SpaceX_Machine_Learning_Prediction_Part_5.jupyterlite.ipynb

{
  "metadata": {
    "kernelspec": {
      "name": "python",
      "display_name": "Python (Pyodide)",
      "language": "python"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    }
  },
  "nbformat_minor": 4,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "source": "<p style=\"text-align:center\">\n    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">\n    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n    </a>\n</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# **Space X  Falcon 9 First Stage Landing Prediction**\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Assignment:  Machine Learning Prediction\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Estimated time needed: **60** minutes\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Space X advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is because Space X can reuse the first stage. Therefore if we can determine if the first stage will land, we can determine the cost of a launch. This information can be used if an alternate company wants to bid against space X for a rocket launch.   In this lab, you will create a machine learning pipeline  to predict if the first stage will land given the data from the preceding labs.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing_1.gif)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Several examples of an unsuccessful landing are shown here:\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Most unsuccessful landings are planed. Space X; performs a controlled landing in the oceans.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Objectives\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Perform exploratory  Data Analysis and determine Training Labels\n\n*   create a column for the class\n*   Standardize the data\n*   Split into training data and test data\n\n\\-Find best Hyperparameter for SVM, Classification Trees and Logistic Regression\n\n*   Find the method performs best using test data\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Import Libraries and Define Auxiliary Functions\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import piplite\nawait piplite.install(['numpy'])\nawait piplite.install(['pandas'])\nawait piplite.install(['seaborn'])",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 1
    },
    {
      "cell_type": "markdown",
      "source": "We will import the following libraries for the lab\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Pandas is a software library written for the Python programming language for data manipulation and analysis.\nimport pandas as pd\n# NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\nimport numpy as np\n# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\nimport matplotlib.pyplot as plt\n#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\nimport seaborn as sns\n# Preprocessing allows us to standarsize our data\nfrom sklearn import preprocessing\n# Allows us to split our data into training and testing data\nfrom sklearn.model_selection import train_test_split\n# Allows us to test parameters of classification algorithms and find the best one\nfrom sklearn.model_selection import GridSearchCV\n# Logistic Regression classification algorithm\nfrom sklearn.linear_model import LogisticRegression\n# Support Vector Machine classification algorithm\nfrom sklearn.svm import SVC\n# Decision Tree classification algorithm\nfrom sklearn.tree import DecisionTreeClassifier\n# K Nearest Neighbors classification algorithm\nfrom sklearn.neighbors import KNeighborsClassifier",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 2
    },
    {
      "cell_type": "markdown",
      "source": "This function is to plot the confusion matrix.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "def plot_confusion_matrix(y,y_predict):\n    \"this function plots the confusion matrix\"\n    from sklearn.metrics import confusion_matrix\n\n    cm = confusion_matrix(y, y_predict)\n    ax= plt.subplot()\n    sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells\n    ax.set_xlabel('Predicted labels')\n    ax.set_ylabel('True labels')\n    ax.set_title('Confusion Matrix'); \n    ax.xaxis.set_ticklabels(['did not land', 'land']); ax.yaxis.set_ticklabels(['did not land', 'landed']) \n    plt.show() ",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 3
    },
    {
      "cell_type": "markdown",
      "source": "## Load the dataframe\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Load the data\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from js import fetch\nimport io\n\nURL1 = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\"\nresp1 = await fetch(URL1)\ntext1 = io.BytesIO((await resp1.arrayBuffer()).to_py())\ndata = pd.read_csv(text1)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 4
    },
    {
      "cell_type": "code",
      "source": "data.head()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 5,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n\n       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n0    None None        1     False   False  False        NaN    1.0   \n1    None None        1     False   False  False        NaN    1.0   \n2    None None        1     False   False  False        NaN    1.0   \n3  False Ocean        1     False   False  False        NaN    1.0   \n4    None None        1     False   False  False        NaN    1.0   \n\n   ReusedCount Serial   Longitude   Latitude  Class  \n0            0  B0003  -80.577366  28.561857      0  \n1            0  B0005  -80.577366  28.561857      0  \n2            0  B0007  -80.577366  28.561857      0  \n3            0  B1003 -120.610829  34.632093      0  \n4            0  B1004  -80.577366  28.561857      0  ",
