P7_SpaceX_Machine_Learning_Prediction.ipynb

{
  "cells": [
    {
      "attachments": {},
      "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"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# **Space X  Falcon 9 First Stage Landing Prediction**\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Assignment:  Machine Learning Prediction\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Estimated time needed: **60** minutes\n"
      ]
    },
    {
      "attachments": {},
      "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"
      ]
    },
    {
      "attachments": {},
      "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"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Several examples of an unsuccessful landing are shown here:\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Most unsuccessful landings are planed. Space X; performs a controlled landing in the oceans.\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Objectives\n"
      ]
    },
    {
      "attachments": {},
      "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"
      ]
    },
    {
      "attachments": {},
      "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'])"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We will import the following libraries for the lab\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "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"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "This function is to plot the confusion matrix.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "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() "
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Load the dataframe\n"
      ]
    },
    {
      "attachments": {},
      "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",
        "text1 = 'dataset_part_2.csv'\n",
        "data = pd.read_csv(text1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
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              "  <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",
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              "      <td>28.561857</td>\n",
              "      <td>0</td>\n",
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              "    <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",
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              "    <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",
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              "      <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",
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            "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": 6,
      "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",
        "text2 = 'dataset_part_3.csv'\n",
        "X = pd.read_csv(text2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
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              "      <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",
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              "      <th>Reused_False</th>\n",
              "      <th>Reused_True</th>\n",
              "      <th>Legs_False</th>\n",
              "      <th>Legs_True</th>\n",
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              "    <tr>\n",
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              "      <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",
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              "      <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": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X.head(100)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  1\n"
      ]
    },
    {
      "attachments": {},
      "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": 8,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "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)"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y = data[\"Class\"].to_numpy()\n",
        "Y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "type(Y)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  2\n"
      ]
    },
    {
      "attachments": {},
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "X = transform.fit_transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[-1.71291154e+00, -1.94814463e-16, -6.53912840e-01, ...,\n",
              "        -8.35531692e-01,  1.93309133e+00, -1.93309133e+00],\n",
