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": null,
      "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": {},
      "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": {},
      "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": {},
      "outputs": [],
      "source": [
        "# from js import fetch\n",
        "import io\n",
        "import requests\n",
        "\n",
        "\n",
        "URL1 = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\"\n",
        "\n",
        "# Send an HTTP GET request to the specified URL\n",
        "response = requests.get(URL1)\n",
        "\n",
        "# Check if the request was successful\n",
        "response.raise_for_status()\n",
        "\n",
        "# Read the content of the response in bytes\n",
        "text1 = io.BytesIO(response.content)\n",
        "\n",
        "# Read the content into a pandas DataFrame\n",
        "data = pd.read_csv(text1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "URL2 = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_3.csv'\n",
        "resp2 =requests.get(URL2)\n",
        "# Check if the request was successful\n",
        "resp2.raise_for_status()\n",
        "\n",
        "# Read the content of the response in bytes\n",
        "text2 = io.BytesIO(response.content)\n",
        "\n",
        "X = pd.read_csv(text2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "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",
              "    <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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>86</td>\n",
              "      <td>2020-09-03</td>\n",
              "      <td>Falcon 9</td>\n",
              "      <td>15400.000000</td>\n",
              "      <td>VLEO</td>\n",
              "      <td>KSC LC 39A</td>\n",
              "      <td>True ASDS</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>5e9e3032383ecb6bb234e7ca</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>B1060</td>\n",
              "      <td>-80.603956</td>\n",
              "      <td>28.608058</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>86</th>\n",
              "      <td>87</td>\n",
              "      <td>2020-10-06</td>\n",
              "      <td>Falcon 9</td>\n",
              "      <td>15400.000000</td>\n",
              "      <td>VLEO</td>\n",
              "      <td>KSC LC 39A</td>\n",
              "      <td>True ASDS</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>5e9e3032383ecb6bb234e7ca</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>B1058</td>\n",
              "      <td>-80.603956</td>\n",
              "      <td>28.608058</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>88</td>\n",
              "      <td>2020-10-18</td>\n",
              "      <td>Falcon 9</td>\n",
              "      <td>15400.000000</td>\n",
              "      <td>VLEO</td>\n",
              "      <td>KSC LC 39A</td>\n",
              "      <td>True ASDS</td>\n",
              "      <td>6</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>5e9e3032383ecb6bb234e7ca</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5</td>\n",
              "      <td>B1051</td>\n",
              "      <td>-80.603956</td>\n",
              "      <td>28.608058</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>89</td>\n",
              "      <td>2020-10-24</td>\n",
              "      <td>Falcon 9</td>\n",
              "      <td>15400.000000</td>\n",
              "      <td>VLEO</td>\n",
              "      <td>CCAFS SLC 40</td>\n",
              "      <td>True ASDS</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>5e9e3033383ecbb9e534e7cc</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2</td>\n",
              "      <td>B1060</td>\n",
              "      <td>-80.577366</td>\n",
              "      <td>28.561857</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>90</td>\n",
              "      <td>2020-11-05</td>\n",
              "      <td>Falcon 9</td>\n",
              "      <td>3681.000000</td>\n",
              "      <td>MEO</td>\n",
              "      <td>CCAFS SLC 40</td>\n",
              "      <td>True ASDS</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>5e9e3032383ecb6bb234e7ca</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0</td>\n",
              "      <td>B1062</td>\n",
              "      <td>-80.577366</td>\n",
              "      <td>28.561857</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>90 rows × 18 columns</p>\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",
              "85            86  2020-09-03       Falcon 9  15400.000000  VLEO    KSC LC 39A   \n",
              "86            87  2020-10-06       Falcon 9  15400.000000  VLEO    KSC LC 39A   \n",
              "87            88  2020-10-18       Falcon 9  15400.000000  VLEO    KSC LC 39A   \n",
              "88            89  2020-10-24       Falcon 9  15400.000000  VLEO  CCAFS SLC 40   \n",
