FailureMode_shear_wall.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nTdUHlmOTzqS"
   },
   "source": [
    "# Steps Involved : Data Exploration, Model Fitting, Best Model Selection, Inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Created by Sujith Mangalathu and Jong-Su Jeon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "shGUCaYcTzqX"
   },
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M3xisLC_Tzqc",
    "outputId": "e700195e-d19e-458d-f631-939b7cf24964"
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   "outputs": [
    {
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       "      <th>Author</th>\n",
       "      <th>Specimen</th>\n",
       "      <th>FailureMode</th>\n",
       "      <th>M/Vlw</th>\n",
       "      <th>lw/tw</th>\n",
       "      <th>ρvwFy,vw/fc</th>\n",
       "      <th>ρhwFy,vw/fc</th>\n",
       "      <th>ρvcFy,vc/fc</th>\n",
       "      <th>ρhcFy,hc/fc</th>\n",
       "      <th>P/fcAg</th>\n",
       "      <th>Section</th>\n",
       "      <th>Ab/Ag</th>\n",
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       "      <th>0</th>\n",
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       "      <td>Lefas et al. (1990a)</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Lefas et al. (1990a)</td>\n",
       "      <td>SW13</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>0.346364</td>\n",
       "      <td>0.176108</td>\n",
       "      <td>0.448584</td>\n",
       "      <td>0.192118</td>\n",
       "      <td>0.208187</td>\n",
       "      <td>R</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Lefas et al. (1990a)</td>\n",
       "      <td>SW14</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Lefas et al. (1990a)</td>\n",
       "      <td>SW15</td>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>0.324766</td>\n",
       "      <td>0.165127</td>\n",
       "      <td>0.420612</td>\n",
       "      <td>0.180139</td>\n",
       "      <td>0.101727</td>\n",
       "      <td>R</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                Author Specimen  FailureMode  M/Vlw      lw/tw  \\\n",
       "0           1  Lefas et al. (1990a)     SW11            1    1.1  10.714286   \n",
       "1           2  Lefas et al. (1990a)     SW12            1    1.1  10.714286   \n",
       "2           3  Lefas et al. (1990a)     SW13            1    1.1  10.714286   \n",
       "3           4  Lefas et al. (1990a)     SW14            1    1.1  10.714286   \n",
       "4           5  Lefas et al. (1990a)     SW15            1    1.1  10.714286   \n",
       "\n",
       "   ρvwFy,vw/fc  ρhwFy,vw/fc  ρvcFy,vc/fc  ρhcFy,hc/fc    P/fcAg Section  Ab/Ag  \n",
       "0     0.268879     0.136711     0.348231     0.149140  0.000000       R    0.0  \n",
       "1     0.262358     0.133396     0.339785     0.145522  0.102168       R    0.0  \n",
       "2     0.346364     0.176108     0.448584     0.192118  0.208187       R    0.0  \n",
       "3     0.334023     0.169834     0.432601     0.185273  0.000000       R    0.0  \n",
       "4     0.324766     0.165127     0.420612     0.180139  0.101727       R    0.0  "
      ]
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     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import preprocessing\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "sns.set(style=\"white\")\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "\n",
    "\n",
    "data1 = pd.read_excel('Shear_Wall_Database.xlsx', sheet_name='Database')\n",
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "905_F1uHTzqt",
    "outputId": "68eea354-0c49-49dd-d2cc-dd4ebc40dce3"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FailureMode</th>\n",
       "      <th>M/Vlw</th>\n",
       "      <th>lw/tw</th>\n",
       "      <th>ρvwFy,vw/fc</th>\n",
       "      <th>ρhwFy,vw/fc</th>\n",
       "      <th>ρvcFy,vc/fc</th>\n",
       "      <th>ρhcFy,hc/fc</th>\n",
       "      <th>P/fcAg</th>\n",
       "      <th>Section</th>\n",
       "      <th>Ab/Ag</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>0.324766</td>\n",
       "      <td>0.165127</td>\n",
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       "      <td>0.180139</td>\n",
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      ],
      "text/plain": [
       "   FailureMode  M/Vlw      lw/tw  ρvwFy,vw/fc  ρhwFy,vw/fc  ρvcFy,vc/fc  \\\n",
       "0            1    1.1  10.714286     0.268879     0.136711     0.348231   \n",
       "1            1    1.1  10.714286     0.262358     0.133396     0.339785   \n",
       "2            1    1.1  10.714286     0.346364     0.176108     0.448584   \n",
       "3            1    1.1  10.714286     0.334023     0.169834     0.432601   \n",
       "4            1    1.1  10.714286     0.324766     0.165127     0.420612   \n",
       "\n",
       "   ρhcFy,hc/fc    P/fcAg Section  Ab/Ag  \n",
       "0     0.149140  0.000000       R    0.0  \n",
       "1     0.145522  0.102168       R    0.0  \n",
       "2     0.192118  0.208187       R    0.0  \n",
       "3     0.185273  0.000000       R    0.0  \n",
       "4     0.180139  0.101727       R    0.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=data1.loc[:, 'FailureMode':'Ab/Ag']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XmPTcUZyTzq3",
    "outputId": "fb918a60-1c14-423c-f8dd-36f453d1e543"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(393, 10)\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nhsGgZFLTzrA"
   },
   "source": [
    "# Checking the data types, If string exists it should be converted into numerical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "z6i272sCTzrC",
    "outputId": "e7872b04-4292-49d4-8818-af9cfa0416bb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 393 entries, 0 to 392\n",
      "Data columns (total 10 columns):\n",
      "FailureMode    393 non-null int64\n",
      "M/Vlw          393 non-null float64\n",
      "lw/tw          393 non-null float64\n",
      "ρvwFy,vw/fc    393 non-null float64\n",
      "ρhwFy,vw/fc    393 non-null float64\n",
      "ρvcFy,vc/fc    393 non-null float64\n",
      "ρhcFy,hc/fc    393 non-null float64\n",
      "P/fcAg         393 non-null float64\n",
      "Section        393 non-null object\n",
      "Ab/Ag          393 non-null float64\n",
      "dtypes: float64(8), int64(1), object(1)\n",
      "memory usage: 30.8+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(data.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IkTBM4NaTzrJ"
   },
   "source": [