            "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>FlightNumber</th>\n      <th>Date</th>\n      <th>BoosterVersion</th>\n      <th>PayloadMass</th>\n      <th>Orbit</th>\n      <th>LaunchSite</th>\n      <th>Outcome</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>LandingPad</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2010-06-04</td>\n      <td>Falcon 9</td>\n      <td>6104.959412</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0003</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2012-05-22</td>\n      <td>Falcon 9</td>\n      <td>525.000000</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0005</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>2013-03-01</td>\n      <td>Falcon 9</td>\n      <td>677.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0007</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>2013-09-29</td>\n      <td>Falcon 9</td>\n      <td>500.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>False Ocean</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1003</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>2013-12-03</td>\n      <td>Falcon 9</td>\n      <td>3170.000000</td>\n      <td>GTO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1004</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 5
    },
    {
      "cell_type": "code",
      "source": "URL2 = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_3.csv'\nresp2 = await fetch(URL2)\ntext2 = io.BytesIO((await resp2.arrayBuffer()).to_py())\nX = pd.read_csv(text2)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 6
    },
    {
      "cell_type": "code",
      "source": "X.head(100)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 7,
          "output_type": "execute_result",
          "data": {
            "text/plain": "    FlightNumber   PayloadMass  Flights  Block  ReusedCount  Orbit_ES-L1  \\\n0            1.0   6104.959412      1.0    1.0          0.0          0.0   \n1            2.0    525.000000      1.0    1.0          0.0          0.0   \n2            3.0    677.000000      1.0    1.0          0.0          0.0   \n3            4.0    500.000000      1.0    1.0          0.0          0.0   \n4            5.0   3170.000000      1.0    1.0          0.0          0.0   \n..           ...           ...      ...    ...          ...          ...   \n85          86.0  15400.000000      2.0    5.0          2.0          0.0   \n86          87.0  15400.000000      3.0    5.0          2.0          0.0   \n87          88.0  15400.000000      6.0    5.0          5.0          0.0   \n88          89.0  15400.000000      3.0    5.0          2.0          0.0   \n89          90.0   3681.000000      1.0    5.0          0.0          0.0   \n\n    Orbit_GEO  Orbit_GTO  Orbit_HEO  Orbit_ISS  ...  Serial_B1058  \\\n0         0.0        0.0        0.0        0.0  ...           0.0   \n1         0.0        0.0        0.0        0.0  ...           0.0   \n2         0.0        0.0        0.0        1.0  ...           0.0   \n3         0.0        0.0        0.0        0.0  ...           0.0   \n4         0.0        1.0        0.0        0.0  ...           0.0   \n..        ...        ...        ...        ...  ...           ...   \n85        0.0        0.0        0.0        0.0  ...           0.0   \n86        0.0        0.0        0.0        0.0  ...           1.0   \n87        0.0        0.0        0.0        0.0  ...           0.0   \n88        0.0        0.0        0.0        0.0  ...           0.0   \n89        0.0        0.0        0.0        0.0  ...           0.0   \n\n    Serial_B1059  Serial_B1060  Serial_B1062  GridFins_False  GridFins_True  \\\n0            0.0           0.0           0.0             1.0            0.0   \n1            0.0           0.0           0.0             1.0            0.0   \n2            0.0           0.0           0.0             1.0            0.0   \n3            0.0           0.0           0.0             1.0            0.0   \n4            0.0           0.0           0.0             1.0            0.0   \n..           ...           ...           ...             ...            ...   \n85           0.0           1.0           0.0             0.0            1.0   \n86           0.0           0.0           0.0             0.0            1.0   \n87           0.0           0.0           0.0             0.0            1.0   \n88           0.0           1.0           0.0             0.0            1.0   \n89           0.0           0.0           1.0             0.0            1.0   \n\n    Reused_False  Reused_True  Legs_False  Legs_True  \n0            1.0          0.0         1.0        0.0  \n1            1.0          0.0         1.0        0.0  \n2            1.0          0.0         1.0        0.0  \n3            1.0          0.0         1.0        0.0  \n4            1.0          0.0         1.0        0.0  \n..           ...          ...         ...        ...  \n85           0.0          1.0         0.0        1.0  \n86           0.0          1.0         0.0        1.0  \n87           0.0          1.0         0.0        1.0  \n88           0.0          1.0         0.0        1.0  \n89           1.0          0.0         0.0        1.0  \n\n[90 rows x 83 columns]",
