SpaceX_Machine_Learning_Prediction_Part_5.jupyterlite.ipynb

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      "language": "python"
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    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    }
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  "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": 9
    },
    {
      "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": 10
    },
    {
      "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": 6
    },
    {
      "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": 48
    },
    {
      "cell_type": "code",
      "source": "data.head()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 49,
          "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": 49
    },
    {
      "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())\ndf_fe = pd.read_csv(text2)\n\nX = df_fe.reset_index().drop(['index'], axis=1)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 50
    },
    {
      "cell_type": "code",
      "source": "X.head(100)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 51,
          "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": 51
    },
    {
      "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": "# It seems weird to convert a column to a numpy array, then reconvert it to a pandas series\n# we can immediately extract the pandas series from the dataframe\nY = data['Class'].to_numpy()\ndata.drop(['Class'], axis=1, inplace=True)\ntype(Y)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 53,
          "output_type": "execute_result",
          "data": {
            "text/plain": "numpy.ndarray"
          },
          "metadata": {}
        }
      ],
      "execution_count": 53
    },
    {
      "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": "# Create the StandardScaler\nscaler = preprocessing.StandardScaler()\n\n# Standardize the data and reassign it to X\nX = scaler.fit_transform(X)\n",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 58
    },
    {
      "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": "# Split the data into training and test sets\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 60
    },
    {
      "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": 61,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(18,)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 61
    },
    {
      "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": "# Create a logistic regression object\nlogreg = LogisticRegression()\n\n# Define the parameters grid\nparameters = {'C': [0.01, 0.1, 1],\n              'penalty': ['l2'],\n              'solver': ['lbfgs']}\n\n# Create a GridSearchCV object with cross-validation (cv=10)\nlogreg_cv = GridSearchCV(logreg, parameters, cv=10)\n\n# Fit the GridSearchCV object to find the best parameters\nlogreg_cv.fit(X_train, Y_train)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 62,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=10, estimator=LogisticRegression(),\n             param_grid={'C': [0.01, 0.1, 1], 'penalty': ['l2'],\n                         'solver': ['lbfgs']})",
            "text/html": "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=LogisticRegression(),\n             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1], &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=LogisticRegression(),\n             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1], &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 62
    },
    {
      "cell_type": "code",
      "source": "parameters ={\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge\nlr=LogisticRegression()\n",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 64,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=10, estimator=LogisticRegression(),\n             param_grid={'C': [0.01, 0.1, 1], 'penalty': ['l2'],\n                         'solver': ['lbfgs']})",
            "text/html": "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=LogisticRegression(),\n             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1], &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=LogisticRegression(),\n             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1], &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 64
    },
    {
      "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 hpyerparameters :(best parameters) \",logreg_cv.best_params_)\nprint(\"accuracy :\",logreg_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\naccuracy : 0.8464285714285713\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 65
    },
    {
      "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": "print('score on train data: ', logreg_cv.score(X_train, Y_train))  # R² score on train data\nprint('score on test data : ', logreg_cv.score(X_test, Y_test))  # R² score on test data\n\nprint(\"accuracy :\",logreg_cv.score(X_test, Y_test))",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "score on train data:  0.875\nscore on test data :  0.8333333333333334\naccuracy : 0.8333333333333334\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 70
    },
    {
      "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": 71
    },
    {
      "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": 72
    },
    {
      "cell_type": "code",
      "source": "svm_cv = GridSearchCV(estimator=svm, cv=10, param_grid=parameters)\nsvm_cv.fit(X_train, Y_train)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 73,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=10, estimator=SVC(),\n             param_grid={'C': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n       1.00000000e+03]),\n                         'gamma': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n       1.00000000e+03]),\n                         'kernel': ('linear', 'rbf', 'poly', 'rbf', 'sigmoid')})",