    "Section contains string values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YbXN3todTzrL",
    "outputId": "4f70aee3-d0f7-49c5-fce2-aba1f4ce54c8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['R' 'B' 'F']\n"
     ]
    }
   ],
   "source": [
    "print(data['Section'].unique())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "z2EW4ytBTzrS",
    "outputId": "23acbf2e-f372-4c8b-b600-87b50d1e798c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Section\n",
      "B     95\n",
      "F     60\n",
      "R    238\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data.groupby('Section').size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YeJE-rDYTzrc"
   },
   "source": [
    "Rectnagular section is more than Barbel and flanged section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YBBgvm6dTzrf",
    "outputId": "93313868-6a34-49b7-c651-bbcd2dc8f072"
   },
   "outputs": [
    {
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       "      <td>1.100000</td>\n",
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       "      <td>0.334023</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>1.100000</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>0.271999</td>\n",
       "      <td>0.138298</td>\n",
       "      <td>0.352273</td>\n",
       "      <td>0.150870</td>\n",
       "      <td>0.211845</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>0.291146</td>\n",
       "      <td>0.049793</td>\n",
       "      <td>0.401398</td>\n",
       "      <td>0.161491</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2.115385</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.342420</td>\n",
       "      <td>0.121495</td>\n",
       "      <td>0.452979</td>\n",
       "      <td>0.136682</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>2.115385</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.290267</td>\n",
       "      <td>0.102767</td>\n",
       "      <td>0.383152</td>\n",
       "      <td>0.115613</td>\n",
       "      <td>0.106415</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2.115385</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.307270</td>\n",
       "      <td>0.108787</td>\n",
       "      <td>0.405596</td>\n",
       "      <td>0.122385</td>\n",
       "      <td>0.212300</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FailureMode     M/Vlw      lw/tw  ρvwFy,vw/fc  ρhwFy,vw/fc  ρvcFy,vc/fc  \\\n",
       "0            1  1.100000  10.714286     0.268879     0.136711     0.348231   \n",
       "1            1  1.100000  10.714286     0.262358     0.133396     0.339785   \n",
       "2            1  1.100000  10.714286     0.346364     0.176108     0.448584   \n",
       "3            1  1.100000  10.714286     0.334023     0.169834     0.432601   \n",
       "4            1  1.100000  10.714286     0.324766     0.165127     0.420612   \n",
       "5            1  1.100000  10.714286     0.271999     0.138298     0.352273   \n",
       "6            1  1.100000  10.714286     0.291146     0.049793     0.401398   \n",
       "7            1  2.115385  10.000000     0.342420     0.121495     0.452979   \n",
       "8            1  2.115385  10.000000     0.290267     0.102767     0.383152   \n",
       "9            1  2.115385  10.000000     0.307270     0.108787     0.405596   \n",
       "\n",
       "   ρhcFy,hc/fc    P/fcAg  Section  Ab/Ag  \n",
       "0     0.149140  0.000000        2    0.0  \n",
       "1     0.145522  0.102168        2    0.0  \n",
       "2     0.192118  0.208187        2    0.0  \n",
       "3     0.185273  0.000000        2    0.0  \n",
       "4     0.180139  0.101727        2    0.0  \n",
       "5     0.150870  0.211845        2    0.0  \n",
       "6     0.161491  0.000000        2    0.0  \n",
       "7     0.136682  0.000000        2    0.0  \n",
       "8     0.115613  0.106415        2    0.0  \n",
       "9     0.122385  0.212300        2    0.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mod_lc = data.copy()\n",
    "data_mod_lc['Section'] = data_mod_lc['Section'].astype('category')\n",
    "#print(cat_bird_lc.dtypes)\n",
    "data_mod_lc['Section'] = data_mod_lc['Section'].cat.codes\n",
    "data_mod_lc.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MgP5ZjtZTzrm",
    "outputId": "c6037a54-d449-4eb3-e4ae-cc7db17b5617"
   },
   "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>FailureMode</th>\n",
       "      <th>M/Vlw</th>\n",
       "      <th>lw/tw</th>\n",
       "      <th>ρvwFy,vw/fc</th>\n",
       "      <th>ρhwFy,vw/fc</th>\n",
       "      <th>ρvcFy,vc/fc</th>\n",
       "      <th>ρhcFy,hc/fc</th>\n",
       "      <th>P/fcAg</th>\n",
       "      <th>Section</th>\n",
       "      <th>Ab/Ag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>0.268879</td>\n",
       "      <td>0.136711</td>\n",
       "      <td>0.348231</td>\n",
       "      <td>0.149140</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>0.262358</td>\n",
       "      <td>0.133396</td>\n",
       "      <td>0.339785</td>\n",
       "      <td>0.145522</td>\n",
       "      <td>0.102168</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>0.346364</td>\n",
       "      <td>0.176108</td>\n",
       "      <td>0.448584</td>\n",
       "      <td>0.192118</td>\n",
       "      <td>0.208187</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>0.334023</td>\n",
       "      <td>0.169834</td>\n",
       "      <td>0.432601</td>\n",
       "      <td>0.185273</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>0.324766</td>\n",
       "      <td>0.165127</td>\n",
       "      <td>0.420612</td>\n",
       "      <td>0.180139</td>\n",
       "      <td>0.101727</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FailureMode  M/Vlw      lw/tw  ρvwFy,vw/fc  ρhwFy,vw/fc  ρvcFy,vc/fc  \\\n",
       "0            1    1.1  10.714286     0.268879     0.136711     0.348231   \n",
       "1            1    1.1  10.714286     0.262358     0.133396     0.339785   \n",
       "2            1    1.1  10.714286     0.346364     0.176108     0.448584   \n",
       "3            1    1.1  10.714286     0.334023     0.169834     0.432601   \n",
       "4            1    1.1  10.714286     0.324766     0.165127     0.420612   \n",
       "\n",
       "   ρhcFy,hc/fc    P/fcAg  Section  Ab/Ag  \n",
       "0     0.149140  0.000000        2    0.0  \n",
       "1     0.145522  0.102168        2    0.0  \n",
       "2     0.192118  0.208187        2    0.0  \n",
       "3     0.185273  0.000000        2    0.0  \n",