              "89            90  2020-11-05       Falcon 9   3681.000000   MEO  CCAFS SLC 40   \n",
              "\n",
              "        Outcome  Flights  GridFins  Reused   Legs                LandingPad  \\\n",
              "0     None None        1     False   False  False                       NaN   \n",
              "1     None None        1     False   False  False                       NaN   \n",
              "2     None None        1     False   False  False                       NaN   \n",
              "3   False Ocean        1     False   False  False                       NaN   \n",
              "4     None None        1     False   False  False                       NaN   \n",
              "..          ...      ...       ...     ...    ...                       ...   \n",
              "85    True ASDS        2      True    True   True  5e9e3032383ecb6bb234e7ca   \n",
              "86    True ASDS        3      True    True   True  5e9e3032383ecb6bb234e7ca   \n",
              "87    True ASDS        6      True    True   True  5e9e3032383ecb6bb234e7ca   \n",
              "88    True ASDS        3      True    True   True  5e9e3033383ecbb9e534e7cc   \n",
              "89    True ASDS        1      True   False   True  5e9e3032383ecb6bb234e7ca   \n",
              "\n",
              "    Block  ReusedCount Serial   Longitude   Latitude  Class  \n",
              "0     1.0            0  B0003  -80.577366  28.561857      0  \n",
              "1     1.0            0  B0005  -80.577366  28.561857      0  \n",
              "2     1.0            0  B0007  -80.577366  28.561857      0  \n",
              "3     1.0            0  B1003 -120.610829  34.632093      0  \n",
              "4     1.0            0  B1004  -80.577366  28.561857      0  \n",
              "..    ...          ...    ...         ...        ...    ...  \n",
              "85    5.0            2  B1060  -80.603956  28.608058      1  \n",
              "86    5.0            2  B1058  -80.603956  28.608058      1  \n",
              "87    5.0            5  B1051  -80.603956  28.608058      1  \n",
              "88    5.0            2  B1060  -80.577366  28.561857      1  \n",
              "89    5.0            0  B1062  -80.577366  28.561857      1  \n",
              "\n",
              "[90 rows x 18 columns]"
            ]
          },
          "execution_count": 6,
          "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": 8,
      "metadata": {},
      "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": 30,
      "metadata": {},
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "# students get this \n",
        "transform = preprocessing.StandardScaler()\n",
        "\n",
        "# Assuming `data` is your DataFrame\n",
        "X = data.drop(['Class', 'Date', 'BoosterVersion','Orbit','LaunchSite','Outcome','GridFins','Reused','Legs','LandingPad','Serial'], axis=1)\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": 31,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "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": 32,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(18,)"
            ]
          },
          "execution_count": 32,
          "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": 33,
      "metadata": {},
      "outputs": [],
      "source": [
        "parameters ={'C':[0.01,0.1,1],\n",
        "             'penalty':['l2'],\n",
        "             'solver':['lbfgs']}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tuned hyperparameters :(best parameters)  {'C': 1, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
            "accuracy : 0.7446428571428572\n"
          ]
        }
      ],
      "source": [
        "# Parameters to search over\n",
        "parameters = {'C': [0.01, 0.1, 1], 'penalty': ['l2'], 'solver': ['lbfgs']}\n",
        "\n",
        "# Create a logistic regression object\n",
        "lr = LogisticRegression()\n",
        "\n",
        "# Create GridSearchCV object\n",
        "logreg_cv = GridSearchCV(lr, parameters, cv=10)\n",
        "\n",
        "# Assuming X_train and Y_train are your training data\n",
        "logreg_cv.fit(X_train, Y_train)\n",
        "\n",
        "# Output the results\n",
        "print(\"tuned hyperparameters :(best parameters) \", logreg_cv.best_params_)\n",
        "print(\"accuracy :\", logreg_cv.best_score_)\n"
      ]
    },
    {
      "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": 35,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tuned hpyerparameters :(best parameters)  {'C': 1, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
            "accuracy : 0.7446428571428572\n"
          ]
        }
      ],
      "source": [
        "print(\"tuned hpyerparameters :(best parameters) \",logreg_cv.best_params_)\n",
        "print(\"accuracy :\",logreg_cv.best_score_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tuned hpyerparameters :(best parameters)  {'C': 1, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