            "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>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Flights</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Orbit_ES-L1</th>\n      <th>Orbit_GEO</th>\n      <th>Orbit_GTO</th>\n      <th>Orbit_HEO</th>\n      <th>Orbit_ISS</th>\n      <th>...</th>\n      <th>Serial_B1058</th>\n      <th>Serial_B1059</th>\n      <th>Serial_B1060</th>\n      <th>Serial_B1062</th>\n      <th>GridFins_False</th>\n      <th>GridFins_True</th>\n      <th>Reused_False</th>\n      <th>Reused_True</th>\n      <th>Legs_False</th>\n      <th>Legs_True</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>6104.959412</td>\n      <td>1.0</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>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>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.0</td>\n      <td>525.000000</td>\n      <td>1.0</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>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>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.0</td>\n      <td>677.000000</td>\n      <td>1.0</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>0.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>500.000000</td>\n      <td>1.0</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>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>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3170.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</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>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</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>85</th>\n      <td>86.0</td>\n      <td>15400.000000</td>\n      <td>2.0</td>\n      <td>5.0</td>\n      <td>2.0</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>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>87.0</td>\n      <td>15400.000000</td>\n      <td>3.0</td>\n      <td>5.0</td>\n      <td>2.0</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>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>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>88.0</td>\n      <td>15400.000000</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>5.0</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>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>89.0</td>\n      <td>15400.000000</td>\n      <td>3.0</td>\n      <td>5.0</td>\n      <td>2.0</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>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>90.0</td>\n      <td>3681.000000</td>\n      <td>1.0</td>\n      <td>5.0</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>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>90 rows × 83 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 7
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  1\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a NumPy array from the column <code>Class</code> in <code>data</code>, by applying the method <code>to_numpy()</code>  then\nassign it  to the variable <code>Y</code>,make sure the output is a  Pandas series (only one bracket df\\['name of  column']).\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "Y = data['Class'].to_numpy()",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 13
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  2\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Standardize the data in <code>X</code> then reassign it to the variable  <code>X</code> using the transform provided below.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# students get this\ntransform = preprocessing.StandardScaler()\nX = transform.fit_transform(X)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 15
    },
    {
      "cell_type": "markdown",
      "source": "We split the data into training and testing data using the  function  <code>train_test_split</code>.   The training data is divided into validation data, a second set used for training  data; then the models are trained and hyperparameters are selected using the function <code>GridSearchCV</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  3\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Use the function train_test_split to split the data X and Y into training and test data. Set the parameter test_size to  0.2 and random_state to 2. The training data and test data should be assigned to the following labels.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<code>X_train, X_test, Y_train, Y_test</code>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "X_train, X_test, Y_train, Y_test = train_test_split(X, Y)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 16
    },
    {
      "cell_type": "markdown",
      "source": "we can see we only have 18 test samples.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "Y_test.shape",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 22,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(23,)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 22
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  4\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a logistic regression object  then create a  GridSearchCV object  <code>logreg_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "parameters ={'C':[0.01,0.1,1],\n             'penalty':['l2'],\n             'solver':['lbfgs']}",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 23
    },
    {
      "cell_type": "code",
      "source": "parameters ={\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge\nlr=LogisticRegression()\nlogreg_cv = GridSearchCV(LogisticRegression(), parameters, cv=10)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 47
    },
    {
      "cell_type": "markdown",