       "4     0.180139  0.101727        2    0.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Section'] = data_mod_lc['Section']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 0, 1: 1, 2: 2}\n"
     ]
    }
   ],
   "source": [
    "c = data_mod_lc.Section.astype('category')\n",
    "d = dict(enumerate(c.cat.categories))\n",
    "print (d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DEXixTQpTzru",
    "outputId": "c86194d7-44d4-4a6b-f737-97da38d54664"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FailureMode    0\n",
       "M/Vlw          0\n",
       "lw/tw          0\n",
       "ρvwFy,vw/fc    0\n",
       "ρhwFy,vw/fc    0\n",
       "ρvcFy,vc/fc    0\n",
       "ρhcFy,hc/fc    0\n",
       "P/fcAg         0\n",
       "Section        0\n",
       "Ab/Ag          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking missing values by column\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nfB8qzl5TzsG",
    "outputId": "2f2ed201-82a8-479b-af35-9f617760980c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = data.drop('FailureMode', axis=1).corr() # examining correlations\n",
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "sns.heatmap(corr[(corr >= 0.0) | (corr <= -0.0)], \n",
    "            cmap='viridis', vmax=1.0, vmin=-1.0, linewidths=0.1,\n",
    "            annot=True, annot_kws={\"size\": 8}, square=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7RW9Qk84TzsM",
    "outputId": "cd970685-fb65-43d1-9d52-157af665822f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting correlation above or below 0.5\n",
    "corr = data.drop('FailureMode', axis=1).corr() \n",
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "sns.heatmap(corr[(corr >= 0.5) | (corr <= -0.5)], \n",
    "            cmap='viridis', vmax=1.0, vmin=-1.0, linewidths=0.1,\n",
    "            annot=True, annot_kws={\"size\": 8}, square=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ka-rpbZNTzsc",
    "outputId": "f02f7827-b505-42d9-d800-e6632db50281"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['FailureMode', 'M/Vlw', 'lw/tw', 'ρvwFy,vw/fc', 'ρhwFy,vw/fc', 'ρvcFy,vc/fc', 'ρhcFy,hc/fc', 'P/fcAg', 'Section', 'Ab/Ag']\n"
     ]
    }
   ],
   "source": [
    "features = list(data.columns.values)\n",
    "print(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cOe0WTrtTzsW",
    "outputId": "c9cc41d7-28b0-4826-f8ea-732416d958c1"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sujit\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:487: RuntimeWarning: invalid value encountered in true_divide\n",
      "  binned = fast_linbin(X, a, b, gridsize) / (delta * nobs)\n",
      "C:\\Users\\sujit\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\statsmodels\\nonparametric\\kdetools.py:34: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  FAC1 = 2*(np.pi*bw/RANGE)**2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x26bce1e4ac8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 965.425x900 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "quantitative_features_list1 = ['FailureMode', 'M/Vlw', 'lw/tw', 'ρvwFy,vw/fc', 'ρhwFy,vw/fc']\n",
    "\n",
    "data_plot_data=data_mod_num = data[quantitative_features_list1]\n",
    "sns.pairplot(data_plot_data, hue=\"FailureMode\", palette=\"husl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XXpIweTmTzsi",
    "outputId": "17a1d8ed-a7cd-4510-daa8-7bfdf854e9fe"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x26bd194eb00>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1145.42x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "quantitative_features_list1 = ['FailureMode', 'ρvcFy,vc/fc', 'ρhcFy,hc/fc', 'P/fcAg', 'Section', 'Ab/Ag']\n",
    "\n",
    "data_plot_data=data_mod_num = data[quantitative_features_list1]\n",
    "sns.pairplot(data_plot_data, hue=\"FailureMode\", palette=\"husl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4mt5RhS3Tzsy",
    "outputId": "02edebbb-d1c7-40b9-d406-63bc23a633a1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Failure Mode')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "table=pd.crosstab(data.Section,data.FailureMode)\n",
    "table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
    "plt.title('Stacked Bar Section vs Failure')\n",
    "plt.xlabel('Section')\n",
    "plt.ylabel('Failure Mode')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "U-aUZRCfTzs4"
   },
   "source": [
    "Section type seems to have an influence on the failure mode. However further analysis is needed to check whether its statistically significant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Hi53ZsTzTzs5",
    "outputId": "89b17a7e-dcc2-40d5-9052-f8aa6410d35f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Lets check wheter data is imbalanced\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set(style=\"white\")\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "sns.countplot(x='FailureMode',data=data, palette='hls')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8FESSWWzTzs9"
   },
   "source": [
    "Data set is imbalanced with respect to the Failure Mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "q8y6e4m8Tzs_",
    "outputId": "ba13fbea-06e5-459f-cec4-fc1a04ebc4bf"
   },
   "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>FailureMode</th>\n",
       "      <th>M/Vlw</th>\n",
       "      <th>lw/tw</th>\n",
       "      <th>ρvwFy,vw/fc</th>\n",
       "      <th>ρhwFy,vw/fc</th>\n",
       "      <th>ρvcFy,vc/fc</th>\n",
       "      <th>ρhcFy,hc/fc</th>\n",
       "      <th>P/fcAg</th>\n",
       "      <th>Ab/Ag</th>\n",
       "      <th>Section_0</th>\n",
       "      <th>Section_1</th>\n",
       "      <th>Section_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>0.268879</td>\n",
       "      <td>0.136711</td>\n",
       "      <td>0.348231</td>\n",
       "      <td>0.149140</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>0.262358</td>\n",
       "      <td>0.133396</td>\n",
       "      <td>0.339785</td>\n",
       "      <td>0.145522</td>\n",
       "      <td>0.102168</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>0.346364</td>\n",
       "      <td>0.176108</td>\n",
       "      <td>0.448584</td>\n",
       "      <td>0.192118</td>\n",
       "      <td>0.208187</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>0.334023</td>\n",
       "      <td>0.169834</td>\n",
       "      <td>0.432601</td>\n",
       "      <td>0.185273</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>0.324766</td>\n",
       "      <td>0.165127</td>\n",
       "      <td>0.420612</td>\n",
       "      <td>0.180139</td>\n",