              "       [-1.67441914e+00, -1.19523159e+00, -6.53912840e-01, ...,\n",
              "        -8.35531692e-01,  1.93309133e+00, -1.93309133e+00],\n",
              "       [-1.63592675e+00, -1.16267307e+00, -6.53912840e-01, ...,\n",
              "        -8.35531692e-01,  1.93309133e+00, -1.93309133e+00],\n",
              "       ...,\n",
              "       [ 1.63592675e+00,  1.99100483e+00,  3.49060516e+00, ...,\n",
              "         1.19684269e+00, -5.17306132e-01,  5.17306132e-01],\n",
              "       [ 1.67441914e+00,  1.99100483e+00,  1.00389436e+00, ...,\n",
              "         1.19684269e+00, -5.17306132e-01,  5.17306132e-01],\n",
              "       [ 1.71291154e+00, -5.19213966e-01, -6.53912840e-01, ...,\n",
              "        -8.35531692e-01, -5.17306132e-01,  5.17306132e-01]])"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "attachments": {},
      "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"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  3\n"
      ]
    },
    {
      "attachments": {},
      "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"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<code>X_train, X_test, Y_train, Y_test</code>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "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)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "we can see we only have 18 test samples.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(18,)"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Y_test.shape"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  4\n"
      ]
    },
    {
      "attachments": {},
      "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": 15,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "parameters ={'C':[0.01, 0.1, 1],\n",
        "             'penalty': ['l2'],\n",
        "             'solver': ['lbfgs']}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "tags": [],
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "             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(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>"
            ],
            "text/plain": [
              "GridSearchCV(estimator=LogisticRegression(),\n",
              "             param_grid={'C': [0.01, 0.1, 1], 'penalty': ['l2'],\n",
              "                         'solver': ['lbfgs']})"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "parameters ={\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge\n",
        "lr=LogisticRegression()\n",
        "\n",
        "logreg_cv = GridSearchCV(lr, parameters)\n",
        "logreg_cv.fit(X, Y)"
      ]
    },
    {
      "attachments": {},
      "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": 17,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tuned hpyerparameters :(best parameters)  {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
            "accuracy : 0.8222222222222222\n"
          ]
        }
      ],
      "source": [
        "print(\"tuned hpyerparameters :(best parameters) \",logreg_cv.best_params_)\n",
        "print(\"accuracy :\",logreg_cv.best_score_)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  5\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Calculate the accuracy on the test data using the method <code>score</code>:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9444444444444444"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "logreg_cv.score(X_test, Y_test)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Lets look at the confusion matrix:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "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=logreg_cv.predict(X_test)\n",
        "plot_confusion_matrix(Y_test,yhat)"
      ]
    },
    {
      "attachments": {},
      "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"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  6\n"
      ]
    },
    {
      "attachments": {},
      "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": 20,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "parameters = {'kernel':('linear', 'rbf','poly','rbf', 'sigmoid'),\n",
        "              'C': np.logspace(-3, 3, 5),\n",
        "              'gamma':np.logspace(-3, 3, 5)}\n",
        "svm = SVC()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#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=SVC(),\n",
              "             param_grid={&#x27;C&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