            "text/html": "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=SVC(),\n             param_grid={&#x27;C&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n       1.00000000e+03]),\n                         &#x27;gamma&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n       1.00000000e+03]),\n                         &#x27;kernel&#x27;: (&#x27;linear&#x27;, &#x27;rbf&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=SVC(),\n             param_grid={&#x27;C&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n       1.00000000e+03]),\n                         &#x27;gamma&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n       1.00000000e+03]),\n                         &#x27;kernel&#x27;: (&#x27;linear&#x27;, &#x27;rbf&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;)})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 73
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",svm_cv.best_params_)\nprint(\"accuracy :\",svm_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'C': 1.0, 'gamma': 0.03162277660168379, 'kernel': 'sigmoid'}\naccuracy : 0.8482142857142856\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 74
    },
    {
      "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": "print(\"accuracy :\", svm_cv.score(X_test, Y_test))",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "accuracy : 0.8333333333333334\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 75
    },
    {
      "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": 76
    },
    {
      "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": 77
    },
    {
      "cell_type": "code",
      "source": "tree_cv = GridSearchCV(estimator=tree, cv=10, param_grid=parameters)\ntree_cv.fit(X_train, Y_train)",
      "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 {'sqrt', 'log2'} 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 0.79464286 0.77857143 0.74642857 0.77857143 0.7625     0.77678571\n 0.83392857 0.7625     0.81964286 0.69107143 0.84821429 0.73392857\n 0.74821429 0.81964286 0.77678571 0.78928571 0.79107143 0.84821429\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.86071429 0.83392857 0.81964286 0.80714286 0.75       0.83214286\n 0.73571429 0.72321429 0.73571429 0.82321429 0.79107143 0.84464286\n 0.76428571 0.75178571 0.86071429 0.7625     0.76071429 0.77678571\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.80535714 0.78035714 0.68928571 0.79285714 0.80357143 0.79285714\n 0.7875     0.84642857 0.77857143 0.78928571 0.75       0.75178571\n 0.77678571 0.81964286 0.73928571 0.84821429 0.83392857 0.80892857\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.7375     0.83214286 0.79107143 0.775      0.80535714 0.86071429\n 0.72321429 0.81964286 0.81607143 0.87678571 0.81964286 0.79464286\n 0.77321429 0.7625     0.875      0.83392857 0.63392857 0.79107143\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.81785714 0.79107143 0.79107143 0.83392857 0.80357143 0.80714286\n 0.81785714 0.81964286 0.73392857 0.7625     0.725      0.83214286\n 0.75178571 0.83214286 0.75357143 0.83214286 0.81964286 0.83392857\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.75       0.77678571 0.78928571 0.73571429 0.77678571 0.75\n 0.73571429 0.775      0.78928571 0.83214286 0.81785714 0.84642857\n 0.75       0.77678571 0.7625     0.8625     0.73571429 0.77678571\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.76428571 0.80535714 0.76428571 0.71071429 0.78035714 0.80714286\n 0.76428571 0.79107143 0.74642857 0.7625     0.80535714 0.80892857\n 0.78928571 0.80357143 0.81785714 0.875      0.83392857 0.80357143\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.75       0.79642857 0.76071429 0.65535714 0.79464286 0.83214286\n 0.69642857 0.83214286 0.76071429 0.77678571 0.73571429 0.78214286\n 0.76428571 0.80535714 0.80357143 0.78928571 0.71964286 0.75178571\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.75       0.74821429 0.70892857 0.79285714 0.81607143 0.77678571\n 0.74642857 0.77678571 0.75892857 0.79285714 0.77678571 0.83214286\n 0.73571429 0.79642857 0.73928571 0.83571429 0.73214286 0.77678571\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.83392857 0.79642857 0.77678571 0.76428571 0.71785714 0.7875\n 0.64821429 0.75357143 0.77321429 0.78035714 0.79107143 0.7625\n 0.79285714 0.81785714 0.79107143 0.70892857 0.75357143 0.76607143\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.78928571 0.77857143 0.79107143 0.80357143 0.81785714 0.80357143\n 0.74642857 0.79107143 0.80357143 0.80535714 0.75892857 0.80178571\n 0.72321429 0.80535714 0.80535714 0.83214286 0.77678571 0.77678571\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.81785714 0.79107143 0.79107143 0.78035714 0.78928571 0.83214286\n 0.75       0.7625     0.73392857 0.81964286 0.81785714 0.81964286\n 0.76607143 0.80357143 0.7625     0.8625     0.72321429 0.83392857\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.70892857 0.80714286 0.73571429 0.80714286 0.76428571 0.80357143\n 0.81964286 0.84642857 0.70714286 0.76428571 0.78928571 0.76071429\n 0.72321429 0.83214286 0.79107143 0.77857143 0.73392857 0.76607143\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.80178571 0.79107143 0.7375     0.83214286 0.85892857 0.83214286\n 0.76071429 0.78214286 0.77857143 0.68392857 0.73392857 0.80535714\n 0.81785714 0.80357143 0.83392857 0.775      0.78214286 0.81785714\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.82142857 0.74821429 0.72142857 0.69285714 0.81785714 0.77857143\n 0.83392857 0.73392857 0.81785714 0.75       0.78928571 0.77678571\n 0.76607143 0.77857143 0.76607143 0.76607143 0.775      0.80535714\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.74821429 0.80714286 0.77678571 0.7625     0.83214286 0.75178571\n 0.74821429 0.78928571 0.78928571 0.80357143 0.76428571 0.84821429\n 0.79285714 0.82321429 0.80535714 0.80535714 0.71071429 0.80357143\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.78928571 0.73928571 0.77857143 0.77857143 0.78928571 0.77857143\n 0.74821429 0.77678571 0.75       0.80357143 0.73571429 0.79107143\n 0.78928571 0.80535714 0.69642857 0.71071429 0.73571429 0.74642857\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.81964286 0.77857143 0.73571429 0.80714286 0.75178571 0.80714286\n 0.73392857 0.68392857 0.7875     0.79107143 0.76071429 0.84821429\n 0.72142857 0.75       0.76071429 0.74642857 0.78928571 0.81964286]\n  warnings.warn(\n",