      "source": "We output the <code>GridSearchCV</code> object for logistic regression. We display the best parameters using the data attribute <code>best_params\\_</code> and the accuracy on the validation data using the data attribute <code>best_score\\_</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hyperparameters :(best parameters) \",logreg_cv.best_params_)\nprint(\"accuracy :\",logreg_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "ename": "<class 'AttributeError'>",
          "evalue": "'GridSearchCV' object has no attribute 'best_params_'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[41], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtuned hyperparameters :(best parameters) \u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[43mlogreg_cv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbest_params_\u001b[49m)\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maccuracy :\u001b[39m\u001b[38;5;124m\"\u001b[39m,logreg_cv\u001b[38;5;241m.\u001b[39mbest_score_)\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'GridSearchCV' object has no attribute 'best_params_'"
          ],
          "output_type": "error"
        }
      ],
      "execution_count": 41
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  5\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Calculate the accuracy on the test data using the method <code>score</code>:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "logreg_cv.fit(X_train, Y_train)\nlogreg_cv.score(X_test, Y_test)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 54,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.8260869565217391"
          },
          "metadata": {}
        }
      ],
      "execution_count": 54
    },
    {
      "cell_type": "markdown",
      "source": "Lets look at the confusion matrix:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "yhat=logreg_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": "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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 52
    },
    {
      "cell_type": "markdown",
      "source": "Examining the confusion matrix, we see that logistic regression can distinguish between the different classes.  We see that the major problem is false positives.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  6\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a support vector machine object then  create a  <code>GridSearchCV</code> object  <code>svm_cv</code> with cv - 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "parameters = {'kernel':('linear', 'rbf','poly','rbf', 'sigmoid'),\n              'C': np.logspace(-3, 3, 5),\n              'gamma':np.logspace(-3, 3, 5)}\nsvm = SVC()",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 55
    },
    {
      "cell_type": "code",
      "source": "svm_cv = GridSearchCV(svm, parameters, cv = 10)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 56
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",svm_cv.best_params_)\nprint(\"accuracy :\",svm_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "ename": "<class 'AttributeError'>",
          "evalue": "'GridSearchCV' object has no attribute 'best_params_'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[57], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtuned hpyerparameters :(best parameters) \u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[43msvm_cv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbest_params_\u001b[49m)\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maccuracy :\u001b[39m\u001b[38;5;124m\"\u001b[39m,svm_cv\u001b[38;5;241m.\u001b[39mbest_score_)\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'GridSearchCV' object has no attribute 'best_params_'"
          ],
          "output_type": "error"
        }
      ],
      "execution_count": 57
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  7\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Calculate the accuracy on the test data using the method <code>score</code>:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "svm_cv.fit(X_train, Y_train)\nsvm_cv.score(X_test, Y_test)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 58,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.8695652173913043"
          },
          "metadata": {}
        }
      ],
      "execution_count": 58
    },
    {
      "cell_type": "markdown",
      "source": "We can plot the confusion matrix\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "yhat=svm_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": "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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 59
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  8\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a decision tree classifier object then  create a  <code>GridSearchCV</code> object  <code>tree_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "parameters = {'criterion': ['gini', 'entropy'],\n     'splitter': ['best', 'random'],\n     'max_depth': [2*n for n in range(1,10)],\n     'max_features': ['auto', 'sqrt'],\n     'min_samples_leaf': [1, 2, 4],\n     'min_samples_split': [2, 5, 10]}\n\ntree = DecisionTreeClassifier()",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 61
    },
    {
      "cell_type": "code",
      "source": "tree_cv = GridSearchCV(tree, parameters, cv=10)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 62
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",tree_cv.best_params_)\nprint(\"accuracy :\",tree_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "ename": "<class 'AttributeError'>",