       "      <td>0.101727</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FailureMode  M/Vlw      lw/tw  ρvwFy,vw/fc  ρhwFy,vw/fc  ρvcFy,vc/fc  \\\n",
       "0            1    1.1  10.714286     0.268879     0.136711     0.348231   \n",
       "1            1    1.1  10.714286     0.262358     0.133396     0.339785   \n",
       "2            1    1.1  10.714286     0.346364     0.176108     0.448584   \n",
       "3            1    1.1  10.714286     0.334023     0.169834     0.432601   \n",
       "4            1    1.1  10.714286     0.324766     0.165127     0.420612   \n",
       "\n",
       "   ρhcFy,hc/fc    P/fcAg  Ab/Ag  Section_0  Section_1  Section_2  \n",
       "0     0.149140  0.000000    0.0          0          0          1  \n",
       "1     0.145522  0.102168    0.0          0          0          1  \n",
       "2     0.192118  0.208187    0.0          0          0          1  \n",
       "3     0.185273  0.000000    0.0          0          0          1  \n",
       "4     0.180139  0.101727    0.0          0          0          1  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#One Hot Encoding for the categorical values\n",
    "data = pd.get_dummies(data = data , columns=['Section'] )\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KbX47FMPTztE",
    "outputId": "d3df6be1-0fb4-4dbe-8012-30a17fd8de20"
   },
   "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>M/Vlw</th>\n",
       "      <th>lw/tw</th>\n",
       "      <th>ρvwFy,vw/fc</th>\n",
       "      <th>ρhwFy,vw/fc</th>\n",
       "      <th>ρvcFy,vc/fc</th>\n",
       "      <th>ρhcFy,hc/fc</th>\n",
       "      <th>P/fcAg</th>\n",
       "      <th>Ab/Ag</th>\n",
       "      <th>Section_0</th>\n",
       "      <th>Section_1</th>\n",
       "      <th>Section_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>0.268879</td>\n",
       "      <td>0.136711</td>\n",
       "      <td>0.348231</td>\n",
       "      <td>0.149140</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.1</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>0.262358</td>\n",
       "      <td>0.133396</td>\n",
       "      <td>0.339785</td>\n",
       "      <td>0.145522</td>\n",
       "      <td>0.102168</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.1</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>0.346364</td>\n",
       "      <td>0.176108</td>\n",
       "      <td>0.448584</td>\n",
       "      <td>0.192118</td>\n",
       "      <td>0.208187</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.1</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>0.334023</td>\n",
       "      <td>0.169834</td>\n",
       "      <td>0.432601</td>\n",
       "      <td>0.185273</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.1</td>\n",
       "      <td>10.714286</td>\n",
       "      <td>0.324766</td>\n",
       "      <td>0.165127</td>\n",
       "      <td>0.420612</td>\n",
       "      <td>0.180139</td>\n",
       "      <td>0.101727</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   M/Vlw      lw/tw  ρvwFy,vw/fc  ρhwFy,vw/fc  ρvcFy,vc/fc  ρhcFy,hc/fc  \\\n",
       "0    1.1  10.714286     0.268879     0.136711     0.348231     0.149140   \n",
       "1    1.1  10.714286     0.262358     0.133396     0.339785     0.145522   \n",
       "2    1.1  10.714286     0.346364     0.176108     0.448584     0.192118   \n",
       "3    1.1  10.714286     0.334023     0.169834     0.432601     0.185273   \n",
       "4    1.1  10.714286     0.324766     0.165127     0.420612     0.180139   \n",
       "\n",
       "     P/fcAg  Ab/Ag  Section_0  Section_1  Section_2  \n",
       "0  0.000000    0.0          0          0          1  \n",
       "1  0.102168    0.0          0          0          1  \n",
       "2  0.208187    0.0          0          0          1  \n",
       "3  0.000000    0.0          0          0          1  \n",
       "4  0.101727    0.0          0          0          1  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = data.loc[:, data.columns != 'FailureMode']\n",
    "y=data['FailureMode']\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "366LZyd_TztP"
   },
   "source": [
    "##### Spliiting the data into training and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LIiqbZMBTztQ",
    "outputId": "6f92e63c-d75b-4a95-b544-b80fb640a171"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: FailureMode, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uOuPU1J8TztY"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "f-fFPc4oTztc"
   },
   "source": [
    "## Naive Bayes techique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iPMjAj4-Tztc",
    "outputId": "10ae5a9d-54cc-4a68-a9f3-73df5c692587"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of GNB classifier on training set: 0.69\n",
      "Accuracy of GNB classifier on test set: 0.76\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "gnb = GaussianNB()\n",
    "gnb.fit(X_train, y_train)\n",
    "print('Accuracy of GNB classifier on training set: {:.2f}'\n",
    "     .format(gnb.score(X_train, y_train)))\n",
    "print('Accuracy of GNB classifier on test set: {:.2f}'\n",
    "     .format(gnb.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KTsXp13vTztg"
   },
   "source": [
    "Confusion Matrix of training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "k-0G6SE7Tzth",
    "outputId": "6c626eb9-a555-4a2b-d372-38c2e05edca3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[82 17  5  2]\n",
      " [15 39 12  0]\n",
      " [ 2 20 61  1]\n",
      " [ 4  3  5  7]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_train = gnb.predict(X_train)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_train, y_pred_train)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0HtbDVi8Tzto",
    "outputId": "593ad5f5-2c90-413b-ed80-b6bd3ceda516"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.80      0.77      0.78       106\n",
      "           2       0.49      0.59      0.54        66\n",
      "           3       0.73      0.73      0.73        84\n",
      "           4       0.70      0.37      0.48        19\n",
      "\n",
      "    accuracy                           0.69       275\n",
      "   macro avg       0.68      0.61      0.63       275\n",
      "weighted avg       0.70      0.69      0.69       275\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_train, y_pred_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IyAogJmnTztt"
   },
   "source": [
    "Confusion Matrix of test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RbbgiCbiTztu",
