Machine learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8052085c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c2968c1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    year  per capita income (US$)\n",
      "0   1970              3399.299037\n",
      "1   1971              3768.297935\n",
      "2   1972              4251.175484\n",
      "3   1973              4804.463248\n",
      "4   1974              5576.514583\n",
      "5   1975              5998.144346\n",
      "6   1976              7062.131392\n",
      "7   1977              7100.126170\n",
      "8   1978              7247.967035\n",
      "9   1979              7602.912681\n",
      "10  1980              8355.968120\n",
      "11  1981              9434.390652\n",
      "12  1982              9619.438377\n",
      "13  1983             10416.536590\n",
      "14  1984             10790.328720\n",
      "15  1985             11018.955850\n",
      "16  1986             11482.891530\n",
      "17  1987             12974.806620\n",
      "18  1988             15080.283450\n",
      "19  1989             16426.725480\n",
      "20  1990             16838.673200\n",
      "21  1991             17266.097690\n",
      "22  1992             16412.083090\n",
      "23  1993             15875.586730\n",
      "24  1994             15755.820270\n",
      "25  1995             16369.317250\n",
      "26  1996             16699.826680\n",
      "27  1997             17310.757750\n",
      "28  1998             16622.671870\n",
      "29  1999             17581.024140\n",
      "30  2000             18987.382410\n",
      "31  2001             18601.397240\n",
      "32  2002             19232.175560\n",
      "33  2003             22739.426280\n",
      "34  2004             25719.147150\n",
      "35  2005             29198.055690\n",
      "36  2006             32738.262900\n",
      "37  2007             36144.481220\n",
      "38  2008             37446.486090\n",
      "39  2009             32755.176820\n",
      "40  2010             38420.522890\n",
      "41  2011             42334.711210\n",
      "42  2012             42665.255970\n",
      "43  2013             42676.468370\n",
      "44  2014             41039.893600\n",
      "45  2015             35175.188980\n",
      "46  2016             34229.193630\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv('canada_per_capita_income.csv')\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3c1d430c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1edeef59910>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('per capita income (US$)')\n",
    "plt.scatter(df['year'], df['per capita income (US$)'], color='orange', marker='*')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4f7c1d61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line=linear_model.LinearRegression()\n",
    "X = df['year'].values.reshape(-1, 1)\n",
    "y = df['per capita income (US$)'].values\n",
    "line = linear_model.LinearRegression()\n",
    "line.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dcd43691",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12292.41646162]\n"
     ]
    }
   ],
   "source": [
    "predicted_value = line.predict(np.array([[1985]]))\n",
    "print(predicted_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "45151df4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[828.46507522]\n",
      "-1632210.7578554575\n"
     ]
    }
   ],
   "source": [
    "print(line.coef_)\n",
    "print(line.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "de582eb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1edecdf7cd0>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('per capita income (US$)')\n",
    "plt.scatter(df['year'], df['per capita income (US$)'], color='orange', marker='*')\n",
    "plt.plot(df['year'], line.predict(df['year'].values.reshape(-1, 1)), color='green')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "769241c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting word2number\n",
      "  Downloading word2number-1.1.zip (9.7 kB)\n",
      "  Installing build dependencies: started\n",
      "  Installing build dependencies: finished with status 'done'\n",
      "  Getting requirements to build wheel: started\n",
      "  Getting requirements to build wheel: finished with status 'done'\n",
      "  Preparing metadata (pyproject.toml): started\n",
      "  Preparing metadata (pyproject.toml): finished with status 'done'\n",
      "Building wheels for collected packages: word2number\n",
      "  Building wheel for word2number (pyproject.toml): started\n",
      "  Building wheel for word2number (pyproject.toml): finished with status 'done'\n",
      "  Created wheel for word2number: filename=word2number-1.1-py3-none-any.whl size=5588 sha256=eab78255ad326926ad884ffb165a01ec0d45b1fc6c01a3a747940316cdbe0524\n",
      "  Stored in directory: c:\\users\\ratna\\appdata\\local\\pip\\cache\\wheels\\cd\\ef\\ae\\073b491b14d25e2efafcffca9e16b2ee6d114ec5c643ba4f06\n",
      "Successfully built word2number\n",
      "Installing collected packages: word2number\n",
      "Successfully installed word2number-1.1\n"
     ]
    }
   ],
   "source": [
    "!pip install word2number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6be874be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
      "0        NaN                    8.0                           9      50000\n",
      "1        NaN                    8.0                           6      45000\n",
      "2       five                    6.0                           7      60000\n",
      "3        two                   10.0                          10      65000\n",
      "4      seven                    9.0                           6      70000\n",
      "5      three                    7.0                          10      62000\n",
      "6        ten                    NaN                           7      72000\n",
      "7     eleven                    7.0                           8      80000\n"
     ]
    }
   ],
   "source": [
    "df2=pd.read_csv('hiring.csv')\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3217ddff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
      "0           0                    8.0                           9      50000\n",
      "1           0                    8.0                           6      45000\n",
      "2           5                    6.0                           7      60000\n",
      "3           2                   10.0                          10      65000\n",
      "4           7                    9.0                           6      70000\n",
      "5           3                    7.0                          10      62000\n",
      "6          10                    NaN                           7      72000\n",
      "7          11                    7.0                           8      80000\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "column_change = 'experience'\n",
    "df2[column_change] = df2[column_change].fillna('')\n",
    "def convert_to_number(value):\n",
    "    try:\n",
    "        if isinstance(value, (int, float)):\n",
    "            return int(value)\n",
    "        return int(value)\n",
    "    except ValueError as e:\n",
    "        print(f\"Error converting '{value}' to a number. Details: {e}\")\n",
    "        return 0 \n",
    "df2[column_change] = df2[column_change].apply(convert_to_number)\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8b806cdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   experience  test_score(out of 10)  interview_score(out of 10)  salary($)\n",
      "0           0                      8                           9      50000\n",
      "1           0                      8                           6      45000\n",
      "2           5                      6                           7      60000\n",
      "3           2                     10                          10      65000\n",
      "4           7                      9                           6      70000\n",
      "5           3                      7                          10      62000\n",
      "6          10                      8                           7      72000\n",
      "7          11                      7                           8      80000\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "mid=df2['test_score(out of 10)'].median()\n",
    "df2['test_score(out of 10)']=(df2['test_score(out of 10)'].fillna(mid)).apply(math.floor)\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3d6f33cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line2=linear_model.LinearRegression()\n",
    "a=df2['experience'].values.reshape(-1,1)\n",
    "b=df2['test_score(out of 10)'].values.reshape(-1,1)\n",
    "c=df2['interview_score(out of 10)'].values.reshape(-1,1)\n",
    "d=df2['salary($)'].values\n",
    "X = pd.concat([pd.Series(a.flatten()), pd.Series(b.flatten()), pd.Series(c.flatten())], axis=1).values\n",
    "y=d\n",
    "line2.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "94749608",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2812.95487627 1845.70596798 2205.24017467]\n",
      "17737.26346433768\n"
     ]
    }
   ],
   "source": [
    "print(line2.coef_)\n",
    "print(line2.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "caf2bc7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[53205.96797671]\n",
      "[86376.27365357]\n"
     ]
    }
   ],
   "source": [
    "print(line2.predict([[2,9,6]]))\n",
    "print(line2.predict([[10,10,10]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7e001f1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     name  math  cs\n",
      "0   david    92  98\n",
      "1   laura    56  68\n",
      "2  sanjay    88  81\n",
      "3     wei    70  80\n",
      "4    jeff    80  83\n",
      "5   aamir    49  52\n",
      "6  venkat    65  66\n",
      "7   virat    35  30\n",
      "8  arthur    66  68\n",
      "9    paul    67  73\n"
     ]
    }
   ],
   "source": [
    "df3=pd.read_csv('test_scores.csv')\n",
    "print(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ca96995d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "m -9169.587968000002, b -129.386408, cost 45366108.340850405 iteration 1\n",
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      "m -80513336.3321037, b -1136112.3853868274, cost 3496779946139640.5 iteration 3\n",
      "m 7544007292.656351, b 106452428.60001147, cost 3.0699891720627802e+19 iteration 4\n",
      "m -706864823521.7386, b -9974470361.562872, cost 2.6952892837845242e+23 iteration 5\n",
      "m 66232422560018.44, b 934596423272.1094, cost 2.3663224578744982e+27 iteration 6\n",
      "m -6205901966243919.0, b -87570612045376.58, cost 2.077506859218773e+31 iteration 7\n",
      "m 5.814858905354231e+17, b 8205265827097408.0, cost 1.8239419296970382e+35 iteration 8\n",
      "m -5.448456046050343e+19, b -7.688239892447695e+17, cost 1.6013252366146002e+39 iteration 9\n",
      "m 5.105140772789593e+21, b 7.203792526577272e+19, cost 1.4058794699920812e+43 iteration 10\n",
      "m -4.783458302630841e+23, b -6.74987090568644e+21, cost 1.2342883500192473e+47 iteration 11\n",
      "m 4.482045520657572e+25, b 6.324551557439078e+23, cost 1.0836403571650532e+51 iteration 12\n",
      "m -4.1996252038400877e+27, b -5.926032210335615e+25, cost 9.513793301690755e+54 iteration 13\n",
      "m 3.9350006088606955e+29, b 5.552624156669068e+27, cost 8.352610936721472e+58 iteration 14\n",
      "m -3.6870504009680314e+31, b -5.202745096702534e+29, cost 7.3331537955360665e+62 iteration 15\n",
      "m 3.4547239023717715e+33, b 4.874912433745606e+31, cost 6.43812395864958e+66 iteration 16\n",
      "m -3.237036640043024e+35, b -4.567736991717981e+33, cost 5.652334761091385e+70 iteration 17\n",
      "m 3.0330661740543987e+37, b 4.279917128578644e+35, cost 4.962453108489603e+74 iteration 18\n",
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      "m 2.662872830409765e+41, b 3.757542494650841e+39, cost 3.8250181282815046e+82 iteration 20\n",
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      "m 2.3378625140433015e+45, b 3.298924996661283e+43, cost 2.948292580669893e+90 iteration 22\n",
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      "m 1.5820659737284843e+57, b 2.2324311013797718e+55, cost 1.35014939088815e+114 iteration 28\n",
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      "m 1.219443468250852e+65, b 1.7207395709812706e+63, cost 8.0215073975038e+129 iteration 32\n",
      "m -1.1426045318640812e+67, b -1.6123132257875217e+65, cost 7.042462274797934e+133 iteration 33\n",
      "m 1.0706073304972364e+69, b 1.510718984957695e+67, cost 6.182912068046699e+137 iteration 34\n",
      "m -1.0031467792662015e+71, b -1.4155263474917225e+69, cost 5.428272122663853e+141 iteration 35\n",
      "m 9.399370171366024e+72, b 1.3263319388942262e+71, cost 4.765737877782639e+145 iteration 36\n",
      "m -8.807101956006055e+74, b -1.2427578018933375e+73, cost 4.184067601346858e+149 iteration 37\n",
      "m 8.252153436809802e+76, b 1.164449794863854e+75, cost 3.6733916429297385e+153 iteration 38\n",
      "m -7.732173044529361e+78, b -1.0910760911681242e+77, cost 3.225044967725275e+157 iteration 39\n",
      "m 7.244957385772907e+80, b 1.0223257730556751e+79, cost 2.831420130186499e+161 iteration 40\n",
      "m -6.788441906224863e+82, b -9.579075141633148e+80, cost 2.485838192600437e+165 iteration 41\n",
      "m 6.360692142190374e+84, b 8.975483450328412e+82, cost 2.1824353983751556e+169 iteration 42\n",
      "m -5.959895523392907e+86, b -8.409924964153124e+84, cost 1.9160636771367335e+173 iteration 43\n",
      "m 5.584353692289688e+88, b 7.880003154604224e+86, cost 1.68220329342901e+177 iteration 44\n",
      "m -5.232475307358434e+90, b -7.3834725019839e+88, cost 1.4768861568589032e+181 iteration 45\n",
      "m 4.9027692998596465e+92, b 6.918228980111427e+90, cost 1.2966284924310824e+185 iteration 46\n",
      "m -4.5938385555002596e+94, b -6.482301140607395e+92, cost 1.1383717286373908e+189 iteration 47\n",
      "m 4.304373994225029e+96, b 6.073841758970393e+94, cost 9.994306003033937e+192 iteration 48\n",
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      "m 3.779014338425252e+100, b 5.332514118724415e+98, cost 7.703529042077515e+200 iteration 50\n",
      "m -3.540893056123038e+102, b -4.996504517772638e+100, cost 6.763293976207482e+204 iteration 51\n",
      "m 3.317776147979637e+104, b 4.681667378706207e+102, cost 5.937816961389488e+208 iteration 52\n",
      "m -3.108718166189119e+106, b -4.386668593389475e+104, cost 5.213091489294607e+212 iteration 53\n",
      "m 2.912833237010043e+108, b 4.110258117813446e+106, cost 4.5768205811107413e+216 iteration 54\n",
      "m -2.7292913069155488e+110, b -3.8512646750908465e+108, cost 4.0182081351718755e+220 iteration 55\n",
      "m 2.557314625279079e+112, b 3.608590792223282e+110, cost 3.5277757411331614e+224 iteration 56\n",
      "m -2.3961744486912823e+114, b -3.381208149609062e+112, cost 3.097201852435974e+228 iteration 57\n",
      "m 2.2451879529427813e+116, b 3.1681532235853846e+114, cost 2.7191805881775087e+232 iteration 58\n",
      "m -2.103715339587469e+118, b -2.968523203540419e+116, cost 2.387297768566809e+236 iteration 59\n",
      "m 1.9711571248255345e+120, b 2.7814721662941635e+118, cost 2.095922080564669e+240 iteration 60\n",
      "m -1.8469515992179773e+122, b -2.606207491537232e+120, cost 1.840109527030539e+244 iteration 61\n",
      "m 1.7305724474682703e+124, b 2.441986503138153e+122, cost 1.615519538090995e+248 iteration 62\n",
      "m -1.621526518185095e+126, b -2.288113322086855e+124, cost 1.4183413213263731e+252 iteration 63\n",
      "m 1.5193517341756278e+128, b 2.143935917738836e+126, cost 1.2452292011020746e+256 iteration 64\n",
      "m -1.4236151344143378e+130, b -2.0088433448647983e+128, cost 1.0932458498968773e+260 iteration 65\n",
      "m 1.3339110393902257e+132, b 1.8822631547978803e+130, cost 9.598124483901925e+263 iteration 66\n",
      "m -1.2498593320582444e+134, b -1.7636589697083224e+132, cost 8.426649286358364e+267 iteration 67\n",
      "m 1.1711038471105164e+136, b 1.6525282097266731e+134, cost 7.398155578663304e+271 iteration 68\n",
      "m -1.0973108617419513e+138, b -1.5483999632843285e+136, cost 6.49519211090368e+275 iteration 69\n",
      "m 1.028167681514955e+140, b 1.4508329916470608e+138, cost 5.702437602044571e+279 iteration 70\n",
      "m -9.633813153308937e+141, b -1.359413859185839e+140, cost 5.006440771878509e+283 iteration 71\n",
      "m 9.026772338935685e+143, b 1.2737551814620527e+142, cost 4.395392102728275e+287 iteration 72\n",
      "m -8.457982064037382e+145, b -1.1934939837034794e+144, cost 3.8589234582070315e+291 iteration 73\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ratna\\AppData\\Local\\Temp\\ipykernel_17844\\1353043702.py:12: RuntimeWarning: overflow encountered in scalar add\n",
      "  cost = (1/n) * sum([val**2 for val in (y - y_predicted)])\n",
      "C:\\Users\\ratna\\AppData\\Local\\Temp\\ipykernel_17844\\1353043702.py:12: RuntimeWarning: overflow encountered in scalar power\n",
      "  cost = (1/n) * sum([val**2 for val in (y - y_predicted)])\n",
      "C:\\Users\\ratna\\AppData\\Local\\Temp\\ipykernel_17844\\1353043702.py:13: RuntimeWarning: overflow encountered in scalar add\n",
      "  md = -(2/n) * sum(x * (y - y_predicted))\n",
      "C:\\Users\\ratna\\AppData\\Local\\Temp\\ipykernel_17844\\1353043702.py:15: RuntimeWarning: invalid value encountered in scalar subtract\n",
      "  m_curr = m_curr - lr * md\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.array(df3['math'])\n",
    "y = np.array(df3['cs'])\n",
    "def grad(x, y):\n",
    "    m_curr = 0\n",
    "    b_curr = 0\n",
    "    lr = 0.01\n",
    "    n = len(x)\n",
    "    iterations = 1000\n",
    "    for i in range(iterations):\n",
    "        y_predicted = m_curr * x + b_curr\n",
    "        cost = (1/n) * sum([val**2 for val in (y - y_predicted)])\n",
    "        md = -(2/n) * sum(x * (y - y_predicted))\n",
    "        bd = -(2/n) * sum(y - y_predicted)\n",
    "        m_curr = m_curr - lr * md\n",
    "        b_curr = b_curr - lr * bd\n",
    "        print(\"m {}, b {}, cost {} iteration {}\".format(m_curr, b_curr, cost, i))\n",
    "grad(x, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a2f597f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Car Model  Mileage  Sell Price($)  Age(yrs)\n",
      "0                  BMW X5    69000          18000         6\n",
      "1                  BMW X5    35000          34000         3\n",
      "2                  BMW X5    57000          26100         5\n",
      "3                  BMW X5    22500          40000         2\n",
      "4                  BMW X5    46000          31500         4\n",
      "5                 Audi A5    59000          29400         5\n",
      "6                 Audi A5    52000          32000         5\n",
      "7                 Audi A5    72000          19300         6\n",
      "8                 Audi A5    91000          12000         8\n",
      "9   Mercedez Benz C class    67000          22000         6\n",
      "10  Mercedez Benz C class    83000          20000         7\n",
      "11  Mercedez Benz C class    79000          21000         7\n",
      "12  Mercedez Benz C class    59000          33000         5\n"
     ]
    }
   ],
   "source": [
    "df4=pd.read_csv('carprices.csv')\n",
    "print(df4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "af8ce470",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Car Model</th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Sell Price($)</th>\n",
       "      <th>Age(yrs)</th>\n",
       "      <th>Audi A5</th>\n",
       "      <th>BMW X5</th>\n",
       "      <th>Mercedez Benz C class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BMW X5</td>\n",
       "      <td>69000</td>\n",
       "      <td>18000</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BMW X5</td>\n",
       "      <td>35000</td>\n",
       "      <td>34000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BMW X5</td>\n",
       "      <td>57000</td>\n",
       "      <td>26100</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BMW X5</td>\n",
       "      <td>22500</td>\n",
       "      <td>40000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BMW X5</td>\n",
       "      <td>46000</td>\n",
       "      <td>31500</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Audi A5</td>\n",
       "      <td>59000</td>\n",
       "      <td>29400</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Audi A5</td>\n",
       "      <td>52000</td>\n",
       "      <td>32000</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Audi A5</td>\n",
       "      <td>72000</td>\n",
       "      <td>19300</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Audi A5</td>\n",
