08_SpaceX_Machine Learning Prediction_Part_5.ipynb

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  "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\" target=\"_blank\">\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,
        "tags": [],
        "editable": true,
        "slideshow": {
          "slide_type": ""
        }
      },
      "outputs": [],
      "execution_count": 1
    },
    {
      "cell_type": "markdown",
      "source": "We will import the following libraries for the lab\n",
      "metadata": {
        "tags": [],
        "editable": true,
        "slideshow": {
          "slide_type": ""
        }
      }
    },
    {
      "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())\nbeta = pd.read_csv(text2)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 6
    },
    {
      "cell_type": "code",
      "source": "beta.head(10)",
      "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   \n5           6.0  3325.000000      1.0    1.0          0.0          0.0   \n6           7.0  2296.000000      1.0    1.0          0.0          0.0   \n7           8.0  1316.000000      1.0    1.0          0.0          0.0   \n8           9.0  4535.000000      1.0    1.0          0.0          0.0   \n9          10.0  4428.000000      1.0    1.0          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   \n5        0.0        1.0        0.0        0.0  ...           0.0   \n6        0.0        0.0        0.0        1.0  ...           0.0   \n7        0.0        0.0        0.0        0.0  ...           0.0   \n8        0.0        1.0        0.0        0.0  ...           0.0   \n9        0.0        1.0        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   \n5           0.0           0.0           0.0             1.0            0.0   \n6           0.0           0.0           0.0             1.0            0.0   \n7           0.0           0.0           0.0             1.0            0.0   \n8           0.0           0.0           0.0             1.0            0.0   \n9           0.0           0.0           0.0             1.0            0.0   \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  \n5           1.0          0.0         1.0        0.0  \n6           1.0          0.0         0.0        1.0  \n7           1.0          0.0         0.0        1.0  \n8           1.0          0.0         1.0        0.0  \n9           1.0          0.0         1.0        0.0  \n\n[10 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>5</th>\n      <td>6.0</td>\n      <td>3325.000000</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>6</th>\n      <td>7.0</td>\n      <td>2296.000000</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>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8.0</td>\n      <td>1316.000000</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>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9.0</td>\n      <td>4535.000000</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>9</th>\n      <td>10.0</td>\n      <td>4428.000000</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  </tbody>\n</table>\n<p>10 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()\nY",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 8,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n       1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,\n       1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 1], dtype=int64)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 8
    },
    {
      "cell_type": "code",
      "source": "len(Y)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 9,
          "output_type": "execute_result",
          "data": {
            "text/plain": "90"
          },
          "metadata": {}
        }
      ],
      "execution_count": 9
    },
    {
      "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()\n\nteta = transform.fit_transform(beta)\n\nX = pd.DataFrame(teta, columns = beta.columns)\nX",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 10,
          "output_type": "execute_result",
          "data": {
            "text/plain": "    FlightNumber   PayloadMass   Flights     Block  ReusedCount  Orbit_ES-L1  \\\n0      -1.712912 -1.948145e-16 -0.653913 -1.575895    -0.973440       -0.106   \n1      -1.674419 -1.195232e+00 -0.653913 -1.575895    -0.973440       -0.106   \n2      -1.635927 -1.162673e+00 -0.653913 -1.575895    -0.973440       -0.106   \n3      -1.597434 -1.200587e+00 -0.653913 -1.575895    -0.973440       -0.106   \n4      -1.558942 -6.286706e-01 -0.653913 -1.575895    -0.973440       -0.106   \n..           ...           ...       ...       ...          ...          ...   \n85      1.558942  1.991005e+00  0.174991  0.945537     0.202528       -0.106   \n86      1.597434  1.991005e+00  1.003894  0.945537     0.202528       -0.106   \n87      1.635927  1.991005e+00  3.490605  0.945537     1.966480       -0.106   \n88      1.674419  1.991005e+00  1.003894  0.945537     0.202528       -0.106   \n89      1.712912 -5.192140e-01 -0.653913  0.945537    -0.973440       -0.106   \n\n    Orbit_GEO  Orbit_GTO  Orbit_HEO  Orbit_ISS  ...  Serial_B1058  \\\n0      -0.106  -0.654654     -0.106  -0.551677  ...     -0.185695   \n1      -0.106  -0.654654     -0.106  -0.551677  ...     -0.185695   \n2      -0.106  -0.654654     -0.106   1.812654  ...     -0.185695   \n3      -0.106  -0.654654     -0.106  -0.551677  ...     -0.185695   \n4      -0.106   1.527525     -0.106  -0.551677  ...     -0.185695   \n..        ...        ...        ...        ...  ...           ...   \n85     -0.106  -0.654654     -0.106  -0.551677  ...     -0.185695   \n86     -0.106  -0.654654     -0.106  -0.551677  ...      5.385165   \n87     -0.106  -0.654654     -0.106  -0.551677  ...     -0.185695   \n88     -0.106  -0.654654     -0.106  -0.551677  ...     -0.185695   \n89     -0.106  -0.654654     -0.106  -0.551677  ...     -0.185695   \n\n    Serial_B1059  Serial_B1060  Serial_B1062  GridFins_False  GridFins_True  \\\n0      -0.215666     -0.185695     -0.106000        1.870829      -1.870829   \n1      -0.215666     -0.185695     -0.106000        1.870829      -1.870829   \n2      -0.215666     -0.185695     -0.106000        1.870829      -1.870829   \n3      -0.215666     -0.185695     -0.106000        1.870829      -1.870829   \n4      -0.215666     -0.185695     -0.106000        1.870829      -1.870829   \n..           ...           ...           ...             ...            ...   \n85     -0.215666      5.385165     -0.106000       -0.534522       0.534522   \n86     -0.215666     -0.185695     -0.106000       -0.534522       0.534522   \n87     -0.215666     -0.185695     -0.106000       -0.534522       0.534522   \n88     -0.215666      5.385165     -0.106000       -0.534522       0.534522   \n89     -0.215666     -0.185695      9.433981       -0.534522       0.534522   \n\n    Reused_False  Reused_True  Legs_False  Legs_True  \n0       0.835532    -0.835532    1.933091  -1.933091  \n1       0.835532    -0.835532    1.933091  -1.933091  \n2       0.835532    -0.835532    1.933091  -1.933091  \n3       0.835532    -0.835532    1.933091  -1.933091  \n4       0.835532    -0.835532    1.933091  -1.933091  \n..           ...          ...         ...        ...  \n85     -1.196843     1.196843   -0.517306   0.517306  \n86     -1.196843     1.196843   -0.517306   0.517306  \n87     -1.196843     1.196843   -0.517306   0.517306  \n88     -1.196843     1.196843   -0.517306   0.517306  \n89      0.835532    -0.835532   -0.517306   0.517306  \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>-1.712912</td>\n      <td>-1.948145e-16</td>\n      <td>-0.653913</td>\n      <td>-1.575895</td>\n      <td>-0.973440</td>\n      <td>-0.106</td>\n      <td>-0.106</td>\n      <td>-0.654654</td>\n      <td>-0.106</td>\n      <td>-0.551677</td>\n      <td>...</td>\n      <td>-0.185695</td>\n      <td>-0.215666</td>\n      <td>-0.185695</td>\n      <td>-0.106000</td>\n      <td>1.870829</td>\n      <td>-1.870829</td>\n      <td>0.835532</td>\n      <td>-0.835532</td>\n      <td>1.933091</td>\n      <td>-1.933091</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-1.674419</td>\n      <td>-1.195232e+00</td>\n      <td>-0.653913</td>\n      <td>-1.575895</td>\n      <td>-0.973440</td>\n      <td>-0.106</td>\n      <td>-0.106</td>\n      <td>-0.654654</td>\n      <td>-0.106</td>\n      <td>-0.551677</td>\n      <td>...</td>\n      <td>-0.185695</td>\n      <td>-0.215666</td>\n      <td>-0.185695</td>\n      <td>-0.106000</td>\n      <td>1.870829</td>\n      <td>-1.870829</td>\n      <td>0.835532</td>\n      <td>-0.835532</td>\n      <td>1.933091</td>\n      <td>-1.933091</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-1.635927</td>\n      <td>-1.162673e+00</td>\n      <td>-0.653913</td>\n      <td>-1.575895</td>\n      <td>-0.973440</td>\n      <td>-0.106</td>\n      <td>-0.106</td>\n      <td>-0.654654</td>\n      <td>-0.106</td>\n      <td>1.812654</td>\n      <td>...</td>\n      <td>-0.185695</td>\n      <td>-0.215666</td>\n      <td>-0.185695</td>\n      <td>-0.106000</td>\n      <td>1.870829</td>\n      <td>-1.870829</td>\n      <td>0.835532</td>\n      <td>-0.835532</td>\n      <td>1.933091</td>\n      <td>-1.933091</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-1.597434</td>\n      <td>-1.200587e+00</td>\n      <td>-0.653913</td>\n      <td>-1.575895</td>\n      <td>-0.973440</td>\n      <td>-0.106</td>\n      <td>-0.106</td>\n      <td>-0.654654</td>\n      <td>-0.106</td>\n      <td>-0.551677</td>\n      <td>...</td>\n      <td>-0.185695</td>\n      <td>-0.215666</td>\n      <td>-0.185695</td>\n      <td>-0.106000</td>\n      <td>1.870829</td>\n      <td>-1.870829</td>\n      <td>0.835532</td>\n      <td>-0.835532</td>\n      <td>1.933091</td>\n      <td>-1.933091</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-1.558942</td>\n      <td>-6.286706e-01</td>\n      <td>-0.653913</td>\n      <td>-1.575895</td>\n      <td>-0.973440</td>\n      <td>-0.106</td>\n      <td>-0.106</td>\n      <td>1.527525</td>\n      <td>-0.106</td>\n      <td>-0.551677</td>\n      <td>...</td>\n      <td>-0.185695</td>\n      <td>-0.215666</td>\n      <td>-0.185695</td>\n      <td>-0.106000</td>\n      <td>1.870829</td>\n      <td>-1.870829</td>\n      <td>0.835532</td>\n      <td>-0.835532</td>\n      <td>1.933091</td>\n      <td>-1.933091</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>1.558942</td>\n      <td>1.991005e+00</td>\n      <td>0.174991</td>\n      <td>0.945537</td>\n      <td>0.202528</td>\n      <td>-0.106</td>\n      <td>-0.106</td>\n      <td>-0.654654</td>\n      <td>-0.106</td>\n      <td>-0.551677</td>\n      <td>...</td>\n      <td>-0.185695</td>\n      <td>-0.215666</td>\n      <td>5.385165</td>\n      <td>-0.106000</td>\n      <td>-0.534522</td>\n      <td>0.534522</td>\n      <td>-1.196843</td>\n      <td>1.196843</td>\n      <td>-0.517306</td>\n      <td>0.517306</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>1.597434</td>\n      <td>1.991005e+00</td>\n      <td>1.003894</td>\n      <td>0.945537</td>\n      <td>0.202528</td>\n      <td>-0.106</td>\n      <td>-0.106</td>\n      <td>-0.654654</td>\n      <td>-0.106</td>\n      <td>-0.551677</td>\n      <td>...</td>\n      <td>5.385165</td>\n      <td>-0.215666</td>\n      <td>-0.185695</td>\n      <td>-0.106000</td>\n      <td>-0.534522</td>\n      <td>0.534522</td>\n      <td>-1.196843</td>\n      <td>1.196843</td>\n      <td>-0.517306</td>\n      <td>0.517306</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>1.635927</td>\n      <td>1.991005e+00</td>\n      <td>3.490605</td>\n      <td>0.945537</td>\n      <td>1.966480</td>\n      <td>-0.106</td>\n      <td>-0.106</td>\n      <td>-0.654654</td>\n      <td>-0.106</td>\n      <td>-0.551677</td>\n      <td>...