SpaceX_Machine_Learning_Prediction_Part_5.jupyterlite.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<p style=\"text-align:center\">\n",
        "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">\n",
        "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
        "    </a>\n",
        "</p>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# **Space X  Falcon 9 First Stage Landing Prediction**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Assignment:  Machine Learning Prediction\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Estimated time needed: **60** minutes\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing_1.gif)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Several examples of an unsuccessful landing are shown here:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Most unsuccessful landings are planed. Space X; performs a controlled landing in the oceans.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Objectives\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Import Libraries and Define Auxiliary Functions\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "import piplite\n",
        "await piplite.install(['numpy'])\n",
        "await piplite.install(['pandas'])\n",
        "await piplite.install(['seaborn'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We will import the following libraries for the lab\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# Pandas is a software library written for the Python programming language for data manipulation and analysis.\n",
        "import 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\n",
        "import 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.\n",
        "import 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\n",
        "import seaborn as sns\n",
        "# Preprocessing allows us to standarsize our data\n",
        "from sklearn import preprocessing\n",
        "# Allows us to split our data into training and testing data\n",
        "from sklearn.model_selection import train_test_split\n",
        "# Allows us to test parameters of classification algorithms and find the best one\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "# Logistic Regression classification algorithm\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "# Support Vector Machine classification algorithm\n",
        "from sklearn.svm import SVC\n",
        "# Decision Tree classification algorithm\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "# K Nearest Neighbors classification algorithm\n",
        "from sklearn.neighbors import KNeighborsClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This function is to plot the confusion matrix.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "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() "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Load the dataframe\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Load the data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "#from js import fetch\n",
        "#import io\n",
        "\n",
        "URL1 = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\"\n",
        "#resp1 = await fetch(URL1)\n",
        "#text1 = io.BytesIO((await resp1.arrayBuffer()).to_py())\n",
        "data = pd.read_csv(URL1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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>"
            ],
            "text/plain": [
              "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n",
              "0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n",
              "1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n",
              "2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n",
              "3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n",
              "4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n",
              "\n",
              "       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n",
              "0    None None        1     False   False  False        NaN    1.0   \n",
              "1    None None        1     False   False  False        NaN    1.0   \n",
              "2    None None        1     False   False  False        NaN    1.0   \n",
              "3  False Ocean        1     False   False  False        NaN    1.0   \n",
              "4    None None        1     False   False  False        NaN    1.0   \n",
              "\n",
              "   ReusedCount Serial   Longitude   Latitude  Class  \n",
              "0            0  B0003  -80.577366  28.561857      0  \n",
              "1            0  B0005  -80.577366  28.561857      0  \n",
              "2            0  B0007  -80.577366  28.561857      0  \n",
              "3            0  B1003 -120.610829  34.632093      0  \n",
              "4            0  B1004  -80.577366  28.561857      0  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "URL2 = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_3.csv'\n",
        "#resp2 = await fetch(URL2)\n",
        "#text2 = io.BytesIO((await resp2.arrayBuffer()).to_py())\n",
        "X = pd.read_csv(URL2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>FlightNumber</th>\n",
              "      <th>PayloadMass</th>\n",
              "      <th>Flights</th>\n",
              "      <th>Block</th>\n",
              "      <th>ReusedCount</th>\n",
              "      <th>Orbit_ES-L1</th>\n",
              "      <th>Orbit_GEO</th>\n",
              "      <th>Orbit_GTO</th>\n",
              "      <th>Orbit_HEO</th>\n",
              "      <th>Orbit_ISS</th>\n",
              "      <th>...</th>\n",
              "      <th>Serial_B1058</th>\n",
              "      <th>Serial_B1059</th>\n",
              "      <th>Serial_B1060</th>\n",
              "      <th>Serial_B1062</th>\n",
              "      <th>GridFins_False</th>\n",
              "      <th>GridFins_True</th>\n",
              "      <th>Reused_False</th>\n",
              "      <th>Reused_True</th>\n",
              "      <th>Legs_False</th>\n",
              "      <th>Legs_True</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>6104.959412</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.0</td>\n",