    "outputId": "ac8fdfae-343b-499e-b9f2-f0b19f2a9488"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[38  4  3  1]\n",
      " [ 6 21  3  0]\n",
      " [ 2  5 30  1]\n",
      " [ 1  1  1  1]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = gnb.predict(X_test)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RTE2oDbMTztx",
    "outputId": "ea5ac5a4-0ad3-4ec0-de34-8b2a5143ed44"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.81      0.83      0.82        46\n",
      "           2       0.68      0.70      0.69        30\n",
      "           3       0.81      0.79      0.80        38\n",
      "           4       0.33      0.25      0.29         4\n",
      "\n",
      "    accuracy                           0.76       118\n",
      "   macro avg       0.66      0.64      0.65       118\n",
      "weighted avg       0.76      0.76      0.76       118\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "v3Kg1AZrTzt5"
   },
   "source": [
    "## K-Nearest neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0tnXuEmzTzt6",
    "outputId": "79b65c9e-43d5-493b-8a2e-a47343b58665"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of K-NN classifier on training set: 0.80\n",
      "Accuracy of K-NN classifier on test set: 0.85\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X_train, y_train)\n",
    "print('Accuracy of K-NN classifier on training set: {:.2f}'\n",
    "     .format(knn.score(X_train, y_train)))\n",
    "print('Accuracy of K-NN classifier on test set: {:.2f}'\n",
    "     .format(knn.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "29_0EemhTzuA"
   },
   "source": [
    "Confusion Matrix of training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "31rFd3wKTzuB",
    "outputId": "f6f18e56-5278-4cb7-b630-1c01fb97906b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[96  5  3  2]\n",
      " [15 46  5  0]\n",
      " [ 4  8 72  0]\n",
      " [ 4  3  6  6]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_train = knn.predict(X_train)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_train, y_pred_train)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RehZDmFMTzuF",
    "outputId": "c623f37e-849b-4754-b6ab-cb1ec0b79ec4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.81      0.91      0.85       106\n",
      "           2       0.74      0.70      0.72        66\n",
      "           3       0.84      0.86      0.85        84\n",
      "           4       0.75      0.32      0.44        19\n",
      "\n",
      "    accuracy                           0.80       275\n",
      "   macro avg       0.78      0.69      0.72       275\n",
      "weighted avg       0.80      0.80      0.79       275\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_train, y_pred_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PE_QYJQXTzuK"
   },
   "source": [
    "Confusion Matrix of test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dZo3mv6QTzuM",
    "outputId": "0dfc2261-b314-4ef3-a24c-e01b82d613c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[43  1  0  2]\n",
      " [ 7 20  3  0]\n",
      " [ 1  3 34  0]\n",
      " [ 0  0  1  3]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = knn.predict(X_test)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4x4Iin7gTzuO",
    "outputId": "3bbd5f90-38a0-4fa9-a931-85307ec284aa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.84      0.93      0.89        46\n",
      "           2       0.83      0.67      0.74        30\n",
      "           3       0.89      0.89      0.89        38\n",
      "           4       0.60      0.75      0.67         4\n",
      "\n",
      "    accuracy                           0.85       118\n",
      "   macro avg       0.79      0.81      0.80       118\n",
      "weighted avg       0.85      0.85      0.84       118\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qXUhD2IeTzuU"
   },
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yH0FxUy-TzuV",
    "outputId": "14c6ac93-3fe5-4772-cf96-4d525b07ef4f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Decision Tree classifier on training set: 1.00\n",
      "Accuracy of Decision Tree classifier on test set: 0.79\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf = DecisionTreeClassifier().fit(X_train, y_train)\n",
    "\n",
    "print('Accuracy of Decision Tree classifier on training set: {:.2f}'\n",
    "     .format(clf.score(X_train, y_train)))\n",
    "print('Accuracy of Decision Tree classifier on test set: {:.2f}'\n",
    "     .format(clf.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uB08Jb66TzuZ"
   },
   "source": [
    "Confusion Matrix of training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M1fAeRF5Tzua",
    "outputId": "d801af9f-dec2-4e7e-cf83-3133765f2f24"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[106   0   0   0]\n",
      " [  0  66   0   0]\n",
      " [  0   0  84   0]\n",
      " [  0   0   0  19]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_train = clf.predict(X_train)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_train, y_pred_train)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1uVvWo8VTzuf",
    "outputId": "425d9605-18e5-42fe-a7fe-e998f335dc27"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00       106\n",
      "           2       1.00      1.00      1.00        66\n",
      "           3       1.00      1.00      1.00        84\n",
      "           4       1.00      1.00      1.00        19\n",
      "\n",
      "    accuracy                           1.00       275\n",
      "   macro avg       1.00      1.00      1.00       275\n",
      "weighted avg       1.00      1.00      1.00       275\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_train, y_pred_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pNRazBnhTzuj"
   },
   "source": [
    "Confusion Matrix of test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3jfh7yguTzuz",
    "outputId": "53b0b7c9-218d-49fe-b2b1-a293e0a88ecf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[37  6  3  0]\n",
      " [ 4 22  1  3]\n",
      " [ 4  1 31  2]\n",
      " [ 0  1  0  3]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = clf.predict(X_test)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vSIu4oNRTzu2",
    "outputId": "b717a1be-438e-439c-ee96-31820d943ef1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.82      0.80      0.81        46\n",
      "           2       0.73      0.73      0.73        30\n",
      "           3       0.89      0.82      0.85        38\n",
      "           4       0.38      0.75      0.50         4\n",
      "\n",