       "      <td>91000</td>\n",
       "      <td>12000</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mercedez Benz C class</td>\n",
       "      <td>67000</td>\n",
       "      <td>22000</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mercedez Benz C class</td>\n",
       "      <td>83000</td>\n",
       "      <td>20000</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Mercedez Benz C class</td>\n",
       "      <td>79000</td>\n",
       "      <td>21000</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Mercedez Benz C class</td>\n",
       "      <td>59000</td>\n",
       "      <td>33000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Car Model  Mileage  Sell Price($)  Age(yrs)  Audi A5  BMW X5  \\\n",
       "0                  BMW X5    69000          18000         6        0       1   \n",
       "1                  BMW X5    35000          34000         3        0       1   \n",
       "2                  BMW X5    57000          26100         5        0       1   \n",
       "3                  BMW X5    22500          40000         2        0       1   \n",
       "4                  BMW X5    46000          31500         4        0       1   \n",
       "5                 Audi A5    59000          29400         5        1       0   \n",
       "6                 Audi A5    52000          32000         5        1       0   \n",
       "7                 Audi A5    72000          19300         6        1       0   \n",
       "8                 Audi A5    91000          12000         8        1       0   \n",
       "9   Mercedez Benz C class    67000          22000         6        0       0   \n",
       "10  Mercedez Benz C class    83000          20000         7        0       0   \n",
       "11  Mercedez Benz C class    79000          21000         7        0       0   \n",
       "12  Mercedez Benz C class    59000          33000         5        0       0   \n",
       "\n",
       "    Mercedez Benz C class  \n",
       "0                       0  \n",
       "1                       0  \n",
       "2                       0  \n",
       "3                       0  \n",
       "4                       0  \n",
       "5                       0  \n",
       "6                       0  \n",
       "7                       0  \n",
       "8                       0  \n",
       "9                       1  \n",
       "10                      1  \n",
       "11                      1  \n",
       "12                      1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy=pd.get_dummies(df4['Car Model'])\n",
    "merged=pd.concat([df4,dummy],axis=1)\n",
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "15873f0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mileage</th>\n",
       "      <th>Sell Price($)</th>\n",
       "      <th>Age(yrs)</th>\n",
       "      <th>Audi A5</th>\n",
       "      <th>BMW X5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69000</td>\n",
       "      <td>18000</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35000</td>\n",
       "      <td>34000</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57000</td>\n",
       "      <td>26100</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22500</td>\n",
       "      <td>40000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46000</td>\n",
       "      <td>31500</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>59000</td>\n",
       "      <td>29400</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>52000</td>\n",
       "      <td>32000</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>72000</td>\n",
       "      <td>19300</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>91000</td>\n",
       "      <td>12000</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>67000</td>\n",
       "      <td>22000</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>83000</td>\n",
       "      <td>20000</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>79000</td>\n",
       "      <td>21000</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>59000</td>\n",
       "      <td>33000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mileage  Sell Price($)  Age(yrs)  Audi A5  BMW X5\n",
       "0     69000          18000         6        0       1\n",
       "1     35000          34000         3        0       1\n",
       "2     57000          26100         5        0       1\n",
       "3     22500          40000         2        0       1\n",
       "4     46000          31500         4        0       1\n",
       "5     59000          29400         5        1       0\n",
       "6     52000          32000         5        1       0\n",
       "7     72000          19300         6        1       0\n",
       "8     91000          12000         8        1       0\n",
       "9     67000          22000         6        0       0\n",
       "10    83000          20000         7        0       0\n",
       "11    79000          21000         7        0       0\n",
       "12    59000          33000         5        0       0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final=merged.drop(['Car Model','Mercedez Benz C class'],axis=1)\n",
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9f6abbc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "line3=linear_model.LinearRegression()\n",
    "a=final['Mileage'].values.reshape(-1,1)\n",
    "b=final['Age(yrs)'].values.reshape(-1,1)\n",
    "c=final['Audi A5'].values.reshape(-1,1)\n",
    "d=final['BMW X5'].values.reshape(-1,1)\n",
    "X=pd.concat([pd.Series(a.flatten()),pd.Series(b.flatten()),pd.Series(c.flatten()),pd.Series(d.flatten())],axis=1)\n",
    "y=final['Sell Price($)'].values\n",
    "line3.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a7c9d4ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-3.70122094e-01 -1.33245363e+03 -2.45354074e+03 -6.73820733e+03]\n",
      "58976.62596853723\n"
     ]
    }
   ],
   "source": [
    "print(line3.coef_)\n",
    "print(line3.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "acffde35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[36991.31721061]\n"
     ]
    }
   ],
   "source": [
    "print(line3.predict([[45000,4,0,0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7b04efa1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11080.74313219]\n"
     ]
    }
   ],
   "source": [
    "print(line3.predict([[86000,7,0,1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2174b44c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9417050937281083\n"
     ]
    }
   ],
   "source": [
    "print(line3.score(X,y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a02ae4ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>last_evaluation</th>\n",
       "      <th>number_project</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>time_spend_company</th>\n",
       "      <th>Work_accident</th>\n",
       "      <th>left</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>Department</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>157</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>5</td>\n",
       "      <td>262</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.88</td>\n",
       "      <td>7</td>\n",
       "      <td>272</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.87</td>\n",
       "      <td>5</td>\n",
       "      <td>223</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>159</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>sales</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>0.40</td>\n",
       "      <td>0.57</td>\n",
       "      <td>2</td>\n",
       "      <td>151</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.48</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.96</td>\n",
       "      <td>6</td>\n",
       "      <td>280</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2</td>\n",
       "      <td>158</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>support</td>\n",
       "      <td>low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  last_evaluation  number_project  \\\n",
       "0                    0.38             0.53               2   \n",
       "1                    0.80             0.86               5   \n",
       "2                    0.11             0.88               7   \n",
       "3                    0.72             0.87               5   \n",
       "4                    0.37             0.52               2   \n",
       "...                   ...              ...             ...   \n",
       "14994                0.40             0.57               2   \n",
       "14995                0.37             0.48               2   \n",
       "14996                0.37             0.53               2   \n",
       "14997                0.11             0.96               6   \n",
       "14998                0.37             0.52               2   \n",
       "\n",
       "       average_montly_hours  time_spend_company  Work_accident  left  \\\n",
       "0                       157                   3              0     1   \n",
       "1                       262                   6              0     1   \n",
       "2                       272                   4              0     1   \n",
       "3                       223                   5              0     1   \n",
       "4                       159                   3              0     1   \n",
       "...                     ...                 ...            ...   ...   \n",
       "14994                   151                   3              0     1   \n",
       "14995                   160                   3              0     1   \n",
       "14996                   143                   3              0     1   \n",
       "14997                   280                   4              0     1   \n",
       "14998                   158                   3              0     1   \n",
       "\n",
       "       promotion_last_5years Department  salary  \n",
       "0                          0      sales     low  \n",
       "1                          0      sales  medium  \n",
       "2                          0      sales  medium  \n",
       "3                          0      sales     low  \n",
       "4                          0      sales     low  \n",
       "...                      ...        ...     ...  \n",
       "14994                      0    support     low  \n",
       "14995                      0    support     low  \n",
       "14996                      0    support     low  \n",
       "14997                      0    support     low  \n",
       "14998                      0    support     low  \n",
       "\n",
       "[14999 rows x 10 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5=pd.read_csv('HR_comma_sep.csv')\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e6d86612",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='left'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yfPlyxcfH64EHHpDT6ZTb7dZ//ud/yt/f3+rTv39/VVdXe1zrkpCQcMG2hszmdrvd3i7iRnC5XHI4HCovL5fdbvd2Ofixm70y5jWKi27r7RKuqDCl0NslAD7h7NmzOnjwoKKjo9WkSRNvl4PLuNzP6mp/f3MEBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAMAtIiEhQWlpadb79u3ba8GCBV6r53o09nYBAADcSHEr4m7q55n0eI+8vDxjn1xNgAEA4BbVqlUrb5dwzTiFBACAlyUkJOjpp59WWlqabrvtNoWHh2vp0qU6deqUxo4dq+bNm6tDhw5av369tc+XX36pIUOGKDg4WOHh4Ro1apSOHTtmtZ86dUqjR49WcHCwIiMj9eqrr17wueefQvr2229ls9lUUFBgtZeVlclmsyk7O1uSlJ2dLZvNpo0bN6p79+5q2rSpBg4cqNLSUq1fv16dO3eW3W7XE088odOnT9+Qv6taBBgAAHzAihUr1LJlS+3YsUNPP/20Jk2apEceeUR9+vTRrl27NHjwYI0aNUqnT59WWVmZBg4cqO7du2vnzp3asGGDSkpK9Oijj1rjTZ8+XTk5Ofrggw/08ccfKzs7W7t27aqXWufMmaPXX39d27Zt06FDh/Too49qwYIFWrVqlTIzM/Xxxx/rtddeq5fPuhROIQEA4AO6du2q2bNnS5JmzZqll156SS1bttSECRMkSenp6XrjjTf0xRdf6JNPPlH37t3129/+1tr/rbfeUlRUlP72t7+pdevWevPNN/XnP/9ZgwYNkvRDQGrTpk291Priiy/qnnvukSSNHz9es2bN0jfffKM77rhDkvTwww/r008/1cyZM+vl8y6GAAMAgA/o0qWL9Wc/Pz+FhoYqLu7/L0AODw+XJJWWlmr37t369NNPFRwcfME433zzjc6cOaPKykr16tXL2t6iRQvFxMTUe63h4eEKCgqywkvtth07dtTLZ10KAQYAAB/g7+/v8d5ms3lss9lskqSamhqdPHlSDz74oH73u99dME5kZKS+/vrrOn9+o0Y/XFXidrutbVVVVVes9cd11m6rqampcw11wTUwAAAYpkePHtqzZ4/at2+vDh06eLyaNWumf/qnf5K/v7+2b99u7XP8+HH97W9/u+SYtXckHTlyxNp2/gW9voYAAwCAYVJTU/X999/r8ccfV15enr755htt3LhRY8eOVXV1tYKDgzV+/HhNnz5dmzdv1pdffqkxY8ZYR1kupmnTpurdu7deeukl7du3Tzk5OdY1Ob6IAAMAgGFat26tzz77TNXV1Ro8eLDi4uKUlpamkJAQK6S8/PLL6tu3rx588EElJibq3nvvVXx8/GXHfeutt3Tu3DnFx8crLS1NL7744s2YzjWxuc8/2dWAuFwuORwOlZeXy263e7sc/Ngch7cruCpx0W29XcIVmbTqJ3AjnT17VgcPHlR0dLSaNGni7XJwGZf7WV3t72+OwAAAAOMQYAAAgHEIMAAAwDgEGAAAYJzrCjAvvfSSbDab0tLSrG1nz55VamqqQkNDFRwcrBEjRqikpMRjv6KiIiUnJysoKEhhYWGaPn26zp0759EnOztbPXr0UGBgoDp06KCMjIzrKRUAcItooPemNCj18TO65gCTl5enP/7xjx7LCUvSlClT9NFHH+m9995TTk6ODh8+rOHDh1vt1dXVSk5OVmVlpbZt26YVK1YoIyND6enpVp+DBw8qOTlZAwYMUEFBgdLS0vTkk09q48aN11ouAKCBq10N9kY/BRnXr/Zn9OMVfOvimh4lcPLkSY0cOVJLly71uEe8vLxcb775platWqWBAwdKkpYvX67OnTvr888/V+/evfXxxx9r7969+uSTTxQeHq5u3brp17/+tWbOnKk5c+YoICBAS5YsUXR0tPXo786dO2vr1q2aP3++kpKSrnmyAICGy8/PTyEhISotLZUkBQUFWcvvwze43W6dPn1apaWlCgkJkZ+f3zWPdU0BJjU1VcnJyUpMTPQIMPn5+aqqqlJiYqK1rVOnTmrbtq1yc3PVu3dv5ebmKi4uznoolSQlJSVp0qRJ2rNnj7p3767c3FyPMWr7nH+q6scqKipUUVFhvXe5XNcyNQCAwSIiIiTJCjHwTSEhIdbP6lrVOcCsXr1au3btUl5e3gVtxcXFCggIUEhIiMf28PBwFRcXW33ODy+17bVtl+vjcrl05swZNW3a9ILPnjt3rp5//vm6TgcA0IDYbDZFRkYqLCzskg8ihHf5+/tf15GXWnUKMIcOHdK//du/KSsry+dWOZw1a5amTp1qvXe5XIqKivJiRQAAb/Hz86uXX5LwXXW6iDc/P1+lpaXq0aOHGjdurMaNGysnJ0eLFi1S48aNFR4ersrKSpWVlXnsV1JSYh0qioiIuOCupNr3V+pjt9svevRFkgIDA2W32z1eAACgYapTgBk0aJAKCwtVUFBgvXr27KmRI0daf/b399emTZusfQ4cOKCioiI5nU5JktPpVGFhocf5yaysLNntdsXGxlp9zh+jtk/tGAAA4NZWp1NIzZs3109+8hOPbc2aNVNoaKi1ffz48Zo6dapatGghu92up59+Wk6nU71795YkDR48WLGxsRo1apTmzZun4uJizZ49W6mpqQoMDJQkTZw4Ua+//rpmzJihcePGafPmzXr33XeVmZlZH3MGAACGu6a7kC5n/vz5atSokUaMGKGKigolJSVp8eLFVrufn5/WrVunSZMmyel0qlmzZkpJSdELL7xg9YmOjlZmZqamTJmihQsXqk2bNlq2bBm3UAMAAEmSzd1Alyy82sdxw0vmOLxdwVWJi27r7RKuqDCl0NslAEC9udrf3zwLCQAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMU6cA88Ybb6hLly6y2+2y2+1yOp1av3691X727FmlpqYqNDRUwcHBGjFihEpKSjzGKCoqUnJysoKCghQWFqbp06fr3LlzHn2ys7PVo0cPBQYGqkOHDsrIyLj2GQIAgAanTgGmTZs2eumll5Sfn6+dO3dq4MCBeuihh7Rnzx5J0pQpU/TRRx/pvffeU05Ojg4fPqzhw4db+1dXVys5OVmVlZXatm2bVqxYoYyMDKWnp1t9Dh48qOTkZA0YMEAFBQVKS0vTk08+qY0bN9bTlAEAgOlsbrfbfT0DtGjRQi+//LIefvhhtWrVSqtWrdLDDz8sSdq/f786d+6s3Nxc9e7dW+vXr9cDDzygw4cPKzw8XJK0ZMkSzZw5U0ePHlVAQIBmzpypzMxMffnll9ZnPPbYYyorK9OGDRuuui6XyyWHw6Hy8nLZ7fbrmSJuhDkOb1dwVeKi23q7hCsqTCn0dgkAUG+u9vf3NV8DU11drdWrV+vUqVNyOp3Kz89XVVWVEhMTrT6dOnVS27ZtlZubK0nKzc1VXFycFV4kKSkpSS6XyzqKk5ub6zFGbZ/aMS6loqJCLpfL4wUAABqmOgeYwsJCBQcHKzAwUBMnTtSaNWsUGxur4uJiBQQEKCQkxKN/eHi4iouLJUnFxcUe4aW2vbbtcn1cLpfOnDlzybrmzp0rh8NhvaKiouo6NQAAYIg6B5iYmBgVFBRo+/btmjRpklJSUrR3794bUVudzJo1S+Xl5dbr0KFD3i4JAADcII3rukNAQIA6dOggSYqPj1deXp4WLlyon/3sZ6qsrFRZWZnHUZiSkhJFRERIkiIiIrRjxw6P8WrvUjq/z4/vXCopKZHdblfTpk0vWVdgYKACAwPrOh0AAGCg614HpqamRhUVFYqPj5e/v782bdpktR04cEBFRUVyOp2SJKfTqcLCQpWWllp9srKyZLfbFRsba/U5f4zaPrVjAAAA1OkIzKxZszRkyBC1bdtWJ06c0KpVq5Sdna2NGzfK4XBo/Pjxmjp1qlq0aCG73a6nn35aTqdTvXv3liQNHjxYsbGxGjVqlObNm6fi4mLNnj1bqamp1tGTiRMn6vXXX9eMGTM0btw4bd68We+++64yMzPrf/YAAMBIdQowpaWlGj16tI4cOSKHw6EuXbpo48aNuu+++yRJ8+fPV6NGjTRixAhVVFQoKSlJixcvtvb38/PTunXrNGnSJDmdTjVr1kwpKSl64YUXrD7R0dHKzMzUlClTtHDhQrVp00bLli1TUlJSPU0ZAACY7rrXgfFVrAPj41gHpt6wDgyAhuSGrwMDAADgLQQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOI29XQAAoIGZ4/B2BVdnTrm3K8B14AgMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4dQowc+fO1d13363mzZsrLCxMw4YN04EDBzz6nD17VqmpqQoNDVVwcLBGjBihkpISjz5FRUVKTk5WUFCQwsLCNH36dJ07d86jT3Z2tnr06KHAwEB16NBBGRkZ1zZDAADQ4NQpwOTk5Cg1NVWff/65srKyVFVVpcGDB+vUqVNWnylTpuijjz7Se++9p5ycHB0+fFjDhw+32qurq5WcnKzKykpt27ZNK1asUEZGhtLT060+Bw8eVHJysgYMGKCCggKlpaXpySef1MaNG+thygAAwHQ2t9vtvtadjx49qrCwMOXk5Khfv34qLy9Xq1attGrVKj388MOSpP3796tz587Kzc1V7969tX79ej3wwAM6fPiwwsPDJUlLlizRzJkzdfToUQUEBGjmzJnKzMzUl19+aX3WY489prKyMm3YsOGqanO5XHI4HCovL5fdbr/WKeJGMWSlzrjott4u4YoKUwq9XQLgyZDvNyvx+qar/f19XdfAlJf/8MNv0aKFJCk/P19VVVVKTEy0+nTq1Elt27ZVbm6uJCk3N1dxcXFWeJGkpKQkuVwu7dmzx+pz/hi1fWrHuJiKigq5XC6PFwAAaJiuOcDU1NQoLS1N99xzj37yk59IkoqLixUQEKCQkBCPvuHh4SouLrb6nB9eattr2y7Xx+Vy6cyZMxetZ+7cuXI4HNYrKirqWqcGAAB83DUHmNTUVH355ZdavXp1fdZzzWbNmqXy8nLrdejQIW+XBAAAbpBrehr15MmTtW7dOm3ZskVt2rSxtkdERKiyslJlZWUeR2FKSkoUERFh9dmxY4fHeLV3KZ3f58d3LpWUlMhut6tp06YXrSkwMFCBgYHXMh0AAGCYOh2Bcbvdmjx5stasWaPNmzcrOjraoz0+Pl7+/v7atGmTte3AgQMqKiqS0+mUJDmdThUWFqq0tNTqk5WVJbvdrtjYWKvP+WPU9qkdAwAA3NrqdAQmNTVVq1at0gcffKDmzZtb16w4HA41bdpUDodD48eP19SpU9WiRQvZ7XY9/fTTcjqd6t27tyRp8ODBio2N1ahRozRv3jwVFxdr9uzZSk1NtY6gTJw4Ua+//rpmzJihcePGafPmzXr33XeVmZlZz9MHAAAmqtMRmDfeeEPl5eVKSEhQZGSk9XrnnXesPvPnz9cDDzygESNGqF+/foqIiND7779vtfv5+WndunXy8/OT0+nUz3/+c40ePVovvPCC1Sc6OlqZmZnKyspS165d9eqrr2rZsmVKSkqqhykDAADTXdc6ML6MdWB8nCHrRLAODHANDPl+sw6Mb7op68AAAAB4AwEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYJzG3i4AAABviFsR5+0SrqgwpdDbJfgsjsAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHF4lEAD1P6ZTG+XcEXfNvF2BQAAk3EEBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOHUOMFu2bNGDDz6o1q1by2azae3atR7tbrdb6enpioyMVNOmTZWYmKivvvrKo8/333+vkSNHym63KyQkROPHj9fJkyc9+nzxxRfq27evmjRpoqioKM2bN6/uswMAAA1SnQPMqVOn1LVrV/3hD3+4aPu8efO0aNEiLVmyRNu3b1ezZs2UlJSks2fPWn1GjhypPXv2KCsrS+vWrdOWLVv01FNPWe0ul0uDBw9Wu3btlJ+fr5dffllz5szRn/70p2uYIgAAaGjqvJDdkCFDNGTIkIu2ud1uLViwQLNnz9ZDDz0kSVq5cqXCw8O1du1aPfbYY9q3b582bNigvLw89ezZU5L02muvaejQoXrllVfUunVrvf3226qsrNRbb72lgIAA3XXXXSooKNDvf/97j6BzvoqKClVUVFjvXS5XXacGAAAMUa/XwBw8eFDFxcVKTEy0tjkcDvXq1Uu5ubmSpNzcXIWEhFjhRZISExPVqFEjbd++3erTr18/BQQEWH2SkpJ04MABHT9+/KKfPXfuXDkcDusVFRVVn1MDAAA+pF4DTHFxsSQpPDzcY3t4eLjVVlxcrLCwMI/2xo0bq0WLFh59LjbG+Z/xY7NmzVJ5ebn1OnTo0PVPCAAA+KQG8yykwMBABQYGersMAABwE9TrEZiIiAhJUklJicf2kpISqy0iIkKlpaUe7efOndP333/v0ediY5z/GQAA4NZVrwEmOjpaERER2rRpk7XN5XJp+/btcjqdkiSn06mysjLl5+dbfTZv3qyamhr16tXL6rNlyxZVVVVZfbKyshQTE6PbbrutPksGAAAGqnOAOXnypAoKClRQUCDphwt3CwoKVFRUJJvNprS0NL344ov68MMPVVhYqNGjR6t169YaNmyYJKlz5866//77NWHCBO3YsUOfffaZJk+erMcee0ytW7eWJD3xxBMKCAjQ+PHjtWfPHr3zzjtauHChpk6dWm8TBwAA5qrzNTA7d+7UgAEDrPe1oSIlJUUZGRmaMWOGTp06paeeekplZWW69957tWHDBjVp0sTa5+2339bkyZM1aNAgNWrUSCNGjNCiRYusdofDoY8//lipqamKj49Xy5YtlZ6efslbqAEAwK3F5na73d4u4kZwuVxyOBwqLy+X3W73djk3VftnMr1dwhV92+QJb5dwVeKi23q7hCsqTCn0dgmApzkOb1dwVfh++6ar/f3Ns5AAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMZp7O0CAABXr/0zmd4u4Yq+beLtCnAr4AgMAAAwjk8HmD/84Q9q3769mjRpol69emnHjh3eLgkAAPgAnw0w77zzjqZOnarnnntOu3btUteuXZWUlKTS0lJvlwYAALzMZwPM73//e02YMEFjx45VbGyslixZoqCgIL311lveLg0AAHiZT17EW1lZqfz8fM2aNcva1qhRIyUmJio3N/ei+1RUVKiiosJ6X15eLklyuVw3tlgfVFNx2tslXJHL5vZ2CVel+ky1t0u4olvx3/itjO93/eH77Ztq5+x2X/7fkU8GmGPHjqm6ulrh4eEe28PDw7V///6L7jN37lw9//zzF2yPioq6ITXi+ji8XcBV2+ftAq7IMcmcv03cGsz5F8n325edOHFCDsel5++TAeZazJo1S1OnTrXe19TU6Pvvv1doaKhsNpsXK8PN4HK5FBUVpUOHDslut3u7HAD1iO/3rcXtduvEiRNq3br1Zfv5ZIBp2bKl/Pz8VFJS4rG9pKREERERF90nMDBQgYGBHttCQkJuVInwUXa7nf/AAQ0U3+9bx+WOvNTyyYt4AwICFB8fr02bNlnbampqtGnTJjmdTi9WBgAAfIFPHoGRpKlTpyolJUU9e/bUT3/6Uy1YsECnTp3S2LFjvV0aAADwMp8NMD/72c909OhRpaenq7i4WN26ddOGDRsuuLAXkH44hfjcc89dcBoRgPn4fuNibO4r3acEAADgY3zyGhgAAIDLIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcn72NGricY8eO6a233lJubq6Ki4slSREREerTp4/GjBmjVq1aeblCAMCNxBEYGCcvL08dO3bUokWL5HA41K9fP/Xr108Oh0OLFi1Sp06dtHPnTm+XCeAGOHTokMaNG+ftMuADWAcGxundu7e6du2qJUuWXPCgTrfbrYkTJ+qLL75Qbm6ulyoEcKPs3r1bPXr0UHV1tbdLgZdxCgnG2b17tzIyMi76lHGbzaYpU6aoe/fuXqgMwPX68MMPL9v+97///SZVAl9HgIFxIiIitGPHDnXq1Omi7Tt27OCRE4Chhg0bJpvNpsudHLjY/7zg1kOAgXGmTZump556Svn5+Ro0aJAVVkpKSrRp0yYtXbpUr7zyiperBHAtIiMjtXjxYj300EMXbS8oKFB8fPxNrgq+iAAD46Smpqply5aaP3++Fi9ebJ0L9/PzU3x8vDIyMvToo496uUoA1yI+Pl75+fmXDDBXOjqDWwcX8cJoVVVVOnbsmCSpZcuW8vf393JFAK7Hf/3Xf+nUqVO6//77L9p+6tQp7dy5U/3797/JlcHXEGAAAIBxWAcGAAAYhwADAACMQ4ABAADGIcAAAADjEGAA+JSEhASlpaVddf+1a9eqQ4cO8vPzq9N+AMzGOjAAjPav//qvGjt2rH7xi1+oefPmGjNmjMrKyrR27VpvlwbgBiLAADDWyZMnVVpaqqSkJLVu3drb5QC4iTiFBMBnVVRUaNq0abr99tvVrFkz9erVS9nZ2ZKk7OxsNW/eXJI0cOBA2Ww2JSQkaMWKFfrggw9ks9lks9ms/gAaFo7AAPBZkydP1t69e7V69Wq1bt1aa9as0f3336/CwkL16dNHBw4cUExMjP7617+qT58+CgoK0oQJE+RyubR8+XJJUosWLbw8CwA3AgEGgE8qKirS8uXLVVRUZJ0emjZtmjZs2KDly5frt7/9rcLCwiT9EFIiIiIkSU2bNlVFRYX1HkDDRIAB4JMKCwtVXV2tjh07emyvqKhQaGiol6oC4CsIMAB80smTJ+Xn56f8/Hz5+fl5tAUHB3upKgC+ggADwCd1795d1dXVKi0tVd++fa96v4CAAFVXV9/AygD4Au5CAuCTOnbsqJEjR2r06NF6//33dfDgQe3YsUNz585VZmbmJfdr3769vvjiCx04cEDHjh1TVVXVTawawM1CgAHgs5YvX67Ro0frl7/8pWJiYjRs2DDl5eWpbdu2l9xnwoQJiomJUc+ePdWqVSt99tlnN7FiADeLze12u71dBAAAQF1wBAYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxvk//6dM/PreztUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df5.left,df5.salary).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "56822ee3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='left'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(df5.left,df5.Department).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0cc2454d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction_level</th>\n",
       "      <th>average_montly_hours</th>\n",
       "      <th>promotion_last_5years</th>\n",
       "      <th>salary</th>\n",
       "      <th>left</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.38</td>\n",