          "evalue": "'GridSearchCV' object has no attribute 'best_params_'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[63], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtuned hpyerparameters :(best parameters) \u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[43mtree_cv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbest_params_\u001b[49m)\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maccuracy :\u001b[39m\u001b[38;5;124m\"\u001b[39m,tree_cv\u001b[38;5;241m.\u001b[39mbest_score_)\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'GridSearchCV' object has no attribute 'best_params_'"
          ],
          "output_type": "error"
        }
      ],
      "execution_count": 63
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  9\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Calculate the accuracy of tree_cv on the test data using the method <code>score</code>:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "tree_cv.fit(X_train, Y_train)\ntree_cv.score(X_test, Y_test)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stderr",
          "text": "/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:425: FitFailedWarning: \n3240 fits failed out of a total of 6480.\nThe score on these train-test partitions for these parameters will be set to nan.\nIf these failures are not expected, you can try to debug them by setting error_score='raise'.\n\nBelow are more details about the failures:\n--------------------------------------------------------------------------------\n3240 fits failed with the following error:\nTraceback (most recent call last):\n  File \"/lib/python3.11/site-packages/sklearn/model_selection/_validation.py\", line 729, in _fit_and_score\n    estimator.fit(X_train, y_train, **fit_params)\n  File \"/lib/python3.11/site-packages/sklearn/base.py\", line 1145, in wrapper\n    estimator._validate_params()\n  File \"/lib/python3.11/site-packages/sklearn/base.py\", line 638, in _validate_params\n    validate_parameter_constraints(\n  File \"/lib/python3.11/site-packages/sklearn/utils/_param_validation.py\", line 95, in validate_parameter_constraints\n    raise InvalidParameterError(\nsklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of DecisionTreeClassifier must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n\n  warnings.warn(some_fits_failed_message, FitFailedWarning)\n/lib/python3.11/site-packages/sklearn/model_selection/_search.py:979: UserWarning: One or more of the test scores are non-finite: [       nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.7        0.7047619  0.80238095 0.79047619 0.79285714 0.76190476\n 0.75714286 0.7452381  0.77857143 0.77380952 0.8547619  0.70238095\n 0.76190476 0.73571429 0.68809524 0.8047619  0.80238095 0.79285714\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.80714286 0.77857143 0.75714286 0.82142857 0.78809524 0.79285714\n 0.84047619 0.73095238 0.8547619  0.8047619  0.83809524 0.75952381\n 0.79047619 0.83571429 0.77380952 0.70714286 0.82380952 0.76666667\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.78809524 0.86666667 0.82380952 0.80714286 0.82380952 0.75238095\n 0.7452381  0.75       0.79285714 0.74761905 0.68333333 0.73571429\n 0.79285714 0.76190476 0.77857143 0.80952381 0.77619048 0.80714286\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.84047619 0.83571429 0.78095238 0.79047619 0.83571429 0.82142857\n 0.80238095 0.85       0.77857143 0.71428571 0.80714286 0.76190476\n 0.77142857 0.78809524 0.83809524 0.83571429 0.77380952 0.72857143\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.78809524 0.79285714 0.71904762 0.79285714 0.82380952 0.71904762\n 0.77142857 0.76190476 0.83571429 0.80952381 0.79285714 0.80952381\n 0.83333333 0.7452381  0.78333333 0.7452381  0.7        0.70714286\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.81666667 0.81904762 0.7452381  0.77857143 0.80714286 0.80714286\n 0.76666667 0.75       0.76190476 0.68809524 0.83571429 0.75\n 0.81666667 0.73333333 0.79761905 0.77380952 0.72857143 0.78809524\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.76428571 0.77142857 0.73571429 0.80714286 0.83571429 0.79047619\n 0.82380952 0.7952381  0.81666667 0.70714286 0.78571429 0.79285714\n 0.8547619  0.74761905 0.77619048 0.75714286 0.8047619  0.74285714\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.82142857 0.83571429 0.7452381  0.76666667 0.83809524 0.8047619\n 0.78333333 0.82380952 0.71904762 0.74761905 0.86666667 0.79285714\n 0.72857143 0.75714286 0.77380952 0.8047619  0.83571429 0.85\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.76428571 0.75952381 0.80714286 0.80714286 0.83809524 0.82142857\n 0.75       0.79285714 0.79047619 0.80714286 0.74285714 0.71904762\n 0.77142857 0.79285714 0.79285714 0.81904762 0.79047619 0.77619048\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.76666667 0.77619048 0.85       0.77619048 0.80952381 0.7452381\n 0.76666667 0.77619048 0.8047619  0.73809524 0.82380952 0.77619048\n 0.85       0.71428571 0.79761905 0.79047619 0.86666667 0.72619048\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.76428571 0.85238095 0.7452381  0.8047619  0.82142857 0.80952381\n 0.8047619  0.77857143 0.88333333 0.7952381  0.82380952 0.79047619\n 0.8047619  0.79285714 0.76666667 0.80952381 0.81904762 0.75952381\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.79285714 0.82380952 0.85238095 0.7952381  0.80714286 0.80952381\n 0.77380952 0.73095238 0.7952381  0.85       0.85       0.79285714\n 0.77857143 0.83333333 0.79285714 0.7452381  0.7952381  0.79285714\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.77380952 0.82142857 0.77619048 0.8047619  