          "output_type": "stream"
        },
        {
          "execution_count": 84,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n             param_grid={'criterion': ['gini', 'entropy'],\n                         'max_depth': [2, 4, 6, 8, 10, 12, 14, 16, 18],\n                         'max_features': ['auto', 'sqrt'],\n                         'min_samples_leaf': [1, 2, 4],\n                         'min_samples_split': [2, 5, 10],\n                         'splitter': ['best', 'random']})",
            "text/html": "<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n                         &#x27;max_depth&#x27;: [2, 4, 6, 8, 10, 12, 14, 16, 18],\n                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n                         &#x27;max_depth&#x27;: [2, 4, 6, 8, 10, 12, 14, 16, 18],\n                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 84
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",tree_cv.best_params_)\nprint(\"accuracy :\",tree_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 16, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'splitter': 'random'}\naccuracy : 0.9017857142857144\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 79
    },
    {
      "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": "print(\"accuracy :\", tree_cv.score(X_test, Y_test))",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "accuracy : 0.8333333333333334\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 80
    },
    {
      "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": 85
    },
    {
      "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": 86
    },
    {
      "cell_type": "code",
      "source": "knn_cv = GridSearchCV(estimator=KNN, cv=10, param_grid=parameters)\nknn_cv.fit(X_train, Y_train)",
      "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": 87,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n             param_grid={'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                         'p': [1, 2]})",
            "text/html": "<style>#sk-container-id-8 {color: black;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n             param_grid={&#x27;algorithm&#x27;: [&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;],\n                         &#x27;n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                         &#x27;p&#x27;: [1, 2]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n             param_grid={&#x27;algorithm&#x27;: [&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;],\n                         &#x27;n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                         &#x27;p&#x27;: [1, 2]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 87
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\nprint(\"accuracy :\",knn_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 10, 'p': 1}\naccuracy : 0.8482142857142858\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 88
    },
    {
      "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": "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\nprint(\"accuracy :\",knn_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 10, 'p': 1}\naccuracy : 0.8482142857142858\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 89
    },
    {
      "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": 90
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  12\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Find the method performs best:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "print(\"Model\\t\\tAccuracy\\tTestAccuracy\")#,logreg_cv.best_score_)\nprint(\"LogReg\\t\\t{}\\t\\t{}\".format((logreg_cv.best_score_).round(5), logreg_cv.score(X_test, Y_test).round(5)))\nprint(\"SVM\\t\\t{}\\t\\t{}\".format((svm_cv.best_score_).round(5), svm_cv.score(X_test, Y_test).round(5)))\nprint(\"Tree\\t\\t{}\\t\\t{}\".format((tree_cv.best_score_).round(5), tree_cv.score(X_test, Y_test).round(5)))\nprint(\"KNN\\t\\t{}\\t\\t{}\".format((knn_cv.best_score_).round(5), knn_cv.score(X_test, Y_test).round(5)))\n\ncomparison = {}\n\ncomparison['LogReg'] = {'Accuracy': logreg_cv.best_score_.round(5), 'TestAccuracy': logreg_cv.score(X_test, Y_test).round(5)}\ncomparison['SVM'] = {'Accuracy': svm_cv.best_score_.round(5), 'TestAccuracy': svm_cv.score(X_test, Y_test).round(5)}\ncomparison['Tree'] = {'Accuracy': tree_cv.best_score_.round(5), 'TestAccuracy': tree_cv.score(X_test, Y_test).round(5)}\ncomparison['KNN'] = {'Accuracy': knn_cv.best_score_.round(5), 'TestAccuracy': knn_cv.score(X_test, Y_test).round(5)}",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Model\t\tAccuracy\tTestAccuracy\nLogReg\t\t0.84643\t\t0.83333\nSVM\t\t0.84821\t\t0.83333\nTree\t\t0.87679\t\t0.77778\nKNN\t\t0.84821\t\t0.83333\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 91
    },
    {
      "cell_type": "code",
      "source": "x = []\ny1 = []\ny2 = []\nfor meth in comparison.keys():\n    x.append(meth)    \n    y1.append(comparison[meth]['Accuracy'])\n    y2.append(comparison[meth]['TestAccuracy'])\n    \n\nx_axis = np.arange(len(x))\n\nplt.bar(x_axis - 0.2, y1, 0.4, label = 'Accuracy')\nplt.bar(x_axis + 0.2, y2, 0.4, label = 'Test Accuracy')\n\nplt.ylim([0,1])\nplt.xticks(x_axis, x)\n\nplt.xlabel(\"Methods\")\nplt.ylabel(\"Accuracy\")\nplt.title(\"Accuracy of Each Method\")\nplt.legend(loc='lower left')\nplt.show()\n",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 92
    },
    {
      "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": {}
    }
  ]
}