       "      <td>157</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.80</td>\n",
       "      <td>262</td>\n",
       "      <td>0</td>\n",
       "      <td>medium</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.11</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>medium</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.72</td>\n",
       "      <td>223</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14994</th>\n",
       "      <td>0.40</td>\n",
       "      <td>151</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14995</th>\n",
       "      <td>0.37</td>\n",
       "      <td>160</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14996</th>\n",
       "      <td>0.37</td>\n",
       "      <td>143</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14997</th>\n",
       "      <td>0.11</td>\n",
       "      <td>280</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14998</th>\n",
       "      <td>0.37</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  average_montly_hours  promotion_last_5years  \\\n",
       "0                    0.38                   157                      0   \n",
       "1                    0.80                   262                      0   \n",
       "2                    0.11                   272                      0   \n",
       "3                    0.72                   223                      0   \n",
       "4                    0.37                   159                      0   \n",
       "...                   ...                   ...                    ...   \n",
       "14994                0.40                   151                      0   \n",
       "14995                0.37                   160                      0   \n",
       "14996                0.37                   143                      0   \n",
       "14997                0.11                   280                      0   \n",
       "14998                0.37                   158                      0   \n",
       "\n",
       "       salary  left  \n",
       "0         low     1  \n",
       "1      medium     1  \n",
       "2      medium     1  \n",
       "3         low     1  \n",
       "4         low     1  \n",
       "...       ...   ...  \n",
       "14994     low     1  \n",
       "14995     low     1  \n",
       "14996     low     1  \n",
       "14997     low     1  \n",
       "14998     low     1  \n",
       "\n",
       "[14999 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subdf=df5[['satisfaction_level','average_montly_hours','promotion_last_5years','salary','left']]\n",
    "subdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c8344fe6",
   "metadata": {},
   "outputs": [
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       "[14999 rows x 3 columns]"
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     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "dummy=pd.get_dummies(subdf['salary'])\n",
    "dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "57b188c2",
   "metadata": {},
   "outputs": [
    {
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       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  average_montly_hours  promotion_last_5years  \\\n",
       "0                    0.38                   157                      0   \n",
       "1                    0.80                   262                      0   \n",
       "2                    0.11                   272                      0   \n",
       "3                    0.72                   223                      0   \n",
       "4                    0.37                   159                      0   \n",
       "...                   ...                   ...                    ...   \n",
       "14994                0.40                   151                      0   \n",
       "14995                0.37                   160                      0   \n",
       "14996                0.37                   143                      0   \n",
       "14997                0.11                   280                      0   \n",
       "14998                0.37                   158                      0   \n",
       "\n",
       "       salary  left  high  low  medium  \n",
       "0         low     1     0    1       0  \n",
       "1      medium     1     0    0       1  \n",
       "2      medium     1     0    0       1  \n",
       "3         low     1     0    1       0  \n",
       "4         low     1     0    1       0  \n",
       "...       ...   ...   ...  ...     ...  \n",
       "14994     low     1     0    1       0  \n",
       "14995     low     1     0    1       0  \n",
       "14996     low     1     0    1       0  \n",
       "14997     low     1     0    1       0  \n",
       "14998     low     1     0    1       0  \n",
       "\n",
       "[14999 rows x 8 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge=pd.concat([subdf,dummy],axis=1)\n",
    "merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5f9946e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>14999 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       satisfaction_level  average_montly_hours  promotion_last_5years  left  \\\n",
       "0                    0.38                   157                      0     1   \n",
       "1                    0.80                   262                      0     1   \n",
       "2                    0.11                   272                      0     1   \n",
       "3                    0.72                   223                      0     1   \n",
       "4                    0.37                   159                      0     1   \n",
       "...                   ...                   ...                    ...   ...   \n",
       "14994                0.40                   151                      0     1   \n",
       "14995                0.37                   160                      0     1   \n",
       "14996                0.37                   143                      0     1   \n",
       "14997                0.11                   280                      0     1   \n",
       "14998                0.37                   158                      0     1   \n",
       "\n",
       "       high  low  \n",
       "0         0    1  \n",
       "1         0    0  \n",
       "2         0    0  \n",
       "3         0    1  \n",
       "4         0    1  \n",
       "...     ...  ...  \n",
       "14994     0    1  \n",
       "14995     0    1  \n",
       "14996     0    1  \n",
       "14997     0    1  \n",
       "14998     0    1  \n",
       "\n",
       "[14999 rows x 6 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new=merge.drop(['salary','medium'],axis=1)\n",
    "new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "35af4664",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "20563d20",
   "metadata": {},
   "outputs": [],
   "source": [
    "a=new['satisfaction_level'].values.reshape(-1,1)\n",
    "b=new['average_montly_hours'].values.reshape(-1,1)\n",
    "c=new['promotion_last_5years'].values.reshape(-1,1)\n",
    "d=new['high'].values.reshape(-1,1)\n",
    "e=new['low'].values.reshape(-1,1)\n",
    "X=pd.concat([pd.Series(a.flatten()),pd.Series(b.flatten()),pd.Series(c.flatten()),pd.Series(d.flatten()),pd.Series(e.flatten())],axis=1)\n",
    "y=new['left'].values\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "108a74e1",
   "metadata": {},
   "outputs": [
    {
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       "      <th>8264</th>\n",
       "      <td>0.84</td>\n",
       "      <td>234</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3166</th>\n",
       "      <td>0.61</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11722</th>\n",
       "      <td>0.16</td>\n",
       "      <td>117</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11999 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0    1  2  3  4\n",
       "9409   0.63  265  0  0  1\n",
       "13963  0.78  209  0  0  0\n",
       "5028   0.64  180  0  0  0\n",
       "3403   0.62  231  0  0  1\n",
       "3206   0.40  280  0  0  1\n",
       "...     ...  ... .. .. ..\n",
       "3495   0.66  186  0  0  0\n",
       "11622  0.70  225  0  0  0\n",
       "8264   0.84  234  0  0  0\n",
       "3166   0.61  112  0  0  1\n",
       "11722  0.16  117  0  0  0\n",
       "\n",
       "[11999 rows x 5 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9e24b3e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "log=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "cb829406",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "cc624610",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "16557bd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0], dtype=int64)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log.predict([[0.75,200,1,0,0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "bc9d2432",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7893333333333333"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8e9d3659",
   "metadata": {},
   "outputs": [
    {
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       "      <td>887</td>\n",
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       "      <td>2</td>\n",
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       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
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       "      <td>889</td>\n",
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       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
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       "      <td>0</td>\n",
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       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
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       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "</table>\n",
       "<p>891 rows × 12 columns</p>\n",
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      ],
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       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6=pd.read_csv('titanic.csv')\n",
    "df6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ada9232b",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
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       "      <td>19.0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.4500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
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       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>7.7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass     Sex   Age     Fare\n",
       "0           0       3    male  22.0   7.2500\n",
       "1           1       1  female  38.0  71.2833\n",
       "2           1       3  female  26.0   7.9250\n",
       "3           1       1  female  35.0  53.1000\n",
       "4           0       3    male  35.0   8.0500\n",
       "..        ...     ...     ...   ...      ...\n",
       "886         0       2    male  27.0  13.0000\n",
       "887         1       1  female  19.0  30.0000\n",
       "888         0       3  female   NaN  23.4500\n",
       "889         1       1    male  26.0  30.0000\n",
       "890         0       3    male  32.0   7.7500\n",
       "\n",
       "[891 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df6=df6.drop(['PassengerId','Name','SibSp','Parch','Ticket','Cabin','Embarked'],axis=1)\n",
    "df6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ce5ca13c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>891 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "     female  male\n",
       "0         0     1\n",
       "1         1     0\n",
       "2         1     0\n",
       "3         1     0\n",
       "4         0     1\n",
       "..      ...   ...\n",
       "886       0     1\n",
       "887       1     0\n",
       "888       1     0\n",
       "889       0     1\n",
       "890       0     1\n",
       "\n",
       "[891 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy=pd.get_dummies(df6['Sex'])\n",
    "dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "44df4ee1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>Pclass</th>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass     Sex   Age     Fare  female  male\n",
       "0           0       3    male  22.0   7.2500       0     1\n",
       "1           1       1  female  38.0  71.2833       1     0\n",
       "2           1       3  female  26.0   7.9250       1     0\n",
       "3           1       1  female  35.0  53.1000       1     0\n",
       "4           0       3    male  35.0   8.0500       0     1\n",
       "..        ...     ...     ...   ...      ...     ...   ...\n",
       "886         0       2    male  27.0  13.0000       0     1\n",
       "887         1       1  female  19.0  30.0000       1     0\n",
       "888         0       3  female   NaN  23.4500       1     0\n",
       "889         1       1    male  26.0  30.0000       0     1\n",
       "890         0       3    male  32.0   7.7500       0     1\n",
       "\n",
       "[891 rows x 7 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final=pd.concat([df6,dummy],axis=1)\n",
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d395f2e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>28</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass     Sex  Age     Fare  female  male  Age)\n",
       "0           0       3    male   22   7.2500       0     1    22\n",
       "1           1       1  female   38  71.2833       1     0    38\n",
       "2           1       3  female   26   7.9250       1     0    26\n",
       "3           1       1  female   35  53.1000       1     0    35\n",
       "4           0       3    male   35   8.0500       0     1    35\n",
       "..        ...     ...     ...  ...      ...     ...   ...   ...\n",
       "886         0       2    male   27  13.0000       0     1    27\n",
       "887         1       1  female   19  30.0000       1     0    19\n",
       "888         0       3  female   28  23.4500       1     0    28\n",
       "889         1       1    male   26  30.0000       0     1    26\n",
       "890         0       3    male   32   7.7500       0     1    32\n",
       "\n",
       "[891 rows x 8 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "mid=final['Age'].median()\n",
    "final['Age']=(final['Age'].fillna(mid)).apply(math.floor)\n",
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a059bcd0",
   "metadata": {},
   "outputs": [
    {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass  Age     Fare  female  male\n",
       "0           0       3   22   7.2500       0     1\n",
       "1           1       1   38  71.2833       1     0\n",
       "2           1       3   26   7.9250       1     0\n",
       "3           1       1   35  53.1000       1     0\n",
       "4           0       3   35   8.0500       0     1\n",
       "..        ...     ...  ...      ...     ...   ...\n",
       "886         0       2   27  13.0000       0     1\n",
       "887         1       1   19  30.0000       1     0\n",
       "888         0       3   28  23.4500       1     0\n",
       "889         1       1   26  30.0000       0     1\n",
       "890         0       3   32   7.7500       0     1\n",
       "\n",
       "[891 rows x 6 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge=final.drop(['Sex','Age)'],axis=1)\n",
    "merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a6ac1ea3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "from sklearn.model_selection import train_test_split\n",
    "model=tree.DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4218be60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
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       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>7.9250</td>\n",
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       "    <tr>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>2</td>\n",
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       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass  Age     Fare  female\n",
       "0           0       3   22   7.2500       0\n",
       "1           1       1   38  71.2833       1\n",
       "2           1       3   26   7.9250       1\n",
       "3           1       1   35  53.1000       1\n",
       "4           0       3   35   8.0500       0\n",
       "..        ...     ...  ...      ...     ...\n",
       "886         0       2   27  13.0000       0\n",
       "887         1       1   19  30.0000       1\n",
       "888         0       3   28  23.4500       1\n",
       "889         1       1   26  30.0000       0\n",
       "890         0       3   32   7.7500       0\n",
       "\n",
       "[891 rows x 5 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge=merge.drop(['male'],axis=1)\n",
    "merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1d4e9e9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "a=merge['Pclass'].values.reshape(-1,1)\n",
    "b=merge['Age'].values.reshape(-1,1)\n",
    "c=merge['Fare'].values.reshape(-1,1)\n",
    "d=merge['female'].values.reshape(-1,1)\n",
    "X = pd.concat([pd.Series(a.flatten()), pd.Series(b.flatten()), pd.Series(c.flatten()),pd.Series(d.flatten())], axis=1)\n",
    "y=merge['Survived'].values\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "515c16ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>85</th>\n",
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       "      <td>33</td>\n",
       "      <td>15.850</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>504</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>86.500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>13.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>29.000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "      <td>7.225</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>21.075</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>712 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     0   1       2  3\n",
       "461  3  34   8.050  0\n",
       "848  2  28  33.000  0\n",
       "555  1  62  26.550  0\n",
       "614  3  35   8.050  0\n",
       "85   3  33  15.850  1\n",
       "..  ..  ..     ... ..\n",
       "504  1  16  86.500  1\n",
       "714  2  52  13.000  0\n",
       "78   2   0  29.000  0\n",
       "354  3  28   7.225  0\n",
       "24   3   8  21.075  1\n",
       "\n",
       "[712 rows x 4 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a8f0fe31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f3737175",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1], dtype=int64)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[2,35,10.002,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "13846f93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9888268156424581"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3b90aa4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "digits=load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cec40d0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1797 rows × 64 columns</p>\n",
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      ],
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       "3   0.0  7.0  13.0  13.0   9.0  0.0  0.0  \n",
       "4   0.0  0.0   2.0  16.0   4.0  0.0  0.0  \n",
       "..  ...  ...   ...   ...   ...  ...  ...  \n",
       "9   0.0  2.0  14.0  15.0   9.0  0.0  0.0  \n",
       "0   0.0  6.0  16.0  14.0   6.0  0.0  0.0  \n",
       "8   0.0  2.0   9.0  13.0   6.0  0.0  0.0  \n",
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       "8   1.0  8.0  12.0  14.0  12.0  1.0  0.0  \n",
       "\n",
       "[1797 rows x 64 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7=pd.DataFrame(digits.data,digits.target)\n",
    "df7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c4fba8ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1797 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0    1     2     3     4     5    6    7    8    9  ...   55   56   57  \\\n",
       "0   0.0  0.0   5.0  13.0   9.0   1.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "1   0.0  0.0   0.0  12.0  13.0   5.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "2   0.0  0.0   0.0   4.0  15.0  12.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "3   0.0  0.0   7.0  15.0  13.0   1.0  0.0  0.0  0.0  8.0  ...  0.0  0.0  0.0   \n",
       "4   0.0  0.0   0.0   1.0  11.0   0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "..  ...  ...   ...   ...   ...   ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "9   0.0  0.0   4.0  10.0  13.0   6.0  0.0  0.0  0.0  1.0  ...  0.0  0.0  0.0   \n",
       "0   0.0  0.0   6.0  16.0  13.0  11.0  1.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "8   0.0  0.0   1.0  11.0  15.0   1.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "9   0.0  0.0   2.0  10.0   7.0   0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "8   0.0  0.0  10.0  14.0   8.0   1.0  0.0  0.0  0.0  2.0  ...  0.0  0.0  1.0   \n",