              "      <td>525.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.0</td>\n",
              "      <td>677.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.0</td>\n",
              "      <td>500.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3170.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>86.0</td>\n",
              "      <td>15400.000000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>87.0</td>\n",
              "      <td>15400.000000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>88.0</td>\n",
              "      <td>15400.000000</td>\n",
              "      <td>6.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>89.0</td>\n",
              "      <td>15400.000000</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>90.0</td>\n",
              "      <td>3681.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>90 rows × 83 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    FlightNumber   PayloadMass  Flights  Block  ReusedCount  Orbit_ES-L1  \\\n",
              "0            1.0   6104.959412      1.0    1.0          0.0          0.0   \n",
              "1            2.0    525.000000      1.0    1.0          0.0          0.0   \n",
              "2            3.0    677.000000      1.0    1.0          0.0          0.0   \n",
              "3            4.0    500.000000      1.0    1.0          0.0          0.0   \n",
              "4            5.0   3170.000000      1.0    1.0          0.0          0.0   \n",
              "..           ...           ...      ...    ...          ...          ...   \n",
              "85          86.0  15400.000000      2.0    5.0          2.0          0.0   \n",
              "86          87.0  15400.000000      3.0    5.0          2.0          0.0   \n",
              "87          88.0  15400.000000      6.0    5.0          5.0          0.0   \n",
              "88          89.0  15400.000000      3.0    5.0          2.0          0.0   \n",
              "89          90.0   3681.000000      1.0    5.0          0.0          0.0   \n",
              "\n",
              "    Orbit_GEO  Orbit_GTO  Orbit_HEO  Orbit_ISS  ...  Serial_B1058  \\\n",
              "0         0.0        0.0        0.0        0.0  ...           0.0   \n",
              "1         0.0        0.0        0.0        0.0  ...           0.0   \n",
              "2         0.0        0.0        0.0        1.0  ...           0.0   \n",
              "3         0.0        0.0        0.0        0.0  ...           0.0   \n",
              "4         0.0        1.0        0.0        0.0  ...           0.0   \n",
              "..        ...        ...        ...        ...  ...           ...   \n",
              "85        0.0        0.0        0.0        0.0  ...           0.0   \n",
              "86        0.0        0.0        0.0        0.0  ...           1.0   \n",
              "87        0.0        0.0        0.0        0.0  ...           0.0   \n",
              "88        0.0        0.0        0.0        0.0  ...           0.0   \n",
              "89        0.0        0.0        0.0        0.0  ...           0.0   \n",
              "\n",
              "    Serial_B1059  Serial_B1060  Serial_B1062  GridFins_False  GridFins_True  \\\n",
              "0            0.0           0.0           0.0             1.0            0.0   \n",
              "1            0.0           0.0           0.0             1.0            0.0   \n",
              "2            0.0           0.0           0.0             1.0            0.0   \n",
              "3            0.0           0.0           0.0             1.0            0.0   \n",
              "4            0.0           0.0           0.0             1.0            0.0   \n",
              "..           ...           ...           ...             ...            ...   \n",
              "85           0.0           1.0           0.0             0.0            1.0   \n",
              "86           0.0           0.0           0.0             0.0            1.0   \n",
              "87           0.0           0.0           0.0             0.0            1.0   \n",
              "88           0.0           1.0           0.0             0.0            1.0   \n",
              "89           0.0           0.0           1.0             0.0            1.0   \n",
              "\n",
              "    Reused_False  Reused_True  Legs_False  Legs_True  \n",
              "0            1.0          0.0         1.0        0.0  \n",
              "1            1.0          0.0         1.0        0.0  \n",
              "2            1.0          0.0         1.0        0.0  \n",
              "3            1.0          0.0         1.0        0.0  \n",
              "4            1.0          0.0         1.0        0.0  \n",
              "..           ...          ...         ...        ...  \n",
              "85           0.0          1.0         0.0        1.0  \n",
              "86           0.0          1.0         0.0        1.0  \n",
              "87           0.0          1.0         0.0        1.0  \n",
              "88           0.0          1.0         0.0        1.0  \n",
              "89           1.0          0.0         0.0        1.0  \n",
              "\n",
              "[90 rows x 83 columns]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X.head(100)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  1\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Create a NumPy array from the column <code>Class</code> in <code>data</code>, by applying the method <code>to_numpy()</code>  then\n",
        "assign it  to the variable <code>Y</code>,make sure the output is a  Pandas series (only one bracket df\\['name of  column']).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "Y = data[\"Class\"].to_numpy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  2\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Standardize the data in <code>X</code> then reassign it to the variable  <code>X</code> using the transform provided below.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "# students get this \n",