      "    accuracy                           0.79       118\n",
      "   macro avg       0.70      0.78      0.72       118\n",
      "weighted avg       0.80      0.79      0.79       118\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TA73LJkaTzu7"
   },
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TyxKSXsgTzu8",
    "outputId": "8ab6dcd4-3287-486d-f6ce-ff09db57efff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of RF classifier on training set: 1.00\n",
      "Accuracy of RF classifier on test set: 0.86\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "RF = RandomForestClassifier(n_estimators=200,  random_state=0)\n",
    "RF.fit(X_train, y_train)\n",
    "print('Accuracy of RF classifier on training set: {:.2f}'\n",
    "     .format(RF.score(X_train, y_train)))\n",
    "print('Accuracy of RF classifier on test set: {:.2f}'\n",
    "     .format(RF.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gZo7-oyITzu_"
   },
   "source": [
    "Confusion Matrix of training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yadH4kocTzu_",
    "outputId": "0dac5566-f035-44ee-ecc6-b0efe462402e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[106   0   0   0]\n",
      " [  0  66   0   0]\n",
      " [  0   0  84   0]\n",
      " [  0   0   0  19]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_train = RF.predict(X_train)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_train, y_pred_train)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GkAaSOMqTzvC"
   },
   "source": [
    "Confusion Matrix of test set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "e5gNpfYMTzvD",
    "outputId": "a3616b21-33fd-4be1-e923-81eb3779a348"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[43  2  0  1]\n",
      " [ 5 21  3  1]\n",
      " [ 2  2 34  0]\n",
      " [ 0  0  0  4]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = RF.predict(X_test)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "p_iIF41gTzvH",
    "outputId": "e55e6469-839c-4365-eb06-8756188c112c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.86      0.93      0.90        46\n",
      "           2       0.84      0.70      0.76        30\n",
      "           3       0.92      0.89      0.91        38\n",
      "           4       0.67      1.00      0.80         4\n",
      "\n",
      "    accuracy                           0.86       118\n",
      "   macro avg       0.82      0.88      0.84       118\n",
      "weighted avg       0.87      0.86      0.86       118\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rjGFr1lrTzvK",
    "outputId": "07185949-9017-4c3d-d083-38f2e208aa36"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.19314531, 0.13153915, 0.09183167, 0.09759066, 0.1397623 ,\n",
       "       0.13749226, 0.06990941, 0.07369148, 0.00996661, 0.01949359,\n",
       "       0.03557756])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RF.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QSHwkGM7TzvW",
    "outputId": "ea0b0ae2-1d5b-4b39-abf6-3b2ecd7426e7",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = list(X.columns.values)\n",
    "\n",
    "importances = RF.feature_importances_\n",
    "import numpy as np\n",
    "indices = np.argsort(importances)\n",
    "\n",
    "plt.title('Feature Importances')\n",
    "plt.barh(range(len(indices)), importances[indices], color='b', align='center')\n",
    "plt.yticks(range(len(indices)), [features[i] for i in indices])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8nHBvNZnTzvd"
   },
   "source": [
    "### Hypertune Random Forest Parameters to improve the accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 100 candidates, totalling 1000 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  17 tasks      | elapsed:   12.1s\n",
      "[Parallel(n_jobs=-1)]: Done 138 tasks      | elapsed:   45.4s\n",
      "[Parallel(n_jobs=-1)]: Done 341 tasks      | elapsed:  1.6min\n",
      "[Parallel(n_jobs=-1)]: Done 624 tasks      | elapsed:  2.6min\n",
      "[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:  4.0min finished\n",
      "C:\\Users\\sujit\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 1000, 'min_samples_split': 2, 'min_samples_leaf': 1, 'max_features': 'auto', 'max_depth': 50, 'bootstrap': False}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 200, stop = 2000, num = 10)]\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "#Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]\n",
    "max_depth.append(None)\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10]\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]\n",
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}\n",
    "#print(random_grid)\n",
    "\n",
    "# Random search of parameters\n",
    "rfc_random = RandomizedSearchCV(estimator = RF, param_distributions = random_grid, n_iter = 100, cv = 10, verbose=2, random_state=42, n_jobs = -1)\n",
    "\n",
    "# Fit the model\n",
    "rfc_random.fit(X_train, y_train)\n",
    "\n",
    "# print results\n",
    "print(rfc_random.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of RF classifier on training set: 1.00\n",
      "Accuracy of RF classifier on test set: 0.86\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "RF1 = RandomForestClassifier(n_estimators= 1000, min_samples_split= 2, min_samples_leaf= 1, max_features= 'auto', max_depth=50, bootstrap= 'False')\n",
    "RF1.fit(X_train, y_train)\n",
    "print('Accuracy of RF classifier on training set: {:.2f}'\n",
    "     .format(RF1.score(X_train, y_train)))\n",
    "print('Accuracy of RF classifier on test set: {:.2f}'\n",
    "     .format(RF1.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optimization doeent improve the performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confusion Matrix of training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[106   0   0   0]\n",
      " [  0  66   0   0]\n",
      " [  0   0  84   0]\n",
      " [  0   0   0  19]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_train = RF1.predict(X_train)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_train, y_pred_train)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Confusion Matrix of test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[42  3  0  1]\n",
      " [ 5 21  3  1]\n",
      " [ 2  1 35  0]\n",
      " [ 0  0  0  4]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = RF1.predict(X_test)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.86      0.91      0.88        46\n",
      "           2       0.84      0.70      0.76        30\n",
      "           3       0.92      0.92      0.92        38\n",