       "\n",
       "     58    59    60    61   62   63  target  \n",
       "0   6.0  13.0  10.0   0.0  0.0  0.0       0  \n",
       "1   0.0  11.0  16.0  10.0  0.0  0.0       1  \n",
       "2   0.0   3.0  11.0  16.0  9.0  0.0       2  \n",
       "3   7.0  13.0  13.0   9.0  0.0  0.0       3  \n",
       "4   0.0   2.0  16.0   4.0  0.0  0.0       4  \n",
       "..  ...   ...   ...   ...  ...  ...     ...  \n",
       "9   2.0  14.0  15.0   9.0  0.0  0.0       9  \n",
       "0   6.0  16.0  14.0   6.0  0.0  0.0       0  \n",
       "8   2.0   9.0  13.0   6.0  0.0  0.0       8  \n",
       "9   5.0  12.0  16.0  12.0  0.0  0.0       9  \n",
       "8   8.0  12.0  14.0  12.0  1.0  0.0       8  \n",
       "\n",
       "[1797 rows x 65 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7['target']=digits.target\n",
    "df7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2cff15aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(df7.drop('target',axis=1),df7.target,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4a0d5150",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "rbf_model=SVC(kernel='rbf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2807e6bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbf_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "62cd0980",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9861111111111112"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbf_model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "83ca20e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model=SVC(kernel='linear')\n",
    "linear_model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a91fb85a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.975"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1dcf2b34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR',\n",
       " 'data',\n",
       " 'data_module',\n",
       " 'feature_names',\n",
       " 'filename',\n",
       " 'frame',\n",
       " 'target',\n",
       " 'target_names']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris=load_iris()\n",
    "dir(iris)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "33b3cec7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                5.1               3.5                1.4               0.2\n",
       "1                4.9               3.0                1.4               0.2\n",
       "2                4.7               3.2                1.3               0.2\n",
       "3                4.6               3.1                1.5               0.2\n",
       "4                5.0               3.6                1.4               0.2\n",
       "5                5.4               3.9                1.7               0.4\n",
       "6                4.6               3.4                1.4               0.3\n",
       "7                5.0               3.4                1.5               0.2\n",
       "8                4.4               2.9                1.4               0.2\n",
       "9                4.9               3.1                1.5               0.1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df8 = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "df8.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d680a1b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "5                5.4               3.9                1.7               0.4   \n",
       "6                4.6               3.4                1.4               0.3   \n",
       "7                5.0               3.4                1.5               0.2   \n",
       "8                4.4               2.9                1.4               0.2   \n",
       "9                4.9               3.1                1.5               0.1   \n",
       "\n",
       "   target  \n",
       "0       0  \n",
       "1       0  \n",
       "2       0  \n",
       "3       0  \n",
       "4       0  \n",
       "5       0  \n",
       "6       0  \n",
       "7       0  \n",
       "8       0  \n",
       "9       0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8['target']=iris.target\n",
    "df8.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "91a1d0b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(df8.drop('target',axis=1),df8.target,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "988e1242",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "model=RandomForestClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2ce0e0f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "98c3397d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2dacacff",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9c844c96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                  5.1               3.5                1.4               0.2   \n",
       "1                  4.9               3.0                1.4               0.2   \n",
       "2                  4.7               3.2                1.3               0.2   \n",
       "3                  4.6               3.1                1.5               0.2   \n",
       "4                  5.0               3.6                1.4               0.2   \n",
       "..                 ...               ...                ...               ...   \n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "     target  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "..      ...  \n",
       "145       2  \n",
       "146       2  \n",
       "147       2  \n",
       "148       2  \n",
       "149       2  \n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "37491d93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KFold(n_splits=10, random_state=None, shuffle=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kf=KFold(n_splits=10)\n",
    "kf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a25e0ba7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9083333333333333\n",
      "0.925\n",
      "0.95\n"
     ]
    }
   ],
   "source": [
    "def get(model,X_test,y_test,X_train,y_train):\n",
    "    model.fit(X_test,y_test)\n",
    "    print(model.score(X_train,y_train))\n",
    "get(LogisticRegression(),X_test,y_test,X_train,y_train)\n",
    "get(SVC(),X_test,y_test,X_train,y_train)\n",
    "get(RandomForestClassifier(n_estimators=50),X_test,y_test,X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e7bb6b5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StratifiedKFold(n_splits=10, random_state=None, shuffle=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import StratifiedKFold\n",
    "skf=StratifiedKFold(n_splits=10)\n",
    "skf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "66d05ca2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.96666667, 0.96666667, 0.93333333, 0.96666667, 1.        ])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "cross_val_score(LogisticRegression(max_iter=1000),iris.data,iris.target)\n",
    "cross_val_score(SVC(),iris.data,iris.target)\n",
    "cross_val_score(RandomForestClassifier(n_estimators=50),iris.data,iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "694b2478",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                  5.1               3.5                1.4               0.2\n",
       "1                  4.9               3.0                1.4               0.2\n",
       "2                  4.7               3.2                1.3               0.2\n",
       "3                  4.6               3.1                1.5               0.2\n",
       "4                  5.0               3.6                1.4               0.2\n",
       "..                 ...               ...                ...               ...\n",
       "145                6.7               3.0                5.2               2.3\n",
       "146                6.3               2.5                5.0               1.9\n",
       "147                6.5               3.0                5.2               2.0\n",
       "148                6.2               3.4                5.4               2.3\n",
       "149                5.9               3.0                5.1               1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9=pd.DataFrame(iris.data,columns=iris.feature_names)\n",
    "df9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5c4b9eb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
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       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     petal length (cm)  petal width (cm)\n",
       "0                  1.4               0.2\n",
       "1                  1.4               0.2\n",
       "2                  1.3               0.2\n",
       "3                  1.5               0.2\n",
       "4                  1.4               0.2\n",
       "..                 ...               ...\n",
       "145                5.2               2.3\n",
       "146                5.0               1.9\n",
       "147                5.2               2.0\n",
       "148                5.4               2.3\n",
       "149                5.1               1.8\n",
       "\n",
       "[150 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9=df9.drop(['sepal length (cm)','sepal width (cm)'],axis=1)\n",
    "df9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bd2865cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "96c997b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ratna\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "km=KMeans(n_clusters=3)\n",
    "yp=km.fit_predict(df9)\n",
    "yp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1740e6fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>clusters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     petal length (cm)  petal width (cm)  clusters\n",
       "0                  1.4               0.2         0\n",
       "1                  1.4               0.2         0\n",
       "2                  1.3               0.2         0\n",
       "3                  1.5               0.2         0\n",
       "4                  1.4               0.2         0\n",
       "..                 ...               ...       ...\n",
       "145                5.2               2.3         1\n",
       "146                5.0               1.9         1\n",
       "147                5.2               2.0         1\n",
       "148                5.4               2.3         1\n",
       "149                5.1               1.8         1\n",
       "\n",
       "[150 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9['clusters']=yp\n",
    "df9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f171e5dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df9.clusters.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3fa299bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df9a=df9[df9.clusters==0]\n",
    "df9b=df9[df9.clusters==1]\n",
    "df9c=df9[df9.clusters==2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "780a0e8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x20b17510490>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df9a['petal length (cm)'],df9a['petal width (cm)'],color='blue')\n",
    "plt.scatter(df9b['petal length (cm)'],df9b['petal width (cm)'],color='green')\n",
    "plt.scatter(df9c['petal length (cm)'],df9c['petal width (cm)'],color='orange')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d05eff5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ratna\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\ratna\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\ratna\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\ratna\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\ratna\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\ratna\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\ratna\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\ratna\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n",
      "C:\\Users\\ratna\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "sse = []\n",
    "k_rng = range(1,10)\n",
    "for k in k_rng:\n",
    "    km = KMeans(n_clusters=k)\n",
    "    km.fit(df9)\n",
    "    sse.append(km.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e9f0e83d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x20b175587d0>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ff/5Zffv2ldV68tcvuugi9ezZ0+sFokr1lZQz9heYWwwAAE2IR2EnMDBQHTp0UGUl0yhmSHTvgM7j5wAA1JXHPTsPP/ywHnroIeXn5/uiHvwC9sgCAMBzHvfsvPzyy/ruu+8UGxurjh07KiwsrMb5zZs3e6041NQn7mST8rGyCrUI8vi/PgAAmh2P/1recMMNPigDdREdEeJuUt6d7VC/jq3NLgkAgEbP47Dz6KOP+qIO1FFSe7tWf5OnjP2FhB0AAOqg3vMg6enp2r17tySpd+/erLHTQFxhZzt9OwAA1InHYScvL0+33nqrPv/8c0VGRkqq2hD0iiuu0Pz589W2bVtv14hqaFIGAMAzHj+NNWHCBBUVFWnnzp3Kz89Xfn6+duzYIYfDoYkTJ/qiRlSTdKJJ+bu8ozpWVmFyNQAANH4eh52lS5fqlVdeUa9evdzHEhISNGvWLC1ZssSrxaG26IgQRYUHy2lIu7NZbwcAgLPxOOw4nU4FBgbWOh4YGCin0+mVovDLXFNZ29kUFACAs/I47Fx55ZWaNGmSDh486D524MABTZkyRUOGDPFqcTi9kyspE3YAADgbj8POyy+/LIfDoU6dOqlLly7q0qWLOnfuLIfDoZdeeskXNeIUrsUFaVIGAODsPH4aKz4+Xps3b9bKlSv1zTffSJJ69eqllJQUrxeH03NNY7malFlJGQCAM/Por2R5eblCQ0O1detWXXXVVbrqqqt8VRd+QdSJJuW8olLtOuhQ/04sLggAwJmw63kT5ZrKom8HAIBfxq7nTZS7SZknsgAA+EXset5EJfFEFgAAdcKu502UK+x8f+ioiksrFBZMkzIAAKfj0V/IiooKWSwW3XXXXYqLi/NVTaiDqIgQRUcEK9dRql3ZDg2gSRkAgNPyqGcnICBAzz77rCoq2JOpMUiibwcAgLOq1wrKa9eu9UUt8FBS+0hJLC4IAMAv8bjR4+qrr9aDDz6ojIwM9evXr1aD8vXXX++14vDLkuIiJEnbCTsAAJyRx2HnT3/6kyTp+eefr3XOYrGwBk8DSqRJGQCAs6rXrudnehF0GlZUeIhiIkJkGNKubIfZ5QAA0Ch5HHaqKykp8VYdqCfX6M52mpQBADgtj8NOZWWlnnjiCbVv314tW7bUDz/8IEn661//qjfeeMPrBeKXuZ7IokkZAIDT8zjsPPnkk5o7d66eeeYZBQUFuY8nJibq9ddf92pxODv2yAIA4Jd5HHbeeecdvfbaaxo9erRsNpv7+AUXXKBvvvmm3oX8/e9/l8Vi0eTJk93HSkpKlJqaqjZt2qhly5YaNWqUcnNza/xeZmamhg8frhYtWigqKkoPPPBAs1oHqHqT8tHS5nPfAADUlcdh58CBA+ratWut406nU+Xl5fUqYuPGjXr11VfVp0+fGsenTJmiRYsW6YMPPtDatWt18OBBjRw50n2+srJSw4cPV1lZmTZs2KC3335bc+fO1fTp0+tVR1PUNjz4ZJPyQZqUAQA4lcdhJyEhQV988UWt4x9++KH69u3rcQFHjx7V6NGj9c9//lOtWrVyHy8sLNQbb7yh559/XldeeaX69eunt956Sxs2bND//vc/SdLy5cu1a9cuvfvuu/rVr36lq6++Wk888YRmzZqlsrKyM35naWmpHA5HjVdTlsimoAAAnJHHYWf69OkaP368nn76aTmdTi1YsEB//OMf9eSTT9ZrRCU1NVXDhw9XSkpKjePp6ekqLy+vcbxnz57q0KGD0tLSJElpaWlKSkpSdHS0+5phw4bJ4XBo586dZ/zOGTNmyG63u1/x8fEe192YuPt29heYWwgAAI2Qx2FnxIgRWrRokVauXKmwsDBNnz5du3fv1qJFi3TVVVd59Fnz58/X5s2bNWPGjFrncnJyFBQUpMjIyBrHo6OjlZOT476metBxnXedO5Np06apsLDQ/crKyvKo7sYmiZEdAADOqF5L7g4ePFgrVqw4py/OysrSpEmTtGLFCoWEhJzTZ3kqODhYwcHBDfqdvuSaxvrh52IdLa1QS1ZSBgDA7ZwWFTwX6enpysvL04UXXqiAgAAFBARo7dq1mjlzpgICAhQdHa2ysjIVFBTU+L3c3FzFxMRIkmJiYmo9neV677qmOWgbHqx29qom5Z2M7gAAUINpYWfIkCHKyMjQ1q1b3a/+/ftr9OjR7p8DAwO1atUq9+/s2bNHmZmZSk5OliQlJycrIyNDeXl57mtWrFihiIgIJSQkNPg9mYkmZQAATs+0+Y7w8HAlJibWOBYWFqY2bdq4j48bN05Tp05V69atFRERoQkTJig5OVmDBg2SJA0dOlQJCQm644479MwzzygnJ0ePPPKIUlNT/Wqaqi6S2tu1YlcuKykDAHCKRt3c8cILL8hqtWrUqFEqLS3VsGHD9Morr7jP22w2LV68WPfee6+Sk5MVFhamMWPG6PHHHzexanMksZIyAACnZTEMwzC7CLM5HA7Z7XYVFhYqIiLC7HLq5eejper/t5WyWKSMx4bRpAwA8Ht1/ftdp7+IU6dOrfMXP//883W+Ft5zXsuqJuXswhLtPFCogee3MbskAAAahTqFnS1bttR4v3nzZlVUVKhHjx6SpL1798pms6lfv37erxB1ltTeruzCEmUQdgAAcKtT2FmzZo375+eff17h4eF6++233ds7HDlyRGPHjtXgwYN9UyXqJKm9Xct35dK3AwBANR4/ev7cc89pxowZNfaxatWqlf72t7/pueee82px8EwiTcoAANTicdhxOBw6dOhQreOHDh1SUVGRV4pC/bi2jfjx52IVldRvB3oAAPyNx2Hnxhtv1NixY7VgwQLt379f+/fv10cffaRx48Zp5MiRvqgRdXRey2DFulZSPti0d3IHAMBbPH4+ec6cObr//vv1u9/9TuXlVaMHAQEBGjdunJ599lmvFwjPJLa362BhiXYcKNQgmpQBAPA87LRo0UKvvPKKnn32WX3//feSpC5duigsLMzrxcFzfeJoUgYAoLp6742VnZ2t7OxsdevWTWFhYWJtwsbBvUfWfsIOAABSPcLO4cOHNWTIEHXv3l3XXHONsrOzJVXtY3Xfffd5vUB4xtWk/ANNygAASKpH2JkyZYoCAwOVmZmpFi1auI/fcsstWrp0qVeLg+fatAxW+8hQSTQpAwAg1SPsLF++XE8//bTi4uJqHO/WrZv27dvntcJQf4ntq/YHYSoLAIB6hJ3i4uIaIzou+fn5Cg4O9kpRODeuqSyalAEAqEfYGTx4sN555x33e4vFIqfTqWeeeUZXXHGFV4tD/STFRUqSdhB2AADw/NHzZ555RkOGDNGmTZtUVlamP//5z9q5c6fy8/O1fv16X9QID1VvUnaUlCsiJNDkigAAMI/HIzuJiYnau3evLr30Uo0YMULFxcUaOXKktmzZoi5duviiRniodVjQySblAzQpAwCaN49GdsrLy/Wb3/xGc+bM0cMPP+yrmuAFSe3tOlBwXDsOFCq5CyspAwCaL49GdgIDA7V9+3Zf1QIvSjqxA/p2+nYAAM2cx9NYt99+u9544w1f1AIvcq2kTJMyAKC587hBuaKiQm+++aZWrlypfv361doT6/nnn/dacag/V5PyjzQpAwCaOY/Dzo4dO3ThhRdKkvbu3VvjnMVi8U5VOGeuJmVX387FXc4zuyQAAEzhcdhZs2aNL+qAD1RvUibsAACaq3rveo7Gz9WknMHj5wCAZszjkR1J2rRpk/7zn/8oMzNTZWVlNc4tWLDAK4Xh3Lm3jdhfYG4hAACYyOORnfnz5+viiy/W7t27tXDhQpWXl2vnzp1avXq17Ha7L2pEPbnCzk+Hj8lRUm5yNQAAmMPjsPPUU0/phRde0KJFixQUFKR//OMf+uabb3TzzTerQ4cOvqgR9dSq2krKPIIOAGiuPA4733//vYYPHy5JCgoKUnFxsSwWi6ZMmaLXXnvN6wXi3PSJY70dAEDz5nHYadWqlYqKiiRJ7du3144dOyRJBQUFOnbsmHerwzlzLS64fT9hBwDQPHncoHzZZZdpxYoVSkpK0k033aRJkyZp9erVWrFihYYMGeKLGnEOklhJGQDQzHkcdl5++WWVlJRIkh5++GEFBgZqw4YNGjVqlB555BGvF4hzU71JufB4ueyhrKQMAGhePA47rVu3dv9stVr14IMPerUgeFersCDFtQrV/iPHtfNAoS7uyuKCAIDmxeOwk5mZ+YvneSKr8Ulqb9f+I8eVQdgBADRDHoedTp06/eIeWJWVledUELwvKc6uJTtylEHfDgCgGfI47GzZsqXG+/Lycm3ZskXPP/+8nnzySa8VBu9xr6RM2AEANEMeh50LLrig1rH+/fsrNjZWzz77rEaOHOmVwuA9ibFVYWcfTcoAgGbIaxuB9ujRQxs3bvTWx8GLWoUFKb511UrKOxndAQA0Mx6P7DgcNXfQNgxD2dnZeuyxx9StWzevFQbvSmpvV1b+cW2nSRkA0Mx4HHYiIyNrNSgbhqH4+HjNnz/fa4XBuxLb2/VZBk3KAIDmx+Ows2bNmhrvrVar2rZtq65duyogwOOPQwPp0z5SEispAwCaH4/Tya9//Wtf1AEfS2wfIelEk/Kxctlb0KQMAGgePA47n3zySZ2vvf766z39ePhIZIuqJuWs/OPacbBQl9C3AwBoJjwOOzfccIMsFosMw6hx/NRjFovlrAsMzp49W7Nnz9ZPP/0kSerdu7emT5+uq6++WpJUUlKi++67T/Pnz1dpaamGDRumV155RdHR0e7PyMzM1L333qs1a9aoZcuWGjNmjGbMmMGU2mn0aR+prPyqlZQJOwCA5sLjR8+XL1+uX/3qV1qyZIkKCgpUUFCgJUuW6MILL9SyZcvkdDrldDrrtJJyXFyc/v73vys9PV2bNm3SlVdeqREjRmjnzp2SpClTpmjRokX64IMPtHbtWh08eLDGOj6VlZUaPny4ysrKtGHDBr399tuaO3eupk+f7ultNQuJrsUF99O3AwBoRgwP9e7d2/jiiy9qHV+3bp3Rs2dPTz+ullatWhmvv/66UVBQYAQGBhoffPCB+9zu3bsNSUZaWpphGIbx2WefGVar1cjJyXFfM3v2bCMiIsIoLS0943eUlJQYhYWF7ldWVpYhySgsLDzn+huzL/YeMjr+ZbEx+OnVZpcCAMA5KywsrNPfb49Hdr7//ntFRkbWOm63293TUfVRWVmp+fPnq7i4WMnJyUpPT1d5eblSUlLc1/Ts2VMdOnRQWlqaJCktLU1JSUk1prWGDRsmh8PhHh06nRkzZshut7tf8fHx9a67KXFtG5GZX9WkDABAc+Bx2BkwYICmTp2q3Nxc97Hc3Fw98MADuuiiizwuICMjQy1btlRwcLDuueceLVy4UAkJCcrJyVFQUFCtYBUdHa2cnBxJUk5OTo2g4zrvOncm06ZNU2FhofuVlZXlcd1Nkb1FoDq0biGJfbIAAM2Hx128b775pm688UZ16NDBPSKSlZWlbt266eOPP/a4gB49emjr1q0qLCzUhx9+qDFjxmjt2rUef44ngoODFRwc7NPvaKyS2tuVmX9MGQcKdWk3mpQBAP7P47DTtWtXbd++XStWrNA333wjSerVq5dSUlJqraxcF0FBQerataskqV+/ftq4caP+8Y9/6JZbblFZWZkKCgpqjO7k5uYqJiZGkhQTE6Ovv/66xue5Rpxc16CmxPZ2fZqRzeKCAIBmo17PZ1ssFg0dOlRDhw6VJBUUFNQr6JyO0+lUaWmp+vXrp8DAQK1atUqjRo2SJO3Zs0eZmZlKTk6WJCUnJ+vJJ59UXl6eoqKiJEkrVqxQRESEEhISvFKPv+kTV9W3s/1AgbmFAADQQDzu2Xn66af1/vvvu9/ffPPNatOmjdq3b69t27Z59FnTpk3TunXr9NNPPykjI0PTpk3T559/rtGjR8tut2vcuHGaOnWq1qxZo/T0dI0dO1bJyckaNGiQJGno0KFKSEjQHXfcoW3btmnZsmV65JFHlJqa2mynqc4mMbYq7GTlH1fBsTKTqwEAwPc8Djtz5sxx9+qsWLFCK1as0JIlS3T11VfrgQce8Oiz8vLydOedd6pHjx4aMmSINm7cqGXLlumqq66SJL3wwgu69tprNWrUKF122WWKiYnRggUL3L9vs9m0ePFi2Ww2JScn6/bbb9edd96pxx9/3NPbajaqNynvOOA4y9UAADR9FsM4ZSnkswgNDdXevXsVHx+vSZMmqaSkRK+++qr27t2rgQMH6siRI76q1WccDofsdrsKCwsVERFhdjk+lzpvsz7dnq0//6aH/nR5V7PLAQCgXur699vjkZ1WrVq5H9VeunSpex0cwzDqtGoyzOdab4cmZQBAc+Bxg/LIkSP1u9/9Tt26ddPhw4fd+1ht2bLF/VQVGjdX2GGtHQBAc+Bx2HnhhRfUqVMnZWVl6ZlnnlHLli0lSdnZ2frTn/7k9QLhfac2KUe2CDK5IgAAfMfjnh1/1Nx6diTp18+u0b7Dx/SvcRdpcLe2ZpcDAIDHfNazA/+QyFQWAKCZIOw0U31oUgYANBOEnWbK1aS8fT9hBwDg3+oUdmbOnKmSkhJJUmZmpmjzafp6nwg7+48c15FiVlIGAPivOoWdqVOnyuGoWm23c+fOOnTokE+Lgu/ZQwPVqc2JlZQPMroDAPBfdXr0PDY2Vh999JGuueYaGYah/fv3u0d6TtWhQwevFgjfSWxv10+Hj2n7/kKeyAIA+K06hZ1HHnlEEyZM0Pjx42WxWDRgwIBa1xiGIYvFwirKTUhSe7sWb8+mSRkA4NfqFHbuvvtu3Xbbbdq3b5/69OmjlStXqk2bNr6uDT6WFMfj5wAA/1fnFZTDw8OVmJiot956S5dccomCg4N9WRcaQOIpTcqtwlhJGQDgfzzeLmLMmDGSpPT0dO3evVuSlJCQoAsvvNC7lcHnIkKqmpR/OnxMGQcKdVl3+nYAAP7H47CTl5enW2+9VZ9//rkiIyMlSQUFBbriiis0f/58tW3LH8ymJCkukrADAPBrHi8qOGHCBBUVFWnnzp3Kz89Xfn6+duzYIYfDoYkTJ/qiRvhQUvuqvUQyWFwQAOCnPB7ZWbp0qVauXKlevXq5jyUkJGjWrFkaOnSoV4uD77FHFgDA33k8suN0OhUYGFjreGBgoJxOp1eKQsNxhZ0DBceVz0rKAAA/5HHYufLKKzVp0iQdPHjQfezAgQOaMmWKhgwZ4tXi4HsRIYHqfF6YJEZ3AAD+yeOw8/LLL8vhcKhTp07q0qWLunTpos6dO8vhcOill17yRY3wsUR2QAcA+DGPe3bi4+O1efNmrVy5Ut98840kqVevXkpJSfF6cWgYfdrbtWjbQZqUAQB+yeOwI0kWi0VXXXWVrrrqKm/XAxPQpAwA8GceT2PB//Q+8fg5TcoAAH9E2AFNygAAv0bYgaSqHdAlKWN/gbmFAADgZYQdSKoWdhjZAQD4mXo1KEtVe2Tl5eXVWkiwT58+51wUGt7Jx88dJlcCAIB3eRx20tPTNWbMGO3evVuGYUiqejrLMAxZLBZVVlZ6vUj4XmK1JuXDR0vVpmWwyRUBAOAdHoedu+66S927d9cbb7yh6OhoWSwWX9SFBhYeEqjzzwvTDz8XK+NAoS7vEWV2SQAAeIXHYeeHH37QRx99pK5du/qiHpgosb1dP/xcrB2EHQCAH/G4QXnIkCHatm2bL2qByfrE0aQMAPA/Ho/svP766xozZox27NihxMTEWjugX3/99V4rDg3LvZIy20YAAPyIx2EnLS1N69ev15IlS2qdo0G5aesdW9WkfLCwhCZlAIDf8Hgaa8KECbr99tuVnZ0tp9NZ40XQadrCQwJ1fltWUgYA+BePw87hw4c1ZcoURUdH+6IemCyJqSwAgJ/xOOyMHDlSa9as8UUtaARYSRkA4G887tnp3r27pk2bpi+//FJJSUm1GpQnTpzoteLQ8JLcKykTdgAA/sFiuJZBrqPOnTuf+cMsFv3www/nXFRDczgcstvtKiwsVEREhNnlmOpoaYWSHlsmw5A2PZKi82hSBgA0UnX9++3xyM6PP/54ToWhcWsZHKDO54Xph0NVKylfweKCAIAmjl3PUUsf11QWTcoAAD9Qr72xfsmbb75Z72LQOCS2t+vjrQe1nb4dAIAf8Hhk58iRIzVeeXl5Wr16tRYsWKCCggKPPmvGjBkaMGCAwsPDFRUVpRtuuEF79uypcU1JSYlSU1PVpk0btWzZUqNGjVJubm6NazIzMzV8+HC1aNFCUVFReuCBB1RRUeHpreEEmpQBAP7E45GdhQsX1jrmdDp17733qkuXLh591tq1a5WamqoBAwaooqJCDz30kIYOHapdu3YpLKxqcbspU6bo008/1QcffCC73a7x48dr5MiRWr9+vSSpsrJSw4cPV0xMjDZs2KDs7GzdeeedCgwM1FNPPeXp7UFS7/Z2WSxSdmGJDhWVqm04TcoAgKbL46exzmTPnj26/PLLlZ2dXe/POHTokKKiorR27VpddtllKiwsVNu2bTVv3jz99re/lSR988036tWrl9LS0jRo0CAtWbJE1157rQ4ePOhe6HDOnDn6y1/+okOHDikoKKjW95SWlqq0tNT93uFwKD4+nqexqhny3Of6/lCx3vr9AF3RkyZlAEDjU9ensbzWoPz999+f89RRYWHVtEnr1q0lSenp6SovL1dKSor7mp49e6pDhw5KS0uTVLVXV1JSUo0VnYcNGyaHw6GdO3ee9ntmzJghu93ufsXHx59T3f6IxQUBAP7C42msqVOn1nhvGIays7P16aefasyYMfUuxOl0avLkybrkkkuUmJgoScrJyVFQUJAiIyNrXBsdHa2cnBz3NaduXeF677rmVNOmTatxH66RHZyUFBepj7ceJOwAAJo8j8POli1bary3Wq1q27atnnvuubM+qfVLUlNTtWPHDn355Zf1/oy6Cg4OVnAwfSi/hD2yAAD+wuOw44t9scaPH6/Fixdr3bp1iouLcx+PiYlRWVmZCgoKaozu5ObmKiYmxn3N119/XePzXE9rua6B53rHRshikXIcNCkDAJo2j3t2jh8/rmPHjrnf79u3Ty+++KKWL1/u8ZcbhqHx48dr4cKFWr16da2tKPr166fAwECtWrXKfWzPnj3KzMxUcnKyJCk5OVkZGRnKy8tzX7NixQpFREQoISHB45pQJSw4QOefV/VEHI+gAwCaMo/DzogRI/TOO+9IkgoKCnTRRRfpueee04gRIzR79myPPis1NVXvvvuu5s2bp/DwcOXk5CgnJ0fHjx+XJNntdo0bN05Tp07VmjVrlJ6errFjxyo5OVmDBg2SJA0dOlQJCQm64447tG3bNi1btkyPPPKIUlNTmao6R33iIiVJ25nKAgA0YR6Hnc2bN2vw4MGSpA8//FAxMTHat2+f3nnnHc2cOdOjz5o9e7YKCwt1+eWXq127du7X+++/777mhRde0LXXXqtRo0bpsssuU0xMjBYsWOA+b7PZtHjxYtlsNiUnJ+v222/XnXfeqccff9zTW8MpEnkiCwDgBzzu2Tl27JjCw8MlScuXL9fIkSNltVo1aNAg7du3z6PPqssSPyEhIZo1a5ZmzZp1xms6duyozz77zKPvxtmxkjIAwB94PLLTtWtXffzxx8rKytKyZcs0dOhQSVJeXh4L8vmZ6k3KeUUlZpcDAEC9eBx2pk+frvvvv1+dOnXSwIED3Y3Cy5cvV9++fb1eIMwTFhygLm1bSmJ0BwDQdHkcdn77298qMzNTmzZt0tKlS93HhwwZohdeeMGrxcF8J9fbcZhcCQAA9eNxz45UtX7NqWvYXHTRRV4pCI1LUnu7Fm45oIwDBWaXAgBAvXhtbyz4p6Q4nsgCADRthB38ooR2VU3KuY5SmpQBAE0SYQe/KCw4QF1pUgYANGGEHZyVq0mZlZQBAE0RYQdnlcjiggCAJoywg7PqQ5MyAKAJI+zgrBJiI2R1NSk7aFIGADQthB2cVYugkyspM7oDAGhqCDuoE9bbAQA0VYQd1MnJbSMIOwCApoWwgzpxhx1GdgAATQxhB3XialLOKypVLk3KAIAmhLCDOmkRFKCuUSealJnKAgA0IYQd1FkiU1kAgCaIsIM668NKygCAJoiwgzpzPX6+nbADAGhCCDuos4R2dlkt0iGalAEATQhhB3UWGmRTt6hwSTQpAwCaDsIOPOJqUmYqCwDQVBB24JGk9hGSaFIGADQdhB14pPoeWYZhmFwNAABnR9iBR2o2KZeaXQ4AAGdF2IFHajQpM5UFAGgCCDvwGCspAwCaEsIOPNbH1bezv8DcQgAAqAPCDjx2cmTHQZMyAKDRI+zAYwntImS1SD8fpUkZAND4EXbgsdAgm7pHVzUpb2cqCwDQyBF2UC+J7IAOAGgiCDuolySeyAIANBGEHdTLyZWUaVIGADRuhB3US0K7CNmsFv18tFQ5jhKzywEA4IwIO6iXkECbukW1lCRl7GcqCwDQeBF2UG+uvp1Pth1UeaXT5GoAADg9wg7qbVjvGEnS4u3ZuuXVNB0oOG5yRQAA1EbYQb2lJETrldEXKjw4QJszCzR85hdauSvX7LIAAKiBsINzck1SO306cbD6xNlVcKxcf3hnk/62eJfKKpjWAgA0DqaGnXXr1um6665TbGysLBaLPv744xrnDcPQ9OnT1a5dO4WGhiolJUXffvttjWvy8/M1evRoRUREKDIyUuPGjdPRo0cb8C7QoU0LfXBPsn5/cSdJ0utf/qibX03T/iPHzC0MAACZHHaKi4t1wQUXaNasWac9/8wzz2jmzJmaM2eOvvrqK4WFhWnYsGEqKTn5qPPo0aO1c+dOrVixQosXL9a6det09913N9Qt4ITgAJseu7635tzeTxEhAdqaVaBr/vGFlu/MMbs0AEAzZzEayYpwFotFCxcu1A033CCpalQnNjZW9913n+6//35JUmFhoaKjozV37lzdeuut2r17txISErRx40b1799fkrR06VJdc8012r9/v2JjY0/7XaWlpSotPbmBpcPhUHx8vAoLCxUREeHbG20GsvKPafx7W7Qtq0CSNPaSTpp2dS8FBTBrCgDwHofDIbvdfta/3432r8+PP/6onJwcpaSkuI/Z7XYNHDhQaWlpkqS0tDRFRka6g44kpaSkyGq16quvvjrjZ8+YMUN2u939io+P992NNEPxrVvog/+XrD9c2lmS9Nb6n3TTnA3KymdaCwDQ8Bpt2MnJqZr+iI6OrnE8OjrafS4nJ0dRUVE1zgcEBKh169bua05n2rRpKiwsdL+ysrK8XD2CAqx65NoE/fPO/rKHBmrb/kJdM/MLLd2RbXZpAIBmptGGHV8KDg5WREREjRd846qEaH068VL17RCpopIK3fPuZj363x0qrag0uzQAQDPRaMNOTEzVgnW5uTXXbcnNzXWfi4mJUV5eXo3zFRUVys/Pd18D88W1aqH//L9k/b/LzpckvZ22T7+dnaZ9h4tNrgwA0Bw02rDTuXNnxcTEaNWqVe5jDodDX331lZKTkyVJycnJKigoUHp6uvua1atXy+l0auDAgQ1eM84s0GbVtGt66c3f91dki0BlHCjUtTO/1KfbmdYCAPiWqWHn6NGj2rp1q7Zu3Sqpqil569atyszMlMVi0eTJk/W3v/1Nn3zyiTIyMnTnnXcqNjbW/cRWr1699Jvf/EZ//OMf9fXXX2v9+vUaP368br311jM+iQVzXdkzWp9NHKz+HVupqLRCqfM2668f71BJOdNaAADfMPXR888//1xXXHFFreNjxozR3LlzZRiGHn30Ub322msqKCjQpZdeqldeeUXdu3d3X5ufn6/x48dr0aJFslqtGjVqlGbOnKmWLVvWuY66ProG7ymvdOr5FXs1+/PvJUkJ7SI0a/SF6nxemMmVAQCairr+/W406+yYibBjns/35Gnqf7Ypv7hMYUE2zRjVR9dfwKgcAODsmvw6O2geLu8Rpc8mDtZFnVqruKxSE9/boocWZjCtBQDwGsIOTBdjD9G8Pw7U+Cu6ymKR5n2VqRtmrdf3h9jjDABw7gg7aBQCbFbdP6yH3h57kdqEBembnCJd99KX+njLAbNLAwA0cYQdNCqXdW+rzyYN1qDzW+tYWaUmv79VD360XcfLmNYCANQPYQeNTnREiP79h0GaOKSbLBZp/sYs3TBrvb7LKzK7NABAE0TYQaNks1o09aruenfcQJ3XMlh7cot03Uvr9VH6frNLAwA0MYQdNGqXdD1Pn026VBd3aaPj5ZW674NteuCDbTpWVmF2aQCAJoKwg0YvKjxE/xo3UFNSustqkT5I368RL6/X3lymtQAAZ0fYQZNgs1o0KaWb/v2HQWobHqxv847q+pe/1AebsswuDQDQyBF20KQkd2mjJZMGa3C381RS7tQDH27X1P9sVXEp01oAgNMj7KDJOa9lsN4ee5EeGNZDVou0YPMBXf/yl/omx2F2aQCARoiwgybJarUo9Yqueu+PgxQdEazvDxVrxMvrNf/rTLHdGwCgOsIOmrSB57fRZxMH69fd26q0wqkHF2Ro8vtbdZRpLQDACYQdNHltWgbrrd8P0F9+01M2q0X/3XpQ17/0pXYdZFoLAEDYgZ+wWi269/Iumn/3ILWzh+iHn4t1wyvr9e+v9jGtBQDNHGEHfmVAp9b6dOJgXdkzSmUVTj28cIcmvLdFRSXlZpcGADAJYQd+p3VYkF6/s78euqanAqwWLd6erete+lI7DhSaXRoAwASEHfglq9Wiuy/rovf/X7Ji7SH66fAxjXxlg/6V9hPTWgDQzBB24Nf6dWylzyYNVkqvKJVVOvXX/+5U6rzNcjCtBQDNBmEHfi+yRZD+eWd/PTK8lwKsFn2WkaNrZ36pjP1MawFAc0DYQbNgsVj0h8Hn64N7ktU+MlSZ+cc0avYGzV3/I9NaAODnCDtoVvp2aKXPJg7W0IRolVU69diiXbr33c0qPM60FgD4K8IOmh17i0C9ekc/PXpdggJtFi3dmaNrX/pC27IKzC4NAOADhB00SxaLRWMv6awP77lY8a1DlZV/XL+ds0FvfMm0FgD4G8IOmrUL4iO1eMJgXZ0Yo/JKQ08s3qW7/5WugmNlZpcGAPASwg6aPXtooF4ZfaEeH9FbQTarVuzK1fCZX2pz5hGzSwMAeAFhB1DVtNadyZ204E8Xq2ObFjpQcFw3z0nTP9f9wLQWADRxFoP/Sy6HwyG73a7CwkJFRESYXQ5MVlRSrgcXZOjT7dmSpI5tWigmIkStw4LUOixIbcKC1Mr9c7BahQW6/w0OsJlcPQA0H3X9+x3QgDUBTUJ4SKBevq2vks9vo8cX79K+w8e07/CxOv1uy+AAtT4RhtqEBalViyC1aVkVjFq3OPFvyxM/twxSeHCALBaLj+8IAJo3RnbEyA7OLK+oRN/lHtXh4jLlV38dK1P+0ZM/HykuU4XT8/9VCrRZ1MoVgk4ZOTrdCFLrFkEKsDH7DAASIzuAV0SFhygqPOSs1xmGIcfxiqoQVFyq/OJy5ReX6nBxVRBy/Ztf7efiskqVVxrKKypVXlFpnWuKCAlQm5bBVSNILapCkXu0KKz2q0WQjdEjAM0aYQfwAovFInuLQNlbBKrzeWF1+p2S8soao0VHjpXp8NFTRo6O1TxvGJKjpEKOkgr9+HNxnb4nOMBaY5TotCNIJ4JSq7AgRYYGMnoEwK8QdgCThATaFBsZqtjI0DpdX+k0VHi8vMbI0WlHkE4EpcPFZSqtcKq0wqmDhSU6WFhS59rsoYEnRo4C3SNIrjDUukWQIl3HT7y3hwbKamX0CEDjRNgBmgib1eIelakLwzB0vLyy1mjRkWNVQaj6yNGRE31HBcfLZRhS4fFyFR4v1491rM1qqdpd/kzhqGpUKbDGcZqzATQUwg7gpywWi1oEBahF6wDFt25Rp985OXp0YoTI1Wt0zNVzVH7y+Il/i0oq5DTknm77/lDdptcCrJYzjhTVCkct6D8CUH+EHQBuno4eSVJ5pVNHjpWp4Fh5ncKRqzm7wmnoUFGpDnnQnB0UYP3FMHQyLJ08FxLI2kdAc0fYAXBOAm3WOj+15lJSXnkyHJ0mDOUfK1dBtfeu/qOyCqdyHCXKcdS9/ygk0KrQQJuCAqwKCrAq0GZVkK3q5yDbifcBJ99XXWM5eW2AVcHVrgu0nXqttdpnWxQccMq1NmutY4E2CyNUQAMi7ABocCGBNsXYbYqx1z0gHS+rrDZidLppttrhqbzSUEm5UyXlTh/eTf0EnRKSThfETh+uLCfe2xQYYFGwzaoAm1UBNosCrVX/BtisCrBaFGC1KPDEuQDriWO2E8es1a6rdizQZpXNdezE57nO2ayENDRNhB0ATUJokE3tg0LVvo5PrxmGoaOlFSo4Vq6S8kqVVVaNDJVXGiqrcKqsslJlFYbKKp0qr3BWO1/1BFt5tfdl7vM1r3ddW3bG613XGao8ZdHJssqqc6r7LF6jEHBKELJZqwLYaY+dCFSBrmMnfjfA5vr5ZNgKcH+OK6i5glbV79osks1mlc1ikc0qWS1V4ctmtbh/PnlMJ37HIqtVJ37HIqvVcvLnatdaLVXfZ7VWTeVW/V61f90/n/wsQl/TQtgB4JcsFovCQwIVHhJodimSqpq/zxSOSk8JSSd/PhHMThOiyk8JUxWVTlWc+I6KSkMVTkMVzqqfy0+cq3CeuK7SUPmJc666qv9upbPq/OnW13d9Toka32hZQ7JaVDM0VQtG1hOBzHUs4AxBq+rYKZ9zSnhzf0+t35c7dJ16vPq1J8/r9DVYVDPYnRIMq5+vcbza/dX8z6BmYLRaTh6PjghRoElreBF2AKABVP0hszWphunqQajC/e8Zjp0IT+5jTlcIOxm6Th6rGb7KK6sHM0OVTqfKqwWzCqehSsNQZWXVv07Xe6chp+tfp1RpVF3rdNY8V/13nM6q+6qo8bun+byz7P7iNCRnpSGp2e+4VGer7vu1urRtacp3+03YmTVrlp599lnl5OToggsu0EsvvaSLLrrI7LIAoMlyBbTmyKgRlHQyDDlrB67KGuFKpw1aFacNVTolYNX+LKdx5uOu9yc/V7WudV9/mrqd1e7tTIGv9u9VO3aaEFkzWFZ9huu4zcSpP78IO++//76mTp2qOXPmaODAgXrxxRc1bNgw7dmzR1FRUWaXBwBoYiyWE71EZhcCr/CLXc8HDhyoAQMG6OWXX5YkOZ1OxcfHa8KECXrwwQdrXV9aWqrS0pNdgQ6HQ/Hx8ex6DgBAE1LXXc+b/G5/ZWVlSk9PV0pKivuY1WpVSkqK0tLSTvs7M2bMkN1ud7/i4+MbqlwAANDAmnzY+fnnn1VZWano6Ogax6Ojo5WTk3Pa35k2bZoKCwvdr6ysrIYoFQAAmKBZTkcGBwcrODjY7DIAAEADaPIjO+edd55sNptyc3NrHM/NzVVMTIxJVQEAgMaiyYedoKAg9evXT6tWrXIfczqdWrVqlZKTk02sDAAANAZ+MY01depUjRkzRv3799dFF12kF198UcXFxRo7dqzZpQEAAJP5Rdi55ZZbdOjQIU2fPl05OTn61a9+paVLl9ZqWgYAAM2PX6yzc67q+pw+AABoPJrNOjsAAAC/hLADAAD8GmEHAAD4NcIOAADwa4QdAADg1/zi0fNz5XogzeFwmFwJAACoK9ff7bM9WE7YkVRUVCRJ7H4OAEATVFRUJLvdfsbzrLOjqu0lDh48qPDwcFksFq99rsPhUHx8vLKysvx2/R5/v0fur+nz93vk/po+f79HX96fYRgqKipSbGysrNYzd+YwsiPJarUqLi7OZ58fERHhl/8DXJ2/3yP31/T5+z1yf02fv9+jr+7vl0Z0XGhQBgAAfo2wAwAA/Bphx4eCg4P16KOPKjg42OxSfMbf75H7a/r8/R65v6bP3++xMdwfDcoAAMCvMbIDAAD8GmEHAAD4NcIOAADwa4QdAADg1wg7PrBu3Tpdd911io2NlcVi0ccff2x2SV41Y8YMDRgwQOHh4YqKitINN9ygPXv2mF2WV82ePVt9+vRxL4KVnJysJUuWmF2Wz/z973+XxWLR5MmTzS7FKx577DFZLJYar549e5pdltcdOHBAt99+u9q0aaPQ0FAlJSVp06ZNZpflFZ06dar136HFYlFqaqrZpXlFZWWl/vrXv6pz584KDQ1Vly5d9MQTT5x1j6empqioSJMnT1bHjh0VGhqqiy++WBs3bmzwOlhB2QeKi4t1wQUX6K677tLIkSPNLsfr1q5dq9TUVA0YMEAVFRV66KGHNHToUO3atUthYWFml+cVcXFx+vvf/65u3brJMAy9/fbbGjFihLZs2aLevXubXZ5Xbdy4Ua+++qr69Oljdile1bt3b61cudL9PiDAv/7P3ZEjR3TJJZfoiiuu0JIlS9S2bVt9++23atWqldmlecXGjRtVWVnpfr9jxw5dddVVuummm0ysynuefvppzZ49W2+//bZ69+6tTZs2aezYsbLb7Zo4caLZ5XnNH/7wB+3YsUP/+te/FBsbq3fffVcpKSnatWuX2rdv33CFGPApScbChQvNLsOn8vLyDEnG2rVrzS7Fp1q1amW8/vrrZpfhVUVFRUa3bt2MFStWGL/+9a+NSZMmmV2SVzz66KPGBRdcYHYZPvWXv/zFuPTSS80uo8FMmjTJ6NKli+F0Os0uxSuGDx9u3HXXXTWOjRw50hg9erRJFXnfsWPHDJvNZixevLjG8QsvvNB4+OGHG7QWprFwzgoLCyVJrVu3NrkS36isrNT8+fNVXFys5ORks8vxqtTUVA0fPlwpKSlml+J13377rWJjY3X++edr9OjRyszMNLskr/rkk0/Uv39/3XTTTYqKilLfvn31z3/+0+yyfKKsrEzvvvuu7rrrLq9u1mymiy++WKtWrdLevXslSdu2bdOXX36pq6++2uTKvKeiokKVlZUKCQmpcTw0NFRffvllg9biX+O6aHBOp1OTJ0/WJZdcosTERLPL8aqMjAwlJyerpKRELVu21MKFC5WQkGB2WV4zf/58bd682ZT5c18bOHCg5s6dqx49eig7O1v/93//p8GDB2vHjh0KDw83uzyv+OGHHzR79mxNnTpVDz30kDZu3KiJEycqKChIY8aMMbs8r/r4449VUFCg3//+92aX4jUPPvigHA6HevbsKZvNpsrKSj355JMaPXq02aV5TXh4uJKTk/XEE0+oV69eio6O1nvvvae0tDR17dq1YYtp0HGkZkh+Po11zz33GB07djSysrLMLsXrSktLjW+//dbYtGmT8eCDDxrnnXeesXPnTrPL8orMzEwjKirK2LZtm/uYP01jnerIkSNGRESEX01DBgYGGsnJyTWOTZgwwRg0aJBJFfnO0KFDjWuvvdbsMrzqvffeM+Li4oz33nvP2L59u/HOO+8YrVu3NubOnWt2aV713XffGZdddpkhybDZbMaAAQOM0aNHGz179mzQOhjZQb2NHz9eixcv1rp16xQXF2d2OV4XFBTk/v8++vXrp40bN+of//iHXn31VZMrO3fp6enKy8vThRde6D5WWVmpdevW6eWXX1ZpaalsNpuJFXpXZGSkunfvru+++87sUrymXbt2tUYae/XqpY8++sikinxj3759WrlypRYsWGB2KV71wAMP6MEHH9Stt94qSUpKStK+ffs0Y8YMvxqZ69Kli9auXavi4mI5HA61a9dOt9xyi84///wGrYOeHXjMMAyNHz9eCxcu1OrVq9W5c2ezS2oQTqdTpaWlZpfhFUOGDFFGRoa2bt3qfvXv31+jR4/W1q1b/SroSNLRo0f1/fffq127dmaX4jWXXHJJrSUf9u7dq44dO5pUkW+89dZbioqK0vDhw80uxauOHTsmq7Xmn2CbzSan02lSRb4VFhamdu3a6ciRI1q2bJlGjBjRoN/PyI4PHD16tMb/B/njjz9q69atat26tTp06GBiZd6RmpqqefPm6b///a/Cw8OVk5MjSbLb7QoNDTW5Ou+YNm2arr76anXo0EFFRUWaN2+ePv/8cy1btszs0rwiPDy8Vo9VWFiY2rRp4xe9V/fff7+uu+46dezYUQcPHtSjjz4qm82m2267zezSvGbKlCm6+OKL9dRTT+nmm2/W119/rddee02vvfaa2aV5jdPp1FtvvaUxY8b43dIB1113nZ588kl16NBBvXv31pYtW/T888/rrrvuMrs0r1q2bJkMw1CPHj303Xff6YEHHlDPnj01duzYhi2kQSfNmok1a9YYkmq9xowZY3ZpXnG6e5NkvPXWW2aX5jV33XWX0bFjRyMoKMho27atMWTIEGP58uVml+VT/tSzc8sttxjt2rUzgoKCjPbt2xu33HKL8d1335ldltctWrTISExMNIKDg42ePXsar732mtkledWyZcsMScaePXvMLsXrHA6HMWnSJKNDhw5GSEiIcf755xsPP/ywUVpaanZpXvX+++8b559/vhEUFGTExMQYqampRkFBQYPXYTEMP1uuEQAAoBp6dgAAgF8j7AAAAL9G2AEAAH6NsAMAAPwaYQcAAPg1wg4AAPBrhB0AAODXCDsAAMCvEXYA+KXLL79ckydPNrsMAI0AYQcAAPg1wg4AAPBrhB0AzcKnn34qu92uf//732aXAqCBBZhdAAD42rx583TPPfdo3rx5uvbaa80uB0ADY2QHgF+bNWuW/vSnP2nRokUEHaCZYmQHgN/68MMPlZeXp/Xr12vAgAFmlwPAJIzsAPBbffv2Vdu2bfXmm2/KMAyzywFgEsIOAL/VpUsXrVmzRv/97381YcIEs8sBYBKmsQD4te7du2vNmjW6/PLLFRAQoBdffNHskgA0MMIOAL/Xo0cPrV69WpdffrlsNpuee+45s0sC0IAsBhPZAADAj9GzAwAA/BphBwAA+DXCDgAA8GuEHQAA4NcIOwAAwK8RdgAAgF8j7AAAAL9G2AEAAH6NsAMAAPwaYQcAAPg1wg4AAPBr/x8naJQaj/N3GQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('k')\n",
    "plt.ylabel('sum of squared errors')\n",
    "plt.plot(k_rng,sse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1427c139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data': array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,\n",
       "         1.065e+03],\n",
       "        [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,\n",
       "         1.050e+03],\n",
       "        [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,\n",
       "         1.185e+03],\n",
       "        ...,\n",
       "        [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,\n",
       "         8.350e+02],\n",
       "        [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,\n",
       "         8.400e+02],\n",
       "        [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,\n",
       "         5.600e+02]]),\n",
       " 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "        2, 2]),\n",
       " 'frame': None,\n",
       " 'target_names': array(['class_0', 'class_1', 'class_2'], dtype='<U7'),\n",
       " 'DESCR': '.. _wine_dataset:\\n\\nWine recognition dataset\\n------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 178\\n    :Number of Attributes: 13 numeric, predictive attributes and the class\\n    :Attribute Information:\\n \\t\\t- Alcohol\\n \\t\\t- Malic acid\\n \\t\\t- Ash\\n\\t\\t- Alcalinity of ash  \\n \\t\\t- Magnesium\\n\\t\\t- Total phenols\\n \\t\\t- Flavanoids\\n \\t\\t- Nonflavanoid phenols\\n \\t\\t- Proanthocyanins\\n\\t\\t- Color intensity\\n \\t\\t- Hue\\n \\t\\t- OD280/OD315 of diluted wines\\n \\t\\t- Proline\\n\\n    - class:\\n            - class_0\\n            - class_1\\n            - class_2\\n\\t\\t\\n    :Summary Statistics:\\n    \\n    ============================= ==== ===== ======= =====\\n                                   Min   Max   Mean     SD\\n    ============================= ==== ===== ======= =====\\n    Alcohol:                      11.0  14.8    13.0   0.8\\n    Malic Acid:                   0.74  5.80    2.34  1.12\\n    Ash:                          1.36  3.23    2.36  0.27\\n    Alcalinity of Ash:            10.6  30.0    19.5   3.3\\n    Magnesium:                    70.0 162.0    99.7  14.3\\n    Total Phenols:                0.98  3.88    2.29  0.63\\n    Flavanoids:                   0.34  5.08    2.03  1.00\\n    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\\n    Proanthocyanins:              0.41  3.58    1.59  0.57\\n    Colour Intensity:              1.3  13.0     5.1   2.3\\n    Hue:                          0.48  1.71    0.96  0.23\\n    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\\n    Proline:                       278  1680     746   315\\n    ============================= ==== ===== ======= =====\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThis is a copy of UCI ML Wine recognition datasets.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\\n\\nThe data is the results of a chemical analysis of wines grown in the same\\nregion in Italy by three different cultivators. There are thirteen different\\nmeasurements taken for different constituents found in the three types of\\nwine.\\n\\nOriginal Owners: \\n\\nForina, M. et al, PARVUS - \\nAn Extendible Package for Data Exploration, Classification and Correlation. \\nInstitute of Pharmaceutical and Food Analysis and Technologies,\\nVia Brigata Salerno, 16147 Genoa, Italy.\\n\\nCitation:\\n\\nLichman, M. (2013). UCI Machine Learning Repository\\n[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\\nSchool of Information and Computer Science. \\n\\n.. topic:: References\\n\\n  (1) S. Aeberhard, D. Coomans and O. de Vel, \\n  Comparison of Classifiers in High Dimensional Settings, \\n  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  \\n  Mathematics and Statistics, James Cook University of North Queensland. \\n  (Also submitted to Technometrics). \\n\\n  The data was used with many others for comparing various \\n  classifiers. The classes are separable, though only RDA \\n  has achieved 100% correct classification. \\n  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \\n  (All results using the leave-one-out technique) \\n\\n  (2) S. Aeberhard, D. Coomans and O. de Vel, \\n  \"THE CLASSIFICATION PERFORMANCE OF RDA\" \\n  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \\n  Mathematics and Statistics, James Cook University of North Queensland. \\n  (Also submitted to Journal of Chemometrics).\\n',\n",
       " 'feature_names': ['alcohol',\n",
       "  'malic_acid',\n",
       "  'ash',\n",
       "  'alcalinity_of_ash',\n",
       "  'magnesium',\n",
       "  'total_phenols',\n",
       "  'flavanoids',\n",
       "  'nonflavanoid_phenols',\n",
       "  'proanthocyanins',\n",
       "  'color_intensity',\n",
       "  'hue',\n",
       "  'od280/od315_of_diluted_wines',\n",
       "  'proline']}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_wine\n",
    "wine=load_wine()\n",
    "wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2af9cd63",
   "metadata": {},
   "outputs": [
    {
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       "      <th>alcohol</th>\n",
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       "      <th>hue</th>\n",
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       "      <th>proline</th>\n",
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       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
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       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
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       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</td>\n",
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       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740.0</td>\n",
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       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750.0</td>\n",
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       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835.0</td>\n",
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       "      <th>176</th>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840.0</td>\n",
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       "    <tr>\n",
       "      <th>177</th>\n",
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       "      <td>96.0</td>\n",
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       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "..       ...         ...   ...                ...        ...            ...   \n",
       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
       "\n",
       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0          3.06                  0.28             2.29             5.64  1.04   \n",
       "1          2.76                  0.26             1.28             4.38  1.05   \n",
       "2          3.24                  0.30             2.81             5.68  1.03   \n",
       "3          3.49                  0.24             2.18             7.80  0.86   \n",
       "4          2.69                  0.39             1.82             4.32  1.04   \n",
       "..          ...                   ...              ...              ...   ...   \n",
       "173        0.61                  0.52             1.06             7.70  0.64   \n",
       "174        0.75                  0.43             1.41             7.30  0.70   \n",
       "175        0.69                  0.43             1.35            10.20  0.59   \n",
       "176        0.68                  0.53             1.46             9.30  0.60   \n",
       "177        0.76                  0.56             1.35             9.20  0.61   \n",
       "\n",
       "     od280/od315_of_diluted_wines  proline  \n",
       "0                            3.92   1065.0  \n",
       "1                            3.40   1050.0  \n",
       "2                            3.17   1185.0  \n",
       "3                            3.45   1480.0  \n",
       "4                            2.93    735.0  \n",
       "..                            ...      ...  \n",
       "173                          1.74    740.0  \n",
       "174                          1.56    750.0  \n",
       "175                          1.56    835.0  \n",
       "176                          1.62    840.0  \n",
       "177                          1.60    560.0  \n",
       "\n",
       "[178 rows x 13 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10=pd.DataFrame(wine.data,columns=wine.feature_names)\n",
    "df10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "60becdf9",
   "metadata": {},
   "outputs": [
    {
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       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
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       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
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       "      <td>2.80</td>\n",
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       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
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       "      <td>95.0</td>\n",
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       "      <td>0.61</td>\n",
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       "      <td>1.06</td>\n",
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       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740.0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1      13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2      13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3      14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4      13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "..       ...         ...   ...                ...        ...            ...   \n",
       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
       "174    13.40        3.91  2.48               23.0      102.0           1.80   \n",
       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
       "176    13.17        2.59  2.37               20.0      120.0           1.65   \n",
       "177    14.13        4.10  2.74               24.5       96.0           2.05   \n",
       "\n",
       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0          3.06                  0.28             2.29             5.64  1.04   \n",