</td>\n      <td>-0.185695</td>\n      <td>-0.215666</td>\n      <td>-0.185695</td>\n      <td>-0.106000</td>\n      <td>-0.534522</td>\n      <td>0.534522</td>\n      <td>-1.196843</td>\n      <td>1.196843</td>\n      <td>-0.517306</td>\n      <td>0.517306</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>1.674419</td>\n      <td>1.991005e+00</td>\n      <td>1.003894</td>\n      <td>0.945537</td>\n      <td>0.202528</td>\n      <td>-0.106</td>\n      <td>-0.106</td>\n      <td>-0.654654</td>\n      <td>-0.106</td>\n      <td>-0.551677</td>\n      <td>...</td>\n      <td>-0.185695</td>\n      <td>-0.215666</td>\n      <td>5.385165</td>\n      <td>-0.106000</td>\n      <td>-0.534522</td>\n      <td>0.534522</td>\n      <td>-1.196843</td>\n      <td>1.196843</td>\n      <td>-0.517306</td>\n      <td>0.517306</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>1.712912</td>\n      <td>-5.192140e-01</td>\n      <td>-0.653913</td>\n      <td>0.945537</td>\n      <td>-0.973440</td>\n      <td>-0.106</td>\n      <td>-0.106</td>\n      <td>-0.654654</td>\n      <td>-0.106</td>\n      <td>-0.551677</td>\n      <td>...</td>\n      <td>-0.185695</td>\n      <td>-0.215666</td>\n      <td>-0.185695</td>\n      <td>9.433981</td>\n      <td>-0.534522</td>\n      <td>0.534522</td>\n      <td>0.835532</td>\n      <td>-0.835532</td>\n      <td>-0.517306</td>\n      <td>0.517306</td>\n    </tr>\n  </tbody>\n</table>\n<p>90 rows × 83 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 10
    },
    {
      "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, test_size=0.25, random_state=2)",
      "metadata": {
        "trusted": true,
        "tags": [],
        "editable": true,
        "slideshow": {
          "slide_type": ""
        }
      },
      "outputs": [],
      "execution_count": 11
    },
    {
      "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": 12,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(23,)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 12
    },
    {
      "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": 13
    },
    {
      "cell_type": "code",
      "source": "parameters ={\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge\nlr=LogisticRegression()\n\nlogreg_cv = GridSearchCV(estimator=lr, param_grid=parameters, cv=10)\n\n# Fit the GridSearchCV object to the training data to find the best parameters\nlogreg_cv.fit(X_train, Y_train)\n",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 14,
          "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": 14
    },
    {
      "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_)\nlr_b_acc = logreg_cv.best_score_\nprint(\"accuracy :\",lr_b_acc)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'C': 1, 'penalty': 'l2', 'solver': 'lbfgs'}\naccuracy : 0.8238095238095238\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 15
    },
    {
      "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": "test_lr_accuracy = logreg_cv.score(X_test, Y_test)\nprint(\"Test accuracy: \", test_lr_accuracy)",
      "metadata": {
        "trusted": true,
        "tags": [],
        "editable": true,
        "slideshow": {
          "slide_type": ""
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Test accuracy:  0.8260869565217391\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 16
    },
    {
      "cell_type": "markdown",
      "source": "Lets look at the confusion matrix:\n",
      "metadata": {
        "tags": [],
        "editable": true,
        "slideshow": {
          "slide_type": ""
        }
      }
    },
    {
      "cell_type": "code",
      "source": "yhat = logreg_cv.predict(X_test)\nplot_confusion_matrix(Y_test, yhat)",
      "metadata": {
        "trusted": true,
        "tags": [],
        "editable": true,
        "slideshow": {
          "slide_type": ""
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": 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4jakNAACAjBiRAADA05jaAAAAbmNqAwAAICNGJAAA8DSmNgAAgNt8OJGwZGojPDxc+fPnz9YFAADcs2LFCrVo0UKRkZGy2WyaM2eO896lS5c0YMAAVahQQUFBQYqMjNTjjz+ugwcP5qgPS0Ykxo0b5/z52LFjGj58uBo3bqwaNWpIklauXKmFCxdq0KBBVoQHAIC5LFpsmZqaqkqVKumJJ55Q27ZtXe6dO3dO69at06BBg1SpUiWdOHFCPXv2VMuWLbVmzZps92EzDMMwO/CcePjhhxUXF6fnnnvOpfztt9/WkiVLXLKn7BoY3cmk6ADfsuLSYatDALzOzweWeryP81+PMqWdwJZ93f6uzWbT7Nmz1bp16yzrrF69Wg888ID27t2r4sWLZ6tdy3dtLFy4UE2aNMlQ3qRJEy1ZssSCiAAAMJnhMOVKS0vT6dOnXa60tDTTwjx16pRsNpvCwsKy/R3LE4kCBQpo7ty5Gcrnzp2rAgUKWBARAADeKSkpSaGhoS5XUlKSKW1fuHBBAwYMUMeOHRUSEpLt71m+ayMxMVHdunXT8uXLVb16dUnSqlWrtGDBAr333nsWRwcAgAlM2rUxcOBA9e7d26XMbrffcruXLl1Su3btZBiGJk6cmKPvWp5IdOnSRbGxsRo/fry++uorSVJsbKx++uknZ2IBAMAdzaTFlna73ZTE4VpXk4i9e/dq6dKlORqNkLwgkZCk6tWra+bMmVaHAQDAP8rVJGLHjh1atmyZW0sKvCKRcDgc2rlzp44cOSLHdcM/derUsSgqAABMYtGBVGfPntXOnTudn1NSUpScnKz8+fMrIiJCjzzyiNatW6d58+YpPT1dhw9f2dmVP39++fv7Z6sPyxOJX3/9VZ06ddLevXt1/U5Um82m9PR0iyIDAMAkFiUSa9asUVxcnPPz1fUV8fHxGjJkiL7++mtJUuXKlV2+t2zZMtWrVy9bfVieSDz99NOqVq2avv32W0VERMhms1kdEgAAPqFevXoZ/pF+LTOOkrI8kdixY4e++OILlSpVyupQAADwDGvPfvQoy8+RqF69usv8DQAAPsfhMOfyQpaPSDz//PPq06ePDh8+rAoVKihPnjwu9ytWrGhRZAAA4GYsTyQefvhhSdITTzzhLLPZbDIMg8WWAADf4KWjCWawPJFISUmxOgQAADzLord/3g6WJxJRUVFWhwAAgGcxIuF5f/zxh/bt26eLFy+6lLds2dKiiAAAwM1Ynkjs3r1bbdq00aZNm5xrIyQ5z5NgjQQA4I7H9k/P6dmzp2JiYnTkyBHlzZtXmzdv1ooVK1StWjUtX77c6vAAALh1bP/0nJUrV2rp0qUqWLCg/Pz85Ofnp1q1aikpKUk9evTQ+vXrrQ4RAABkwfIRifT0dAUHB0uSChYsqIMHD0q6sghz27ZtVoYGAIA5GJHwnPLly2vDhg2KiYlR9erVNWLECPn7+2vy5MkqUaKE1eEBAHDr2P7pOS+//LJSU1MlSUOHDlXz5s1Vu3ZtFShQQJ999pnF0QEAgBuxPJFo3Lix8+dSpUpp69atOn78uMLDw3kTKADAJxgO3921YXkikZn8+fNbHQIAAObx0vUNZrAkkWjbtm2263711VcejAQAANwKSxKJ0NBQK7oFAMAaLLY015QpU6zoFgAAa7BGAgAAuM2H10hYfiAVAAC4czEiAQCAp/nwiASJBAAAnsbbPz1n+vTpSktLy1B+8eJFTZ8+3YKIAABAdlk+IpGQkKAmTZqocOHCLuVnzpxRQkKCHn/8cYsig7vqv/CwGrzwsEvZkV0HNbZ+X4siArxD68dbqs1jLRRRrKgkKWX7Hk0ZO0O/LvvN4sjgcUxteI5hGJkehf3nn39y3sQd7PC2/frg0decnx2XffcPEZBdRw8d1aSk97U/5U/ZbDY1/Xcjvf7hMCU0/o9Stu+xOjx4Ets/zVelShXZbDbZbDbVr19fuXP/L5T09HSlpKSoSZMmVoWHW+RIT9fZo6esDgPwKj8vXunyefIbH6rNYy11b9VYEgncsSxLJFq3bi1JSk5OVuPGjZUvXz7nPX9/f0VHR+vhhx/O4tvwdgWji2rgqnd0Oe2S9q3boQUjPtWpg8esDgvwGn5+foprXlcBeQP0+9o/rA4HnsbJluYbPHiwJCk6Olrt27dXQECAVaHAZPuTd+rzvu/q790HFVw4XPV7ttV/Zr2icY0H6GLqBavDAyxVomyM3v36bfnb/XU+9bz+222w9uzYa3VY8DSmNjwnPj5ekrR27Vpt2bJFknTvvfeqSpUq2fp+Wlpahl0fl4105bblMjdQZNv25RucPx/eul/7k3dqwE/jVbHZ/2nNrOXWBQZ4gX279qtLoyeVLzhIcc3q6qVxA/Tcw71IJnDHsjyROHLkiDp06KDly5crLCxMknTy5EnFxcXp008/VaFChW74/aSkJCUmJrqU1Qwtr9phFTwVMnLowulz+jvlkApEF7E6FMByly9d1oE9ByVJ2zbtUNnKZfTvbm01csBYiyODJxk+vGvD8nMknn/+eZ05c0abN2/W8ePHdfz4cf3+++86ffq0evTocdPvDxw4UKdOnXK5aoSWuw2RI7v889qVP6qIzhw5aXUogNfx8/OTv38eq8OApzkMcy4vZPmIxIIFC7RkyRLFxsY6y8qVK6d33nlHjRo1uun37Xa77Ha7SxnTGtZq+t9O2vr9Op048LdCCoerQa9H5Eh3aMPXv1gdGmCpp1/sppXLftNfB/5S3nx51ah1fVWpUUm9Ow2wOjR4GostPcfhcChPnozZeJ48eeTw4aEgXxYaUUAdxj+vvGH5lHr8tPas2a6JbV5R6vEzVocGWCqsYJgGvfmiChTOr9Qzqdq5Zbd6dxqg1T+utTo0wG2WJxL/+te/1LNnT33yySeKjIyUJB04cEC9evVS/fr1LY4O7vj0+besDgHwSq/3HWV1CLCKl05LmMHyNRJvv/22Tp8+rejoaJUsWVIlS5ZUTEyMTp8+rbfe4i8kAIAPcDjMubyQ5SMSxYoV07p167RkyRJt3bpVkhQbG6sGDRpYHBkAALgZyxMJSbLZbGrYsKEaNmxodSgAAJjPh6c2vCKR+P777/X999/ryJEjGRZYfvjhhxZFBQCASdi14TmJiYkaOnSoqlWrpoiIiEzfBAoAALyT5YnEpEmTNHXqVD322GNWhwIAgGcwteE5Fy9e1IMPPmh1GAAAeAxHZHtQt27d9PHHH1sdBgAAcIPlIxIXLlzQ5MmTtWTJElWsWDHDKZdjxoyxKDIAAEzC1IbnbNy4UZUrV5Yk/f777y73WHgJAPAJJBKes2zZMqtDAADAs3x4+6flayQAAMCdi0QCAABPcxjmXDm0YsUKtWjRQpGRkbLZbJozZ47LfcMw9MorrygiIkKBgYFq0KCBduzYkaM+SCQAAPAww2GYcuVUamqqKlWqpHfeeSfT+yNGjND48eM1adIkrVq1SkFBQWrcuLEuXLiQ7T4sXyMBAAA8o2nTpmratGmm9wzD0Lhx4/Tyyy+rVatWkqTp06erSJEimjNnjjp06JCtPhiRAADA00ya2khLS9Pp06ddrrS0NLdCSklJ0eHDh13eth0aGqrq1atr5cqV2W7HkhGJr7/+Ott1W7Zs6cFIAAC4DUw62TIpKUmJiYkuZYMHD9aQIUNy3Nbhw4clSUWKFHEpL1KkiPNedliSSLRu3drls81mk2EYLp+vSk9Pv11hAQDg1QYOHKjevXu7lNntdouiucKSqQ2Hw+G8Fi1apMqVK2v+/Pk6efKkTp48qe+++05Vq1bVggULrAgPAABzmTS1YbfbFRIS4nK5m0gULVpUkvTXX3+5lP/111/Oe9lh+WLLF154QZMmTVKtWrWcZY0bN1bevHn11FNPacuWLRZGBwCACbzwZMuYmBgVLVpU33//vfOE6dOnT2vVqlV65plnst2O5YnErl27FBYWlqE8NDRUe/bsue3xAADgK86ePaudO3c6P6ekpCg5OVn58+dX8eLF9cILL2j48OEqXbq0YmJiNGjQIEVGRmZYgnAjlicS999/v3r37q0ZM2Y4F3z89ddf6tevnx544AGLowMA4NZduw7wdlqzZo3i4uKcn6+ur4iPj9fUqVPVv39/paam6qmnntLJkydVq1YtLViwQAEBAdnuw2ZY9XT/386dO9WmTRtt375dxYoVkyTt379fpUuX1pw5c1SqVKkctzkwupPZYQI+YcWl7K/EBv4pfj6w1ON9nH6ykSnthLy3yJR2zGT5iESpUqW0ceNGLV68WFu3bpUkxcbGqkGDBrz9EwDgG7xwjYRZLE8kpCvbPRs1aqRGjczJ2AAAwO1hSSIxfvx4PfXUUwoICND48eNvWLdHjx63KSoAADzDnfdk3CksSSTGjh2rzp07KyAgQGPHjs2yns1mI5EAANz5SCTMlZKSkunPAADgzuIVayQAAPBp5rxqwytZkkhcf074jYwZM8aDkQAA4HmskTDZ+vXrXT6vW7dOly9fVpkyZSRJ27dvV65cuXTfffdZER4AAMgmSxKJZcuWOX8eM2aMgoODNW3aNIWHh0uSTpw4oYSEBNWuXduK8AAAMJcPj0hY8vbPa40ePVpJSUnOJEKSwsPDNXz4cI0ePdrCyAAAMInDpMsLWZ5InD59WkePHs1QfvToUZ05c8aCiAAAQHZZvmujTZs2SkhI0OjRo50v6Vq1apX69euntm3bWhwdAAC3jsWWHjRp0iT17dtXnTp10qVLlyRJuXPnVteuXTVy5EiLowMAwAReOi1hBssTibx582rChAkaOXKkdu3aJUkqWbKkgoKCLI4MAABzMCJxGwQFBalixYpWhwEAAHLAaxIJAAB8FlMbAADAXYYPJxKWb/8EAAB3LkYkAADwNB8ekSCRAADAw5jaAAAAyAQjEgAAeJoPj0iQSAAA4GG+PLVBIgEAgIf5ciLBGgkAAOA2RiQAAPAwXx6RIJEAAMDTDJvVEXgMUxsAAMBtpoxInDx5UmFhYWY0BQCAz/HlqY0cj0i88cYb+uyzz5yf27VrpwIFCuiuu+7Shg0bTA0OAABfYDhsplzeKMeJxKRJk1SsWDFJ0uLFi7V48WLNnz9fTZs2Vb9+/UwPEAAAeK8cT20cPnzYmUjMmzdP7dq1U6NGjRQdHa3q1aubHiAAAHc6pjauER4erv3790uSFixYoAYNGkiSDMNQenq6udEBAOADDMNmyuWNcjwi0bZtW3Xq1EmlS5fWsWPH1LRpU0nS+vXrVapUKdMDBAAA3ivHicTYsWMVHR2t/fv3a8SIEcqXL58k6dChQ3r22WdNDxAAgDudL09t5DiRyJMnj/r27ZuhvFevXqYEBACAr/HWHRdmyFYi8fXXX2e7wZYtW7odDAAAvsgwrI7Ac7KVSLRu3TpbjdlsNhZcAgDwD5KtRMLh8OHJHQAAPOwfP7WRlQsXLiggIMCsWAAA8Em+nEjk+ByJ9PR0DRs2THfddZfy5cun3bt3S5IGDRqkDz74wPQAAQCA98pxIvHqq69q6tSpGjFihPz9/Z3l5cuX1/vvv29qcAAA+ALDMOfyRjlOJKZPn67Jkyerc+fOypUrl7O8UqVK2rp1q6nBAQDgC3hp1zUOHDiQ6QmWDodDly5dMiUoAABwZ8hxIlGuXDn9+OOPGcq/+OILValSxZSgAADwJbxr4xqvvPKK4uPjdeDAATkcDn311Vfatm2bpk+frnnz5nkiRgAA7mhWHJGdnp6uIUOG6KOPPtLhw4cVGRmpLl266OWXX5bNZl5SkuNEolWrVvrmm280dOhQBQUF6ZVXXlHVqlX1zTffqGHDhqYFBgAA3PfGG29o4sSJmjZtmu69916tWbNGCQkJCg0NVY8ePUzrx61zJGrXrq3FixebFgQAAL7MYcG0xC+//KJWrVqpWbNmkqTo6Gh98skn+u2330ztx+0DqdasWaMtW7ZIurJu4r777jMtKAAAfIkV6xsefPBBTZ48Wdu3b9c999yjDRs26KefftKYMWNM7SfHicSff/6pjh076ueff1ZYWJgk6eTJk3rwwQf16aef6u677zY1QAAA7nRmbd1MS0tTWlqaS5ndbpfdbs9Q98UXX9Tp06dVtmxZ5cqVS+np6Xr11VfVuXNnU2K5Kse7Nrp166ZLly5py5YtOn78uI4fP64tW7bI4XCoW7dupgYHAAD+JykpSaGhoS5XUlJSpnVnzZqlmTNn6uOPP9a6des0bdo0jRo1StOmTTM1Jpth5OysrMDAQP3yyy8ZtnquXbtWtWvX1rlz50wN0B0DoztZHQLglVZcOmx1CIDX+fnAUo/3saX0Q6a0U+L32dkekShWrJhefPFFde/e3Vk2fPhwffTRR6YeIJnjqY1ixYplevBUenq6IiMjTQkKAABfYtbURlZJQ2bOnTsnPz/XiYdcuXKZ/kbvHE9tjBw5Us8//7zWrFnjLFuzZo169uypUaNGmRocAABwT4sWLfTqq6/q22+/1Z49ezR79myNGTNGbdq0MbWfbE1thIeHuxxekZqaqsuXLyt37isDGld/DgoK0vHjx00N0B1MbQCZY2oDyOh2TG38XqK5Ke2U3539gx/PnDmjQYMGafbs2Tpy5IgiIyPVsWNHvfLKKy4v3bxV2ZraGDdunGkdAgDwT2PF9s/g4GCNGzfO43+HZyuRiI+P92gQAADgzuT2gVSSdOHCBV28eNGlLCQk5JYCAgDA1+Rsf+SdJceJRGpqqgYMGKBZs2bp2LFjGe6np6ebEhgAAL7CiiOyb5cc79ro37+/li5dqokTJ8put+v9999XYmKiIiMjNX36dE/ECAAAvFSORyS++eYbTZ8+XfXq1VNCQoJq166tUqVKKSoqSjNnzjT96E0AAO50Viy2vF1yPCJx/PhxlShRQtKV9RBXt3vWqlVLK1asMDc6AAB8gGGYc3mjHCcSJUqUUEpKiiSpbNmymjVrlqQrIxVXX+IFAAD+x2HYTLm8UY4TiYSEBG3YsEHSlTeLvfPOOwoICFCvXr3Ur18/0wMEAADeK8cv7bre3r17tXbtWpUqVUoVK1Y0K65bktv/LqtDALzS+YM/Wh0C4HXyFCzh8T5W32XOsdT3H5htSjtmuqVzJCQpKipKUVFRZsQCAIBP8tZpCTNkK5EYP358thvs0aOH28EAAIA7S7YSibFjx2arMZvNRiIBAMB1vHTDhSmylUhc3aUBAAByzpenNnK8awMAAOCqW15sCQAAbsyXT7YkkQAAwMMcVgfgQUxtAAAAtzEiAQCAhxny3akNt0YkfvzxRz366KOqUaOGDhw4IEmaMWOGfvrpJ1ODAwDAFzgMcy5vlONE4ssvv1Tjxo0VGBio9evXKy0tTZJ06tQpvfbaa6YHCADAnc4hmymXN8pxIjF8+HBNmjRJ7733nvLkyeMsr1mzptatW2dqcAAAwLvleI3Etm3bVKdOnQzloaGhOnnypBkxAQDgU1gjcY2iRYtq586dGcp/+uknlSjh+TeoAQBwp3GYdHmjHCcSTz75pHr27KlVq1bJZrPp4MGDmjlzpvr27atnnnnGEzECAAAvleOpjRdffFEOh0P169fXuXPnVKdOHdntdvXt21fPP/+8J2IEAOCO5stTGzbDMNzaUHLx4kXt3LlTZ8+eVbly5ZQvXz6zY3Nbbv+7rA4B8ErnD/5odQiA18lT0PPT8guKdDClnSZ/fWpKO2Zy+0Aqf39/lStXzsxYAADAHSbHiURcXJxstqyHaJYuXXpLAQEA4Gu8daGkGXKcSFSuXNnl86VLl5ScnKzff/9d8fHxZsUFAIDP8OU1EjlOJMaOHZtp+ZAhQ3T27NlbDggAANw5THv756OPPqoPP/zQrOYAAPAZDps5lzcy7e2fK1euVEBAgFnNAQDgM7z1PRlmyHEi0bZtW5fPhmHo0KFDWrNmjQYNGmRaYAAA+AovfXGnKXKcSISGhrp89vPzU5kyZTR06FA1atTItMAAAID3y1EikZ6eroSEBFWoUEHh4eGeigkAAJ/iy9s/c7TYMleuXGrUqBFv+QQAIAccNpsplzfK8a6N8uXLa/fu3Z6IBQAA3GFynEgMHz5cffv21bx583To0CGdPn3a5QIAAK4Mky5vlO01EkOHDlWfPn300EMPSZJatmzpclS2YRiy2WxKT083P0oAAO5gvrxGItuJRGJiop5++mktW7bMk/EAAIA7SLYTiatvG69bt67HggEAwBd566mUZsjR9s8bvfUTAABkjpMt/7977rnnpsnE8ePHbykgAABw58hRIpGYmJjhZEsAAHBj3rrjwgw5SiQ6dOigwoULeyoWAAB8EmskxPoIAADc5cvbP7N9INXVXRsAAODOcODAAT366KMqUKCAAgMDVaFCBa1Zs8bUPrI9IuFw+HI+BQCA51jxT/ETJ06oZs2aiouL0/z581WoUCHt2LHD9Jdu5vg14gAAIGesWCPxxhtvqFixYpoyZYqzLCYmxvR+cvyuDQAA4P2+/vprVatWTf/+979VuHBhValSRe+9957p/ZBIAADgYQ6TrrS0tAwvy0xLS8u0z927d2vixIkqXbq0Fi5cqGeeeUY9evTQtGnTTH02EgkAADzMrEQiKSlJoaGhLldSUlLmfTocqlq1ql577TVVqVJFTz31lJ588klNmjTJ1GdjjQQAAHeIgQMHqnfv3i5ldrs907oREREqV66cS1lsbKy+/PJLU2MikQAAwMMMkxZb2u32LBOH69WsWVPbtm1zKdu+fbuioqLMCeb/I5EAAMDDrDhAoVevXnrwwQf12muvqV27dvrtt980efJkTZ482dR+WCMBAIAPuv/++zV79mx98sknKl++vIYNG6Zx48apc+fOpvbDiAQAAB5m1ZGOzZs3V/PmzT3aB4kEAAAe5ssvmSCRAADAw3z57Z+skQAAAG5jRAIAAA/z5ddekkgAAOBhvpxIMLUBAADcxogEAAAexq4NAADgNnZtAAAAZIIRCQAAPMyXF1uSSAAA4GG+vEaCqQ0AAOA2RiQAAPAwhw+PSZBIAADgYayRAAAAbvPd8QjWSAAAgFvAiAQAAB7G1AYAAHAbJ1sCAABkghEJAAA8jO2fAADAbb6bRjC1AQAAbgEjEgAAeBi7Njxg48aN2a5bsWJFD0YCAIBnsUbCAypXriybzSbDMGSz3XhfTHp6+m2KCgAA5IRlayRSUlK0e/dupaSk6Msvv1RMTIwmTJig9evXa/369ZowYYJKliypL7/80qoQAQAwhWHS5Y0sG5GIiopy/vzvf/9b48eP10MPPeQsq1ixoooVK6ZBgwapdevWFkQIAIA5WCPhYZs2bVJMTEyG8piYGP3xxx8WRAQAgHl8eY2EV2z/jI2NVVJSki5evOgsu3jxopKSkhQbG2thZAAA4Ea8YkRi0qRJatGihe6++27nDo2NGzfKZrPpm2++sTg6AABuje+OR3hJIvHAAw9o9+7dmjlzprZu3SpJat++vTp16qSgoCCLowMA4NawRuI2CAoK0lNPPWV1GAAAIAe8Yo2EJM2YMUO1atVSZGSk9u7dK0kaO3as5s6da3FkAADcGsOk/3kjr0gkJk6cqN69e6tp06Y6ceKE8wCq8PBwjRs3ztrgAAC4RQ6TLm/kFYnEW2+9pffee08vvfSScuf+32xLtWrVtGnTJgsjAwAAN+IVayRSUlJUpUqVDOV2u12pqakWRAQAgHk4R8LDYmJilJycnKF8wYIFnCMBALjjcUS2h/Xu3Vvdu3fXhQsXZBiGfvvtN33yySdKSkrS+++/b3V4AAAgC14xItGtWze98cYbevnll3Xu3Dl16tRJEydO1JtvvqkOHTpYHR7cULtWdc2ZPVX79qzV5YsH1LJlY6tDAm67Ncmb1L3/YMW17KzyNZvq+xW/ZFk3ccRbKl+zqWZ8Nvs2RojbxSHDlMsbeUUiIUmdO3fWjh07dPbsWR0+fFh//vmnunbtanVYcFNQUF5t3PiHnu/5ktWhAJY5f/6CypQqoZf6PHvDekt++FkbN29V4YIFblNkuN18edeGV0xtXCtv3rzKmzev1WHgFi1YuEwLFi6zOgzAUrVr3K/aNe6/YZ2/jv6tpLET9e6YV/Vsv1duU2S43bz1DAgzWJZIVKlSRTabLVt1161b5+FoAOD2czgcGjh0lLp0ekSlSkRZHQ7gFssSidatWzt/vnDhgiZMmKBy5cqpRo0akqRff/1Vmzdv1rPP3nhIMC0tTWlpaS5lhmFkO0kBAKt88NHnypXLT4/+u5XVocDDvHVawgyWJRKDBw92/tytWzf16NFDw4YNy1Bn//79N2wnKSlJiYmJLmU2v3yy5QoxL1gAMNnmrTv00edz9fmHb/EPn38AX57a8IrFlp9//rkef/zxDOWPPvqovvzyyxt+d+DAgTp16pTLZfML9lSoAGCKdRt+1/ETJ9Xw4cdVqU4zVarTTAcPH9HIt99Xo4fjrQ4PyDavWGwZGBion3/+WaVLl3Yp//nnnxUQEHDD79rtdtntdpcysnsA3q5Fk/r6v/tdT/T9T6+X1aLJv9T6oUYWRQVPYWrDw1544QU988wzWrdunR544AFJ0qpVq/Thhx9q0KBBFkcHdwQF5VWpUjHOzzHRxVWp0r06fvyE9u8/aGFkwO1z7tx57fvzf/+9Hzj4l7Zu36XQkGBFFC2ssFDXKdjcuXOpYP5wxUTdfbtDhYc5DOunNl5//XUNHDhQPXv2NPWFmF6RSLz44osqUaKE3nzzTX300UeSpNjYWE2ZMkXt2rWzODq4o9p9lfT9ki+cn0ePGiJJmjZ9lrp262VRVMDt9fvWHXri+QHOzyPemixJatW0gV59uY9VYeEfaPXq1Xr33XdVsWJF09u2GYYXpEkmy+1/l9UhAF7p/MEfrQ4B8Dp5CpbweB+PRrU1pZ2P9n6V4++cPXtWVatW1YQJEzR8+HBVrlzZ90Ykrrp48aKOHDkih8N1Nql48eIWRQQAwK0z63jrzI48yGyt4LW6d++uZs2aqUGDBho+fLgpcVzLK3Zt7NixQ7Vr11ZgYKCioqIUExOjmJgYRUdHKyYm5uYNAADwD5CUlKTQ0FCXKykpKcv6n376qdatW3fDOrfKK0YkunTpoty5c2vevHmKiIhg1wUAwKeYdY7EwIED1bt3b5eyrEYj9u/fr549e2rx4sU33QF5K7wikUhOTtbatWtVtmxZq0MBAMB0Zm3/vNk0xrXWrl2rI0eOqGrVqs6y9PR0rVixQm+//bbS0tKUK1euW47JKxKJcuXK6e+//7Y6DAAAPMKKV4DXr19fmzZtcilLSEhQ2bJlNWDAAFOSCMlLEok33nhD/fv312uvvaYKFSooT548LvdDQjjuGgCAnAgODlb58uVdyoKCglSgQIEM5bfCKxKJBg0aSLqSPV3r6su30tPTrQgLAABT+PK7NrwikVi2bJnVIQAA4DHeckT28uXLTW/TKxKJunXrWh0CAABwg1ckEledO3dO+/bt08WLF13KPXGkJwAAt4sPHiLt5BWJxNGjR5WQkKD58+dnep81EgCAO5kVuzZuF6842fKFF17QyZMntWrVKgUGBmrBggWaNm2aSpcura+//trq8AAAQBa8YkRi6dKlmjt3rqpVqyY/Pz9FRUWpYcOGCgkJUVJSkpo1a2Z1iAAAuM1bFlt6gleMSKSmpqpw4cKSpPDwcB09elSSVKFCBa1bt87K0AAAuGWGSf/zRl6RSJQpU0bbtm2TJFWqVEnvvvuuDhw4oEmTJikiIsLi6AAAQFa8YmqjZ8+eOnTokCRp8ODBatKkiT766CP5+/tr2rRpFkcHAMCt8eXFll6RSDz66KPOn++77z7t3btXW7duVfHixVWwYEELIwMA4Nax/dMDrn8N6o2MGTPGg5EAAOBZvrzY0rJEYv369dmqZ7PZPBwJAABwl2WJBO/XAAD8U3jrjgszeMUaCQAAfJkvL7b0iu2fAADgzsSIBAAAHsauDQAA4DamNgAAADLBiAQAAB7Grg0AAOA2hw+vkWBqAwAAuI0RCQAAPMx3xyNIJAAA8Dhf3rVBIgEAgIf5ciLBGgkAAOA2RiQAAPAwTrYEAABuY2oDAAAgE4xIAADgYZxsCQAA3ObLaySY2gAAAG5jRAIAAA/z5cWWJBIAAHgYUxsAAACZYEQCAAAPY2oDAAC4je2fAADAbQ7WSAAAAGTEiAQAAB7G1AYAAHAbUxsAAACZYEQCAAAPY2oDAAC4jakNAACATDAiAQCAhzG1AQAA3MbUBgAAQCZIJAAA8DDDpP/lRFJSku6//34FBwercOHCat26tbZt22b6s5FIAADgYYbhMOXKiR9++EHdu3fXr7/+qsWLF+vSpUtq1KiRUlNTTX02m2H43sRNbv+7rA4B8ErnD/5odQiA18lTsITH+4gqUNGUdvYe2+j2d48eParChQvrhx9+UJ06dUyJR2KxJQAAd4y0tDSlpaW5lNntdtnt9pt+99SpU5Kk/PnzmxoTUxsAAHiYYRimXElJSQoNDXW5kpKSbtq/w+HQCy+8oJo1a6p8+fKmPhtTG8A/CFMbQEa3Y2rj7vzm/OW969Bat0YknnnmGc2fP18//fST7r77blNiuYqpDQAA7hDZnca41nPPPad58+ZpxYoVpicREokEAAAeZ8Xgv2EYev755zV79mwtX75cMTExHumHRAIAAA+z4mTL7t276+OPP9bcuXMVHBysw4cPS5JCQ0MVGBhoWj+skQD+QVgjAWR0O9ZIRISVM6WdQyf/yHZdm82WafmUKVPUpUsXU+KRGJEAAMDjrHhp1+0aJyCRAADAw3xw8N+JcyQAAIDbGJEAAMDDHBZMbdwuJBIAAHiYL09tkEgAAOBhVmz/vF1YIwEAANzGiAQAAB7G1AYAAHCbLy+2ZGoDAAC4jREJAAA8jKkNAADgNnZtAAAAZIIRCQAAPMyKl3bdLiQSAAB4GFMbAAAAmWBEAgAAD2PXBgAAcBtrJAAAgNt8eUSCNRIAAMBtjEgAAOBhvjwiQSIBAICH+W4awdQGAAC4BTbDl8dbYKm0tDQlJSVp4MCBstvtVocDeA3+bMCXkEjAY06fPq3Q0FCdOnVKISEhVocDeA3+bMCXMLUBAADcRiIBAADcRiIBAADcRiIBj7Hb7Ro8eDCLyYDr8GcDvoTFlgAAwG2MSAAAALeRSAAAALeRSAAAALeRSPigevXq6YUXXnB+jo6O1rhx4274HZvNpjlz5ng0ruzq0qWLWrdu7fF+pk6dqrCwMI/3A992/Z83TxgyZIgqV67s0T4Ad/HSrn+A1atXKygoyOowNGTIEM2ZM0fJyclWhwIAMAmJxD9AoUKFrA4BAOCjmNq4w6Wmpurxxx9Xvnz5FBERodGjR2eoc/3Uxo4dO1SnTh0FBASoXLlyWrx48U37qVevnnr06KH+/fsrf/78Klq0qIYMGeJSZ9++fWrVqpXy5cunkJAQtWvXTn/99ZekK9MIiYmJ2rBhg2w2m2w2m6ZOnZqtZ1ywYIFq1aqlsLAwFShQQM2bN9euXbuc9/fs2SObzaavvvpKcXFxyps3rypVqqSVK1e6tDN16lQVL15cefPmVZs2bXTs2LFs9Q9k14wZM1StWjUFBweraNGi6tSpk44cOeK8v3z5ctlsNn3//feqVq2a8ubNqwcffFDbtm1zaef1119XkSJFFBwcrK5du+rChQu3+1GAbCORuMP169dPP/zwg+bOnatFixZp+fLlWrduXZb1HQ6H2rZtK39/f61atUqTJk3SgAEDstXXtGnTFBQUpFWrVmnEiBEaOnSoMwlxOBxq1aqVjh8/rh9++EGLFy/W7t271b59e0lS+/bt1adPH9177706dOiQDh065Lx3M6mpqerdu7fWrFmj77//Xn5+fmrTpo0cDodLvZdeekl9+/ZVcnKy7rnnHnXs2FGXL1+WJK1atUpdu3bVc889p+TkZMXFxWn48OHZ6h/IrkuXLmnYsGHasGGD5syZoz179qhLly4Z6r300ksaPXq01qxZo9y5c+uJJ55w3ps1a5aGDBmi1157TWvWrFFERIQmTJhwG58CyCEDd6wzZ84Y/v7+xqxZs5xlx44dMwIDA42ePXs6y6KiooyxY8cahmEYCxcuNHLnzm0cOHDAeX/+/PmGJGP27NlZ9lW3bl2jVq1aLmX333+/MWDAAMMwDGPRokVGrly5jH379jnvb9682ZBk/Pbbb4ZhGMbgwYONSpUq3fS54uPjjVatWmV5/+jRo4YkY9OmTYZhGEZKSoohyXj//fcz9L1lyxbDMAyjY8eOxkMPPeTSTvv27Y3Q0NCbxgPcSN26dV3+vF1r9erVhiTjzJkzhmEYxrJlywxJxpIlS5x1vv32W0OScf78ecMwDKNGjRrGs88+69JO9erVs/VnB7ACIxJ3sF27dunixYuqXr26syx//vwqU6ZMlt/ZsmWLihUrpsjISGdZjRo1stVfxYoVXT5HREQ4h22vtlusWDHn/XLlyiksLExbtmzJVvtZ2bFjhzp27KgSJUooJCRE0dHRkq5MpWQVX0REhCS5xHft70nK/nMD2bV27Vq1aNFCxYsXV3BwsOrWrSuJ/1bh20gkkG158uRx+Wyz2TJML3hCixYtdPz4cb333ntatWqVVq1aJUm6ePFilvHZbDZJui3xAdKVKbjGjRsrJCREM2fO1OrVqzV79mxJ/LcK30YicQcrWbKk8uTJ4/yLVZJOnDih7du3Z/md2NhY7d+/X4cOHXKW/frrr7ccy9V29+/f7yz7448/dPLkSZUrV06S5O/vr/T09By1e+zYMW3btk0vv/yy6tevr9jYWJ04ccKt+K79PUnmPDdw1datW3Xs2DG9/vrrql27tsqWLeuy0DK7+G8Vdxq2f97B8uXLp65du6pfv34qUKCAChcurJdeekl+flnnhw0aNNA999yj+Ph4jRw5UqdPn9ZLL710y7E0aNBAFSpUUOfOnTVu3DhdvnxZzz77rOrWratq1apJurJ7JCUlRcnJybr77rsVHBx807cfhoeHq0CBApo8ebIiIiK0b98+vfjiizmOr0ePHqpZs6ZGjRqlVq1aaeHChVqwYIFbzwpkpnjx4vL399dbb72lp59+Wr///ruGDRuW43Z69uypLl26qFq1aqpZs6ZmzpypzZs3q0SJEh6IGrh1jEjc4UaOHKnatWurRYsWatCggWrVqqX77rsvy/p+fn6aPXu2zp8/rwceeEDdunXTq6++estx2Gw2zZ07V+Hh4apTp44aNGigEiVK6LPPPnPWefjhh9WkSRPFxcWpUKFC+uSTT27arp+fnz799FOtXbtW5cuXV69evTRy5Mgcx/d///d/eu+99/Tmm2+qUqVKWrRokV5++eUctwNkpVChQpo6dao+//xzlStXTq+//rpGjRqV43bat2+vQYMGqX///rrvvvu0d+9ePfPMMx6IGDAHrxEHAABuY0QCAAC4jUQCAAC4jUQCAAC4jUQCAAC4jUQCAAC4jUQCAAC4jUQCAAC4jUQCsFCXLl3UunVr5+d69erphRdeuO1xLF++XDabTSdPnsyyjs1m05w5c7Ld5pAhQ1S5cuVbimvPnj2y2WxKTk6+pXYAeA6JBHCdLl26yGazyWazyd/fX6VKldLQoUN1+fJlj/f91VdfZftY5ez85Q8Ansa7NoBMNGnSRFOmTFFaWpq+++47de/eXXny5NHAgQMz1L148aL8/f1N6Td//vymtAMAtwsjEkAm7Ha7ihYtqqioKD3zzDNq0KCBvv76a0n/m4549dVXFRkZqTJlykiS9u/fr3bt2iksLEz58+dXq1attGfPHmeb6enp6t27t8LCwlSgQAH1799f159Qf/3URlpamgYMGKBixYrJbrerVKlS+uCDD7Rnzx7FxcVJuvJiM5vNpi5duki68jrqpKQkxcTEKDAwUJUqVdIXX3zh0s93332ne+65R4GBgYqLi3OJM7sGDBige+65R3nz5lWJEiU0aNAgXbp0KUO9d999V8WKFVPevHnVrl07nTp1yuX++++/r9jYWAUEBKhs2bKaMGFCln2eOHFCnTt3VqFChRQYGKjSpUtrypQpOY4dgHkYkQCyITAwUMeOHXN+/v777xUSEqLFixdLki5duqTGjRurRo0a+vHHH5U7d24NHz5cTZo00caNG+Xv76/Ro0dr6tSp+vDDDxUbG6vRo0dr9uzZ+te//pVlv48//rhWrlyp8ePHq1KlSkpJSdHff/+tYsWK6csvv9TDDz+sbdu2KSQkRIGBgZKkpKQkffTRR5o0aZJKly6tFStW6NFHH1WhQoVUt25d7d+/X23btlX37t311FNPac2aNerTp0+OfyfBwcGaOnWqIiMjtWnTJj355JMKDg5W//79nXV27typWbNm6ZtvvtHp06fVtWtXPfvss5o5c6YkaebMmXrllVf09ttvq0qVKlq/fr2efPJJBQUFKT4+PkOfgwYN0h9//KH58+erYMGC2rlzp86fP5/j2AGYyADgIj4+3mjVqpVhGIbhcDiMxYsXG3a73ejbt6/zfpEiRYy0tDTnd2bMmGGUKVPGcDgczrK0tDQjMDDQWLhwoWEYhhEREWGMGDHCef/SpUvG3Xff7ezLMAyjbt26Rs+ePQ3DMIxt27YZkozFixdnGueyZcsMScaJEyecZRcuXDDy5s1r/PLLLy51u3btanTs2NEwDMMYOHCgUa5cOZf7AwYMyNDW9SQZs2fPzvL+yJEjjfvuu8/5efDgwUauXLmMP//801k2f/58w8/Pzzh06JBhGIZRsmRJ4+OPP3ZpZ9iwYUaNGjUMwzCMlJQUQ5Kxfv16wzAMo0WLFkZCQkKWMQC4/RiRADIxb9485cuXT5cuXZLD4VCnTp00ZMgQ5/0KFSq4rIvYsGGDdu7cqeDgYJd2Lly4oF27dunUqVM6dOiQqlev7ryXO3duVatWLcP0xlXJycnKlSuX6tatm+24d+7cqXPnzqlhw4Yu5RcvXlSVKlUkSVu2bHGJQ5Jq1KiR7T6u+uyzzzR+/Hjt2rVLZ8+e1eXLlxUSEuJSp3jx4rrrrrtc+nE4HNq2bZuCg4O1a9cude3aVU8++aSzzuXLlxUaGpppn88884wefvhhrVu3To0aNVLr1q314IMP5jh2AOYhkQAyERcXp4kTJ8rf31+RkZHKndv1j0pQUJDL57Nnz+q+++5zDtlfq1ChQm7FcHWqIifOnj0rSfr2229d/gKXrqz7MMvKlSvVuXNnJSYmqnHjxgoNDdWnn36q0aNH5zjW9957L0NikytXrky/07RpU+3du1ffffedFi9erPr166t79+4aNWqU+w8D4JaQSACZCAoKUqlSpbJdv2rVqvrss89UuHDhDP8qvyoiIkKrVq1SnTp1JF35l/fatWtVtWrVTOtXqFBBDodDP/zwgxo0aJDh/tURkfT0dGdZuXLlZLfbtW/fvixHMmJjY50LR6/69ddfb/6Q1/jll18UFRWll156yVm2d+/eDPX27dungwcPKjIy0tmPn5+fypQpoyJFiigyMlK7d+9W586ds913oUKFFB8fr/j4eNWuXVv9+vUjkQAsxK4NwASdO3dWwYIF1apVK/34449KSUnR8uXL1aNHD/3555+SpJ49e+r111/XnDlztHXrVj377LM3PAMiOjpa8fHxeuKJJzRnzhxnm7NmzZIkRUVFyWazad68eTp69KjOnj2r4OBg9e3bV7169dK0adO0a9curVu3Tm+99ZamTZsmSXr66ae1Y8cO9evXT9u2bdPHH3+sqVOn5uh5S5curX379unTTz/Vrl27NH78eM2ePTtDvYCAAMXHx2vDhg368ccf1aNHD7Vr105FixaVJCUmJiopKUnjx4/X9u3btWnTJk2ZMkVjxozJtN9XXnlFc+fO1c6dO7V582bNmzdPsbGxOYodgLlIJAAT5M2bVytWrFDx4sXVtm1bxcbGqmvXrrpw4YJzhKJPnz567LHHFB8frxo1aig4OFht2rS5YbsTJ07UI488omeffVZly5bVk08+qdTUVEnSXXfdpcTERL344osqUqSInnvuOUnSsGHDNGjQICUlJSk2NlZNmjTRt99+q5iYGElX1i18+eWXmjNnjipVqqRJkybptddey9HztmzZUr169dJzzz2nypUr65dfftGgQYMy1CtVqpTatm2rhx56SI0aNVLFihVdtnd269ZN77//vqZMmaIKFSqobt26mjp1qjPW6/n7+2vgwIGqWLGi6tSpo1y5cunTTz/NUewAzGUzslrpBQAAcBOMSAAAALeRSAAAALeRSAAAALeRSAAAALeRSAAAALeRSAAAALeRSAAAALeRSAAAALeRSAAAALeRSAAAALeRSAAAALeRSAAAALf9P4aZnsEVCHpLAAAAAElFTkSuQmCC"
          },
          "metadata": {}
        }
      ],
      "execution_count": 17
    },
    {
      "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": {
        "tags": [],
        "editable": true,
        "slideshow": {
          "slide_type": ""
        }
      }
    },
    {
      "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": 18
    },
    {
      "cell_type": "code",
      "source": "# Create a GridSearchCV object with 10-fold cross-validation\nsvm_cv = GridSearchCV(estimator = svm, param_grid = parameters, cv = 10)\n\n# Fit the GridSearchCV object to the training data to find the best parameters\nsvm_cv.fit(X_train, Y_train)\n\n# Calculate the accuracy on the test data\ntest_svm_accuracy = svm_cv.score(X_test, Y_test)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 19
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",svm_cv.best_params_)\nsvm_b_acc = svm_cv.best_score_\nprint(\"accuracy :\",svm_b_acc)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'C': 0.03162277660168379, 'gamma': 0.001, 'kernel': 'linear'}\naccuracy : 0.838095238095238\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 20
    },
    {
      "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(\"Test accuracy: \", test_svm_accuracy)",