        "transform = preprocessing.StandardScaler()\n",
        "X = transform.fit_transform(X)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  3\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<code>X_train, X_test, Y_train, Y_test</code>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "we can see we only have 18 test samples.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(18,)"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y_test.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  4\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "parameters ={'C':[0.01,0.1,1],\n",
        "             'penalty':['l2'],\n",
        "             'solver':['lbfgs']}\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "parameters ={\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge\n",
        "lr=LogisticRegression()\n",
        "grid_search_lr = GridSearchCV(\n",
        "    estimator = lr,\n",
        "    param_grid = parameters,\n",
        "    scoring = 'accuracy',\n",
        "    cv = 10\n",
        ")\n",
        "# execute search\n",
        "logreg_cv = grid_search_lr.fit(X_train,Y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tuned hpyerparameters :(best parameters)  {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
            "accuracy : 0.8464285714285713\n"
          ]
        }
      ],
      "source": [
        "print(\"tuned hpyerparameters :(best parameters) \",logreg_cv.best_params_)\n",
        "print(\"accuracy :\",logreg_cv.best_score_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  5\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Calculate the accuracy on the test data using the method <code>score</code>:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8333333333333334"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "logreg_cv.score(X_test, Y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Lets look at the confusion matrix:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhIAAAHHCAYAAADqJrG+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAABD30lEQVR4nO3dd3RU1fr/8c+EkEkISQg9USABFIJUQbh0uESKghSV6jU0vQpIL3IVSACNghRBEUEFRBS5IiCoKFX0ggiEANJLKNKlSguQOb8//DFfxySaDGc4k+H9cs1amX327P2cLAcedjs2wzAMAQAAuMHP6gAAAEDORSIBAADcRiIBAADcRiIBAADcRiIBAADcRiIBAADcRiIBAADcRiIBAADcRiIBAADcRiIBeNDevXvVuHFjhYWFyWazaeHChaa2f/DgQdlsNs2cOdPUdnOyBg0aqEGDBlaHAdw1SCTg8/bv369///vfKlmypAIDAxUaGqratWvrzTff1NWrVz3ad1xcnLZt26ZXXnlFs2fPVrVq1Tza353UuXNn2Ww2hYaGZvh73Lt3r2w2m2w2m954441st3/s2DHFx8crOTnZhGgBeIq/1QEAnvTll1/qySeflN1u19NPP63y5cvr+vXr+uGHHzRo0CBt375d06ZN80jfV69e1bp16/TSSy+pV69eHumjRIkSunr1qnLnzu2R9v+Ov7+/rly5osWLF6tt27Yu1+bMmaPAwEBdu3bNrbaPHTumhIQERUVFqXLlyln+3LfffutWfwDcQyIBn5WSkqL27durRIkSWrlypSIiIpzXevbsqX379unLL7/0WP+nT5+WJOXLl89jfdhsNgUGBnqs/b9jt9tVu3ZtffLJJ+kSiY8//liPPvqo5s+ff0diuXLlivLkyaOAgIA70h+A3zG1AZ81ZswYXbp0Se+//75LEnFL6dKl1adPH+f7mzdvatSoUSpVqpTsdruioqL0n//8R6mpqS6fi4qKUvPmzfXDDz+oevXqCgwMVMmSJfXhhx8668THx6tEiRKSpEGDBslmsykqKkrS71MCt37+o/j4eNlsNpeyZcuWqU6dOsqXL5/y5s2rMmXK6D//+Y/zemZrJFauXKm6desqODhY+fLlU8uWLbVz584M+9u3b586d+6sfPnyKSwsTF26dNGVK1cy/8X+SceOHfX111/r/PnzzrINGzZo79696tixY7r6Z8+e1cCBA1WhQgXlzZtXoaGhatasmbZs2eKss3r1aj300EOSpC5dujinSG7dZ4MGDVS+fHlt2rRJ9erVU548eZy/lz+vkYiLi1NgYGC6+2/SpInCw8N17NixLN8rgPRIJOCzFi9erJIlS6pWrVpZqt+9e3cNHz5cDz74oCZMmKD69esrMTFR7du3T1d33759euKJJ/Twww9r3LhxCg8PV+fOnbV9+3ZJUps2bTRhwgRJUocOHTR79mxNnDgxW/Fv375dzZs3V2pqqkaOHKlx48bpscce0//+97+//Nzy5cvVpEkTnTp1SvHx8erfv7/Wrl2r2rVr6+DBg+nqt23bVr/99psSExPVtm1bzZw5UwkJCVmOs02bNrLZbPr888+dZR9//LHKli2rBx98MF39AwcOaOHChWrevLnGjx+vQYMGadu2bapfv77zL/WYmBiNHDlSkvTss89q9uzZmj17turVq+ds58yZM2rWrJkqV66siRMnqmHDhhnG9+abb6pQoUKKi4tTWlqaJOndd9/Vt99+q8mTJysyMjLL9wogAwbggy5cuGBIMlq2bJml+snJyYYko3v37i7lAwcONCQZK1eudJaVKFHCkGSsWbPGWXbq1CnDbrcbAwYMcJalpKQYkoyxY8e6tBkXF2eUKFEiXQwjRoww/viVnDBhgiHJOH36dKZx3+pjxowZzrLKlSsbhQsXNs6cOeMs27Jli+Hn52c8/fTT6frr2rWrS5utW7c2ChQokGmff7yP4OBgwzAM44knnjAaNWpkGIZhpKWlGUWLFjUSEhIy/B1cu3bNSEtLS3cfdrvdGDlypLNsw4YN6e7tlvr16xuSjKlTp2Z4rX79+i5l33zzjSHJGD16tHHgwAEjb968RqtWrf72HgH8PUYk4JMuXrwoSQoJCclS/a+++kqS1L9/f5fyAQMGSFK6tRTlypVT3bp1ne8LFSqkMmXK6MCBA27H/Ge31lYsWrRIDocjS585fvy4kpOT1blzZ+XPn99ZXrFiRT388MPO+/yj5557zuV93bp1debMGefvMCs6duyo1atX68SJE1q5cqVOnDiR4bSG9Pu6Cj+/3//oSUtL05kzZ5zTNklJSVnu0263q0uXLlmq27hxY/373//WyJEj1aZNGwUGBurdd9/Ncl8AMkciAZ8UGhoqSfrtt9+yVP/QoUPy8/NT6dKlXcqLFi2qfPny6dChQy7lxYsXT9dGeHi4zp0752bE6bVr1061a9dW9+7dVaRIEbVv317z5s37y6TiVpxlypRJdy0mJka//vqrLl++7FL+53sJDw+XpGzdyyOPPKKQkBB9+umnmjNnjh566KF0v8tbHA6HJkyYoPvuu092u10FCxZUoUKFtHXrVl24cCHLfd5zzz3ZWlj5xhtvKH/+/EpOTtakSZNUuHDhLH8WQOZIJOCTQkNDFRkZqZ9//jlbn/vzYsfM5MqVK8NywzDc7uPW/P0tQUFBWrNmjZYvX65//etf2rp1q9q1a6eHH344Xd3bcTv3covdblebNm00a9YsLViwINPRCEl69dVX1b9/f9WrV08fffSRvvnmGy1btkwPPPBAlkdepN9/P9mxefNmnTp1SpK0bdu2bH0WQOZIJOCzmjdvrv3792vdunV/W7dEiRJyOBzau3evS/nJkyd1/vx55w4MM4SHh7vscLjlz6MekuTn56dGjRpp/Pjx2rFjh1555RWtXLlSq1atyrDtW3Hu3r073bVdu3apYMGCCg4Ovr0byETHjh21efNm/fbbbxkuUL3ls88+U8OGDfX++++rffv2aty4sWJjY9P9TrKa1GXF5cuX1aVLF5UrV07PPvusxowZow0bNpjWPnA3I5GAzxo8eLCCg4PVvXt3nTx5Mt31/fv3680335T0+9C8pHQ7K8aPHy9JevTRR02Lq1SpUrpw4YK2bt3qLDt+/LgWLFjgUu/s2bPpPnvrYKY/b0m9JSIiQpUrV9asWbNc/mL++eef9e233zrv0xMaNmyoUaNG6a233lLRokUzrZcrV650ox3//e9/dfToUZeyWwlPRklXdg0ZMkSHDx/WrFmzNH78eEVFRSkuLi7T3yOArONAKvisUqVK6eOPP1a7du0UExPjcrLl2rVr9d///ledO3eWJFWqVElxcXGaNm2azp8/r/r16+unn37SrFmz1KpVq0y3Frqjffv2GjJkiFq3bq3evXvrypUreuedd3T//fe7LDYcOXKk1qxZo0cffVQlSpTQqVOnNGXKFN17772qU6dOpu2PHTtWzZo1U82aNdWtWzddvXpVkydPVlhYmOLj4027jz/z8/PTyy+//Lf1mjdvrpEjR6pLly6qVauWtm3bpjlz5qhkyZIu9UqVKqV8+fJp6tSpCgkJUXBwsGrUqKHo6OhsxbVy5UpNmTJFI0aMcG5HnTFjhho0aKBhw4ZpzJgx2WoPwJ9YvGsE8Lg9e/YYzzzzjBEVFWUEBAQYISEhRu3atY3Jkycb165dc9a7ceOGkZCQYERHRxu5c+c2ihUrZgwdOtSljmH8vv3z0UcfTdfPn7cdZrb90zAM49tvvzXKly9vBAQEGGXKlDE++uijdNs/V6xYYbRs2dKIjIw0AgICjMjISKNDhw7Gnj170vXx5y2Sy5cvN2rXrm0EBQUZoaGhRosWLYwdO3a41LnV35+3l86YMcOQZKSkpGT6OzUM1+2fmcls++eAAQOMiIgIIygoyKhdu7axbt26DLdtLlq0yChXrpzh7+/vcp/169c3HnjggQz7/GM7Fy9eNEqUKGE8+OCDxo0bN1zq9evXz/Dz8zPWrVv3l/cA4K/ZDCMbK6oAAAD+gDUSAADAbSQSAADAbSQSAADAbSQSAAD4qDVr1qhFixaKjIyUzWbTwoULnddu3LihIUOGqEKFCgoODlZkZKSefvrpbD8Rl0QCAAAfdfnyZVWqVElvv/12umtXrlxRUlKShg0bpqSkJH3++efavXu3HnvssWz1wa4NAADuAjabTQsWLFCrVq0yrbNhwwZVr15dhw4dyvCZQhnhQCoAAHKI1NTUdCey2u122e12U9q/cOGCbDab8+nDWeGTiUTXqCesDgEAkEN8cPAzj/dx49cDprST+NaHSkhIcCkbMWKEKafWXrt2TUOGDFGHDh2cT1DOCp9MJAAA8EVDhw5V//79XcrMGI24ceOG2rZtK8Mw9M4772TrsyQSAAB4miPNlGbMnMa45VYScejQIa1cuTJboxESiQQAAJ5nOKyOIEO3koi9e/dq1apVKlCgQLbbIJEAAMDTHNYkEpcuXdK+ffuc71NSUpScnKz8+fMrIiJCTzzxhJKSkrRkyRKlpaXpxIkTkqT8+fMrICAgS3345PZPFlsCALLqjiy2PL7TlHZyR8Rkq/7q1avVsGHDdOVxcXGKj49XdHR0hp9btWqVGjRokKU+GJEAAMDDDIumNho0aKC/Gi8wYyyBRAIAAE+zaGrjTuCIbAAA4DZGJAAA8DQv3bVhBhIJAAA8zaRzJLwRUxsAAMBtjEgAAOBpTG0AAAC3sWsDAAAgPUYkAADwMKsOpLoTSCQAAPA0H57aIJEAAMDTfHhEgjUSAADAbYxIAADgaT58IBWJBAAAnsbUBgAAQHqMSAAA4Gns2gAAAG5jagMAACA9RiQAAPA0pjYAAIC7DMN3t38ytQEAANzGiAQAAJ7mw4stSSQAAPA01kgAAAC3+fCIBGskAACA2xiRAADA03hoFwAAcBtTGwAAAOkxIgEAgKexawMAALiNqQ0AAID0GJEAAMDTmNoAAABu8+FEgqkNAADgNkYkAADwMF9+jDiJBAAAnubDUxskEgAAeBrbPwEAANJjRAIAAE9jagMAALiNqQ0AAID0GJEAAMDTmNoAAABuY2oDAAAgPUYkAADwNKY2AACA20gkzBUeHi6bzZalumfPnvVwNAAAwF2WJBITJ050/nzmzBmNHj1aTZo0Uc2aNSVJ69at0zfffKNhw4ZZER4AAOby4cWWNsMwDCsDePzxx9WwYUP16tXLpfytt97S8uXLtXDhwmy32TXqCZOiAwD4ug8OfubxPq5+8YYp7QQ9NtCUdsxk+a6Nb775Rk2bNk1X3rRpUy1fvtyCiAAAMJnhMOflhSxPJAoUKKBFixalK1+0aJEKFChgQUQAAPiGNWvWqEWLFoqMjJTNZks3ym8YhoYPH66IiAgFBQUpNjZWe/fuzVYflu/aSEhIUPfu3bV69WrVqFFDkrR+/XotXbpU06dPtzg6AABMYNGujcuXL6tSpUrq2rWr2rRpk+76mDFjNGnSJM2aNUvR0dEaNmyYmjRpoh07digwMDBLfVieSHTu3FkxMTGaNGmSPv/8c0lSTEyMfvjhB2diAQBAjmbRtESzZs3UrFmzDK8ZhqGJEyfq5ZdfVsuWLSVJH374oYoUKaKFCxeqffv2WerD8kRCkmrUqKE5c+ZYHQYAAHeNlJQUnThxQrGxsc6ysLAw1ahRQ+vWrctZiYTD4dC+fft06tQpOf40/FOvXj2LogIAwCQmTW2kpqYqNTXVpcxut8tut2e7rRMnTkiSihQp4lJepEgR57WssDyR+PHHH9WxY0cdOnRIf96JarPZlJaWZlFkAACYxKREIjExUQkJCS5lI0aMUHx8vCntu8PyROK5555TtWrV9OWXXyoiIiLLJ14CAHC3GTp0qPr37+9S5s5ohCQVLVpUknTy5ElFREQ4y0+ePKnKlStnuR3LE4m9e/fqs88+U+nSpa0OBQAAzzDp7Ed3pzEyEh0draJFi2rFihXOxOHixYtav369nn/++Sy3Y3kiUaNGDe3bt49EAgDguyza/nnp0iXt27fP+T4lJUXJycnKnz+/ihcvrr59+2r06NG67777nNs/IyMj1apVqyz3YXki8cILL2jAgAE6ceKEKlSooNy5c7tcr1ixokWRAQCQs23cuFENGzZ0vr81LRIXF6eZM2dq8ODBunz5sp599lmdP39ederU0dKlS7N8hoTkBc/a8PNLf7imzWaTYRhuL7bkWRsAgKy6I8/amGPOQyiDOo0ypR0zWT4ikZKSYnUIAAB4lpc+J8MMlicSJUqUsDoEAAA8y6I1EneC5YnELTt27NDhw4d1/fp1l/LHHnvMoogAAMDfsTyROHDggFq3bq1t27Y510ZIcp4nwYFUAIAcz9rliB5l+WPE+/Tpo+joaJ06dUp58uTR9u3btWbNGlWrVk2rV6+2OjwAAG6fw2HOywtZPiKxbt06rVy5UgULFpSfn5/8/PxUp04dJSYmqnfv3tq8ebPVIQIAgExYPiKRlpamkJAQSVLBggV17NgxSb8vwty9e7eVoQEAYA5GJDynfPny2rJli6Kjo1WjRg2NGTNGAQEBmjZtmkqWLGl1eAAA3D62f3rOyy+/rMuXL0uSRo4cqebNm6tu3boqUKCAPv30U4ujAwAAf8XyRKJJkybOn0uXLq1du3bp7NmzCg8P50mgAACfYDh8d9eG5YlERvLnz291CAAAmMdL1zeYwZJEok2bNlmu+/nnn3swEgAAcDssSSTCwsKs6BYAAGuw2NJcM2bMsKJbAACswRoJAADgNh9eI2H5gVQAACDnYkQCAABP8+ERCRIJAAA8jad/es6HH36o1NTUdOXXr1/Xhx9+aEFEAAAgq2yGYW2alCtXLh0/flyFCxd2KT9z5owKFy6stLS0bLfZNeoJs8KDGxo81VgNOzVRwXsLSZKO7j2ixZM+07bVPMkVdze+G97pg4OfebyPK+OfMaWdPP2nm9KOmSyf2jAMI8OjsH/55RfOm8ihzh0/o89e/0gnDx6XzWZT7ccb6IVpgxX/6CAd2/uL1eEBluG7cRdj+6f5qlSpIpvNJpvNpkaNGsnf//9CSUtLU0pKipo2bWpVeLgNW1Zscnn/+RufqMFTjVWqyv38YYm7Gt8N+CLLEolWrVpJkpKTk9WkSRPlzZvXeS0gIEBRUVF6/PHHLYoOZrH5+emhR2vKHhSo/Ul7rA4H8Bp8N+4ynGxpvhEjRkiSoqKi1K5dOwUGBloVCjzgnjLF9dLnryi3PUCpV67prX+P0bF9/IsL4Ltxl/LhqQ3LF1vesmnTJu3cuVOS9MADD6hKlSpZ+lxqamq6XR8vVIhTLlsu02NE1uXK7a8CkQUVFJJH1R75h+q1a6TX243gD0zc9fhueJ87stjy9S6mtJNniPc9YsLyxZanTp1S+/bttXr1auXLl0+SdP78eTVs2FBz585VoUKF/vLziYmJSkhIcCmrHBajKvnKeSpkZEHajZs6deiEJOnQzwcUXbG0Yrs+og//M83iyABr8d24Oxk+fCCV5edIvPDCC/rtt9+0fft2nT17VmfPntXPP/+sixcvqnfv3n/7+aFDh+rChQsur4phZe5A5MgOm59N/gG5rQ4D8Dp8N+4SDsOclxeyfERi6dKlWr58uWJiYpxl5cqV09tvv63GjRv/7eftdrvsdrtLGdMa1np8cEdtW71ZZ479qsDgIP2jZR2V+ccDGv/0aKtDAyzFd+MuxmJLz3E4HMqdO302njt3bjl8eCjIl4UWCFP38S8orFC4rv52Rb/sOqTxT4/Wjh+2Wh0aYCm+G/BFlicS//znP9WnTx998sknioyMlCQdPXpU/fr1U6NGjSyODu6YMeQdq0MAvBLfjbuYl05LmMHyNRJvvfWWLl68qKioKJUqVUqlSpVSdHS0Ll68qMmTJ1sdHgAAt8/hMOflhSwfkShWrJiSkpK0fPly7dq1S5IUExOj2NhYiyMDAAB/x/JEQpJsNpsefvhhPfzww1aHAgCA+Xx4asMrEokVK1ZoxYoVOnXqVLoFlh988IFFUQEAYBJ2bXhOQkKCRo4cqWrVqikiIiLDJ4ECAADvZHkiMXXqVM2cOVP/+te/rA4FAADPYGrDc65fv65atWpZHQYAAB7DEdke1L17d3388cdWhwEAANxg+YjEtWvXNG3aNC1fvlwVK1ZMd8rl+PHjLYoMAACTMLXhOVu3blXlypUlST///LPLNRZeAgB8AomE56xatcrqEAAA8Cwf3v5p+RoJAACQc1k+IgEAgM9jagMAALjL8OFEgqkNAADgNkYkAADwNB8ekbAkkfjiiy+yXPexxx7zYCQAANwBPnyypSWJRKtWrVze22w2GYbh8v6WtLS0OxUWAADIJkvWSDgcDufr22+/VeXKlfX111/r/PnzOn/+vL766is9+OCDWrp0qRXhAQBgLodhzssLWb5Gom/fvpo6darq1KnjLGvSpIny5MmjZ599Vjt37rQwOgAATOClSYAZLN+1sX//fuXLly9deVhYmA4ePHjH4wEAAFlneSLx0EMPqX///jp58qSz7OTJkxo0aJCqV69uYWQAAJjDMAxTXtmRlpamYcOGKTo6WkFBQSpVqpRGjRqV7Xb+juVTGx988IFat26t4sWLq1ixYpKkI0eO6L777tPChQutDQ4AADNYMLXx+uuv65133tGsWbP0wAMPaOPGjerSpYvCwsLUu3dv0/qxPJEoXbq0tm7dqmXLlmnXrl2SpJiYGMXGxvL0TwCAb7AgkVi7dq1atmypRx99VJIUFRWlTz75RD/99JOp/VieSEi/b/ds3LixGjdubHUoAAB4rdTUVKWmprqU2e122e32dHVr1aqladOmac+ePbr//vu1ZcsW/fDDDxo/frypMVmSSEyaNEnPPvusAgMDNWnSpL+sa+bwCwAAVjDrWRuJiYlKSEhwKRsxYoTi4+PT1X3xxRd18eJFlS1bVrly5VJaWppeeeUVderUyZRYbrEZZq+6yILo6Ght3LhRBQoUUHR0dKb1bDabDhw4kO32u0Y9cTvhAQDuIh8c/MzjfVyIa2RKO4HTvsryiMTcuXM1aNAgjR07Vg888ICSk5PVt29fjR8/XnFxcabEI1k0IpGSkpLhzwAAIHOZJQ0ZGTRokF588UW1b99eklShQgUdOnRIiYmJOT+RAADgrmLBozauXLkiPz/XUx5y5colh8nP/bAkkejfv3+W65q9KAQAgDvNrDUS2dGiRQu98sorKl68uB544AFt3rxZ48ePV9euXU3tx5JEYvPmzS7vk5KSdPPmTZUpU0aStGfPHuXKlUtVq1a1IjwAAHK8yZMna9iwYerRo4dOnTqlyMhI/fvf/9bw4cNN7ceSRGLVqlXOn8ePH6+QkBDNmjVL4eHhkqRz586pS5cuqlu3rhXhAQBgLgtGJEJCQjRx4kRNnDjRo/1YfkT2uHHjlJiY6EwiJCk8PFyjR4/WuHHjLIwMAACTOEx6eSHLE4mLFy/q9OnT6cpPnz6t3377zYKIAABAVlm+a6N169bq0qWLxo0b53xI1/r16zVo0CC1adPG4ugAALh9Viy2vFMsTySmTp2qgQMHqmPHjrpx44Ykyd/fX926ddPYsWMtjg4AABN46bSEGSxPJPLkyaMpU6Zo7Nix2r9/vySpVKlSCg4OtjgyAADMwYjEHRAcHKyKFStaHQYAAMgGr0kkAADwWUxtAAAAdxk+nEhYvv0TAADkXIxIAADgaT48IkEiAQCAhzG1AQAAkAFGJAAA8DQfHpEgkQAAwMN8eWqDRAIAAA/z5USCNRIAAMBtjEgAAOBhvjwiQSIBAICnGTarI/AYpjYAAIDbTBmROH/+vPLly2dGUwAA+BxfntrI9ojE66+/rk8//dT5vm3btipQoIDuuecebdmyxdTgAADwBYbDZsrLG2U7kZg6daqKFSsmSVq2bJmWLVumr7/+Ws2aNdOgQYNMDxAAAHivbE9tnDhxwplILFmyRG3btlXjxo0VFRWlGjVqmB4gAAA5HVMbfxAeHq4jR45IkpYuXarY2FhJkmEYSktLMzc6AAB8gGHYTHl5o2yPSLRp00YdO3bUfffdpzNnzqhZs2aSpM2bN6t06dKmBwgAALxXthOJCRMmKCoqSkeOHNGYMWOUN29eSdLx48fVo0cP0wMEACCn8+WpjWwnErlz59bAgQPTlffr18+UgAAA8DXeuuPCDFlKJL744ossN/jYY4+5HQwAAL7IMKyOwHOylEi0atUqS43ZbDYWXAIAcBfJUiLhcPjw5A4AAB52109tZObatWsKDAw0KxYAAHySLycS2T5HIi0tTaNGjdI999yjvHnz6sCBA5KkYcOG6f333zc9QAAA4L2ynUi88sormjlzpsaMGaOAgABnefny5fXee++ZGhwAAL7AMMx5eaNsJxIffvihpk2bpk6dOilXrlzO8kqVKmnXrl2mBgcAgC/goV1/cPTo0QxPsHQ4HLpx44YpQQEAgJwh24lEuXLl9P3336cr/+yzz1SlShVTggIAwJfwrI0/GD58uOLi4nT06FE5HA59/vnn2r17tz788EMtWbLEEzECAJCj+fIR2dkekWjZsqUWL16s5cuXKzg4WMOHD9fOnTu1ePFiPfzww56IEQAAeCm3zpGoW7euli1bZnYsAAD4JIeXTkuYwe0DqTZu3KidO3dK+n3dRNWqVU0LCgAAX+Kt6xvMkO1E4pdfflGHDh30v//9T/ny5ZMknT9/XrVq1dLcuXN17733mh0jAAA5mrdu3TRDttdIdO/eXTdu3NDOnTt19uxZnT17Vjt37pTD4VD37t09ESMAAPBS2R6R+O6777R27VqVKVPGWVamTBlNnjxZdevWNTU4AAB8gbeeSmmGbCcSxYoVy/DgqbS0NEVGRpoSFAAAvoSpjT8YO3asXnjhBW3cuNFZtnHjRvXp00dvvPGGqcEBAADvlqURifDwcNls/5dNXb58WTVq1JC//+8fv3nzpvz9/dW1a1e1atXKI4ECAJBT3fXbPydOnOjhMAAA8F13/fbPuLg4T8cBAAByILcPpJKka9eu6fr16y5loaGhtxUQAAC+hl0bf3D58mUNGTJE8+bN05kzZ9JdT0tLMyUwAAB8hS+vkcj2ro3Bgwdr5cqVeuedd2S32/Xee+8pISFBkZGR+vDDDz0RIwAA8FLZTiQWL16sKVOm6PHHH5e/v7/q1q2rl19+Wa+++qrmzJnjiRgBAMjRDMNmyiu7jh49qqeeekoFChRQUFCQKlSo4HJ8gxmyPbVx9uxZlSxZUtLv6yHOnj0rSapTp46ef/55U4MDAMAXWLFG4ty5c6pdu7YaNmyor7/+WoUKFdLevXsVHh5uaj/ZTiRKliyplJQUFS9eXGXLltW8efNUvXp1LV682PkQLwAA8H+sWCPx+uuvq1ixYpoxY4azLDo62vR+sj210aVLF23ZskWS9OKLL+rtt99WYGCg+vXrp0GDBpkeIAAA+F1qaqouXrzo8kpNTc2w7hdffKFq1arpySefVOHChVWlShVNnz7d9JhshnF7Ay6HDh3Spk2bVLp0aVWsWNGsuG6Lf8A9VocAeKWrx763OgTA6+QuWNLjfWy4p7Up7Xz5TCUlJCS4lI0YMULx8fHp6gYGBkqS+vfvryeffFIbNmxQnz59NHXqVFPPh7rtRMIbkUgAGSORANK7E4nE+sg2prRTOeWTdCMQdrtddrs9Xd2AgABVq1ZNa9eudZb17t1bGzZs0Lp160yJR8riGolJkyZlucHevXu7HQwAAMhcZklDRiIiIlSuXDmXspiYGM2fP9/UmLKUSEyYMCFLjdlsNhIJAAD+xIqh/9q1a2v37t0uZXv27FGJEiVM7SdLiURKSoqpnQIAcDexYtdGv379VKtWLb366qtq27atfvrpJ02bNk3Tpk0ztZ9s79oAAADe76GHHtKCBQv0ySefqHz58ho1apQmTpyoTp06mdrPbT20CwAA/D2rHiPevHlzNW/e3KN9kEgAAOBhDqsD8CCmNgAAgNsYkQAAwMMM8RhxF99//72eeuop1axZU0ePHpUkzZ49Wz/88IOpwQEA4Aschjkvb5TtRGL+/Plq0qSJgoKCtHnzZucJWxcuXNCrr75qeoAAAOR0DtlMeXmjbCcSo0eP1tSpUzV9+nTlzp3bWV67dm0lJSWZGhwAAPBu2V4jsXv3btWrVy9deVhYmM6fP29GTAAA+BTWSPxB0aJFtW/fvnTlP/zwg0qW9PyDTwAAyGkcJr28UbYTiWeeeUZ9+vTR+vXrZbPZdOzYMc2ZM0cDBw7U888/74kYAQCAl8r21MaLL74oh8OhRo0a6cqVK6pXr57sdrsGDhyoF154wRMxAgCQo/ny1IbNMAy3NpRcv35d+/bt06VLl1SuXDnlzZvX7Njc5h9wj9UhAF7p6rHvrQ4B8Dq5C3p+Wn5pkfamtNP05FxT2jGT2wdSBQQEpHvOOQAAuLtkO5Fo2LChbLbMh2hWrlx5WwEBAOBrvHWhpBmynUhUrlzZ5f2NGzeUnJysn3/+WXFxcWbFBQCAz/DlNRLZTiQmTJiQYXl8fLwuXbp02wEBAICcw7Snfz711FP64IMPzGoOAACf4bCZ8/JGpj39c926dQoMDDSrOQAAfIa3PifDDNlOJNq0aePy3jAMHT9+XBs3btSwYcNMCwwAAF/hpQ/uNEW2E4mwsDCX935+fipTpoxGjhypxo0bmxYYAADwftlKJNLS0tSlSxdVqFBB4eHhnooJAACf4svbP7O12DJXrlxq3LgxT/kEACAbHDabKS9vlO1dG+XLl9eBAwc8EQsAAMhhsp1IjB49WgMHDtSSJUt0/PhxXbx40eUFAABcGSa9vFGW10iMHDlSAwYM0COPPCJJeuyxx1yOyjYMQzabTWlpaeZHCQBADubLaySynEgkJCToueee06pVqzwZDwAAyEGynEjcetp4/fr1PRYMAAC+yFtPpTRDtrZ//tVTPwEAQMY42fL/u//++/82mTh79uxtBQQAAHKObCUSCQkJ6U62BAAAf81bd1yYIVuJRPv27VW4cGFPxQIAgE9ijYRYHwEAgLt8eftnlg+kurVrAwAA4JYsj0g4HL6cTwEA4Dm+/E/xbD9GHAAAZI8vr5HI9rM2AAAAbmFEAgAAD/PlxQEkEgAAeJgvJxJMbQAAALcxIgEAgIcZPrzYkkQCAAAPY2oDAAAgA4xIAADgYb48IkEiAQCAh3GyJQAAcBsnWwIAAGSAEQkAADyMNRIAAMBtvpxIMLUBAADcxogEAAAexq4NAADgNnZtAACAHO21116TzWZT3759TW2XEQkAADzM6sWWGzZs0LvvvquKFSua3jYjEgAAeJhh0ssdly5dUqdOnTR9+nSFh4ffzm1kiEQCAAAf1rNnTz366KOKjY31SPtMbQAA4GEOk/ZtpKamKjU11aXMbrfLbrdnWH/u3LlKSkrShg0bTOk/I4xIAADgYQ6TXomJiQoLC3N5JSYmZtjnkSNH1KdPH82ZM0eBgYEeuzebYRg+t73VP+Aeq0MAvNLVY99bHQLgdXIXLOnxPkaW6GRKO0P2fJDlEYmFCxeqdevWypUrl7MsLS1NNptNfn5+Sk1NdbnmLqY2AADIIf5qGuPPGjVqpG3btrmUdenSRWXLltWQIUNMSSIkEgkAADzOiu2fISEhKl++vEtZcHCwChQokK78dpBIAADgYb58siWJBAAAd4nVq1eb3iaJBAAAHmbW9k9vRCIBAICH+W4awTkSAADgNjAiAQCAh1n90C5PsiyR2Lp1a5breuJpZQAA3CmskfCAypUry2azyTAM2Wx/vS8mLS3tDkUFAACyw7I1EikpKTpw4IBSUlI0f/58RUdHa8qUKdq8ebM2b96sKVOmqFSpUpo/f75VIQIAYAorHyPuaZaNSJQoUcL585NPPqlJkybpkUcecZZVrFhRxYoV07Bhw9SqVSsLIgQAwByskfCwbdu2KTo6Ol15dHS0duzYYUFEAACYx5fXSHjF9s+YmBglJibq+vXrzrLr168rMTFRMTExFkYGAAD+ileMSEydOlUtWrTQvffe69yhsXXrVtlsNi1evNji6AAAuD2+Ox7hJYlE9erVdeDAAc2ZM0e7du2SJLVr104dO3ZUcHCwxdEBAHB7WCNxBwQHB+vZZ5+1OgwAAJANXrFGQpJmz56tOnXqKDIyUocOHZIkTZgwQYsWLbI4MgAAbo9h0n/eyCsSiXfeeUf9+/dXs2bNdO7cOecBVOHh4Zo4caK1wQEAcJscJr28kVckEpMnT9b06dP10ksvyd///2ZbqlWrpm3btlkYGQAA+CtesUYiJSVFVapUSVdut9t1+fJlCyICAMA8nCPhYdHR0UpOTk5XvnTpUs6RAADkeByR7WH9+/dXz549de3aNRmGoZ9++kmffPKJEhMT9d5771kdHgAAyIRXJBLdu3dXUFCQXn75ZV25ckUdO3ZUZGSk3nzzTbVv397q8OCm55+L04D+z6to0ULaunWH+vQdpg0bk60OC7hjNiZv04yPP9OOXft0+sxZvZk4TI3q1ZIk3bh5U5OnzdL36zbql2PHlTc4WP94qIr6PddFhQsVsDhymI2pjTugU6dO2rt3ry5duqQTJ07ol19+Ubdu3awOC2568snH9MbYERo1erweqtFUW7bu0FdfzlEh/oDEXeTq1WsqU7qkXhrQI921a9dStWP3fv27cwfN++AtTXz1ZR08/It6DUmwIFJ4mi/v2rAZhuFzaZJ/wD1Wh3DXW/vDYm3YuEV9+r4sSbLZbDp4YIPenjJDY8a+bXF0d6+rx763OoS7VvnazVxGJDKybedudejeV8vmz1JE0cJ3MLq7W+6CJT3eR/eoJ0xp572Dn5nSjpksm9qoUqWKbDZbluomJSV5OBqYKXfu3HrwwYp6bcxbzjLDMLRi5Q/6xz+qWhgZ4N0uXboim82mkBAeDYCcw7JEolWrVs6fr127pilTpqhcuXKqWbOmJOnHH3/U9u3b1aNH+iHBP0pNTVVqaqpLmWEYWU5SYL6CBfPL399fp07+6lJ+6tRplS1TyqKoAO+WmnpdE975QI/E1ldenjHkc7x1WsIMliUSI0aMcP7cvXt39e7dW6NGjUpX58iRI3/ZTmJiohISXOcUbX55ZcsVal6wAOBBN27e1IBhr8owDA0b1MvqcOAB3nq8tRm8YrHlf//7Xz399NPpyp966inNnz//Lz87dOhQXbhwweVl8wvxVKjIgl9/PaubN2+qcJGCLuWFCxfSiZOnLYoK8E63kohjJ09p+sRXGY1AjuMViURQUJD+97//pSv/3//+p8DAwL/8rN1uV2hoqMuLaQ1r3bhxQ0lJW/XPhnWcZTabTf9sWEc//rjJwsgA73IriTh85Jjem/iq8oUxkuqrfHnXhlecI9G3b189//zzSkpKUvXq1SVJ69ev1wcffKBhw4ZZHB3cMeHN6Zrx/gRtStqqDRs2q/cLzyg4OEgzZ31qdWjAHXPlylUd/uWY8/3RYye1a89+hYWGqGDB/Or/0ivasWef3h6TIIfDoV/PnJUkhYWGKHfu3FaFDQ9w+N4GSSev2f45b948vfnmm9q5c6ckKSYmRn369FHbtm2z3RbbP71Dj+c7Ow+k2rJlu/r2G66fNmy2Oqy7Gts/76yfkraq6wtD0pW3bBarHt2eUpMnOmf4uQ8mv67qD1b0cHS45U5s//xXiTamtDP70OemtGMmr0kkzEQiAWSMRAJI704kEk+ZlEh85IWJhFdMbdxy/fp1nTp1Sg6H60xQ8eLFLYoIAIDb58tHZHtFIrF371517dpVa9eudSm/dR5EWlqaRZEBAIC/4hWJROfOneXv768lS5YoIiKCXRcAAJ/iy+dIeEUikZycrE2bNqls2bJWhwIAgOm8deumGbwikShXrpx+/fXXv68IAEAO5MtrJLziQKrXX39dgwcP1urVq3XmzBldvHjR5QUAALyTV4xIxMbGSpIaNWrkUs5iSwCAL2CNhIetWrXK6hAAAPAY1kh4WP369a0OAQAAuMErEolbrly5osOHD+v69esu5RUrclQsACDn8sFDpJ28IpE4ffq0unTpoq+//jrD66yRAADkZOza8LC+ffvq/PnzWr9+vYKCgrR06VLNmjVL9913n7744gurwwMAAJnwihGJlStXatGiRapWrZr8/PxUokQJPfzwwwoNDVViYqIeffRRq0MEAMBtvrzY0itGJC5fvqzChQtLksLDw3X69GlJUoUKFZSUlGRlaAAA3DbDpP+8kVckEmXKlNHu3bslSZUqVdK7776ro0ePaurUqYqIiLA4OgAAkBmvmNro06ePjh8/LkkaMWKEmjZtqo8++kgBAQGaNWuWxdEBAHB7fHmxpVckEk899ZTz56pVq+rQoUPatWuXihcvroIFC1oYGQAAt4/tnx7Qv3//LNcdP368ByMBAMCzfHmxpWWJxObNm7NUz2azeTgSAADgLssSCZ6vAQC4W3jrjgszeMUaCQAAfJkvL7b0iu2fAADAXImJiXrooYcUEhKiwoULq1WrVs6jFsxEIgEAgIcZhmHKKzu+++479ezZUz/++KOWLVumGzduqHHjxrp8+bKp98bUBgAAHmbF1MbSpUtd3s+cOVOFCxfWpk2bVK9ePdP6IZEAACCHSE1NVWpqqkuZ3W6X3W7/289euHBBkpQ/f35TY2JqAwAADzPrWRuJiYkKCwtzeSUmJv5t/w6HQ3379lXt2rVVvnx5U++NEQkAADzMYdLJlkOHDk13oGNWRiN69uypn3/+WT/88IMpcfwRiQQAADlEVqcx/qhXr15asmSJ1qxZo3vvvdf0mEgkAADwMCtOkTAMQy+88IIWLFig1atXKzo62iP9kEgAAOBhVuza6Nmzpz7++GMtWrRIISEhOnHihCQpLCxMQUFBpvVjM3zwkWT+AfdYHQLgla4e+97qEACvk7tgSY/3UfOehqa0s+5o1h8vkdmzqmbMmKHOnTubEo/EiAQAAD7pTo0TkEgAAOBhPjj470QiAQCAh/HQLgAAgAwwIgEAgIcZPjwiQSIBAICH+fIaCaY2AACA2xiRAADAw3x5sSWJBAAAHsbUBgAAQAYYkQAAwMOY2gAAAG5j+ycAAHCbgzUSAAAA6TEiAQCAhzG1AQAA3MbUBgAAQAYYkQAAwMOY2gAAAG5jagMAACADjEgAAOBhTG0AAAC3MbUBAACQAUYkAADwMKY2AACA2wzDYXUIHkMiAQCAh/nyY8RZIwEAANzGiAQAAB5m+PCuDRIJAAA8jKkNAACADDAiAQCAhzG1AQAA3MbJlgAAABlgRAIAAA/jZEsAAOA2X14jwdQGAABwGyMSAAB4mC+fI0EiAQCAh/ny1AaJBAAAHsb2TwAAgAwwIgEAgIcxtQEAANzmy4stmdoAAABuY0QCAAAPY2oDAAC4jV0bAAAAGWBEAgAAD+OhXQAAwG1MbQAAAGSAEQkAADyMXRsAAMBtvrxGgqkNAAA8zDAMU17uePvttxUVFaXAwEDVqFFDP/30k6n3RiIBAICP+vTTT9W/f3+NGDFCSUlJqlSpkpo0aaJTp06Z1ofN8MGJG/+Ae6wOAfBKV499b3UIgNfJXbCk5/sw6e+lG9ePZqt+jRo19NBDD+mtt96SJDkcDhUrVkwvvPCCXnzxRVNiYkQCAAAPM0x6Zcf169e1adMmxcbGOsv8/PwUGxurdevW3db9/BGLLQEAyCFSU1OVmprqUma322W329PV/fXXX5WWlqYiRYq4lBcpUkS7du0yLSafTCRuZnPoB56RmpqqxMREDR06NMP/yYG7Fd+Nu49Zfy/Fx8crISHBpWzEiBGKj483pX13+OQaCXiHixcvKiwsTBcuXFBoaKjV4QBeg+8G3JWdEYnr168rT548+uyzz9SqVStneVxcnM6fP69FixaZEhNrJAAAyCHsdrtCQ0NdXpmNagUEBKhq1apasWKFs8zhcGjFihWqWbOmaTH55NQGAACQ+vfvr7i4OFWrVk3Vq1fXxIkTdfnyZXXp0sW0PkgkAADwUe3atdPp06c1fPhwnThxQpUrV9bSpUvTLcC8HSQS8Bi73a4RI0awmAz4E74buJN69eqlXr16eax9FlsCAAC3sdgSAAC4jUQCAAC4jUQCAAC4jUTCBzVo0EB9+/Z1vo+KitLEiRP/8jM2m00LFy70aFxZ1blzZ5fDUzxl5syZypcvn8f7gW/78/fNE+Lj41W5cmWP9gG4i10bd4ENGzYoODjY6jAUHx+vhQsXKjk52epQAAAmIZG4CxQqVMjqEAAAPoqpjRzu8uXLevrpp5U3b15FRERo3Lhx6er8eWpj7969qlevngIDA1WuXDktW7bsb/tp0KCBevfurcGDByt//vwqWrRouofEHD58WC1btlTevHkVGhqqtm3b6uTJk5J+n0ZISEjQli1bZLPZZLPZNHPmzCzd49KlS1WnTh3ly5dPBQoUUPPmzbV//37n9YMHD8pms+nzzz9Xw4YNlSdPHlWqVCndY3Jnzpyp4sWLK0+ePGrdurXOnDmTpf6BrJo9e7aqVaumkJAQFS1aVB07dtSpU6ec11evXi2bzaYVK1aoWrVqypMnj2rVqqXdu3e7tPPaa6+pSJEiCgkJUbdu3XTt2rU7fStAlpFI5HCDBg3Sd999p0WLFunbb7/V6tWrlZSUlGl9h8OhNm3aKCAgQOvXr9fUqVM1ZMiQLPU1a9YsBQcHa/369RozZoxGjhzpTEIcDodatmyps2fP6rvvvtOyZct04MABtWvXTtLvp6sNGDBADzzwgI4fP67jx487r/2dy5cvq3///tq4caNWrFghPz8/tW7dWg6Hw6XeSy+9pIEDByo5OVn333+/OnTooJs3b0qS1q9fr27duqlXr15KTk5Ww4YNNXr06Cz1D2TVjRs3NGrUKG3ZskULFy7UwYMH1blz53T1XnrpJY0bN04bN26Uv7+/unbt6rw2b948xcfH69VXX9XGjRsVERGhKVOm3MG7ALLJQI7122+/GQEBAca8efOcZWfOnDGCgoKMPn36OMtKlChhTJgwwTAMw/jmm28Mf39/4+jRo87rX3/9tSHJWLBgQaZ91a9f36hTp45L2UMPPWQMGTLEMAzD+Pbbb41cuXIZhw8fdl7fvn27Icn46aefDMMwjBEjRhiVKlX62/uKi4szWrZsmen106dPG5KMbdu2GYZhGCkpKYYk47333kvX986dOw3DMIwOHToYjzzyiEs77dq1M8LCwv42HuCv1K9f3+X79kcbNmwwJBm//fabYRiGsWrVKkOSsXz5cmedL7/80pBkXL161TAMw6hZs6bRo0cPl3Zq1KiRpe8OYAVGJHKw/fv36/r166pRo4azLH/+/CpTpkymn9m5c6eKFSumyMhIZ1lWnwJXsWJFl/cRERHOYdtb7RYrVsx5vVy5csqXL5927tyZpfYzs3fvXnXo0EElS5ZUaGiooqKiJP0+lZJZfBEREZLkEt8ff09S1u8byKpNmzapRYsWKl68uEJCQlS/fn1J/L8K30YigSzLnTu3y3ubzZZuesETWrRoobNnz2r69Olav3691q9fL0m6fv16pvHZbDZJuiPxAdLvU3BNmjRRaGio5syZow0bNmjBggWS+H8Vvo1EIgcrVaqUcufO7fyLVZLOnTunPXv2ZPqZmJgYHTlyRMePH3eW/fjjj7cdy612jxw54izbsWOHzp8/r3LlykmSAgIClJaWlq12z5w5o927d+vll19Wo0aNFBMTo3PnzrkV3x9/T5I59w3csmvXLp05c0avvfaa6tatq7Jly7ostMwq/l9FTsP2zxwsb9686tatmwYNGqQCBQqocOHCeumll+Tnl3l+GBsbq/vvv19xcXEaO3asLl68qJdeeum2Y4mNjVWFChXUqVMnTZw4UTdv3lSPHj1Uv359VatWTdLvu0dSUlKUnJyse++9VyEhIX/79MPw8HAVKFBA06ZNU0REhA4fPqwXX3wx2/H17t1btWvX1htvvKGWLVvqm2++0dKlS926VyAjxYsXV0BAgCZPnqznnntOP//8s0aNGpXtdvr06aPOnTurWrVqql27tubMmaPt27erZMmSHogauH2MSORwY8eOVd26ddWiRQvFxsaqTp06qlq1aqb1/fz8tGDBAl29elXVq1dX9+7d9corr9x2HDabTYsWLVJ4eLjq1aun2NhYlSxZUp9++qmzzuOPP66mTZuqYcOGKlSokD755JO/bdfPz09z587Vpk2bVL58efXr109jx47Ndnz/+Mc/NH36dL355puqVKmSvv32W7388svZbgfITKFChTRz5kz997//Vbly5fTaa6/pjTfeyHY77dq107BhwzR48GBVrVpVhw4d0vPPP++BiAFz8BhxAADgNkYkAACA20gkAACA20gkAACA20gkAACA20gkAACA20gkAACA20gkAACA20gkAAt17txZrVq1cr5v0KCB+vbte8fjWL16tWw2m86fP59pHZvNpoULF2a5zfj4eFWuXPm24jp48KBsNpuSk5Nvqx0AnkMiAfxJ586dZbPZZLPZFBAQoNKlS2vkyJG6efOmx/v+/PPPs3ysclb+8gcAT+NZG0AGmjZtqhkzZig1NVVfffWVevbsqdy5c2vo0KHp6l6/fl0BAQGm9Js/f35T2gGAO4URCSADdrtdRYsWVYkSJfT8888rNjZWX3zxhaT/m4545ZVXFBkZqTJlykiSjhw5orZt2ypfvnzKnz+/WrZsqYMHDzrbTEtLU//+/ZUvXz4VKFBAgwcP1p9PqP/z1EZqaqqGDBmiYsWKyW63q3Tp0nr//fd18OBBNWzYUNLvDzaz2Wzq3LmzpN8fR52YmKjo6GgFBQWpUqVK+uyzz1z6+eqrr3T//fcrKChIDRs2dIkzq4YMGaL7779fefLkUcmSJTVs2DDduHEjXb13331XxYoVU548edS2bVtduHDB5fp7772nmJgYBQYGqmzZspoyZUqmfZ47d06dOnVSoUKFFBQUpPvuu08zZszIduwAzMOIBJAFQUFBOnPmjPP9ihUrFBoaqmXLlkmSbty4oSZNmqhmzZr6/vvv5e/vr9GjR6tp06baunWrAgICNG7cOM2cOVMffPCBYmJiNG7cOC1YsED//Oc/M+336aef1rp16zRp0iRVqlRJKSkp+vXXX1WsWDHNnz9fjz/+uHbv3q3Q0FAFBQVJkhITE/XRRx9p6tSpuu+++7RmzRo99dRTKlSokOrXr68jR46oTZs26tmzp5599llt3LhRAwYMyPbvJCQkRDNnzlRkZKS2bdumZ555RiEhIRo8eLCzzr59+zRv3jwtXrxYFy9eVLdu3dSjRw/NmTNHkjRnzhwNHz5cb731lqpUqaLNmzfrmWeeUXBwsOLi4tL1OWzYMO3YsUNff/21ChYsqH379unq1avZjh2AiQwALuLi4oyWLVsahmEYDofDWLZsmWG3242BAwc6rxcpUsRITU11fmb27NlGmTJlDIfD4SxLTU01goKCjG+++cYwDMOIiIgwxowZ47x+48YN495773X2ZRiGUb9+faNPnz6GYRjG7t27DUnGsmXLMoxz1apVhiTj3LlzzrJr164ZefLkMdauXetSt1u3bkaHDh0MwzCMoUOHGuXKlXO5PmTIkHRt/ZkkY8GCBZleHzt2rFG1alXn+xEjRhi5cuUyfvnlF2fZ119/bfj5+RnHjx83DMMwSpUqZXz88ccu7YwaNcqoWbOmYRiGkZKSYkgyNm/ebBiGYbRo0cLo0qVLpjEAuPMYkQAysGTJEuXNm1c3btyQw+FQx44dFR8f77xeoUIFl3URW7Zs0b59+xQSEuLSzrVr17R//35duHBBx48fV40aNZzX/P39Va1atXTTG7ckJycrV65cql+/fpbj3rdvn65cuaKHH37Ypfz69euqUqWKJGnnzp0ucUhSzZo1s9zHLZ9++qkmTZqk/fv369KlS7p586ZCQ0Nd6hQvXlz33HOPSz8Oh0O7d+9WSEiI9u/fr27duumZZ55x1rl586bCwsIy7PP555/X448/rqSkJDVu3FitWrVSrVq1sh07APOQSAAZaNiwod555x0FBAQoMjJS/v6uX5Xg4GCX95cuXVLVqlWdQ/Z/VKhQIbdiuDVVkR2XLl2SJH355Zcuf4FLv6/7MMu6devUqVMnJSQkqEmTJgoLC9PcuXM1bty4bMc6ffr0dIlNrly5MvxMs2bNdOjQIX311VdatmyZGjVqpJ49e+qNN95w/2YA3BYSCSADwcHBKl26dJbrP/jgg/r0009VuHDhdP8qvyUiIkLr169XvXr1JP3+L+9NmzbpwQcfzLB+hQoV5HA49N133yk2Njbd9VsjImlpac6ycuXKyW636/Dhw5mOZMTExDgXjt7y448//v1N/sHatWtVokQJvfTSS86yQ4cOpat3+PBhHTt2TJGRkc5+/Pz8VKZMGRUpUkSRkZE6cOCAOnXqlOW+CxUqpLi4OMXFxalu3boaNGgQiQRgIXZtACbo1KmTChYsqJYtW+r7779XSkqKVq9erd69e+uXX36RJPXp00evvfaaFi5cqF27dqlHjx5/eQZEVFSU4uLi1LVrVy1cuNDZ5rx58yRJJUqUkM1m05IlS3T69GldunRJISEhGjhwoPr166dZs2Zp//79SkpK0uTJkzVr1ixJ0nPPPae9e/dq0KBB2r17tz7++GPNnDkzW/d733336fDhw5o7d67279+vSZMmacGCBenqBQYGKi4uTlu2bNH333+v3r17q23btipatKgkKSEhQYmJiZo0aZL27Nmjbdu2acaMGRo/fnyG/Q4fPlyLFi3Svn37tH37di1ZskQxMTHZih2AuUgkABPkyZNHa9asUfHixdWmTRvFxMSoW7duunbtmnOEYsCAAfrXv/6luLg41axZUyEhIWrduvVftvvOO+/oiSeeUI8ePVS2bFk988wzunz5siTpnnvuUUJCgl588UUVKVJEvXr1kiSNGjVKw4YNU2JiomJiYtS0aVN9+eWXio6OlvT7uoX58+dr4cKFqlSpkqZOnapXX301W/f72GOPqV+/furVq5cqV66stWvXatiwYenqlS5dWm3atNEjjzyixo0bq2LFii7bO7t376733ntPM2bMUIUKFVS/fn3NnDnTGeufBQQEaOjQoapYsaLq1aunXLlyae7cudmKHYC5bEZmK70AAAD+BiMSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbf8PkAtlXISM2hwAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "yhat=logreg_cv.predict(X_test)\n",