       "1          2.76                  0.26             1.28             4.38  1.05   \n",
       "2          3.24                  0.30             2.81             5.68  1.03   \n",
       "3          3.49                  0.24             2.18             7.80  0.86   \n",
       "4          2.69                  0.39             1.82             4.32  1.04   \n",
       "..          ...                   ...              ...              ...   ...   \n",
       "173        0.61                  0.52             1.06             7.70  0.64   \n",
       "174        0.75                  0.43             1.41             7.30  0.70   \n",
       "175        0.69                  0.43             1.35            10.20  0.59   \n",
       "176        0.68                  0.53             1.46             9.30  0.60   \n",
       "177        0.76                  0.56             1.35             9.20  0.61   \n",
       "\n",
       "     od280/od315_of_diluted_wines  proline  target  \n",
       "0                            3.92   1065.0       0  \n",
       "1                            3.40   1050.0       0  \n",
       "2                            3.17   1185.0       0  \n",
       "3                            3.45   1480.0       0  \n",
       "4                            2.93    735.0       0  \n",
       "..                            ...      ...     ...  \n",
       "173                          1.74    740.0       2  \n",
       "174                          1.56    750.0       2  \n",
       "175                          1.56    835.0       2  \n",
       "176                          1.62    840.0       2  \n",
       "177                          1.60    560.0       2  \n",
       "\n",
       "[178 rows x 14 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df10['target']=wine.target\n",
    "df10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5cb019cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(df10.drop(['target'],axis=1),df10['target'],test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b716ca5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "model=GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "316e8220",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8dc67f82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "876c5cf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>14.10</td>\n",
       "      <td>2.02</td>\n",
       "      <td>2.40</td>\n",
       "      <td>18.8</td>\n",
       "      <td>103.0</td>\n",
       "      <td>2.75</td>\n",
       "      <td>2.92</td>\n",
       "      <td>0.32</td>\n",
       "      <td>2.38</td>\n",
       "      <td>6.20</td>\n",
       "      <td>1.070</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1060.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>13.71</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.36</td>\n",
       "      <td>16.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.61</td>\n",
       "      <td>2.88</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.69</td>\n",
       "      <td>3.80</td>\n",
       "      <td>1.110</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1035.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>13.05</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.10</td>\n",
       "      <td>17.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.03</td>\n",
       "      <td>5.04</td>\n",
       "      <td>0.880</td>\n",
       "      <td>3.35</td>\n",
       "      <td>885.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>12.29</td>\n",
       "      <td>1.61</td>\n",
       "      <td>2.21</td>\n",
       "      <td>20.4</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.46</td>\n",
       "      <td>3.05</td>\n",
       "      <td>0.906</td>\n",
       "      <td>1.82</td>\n",
       "      <td>870.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>12.22</td>\n",
       "      <td>1.29</td>\n",
       "      <td>1.94</td>\n",
       "      <td>19.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.04</td>\n",
       "      <td>0.39</td>\n",
       "      <td>2.08</td>\n",
       "      <td>2.70</td>\n",
       "      <td>0.860</td>\n",
       "      <td>3.02</td>\n",
       "      <td>312.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>12.29</td>\n",
       "      <td>2.83</td>\n",
       "      <td>2.22</td>\n",
       "      <td>18.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.99</td>\n",
       "      <td>2.15</td>\n",
       "      <td>1.150</td>\n",
       "      <td>3.30</td>\n",
       "      <td>290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>13.49</td>\n",
       "      <td>3.59</td>\n",
       "      <td>2.19</td>\n",
       "      <td>19.5</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.62</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.88</td>\n",
       "      <td>5.70</td>\n",
       "      <td>0.810</td>\n",
       "      <td>1.82</td>\n",
       "      <td>580.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>11.41</td>\n",
       "      <td>0.74</td>\n",
       "      <td>2.50</td>\n",
       "      <td>21.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.48</td>\n",
       "      <td>2.01</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.44</td>\n",
       "      <td>3.08</td>\n",
       "      <td>1.100</td>\n",
       "      <td>2.31</td>\n",
       "      <td>434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>13.83</td>\n",
       "      <td>1.65</td>\n",
       "      <td>2.60</td>\n",
       "      <td>17.2</td>\n",
       "      <td>94.0</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.99</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.60</td>\n",
       "      <td>1.240</td>\n",
       "      <td>3.37</td>\n",
       "      <td>1265.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>12.08</td>\n",
       "      <td>1.33</td>\n",
       "      <td>2.30</td>\n",
       "      <td>23.6</td>\n",
       "      <td>70.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.38</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1.070</td>\n",
       "      <td>3.21</td>\n",
       "      <td>625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>14.34</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.70</td>\n",
       "      <td>25.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2.70</td>\n",
       "      <td>13.00</td>\n",
       "      <td>0.570</td>\n",
       "      <td>1.96</td>\n",
       "      <td>660.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.040</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>13.50</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.61</td>\n",
       "      <td>20.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.53</td>\n",
       "      <td>2.61</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1.66</td>\n",
       "      <td>3.52</td>\n",
       "      <td>1.120</td>\n",
       "      <td>3.82</td>\n",
       "      <td>845.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>11.87</td>\n",
       "      <td>4.31</td>\n",
       "      <td>2.39</td>\n",
       "      <td>21.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>2.86</td>\n",
       "      <td>3.03</td>\n",
       "      <td>0.21</td>\n",
       "      <td>2.91</td>\n",
       "      <td>2.80</td>\n",
       "      <td>0.750</td>\n",
       "      <td>3.64</td>\n",
       "      <td>380.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>13.78</td>\n",
       "      <td>2.76</td>\n",
       "      <td>2.30</td>\n",
       "      <td>22.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1.03</td>\n",
       "      <td>9.58</td>\n",
       "      <td>0.700</td>\n",
       "      <td>1.68</td>\n",
       "      <td>615.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>13.77</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.68</td>\n",
       "      <td>17.1</td>\n",
       "      <td>115.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>2.79</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.68</td>\n",
       "      <td>6.30</td>\n",
       "      <td>1.130</td>\n",
       "      <td>2.93</td>\n",
       "      <td>1375.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>11.64</td>\n",
       "      <td>2.06</td>\n",
       "      <td>2.46</td>\n",
       "      <td>21.6</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1.000</td>\n",
       "      <td>2.75</td>\n",
       "      <td>680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>13.52</td>\n",
       "      <td>3.17</td>\n",
       "      <td>2.72</td>\n",
       "      <td>23.5</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.55</td>\n",
       "      <td>4.35</td>\n",
       "      <td>0.890</td>\n",
       "      <td>2.06</td>\n",
       "      <td>520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>14.22</td>\n",
       "      <td>1.70</td>\n",
       "      <td>2.30</td>\n",
       "      <td>16.3</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.26</td>\n",
       "      <td>2.03</td>\n",
       "      <td>6.38</td>\n",
       "      <td>0.940</td>\n",
       "      <td>3.31</td>\n",
       "      <td>970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>14.21</td>\n",
       "      <td>4.04</td>\n",
       "      <td>2.44</td>\n",
       "      <td>18.9</td>\n",
       "      <td>111.0</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.65</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.24</td>\n",
       "      <td>0.870</td>\n",
       "      <td>3.33</td>\n",
       "      <td>1080.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>12.51</td>\n",
       "      <td>1.73</td>\n",
       "      <td>1.98</td>\n",
       "      <td>20.5</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.92</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.48</td>\n",
       "      <td>2.94</td>\n",
       "      <td>1.040</td>\n",
       "      <td>3.57</td>\n",
       "      <td>672.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>11.96</td>\n",
       "      <td>1.09</td>\n",
       "      <td>2.30</td>\n",
       "      <td>21.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>3.38</td>\n",
       "      <td>2.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>1.65</td>\n",
       "      <td>3.21</td>\n",
       "      <td>0.990</td>\n",
       "      <td>3.13</td>\n",
       "      <td>886.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>13.41</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.12</td>\n",
       "      <td>18.8</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.68</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.48</td>\n",
       "      <td>4.28</td>\n",
       "      <td>0.910</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1035.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>14.06</td>\n",
       "      <td>1.63</td>\n",
       "      <td>2.28</td>\n",
       "      <td>16.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.10</td>\n",
       "      <td>5.65</td>\n",
       "      <td>1.090</td>\n",
       "      <td>3.71</td>\n",
       "      <td>780.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>13.11</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.70</td>\n",
       "      <td>15.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2.98</td>\n",
       "      <td>3.18</td>\n",
       "      <td>0.26</td>\n",
       "      <td>2.28</td>\n",
       "      <td>5.30</td>\n",
       "      <td>1.120</td>\n",
       "      <td>3.18</td>\n",
       "      <td>502.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>12.82</td>\n",
       "      <td>3.37</td>\n",
       "      <td>2.30</td>\n",
       "      <td>19.5</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.97</td>\n",
       "      <td>10.26</td>\n",
       "      <td>0.720</td>\n",
       "      <td>1.75</td>\n",
       "      <td>685.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>13.03</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.71</td>\n",
       "      <td>16.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.03</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.46</td>\n",
       "      <td>4.60</td>\n",
       "      <td>1.190</td>\n",
       "      <td>2.48</td>\n",
       "      <td>392.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>12.16</td>\n",
       "      <td>1.61</td>\n",
       "      <td>2.31</td>\n",
       "      <td>22.8</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.78</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.56</td>\n",
       "      <td>2.45</td>\n",
       "      <td>1.330</td>\n",
       "      <td>2.26</td>\n",
       "      <td>495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>13.68</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2.36</td>\n",
       "      <td>17.2</td>\n",
       "      <td>104.0</td>\n",
       "      <td>2.42</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.97</td>\n",
       "      <td>3.84</td>\n",
       "      <td>1.230</td>\n",
       "      <td>2.87</td>\n",
       "      <td>990.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0.640</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.590</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>12.25</td>\n",
       "      <td>3.88</td>\n",
       "      <td>2.20</td>\n",
       "      <td>18.5</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.38</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.14</td>\n",
       "      <td>8.21</td>\n",
       "      <td>0.650</td>\n",
       "      <td>2.00</td>\n",
       "      <td>855.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>13.05</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.04</td>\n",
       "      <td>12.4</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2.72</td>\n",
       "      <td>3.27</td>\n",
       "      <td>0.17</td>\n",
       "      <td>2.91</td>\n",
       "      <td>7.20</td>\n",
       "      <td>1.120</td>\n",
       "      <td>2.91</td>\n",
       "      <td>1150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.75</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.41</td>\n",
       "      <td>16.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.81</td>\n",
       "      <td>5.60</td>\n",
       "      <td>1.150</td>\n",
       "      <td>2.90</td>\n",
       "      <td>1320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>13.83</td>\n",
       "      <td>1.57</td>\n",
       "      <td>2.62</td>\n",
       "      <td>20.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.72</td>\n",
       "      <td>6.60</td>\n",
       "      <td>1.130</td>\n",
       "      <td>2.57</td>\n",
       "      <td>1130.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>12.37</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1.92</td>\n",
       "      <td>19.6</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2.11</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.04</td>\n",
       "      <td>4.68</td>\n",
       "      <td>1.120</td>\n",
       "      <td>3.48</td>\n",
       "      <td>510.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "48     14.10        2.02  2.40               18.8      103.0           2.75   \n",
       "22     13.71        1.86  2.36               16.6      101.0           2.61   \n",
       "44     13.05        1.77  2.10               17.0      107.0           3.00   \n",
       "70     12.29        1.61  2.21               20.4      103.0           1.10   \n",
       "108    12.22        1.29  1.94               19.0       92.0           2.36   \n",
       "93     12.29        2.83  2.22               18.0       88.0           2.45   \n",
       "138    13.49        3.59  2.19               19.5       88.0           1.62   \n",
       "113    11.41        0.74  2.50               21.0       88.0           2.48   \n",
       "51     13.83        1.65  2.60               17.2       94.0           2.45   \n",
       "89     12.08        1.33  2.30               23.6       70.0           2.20   \n",
       "158    14.34        1.68  2.70               25.0       98.0           2.80   \n",
       "0      14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "24     13.50        1.81  2.61               20.0       96.0           2.53   \n",
       "124    11.87        4.31  2.39               21.0       82.0           2.86   \n",
       "164    13.78        2.76  2.30               22.0       90.0           1.35   \n",
       "53     13.77        1.90  2.68               17.1      115.0           3.00   \n",
       "88     11.64        2.06  2.46               21.6       84.0           1.95   \n",
       "142    13.52        3.17  2.72               23.5       97.0           1.55   \n",
       "56     14.22        1.70  2.30               16.3      118.0           3.20   \n",
       "45     14.21        4.04  2.44               18.9      111.0           2.85   \n",
       "104    12.51        1.73  1.98               20.5       85.0           2.20   \n",
       "74     11.96        1.09  2.30               21.0      101.0           3.38   \n",
       "41     13.41        3.84  2.12               18.8       90.0           2.45   \n",
       "20     14.06        1.63  2.28               16.0      126.0           3.00   \n",
       "66     13.11        1.01  1.70               15.0       78.0           2.98   \n",
       "167    12.82        3.37  2.30               19.5       88.0           1.48   \n",
       "76     13.03        0.90  1.71               16.0       86.0           1.95   \n",
       "86     12.16        1.61  2.31               22.8       90.0           1.78   \n",
       "32     13.68        1.83  2.36               17.2      104.0           2.42   \n",
       "173    13.71        5.65  2.45               20.5       95.0           1.68   \n",
       "175    13.27        4.28  2.26               20.0      120.0           1.59   \n",
       "144    12.25        3.88  2.20               18.5      112.0           1.38   \n",
       "50     13.05        1.73  2.04               12.4       92.0           2.72   \n",
       "12     13.75        1.73  2.41               16.0       89.0           2.60   \n",
       "17     13.83        1.57  2.62               20.0      115.0           2.95   \n",
       "67     12.37        1.17  1.92               19.6       78.0           2.11   \n",
       "\n",
       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity  \\\n",
       "48         2.92                  0.32             2.38             6.20   \n",
       "22         2.88                  0.27             1.69             3.80   \n",
       "44         3.00                  0.28             2.03             5.04   \n",
       "70         1.02                  0.37             1.46             3.05   \n",
       "108        2.04                  0.39             2.08             2.70   \n",
       "93         2.25                  0.25             1.99             2.15   \n",
       "138        0.48                  0.58             0.88             5.70   \n",
       "113        2.01                  0.42             1.44             3.08   \n",
       "51         2.99                  0.22             2.29             5.60   \n",
       "89         1.59                  0.42             1.38             1.74   \n",
       "158        1.31                  0.53             2.70            13.00   \n",
       "0          3.06                  0.28             2.29             5.64   \n",
       "24         2.61                  0.28             1.66             3.52   \n",
       "124        3.03                  0.21             2.91             2.80   \n",
       "164        0.68                  0.41             1.03             9.58   \n",
       "53         2.79                  0.39             1.68             6.30   \n",
       "88         1.69                  0.48             1.35             2.80   \n",
       "142        0.52                  0.50             0.55             4.35   \n",
       "56         3.00                  0.26             2.03             6.38   \n",
       "45         2.65                  0.30             1.25             5.24   \n",
       "104        1.92                  0.32             1.48             2.94   \n",
       "74         2.14                  0.13             1.65             3.21   \n",
       "41         2.68                  0.27             1.48             4.28   \n",
       "20         3.17                  0.24             2.10             5.65   \n",
       "66         3.18                  0.26             2.28             5.30   \n",
       "167        0.66                  0.40             0.97            10.26   \n",
       "76         2.03                  0.24             1.46             4.60   \n",
       "86         1.69                  0.43             1.56             2.45   \n",
       "32         2.69                  0.42             1.97             3.84   \n",