      "metadata": {
        "trusted": true,
        "tags": [],
        "editable": true,
        "slideshow": {
          "slide_type": ""
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Test accuracy:  0.8260869565217391\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 21
    },
    {
      "cell_type": "markdown",
      "source": "We can plot the confusion matrix\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "yhat_svm = svm_cv.predict(X_test)\nplot_confusion_matrix(Y_test, yhat_svm)",
      "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": 22
    },
    {
      "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": 23
    },
    {
      "cell_type": "code",
      "source": "# Create a GridSearchCV object with 10-fold cross-validation\ntree_cv = GridSearchCV(estimator = tree, param_grid = parameters, cv=10)\n\n# Fit the GridSearchCV object to the training data to find the best parameters\ntree_cv.fit(X_train, Y_train)",
      "metadata": {
        "trusted": true,
        "scrolled": 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 0.75       0.77857143 0.7452381  0.71666667 0.80952381 0.78809524\n 0.82142857 0.74047619 0.74285714 0.75       0.7452381  0.79047619\n 0.74761905 0.7952381  0.7547619  0.73571429 0.83571429 0.77857143\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.72857143 0.84761905 0.79047619 0.77857143 0.73571429 0.82380952\n 0.8047619  0.71904762 0.79285714 0.73095238 0.82142857 0.78571429\n 0.77619048 0.82142857 0.80714286 0.73809524 0.77380952 0.77142857\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.76904762 0.82380952 0.69285714 0.75       0.82380952 0.7952381\n 0.83571429 0.77142857 0.72857143 0.71428571 0.74285714 0.76428571\n 0.78571429 0.77619048 0.72857143 0.75714286 0.77857143 0.78809524\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.76190476 0.85       0.79047619 0.72857143 0.69761905 0.79285714\n 0.72857143 0.85       0.84047619 0.76190476 0.76190476 0.7952381\n 0.7547619  0.8047619  0.74285714 0.71190476 0.74761905 0.76190476\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.80238095 0.79047619 0.72857143 0.83571429 0.71428571 0.86666667\n 0.82142857 0.71904762 0.75952381 0.77380952 0.82142857 0.7452381\n 0.74285714 0.78571429 0.80714286 0.76190476 0.7452381  0.73095238\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.74285714 0.7452381  0.77380952 0.79047619 0.83571429 0.77619048\n 0.7452381  0.82142857 0.76190476 0.79285714 0.73095238 0.71904762\n 0.77380952 0.71904762 0.80714286 0.80714286 0.79047619 0.82142857\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.7452381  0.77857143 0.77380952 0.76428571 0.77380952 0.8047619\n 0.79761905 0.78571429 0.73333333 0.72380952 0.80952381 0.75\n 0.74761905 0.78809524 0.68571429 0.82380952 0.78571429 0.79047619\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.80714286 0.76190476 0.75952381 0.81904762 0.85238095 0.79047619\n 0.82142857 0.80714286 0.71428571 0.81666667 0.82380952 0.80714286\n 0.73095238 0.73095238 0.71904762 0.79047619 0.69761905 0.80714286\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.80714286 0.77619048 0.77619048 0.71428571 0.79285714 0.76666667\n 0.77142857 0.80952381 0.7547619  0.8547619  0.8047619  0.77380952\n 0.71428571 0.78809524 0.7452381  0.7952381  0.82142857 0.72142857\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.70238095 0.79047619 0.79285714 0.7547619  0.81904762 0.72619048\n 0.8        0.83809524 0.80238095 0.77857143 0.73095238 0.77380952\n 0.74285714 0.77142857 0.72142857 0.67619048 0.72857143 0.7452381\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.7547619  0.85238095 0.75952381 0.80952381 0.79047619 0.82380952\n 0.79047619 0.83333333 0.74761905 0.76190476 0.79285714 0.80952381\n 0.82619048 0.81904762 0.82380952 0.71428571 0.7452381  0.80714286\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.79285714 0.79047619 0.79285714 0.77142857 0.73809524 0.80952381\n 0.72857143 0.77857143 0.71428571 0.83333333 0.76190476 0.79047619\n 0.77380952 0.82142857 0.67619048 0.82142857 0.78809524 0.75714286\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.79285714 0.76190476 0.66904762 0.71428571 0.77142857 0.79047619\n 0.6952381  0.83571429 0.71666667 0.80238095 0.74285714 0.73095238\n 0.80714286 0.77380952 0.77380952 0.81666667 0.80714286 0.7452381\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.76190476 0.80714286 0.79047619 0.77380952 0.79047619 0.82380952\n 0.73095238 0.70238095 0.75714286 0.76428571 0.80714286 0.80714286\n 0.76190476 0.76190476 0.7452381  0.82380952 0.72857143 0.76428571\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.78809524 0.7452381  0.77380952 0.79047619 0.80714286 0.76190476\n 0.7952381  0.81190476 0.74285714 0.68571429 0.78809524 0.73809524\n 0.74761905 0.76190476 0.77380952 0.76190476 0.68333333 0.81904762\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.71666667 0.83809524 0.81904762 0.71428571 0.71666667 0.86666667\n 0.79047619 0.8047619  0.67619048 0.75952381 0.83571429 0.77857143\n 0.8047619  0.74047619 0.75952381 0.75       0.76190476 0.80238095\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.8547619  0.70238095 0.80714286 0.81666667 0.67142857 0.8047619\n 0.77857143 0.7452381  0.73333333 0.75       0.73333333 0.8047619\n 0.81666667 0.8047619  0.78809524 0.72380952 0.74285714 0.8047619\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n        nan        nan        nan        nan        nan        nan\n 0.72619048 0.79047619 0.77619048 0.7952381  0.69761905 0.77857143\n 0.74761905 0.80238095 0.69761905 0.77380952 0.70238095 0.82142857\n 0.76190476 0.72619048 0.68333333 0.79047619 0.73571429 0.71666667]\n  warnings.warn(\n",
          "output_type": "stream"
        },
        {
          "execution_count": 24,