      "           4       0.67      1.00      0.80         4\n",
      "\n",
      "    accuracy                           0.86       118\n",
      "   macro avg       0.82      0.88      0.84       118\n",
      "weighted avg       0.87      0.86      0.86       118\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RF1.feature_importances_\n",
    "features = list(X.columns.values)\n",
    "\n",
    "importances = RF1.feature_importances_\n",
    "import numpy as np\n",
    "indices = np.argsort(importances)\n",
    "\n",
    "plt.title('Feature Importances')\n",
    "plt.barh(range(len(indices)), importances[indices], color='b', align='center')\n",
    "plt.yticks(range(len(indices)), [features[i] for i in indices])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2dkUBqbITzvn"
   },
   "source": [
    "## ADABOOST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zNy69xhRTzvn",
    "outputId": "30af90ad-3244-4177-dc1b-422e2970b2eb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of ADB classifier on training set: 0.76\n",
      "Accuracy of ADB classifier on test set: 0.67\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "\n",
    "ADB = AdaBoostClassifier(n_estimators=100, random_state=0)\n",
    "ADB.fit(X_train, y_train)\n",
    "print('Accuracy of ADB classifier on training set: {:.2f}'\n",
    "     .format(ADB.score(X_train, y_train)))\n",
    "print('Accuracy of ADB classifier on test set: {:.2f}'\n",
    "     .format(ADB.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DHqmMeqyTzvq"
   },
   "source": [
    "Confusion Matrix of training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "baP35PyFTzvr",
    "outputId": "34322a91-f2f9-42c8-c67b-2be230ea0b77"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[85 17  4  0]\n",
      " [13 46  6  1]\n",
      " [ 7 13 62  2]\n",
      " [ 0  2  2 15]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_train = ADB.predict(X_train)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_train, y_pred_train)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uSzAXxurTzvw",
    "outputId": "ea38a951-db50-445f-df17-3d999276d98f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.81      0.80      0.81       106\n",
      "           2       0.59      0.70      0.64        66\n",
      "           3       0.84      0.74      0.78        84\n",
      "           4       0.83      0.79      0.81        19\n",
      "\n",
      "    accuracy                           0.76       275\n",
      "   macro avg       0.77      0.76      0.76       275\n",
      "weighted avg       0.77      0.76      0.76       275\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_train, y_pred_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "w0iIvbPsTzv1"
   },
   "source": [
    "Confusion Matrix of test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-9nP0vYwTzv3",
    "outputId": "106afec8-a195-4d42-e2ed-5332e8965bbe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[36  6  1  3]\n",
      " [ 9 15  5  1]\n",
      " [ 4  8 26  0]\n",
      " [ 0  2  0  2]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = ADB.predict(X_test)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8nyl3tyoTzv6",
    "outputId": "c7219b4c-56f5-4fae-9444-b44948ab7ea5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.73      0.78      0.76        46\n",
      "           2       0.48      0.50      0.49        30\n",
      "           3       0.81      0.68      0.74        38\n",
      "           4       0.33      0.50      0.40         4\n",
      "\n",
      "    accuracy                           0.67       118\n",
      "   macro avg       0.59      0.62      0.60       118\n",
      "weighted avg       0.68      0.67      0.67       118\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_h-zq05ATzv-"
   },
   "source": [
    "## XGBOOST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "w5VyRmJGTzv-",
    "outputId": "dfa6f7b1-4f6d-4a5a-ede5-3192aa416f6d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of XGB classifier on training set: 1.00\n",
      "Accuracy of XGB classifier on test set: 0.83\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "\n",
    "XGB = XGBClassifier()\n",
    "XGB.fit(X_train, y_train)\n",
    "print('Accuracy of XGB classifier on training set: {:.2f}'\n",
    "     .format(XGB.score(X_train, y_train)))\n",
    "print('Accuracy of XGB classifier on test set: {:.2f}'\n",
    "     .format(XGB.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3U1LjtB-TzwB"
   },
   "source": [
    "Confusion Matrix of training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sX5WmJD-TzwC",
    "outputId": "8f186c1e-656c-498c-ada3-9fe2bb9ee11b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[106   0   0   0]\n",
      " [  0  66   0   0]\n",
      " [  0   0  84   0]\n",
      " [  0   0   0  19]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_train = XGB.predict(X_train)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_train, y_pred_train)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7hOLYB74TzwE",
    "outputId": "c058ce82-46b4-4e3a-ade2-2240de914d53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00       106\n",
      "           2       1.00      1.00      1.00        66\n",
      "           3       1.00      1.00      1.00        84\n",
      "           4       1.00      1.00      1.00        19\n",
      "\n",
      "    accuracy                           1.00       275\n",
      "   macro avg       1.00      1.00      1.00       275\n",
      "weighted avg       1.00      1.00      1.00       275\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_train, y_pred_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sGGzBLVlTzwH"
   },
   "source": [
    "Confusion Matrix of test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "52QXpqJqTzwI",
    "outputId": "d85022e2-b72e-4094-d74f-09f06062dfc0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[41  4  0  1]\n",
      " [ 7 19  3  1]\n",
      " [ 2  2 34  0]\n",
      " [ 0  0  0  4]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = XGB.predict(X_test)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bPx9aLPCTzwM",
    "outputId": "89267532-fb38-4494-dcd8-a9c5c92091b1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.82      0.89      0.85        46\n",
      "           2       0.76      0.63      0.69        30\n",
      "           3       0.92      0.89      0.91        38\n",
      "           4       0.67      1.00      0.80         4\n",
      "\n",