            "accuracy : 0.7446428571428572\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": 38,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy on test data: 0.8333333333333334\n"
          ]
        }
      ],
      "source": [
        "\n",
        "accuracy = logreg_cv.score(X_test, Y_test)\n",
        "print(\"Accuracy on test data:\", accuracy)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Lets look at the confusion matrix:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {},
      "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)"
      ]
    },
    {
      "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": 40,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=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-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" 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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" 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": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "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()\n",
        "\n",
        "\n",
        "\n",
        "# Create an SVM object\n",
        "svm = SVC()\n",
        "\n",
        "# Create GridSearchCV object with cv=10\n",
        "svm_cv = GridSearchCV(svm, parameters, cv=10)\n",
        "\n",
        "# Assuming X_train and Y_train are your training data\n",
        "svm_cv.fit(X_train, Y_train)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tuned hpyerparameters :(best parameters)  {'C': 1000.0, 'gamma': 0.03162277660168379, 'kernel': 'sigmoid'}\n",
            "accuracy : 0.8321428571428571\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": 42,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy on test data: 0.7777777777777778\n"
          ]
        }
      ],
      "source": [
        "# Calculate accuracy on the test data\n",
        "accuracy = svm_cv.score(X_test, Y_test)\n",
        "print(\"Accuracy on test data:\", accuracy)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can plot the confusion matrix\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhIAAAHFCAYAAACn7hC1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA/nUlEQVR4nO3deXwO5/7/8fedyCJBSAihQlAk1lRQaq29KN1QnFqC09KSQ9VxtA3VCnqK0qK0lkNLHS3VRWrvaie1hbYaS0+ptWgQkszvj/7c395NaHKbMbe7r6fHPB65r5m55jN5uOvTz3VdMw7DMAwBAAC4wcfuAAAAwO2LRAIAALiNRAIAALiNRAIAALiNRAIAALiNRAIAALiNRAIAALiNRAIAALiNRAIAALiNRAJebdeuXerTp4+ioqIUGBioQoUK6a677tLEiRN15swZS6+9c+dONW3aVCEhIXI4HJoyZYrp13A4HBo9erTp/f6ZefPmyeFwyOFwaMOGDTn2G4ahSpUqyeFwqFmzZm5dY/r06Zo3b16+ztmwYcN1YwJgjQJ2BwBYZfbs2Ro4cKCqVKmi4cOHKyYmRlevXtW2bds0c+ZMbdy4UcuWLbPs+n379lV6eroWL16sYsWKqXz58qZfY+PGjbrjjjtM7zevChcurLfeeitHsvDZZ5/p4MGDKly4sNt9T58+XcWLF1fv3r3zfM5dd92ljRs3KiYmxu3rAsgfEgl4pY0bN+qJJ55Qq1attHz5cgUEBDj3tWrVSsOGDVNycrKlMezZs0f9+/dXu3btLLvG3XffbVnfedG1a1e9/fbbev3111WkSBFn+1tvvaUGDRro/PnztySOq1evyuFwqEiRIrb/ToC/GoY24JXGjRsnh8OhWbNmuSQR1/j7++v+++93fs7OztbEiRNVtWpVBQQEKDw8XI899ph+/PFHl/OaNWum6tWra+vWrWrcuLGCgoJUoUIFjR8/XtnZ2ZL+r+yfmZmpGTNmOIcAJGn06NHOn3/v2jmHDh1ytq1bt07NmjVTWFiYChYsqMjISD300EO6ePGi85jchjb27NmjTp06qVixYgoMDFTt2rU1f/58l2OuDQEsWrRIo0aNUunSpVWkSBG1bNlSBw4cyNsvWdKjjz4qSVq0aJGz7dy5c3rvvffUt2/fXM8ZM2aM6tevr9DQUBUpUkR33XWX3nrrLf3+/YHly5fX3r179dlnnzl/f9cqOtdiX7BggYYNG6YyZcooICBA33//fY6hjVOnTqls2bJq2LChrl696ux/3759Cg4O1t/+9rc83yuA3JFIwOtkZWVp3bp1qlOnjsqWLZunc5544gmNGDFCrVq10ooVKzR27FglJyerYcOGOnXqlMuxx48fV48ePdSzZ0+tWLFC7dq108iRI7Vw4UJJUvv27bVx40ZJ0sMPP6yNGzc6P+fVoUOH1L59e/n7+2vOnDlKTk7W+PHjFRwcrCtXrlz3vAMHDqhhw4bau3evpk6dqvfff18xMTHq3bu3Jk6cmOP4f/3rXzp8+LDefPNNzZo1S9999506duyorKysPMVZpEgRPfzww5ozZ46zbdGiRfLx8VHXrl2ve29///vftWTJEr3//vt68MEH9dRTT2ns2LHOY5YtW6YKFSooNjbW+fv74zDUyJEjdeTIEc2cOVMffvihwsPDc1yrePHiWrx4sbZu3aoRI0ZIki5evKhHHnlEkZGRmjlzZp7uE8ANGICXOX78uCHJ6NatW56OT01NNSQZAwcOdGnfvHmzIcn417/+5Wxr2rSpIcnYvHmzy7ExMTFGmzZtXNokGYMGDXJpS0xMNHL72s2dO9eQZKSlpRmGYRhLly41JBkpKSk3jF2SkZiY6PzcrVs3IyAgwDhy5IjLce3atTOCgoKMX375xTAMw1i/fr0hybjvvvtcjluyZIkhydi4ceMNr3st3q1btzr72rNnj2EYhlG3bl2jd+/ehmEYRrVq1YymTZtet5+srCzj6tWrxgsvvGCEhYUZ2dnZzn3XO/fa9Zo0aXLdfevXr3dpnzBhgiHJWLZsmdGrVy+jYMGCxq5du254jwDyhooE/vLWr18vSTkm9dWrV0/R0dFau3atS3upUqVUr149l7aaNWvq8OHDpsVUu3Zt+fv7a8CAAZo/f75++OGHPJ23bt06tWjRIkclpnfv3rp48WKOysjvh3ek3+5DUr7upWnTpqpYsaLmzJmj3bt3a+vWrdcd1rgWY8uWLRUSEiJfX1/5+fnp+eef1+nTp3XixIk8X/ehhx7K87HDhw9X+/bt9eijj2r+/PmaNm2aatSokefzAVwfiQS8TvHixRUUFKS0tLQ8HX/69GlJUkRERI59pUuXdu6/JiwsLMdxAQEBunTpkhvR5q5ixYpas2aNwsPDNWjQIFWsWFEVK1bUq6++esPzTp8+fd37uLb/9/54L9fmk+TnXhwOh/r06aOFCxdq5syZqly5sho3bpzrsVu2bFHr1q0l/baq5quvvtLWrVs1atSofF83t/u8UYy9e/fW5cuXVapUKeZGACYikYDX8fX1VYsWLbR9+/YckyVzc+0f02PHjuXY99NPP6l48eKmxRYYGChJysjIcGn/4zwMSWrcuLE+/PBDnTt3Tps2bVKDBg2UkJCgxYsXX7f/sLCw696HJFPv5fd69+6tU6dOaebMmerTp891j1u8eLH8/Pz00UcfqUuXLmrYsKHi4uLcumZuk1av59ixYxo0aJBq166t06dP6+mnn3brmgByIpGAVxo5cqQMw1D//v1znZx49epVffjhh5Kke++9V5KckyWv2bp1q1JTU9WiRQvT4rq28mDXrl0u7ddiyY2vr6/q16+v119/XZK0Y8eO6x7bokULrVu3zpk4XPOf//xHQUFBli2NLFOmjIYPH66OHTuqV69e1z3O4XCoQIEC8vX1dbZdunRJCxYsyHGsWVWerKwsPfroo3I4HFq5cqWSkpI0bdo0vf/++zfdNwCeIwEv1aBBA82YMUMDBw5UnTp19MQTT6hatWq6evWqdu7cqVmzZql69erq2LGjqlSpogEDBmjatGny8fFRu3btdOjQIT333HMqW7as/vGPf5gW13333afQ0FDFx8frhRdeUIECBTRv3jwdPXrU5biZM2dq3bp1at++vSIjI3X58mXnyoiWLVtet//ExER99NFHat68uZ5//nmFhobq7bff1scff6yJEycqJCTEtHv5o/Hjx//pMe3bt9ekSZPUvXt3DRgwQKdPn9a///3vXJfo1qhRQ4sXL9a7776rChUqKDAw0K15DYmJifriiy+0atUqlSpVSsOGDdNnn32m+Ph4xcbGKioqKt99Avg/JBLwWv3791e9evU0efJkTZgwQcePH5efn58qV66s7t2768knn3QeO2PGDFWsWFFvvfWWXn/9dYWEhKht27ZKSkrKdU6Eu4oUKaLk5GQlJCSoZ8+eKlq0qPr166d27dqpX79+zuNq166tVatWKTExUcePH1ehQoVUvXp1rVixwjnHIDdVqlTR119/rX/9618aNGiQLl26pOjoaM2dOzdfT4i0yr333qs5c+ZowoQJ6tixo8qUKaP+/fsrPDxc8fHxLseOGTNGx44dU//+/XXhwgWVK1fO5TkbebF69WolJSXpueeec6kszZs3T7Gxseratau+/PJL+fv7m3F7wF+SwzB+9xQYAACAfGCOBAAAcBuJBAAAcBuJBAAAcBuJBAAAcBuJBAAAcBuJBAAAcBuJBAAAcJtXPpCqY2QHu0MAANwmPjzykeXXuHoqb2/w/TN+xSuY0o+ZqEgAAAC3eWVFAgAAj5KdZXcEliGRAADAaka23RFYhkQCAACrZXtvIsEcCQAA4DYqEgAAWMxgaAMAALiNoQ0AAICcqEgAAGA1hjYAAIDbvPg5EgxtAAAAt1GRAADAagxtAAAAt7FqAwAAICcqEgAAWIwHUgEAAPd58dAGiQQAAFbz4ooEcyQAAIDbqEgAAGA1L34gFYkEAABWY2gDAAAgJyoSAABYjVUbAADAbQxtAAAA5ERFAgAAqzG0AQAA3GUY3rv8k6ENAADgNioSAABYzYsnW5JIAABgNeZIAAAAt3lxRYI5EgAAwG1UJAAAsJoXv7SLigQAAFYzss3Z8unzzz9Xx44dVbp0aTkcDi1fvtw1LMPQ6NGjVbp0aRUsWFDNmjXT3r1783UNEgkAALxUenq6atWqpddeey3X/RMnTtSkSZP02muvaevWrSpVqpRatWqlCxcu5PkaDG0AAGA1m1ZttGvXTu3atct1n2EYmjJlikaNGqUHH3xQkjR//nyVLFlS77zzjv7+97/n6RpUJAAAsJpJQxsZGRk6f/68y5aRkeFWSGlpaTp+/Lhat27tbAsICFDTpk319ddf57kfEgkAAG4TSUlJCgkJcdmSkpLc6uv48eOSpJIlS7q0lyxZ0rkvLxjaAADAaiYNbYwcOVJDhw51aQsICLipPh0Oh8tnwzBytN0IiQQAAFYzKZEICAi46cThmlKlSkn6rTIRERHhbD9x4kSOKsWNMLQBAMBfUFRUlEqVKqXVq1c7265cuaLPPvtMDRs2zHM/VCQAALCYXa8R//XXX/X99987P6elpSklJUWhoaGKjIxUQkKCxo0bpzvvvFN33nmnxo0bp6CgIHXv3j3P1yCRAADAajYt/9y2bZuaN2/u/HxtfkWvXr00b948PfPMM7p06ZIGDhyos2fPqn79+lq1apUKFy6c52s4DMMwTI/cZh0jO9gdAgDgNvHhkY8sv8al9W+a0k/B5v1M6cdMzJEAAABuY2gDAACr2TS0cSuQSAAAYDU3Xrh1u2BoAwAAuI2KBAAAVmNoAwAAuI2hDQAAgJyoSAAAYDWGNgAAgNtIJMxVrFixPL+i9MyZMxZHAwAA3GVLIjFlyhTnz6dPn9aLL76oNm3aqEGDBpKkjRs36tNPP9Vzzz1nR3gAAJjLiydb2v6ujYceekjNmzfXk08+6dL+2muvac2aNVq+fHm+++RdGwCAvLol79pY8W9T+il4/9Om9GMm21dtfPrpp2rbtm2O9jZt2mjNmjU2RAQAgMmMbHM2D2R7IhEWFqZly5blaF++fLnCwsJsiAgAAOSV7as2xowZo/j4eG3YsME5R2LTpk1KTk7Wm2+a89pVAABsxaoN6/Tu3VvR0dGaOnWq3n//fRmGoZiYGH311VeqX7++3eEBAHDzPHRYwgy2JxKSVL9+fb399tt2hwEAAPLJIxKJ7Oxsff/99zpx4oSy/1D+adKkiU1RAQBgEoY2rLNp0yZ1795dhw8f1h9XojocDmVlZdkUGQAAJiGRsM7jjz+uuLg4ffzxx4qIiMjzEy8BAID9bE8kvvvuOy1dulSVKlWyOxQAAKxh77MfLWX7cyTq16+v77//3u4wAACwTna2OZsHsr0i8dRTT2nYsGE6fvy4atSoIT8/P5f9NWvWtCkyAADwZ2xPJB566CFJUt++fZ1tDodDhmEw2RIA4B08tJpgBtsTibS0NLtDAADAWjyQyjrlypWzOwQAAKxFRcJ6+/bt05EjR3TlyhWX9vvvv9+miAAAwJ+xPZH44Ycf9MADD2j37t3OuRGSnM+TYI4EAOC2x/JP6wwZMkRRUVH6+eefFRQUpL179+rzzz9XXFycNmzYYHd4AADcPJZ/Wmfjxo1at26dSpQoIR8fH/n4+KhRo0ZKSkrS4MGDtXPnTrtDBAAA12F7RSIrK0uFChWSJBUvXlw//fSTpN8mYR44cMDO0AAAMAcVCetUr15du3btUoUKFVS/fn1NnDhR/v7+mjVrlipUqGB3eAAA3DyWf1rn2WefVXp6uiTpxRdfVIcOHdS4cWOFhYXp3XfftTk6AABwI7YnEm3atHH+XKFCBe3bt09nzpxRsWLFeBMoAMArGNneu2rD9kQiN6GhoXaHAACAeTx0foMZbEkkHnzwwTwf+/7771sYCQAAuBm2JBIhISF2XBYAAHsw2dJcc+fOteOyAADYgzkSAADAbV48R8L2B1IBAIDbFxUJAACs5sUVCRIJAACsxts/rfOf//xHGRkZOdqvXLmi//znPzZEBAAA8sphGPamSb6+vjp27JjCw8Nd2k+fPq3w8HBlZWXlu8+OkR3MCg9uaNezndr97T6VvKOkJOnIt0e0+NVF2r5hu82RAfbiu+GZPjzykeXXuDipvyn9BA2dbUo/ZrJ9aMMwjFwfhf3jjz/yvInb1KnjpzV//HwdO/Tbm1xbPNxCo958Vgn3DdGRb4/YHB1gH74bf2Es/zRfbGysHA6HHA6HWrRooQIF/i+UrKwspaWlqW3btnaFh5uwdc0Wl88LXl6gdn+7T1Viq/AfS/yl8d2AN7ItkejcubMkKSUlRW3atFGhQoWc+/z9/VW+fHk99NBDNkUHs/j4+Oie9o0UWDBQ+3fstzscwGPw3fiL4cmW5ktMTJQklS9fXl27dlVgYKBdocAC5aqU08vL/y3/AH9dSr+klwa8pKPfHbU7LMB2fDf+orx4aMP2yZbXbN++XampqXI4HIqJiVFsbGyezsvIyMix6qNbta7ydfhaESbyqIBfAZUoXULBIcFq2O4ete7WWiO7/JP/YOIvj++G57klky0n9DGln6ARnveKCdsnW544cULdunXThg0bVLRoURmGoXPnzql58+ZavHixSpQoccPzk5KSNGbMGJe2O4vcqSohla0MG38i82qmjh0+Jkn6ftf3urPWnbq/7/16feTrNkcG2Ivvxl+T4cUPpLL9ORJPPfWUzp8/r7179+rMmTM6e/as9uzZo/Pnz2vw4MF/ev7IkSN17tw5l61SkYq3IHLkh8PhkJ+/n91hAB6H78ZfRLZhzuaBbK9IJCcna82aNYqOjna2xcTE6PXXX1fr1q3/9PyAgAAFBAS4tDGsYa+/PfOYtm/YrlM/nVTB4IJqcn8TVb+7ukY/lmh3aICt+G78hTHZ0jrZ2dny88uZjfv5+Snbi0tB3qxo8aIaOnmoQsNDlX4hXYf2H9LoxxKV8kWK3aEBtuK7AW9k+2TLTp066ZdfftGiRYtUunRpSdL//vc/9ejRQ8WKFdOyZcvy3SdPtgQA5NWtmGyZ/kIPU/oJfv5tU/oxk+1zJF577TVduHBB5cuXV8WKFVWpUiVFRUXpwoULmjZtmt3hAQBw87Kzzdk8kO1DG2XLltWOHTu0evVq7d+/X4ZhKCYmRi1btrQ7NAAA8CdsTySuadWqlVq1amV3GAAAmM9DV1yYwSMSibVr12rt2rU6ceJEjgmWc+bMsSkqAABMwqoN64wZM0YvvPCC4uLiFBERkeubQAEAgGeyPZGYOXOm5s2bp7/97W92hwIAgDUY2rDOlStX1LBhQ7vDAADAMjwi20L9+vXTO++8Y3cYAADADbZXJC5fvqxZs2ZpzZo1qlmzZo6nXE6aNMmmyAAAMAlDG9bZtWuXateuLUnas2ePyz4mXgIAvIINiURmZqZGjx6tt99+W8ePH1dERIR69+6tZ599Vj4+5g1I2J5IrF+/3u4QAACwlg3LPydMmKCZM2dq/vz5qlatmrZt26Y+ffooJCREQ4YMMe06ticSAADAfBs3blSnTp3Uvn17SVL58uW1aNEibdu2zdTr2D7ZEgAAr5dtmLJlZGTo/PnzLltGRkaul2zUqJHWrl2rb7/9VpL0zTff6Msvv9R9991n6q2RSAAAYDEj2zBlS0pKUkhIiMuWlJSU6zVHjBihRx99VFWrVpWfn59iY2OVkJCgRx991NR7Y2gDAIDbxMiRIzV06FCXtoCAgFyPfffdd7Vw4UK98847qlatmlJSUpSQkKDSpUurV69epsVEIgEAgNVMWrUREBBw3cThj4YPH65//vOf6tatmySpRo0aOnz4sJKSkm7/RGLFihV5Pvb++++3MBIAAG4BG55sefHixRzLPH19fXO8HPNm2ZJIdO7c2eWzw+GQYRgun6/Jysq6VWEBAOA1OnbsqJdeekmRkZGqVq2adu7cqUmTJqlv376mXseWyZbZ2dnObdWqVapdu7ZWrlypX375RefOndMnn3yiu+66S8nJyXaEBwCAuUxatZEf06ZN08MPP6yBAwcqOjpaTz/9tP7+979r7Nixpt6aw/h9KcAG1atX18yZM9WoUSOX9i+++EIDBgxQampqvvvsGNnBrPAAAF7uwyMfWX6NC4+3NaWfwjM973+wbV/+efDgQYWEhORoDwkJ0aFDh259QAAAIM9sTyTq1q2rhIQEHTt2zNl2/PhxDRs2TPXq1bMxMgAAzGEYhimbJ7J9+eecOXP0wAMPqFy5coqMjJQkHTlyRJUrV9by5cvtDQ4AADPw9k/rVKpUSbt27dLq1au1f/9+GYahmJgYtWzZkrd/AgC8A4mEtRwOh1q3bq3WrVvbHQoAAMgHWxKJqVOnasCAAQoMDNTUqVNveOzgwYNvUVQAAFjD8OKKhC3LP6OiorRt2zaFhYUpKirqusc5HA798MMP+e6f5Z8AgLy6Fcs/z/VqYUo/IfPXmtKPmWypSKSlpeX6MwAAuL14xBwJAAC82q1/1cYtY0si8cdXoN7IpEmTLIwEAADrefMcCVsSiZ07d7p83r59u7KyslSlShVJ0rfffitfX1/VqVPHjvAAAEAe2ZJIrF+/3vnzpEmTVLhwYc2fP1/FihWTJJ09e1Z9+vRR48aN7QgPAABzeXFFwvaXdpUpU0arVq1StWrVXNr37Nmj1q1b66effsp3n6zaAADk1a1YtfFL1+am9FP03fV/ftAtZvu7Ns6fP6+ff/45R/uJEyd04cIFGyICAAB5ZfuqjQceeEB9+vTRK6+8orvvvluStGnTJg0fPlwPPvigzdEBAHDzmGxpoZkzZ+rpp59Wz549dfXqVUlSgQIFFB8fr5dfftnm6AAAMAHLP60TFBSk6dOn6+WXX9bBgwdlGIYqVaqk4OBgu0MDAMAUVCRugeDgYNWsWdPuMAAAQD54TCIBAIDXYmgDAAC4y/DiRML25Z8AAOD2RUUCAACreXFFgkQCAACLMbQBAACQCyoSAABYzYsrEiQSAABYzJuHNkgkAACwmDcnEsyRAAAAbqMiAQCAxby5IkEiAQCA1QyH3RFYhqENAADgtptOJLKyspSSkqKzZ8+aEQ8AAF7HyDZn80T5TiQSEhL01ltvSfotiWjatKnuuusulS1bVhs2bDA7PgAAbntGtsOUzRPlO5FYunSpatWqJUn68MMPlZaWpv379yshIUGjRo0yPUAAAOC58p1InDp1SqVKlZIkffLJJ3rkkUdUuXJlxcfHa/fu3aYHCADA7Y6hjd8pWbKk9u3bp6ysLCUnJ6tly5aSpIsXL8rX19f0AAEAuN0ZhsOUzRPle/lnnz591KVLF0VERMjhcKhVq1aSpM2bN6tq1aqmBwgAADxXvhOJ0aNHq3r16jp69KgeeeQRBQQESJJ8fX31z3/+0/QAAQC43XnqsIQZ3Hog1cMPP5yjrVevXjcdDAAA3shTV1yYIU+JxNSpU/Pc4eDBg90OBgAAb2QYdkdgnTwlEpMnT85TZw6Hg0QCAIC/kDwlEmlpaVbHAQCA1/LmoQ23H5F95coVHThwQJmZmWbGAwCA1+HJlr9z8eJFxcfHKygoSNWqVdORI0ck/TY3Yvz48aYHCAAAPFe+E4mRI0fqm2++0YYNGxQYGOhsb9mypd59911TgwMAwBsYhjmbJ8r38s/ly5fr3Xff1d133y2H4//KLDExMTp48KCpwQEA4A08dVjCDPmuSJw8eVLh4eE52tPT010SCwAA4P3ynUjUrVtXH3/8sfPzteRh9uzZatCggXmRAQDgJXjXxu8kJSWpbdu22rdvnzIzM/Xqq69q79692rhxoz777DMrYgQA4LbmzY/IzndFomHDhvrqq6908eJFVaxYUatWrVLJkiW1ceNG1alTx4oYAQCAh3LrXRs1atTQ/PnzzY4FAACvlO2hwxJmcCuRyMrK0rJly5SamiqHw6Ho6Gh16tRJBQq41R0AAF7NU+c3mCHf//Lv2bNHnTp10vHjx1WlShVJ0rfffqsSJUpoxYoVqlGjhulBAgBwO2P55+/069dP1apV048//qgdO3Zox44dOnr0qGrWrKkBAwZYESMAAPBQ+a5IfPPNN9q2bZuKFSvmbCtWrJheeukl1a1b19TgAADwBp76VEoz5LsiUaVKFf3888852k+cOKFKlSqZEhQAAN7kL//SrvPnzzu3cePGafDgwVq6dKl+/PFH/fjjj1q6dKkSEhI0YcIEq+MFAAAeJE9DG0WLFnV5/LVhGOrSpYuzzfj/NZuOHTsqKyvLgjABALh9/eWXf65fv97qOAAA8Fp/+eWfTZs2tToOAABwG3L7CVIXL17UkSNHdOXKFZf2mjVr3nRQAAB4E29etZHvROLkyZPq06ePVq5cmet+5kgAAODKm+dI5Hv5Z0JCgs6ePatNmzapYMGCSk5O1vz583XnnXdqxYoVVsQIAAA8VL4TiXXr1mny5MmqW7eufHx8VK5cOfXs2VMTJ05UUlKSFTECAHBbMwyHKVt+/e9//1PPnj0VFhamoKAg1a5dW9u3bzf13vI9tJGenq7w8HBJUmhoqE6ePKnKlSurRo0a2rFjh6nBAQDgDeyYI3H27Fndc889at68uVauXKnw8HAdPHhQRYsWNfU6+U4kqlSpogMHDqh8+fKqXbu23njjDZUvX14zZ85URESEqcEBAOAN7JgjMWHCBJUtW1Zz5851tpUvX97067g1R+LYsWOSpMTERCUnJysyMlJTp07VuHHjTA8QAAD8JiMjw+Vp0+fPn1dGRkaux65YsUJxcXF65JFHFB4ertjYWM2ePdv0mByGcXMFl4sXL2r//v2KjIxU8eLFzYrrphTwL2N3CIBHuvTTF3aHAHgcv+IVLL/G1jIPmNLPx/1racyYMS5tiYmJGj16dI5jAwMDJUlDhw7VI488oi1btighIUFvvPGGHnvsMVPikUxIJDwRiQSQOxIJIKdbkUhsLv2gKf3UTluUowIREBCggICAHMf6+/srLi5OX3/9tbNt8ODB2rp1qzZu3GhKPFIe50gMHTo0zx1OmjTJ7WAAAMD1XS9pyE1ERIRiYmJc2qKjo/Xee++ZGlOeEomdO3fmqbPfv9gLAAD8xo7S/z333KMDBw64tH377bcqV66cqdfhpV0AAFjMjlUb//jHP9SwYUONGzdOXbp00ZYtWzRr1izNmjXL1Ovke9UGAADwfHXr1tWyZcu0aNEiVa9eXWPHjtWUKVPUo0cPU6/j9ku7AABA3tj1GvEOHTqoQ4cOll6DRAIAAItl2x2AhRjaAAAAbqMiAQCAxQx576pGtyoSCxYs0D333KPSpUvr8OHDkqQpU6bogw8+MDU4AAC8QbZhzuaJ8p1IzJgxQ0OHDtV9992nX375RVlZWZKkokWLasqUKWbHBwDAbS9bDlM2T5TvRGLatGmaPXu2Ro0aJV9fX2d7XFycdu/ebWpwAADAs+V7jkRaWppiY2NztAcEBCg9Pd2UoAAA8CbMkfidqKgopaSk5GhfuXJljmd6AwCA35Z/mrF5onxXJIYPH65Bgwbp8uXLMgxDW7Zs0aJFi5SUlKQ333zTihgBAICHynci0adPH2VmZuqZZ57RxYsX1b17d5UpU0avvvqqunXrZkWMAADc1rx5aMNhGIbbC0pOnTql7OxshYeHmxnTTSvgX8buEACPdOmnL+wOAfA4fsUrWH6N5JLm/I92258Xm9KPmW7qgVTFixc3Kw4AAHAbynciERUVJYfj+iWaH3744aYCAgDA23jqREkz5DuRSEhIcPl89epV7dy5U8nJyRo+fLhZcQEA4DW8eY5EvhOJIUOG5Nr++uuva9u2bTcdEAAAuH2Y9vbPdu3a6b333jOrOwAAvEa2w5zNE5n29s+lS5cqNDTUrO4AAPAanvqeDDPkO5GIjY11mWxpGIaOHz+ukydPavr06aYGBwCAN/DQF3eaIt+JROfOnV0++/j4qESJEmrWrJmqVq1qVlwAAOA2kK9EIjMzU+XLl1ebNm1UqlQpq2ICAMCrePPyz3xNtixQoICeeOIJZWRkWBUPAABeJ9vhMGXzRPletVG/fn3t3LnTilgAAMBtJt9zJAYOHKhhw4bpxx9/VJ06dRQcHOyyv2bNmqYFBwCAN2CypaS+fftqypQp6tq1qyRp8ODBzn0Oh0OGYcjhcCgrK8v8KAEAuI158xyJPCcS8+fP1/jx45WWlmZlPAAA4DaS50Ti2tvGy5UrZ1kwAAB4I099KqUZ8jVH4kZv/QQAALnjyZb/X+XKlf80mThz5sxNBQQAAG4f+UokxowZo5CQEKtiAQDAK7Fq4//r1q2bwsPDrYoFAACvxBwJMT8CAAB3efPyzzw/2fLaqg0AAIBr8lyRyM725nwKAADrePP/iuf7EdkAACB/vHmORL5f2gUAAHANFQkAACzmzZMDSCQAALCYNycSDG0AAAC3UZEAAMBihhdPtiSRAADAYgxtAAAA5IKKBAAAFvPmigSJBAAAFuPJlgAAwG082RIAACAXVCQAALAYcyQAAIDbvDmRYGgDAAC4jYoEAAAWY9UGAABwG6s2AAAAckFFAgAAi3nzZEsSCQAALObNcyQY2gAAAG6jIgEAgMWyvbgmQSIBAIDFmCMBAADc5r31COZIAACAm0BFAgAAizG0AQAA3MaTLQEAAHJBRQIAAIux/BMAALjNe9MIhjYAAPhLSEpKksPhUEJCgqn9UpEAAMBidq/a2Lp1q2bNmqWaNWua3rdticSuXbvyfKwVNw4AwK1i5xyJX3/9VT169NDs2bP14osvmt6/bYlE7dq15XA4ZBiGHI4br4vJysq6RVEBAOC5MjIylJGR4dIWEBCggICA654zaNAgtW/fXi1btrQkkbBtjkRaWpp++OEHpaWl6b333lNUVJSmT5+unTt3aufOnZo+fboqVqyo9957z64QAQAwhWHSlpSUpJCQEJctKSnputddvHixduzYccNjbpZtFYly5co5f37kkUc0depU3Xfffc62mjVrqmzZsnruuefUuXNnGyIEAMAcZs2RGDlypIYOHerSdr1qxNGjRzVkyBCtWrVKgYGBJkWQk0dMtty9e7eioqJytEdFRWnfvn02RAQAgHnMmiPxZ8MYv7d9+3adOHFCderUcbZlZWXp888/12uvvaaMjAz5+vredEwesfwzOjpaL774oi5fvuxsy8jI0Isvvqjo6GgbIwMA4PbUokUL7d69WykpKc4tLi5OPXr0UEpKiilJhOQhFYmZM2eqY8eOKlu2rGrVqiVJ+uabb+RwOPTRRx/ZHB0AADfHjjUbhQsXVvXq1V3agoODFRYWlqP9ZnhEIlGvXj2lpaVp4cKF2r9/vwzDUNeuXdW9e3cFBwfbHR4AADfF7udIWMkjEglJCgoK0oABA+wOAwAAr7VhwwbT+/SIORKStGDBAjVq1EilS5fW4cOHJUmTJ0/WBx98YHNkAADcHMOkP57IIxKJGTNmaOjQoWrXrp3Onj3rfABVsWLFNGXKFHuDAwDgJmWbtHkij0gkpk2bptmzZ2vUqFEqUOD/Rlvi4uK0e/duGyMDAAA34hFzJNLS0hQbG5ujPSAgQOnp6TZEBACAeex814bVPKIiERUVpZSUlBztK1euVExMzK0PCAAAE5n1iGxP5BEVieHDh2vQoEG6fPmyDMPQli1btGjRIiUlJenNN9+0OzwAAHAdHpFI9OnTR5mZmXrmmWd08eJFde/eXWXKlNGrr76qbt262R0e3NC4UX0NG/aE7oqtodKlS+nBh/tqxYpP7Q4LuKW2pezW3HeWat/+73Xy9Bm9mvScWjRp6Ny/esNX+u8Hn2jfge/1y7nzWjr3NVWtXNHGiGEVhjZugf79++vw4cM6ceKEjh8/rqNHjyo+Pt7usOCm4OAg7dq1T4MTnrU7FMA2ly5dVpVKFfSvoQNz33/5smJrxCjh8T63ODLcat68asMjKhK/V7x4cbtDgAmSP12v5E/X2x0GYKvGDeqqcYO6191/f9sWkqT/Hfv5VoUEm3jqMyDMYFsiERsbK4fDkadjd+zYYXE0AADAHbYlEp07d3b+fPnyZU2fPl0xMTFq0KCBJGnTpk3au3evBg7MvSR4TUZGhjIyMlzaDMPIc5ICAIDVPHVYwgy2JRKJiYnOn/v166fBgwdr7NixOY45evToDftJSkrSmDFjXNocPoXk8C1iXrAAANwEbx7a8IjJlv/973/12GOP5Wjv2bOn3nvvvRueO3LkSJ07d85lc/gUtipUAADwOx4x2bJgwYL68ssvdeedd7q0f/nllwoMDLzhuQEBAQoICHBpY1gDAOBJGNqwWEJCgp544glt375dd999t6Tf5kjMmTNHzz//vM3RwR3BwUGqVCnK+TmqfKRq1aqmM2fO6ujRn2yMDLh1Ll68pCM//t/f9//99LP2f3tQIUUKK6JUuM6dv6Bjx0/oxKnTkqS0Iz9KkoqHFVPxsFBbYoY1sg3vHdpwGIZn3N2SJUv06quvKjU1VZIUHR2tIUOGqEuXLvnuq4B/GbPDQz41bdJAa9cszdE+/z9LFN/vHzZEBEm69NMXdofwl7Jlxy71fWpEjvZO7VrqpWeHafnHq/XsuEk59j/Rt4cGxfe8FSFCkl/xCpZf42/lHjSlnwWH3zelHzN5TCJhJhIJIHckEkBOtyKR6GlSIrHQAxMJjxjauObKlSs6ceKEsrNdR5MiIyNtiggAgJvnzY/I9ohE4rvvvlPfvn319ddfu7Rfex5EVlaWTZEBAIAb8YhEonfv3ipQoIA++ugjRUREsOoCAOBVvPk5Eh6RSKSkpGj79u2qWrWq3aEAAGA6ln9aLCYmRqdOnbI7DAAALOHNcyQ84smWEyZM0DPPPKMNGzbo9OnTOn/+vMsGAAA8k0dUJFq2bClJatGihUs7ky0BAN6AORIWW79+vd0hAABgGeZIWKxp06Z2hwAAANzgEYnENRcvXtSRI0d05coVl/aaNWvaFBEAADfPCx8i7eQRicTJkyfVp08frVy5Mtf9zJEAANzOWLVhsYSEBJ09e1abNm1SwYIFlZycrPnz5+vOO+/UihUr7A4PAABch0dUJNatW6cPPvhAdevWlY+Pj8qVK6dWrVqpSJEiSkpKUvv27e0OEQAAt3nzZEuPqEikp6crPDxckhQaGqqTJ09KkmrUqKEdO3bYGRoAADfNMOmPJ/KIRKJKlSo6cOCAJKl27dp644039L///U8zZ85URESEzdEBAIDr8YihjYSEBB07dkySlJiYqDZt2mjhwoXy9/fX/PnzbY4OAICb482TLT0ikejRo4fz59jYWB06dEj79+9XZGSkihcvbmNkAADcPJZ/WmDo0KF5PnbSpEkWRgIAgLW8ebKlbYnEzp0783Scw+GwOBIAAOAu2xIJ3q8BAPir8NQVF2bwiDkSAAB4M2+ebOkRyz8BAMDtiYoEAAAWY9UGAABwG0MbAAAAuaAiAQCAxVi1AQAA3JbtxXMkGNoAAABuoyIBAIDFvLceQSIBAIDlvHnVBokEAAAW8+ZEgjkSAADAbVQkAACwGE+2BAAAbmNoAwAAIBdUJAAAsBhPtgQAAG7z5jkSDG0AAAC3UZEAAMBi3jzZkkQCAACLMbQBAACQCyoSAABYjKENAADgNpZ/AgAAt2UzRwIAACAnEgkAACxmmPQnP5KSklS3bl0VLlxY4eHh6ty5sw4cOGD6vZFIAABgsWzDMGXLj88++0yDBg3Spk2btHr1amVmZqp169ZKT0839d6YIwEAgBdKTk52+Tx37lyFh4dr+/btatKkiWnXIZEAAMBinrBq49y5c5Kk0NBQU/slkQAAwGJmrdrIyMhQRkaGS1tAQIACAgJueJ5hGBo6dKgaNWqk6tWrmxLLNcyRAADgNpGUlKSQkBCXLSkp6U/Pe/LJJ7Vr1y4tWrTI9Jgchhc+ALyAfxm7QwA80qWfvrA7BMDj+BWvYPk17ixRx5R+9vz4db4rEk899ZSWL1+uzz//XFFRUabE8XsMbQAAYDGzhjbyMoxxjWEYeuqpp7Rs2TJt2LDBkiRCIpEAAMArDRo0SO+8844++OADFS5cWMePH5ckhYSEqGDBgqZdh6EN4C+EoQ0gp1sxtFGheKwp/fxwameej3U4HLm2z507V7179zYlHomKBAAAljOMbBuueWvqBCQSAABYzJtfI87yTwAA4DYqEgAAWMwLpyM6kUgAAGAxhjYAAAByQUUCAACLMbQBAADcZtaTLT0RQxsAAMBtVCQAALCY4cWTLUkkAACwmDfPkWBoAwAAuI2KBAAAFvPm50iQSAAAYDFvHtogkQAAwGIs/wQAAMgFFQkAACzG0AYAAHCbN0+2ZGgDAAC4jYoEAAAWY2gDAAC4jVUbAAAAuaAiAQCAxXhpFwAAcBtDGwAAALmgIgEAgMVYtQEAANzGHAkAAOA2b65IMEcCAAC4jYoEAAAW8+aKBIkEAAAW8940gqENAABwExyGN9dbYKuMjAwlJSVp5MiRCggIsDscwGPw3YA3IZGAZc6fP6+QkBCdO3dORYoUsTscwGPw3YA3YWgDAAC4jUQCAAC4jUQCAAC4jUQClgkICFBiYiKTyYA/4LsBb8JkSwAA4DYqEgAAwG0kEgAAwG0kEgAAwG0kEl6oWbNmSkhIcH4uX768pkyZcsNzHA6Hli9fbmlcedW7d2917tzZ8uvMmzdPRYsWtfw68G5//L5ZYfTo0apdu7al1wDcxUu7/gK2bt2q4OBgu8PQ6NGjtXz5cqWkpNgdCgDAJCQSfwElSpSwOwQAgJdiaOM2l56erscee0yFChVSRESEXnnllRzH/HFo47vvvlOTJk0UGBiomJgYrV69+k+v06xZMw0ePFjPPPOMQkNDVapUKY0ePdrlmCNHjqhTp04qVKiQihQpoi5duujnn3+W9NswwpgxY/TNN9/I4XDI4XBo3rx5ebrH5ORkNWrUSEWLFlVYWJg6dOiggwcPOvcfOnRIDodD77//vpo3b66goCDVqlVLGzdudOln3rx5ioyMVFBQkB544AGdPn06T9cH8mrhwoWKi4tT4cKFVapUKXXv3l0nTpxw7t+wYYMcDofWrl2ruLg4BQUFqWHDhjpw4IBLP+PHj1fJkiVVuHBhxcfH6/Lly7f6VoA8I5G4zQ0fPlzr16/XsmXLtGrVKm3YsEHbt2+/7vHZ2dl68MEH5evrq02bNmnmzJkaMWJEnq41f/58BQcHa/PmzZo4caJeeOEFZxJiGIY6d+6sM2fO6LPPPtPq1at18OBBde3aVZLUtWtXDRs2TNWqVdOxY8d07Ngx574/k56erqFDh2rr1q1au3atfHx89MADDyg7O9vluFGjRunpp59WSkqKKleurEcffVSZmZmSpM2bN6tv374aOHCgUlJS1Lx5c7344ot5uj6QV1euXNHYsWP1zTffaPny5UpLS1Pv3r1zHDdq1Ci98sor2rZtmwoUKKC+ffs69y1ZskSJiYl66aWXtG3bNkVERGj69Om38C6AfDJw27pw4YLh7+9vLF682Nl2+vRpo2DBgsaQIUOcbeXKlTMmT55sGIZhfPrpp4avr69x9OhR5/6VK1cakoxly5Zd91pNmzY1GjVq5NJWt25dY8SIEYZhGMaqVasMX19f48iRI879e/fuNSQZW7ZsMQzDMBITE41atWr96X316tXL6NSp03X3nzhxwpBk7N692zAMw0hLSzMkGW+++WaOa6emphqGYRiPPvqo0bZtW5d+unbtaoSEhPxpPMCNNG3a1OX79ntbtmwxJBkXLlwwDMMw1q9fb0gy1qxZ4zzm448/NiQZly5dMgzDMBo0aGA8/vjjLv3Ur18/T98dwA5UJG5jBw8e1JUrV9SgQQNnW2hoqKpUqXLdc1JTUxUZGak77rjD2fb782+kZs2aLp8jIiKcZdvU1FSVLVtWZcuWde6PiYlR0aJFlZqamqf+r+fgwYPq3r27KlSooCJFiigqKkrSb0Mp14svIiJCklzi++N95vW+gbzauXOnOnXqpHLlyqlw4cJq1qyZJP6uwruRSNzGDDeebp7bOQ6HI0/n+vn55Tjv2vCCYRi59nO99vzo2LGjTp8+rdmzZ2vz5s3avHmzpN/KyNeL79o1fx8fYKX09HS1bt1ahQoV0sKFC7V161YtW7ZMUv7+rgK3GxKJ21ilSpXk5+enTZs2OdvOnj2rb7/99rrnxMTE6MiRI/rpp5+cbX+clOiOa/0ePXrU2bZv3z6dO3dO0dHRkiR/f39lZWXlq9/Tp08rNTVVzz77rFq0aKHo6GidPXvWrfh+/3uSlOMzcDP279+vU6dOafz48WrcuLGqVq3qMtEyr6Kjo/m7itsKyz9vY4UKFVJ8fLyGDx+usLAwlSxZUqNGjZKPz/Xzw5YtW6pKlSp67LHH9Morr+j8+fMaNWrUTcfSsmVL1axZUz169NCUKVOUmZmpgQMHqmnTpoqLi5P02+qRtLQ0paSk6I477lDhwoX/9O2HxYoVU1hYmGbNmqWIiAgdOXJE//znP/Md3+DBg9WwYUNNnDhRnTt31qpVq5ScnOzWvQK5iYyMlL+/v6ZNm6bHH39ce/bs0dixY/Pdz5AhQ9SrVy/FxcWpUaNGevvtt7V3715VqFDBgqiBm0dF4jb38ssvq0mTJrr//vvVsmVLNWrUSHXq1Lnu8T4+Plq2bJkyMjJUr1499evXTy+99NJNx3HtyZjFihVTkyZN1LJlS1WoUEHvvvuu85iHHnpIbdu2VfPmzVWiRAktWrToT/v18fHR4sWLtX37dlWvXl3/+Mc/9PLLL+c7vrvvvltvvvmmpk2bptq1a2vVqlV69tln890PcD0lSpTQvHnz9N///lcxMTEaP368/v3vf+e7n65du+r555/XiBEjVKdOHR0+fFhPPPGEBRED5uA14gAAwG1UJAAAgNtIJAAAgNtIJAAAgNtIJAAAgNtIJAAAgNtIJAAAgNtIJAAAgNtIJAAbjR49WrVr13Z+7t27tzp37nzL4zh06JAcDodSUlKue0z58uU1ZcqUPPc5b948FS1a9KZju/awMwCeiUQC+IPevXvL4XDI4XDIz89PFSpU0NNPP6309HTLr/3qq69q3rx5eTo2L//4A4DVeNcGkIu2bdtq7ty5unr1qr744gv169dP6enpmjFjRo5jr169muPNqO4KCQkxpR8AuFWoSAC5CAgIUKlSpVS2bFl1795dPXr0cJbXrw1HzJkzRxUqVFBAQIAMw9C5c+c0YMAAhYeHq0iRIrr33nv1zTffuPQ7fvx4lSxZUoULF1Z8fLwuX77ssv+PQxvZ2dmaMGGCKlWqpICAAEVGRjrfjRIVFSVJio2NlcPhULNmzZznzZ07V9HR0QoMDFTVqlU1ffp0l+ts2bJFsbGxCgwMVFxcnHbu3Jnv39GkSZNUo0YNBQcHq2zZsho4cKB+/fXXHMctX75clStXVmBgoFq1auXyhlhJ+vDDD1WnTh0FBgaqQoUKGjNmjDIzM3O95pUrV/Tkk08qIiJCgYGBKl++vJKSkvIdOwDzUJEA8qBgwYK6evWq8/P333+vJUuW6L333pOvr68kqX379goNDdUnn3yikJAQvfHGG2rRooW+/fZbhYaGasmSJUpMTNTrr7+uxo0ba8GCBZo6deoN3+o4cuRIzZ49W5MnT1ajRo107Ngx7d+/X9JvyUC9evW0Zs0aVatWTf7+/pKk2bNnKzExUa+99ppiY2O1c+dO9e/fX8HBwerVq5fS09PVoUMH3XvvvVq4cKHS0tI0ZMiQfP9OfHx8NHXqVOdbXQcOHKhnnnnGJWm5ePGiXnrpJc2fP1/+/v4aOHCgunXrpq+++kqS9Omnn6pnz56aOnWqGjdurIMHD2rAgAGSpMTExBzXnDp1qlasWKElS5YoMjJSR48ezZGYALjFDAAuevXqZXTq1Mn5efPmzUZYWJjRpUsXwzAMIzEx0fDz8zNOnDjhPGbt2rVGkSJFjMuXL7v0VbFiReONN94wDMMwGjRoYDz++OMu++vXr2/UqlUr12ufP3/eCAgIMGbPnp1rnGlpaYYkY+fOnS7tZcuWNd555x2XtrFjxxoNGjQwDMMw3njjDSM0NNRIT0937p8xY0auff1euXLljMmTJ193/5IlS4ywsDDn57lz5xqSjE2bNjnbUlNTDUnG5s2bDcMwjMaNGxvjxo1z6WfBggVGRESE87MkY9myZYZhGMZTTz1l3HvvvUZ2dvZ14wBwa1GRAHLx0UcfqVChQsrMzNTVq1fVqVMnTZs2zbm/XLlyKlGihPPz9u3b9euvvyosLMyln0uXLungwYOSpNTUVD3++OMu+xs0aKD169fnGkNqaqoyMjLUokWLPMd98uRJHT16VPHx8erfv7+zPTMz0zn/IjU1VbVq1VJQUJBLHPm1fv16jRs3Tvv27dP58+eVmZmpy5cvKz09XcHBwZKkAgUKKC4uznlO1apVVbRoUaWmpqpevXravn27tm7d6vIq+6ysLF2+fFkXL150iVH6beinVatWqlKlitq2basOHTqodevW+Y4dgHlIJIBcNG/eXDNmzJCfn59Kly6dYzLltX8or8nOzlZERIQ2bNiQoy93l0AWLFgw3+dkZ2dL+m14o379+i77rg3BGIbhVjy/d/jwYd133316/PHHNXbsWIWGhurLL79UfHy8yxCQ9NvyzT+61padna0xY8bowQcfzHFMYGBgjra77rpLaWlpWrlypdasWaMuXbqoZcuWWrp06U3fEwD3kEgAuQgODlalSpXyfPxdd92l48ePq0CBAipfvnyux0RHR2vTpk167LHHnG2bNm26bp933nmnChYsqLVr16pfv3459l+bE5GVleVsK1mypMqUKaMffvhBPXr0yLXfmJgYLViwQJcuXXImKzeKIzfbtm1TZmamXnnlFfn4/DZne8mSJTmOy8zM1LZt21SvXj1J0oEDB/TLL7+oatWqkn77vR04cCBfv+siRYqoa9eu6tq1qx5++GG1bdtWZ86cUWhoaL7uAYA5SCQAE7Rs2VINGjRQ586dNWHCBFWpUkU//fSTPvnkE3Xu3FlxcXEaMmSIevXqpbi4ODVq1Ehvv/229u7de93JloGBgRoxYoSeeeYZ+fv765577tHJkye1d+9excfHKzw8XAULFlRycrLuuOMOBQYGKiQkRKNHj9bgwYNVpEgRtWvXThkZGdq2bZvOnj2roUOHqnv37ho1apTi4+P17LPP6tChQ/r3v/+dr/utWLGiMjMzNW3aNHXs2FFfffWVZs6cmeM4Pz8/PfXUU5o6dar8/Pz05JNP6u6773YmFs8//7w6dOigsmXL6pFHHpGPj4927dql3bt368UXX8zR3+TJkxUREaHatWvLx8dH//3vf1WqVClTHnwFwD0s/wRM4HA49Mknn6hJkybq27evKleurG7duunQoUMqWbKkJKlr1656/vnnNWLECNWpU0eHDx/WE088ccN+n3vuOQ0bNkzPP/+8oqOj1bVrV504cULSb/MPpk6dqjfeeEOlS5dWp06dJEn9+vXTm2++qXnz5qlGjRpq2rSp5s2b51wuWqhQIX344Yfat2+fYmNjNWrUKE2YMCFf91u7dm1NmjRJEyZMUPXq1fX222/nugwzKChII0aMUPfu3dWgQQMVLFhQixcvdu5v06aNPvroI61evVp169bV3XffrUmTJqlcuXK5XrdQoUKaMGGC4uLiVLduXR06dEiffPKJsyoC4NZzGGYMmAIAgL8k0ngAAOA2EgkAAOA2EgkAAOA2EgkAAOA2EgkAAOA2EgkAAOA2EgkAAOA2EgkAAOA2EgkAAOA2EgkAAOA2EgkAAOA2EgkAAOC2/weDhC/Q4+00eQAAAABJRU5ErkJggg==",
            "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": 44,
      "metadata": {},
      "outputs": [],
      "source": [
        "parameters = {'criterion': ['gini', 'entropy'],\n",
        "     'splitter': ['best', 'random'],\n",
        "     'max_depth': [2*n for n in range(1,10)],\n",
        "     'max_features': ['auto', 'sqrt'],\n",
        "     'min_samples_leaf': [1, 2, 4],\n",
        "     'min_samples_split': [2, 5, 10]}\n",
        "\n",
        "tree = DecisionTreeClassifier()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/Users/timothywickey/anaconda3/lib/python3.11/site-packages/sklearn/tree/_classes.py:269: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "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=DecisionTreeClassifier(),\n",
              "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
              "                         &#x27;max_depth&#x27;: [2, 4, 6, 8, 10, 12, 14, 16, 18],\n",
              "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
              "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