          "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-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=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-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=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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" 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-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" 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": 24
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",tree_cv.best_params_)\ntree_b_acc = tree_cv.best_score_\nprint(\"accuracy :\", tree_b_acc)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'splitter': 'random'}\naccuracy : 0.8666666666666666\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 25
    },
    {
      "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": "test_tree_accuracy = tree_cv.score(X_test, Y_test)\nprint(\"Test accuracy: \", test_tree_accuracy)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Test accuracy:  0.8695652173913043\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 26
    },
    {
      "cell_type": "markdown",
      "source": "We can plot the confusion matrix\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "yhat_tree = tree_cv.predict(X_test)\nplot_confusion_matrix(Y_test, yhat_tree)",
      "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": 27
    },
    {
      "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,
        "tags": [],
        "editable": true,
        "slideshow": {
          "slide_type": ""
        }
      },
      "outputs": [],
      "execution_count": 28
    },
    {
      "cell_type": "code",
      "source": "# Create a GridSearchCV object with 10-fold cross-validation\nKNN_cv = GridSearchCV(estimator=KNN, param_grid=parameters, cv=10)\n\n# Fit the GridSearchCV object to the training data to find the best 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": 29,
          "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]})",
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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=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-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=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-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" 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-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" 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": 29
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",KNN_cv.best_params_)\nknn_b_acc = KNN_cv.best_score_\nprint(\"accuracy :\", knn_b_acc)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 9, 'p': 1}\naccuracy : 0.85\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 30
    },
    {
      "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": "test_knn_accuracy = KNN_cv.score(X_test, Y_test)\nprint(\"Test accuracy: \", test_knn_accuracy)",
      "metadata": {
        "trusted": true,
        "tags": [],
        "editable": true,
        "slideshow": {
          "slide_type": ""
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Test accuracy:  0.8695652173913043\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 31
    },
    {
      "cell_type": "markdown",
      "source": "We can plot the confusion matrix\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "yhat_knn = KNN_cv.predict(X_test)\nplot_confusion_matrix(Y_test, yhat_knn)",
      "metadata": {
        "trusted": true,
        "tags": [],
        "editable": true,
        "slideshow": {
          "slide_type": ""
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 32
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  12\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Find the method performs best:\n",
      "metadata": {
        "tags": [],
        "editable": true,
        "slideshow": {
          "slide_type": ""
        }
      }
    },
    {
      "cell_type": "code",
      "source": "method_dict = {\"Method\": [\"Log Regrression\", \"Decision Tree\", \"SVM\", \"KN Neighbors\"],\n              \"Accuracy\": [test_lr_accuracy,test_tree_accuracy, test_svm_accuracy, test_knn_accuracy]}\n\nacc_df = pd.DataFrame(method_dict, columns = ['Method', 'Accuracy'])\nsns.barplot(x=\"Method\", y = \"Accuracy\", data = acc_df.sort_values('Accuracy', ascending = False))\nplt.title('Prediction methodology scores')\nplt.show()                      ",
      "metadata": {
        "trusted": true,
        "tags": [],
        "editable": true,
        "slideshow": {
          "slide_type": ""
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 33
    },
    {
      "cell_type": "code",
      "source": "method_b_dict = {\"Method\": [\"Log Regrression\", \"Decision Tree\", \"SVM\", \"KN Neighbors\"],\n              \"Accuracy\": [lr_b_acc, tree_b_acc, svm_b_acc, knn_b_acc]}\n\nacc_df = pd.DataFrame(method_b_dict, columns = ['Method', 'Accuracy'])\nsns.barplot(x = \"Method\", y = \"Accuracy\", data = acc_df.sort_values('Accuracy', ascending = False))\nplt.title('Prediction methodology scores')\nplt.show()",
      "metadata": {
        "trusted": true,
        "tags": [],
        "editable": true,
        "slideshow": {
          "slide_type": ""
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 34
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": null
    },
    {
      "cell_type": "markdown",
      "source": "## Authors\n",
      "metadata": {
        "tags": [],
        "editable": true,
        "slideshow": {
          "slide_type": ""
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "[Pratiksha Verma](https://www.linkedin.com/in/pratiksha-verma-6487561b1/)\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": {}
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {
        "trusted": true,
        "tags": [],
        "editable": true,
        "slideshow": {
          "slide_type": ""
        }
      },
      "outputs": [],
      "execution_count": null
    }
  ]
}