      "    accuracy                           0.83       118\n",
      "   macro avg       0.79      0.85      0.81       118\n",
      "weighted avg       0.83      0.83      0.83       118\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ys09efjyTzwW"
   },
   "source": [
    "## Light GBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rCZlxJdzTzwW",
    "outputId": "2d1b5fd6-3bf7-4ba6-b704-44c272842952"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of LGB classifier on training set: 1.00\n",
      "Accuracy of LGB classifier on test set: 0.80\n"
     ]
    }
   ],
   "source": [
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "\n",
    "LGB = LGBMClassifier()\n",
    "LGB.fit(X_train, y_train)\n",
    "print('Accuracy of LGB classifier on training set: {:.2f}'\n",
    "     .format(LGB.score(X_train, y_train)))\n",
    "print('Accuracy of LGB classifier on test set: {:.2f}'\n",
    "     .format(LGB.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "X3_oy953TzwY"
   },
   "source": [
    "Confusion Matrix of training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fEKJczl1TzwY",
    "outputId": "487614c4-f6b0-4f40-cb44-aa0a9a1694c3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[106   0   0   0]\n",
      " [  0  66   0   0]\n",
      " [  0   0  84   0]\n",
      " [  0   0   0  19]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_train = LGB.predict(X_train)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_train, y_pred_train)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fu4ouGJpTzwc",
    "outputId": "4bc4d5df-112a-4018-ce11-0910fecb20e2",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00       106\n",
      "           2       1.00      1.00      1.00        66\n",
      "           3       1.00      1.00      1.00        84\n",
      "           4       1.00      1.00      1.00        19\n",
      "\n",
      "    accuracy                           1.00       275\n",
      "   macro avg       1.00      1.00      1.00       275\n",
      "weighted avg       1.00      1.00      1.00       275\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_train, y_pred_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_SerIgneTzwf"
   },
   "source": [
    "Confusion Matrix of test set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Rpi3JYTVTzwg",
    "outputId": "554fb12e-03ad-455e-b89a-9f8db9f9b52d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[39  7  0  0]\n",
      " [ 7 19  3  1]\n",
      " [ 3  3 32  0]\n",
      " [ 0  0  0  4]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = LGB.predict(X_test)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5VZpZNiPTzwj",
    "outputId": "22038642-7144-4bb7-d8c3-152bf32e08e6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.80      0.85      0.82        46\n",
      "           2       0.66      0.63      0.64        30\n",
      "           3       0.91      0.84      0.88        38\n",
      "           4       0.80      1.00      0.89         4\n",
      "\n",
      "    accuracy                           0.80       118\n",
      "   macro avg       0.79      0.83      0.81       118\n",
      "weighted avg       0.80      0.80      0.80       118\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Q7nmI2H7Tzwk"
   },
   "source": [
    "# Cat BOOST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-Btw9Fs8Tzwl",
    "outputId": "90e0dd9b-895a-4b3b-f162-db210a99c709"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "999:\tlearn: 0.0868362\ttotal: 1m 28s\tremaining: 0us\n",
      "Accuracy of CGB classifier on training set: 1.00\n",
      "Accuracy of CGB classifier on test set: 0.84\n"
     ]
    }
   ],
   "source": [
    "from catboost import CatBoostClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "\n",
    "CGB = CatBoostClassifier()\n",
    "CGB.fit(X_train, y_train)\n",
    "print('Accuracy of CGB classifier on training set: {:.2f}'\n",
    "     .format(CGB.score(X_train, y_train)))\n",
    "print('Accuracy of CGB classifier on test set: {:.2f}'\n",
    "     .format(CGB.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wYz5oAF2Tzwo"
   },
   "source": [
    "Confusion Matrix of training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "O3x-THn8Tzwp",
    "outputId": "b47ce891-d678-46ac-e9d5-286319906f28"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[106   0   0   0]\n",
      " [  0  66   0   0]\n",
      " [  0   0  84   0]\n",
      " [  0   0   0  19]]\n"
     ]
    }
   ],
   "source": [
    "y_pred_train = CGB.predict(X_train)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_train, y_pred_train)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "857nslAZTzwu",
    "outputId": "dbcd82e6-214f-48c2-d463-5ee462afbd99"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       1.00      1.00      1.00       106\n",
      "           2       1.00      1.00      1.00        66\n",
      "           3       1.00      1.00      1.00        84\n",
      "           4       1.00      1.00      1.00        19\n",
      "\n",
      "    accuracy                           1.00       275\n",
      "   macro avg       1.00      1.00      1.00       275\n",
      "weighted avg       1.00      1.00      1.00       275\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_train, y_pred_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WyB-Sm23Tzwx"
   },
   "source": [
    "Confusion Matrix of test set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wOjf4P7MTzwz",
    "outputId": "0c30ead2-c5fb-4237-cbfd-e7d582fdeee5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[40  5  0  1]\n",
      " [ 5 21  3  1]\n",
      " [ 2  2 34  0]\n",
      " [ 0  0  0  4]]\n"
     ]
    }
   ],
   "source": [
    "y_pred = CGB.predict(X_test)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3QzbdGstTzw1",
    "outputId": "645c2b0c-df62-4900-84e8-90911491705b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.85      0.87      0.86        46\n",
      "           2       0.75      0.70      0.72        30\n",
      "           3       0.92      0.89      0.91        38\n",
      "           4       0.67      1.00      0.80         4\n",
      "\n",
      "    accuracy                           0.84       118\n",
      "   macro avg       0.80      0.87      0.82       118\n",
      "weighted avg       0.84      0.84      0.84       118\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PrnneS0uTzw4"
   },
   "source": [
    "## From the models, Random Forest is the one having highest accuracy"
   ]
  }
 ],
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