              "       1.00000000e+03]),\n",
              "                         &#x27;gamma&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
              "       1.00000000e+03]),\n",
              "                         &#x27;kernel&#x27;: (&#x27;linear&#x27;, &#x27;rbf&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-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=SVC(),\n",
              "             param_grid={&#x27;C&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
              "       1.00000000e+03]),\n",
              "                         &#x27;gamma&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
              "       1.00000000e+03]),\n",
              "                         &#x27;kernel&#x27;: (&#x27;linear&#x27;, &#x27;rbf&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;)})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
            ],
            "text/plain": [
              "GridSearchCV(cv=10, estimator=SVC(),\n",
              "             param_grid={'C': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
              "       1.00000000e+03]),\n",
              "                         'gamma': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
              "       1.00000000e+03]),\n",
              "                         'kernel': ('linear', 'rbf', 'poly', 'rbf', 'sigmoid')})"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "svm_cv = GridSearchCV(svm, parameters, cv=10)\n",
        "svm_cv.fit(X, Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tuned hpyerparameters :(best parameters)  {'C': 1.0, 'gamma': 0.03162277660168379, 'kernel': 'sigmoid'}\n",
            "accuracy : 0.8222222222222223\n"
          ]
        }
      ],
      "source": [
        "print(\"tuned hpyerparameters :(best parameters) \",svm_cv.best_params_)\n",
        "print(\"accuracy :\",svm_cv.best_score_)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  7\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Calculate the accuracy on the test data using the method <code>score</code>:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9444444444444444"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "svm_cv.score(X_test, Y_test)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can plot the confusion matrix\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "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)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  8\n"
      ]
    },
    {
      "attachments": {},
      "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": 25,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "parameters = {'criterion': ['gini', 'entropy'],\n",
        "     'splitter': ['best', 'random'],\n",
        "     'max_depth': [2*n for n in range(1,10)],\n",
        "     'max_features': ['sqrt'],\n",
        "     'min_samples_leaf': [1, 2, 4],\n",
        "     'min_samples_split': [2, 5, 10]}\n",
        "\n",
        "tree = DecisionTreeClassifier()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=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;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-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=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;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-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" 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-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" 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>"
            ],
            "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': ['sqrt'],\n",
              "                         'min_samples_leaf': [1, 2, 4],\n",
              "                         'min_samples_split': [2, 5, 10],\n",
              "                         'splitter': ['best', 'random']})"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tree_cv = GridSearchCV(tree, parameters, cv = 10)\n",
        "tree_cv.fit(X, Y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tuned hpyerparameters :(best parameters)  {'criterion': 'entropy', 'max_depth': 16, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 5, 'splitter': 'random'}\n",
            "accuracy : 0.8888888888888887\n"
          ]
        }
      ],
      "source": [
        "print(\"tuned hpyerparameters :(best parameters) \",tree_cv.best_params_)\n",
        "print(\"accuracy :\",tree_cv.best_score_)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  9\n"
      ]
    },