       "173        0.61                  0.52             1.06             7.70   \n",
       "175        0.69                  0.43             1.35            10.20   \n",
       "144        0.78                  0.29             1.14             8.21   \n",
       "50         3.27                  0.17             2.91             7.20   \n",
       "12         2.76                  0.29             1.81             5.60   \n",
       "17         3.40                  0.40             1.72             6.60   \n",
       "67         2.00                  0.27             1.04             4.68   \n",
       "\n",
       "       hue  od280/od315_of_diluted_wines  proline  \n",
       "48   1.070                          2.75   1060.0  \n",
       "22   1.110                          4.00   1035.0  \n",
       "44   0.880                          3.35    885.0  \n",
       "70   0.906                          1.82    870.0  \n",
       "108  0.860                          3.02    312.0  \n",
       "93   1.150                          3.30    290.0  \n",
       "138  0.810                          1.82    580.0  \n",
       "113  1.100                          2.31    434.0  \n",
       "51   1.240                          3.37   1265.0  \n",
       "89   1.070                          3.21    625.0  \n",
       "158  0.570                          1.96    660.0  \n",
       "0    1.040                          3.92   1065.0  \n",
       "24   1.120                          3.82    845.0  \n",
       "124  0.750                          3.64    380.0  \n",
       "164  0.700                          1.68    615.0  \n",
       "53   1.130                          2.93   1375.0  \n",
       "88   1.000                          2.75    680.0  \n",
       "142  0.890                          2.06    520.0  \n",
       "56   0.940                          3.31    970.0  \n",
       "45   0.870                          3.33   1080.0  \n",
       "104  1.040                          3.57    672.0  \n",
       "74   0.990                          3.13    886.0  \n",
       "41   0.910                          3.00   1035.0  \n",
       "20   1.090                          3.71    780.0  \n",
       "66   1.120                          3.18    502.0  \n",
       "167  0.720                          1.75    685.0  \n",
       "76   1.190                          2.48    392.0  \n",
       "86   1.330                          2.26    495.0  \n",
       "32   1.230                          2.87    990.0  \n",
       "173  0.640                          1.74    740.0  \n",
       "175  0.590                          1.56    835.0  \n",
       "144  0.650                          2.00    855.0  \n",
       "50   1.120                          2.91   1150.0  \n",
       "12   1.150                          2.90   1320.0  \n",
       "17   1.130                          2.57   1130.0  \n",
       "67   1.120                          3.48    510.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "987053de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1e180bc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48     0\n",
       "22     0\n",
       "44     0\n",
       "70     1\n",
       "108    1\n",
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       "50     0\n",
       "12     0\n",
       "17     0\n",
       "67     1\n",
       "Name: target, dtype: int32"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7e5dd933",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 1, 1, 2, 1, 0, 1, 2, 0, 0, 1, 2, 0, 1, 2, 0, 0, 1, 1,\n",
       "       0, 0, 1, 2, 1, 1, 0, 2, 2, 2, 0, 0, 0, 1])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9b5ccbca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "model=MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "17687603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MultinomialNB</label><div class=\"sk-toggleable__content\"><pre>MultinomialNB()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "53ad85df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8055555555555556"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5c469bc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48     0\n",
       "22     0\n",
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       "70     1\n",
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       "17     0\n",
       "67     1\n",
       "Name: target, dtype: int32"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "dcc602e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1, 1, 2, 1, 0, 2, 2, 0, 0, 1, 2, 0, 2, 1, 0, 0, 2, 0,\n",
       "       0, 1, 1, 2, 1, 1, 0, 2, 2, 2, 0, 0, 0, 1])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bbd30980",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "digits=datasets.load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "39eb5de9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ecc46d59",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_params = {\n",
    "    'svm': {\n",
    "        'model': svm.SVC(gamma='auto'),\n",
    "        'params' : {\n",
    "            'C': [1,50,100],\n",
    "            'kernel': ['rbf','linear']\n",
    "        }  \n",
    "    },\n",
    "    'random_forest': {\n",
    "        'model': RandomForestClassifier(),\n",
    "        'params' : {\n",
    "            'n_estimators': [1,10,30]\n",
    "        }\n",
    "    },\n",
    "    'logistic_regression' : {\n",
    "        'model': LogisticRegression(solver='liblinear',multi_class='auto'),\n",
    "        'params': {\n",
    "            'C': [1,12,20]\n",
    "        }\n",
    "    },\n",
    "    'naive_bayes_gaussian': {\n",
    "        'model': GaussianNB(),\n",
    "        'params': {}\n",
    "    },\n",
    "    'naive_bayes_multinomial': {\n",
    "        'model': MultinomialNB(),\n",
    "        'params': {}\n",
    "    },\n",
    "    'decision_tree': {\n",
    "        'model': DecisionTreeClassifier(),\n",
    "        'params': {\n",
    "            'criterion': ['gini','entropy'],\n",
    "            \n",
    "        }\n",
    "    }     \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3c65f6f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>best_score</th>\n",
       "      <th>best_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>svm</td>\n",
       "      <td>0.947697</td>\n",
       "      <td>{'C': 1, 'kernel': 'linear'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>random_forest</td>\n",
       "      <td>0.933794</td>\n",
       "      <td>{'n_estimators': 30}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>logistic_regression</td>\n",
       "      <td>0.922114</td>\n",
       "      <td>{'C': 1}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>naive_bayes_gaussian</td>\n",
       "      <td>0.806928</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>naive_bayes_multinomial</td>\n",
       "      <td>0.870350</td>\n",
       "      <td>{}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>decision_tree</td>\n",
       "      <td>0.811925</td>\n",
       "      <td>{'criterion': 'entropy'}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     model  best_score                   best_params\n",
       "0                      svm    0.947697  {'C': 1, 'kernel': 'linear'}\n",
       "1            random_forest    0.933794          {'n_estimators': 30}\n",
       "2      logistic_regression    0.922114                      {'C': 1}\n",
       "3     naive_bayes_gaussian    0.806928                            {}\n",
       "4  naive_bayes_multinomial    0.870350                            {}\n",
       "5            decision_tree    0.811925      {'criterion': 'entropy'}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "import pandas as pd\n",
    "scores = []\n",
    "\n",
    "for model_name, mp in model_params.items():\n",
    "    clf =  GridSearchCV(mp['model'], mp['params'], cv=5, return_train_score=False)\n",
    "    clf.fit(digits.data, digits.target)\n",
    "    scores.append({\n",
    "        'model': model_name,\n",
    "        'best_score': clf.best_score_,\n",
    "        'best_params': clf.best_params_\n",
    "    })\n",
    "    \n",
    "df = pd.DataFrame(scores,columns=['model','best_score','best_params'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aa978023",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_names']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "digits=load_digits()\n",
    "dir(digits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9c7c9b89",
   "metadata": {},
   "outputs": [
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       "      <td>...</td>\n",
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       "      <th>9</th>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <td>7.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1797 rows × 64 columns</p>\n",
       "</div>"
      ],
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       "0   0.0  6.0  13.0  10.0   0.0  0.0  0.0  \n",
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       "2   0.0  0.0   3.0  11.0  16.0  9.0  0.0  \n",
       "3   0.0  7.0  13.0  13.0   9.0  0.0  0.0  \n",
       "4   0.0  0.0   2.0  16.0   4.0  0.0  0.0  \n",
       "..  ...  ...   ...   ...   ...  ...  ...  \n",
       "9   0.0  2.0  14.0  15.0   9.0  0.0  0.0  \n",
       "0   0.0  6.0  16.0  14.0   6.0  0.0  0.0  \n",
       "8   0.0  2.0   9.0  13.0   6.0  0.0  0.0  \n",
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       "8   1.0  8.0  12.0  14.0  12.0  1.0  0.0  \n",
       "\n",
       "[1797 rows x 64 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df11=pd.DataFrame(digits.data,digits.target)\n",
    "df11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8b3de6b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1797 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0    1     2     3     4     5    6    7    8    9  ...   55   56   57  \\\n",
       "0   0.0  0.0   5.0  13.0   9.0   1.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "1   0.0  0.0   0.0  12.0  13.0   5.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "2   0.0  0.0   0.0   4.0  15.0  12.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "3   0.0  0.0   7.0  15.0  13.0   1.0  0.0  0.0  0.0  8.0  ...  0.0  0.0  0.0   \n",
       "4   0.0  0.0   0.0   1.0  11.0   0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "..  ...  ...   ...   ...   ...   ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "9   0.0  0.0   4.0  10.0  13.0   6.0  0.0  0.0  0.0  1.0  ...  0.0  0.0  0.0   \n",
       "0   0.0  0.0   6.0  16.0  13.0  11.0  1.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "8   0.0  0.0   1.0  11.0  15.0   1.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "9   0.0  0.0   2.0  10.0   7.0   0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "8   0.0  0.0  10.0  14.0   8.0   1.0  0.0  0.0  0.0  2.0  ...  0.0  0.0  1.0   \n",
       "\n",
       "     58    59    60    61   62   63  target  \n",
       "0   6.0  13.0  10.0   0.0  0.0  0.0       0  \n",
       "1   0.0  11.0  16.0  10.0  0.0  0.0       1  \n",
       "2   0.0   3.0  11.0  16.0  9.0  0.0       2  \n",
       "3   7.0  13.0  13.0   9.0  0.0  0.0       3  \n",
       "4   0.0   2.0  16.0   4.0  0.0  0.0       4  \n",
       "..  ...   ...   ...   ...  ...  ...     ...  \n",
       "9   2.0  14.0  15.0   9.0  0.0  0.0       9  \n",
       "0   6.0  16.0  14.0   6.0  0.0  0.0       0  \n",
       "8   2.0   9.0  13.0   6.0  0.0  0.0       8  \n",
       "9   5.0  12.0  16.0  12.0  0.0  0.0       9  \n",
       "8   8.0  12.0  14.0  12.0  1.0  0.0       8  \n",
       "\n",
       "[1797 rows x 65 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df11['target']=digits.target\n",
    "df11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4a3c04fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(df11.drop(['target'], axis=1), df11['target'], test_size=0.2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4b7b7481",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "610669d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=10)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=10)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=10)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d8610e46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9833333333333333"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "45b4a4aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[43,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0, 39,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0, 44,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0, 28,  0,  1,  0,  0,  0,  0],\n",
       "       [ 0,  1,  0,  0, 33,  0,  0,  1,  1,  0],\n",
       "       [ 0,  0,  0,  0,  0, 27,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0, 37,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0, 34,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0, 31,  1],\n",
       "       [ 0,  0,  0,  1,  0,  0,  0,  0,  0, 38]], dtype=int64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "y_predict=knn.predict(X_test)\n",
    "cm=confusion_matrix(y_test,y_predict)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "39689f13",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting seaborn\n",
      "  Downloading seaborn-0.13.0-py3-none-any.whl.metadata (5.3 kB)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in c:\\users\\ratna\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from seaborn) (1.24.1)\n",
      "Requirement already satisfied: pandas>=1.2 in c:\\users\\ratna\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from seaborn) (1.5.3)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.3 in c:\\users\\ratna\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from seaborn) (3.6.3)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\ratna\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (1.0.7)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\ratna\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\ratna\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (4.38.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\ratna\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\users\\ratna\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (23.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\ratna\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (9.4.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\ratna\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\ratna\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\ratna\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from pandas>=1.2->seaborn) (2022.7.1)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\ratna\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.3->seaborn) (1.16.0)\n",
      "Downloading seaborn-0.13.0-py3-none-any.whl (294 kB)\n",
      "   ---------------------------------------- 0.0/294.6 kB ? eta -:--:--\n",
      "   ---- ----------------------------------- 30.7/294.6 kB 1.3 MB/s eta 0:00:01\n",
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      "   -------------------------------------- 294.6/294.6 kB 908.4 kB/s eta 0:00:00\n",
      "Installing collected packages: seaborn\n",
      "Successfully installed seaborn-0.13.0\n"
     ]
    }
   ],
   "source": [
    "!pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c415e833",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(58.222222222222214, 0.5, 'Truth')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "plt.figure(figsize=(7,5))\n",
    "sn.heatmap(cm, annot=True)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Truth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dabd07c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        43\n",
      "           1       0.97      1.00      0.99        39\n",
      "           2       1.00      1.00      1.00        44\n",
      "           3       0.97      0.97      0.97        29\n",
      "           4       1.00      0.92      0.96        36\n",
      "           5       0.96      1.00      0.98        27\n",
      "           6       1.00      1.00      1.00        37\n",
      "           7       0.97      1.00      0.99        34\n",
      "           8       0.97      0.97      0.97        32\n",
      "           9       0.97      0.97      0.97        39\n",
      "\n",
      "    accuracy                           0.98       360\n",
      "   macro avg       0.98      0.98      0.98       360\n",
      "weighted avg       0.98      0.98      0.98       360\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "df160724",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "[1797 rows x 64 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5f8cc28b",
   "metadata": {},
   "outputs": [
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       "4           0.0        0.0        0.0        1.0       11.0        0.0   \n",
       "...         ...        ...        ...        ...        ...        ...   \n",
       "1792        0.0        0.0        4.0       10.0       13.0        6.0   \n",
       "1793        0.0        0.0        6.0       16.0       13.0       11.0   \n",
       "1794        0.0        0.0        1.0       11.0       15.0        1.0   \n",
       "1795        0.0        0.0        2.0       10.0        7.0        0.0   \n",
       "1796        0.0        0.0       10.0       14.0        8.0        1.0   \n",
       "\n",
       "      pixel_0_6  pixel_0_7  pixel_1_0  pixel_1_1  ...  pixel_6_6  pixel_6_7  \\\n",
       "0           0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
       "1           0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
       "2           0.0        0.0        0.0        0.0  ...        5.0        0.0   \n",
       "3           0.0        0.0        0.0        8.0  ...        9.0        0.0   \n",
       "4           0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
       "...         ...        ...        ...        ...  ...        ...        ...   \n",
       "1792        0.0        0.0        0.0        1.0  ...        4.0        0.0   \n",
       "1793        1.0        0.0        0.0        0.0  ...        1.0        0.0   \n",
       "1794        0.0        0.0        0.0        0.0  ...        0.0        0.0   \n",
       "1795        0.0        0.0        0.0        0.0  ...        2.0        0.0   \n",
       "1796        0.0        0.0        0.0        2.0  ...        8.0        0.0   \n",
       "\n",
       "      pixel_7_0  pixel_7_1  pixel_7_2  pixel_7_3  pixel_7_4  pixel_7_5  \\\n",
       "0           0.0        0.0        6.0       13.0       10.0        0.0   \n",
       "1           0.0        0.0        0.0       11.0       16.0       10.0   \n",
       "2           0.0        0.0        0.0        3.0       11.0       16.0   \n",
       "3           0.0        0.0        7.0       13.0       13.0        9.0   \n",
       "4           0.0        0.0        0.0        2.0       16.0        4.0   \n",
       "...         ...        ...        ...        ...        ...        ...   \n",
       "1792        0.0        0.0        2.0       14.0       15.0        9.0   \n",
       "1793        0.0        0.0        6.0       16.0       14.0        6.0   \n",
       "1794        0.0        0.0        2.0        9.0       13.0        6.0   \n",
       "1795        0.0        0.0        5.0       12.0       16.0       12.0   \n",
       "1796        0.0        1.0        8.0       12.0       14.0       12.0   \n",
       "\n",
       "      pixel_7_6  pixel_7_7  \n",
       "0           0.0        0.0  \n",
       "1           0.0        0.0  \n",
       "2           9.0        0.0  \n",
       "3           0.0        0.0  \n",
       "4           0.0        0.0  \n",
       "...         ...        ...  \n",
       "1792        0.0        0.0  \n",
       "1793        0.0        0.0  \n",
       "1794        0.0        0.0  \n",
       "1795        0.0        0.0  \n",
       "1796        1.0        0.0  \n",
       "\n",
       "[1797 rows x 64 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df11=df = pd.DataFrame(digits.data, columns=digits.feature_names)\n",
    "df11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "21162391",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df11.drop(['target'],axis=1)\n",
    "y=df11.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "155838eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 29)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca=PCA(0.95)\n",
    "X_pca=pca.fit_transform(X)\n",
    "X_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a555c2c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.n_components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c8696e05",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train_pca, X_test_pca, y_train, y_test = train_test_split(X_pca, y, test_size=0.2, random_state=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a44c5a79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9694444444444444"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model=LogisticRegression(max_iter=1000)\n",
    "model.fit(X_train_pca,y_train)\n",
    "model.score(X_test_pca,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6b4ed5a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1797, 2)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "pca=PCA(n_components=2)\n",
    "X_pca=pca.fit_transform(X)\n",
    "X_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "01786fab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>NAP</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>156</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>ST</td>\n",
       "      <td>98</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>ASY</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>108</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>122</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>45</td>\n",
       "      <td>M</td>\n",
       "      <td>TA</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>132</td>\n",
       "      <td>N</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>68</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>141</td>\n",
       "      <td>N</td>\n",
       "      <td>3.4</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>57</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>115</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>57</td>\n",
       "      <td>F</td>\n",
       "      <td>ATA</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>LVH</td>\n",
       "      <td>174</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>38</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>138</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>173</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>918 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "0     40   M           ATA        140          289          0     Normal   \n",
       "1     49   F           NAP        160          180          0     Normal   \n",
       "2     37   M           ATA        130          283          0         ST   \n",
       "3     48   F           ASY        138          214          0     Normal   \n",
       "4     54   M           NAP        150          195          0     Normal   \n",
       "..   ...  ..           ...        ...          ...        ...        ...   \n",
       "913   45   M            TA        110          264          0     Normal   \n",
       "914   68   M           ASY        144          193          1     Normal   \n",
       "915   57   M           ASY        130          131          0     Normal   \n",
       "916   57   F           ATA        130          236          0        LVH   \n",
       "917   38   M           NAP        138          175          0     Normal   \n",
       "\n",
       "     MaxHR ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "0      172              N      0.0       Up             0  \n",
       "1      156              N      1.0     Flat             1  \n",
       "2       98              N      0.0       Up             0  \n",
       "3      108              Y      1.5     Flat             1  \n",
       "4      122              N      0.0       Up             0  \n",
       "..     ...            ...      ...      ...           ...  \n",
       "913    132              N      1.2     Flat             1  \n",
       "914    141              N      3.4     Flat             1  \n",
       "915    115              Y      1.2     Flat             1  \n",
       "916    174              N      0.0     Flat             1  \n",
       "917    173              N      0.0       Up             0  \n",
       "\n",
       "[918 rows x 12 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df13=pd.read_csv('heart.csv')\n",
    "df13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0a48e583",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age               0\n",
       "Sex               0\n",
       "ChestPainType     0\n",
       "RestingBP         0\n",
       "Cholesterol       0\n",
       "FastingBS         0\n",
       "RestingECG        0\n",
       "MaxHR             0\n",
       "ExerciseAngina    0\n",
       "Oldpeak           0\n",
       "ST_Slope          0\n",
       "HeartDisease      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df13.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6c89fc19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>4</th>\n",
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       "      <th>913</th>\n",
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       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>918 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     N  Y\n",
       "0    1  0\n",
       "1    1  0\n",
       "2    1  0\n",
       "3    0  1\n",
       "4    1  0\n",
       "..  .. ..\n",
       "913  1  0\n",
       "914  1  0\n",
       "915  0  1\n",
       "916  1  0\n",
       "917  1  0\n",
       "\n",
       "[918 rows x 2 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy1=pd.get_dummies(df13['ChestPainType'])\n",
    "dummy2=pd.get_dummies(df13['RestingECG'])\n",
    "dummy3=pd.get_dummies(df13['ExerciseAngina'])\n",
    "\n",
    "dummy1\n",
    "dummy2\n",
    "dummy3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8df6bb6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>RestingECG</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>ExerciseAngina</th>\n",
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       "      <th>HeartDisease</th>\n",
       "      <th>ASY</th>\n",
       "      <th>ATA</th>\n",
       "      <th>NAP</th>\n",
       "      <th>TA</th>\n",
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       "      <th>Normal</th>\n",
       "      <th>ST</th>\n",
       "      <th>N</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>NAP</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>156</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>M</td>\n",
       "      <td>ATA</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>ST</td>\n",
       "      <td>98</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>ASY</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>108</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>122</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>45</td>\n",
       "      <td>M</td>\n",
       "      <td>TA</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>132</td>\n",
       "      <td>N</td>\n",
       "      <td>1.2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>68</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>141</td>\n",
       "      <td>N</td>\n",
       "      <td>3.4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>57</td>\n",
       "      <td>M</td>\n",
       "      <td>ASY</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>115</td>\n",
       "      <td>Y</td>\n",
       "      <td>1.2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>57</td>\n",
       "      <td>F</td>\n",
       "      <td>ATA</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>LVH</td>\n",
       "      <td>174</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>38</td>\n",
       "      <td>M</td>\n",
       "      <td>NAP</td>\n",
       "      <td>138</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>173</td>\n",
       "      <td>N</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>918 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  \\\n",
       "0     40   M           ATA        140          289          0     Normal   \n",
       "1     49   F           NAP        160          180          0     Normal   \n",
       "2     37   M           ATA        130          283          0         ST   \n",
       "3     48   F           ASY        138          214          0     Normal   \n",
       "4     54   M           NAP        150          195          0     Normal   \n",
       "..   ...  ..           ...        ...          ...        ...        ...   \n",
       "913   45   M            TA        110          264          0     Normal   \n",
       "914   68   M           ASY        144          193          1     Normal   \n",
       "915   57   M           ASY        130          131          0     Normal   \n",
       "916   57   F           ATA        130          236          0        LVH   \n",
       "917   38   M           NAP        138          175          0     Normal   \n",
       "\n",
       "     MaxHR ExerciseAngina  Oldpeak  ... HeartDisease  ASY  ATA  NAP  TA  LVH  \\\n",
       "0      172              N      0.0  ...            0    0    1    0   0    0   \n",
       "1      156              N      1.0  ...            1    0    0    1   0    0   \n",
       "2       98              N      0.0  ...            0    0    1    0   0    0   \n",
       "3      108              Y      1.5  ...            1    1    0    0   0    0   \n",
       "4      122              N      0.0  ...            0    0    0    1   0    0   \n",
       "..     ...            ...      ...  ...          ...  ...  ...  ...  ..  ...   \n",
       "913    132              N      1.2  ...            1    0    0    0   1    0   \n",
       "914    141              N      3.4  ...            1    1    0    0   0    0   \n",
       "915    115              Y      1.2  ...            1    1    0    0   0    0   \n",
       "916    174              N      0.0  ...            1    0    1    0   0    1   \n",
       "917    173              N      0.0  ...            0    0    0    1   0    0   \n",
       "\n",
       "     Normal  ST  N  Y  \n",
       "0         1   0  1  0  \n",
       "1         1   0  1  0  \n",
       "2         0   1  1  0  \n",
       "3         1   0  0  1  \n",
       "4         1   0  1  0  \n",
       "..      ...  .. .. ..  \n",
       "913       1   0  1  0  \n",
       "914       1   0  1  0  \n",
       "915       1   0  0  1  \n",
       "916       0   0  1  0  \n",
       "917       1   0  1  0  \n",
       "\n",
       "[918 rows x 21 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df13=pd.concat([df13,dummy1,dummy2,dummy3],axis=1)\n",
    "df13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7fdad6b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
       "      <th>HeartDisease</th>\n",
       "      <th>ASY</th>\n",
       "      <th>ATA</th>\n",
       "      <th>NAP</th>\n",
       "      <th>TA</th>\n",
       "      <th>LVH</th>\n",
       "      <th>Normal</th>\n",
       "      <th>ST</th>\n",
       "      <th>N</th>\n",
       "      <th>Y</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>M</td>\n",
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       "      <td>289</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>F</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>156</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>M</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>F</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>M</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Up</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>45</td>\n",
       "      <td>M</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>68</td>\n",
       "      <td>M</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>3.4</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>57</td>\n",
       "      <td>M</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>1.2</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>57</td>\n",
       "      <td>F</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Flat</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>918 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age Sex  RestingBP  Cholesterol  FastingBS  MaxHR  Oldpeak ST_Slope  \\\n",
       "0     40   M        140          289          0    172      0.0       Up   \n",
       "1     49   F        160          180          0    156      1.0     Flat   \n",
       "2     37   M        130          283          0     98      0.0       Up   \n",
       "3     48   F        138          214          0    108      1.5     Flat   \n",
       "4     54   M        150          195          0    122      0.0       Up   \n",
       "..   ...  ..        ...          ...        ...    ...      ...      ...   \n",
       "913   45   M        110          264          0    132      1.2     Flat   \n",
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       "915   57   M        130          131          0    115      1.2     Flat   \n",
       "916   57   F        130          236          0    174      0.0     Flat   \n",
       "917   38   M        138          175          0    173      0.0       Up   \n",
       "\n",
       "     HeartDisease  ASY  ATA  NAP  TA  LVH  Normal  ST  N  Y  \n",
       "0               0    0    1    0   0    0       1   0  1  0  \n",
       "1               1    0    0    1   0    0       1   0  1  0  \n",
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       "4               0    0    0    1   0    0       1   0  1  0  \n",
       "..            ...  ...  ...  ...  ..  ...     ...  .. .. ..  \n",
       "913             1    0    0    0   1    0       1   0  1  0  \n",
       "914             1    1    0    0   0    0       1   0  1  0  \n",
       "915             1    1    0    0   0    0       1   0  0  1  \n",
       "916             1    0    1    0   0    1       0   0  1  0  \n",
       "917             0    0    0    1   0    0       1   0  1  0  \n",
       "\n",
       "[918 rows x 18 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df13=df13.drop(['ChestPainType','RestingECG','ExerciseAngina'],axis=1)\n",
    "df13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f3be5c5a",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "     Age  RestingBP  Cholesterol  FastingBS  MaxHR  Oldpeak ST_Slope  \\\n",
       "0     40        140          289          0    172      0.0       Up   \n",
       "1     49        160          180          0    156      1.0     Flat   \n",
       "2     37        130          283          0     98      0.0       Up   \n",
       "3     48        138          214          0    108      1.5     Flat   \n",
       "4     54        150          195          0    122      0.0       Up   \n",
       "..   ...        ...          ...        ...    ...      ...      ...   \n",
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       "917   38        138          175          0    173      0.0       Up   \n",
       "\n",
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       "[918 rows x 19 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy4=pd.get_dummies(df13['Sex'])\n",
    "df13=pd.concat([df13,dummy4],axis=1)\n",
    "df13=df13.drop(['Sex'],axis=1)\n",
    "df13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7b958857",
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>45</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>68</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>57</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>57</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>38</td>\n",
       "      <td>138</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>173</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>918 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  RestingBP  Cholesterol  FastingBS  MaxHR  Oldpeak  HeartDisease  \\\n",
       "0     40        140          289          0    172      0.0             0   \n",
       "1     49        160          180          0    156      1.0             1   \n",
       "2     37        130          283          0     98      0.0             0   \n",
       "3     48        138          214          0    108      1.5             1   \n",
       "4     54        150          195          0    122      0.0             0   \n",
       "..   ...        ...          ...        ...    ...      ...           ...   \n",
       "913   45        110          264          0    132      1.2             1   \n",
       "914   68        144          193          1    141      3.4             1   \n",
       "915   57        130          131          0    115      1.2             1   \n",
       "916   57        130          236          0    174      0.0             1   \n",
       "917   38        138          175          0    173      0.0             0   \n",
       "\n",
       "     ASY  ATA  NAP  ...  LVH  Normal  ST  N  Y  F  M  Down  Flat  Up  \n",
       "0      0    1    0  ...    0       1   0  1  0  0  1     0     0   1  \n",
       "1      0    0    1  ...    0       1   0  1  0  1  0     0     1   0  \n",
       "2      0    1    0  ...    0       0   1  1  0  0  1     0     0   1  \n",
       "3      1    0    0  ...    0       1   0  0  1  1  0     0     1   0  \n",
       "4      0    0    1  ...    0       1   0  1  0  0  1     0     0   1  \n",
       "..   ...  ...  ...  ...  ...     ...  .. .. .. .. ..   ...   ...  ..  \n",
       "913    0    0    0  ...    0       1   0  1  0  0  1     0     1   0  \n",
       "914    1    0    0  ...    0       1   0  1  0  0  1     0     1   0  \n",
       "915    1    0    0  ...    0       1   0  0  1  0  1     0     1   0  \n",
       "916    0    1    0  ...    1       0   0  1  0  1  0     0     1   0  \n",
       "917    0    0    1  ...    0       1   0  1  0  0  1     0     0   1  \n",
       "\n",
       "[918 rows x 21 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "dummy6 = pd.get_dummies(df13['ST_Slope'])\n",
    "df13 = pd.concat([df13, dummy6], axis=1)\n",
    "df13 = df13.drop(['ST_Slope'], axis=1)\n",
    "df13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a17a8888",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.4331398 ,  0.41090889,  0.82507026, ..., -0.27144836,\n",
       "        -1.00218103,  1.15067399],\n",
       "       [-0.47848359,  1.49175234, -0.17196105, ..., -0.27144836,\n",
       "         0.99782372, -0.86905588],\n",
       "       [-1.75135854, -0.12951283,  0.7701878 , ..., -0.27144836,\n",
       "        -1.00218103,  1.15067399],\n",
       "       ...,\n",
       "       [ 0.37009972, -0.12951283, -0.62016778, ..., -0.27144836,\n",
       "         0.99782372, -0.86905588],\n",
       "       [ 0.37009972, -0.12951283,  0.34027522, ..., -0.27144836,\n",
       "         0.99782372, -0.86905588],\n",
       "       [-1.64528563,  0.30282455, -0.21769643, ..., -0.27144836,\n",
       "        -1.00218103,  1.15067399]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df13.drop(\"HeartDisease\",axis=1)\n",
    "y = df13.HeartDisease\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "X_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "03661ee0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_test,y_train=train_test_split(X,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6f92bcd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.75      , 0.76630435, 0.79891304, 0.75956284, 0.65027322])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "scores = cross_val_score(DecisionTreeClassifier(), X, y, cv=5)\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d93e234d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7450106913756236"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "457722dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (734, 20)\n",
      "y_train shape: (184,)\n",
      "X_test shape: (184, 20)\n",
      "y_test shape: (734,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X_train shape:\", X_train.shape)\n",
    "print(\"y_train shape:\", y_train.shape)\n",
    "print(\"X_test shape:\", X_test.shape)\n",
    "print(\"y_test shape:\", y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "15b01e14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8097826086956522\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "\n",
    "# Assuming you have already defined X and y\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# Instantiate and fit the BaggingClassifier\n",
    "bag_model = BaggingClassifier()\n",
    "bag_model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions\n",
    "predictions = bag_model.predict(X_test)\n",
    "\n",
    "# Evaluate the model\n",
    "accuracy = bag_model.score(X_test, y_test)\n",
    "print(\"Accuracy:\", accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f1d8fadc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.825623663578047"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "scores = cross_val_score(RandomForestClassifier(n_estimators=50), X, y, cv=5)\n",
    "scores.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15d08307",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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