              "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
              "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n",
              "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
              "                         &#x27;max_depth&#x27;: [2, 4, 6, 8, 10, 12, 14, 16, 18],\n",
              "                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
              "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
              "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
              "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ],
            "text/plain": [
              "GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n",
              "             param_grid={'criterion': ['gini', 'entropy'],\n",
              "                         'max_depth': [2, 4, 6, 8, 10, 12, 14, 16, 18],\n",
              "                         'max_features': ['auto', 'sqrt'],\n",
              "                         'min_samples_leaf': [1, 2, 4],\n",
              "                         'min_samples_split': [2, 5, 10],\n",
              "                         'splitter': ['best', 'random']})"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create a decision tree classifier object\n",
        "tree = DecisionTreeClassifier()\n",
        "\n",
        "# Create GridSearchCV object with cv=10\n",
        "tree_cv = GridSearchCV(tree, parameters, cv=10)\n",
        "\n",
        "# Assuming X_train and Y_train are your training data\n",
        "tree_cv.fit(X_train, Y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tuned hpyerparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'splitter': 'random'}\n",
            "accuracy : 0.8607142857142858\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": 47,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy on test data: 0.6666666666666666\n"
          ]
        }
      ],
      "source": [
        "# Calculate accuracy on the test data\n",
        "accuracy = tree_cv.score(X_test, Y_test)\n",
        "print(\"Accuracy on test data:\", accuracy)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can plot the confusion matrix\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {},
      "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": 50,
      "metadata": {},
      "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=KNeighborsClassifier(),\n",
              "             param_grid={&#x27;algorithm&#x27;: [&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;],\n",
              "                         &#x27;n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
              "                         &#x27;p&#x27;: [1, 2]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
              "             param_grid={&#x27;algorithm&#x27;: [&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;],\n",
              "                         &#x27;n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
              "                         &#x27;p&#x27;: [1, 2]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ],
            "text/plain": [
              "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
              "             param_grid={'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
              "                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
              "                         'p': [1, 2]})"
            ]
          },
          "execution_count": 50,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Parameter grid to search over\n",
        "parameters = {\n",
        "    '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",
        "\n",
        "# Create a KNN classifier object\n",
        "KNN = KNeighborsClassifier()\n",
        "\n",
        "# Create GridSearchCV object with cv=10\n",
        "knn_cv = GridSearchCV(KNN, parameters, cv=10)\n",
        "\n",
        "# Assuming X_train and Y_train are your training data\n",
        "knn_cv.fit(X_train, Y_train)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 3, 'p': 2}\n",
            "accuracy : 0.7785714285714286\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": 54,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy on test data: 0.7222222222222222\n"
          ]
        }
      ],
      "source": [
        "accuracy = knn_cv.score(X_test, Y_test)\n",
        "print(\"Accuracy on test data:\", accuracy)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We can plot the confusion matrix\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {},
      "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": "markdown",
      "metadata": {},
      "source": [
        "Task 8 using a decision tree classifier object with a GridSearchCV object and a cv of 10 worked the best.\n"
      ]
    },
    {
      "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": "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": "3.11.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}