    {
      "attachments": {},
      "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": 28,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.8333333333333334"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tree_cv.score(X_test, Y_test)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can plot the confusion matrix\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "image/png": 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WJxIhISFasWJFjvKVK1cqJCTEgogAAPAMX375pTp16qSyZcvKZrPl6OU3DEPjxo1T2bJlVbhwYbVs2VJ79+7NVxuWr9oYP368+vXrpw0bNtjnSGzevFmJiYl64403LI4OAAATWLRqIzU1VXXq1FGfPn30wAMP5Dg/ZcoUTZ06VQsXLlTVqlX1wgsvqE2bNjpw4ICKFSuWpzYsTyR69+6tyMhIzZgxQx988IEMw1BUVJS++eYbNWrUyOrwAAC4cRYNS3To0EEdOnTI9ZxhGJo+fbrGjh2r+++/X5K0aNEilS5dWu+8844ee+yxPLVheSIhSY0aNdLbb79tdRgAANwyUlJSdOLECbVt29Ze5ufnpxYtWujbb78tWIlEdna2fvzxR508eVLZf+v+ad68uUVRAQBgEpOGNtLT05Wenu5Q5ufnJz8/v3zXdeLECUlS6dKlHcpLly6tI0eO5LkeyxOJzZs3q0ePHjpy5Ij+vhLVZrMpKyvLosgAADCJSYlEQkKCxo8f71AWHx+vcePGOV3n3zeINAwjz5tGSm6QSAwaNEgxMTH65JNPFBYWlq/gAQC4lYwZM0YjRoxwKHOmN0KSypQpI+lKz0RYWJi9/OTJkzl6Ka7H8kTihx9+0PLly1WlShWrQwEAwDVM2vvR2WGM3ERERKhMmTJau3atoqOjJUmXL1/Wxo0bNXny5DzXY3ki0ahRI/34448kEgAAz2XR8s8///xTP/74o/1zSkqKdu3apeDgYIWHhysuLk4TJ07U7bffrttvv10TJ05UQECAevTokec2LE8khg4dqpEjR+rEiROqVauWfHx8HM7Xrl3bosgAACjYtm/frlatWtk/Xx0W6dWrlxYuXKinn35aFy9e1ODBg3Xu3Dk1atRIa9asyfMeEpIbvGvDyyvn5po2m80+2cOZyZa8awMAkFc35V0bb5vzEsrCj0wwpR4zWd4jkZKSYnUIAAC4lpu+J8MMlicSFSpUsDoEAABcy6I5EjeD5YnEVfv27dPRo0d1+fJlh/J7773XoogAAMA/sTyR+Omnn3Tfffdpz5499rkR0v9tkMGGVACAAs/a6YguZflrxIcPH66IiAj99ttvCggI0N69e/Xll18qJiZGGzZssDo8AABuXHa2OYcbsrxHYtOmTVq3bp1KlSolLy8veXl5qWnTpkpISNCwYcOUlJRkdYgAAOAaLO+RyMrKUtGiRSVJJUuW1K+//irpyiTMAwcOWBkaAADmoEfCdWrWrKndu3erUqVKatSokaZMmSJfX1/NnTtXlSpVsjo8AABuHMs/XeeZZ55RamqqJOmFF17QPffco2bNmikkJETvvvuuxdEBAIDrsTyRaNeunf3nSpUqad++fTp79qxKlCjBm0ABAB7ByPbcVRuWJxK5CQ4OtjoEAADM46bzG8xgSSJx//335/naDz74wIWRAACAG2FJIhEUFGRFswAAWIPJluZasGCBFc0CAGAN5kgAAACnefAcCcs3pAIAAAUXPRIAALiaB/dIkEgAAOBqvP3Tdf73v/8pPT09R/nly5f1v//9z4KIAABAXtkMw9o0ydvbW8ePH1doaKhD+ZkzZxQaGqqsrKx819m34oNmhQcntOzZVq0eaaeSt5WSJP3ywzF9NGO59mzgTa64tfHdcE/zDy93eRtpUweYUk/AiHmm1GMmy4c2DMPIdSvsn3/+mf0mCqhzx89o+eTFOnnkhCTpzgdaaujcpzWu4yj9+sPPFkcHWIfvxi2M5Z/mi46Ols1mk81mU+vWrVWo0P+FkpWVpZSUFLVv396q8HADvvtih8PnD/67RC17tlXl6Kr8zxK3NL4b8ESWJRJdunSRJO3atUvt2rVT0aJF7ed8fX1VsWJFPfDAAxZFB7PYvLzUoGNj+RX216GdB60OB3AbfDduMexsab74+HhJUsWKFdWtWzf5+/tbFQpcoFy1cI394EX5+PkqPe2SXn1sin79kX9xAXw3blEePLRh+WTLq3bs2KHk5GTZbDZFRUUpOjo6T/elp6fnWPUxtFYvedu8XREm8sjbp5BCypZUQGAR1e/QSM27tdbkbvH8DxO3PL4b7uemTLac3MeUegJGu98rJiyfbHny5El1795dGzZsUPHixWUYhs6fP69WrVpp6dKlKlWq1HXvT0hI0Pjx4x3K6gZFKrp4lCvDxj/Iysi0Tyg7vOeQImpXUWzfu/W//8y1ODLAWnw3bk2GB29IZfk+EkOHDtWFCxe0d+9enT17VufOndP333+vCxcuaNiwYf94/5gxY3T+/HmHo3ZQtZsQOfLFZlMhXx+rowDcD9+NW0O2Yc7hhizvkUhMTNTnn3+uyMhIe1lUVJRee+01tW3b9h/v9/Pzk5+fn0MZwxrWun9UD+3ZkKSzx0/Lv0hhNep0p6rfEaWpvV60OjTAUnw3bmFMtnSd7Oxs+fjkzMZ9fHyU7cFdQZ4sqGSQBkwbqqBSJXTxjzT9vP+IpvZ6Ufu+3m11aICl+G7AE1meSNx1110aPny4lixZorJly0qSfvnlFz355JNq3bq1xdHBGQtGz7Y6BMAt8d24hbnpsIQZLJ8j8eqrr+qPP/5QxYoVVblyZVWpUkURERH6448/NHPmTKvDAwDgxmVnm3O4Ict7JMqXL6+dO3dq7dq12r9/vwzDUFRUlGJjY60ODQAA/APLE4mr2rRpozZt2lgdBgAA5vPgoQ23SCS++OILffHFFzp58mSOCZbz58+3KCoAAEzCqg3XGT9+vJ5//nnFxMQoLCws1zeBAgAA92R5IjFnzhwtXLhQ//rXv6wOBQAA12Bow3UuX76sJk2aWB0GAAAuwxbZLtS/f3+98847VocBAACcYHmPxKVLlzR37lx9/vnnql27do5dLqdOnWpRZAAAmIShDdfZvXu36tatK0n6/vvvHc4x8RIA4BFIJFxn/fr1VocAAIBrefDyT8vnSAAAgILL8h4JAAA8HkMbAADAWYYHJxIMbQAAAKfRIwEAgKt5cI+EJYnEqlWr8nztvffe68JIAAC4CTx4Z0tLEokuXbo4fLbZbDIMw+HzVVlZWTcrLAAAkE+WzJHIzs62H2vWrFHdunW1evVq/f777zp//rw+/fRT1atXT4mJiVaEBwCAubINcw43ZPkcibi4OM2ZM0dNmza1l7Vr104BAQEaOHCgkpOTLYwOAAATuGkSYAbLV20cOnRIQUFBOcqDgoJ0+PDhmx8QAADIM8sTiQYNGiguLk7Hjx+3l504cUIjR45Uw4YNLYwMAABzGIZhypEfmZmZeuaZZxQREaHChQurUqVKev7555Vt8sRPy4c25s+fr/vuu08VKlRQeHi4JOno0aOqWrWqVq5caW1wAACYwYKhjcmTJ2vOnDlatGiRatSooe3bt6tPnz4KCgrS8OHDTWvH8kSiSpUq2r17t9auXav9+/fLMAxFRUUpNjaWt38CADyDBYnEpk2b1LlzZ3Xs2FGSVLFiRS1ZskTbt283tR3LEwnpynLPtm3bqm3btlaHAgCA20pPT1d6erpDmZ+fn/z8/HJc27RpU82ZM0cHDx5U1apV9d133+nrr7/W9OnTTY3JkkRixowZGjhwoPz9/TVjxozrXjts2LCbFBUAAK5h1rs2EhISNH78eIey+Ph4jRs3Lse1o0eP1vnz51W9enV5e3srKytLL774oh5++GFTYrnKZuR39oYJIiIitH37doWEhCgiIuKa19lsNv3000/5rr9vxQdvJDwAwC1k/uHlLm/jfK/WptTjP/fTPPdILF26VKNGjdJLL72kGjVqaNeuXYqLi9PUqVPVq1cvU+KRLOqRSElJyfVnAABwbddKGnIzatQo/fvf/1b37t0lSbVq1dKRI0eUkJBQ8BMJAABuKRa8aiMtLU1eXo67PHh7e3vG8s8RI0bk+dqpU6e6MBIAAFzPrDkS+dGpUye9+OKLCg8PV40aNZSUlKSpU6eqb9++prZjSSKRlJTk8HnHjh3KyspStWrVJEkHDx6Ut7e36tevb0V4AAAUeDNnztSzzz6rwYMH6+TJkypbtqwee+wxPffcc6a2Y0kisX79evvPU6dOVbFixbRo0SKVKFFCknTu3Dn16dNHzZo1syI8AADMZUGPRLFixTR9+nTTl3v+neVbZL/88stKSEiwJxGSVKJECb3wwgt6+eWXLYwMAACTZJt0uCHLE4kLFy7ot99+y1F+8uRJ/fHHHxZEBAAA8sryVRv33Xef+vTpo5dffll33HGHJGnz5s0aNWqU7r//foujAwDgxlkx2fJmsTyRmDNnjp566in17NlTGRkZkqRChQqpX79+eumllyyODgAAE7jpsIQZLE8kAgICNGvWLL300ks6dOiQDMNQlSpVVKRIEatDAwDAFPRI3ARFihRR7dq1rQ4DAADkg9skEgAAeCyGNgAAgLMMD04kLF/+CQAACi56JAAAcDUP7pEgkQAAwMUY2gAAAMgFPRIAALiaB/dIkEgAAOBinjy0QSIBAICLeXIiwRwJAADgNHokAABwMU/ukSCRAADA1Qyb1RG4DEMbAADAaTecSGRlZWnXrl06d+6cGfEAAOBxjGxzDneU70QiLi5Ob775pqQrSUSLFi1Ur149lS9fXhs2bDA7PgAACjwj22bK4Y7ynUgsX75cderUkSR99NFHSklJ0f79+xUXF6exY8eaHiAAAHBf+U4kTp8+rTJlykiSPv30Uz300EOqWrWq+vXrpz179pgeIAAABR1DG39RunRp7du3T1lZWUpMTFRsbKwkKS0tTd7e3qYHCABAQWcYNlMOd5Tv5Z99+vRR165dFRYWJpvNpjZt2kiStmzZourVq5seIAAAcF/5TiTGjRunmjVr6tixY3rooYfk5+cnSfL29ta///1v0wMEAKCgc9dhCTM4tSHVgw8+mKOsV69eNxwMAACeyF1XXJghT4nEjBkz8lzhsGHDnA4GAABPZBhWR+A6eUokpk2blqfKbDYbiQQAALeQPCUSKSkpro4DAACP5clDG05vkX358mUdOHBAmZmZZsYDAIDHYWfLv0hLS1O/fv0UEBCgGjVq6OjRo5KuzI2YNGmS6QECAAD3le9EYsyYMfruu++0YcMG+fv728tjY2P17rvvmhocAACewDDMOdxRvpd/rly5Uu+++67uuOMO2Wz/180SFRWlQ4cOmRocAACewF2HJcyQ7x6JU6dOKTQ0NEd5amqqQ2IBAAA8X74TiQYNGuiTTz6xf76aPMybN0+NGzc2LzIAADwE79r4i4SEBLVv31779u1TZmamXnnlFe3du1ebNm3Sxo0bXREjAAAFmidvkZ3vHokmTZrom2++UVpamipXrqw1a9aodOnS2rRpk+rXr++KGAEAgJty6l0btWrV0qJFi8yOBQAAj5TtpsMSZnAqkcjKytKKFSuUnJwsm82myMhIde7cWYUKOVUdAAAezV3nN5gh33/zf//99+rcubNOnDihatWqSZIOHjyoUqVKadWqVapVq5bpQQIAUJCx/PMv+vfvrxo1aujnn3/Wzp07tXPnTh07dky1a9fWwIEDXREjAABwU/nukfjuu++0fft2lShRwl5WokQJvfjii2rQoIGpwQEA4AncdVdKM+S7R6JatWr67bffcpSfPHlSVapUMSUoAAA8yS3/0q4LFy7Yj4kTJ2rYsGFavny5fv75Z/38889avny54uLiNHnyZFfHCwAA3EiehjaKFy/usP21YRjq2rWrvcz4/302nTp1UlZWlgvCBACg4Lrll3+uX7/e1XEAAOCxbvnlny1atHB1HAAAoAByegeptLQ0HT16VJcvX3Yor1279g0HBQCAJ/HkVRv5TiROnTqlPn36aPXq1bmeZ44EAACOPHmORL6Xf8bFxencuXPavHmzChcurMTERC1atEi33367Vq1a5YoYAQCAm8p3IrFu3TpNmzZNDRo0kJeXlypUqKCePXtqypQpSkhIcEWMAAAUaIZhM+XIr19++UU9e/ZUSEiIAgICVLduXe3YscPUZ8v30EZqaqpCQ0MlScHBwTp16pSqVq2qWrVqaefOnaYGBwCAJ7BijsS5c+d05513qlWrVlq9erVCQ0N16NAhFS9e3NR28p1IVKtWTQcOHFDFihVVt25dvf7666pYsaLmzJmjsLAwU4MDAMATWDFHYvLkySpfvrwWLFhgL6tYsaLp7Tg1R+L48eOSpPj4eCUmJio8PFwzZszQxIkTTQ8QAABckZ6e7rDb9IULF5Senp7rtatWrVJMTIweeughhYaGKjo6WvPmzTM9Jpth3FiHS1pamvbv36/w8HCVLFnSrLhuSCHfclaHALili79+ZXUIgNvxKVnJ5W1sK3efKfV8MqCOxo8f71AWHx+vcePG5bjW399fkjRixAg99NBD2rp1q+Li4vT666/r0UcfNSUeyYREwh2RSAC5I5EAcroZicSWsvebUk/dlCU5eiD8/Pzk5+eX41pfX1/FxMTo22+/tZcNGzZM27Zt06ZNm0yJR8rjHIkRI0bkucKpU6c6HQwAALi2ayUNuQkLC1NUVJRDWWRkpN5//31TY8pTIpGUlJSnyv76Yi8AAHCFFV3/d955pw4cOOBQdvDgQVWoUMHUdnhpFwAALmbFqo0nn3xSTZo00cSJE9W1a1dt3bpVc+fO1dy5c01tJ9+rNgAAgPtr0KCBVqxYoSVLlqhmzZqaMGGCpk+frkceecTUdpx+aRcAAMgbq14jfs899+iee+5xaRskEgAAuFi21QG4EEMbAADAafRIAADgYoY8d1WjUz0Sb731lu68806VLVtWR44ckSRNnz5dH374oanBAQDgCbINcw53lO9EYvbs2RoxYoTuvvtu/f7778rKypIkFS9eXNOnTzc7PgAACrxs2Uw53FG+E4mZM2dq3rx5Gjt2rLy9ve3lMTEx2rNnj6nBAQAA95bvORIpKSmKjo7OUe7n56fU1FRTggIAwJMwR+IvIiIitGvXrhzlq1evzrGnNwAAuLL804zDHeW7R2LUqFEaMmSILl26JMMwtHXrVi1ZskQJCQl64403XBEjAABwU/lOJPr06aPMzEw9/fTTSktLU48ePVSuXDm98sor6t69uytiBACgQPPkoQ2bYRhOLyg5ffq0srOzFRoaamZMN6yQbzmrQwDc0sVfv7I6BMDt+JSs5PI2Ekub8w/t9r8tNaUeM93QhlQlS5Y0Kw4AAFAA5TuRiIiIkM127S6an3766YYCAgDA07jrREkz5DuRiIuLc/ickZGhpKQkJSYmatSoUWbFBQCAx/DkORL5TiSGDx+ea/lrr72m7du333BAAACg4DDt7Z8dOnTQ+++/b1Z1AAB4jGybOYc7Mu3tn8uXL1dwcLBZ1QEA4DHc9T0ZZsh3IhEdHe0w2dIwDJ04cUKnTp3SrFmzTA0OAABP4KYv7jRFvhOJLl26OHz28vJSqVKl1LJlS1WvXt2suAAAQAGQr0QiMzNTFStWVLt27VSmTBlXxQQAgEfx5OWf+ZpsWahQIT3++ONKT093VTwAAHicbJvNlMMd5XvVRqNGjZSUlOSKWAAAQAGT7zkSgwcP1siRI/Xzzz+rfv36KlKkiMP52rVrmxYcAACegMmWkvr27avp06erW7dukqRhw4bZz9lsNhmGIZvNpqysLPOjBACgAPPkORJ5TiQWLVqkSZMmKSUlxZXxAACAAiTPicTVt41XqFDBZcEAAOCJ3HVXSjPka47E9d76CQAAcsfOlv9f1apV/zGZOHv27A0FBAAACo58JRLjx49XUFCQq2IBAMAjsWrj/+vevbtCQ0NdFQsAAB6JORJifgQAAM7y5OWfed7Z8uqqDQAAgKvy3CORne3J+RQAAK7jyf8Uz/cW2QAAIH88eY5Evl/aBQAAcBU9EgAAuJgnTw4gkQAAwMU8OZFgaAMAADiNHgkAAFzM8ODJliQSAAC4GEMbAAAAuaBHAgAAF/PkHgkSCQAAXIydLQEAgNPY2RIAACAX9EgAAOBizJEAAABO8+REgqENAADgNHokAABwMVZtAAAAp7FqAwAAFGgJCQmy2WyKi4sztV56JAAAcDGrJ1tu27ZNc+fOVe3atU2vmx4JAABczDDpcMaff/6pRx55RPPmzVOJEiVu5DFyRSIBAIAHGzJkiDp27KjY2FiX1M/QBgAALpZt0rqN9PR0paenO5T5+fnJz88v1+uXLl2qnTt3atu2baa0nxt6JAAAcLFsk46EhAQFBQU5HAkJCbm2eezYMQ0fPlyLFy+Wv7+/y57NZhiGxy1vLeRbzuoQALd08devrA4BcDs+JSu5vI3nKzxiSj2jD87Pc4/EypUrdd9998nb29telpWVJZvNJi8vL6WnpzuccxZDGwAAFBDXG8b4u9atW2vPnj0OZX369FH16tU1evRoU5IIiUQCAACXs2L5Z7FixVSzZk2HsiJFiigkJCRH+Y0gkQAAwMU8eWdLEgkAAG4RGzZsML1OEgkAAFzMrOWf7ohEAgAAF/PcNIJ9JAAAwA2gRwIAABez+qVdrmRZIrF79+48X+uKt5UBAHCzMEfCBerWrSubzSbDMGSzXX9dTFZW1k2KCgAA5IdlcyRSUlL0008/KSUlRe+//74iIiI0a9YsJSUlKSkpSbNmzVLlypX1/vvvWxUiAACmsPI14q5mWY9EhQoV7D8/9NBDmjFjhu6++257We3atVW+fHk9++yz6tKliwURAgBgDuZIuNiePXsUERGRozwiIkL79u2zICIAAMzjyXMk3GL5Z2RkpF544QVdunTJXpaenq4XXnhBkZGRFkYGAACuxy16JObMmaNOnTqpfPnyqlOnjiTpu+++k81m08cff2xxdAAA3BjP7Y9wk0SiYcOGSklJ0eLFi7V//34ZhqFu3bqpR48eKlKkiNXhAQBwQ5gjcRMEBARo4MCBVocBAADywS3mSEjSW2+9paZNm6ps2bI6cuSIJGnatGn68MMPLY4MAIAbY5j0nztyi0Ri9uzZGjFihDp06KBz587ZN6AqUaKEpk+fbm1wAADcoGyTDnfkFonEzJkzNW/ePI0dO1aFCv3faEtMTIz27NljYWQAAOB63GKOREpKiqKjo3OU+/n5KTU11YKIAAAwD/tIuFhERIR27dqVo3z16tWKioq6+QEBAGAitsh2sVGjRmnIkCG6dOmSDMPQ1q1btWTJEiUkJOiNN96wOjwAAHANbpFI9OnTR5mZmXr66aeVlpamHj16qFy5cnrllVfUvXt3q8ODkwY91ksjRwxSWFio9u47qJEj4/X1N1utDgu4abbv2qMF7yzXvv0/6tSZs3ol4Vm1bt5EkpSRmamZcxfpq03b9fOvx1W0SBHd0SBaTw7qo9BSIRZHDrMxtHETDBgwQEeOHNHJkyd14sQJHTt2TP369bM6LDjpoYfu1dSXxylh0gzFNGynr7/eqo8/Wqzy5ctaHRpw01y8eEnVqlTSf0YMznHu0qV07TtwSI/1fljL5r+q6ROf0ZGjP+uJ0eMtiBSu5smrNmyGYXhcmlTIt5zVIdzyvv36I+1M+l5PDB1jL9uze4NWrUrU2GcmWRjZre3ir19ZHcItq+adHRx6JHKzJ/mAHu4fp7XvL1JYmdCbGN2tzadkJZe30b/ig6bU88bh5abUYybLhjaio6Nls9nydO3OnTtdHA3M5OPjo3r1amvyS685lK9du1GN74ixKCrA/f35Z5psNpuKFePVACg4LEskunTpYv/50qVLmjVrlqKiotS4cWNJ0ubNm7V3714NHpyzS/Cv0tPTlZ6e7lBmGEaekxSYr2TJYBUqVEgnfzvtUH7y5GmV5l9ZQK7S0y9r2uwFurtNSxXlHUMex12HJcxgWSIRHx9v/7l///4aNmyYJkyYkOOaY8eOXbeehIQEjR/vOKZo8yoqm3egecHCKX8fNbPZbDnKAFyZeDkqfpIMI1vPPjXE6nDgAu66vbUZ3GKy5XvvvadHH300R3nPnj31/vvvX/feMWPG6Pz58w6HzauYq0JFHpw+fVaZmZkqXaaUQ3mpUiE6+dspi6IC3FNGZqZGPjtRPx8/oXnTJ9IbgQLHLRKJwoUL6+uvv85R/vXXX8vf3/+69/r5+SkwMNDhYFjDWhkZGdq5c7diWzd3KI+Nba5Nm7dbFBXgfq4mEUeP/ao3pk9U8SB6Uj2VJ6/acIt9JOLi4vT4449rx44duuOOOyRdmSMxf/58PffccxZHB2dMe2WeFi14RTt2fKfNW3ZoQL+eCi9fTq/Pfcvq0ICbJi3too7+/Kv98y+//qb9Bw8pKLCYSpUM0YixL2rfwR/12pTxys7O1ukzZyVJQYHF5OPjY1XYcIFsDx7WdZvln8uWLdMrr7yi5ORkSVJkZKSGDx+url275rsuln+6h0GP9dJTIx9XWFiovt97QE89NU5ffb3F6rBuaSz/vLm27tytvkNH5yjv3CFWg/v1VLsHe+d63/yZk9WwXm0XR4erbsbyz39VuN+Uet468oEp9ZjJbRIJM5FIALkjkQByuhmJRE+TEonFbphIuMXQxlWXL1/WyZMnlZ3tOBIUHh5uUUQAANw4T94i2y0SiR9++EF9+/bVt99+61B+dT+IrKwsiyIDAADX4xaJRO/evVWoUCF9/PHHCgsLY9UFAMCjePI+Em6RSOzatUs7duxQ9erVrQ4FAADTuevSTTO4RSIRFRWl06dP//OFAAAUQJ48R8ItNqSaPHmynn76aW3YsEFnzpzRhQsXHA4AAOCe3KJHIjY2VpLUunVrh3ImWwIAPAFzJFxs/fr1VocAAIDLMEfCxVq0aGF1CAAAwAlukUhclZaWpqNHj+ry5csO5bVrs1UsAKDg8sBNpO3cIpE4deqU+vTpo9WrV+d6njkSAICCjFUbLhYXF6dz585p8+bNKly4sBITE7Vo0SLdfvvtWrVqldXhAQCAa3CLHol169bpww8/VIMGDeTl5aUKFSqoTZs2CgwMVEJCgjp27Gh1iAAAOM2TJ1u6RY9EamqqQkNDJUnBwcE6deqUJKlWrVrauXOnlaEBAHDDDJP+c0dukUhUq1ZNBw4ckCTVrVtXr7/+un755RfNmTNHYWFhFkcHAACuxS2GNuLi4nT8+HFJUnx8vNq1a6fFixfL19dXixYtsjg6AABujCdPtnSLROKRRx6x/xwdHa3Dhw9r//79Cg8PV8mSJS2MDACAG8fyTxcYMWJEnq+dOnWqCyMBAMC1PHmypWWJRFJSUp6us9lsLo4EAAA4y7JEgvdrAABuFe664sIMbjFHAgAAT+bJky3dYvknAAAwV0JCgho0aKBixYopNDRUXbp0sW+1YCYSCQAAXMwwDFOO/Ni4caOGDBmizZs3a+3atcrMzFTbtm2Vmppq6rMxtAEAgItZMbSRmJjo8HnBggUKDQ3Vjh071Lx5c9PaIZEAAKCASE9PV3p6ukOZn5+f/Pz8/vHe8+fPS7ryKgozMbQBAICLmfWujYSEBAUFBTkcCQkJ/9y+YWjEiBFq2rSpatasaeqz0SMBAICLZZu0s+WYMWNybOiYl96IJ554Qrt379bXX39tShx/RSIBAEABkddhjL8aOnSoVq1apS+//FK33Xab6TGRSAAA4GJW7CJhGIaGDh2qFStWaMOGDYqIiHBJOyQSAAC4mBWrNoYMGaJ33nlHH374oYoVK6YTJ05IkoKCglS4cGHT2rEZHvhKskK+5awOAXBLF3/9yuoQALfjU7KSy9toXK6VKfVs+iXvr5e41ruqFixYoN69e5sSj0SPBAAAHulm9ROQSAAA4GIe2PlvRyIBAICL8dIuAACAXNAjAQCAixke3CNBIgEAgIt58hwJhjYAAIDT6JEAAMDFPHmyJYkEAAAuxtAGAABALuiRAADAxRjaAAAATmP5JwAAcFo2cyQAAAByokcCAAAXY2gDAAA4jaENAACAXNAjAQCAizG0AQAAnMbQBgAAQC7okQAAwMUY2gAAAE5jaAMAACAX9EgAAOBiDG0AAACnGUa21SG4DIkEAAAu5smvEWeOBAAAcBo9EgAAuJjhwas2SCQAAHAxhjYAAAByQY8EAAAuxtAGAABwGjtbAgAA5IIeCQAAXIydLQEAgNM8eY4EQxsAAMBp9EgAAOBinryPBIkEAAAu5slDGyQSAAC4GMs/AQAAckGPBAAALsbQBgAAcJonT7ZkaAMAADiNHgkAAFyMoQ0AAOA0Vm0AAADkgh4JAABcjJd2AQAApzG0AQAAkAt6JAAAcDFWbQAAAKd58hwJhjYAAHAxwzBMOZwxa9YsRUREyN/fX/Xr19dXX31l6rORSAAA4KHeffddxcXFaezYsUpKSlKzZs3UoUMHHT161LQ2bIYHDtwU8i1ndQiAW7r4q7n/EgE8gU/JSq5vw6S/lzIu/5Kv6xs1aqR69epp9uzZ9rLIyEh16dJFCQkJpsREjwQAAC5mmHTkx+XLl7Vjxw61bdvWobxt27b69ttvnX6Wv2OyJQAABUR6errS09Mdyvz8/OTn55fj2tOnTysrK0ulS5d2KC9durROnDhhWkwemUhk5rPrB66Rnp6uhIQEjRkzJtc/5MCtiu/Grcesv5fGjRun8ePHO5TFx8dr3Lhx17zHZrM5fDYMI0fZjfDIORJwDxcuXFBQUJDOnz+vwMBAq8MB3AbfDTgrPz0Sly9fVkBAgN577z3dd9999vLhw4dr165d2rhxoykxMUcCAIACws/PT4GBgQ7HtXq1fH19Vb9+fa1du9ahfO3atWrSpIlpMXnk0AYAAJBGjBihf/3rX4qJiVHjxo01d+5cHT16VIMGDTKtDRIJAAA8VLdu3XTmzBk9//zzOn78uGrWrKlPP/1UFSpUMK0NEgm4jJ+fn+Lj45lMBvwN3w3cTIMHD9bgwYNdVj+TLQEAgNOYbAkAAJxGIgEAAJxGIgEAAJxGIuGBWrZsqbi4OPvnihUravr06de9x2azaeXKlS6NK6969+6tLl26uLydhQsXqnjx4i5vB57t7983Vxg3bpzq1q3r0jYAZ7Fq4xawbds2FSlSxOowNG7cOK1cuVK7du2yOhQAgElIJG4BpUqVsjoEAICHYmijgEtNTdWjjz6qokWLKiwsTC+//HKOa/4+tPHDDz+oefPm8vf3V1RUVI7tU3PTsmVLDRs2TE8//bSCg