        "plot_confusion_matrix(Y_test,yhat)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  6\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "parameters_svm = {'kernel':('linear', 'rbf','poly','rbf', 'sigmoid'),\n",
        "              'C': np.logspace(-3, 3, 5),\n",
        "              'gamma':np.logspace(-3, 3, 5)}\n",
        "svm = SVC()\n",
        "grid_search_svm = GridSearchCV(\n",
        "    estimator = svm,\n",
        "    param_grid = parameters_svm,\n",
        "    scoring = 'accuracy',\n",
        "    cv = 10\n",
        ")\n",
        "# execute search\n",
        "svm_cv = grid_search_svm.fit(X_train,Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tuned hpyerparameters :(best parameters)  {'C': 1.0, 'gamma': 0.03162277660168379, 'kernel': 'sigmoid'}\n",
            "accuracy : 0.8482142857142856\n"
          ]
        }
      ],
      "source": [
        "print(\"tuned hpyerparameters :(best parameters) \",svm_cv.best_params_)\n",
        "print(\"accuracy :\",svm_cv.best_score_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  7\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Calculate the accuracy on the test data using the method <code>score</code>:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8333333333333334"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "svm_cv.score(X_test, Y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can plot the confusion matrix\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "yhat=svm_cv.predict(X_test)\n",
        "plot_confusion_matrix(Y_test,yhat)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  8\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/lib/python3.11/site-packages/sklearn/model_selection/_validation.py:425: FitFailedWarning: \n",
            "3240 fits failed out of a total of 6480.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "3240 fits failed with the following error:\n",
            "Traceback (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(\n",
            "sklearn.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",
            " 0.81785714 0.76785714 0.85892857 0.77678571 0.70892857 0.75\n",
            " 0.76607143 0.73392857 0.725      0.74821429 0.78214286 0.76607143\n",
            " 0.81785714 0.75178571 0.80357143 0.74821429 0.81964286 0.84642857\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            " 0.81785714 0.81964286 0.76071429 0.84642857 0.76428571 0.73392857\n",
            " 0.77678571 0.80535714 0.73392857 0.76964286 0.81964286 0.78928571\n",
            " 0.82321429 0.81785714 0.79107143 0.7375     0.7625     0.77857143\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            " 0.79107143 0.84821429 0.775      0.79285714 0.76607143 0.775\n",
            " 0.74821429 0.78928571 0.83214286 0.79107143 0.73571429 0.73571429\n",
            " 0.84821429 0.83214286 0.775      0.77678571 0.79285714 0.79642857\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            " 0.81964286 0.77857143 0.75178571 0.80357143 0.84642857 0.83214286\n",
            " 0.74821429 0.7625     0.84642857 0.81964286 0.80357143 0.83571429\n",
            " 0.77857143 0.80892857 0.80357143 0.73214286 0.71071429 0.78035714\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            " 0.7125     0.83392857 0.74821429 0.77857143 0.75       0.775\n",
            " 0.76607143 0.85714286 0.73571429 0.76607143 0.81964286 0.80714286\n",
            " 0.77678571 0.70892857 0.70714286 0.86071429 0.77857143 0.81964286\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            " 0.7625     0.75178571 0.70535714 0.81964286 0.80714286 0.77678571\n",
            " 0.71785714 0.80535714 0.79107143 0.77857143 0.72321429 0.7625\n",
            " 0.68571429 0.75178571 0.83392857 0.85892857 0.77678571 0.75178571\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            " 0.78928571 0.7625     0.84642857 0.72142857 0.79107143 0.83214286\n",
            " 0.80178571 0.72321429 0.79107143 0.76428571 0.80357143 0.775\n",
            " 0.71785714 0.7625     0.81785714 0.80357143 0.77678571 0.82142857\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            " 0.70714286 0.80357143 0.74821429 0.80357143 0.73214286 0.80714286\n",
            " 0.69464286 0.72142857 0.79107143 0.79107143 0.7625     0.83392857\n",
            " 0.77678571 0.78928571 0.75       0.83392857 0.83571429 0.84821429\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            " 0.77678571 0.82142857 0.7375     0.69285714 0.76071429 0.81964286\n",
            " 0.80535714 0.80357143 0.7625     0.74821429 0.80714286 0.81964286\n",
            " 0.80357143 0.80357143 0.73392857 0.80714286 0.84464286 0.83392857\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            " 0.80357143 0.81964286 0.72678571 0.7375     0.79107143 0.775\n",
            " 0.84464286 0.73214286 0.78928571 0.80357143 0.76071429 0.71964286\n",
            " 0.69464286 0.8625     0.8625     0.76607143 0.74821429 0.775\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            " 0.80714286 0.81785714 0.77678571 0.81964286 0.81607143 0.85892857\n",
            " 0.83392857 0.77678571 0.83214286 0.80535714 0.83392857 0.81785714\n",
            " 0.79107143 0.77678571 0.73928571 0.83392857 0.76071429 0.83214286\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            " 0.77857143 0.83392857 0.81964286 0.82142857 0.84821429 0.80535714\n",
            " 0.80178571 0.82142857 0.83392857 0.76607143 0.70357143 0.81785714\n",
            " 0.78928571 0.83214286 0.74642857 0.73392857 0.77678571 0.79107143\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            " 0.80535714 0.81964286 0.7875     0.775      0.75       0.775\n",
            " 0.79464286 0.79464286 0.80535714 0.75       0.76607143 0.775\n",