0.76428571 0.79285714\n 0.88095238 0.77619048 0.8047619  0.73571429 0.81190476 0.86904762\n 0.74761905 0.83571429 0.79285714 0.76428571 0.83571429 0.82380952\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.8047619  0.77619048 0.80714286 0.80714286 0.79285714 0.85\n 0.83809524 0.7952381  0.76190476 0.75       0.80714286 0.86666667\n 0.77380952 0.82142857 0.77380952 0.78809524 0.80238095 0.80714286\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.75952381 0.79285714 0.82142857 0.82380952 0.73333333 0.77380952\n 0.80952381 0.72857143 0.78809524 0.73095238 0.79047619 0.74285714\n 0.86904762 0.77619048 0.75714286 0.81666667 0.78333333 0.6952381\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.83333333 0.67619048 0.77619048 0.81904762 0.86666667 0.79285714\n 0.76428571 0.80714286 0.78809524 0.77857143 0.71190476 0.80952381\n 0.77380952 0.79047619 0.74285714 0.75952381 0.77619048 0.76428571\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.85238095 0.77619048 0.78809524 0.81428571 0.83809524 0.81904762\n 0.71904762 0.80714286 0.77857143 0.82142857 0.8547619  0.86666667\n 0.71428571 0.77619048 0.75952381 0.75952381 0.77380952 0.75\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.8        0.8452381  0.79285714 0.77380952 0.79047619 0.86428571\n 0.80714286 0.81666667 0.80952381 0.8047619  0.79285714 0.80952381\n 0.61666667 0.79285714 0.71666667 0.77857143 0.83571429 0.82619048]\n  warnings.warn(\n",
          "output_type": "stream"
        },
        {
          "execution_count": 64,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.8260869565217391"
          },
          "metadata": {}
        }
      ],
      "execution_count": 64
    },
    {
      "cell_type": "markdown",
      "source": "We can plot the confusion matrix\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "yhat = tree_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": "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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 65
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  10\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a k nearest neighbors object then  create a  <code>GridSearchCV</code> object  <code>knn_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "parameters = {'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n              'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n              'p': [1,2]}\n\nKNN = KNeighborsClassifier()",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 67
    },
    {
      "cell_type": "code",
      "source": "knn_cv = GridSearchCV(KNN, parameters, cv=10)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 68
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\nprint(\"accuracy :\",knn_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "ename": "<class 'AttributeError'>",
          "evalue": "'GridSearchCV' object has no attribute 'best_params_'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[69], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtuned hpyerparameters :(best parameters) \u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[43mknn_cv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbest_params_\u001b[49m)\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124maccuracy :\u001b[39m\u001b[38;5;124m\"\u001b[39m,knn_cv\u001b[38;5;241m.\u001b[39mbest_score_)\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'GridSearchCV' object has no attribute 'best_params_'"
          ],
          "output_type": "error"
        }
      ],
      "execution_count": 69
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  11\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Calculate the accuracy of knn_cv on the test data using the method <code>score</code>:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "knn_cv.fit(X_train, Y_train)\nknn_cv.score(X_test, Y_test)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stderr",
          "text": "/lib/python3.11/site-packages/threadpoolctl.py:1019: RuntimeWarning: libc not found. The ctypes module in Python 3.11 is maybe too old for this OS.\n  warnings.warn(\n",
          "output_type": "stream"
        },
        {
          "execution_count": 70,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.8695652173913043"
          },
          "metadata": {}
        }
      ],
      "execution_count": 70
    },
    {
      "cell_type": "markdown",
      "source": "We can plot the confusion matrix\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "yhat = knn_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": "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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 71
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  12\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Find the method performs best:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {},
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": "## Authors\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "[Pratiksha Verma](https://www.linkedin.com/in/pratiksha-verma-6487561b1/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Change Log\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "| Date (YYYY-MM-DD) | Version | Changed By      | Change Description      |\n| ----------------- | ------- | -------------   | ----------------------- |\n| 2022-11-09        | 1.0     | Pratiksha Verma | Converted initial version to Jupyterlite|\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### <h3 align=\"center\"> IBM Corporation 2022. All rights reserved. <h3/>\n",
      "metadata": {}
    }
  ]
}