4NVpkyZHC+JOXr0qDp37qyiRYsqMDBQXbt21W+//SbpyjDC+PHj9d1338lms8lms2nhwoV5esbExEQ1bdpUxYsXV0hIiO655x4dOnTIfv7w4cOy2Wz64IMP1KpVKwUEBKhOnTratGmTQz0LFy5UeHi4AgICdN999+nMmTN5ah/Iq8WLFysmJkbFihVTmTJl1KNHD508edJ+fsOGDbLZbPriiy8UExOjgIAANWnSRAcOHHCoZ9KkSSpdurSKFSumfv366dKlSzf7UYA8I5Eo4EaNGqX169drxYoVWrNmjTZs2KAdO3Zc8/rs7Gzdf//98vb21ubNmzVnzhyNHj06T20tWrRIRYoU0ZYtWzRlyhQ9//zz9iTEMAx16dJFZ8+e1caNG7V27VodOnRI3bp1k3Rld7WRI0eqRo0aOn78uI4fP24/909SU1M1YsQIbdu2TV988YW8vLx03333KTs72+G6sWPH6qmnntKuXbtUtWpVPfzww8rMzJQkbdmyRX379tXgwYO1a9cutWrVSi+88EKe2gfy6vLly5owYYK+++47rVy5UikpKerdu3eO68aOHauXX35Z27dvV6FChdS3b1/7uWXLlik+Pl4vvviitm/frrCwMM2aNesmPgWQTwYKrD/++MPw9fU1li5dai87c+aMUbhwYWP48OH2sgoVKhjTpk0zDMMwPvvsM8Pb29s4duyY/fzq1asNScaKFSuu2VaLFi2Mpk2bOpQ1aNDAGD16tGEYhrFmzRrD29vbOHr0qP383r17DUnG1q1bDcMwjPj4eKNOnTr/+Fy9evUyOnfufM3zJ0+eNCQZe/bsMQzDMFJSUgxJxhtvvJGj7eTkZMMwDOPhhx822rdv71BPt27djKCgoH+MB7ieFi1aOHzf/mrr1q2GJOOPP/4wDMMw1q9fb0gyPv/8c/s1n3zyiSHJuHjxomEYhtG4cWNj0KBBDvU0atQoT98dwAr0SBRghw4d0uXLl9W4cWN7WXBwsKpVq3bNe5KTkxUeHq7bbrvNXvbX+6+ndu3aDp/DwsLs3bbJyckqX768ypcvbz8fFRWl4sWLKzk5OU/1X8uhQ4fUo0cPVapUSYGBgYqIiJB0ZSjlWvGFhYVJkkN8f3/OvD43kFdJSUnq3LmzKlSooGLFiqlly5aS+LMKz0YiUYAZTuxunts9NpstT/f6+PjkuO/q8IJhGLnWc63y/OjUqZPOnDmjefPmacuWLdqyZYukK93I14rvapt/jQ9wpdTUVLVt21ZFixbV4sWLtW3bNq1YsUJS/v6sAgUNiUQBVqVKFfn4+Gjz5s32snPnzungwYPXvCcqKkpHjx7Vr7/+ai/7+6REZ1yt99ixY/ayffv26fz584qMjJQk+fr6KisrK1/1njlzRsnJyXrmmWfUunVrRUZG6ty5c07F99ffk6Qcn4EbsX//fp0+fVqTJk1Ss2bNVL16dYeJlnkVGRnJn1UUKCz/LMCKFi2qfv36adSoUQoJCVHp0qU1duxYeXldOz+MjY1VtWrV9Oijj+rll1/WhQsXNHbs2BuOJTY2VrVr19Yjjzyi6dOnKzMzU4MHD1aLFi0UExMj6crqkZSUFO3atUu33XabihUr9o9vPyxRooRCQkI0d+5chYWF6ejRo/r3v/+d7/iGDRumJk2aaMqUKerSpYvWrFmjxMREp54VyE14eLh8fX01c+ZMDRo0SN9//70mTJiQ73qGDx+uXr16KSYmRk2bNtXbb7+tvXv3qlKlSi6IGrhx9EgUcC+99JKaN2+ue++9V7GxsWratKnq169/zeu9vLy0YsUKpaenq2HDhurfv79efPHFG47j6s6YJUqUUPPmzRUbG6tKlSrp3XfftV/zwAMPqH379mrVqpVKlSqlJUuW/GO9Xl5eWrp0qXbs2KGaNWvqySef1EsvvZTv+O644w698cYbmjlzpurWras1a9bomWeeyXc9wLWUKlVKCxcu1HvvvaeoqChNmjRJ//3vf/NdT7du3fTcc89p9OjRql+/vo4cOaLHH3/cBRED5uA14gAAwGn0SAAAAKeRSAAAAKeRSAAAAKeRSAAAAKeRSAAAAKeRSAAAAKeRSAAAAKeRSAAWGjdunOrWrWv/3Lt3b3Xp0uWmx3H48GHZbDbt2rXrmtdUrFhR06dPz3OdCxcuVPHixW84tqubnQFwTyQSwN/07t1bNptNNptNPj4+qlSpkp566imlpqa6vO1XXnlFCxcuzNO1efnLHwBcjXdtALlo3769FixYoIyMDH311Vfq37+/UlNTNXv27BzXZmRk5HgzqrOCgoJMqQcAbhZ6JIBc+Pn5qUyZMipfvrx69OihRx55xN69fnU4Yv78+apUqZL8/PxkGIbOnz+vgQMHKjQ0VIGBgbrrrrv03XffOdQ7adIklS5dWsWKFVO/fv106dIlh/N/H9rIzs7W5MmTVaVKFfn5+Sk8PNz+bpSIiAhJUnR0tGw2m1q2bGm/b8GCBYqMjJS/v7+qV6+uWbNmObSzdetWRUdHy9/fXzExMUpKSsr372jq1KmqVauWihQpovLly2vw4MH6888/c1y3cuVKVa1aVf7+/mrTpo3DG2Il6aOPPlL9+vXl7++vSpUqafz48crMzMy1zcuXL+uJJ55QWFiY/P39VbFiRSUkJOQ7dgDmoUcCyIPChQsrIyPD/vnHH3/UsmXL9P7778vb21uS1LFjRwUHB+vTTz9VUFCQXn/9dbVu3VoHDx5UcHCwli1bpvj4eL322mtq1qyZ3nrrLc2YMeO6b3UcM2aM5s2bp2nTpqlp06Y6fvy49u/fL+lKMtCwYUN9/vnnqlGjhnx9fSVJ8+bNU3x8vF599VVFR0crKSlJAwYMUJEiRdSrVy+lpqbqnnvu0V133aXFixcrJSVFw4cPz/fvxMvLSzNmzLC/1XXw4MF6+umnHZKWtLQ0vfjii1q0aJF8fX01ePBgde/eXd98840k6bPPPlPPnj01Y8YMNWvWTIcOHdLAgQMlSfHx8TnanDFjhlatWqVly5YpPDxcx44dy5GYALjJDAAOevXqZXTu3Nn+ecuWLUZISIjRtWtXwzAMIz4+3vDx8TFOnjxpv+aLL74wAgMDjUuXLjnUVblyZeP11183DMMwGjdubAwaNMjhfKNGjYw6derk2vaFCxcMPz8/Y968ebnGmZKSYkgykpKSHMrLly9vvPPOOw5lEyZMMBo3bmwYhmG8/vrrRnBwsJGammo/P3v27Fzr+qsKFSoY06ZNu+b5ZcuWGSEhIfbPCxYsMCQZmzdvtpclJycbkowtW7YYhmEYzZo1MyZOnOhQz1tvvWWEhYXZP0syVqxYYRiGYQwdOtS46667jOzs7GvGAeDmokcCyMXHH3+sokWLKjMzUxkZGercubNmzpxpP1+hQgWVKlXK/nnHjh36888/FRIS4lDPxYsXdejQIUlScnKyBg0a5HC+cePGWr9+fa4xJCcnKz09Xa1bt85z3KdOndKxY8fUr18/DRgwwF6emZlpn3+RnJysOnXqKCAgwCGO/Fq/fr0mTpyoffv26cKFC8rMzNSlS5eUmpqqIkWKSJIKFSqkmJgY+z3Vq1dX8eLFlZycrIYNG2rHjh3atm2bw6vss7KydOnSJaWlpTnEKF0Z+mnTpo2qVaum9u3b65577lHbtm3zHTsA85BIALlo1aqVZs+eLR8fH5UtWzbHZMqrf1FelZ2drbCwMG3YsCFHXc4ugSxcuHC+78nOzpZ0ZXijUaNGDueuDsEYhuFUPH915MgR3X333Ro0aJAmTJig4OBgff311+rXr5/DEJB0Zfnm310ty87O1vjx43X//ffnuMbf3z9HWb169ZSSkqLVq1fr888/V9euXRUbG6vly5ff8DMBcA6JBJCLIkWKqEqVKnm+vl69ejpx4oQKFSqkihUr5npNZGSkNm/erEcffdRetnnz5mvWefvtt6tw4cL64osv1L9//xznr86JyMrKspeVLl1a5cqV008//aRHHnkk13qjoqL01ltv6eLFi/Zk5Xpx5Gb79u3KzMzUyy+/LC+vK3O2ly1bluO6zMxMbd++XQ0bNpQkHThwQL///ruqV68u6crv7cCBA/n6XQcGBqpbt27q1q2bHnzwQbVv315nz55VcHBwvp4BgDlIJAATxMbGqnHjxurSpYsmT56satWq6ddff9Wnn36qLl26KCYmRsOHD1evXr0UExOjpk2b6u2339bevXuvOdnS399fo0eP1tNPPy1fX1/deeedOnXqlPbu3at+/fopNDRUhQsXVmJiom677Tb5+/srKChI48aN07BhwxQYGKgOHTooPT1d27dv17lz5zRixAj16NFDY8eOVb9+/fTMM8/o8OHD+u9//5uv561cubIyMzM1c+ZMderUSd98843mzJmT4zofHx8NHTpUM2bMkI+Pj5544gndcccd9sTiueee0z333KPy5cvroYcekpeXl3bv3q09e/bohRdeyFHftGnTFBYWprp168rLy0vvvfeeypQpY8rGVwCcw/JPwAQ2m02ffvqpmjdvrr59+6pq1arq3r27Dh8+rNKlS0uSunXrpueee06jR49W/fr1deTIET3++OPXrffZZ5/VyJEj9dxzzykyMlLdunXTyZMnJV2ZfzBjxgy9/vrrKlu2rDp37ixJ6t+/v9544w0tXLhQtWrVUosWLbRw4UL7ctGiRYvqo48+0r59+xQdHa2xY8dq8uTJ+XreunXraurUqZo8ebJq1qypt99+O9dlmAEBARo9erR69Oihxo0bq3Dhwlq6dKn9fLt27fTxxx9r7dq1atCgge644w5NnTpVFSpUyLXdokWLavLkyYqJiVGDBg10+PBhffrpp/ZeEQA3n80wY8AUAADckkjjAQCA00gkAACA00gkAACA00gkAACA00gkAACA00gkAACA00gkAACA00gkAACA00gkAACA00gkAACA00gkAACA00gkAACA0/4fSJA9PyZ/6KAAAAAASUVORK5CYII=",