            " 0.80535714 0.71071429 0.80535714 0.7375     0.81428571 0.77678571\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            " 0.81785714 0.78035714 0.80178571 0.7125     0.76071429 0.84464286\n",
            " 0.7625     0.73571429 0.70714286 0.725      0.80892857 0.7625\n",
            " 0.79107143 0.76428571 0.74642857 0.77678571 0.81428571 0.77678571\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            " 0.80357143 0.83392857 0.74821429 0.79107143 0.77678571 0.81964286\n",
            " 0.74821429 0.80357143 0.78928571 0.7625     0.81785714 0.80357143\n",
            " 0.80535714 0.79107143 0.84642857 0.7375     0.77678571 0.75178571\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            " 0.84464286 0.7625     0.77321429 0.7625     0.71964286 0.7625\n",
            " 0.80535714 0.82321429 0.73214286 0.75178571 0.7125     0.83214286\n",
            " 0.78928571 0.76428571 0.75       0.79107143 0.77678571 0.76071429\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            " 0.80357143 0.79107143 0.76428571 0.76071429 0.76071429 0.78928571\n",
            " 0.79285714 0.79107143 0.74642857 0.77857143 0.81964286 0.73571429\n",
            " 0.75       0.80535714 0.74464286 0.80535714 0.81785714 0.78214286\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            "        nan        nan        nan        nan        nan        nan\n",
            " 0.72321429 0.83392857 0.7375     0.72321429 0.78928571 0.83571429\n",
            " 0.81964286 0.81964286 0.76428571 0.75178571 0.80357143 0.78928571\n",
            " 0.725      0.80357143 0.79107143 0.80535714 0.76071429 0.83392857]\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "parameters_tree = {'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",
        "\n",
        "tree = DecisionTreeClassifier()\n",
        "grid_search_tree = GridSearchCV(\n",
        "    estimator = tree,\n",
        "    param_grid = parameters_tree,\n",
        "    scoring = 'accuracy',\n",
        "    cv = 10\n",
        ")\n",
        "# execute search\n",
        "tree_cv = grid_search_tree.fit(X_train, Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tuned hpyerparameters :(best parameters)  {'criterion': 'entropy', 'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 2, 'splitter': 'random'}\n",
            "accuracy : 0.8625\n"
          ]
        }
      ],
      "source": [
        "print(\"tuned hpyerparameters :(best parameters) \",tree_cv.best_params_)\n",
        "print(\"accuracy :\",tree_cv.best_score_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  9\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Calculate the accuracy of tree_cv on the test data using the method <code>score</code>:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8333333333333334"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tree_cv.score(X_test, Y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can plot the confusion matrix\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "yhat = tree_cv.predict(X_test)\n",
        "plot_confusion_matrix(Y_test,yhat)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  10\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "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",
        "\n",
        "KNN = KNeighborsClassifier()\n",
        "grid_search_knn = GridSearchCV(\n",
        "    estimator = KNN,\n",
        "    param_grid = parameters,\n",
        "    scoring = 'accuracy',\n",
        "    cv = 10\n",
        ")\n",
        "# execute search\n",
        "knn_cv = grid_search_knn.fit(X_train, Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 10, 'p': 1}\n",
            "accuracy : 0.8482142857142858\n"
          ]
        }
      ],
      "source": [
        "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\n",
        "print(\"accuracy :\",knn_cv.best_score_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  11\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Calculate the accuracy of knn_cv on the test data using the method <code>score</code>:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8333333333333334"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "knn_cv.score(X_test, Y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can plot the confusion matrix\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "yhat = knn_cv.predict(X_test)\n",
        "plot_confusion_matrix(Y_test,yhat)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  12\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Find the method performs best:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best model is DecisionTree with a score of 0.8625\n",
            "Best params is : {'criterion': 'entropy', 'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 2, 'splitter': 'random'}\n"
          ]
        }
      ],
      "source": [
        "models = {'KNeighbors':knn_cv.best_score_,\n",
        "              'DecisionTree':tree_cv.best_score_,\n",
        "              'LogisticRegression':logreg_cv.best_score_,\n",
        "              'SupportVector': svm_cv.best_score_}\n",
        "\n",
        "best = max(models, key=models.get)\n",
        "print('Best model is', best,'with a score of', models[bestalgorithm])\n",
        "if best == 'DecisionTree':\n",
        "    print('Best params is :', tree_cv.best_params_)\n",
        "if best == 'KNeighbors':\n",
        "    print('Best params is :', knn_cv.best_params_)\n",
        "if best == 'LogisticRegression':\n",
        "    print('Best params is :', logreg_cv.best_params_)\n",
        "if best == 'SupportVectorMachine':\n",
        "    print('Best params is :', svm_cv.best_params_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Authors\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "[Pratiksha Verma](https://www.linkedin.com/in/pratiksha-verma-6487561b1/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Change Log\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### <h3 align=\"center\"> IBM Corporation 2022. All rights reserved. <h3/>\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.6"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}