            "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)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  10\n"
      ]
    },
    {
      "attachments": {},
      "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": 30,
      "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()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "source": [
        "knn_cv = GridSearchCV(KNN, parameters, cv = 5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "             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-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, 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-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" 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-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" 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>"
            ],
            "text/plain": [
              "GridSearchCV(cv=5, 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]})"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "knn_cv.fit(X_test, Y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 1, 'p': 1}\n",
            "accuracy : 0.95\n"
          ]
        }
      ],
      "source": [
        "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\n",
        "print(\"accuracy :\",knn_cv.best_score_)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  11\n"
      ]
    },
    {
      "attachments": {},
      "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": 34,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "knn_cv.score(X_test, Y_test)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can plot the confusion matrix\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "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)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## TASK  12\n"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Find the method performs best:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Logistic regression perfomance is: 0.9444444444444444\n",
            "SVM perfomance is: 0.9444444444444444\n",
            "Decision tree classifier perfomance is: 0.8333333333333334\n",
            "KNN perfomance is: 1.0\n"
          ]
        }
      ],
      "source": [
        "print(\"Logistic regression perfomance is:\",logreg_cv.score(X_test, Y_test))\n",
        "print(\"SVM perfomance is:\",svm_cv.score(X_test, Y_test))\n",
        "print(\"Decision tree classifier perfomance is:\",tree_cv.score(X_test, Y_test))\n",
        "print(\"KNN perfomance is:\",knn_cv.score(X_test, Y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "classif_model = [\"Logistic regression\", \"SVM perfomance\", \"Decision tree\", \"KNN perfomance\"]\n",
        "model_score = [logreg_cv.score(X_test, Y_test), svm_cv.score(X_test, Y_test), tree_cv.score(X_test, Y_test), knn_cv.score(X_test, Y_test)]\n",
        "\n",
        "plt.barh(y = classif_model, width = model_score, data = model_score)\n",
        "plt.xlabel(\"Accuracy\",fontsize=20)\n",
        "plt.ylabel(\"Classification model\",fontsize=20)\n",
        "plt.show()"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "KNN indicates an overfitting behaviour, so in this case, we can consider that the methods that perform best are Logistic Regression and SVM with an accuracy metric of 94% of the model against the test data."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Authors\n"
      ]
    },
    {
      "attachments": {},
      "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"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Change Log\n"
      ]
    },
    {
      "attachments": {},
      "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"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### <h3 align=\"center\"> IBM Corporation 2022. All rights reserved. <h3/>\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "base",
      "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": "3.10.9"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}