jupyter-labs-SpaceX-ML-Prediction.ipynb

{
  "metadata": {
    "kernelspec": {
      "name": "python",
      "display_name": "Python (Pyodide)",
      "language": "python"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    },
    "prev_pub_hash": "d01cd38c8e03632c08cdf905ff282a58063434d964488aa1bb4965ef6563e17d"
  },
  "nbformat_minor": 4,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "source": "<p style=\"text-align:center\">\n    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\" target=\"_blank\">\n    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\">\n    </a>\n</p>\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# **Space X  Falcon 9 First Stage Landing Prediction**\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Assignment:  Machine Learning Prediction\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Estimated time needed: **60** minutes\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Space X advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is because Space X can reuse the first stage. Therefore if we can determine if the first stage will land, we can determine the cost of a launch. This information can be used if an alternate company wants to bid against space X for a rocket launch.   In this lab, you will create a machine learning pipeline  to predict if the first stage will land given the data from the preceding labs.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing_1.gif)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Several examples of an unsuccessful landing are shown here:\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Most unsuccessful landings are planed. Space X; performs a controlled landing in the oceans.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Objectives\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Perform exploratory  Data Analysis and determine Training Labels\n\n*   create a column for the class\n*   Standardize the data\n*   Split into training data and test data\n\n\\-Find best Hyperparameter for SVM, Classification Trees and Logistic Regression\n\n*   Find the method performs best using test data\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Import Libraries and Define Auxiliary Functions\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import piplite\nawait piplite.install(['numpy'])\nawait piplite.install(['pandas'])\nawait piplite.install(['seaborn'])",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 1
    },
    {
      "cell_type": "markdown",
      "source": "We will import the following libraries for the lab\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Pandas is a software library written for the Python programming language for data manipulation and analysis.\nimport pandas as pd\n# NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\nimport numpy as np\n# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\nimport matplotlib.pyplot as plt\n#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\nimport seaborn as sns\n# Preprocessing allows us to standarsize our data\nfrom sklearn import preprocessing\n# Allows us to split our data into training and testing data\nfrom sklearn.model_selection import train_test_split\n# Allows us to test parameters of classification algorithms and find the best one\nfrom sklearn.model_selection import GridSearchCV\n# Logistic Regression classification algorithm\nfrom sklearn.linear_model import LogisticRegression\n# Support Vector Machine classification algorithm\nfrom sklearn.svm import SVC\n# Decision Tree classification algorithm\nfrom sklearn.tree import DecisionTreeClassifier\n# K Nearest Neighbors classification algorithm\nfrom sklearn.neighbors import KNeighborsClassifier",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 2
    },
    {
      "cell_type": "markdown",
      "source": "This function is to plot the confusion matrix.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "def plot_confusion_matrix(y,y_predict):\n    \"this function plots the confusion matrix\"\n    from sklearn.metrics import confusion_matrix\n\n    cm = confusion_matrix(y, y_predict)\n    ax= plt.subplot()\n    sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells\n    ax.set_xlabel('Predicted labels')\n    ax.set_ylabel('True labels')\n    ax.set_title('Confusion Matrix'); \n    ax.xaxis.set_ticklabels(['did not land', 'land']); ax.yaxis.set_ticklabels(['did not land', 'landed']) \n    plt.show() ",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 3
    },
    {
      "cell_type": "markdown",
      "source": "## Load the dataframe\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Load the data\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from js import fetch\nimport io\n\nURL1 = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\"\nresp1 = await fetch(URL1)\ntext1 = io.BytesIO((await resp1.arrayBuffer()).to_py())\ndata = pd.read_csv(text1)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 4
    },
    {
      "cell_type": "code",
      "source": "data.head()",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 5,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n\n       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n0    None None        1     False   False  False        NaN    1.0   \n1    None None        1     False   False  False        NaN    1.0   \n2    None None        1     False   False  False        NaN    1.0   \n3  False Ocean        1     False   False  False        NaN    1.0   \n4    None None        1     False   False  False        NaN    1.0   \n\n   ReusedCount Serial   Longitude   Latitude  Class  \n0            0  B0003  -80.577366  28.561857      0  \n1            0  B0005  -80.577366  28.561857      0  \n2            0  B0007  -80.577366  28.561857      0  \n3            0  B1003 -120.610829  34.632093      0  \n4            0  B1004  -80.577366  28.561857      0  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>Date</th>\n      <th>BoosterVersion</th>\n      <th>PayloadMass</th>\n      <th>Orbit</th>\n      <th>LaunchSite</th>\n      <th>Outcome</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>LandingPad</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2010-06-04</td>\n      <td>Falcon 9</td>\n      <td>6104.959412</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0003</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2012-05-22</td>\n      <td>Falcon 9</td>\n      <td>525.000000</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0005</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>2013-03-01</td>\n      <td>Falcon 9</td>\n      <td>677.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0007</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>2013-09-29</td>\n      <td>Falcon 9</td>\n      <td>500.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>False Ocean</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1003</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>2013-12-03</td>\n      <td>Falcon 9</td>\n      <td>3170.000000</td>\n      <td>GTO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1004</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 5
    },
    {
      "cell_type": "code",
      "source": "URL2 = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_3.csv'\nresp2 = await fetch(URL2)\ntext2 = io.BytesIO((await resp2.arrayBuffer()).to_py())\nX = pd.read_csv(text2)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 6
    },
    {
      "cell_type": "code",
      "source": "X.head(100)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 7,
          "output_type": "execute_result",
          "data": {
            "text/plain": "    FlightNumber   PayloadMass  Flights  Block  ReusedCount  Orbit_ES-L1  \\\n0            1.0   6104.959412      1.0    1.0          0.0          0.0   \n1            2.0    525.000000      1.0    1.0          0.0          0.0   \n2            3.0    677.000000      1.0    1.0          0.0          0.0   \n3            4.0    500.000000      1.0    1.0          0.0          0.0   \n4            5.0   3170.000000      1.0    1.0          0.0          0.0   \n..           ...           ...      ...    ...          ...          ...   \n85          86.0  15400.000000      2.0    5.0          2.0          0.0   \n86          87.0  15400.000000      3.0    5.0          2.0          0.0   \n87          88.0  15400.000000      6.0    5.0          5.0          0.0   \n88          89.0  15400.000000      3.0    5.0          2.0          0.0   \n89          90.0   3681.000000      1.0    5.0          0.0          0.0   \n\n    Orbit_GEO  Orbit_GTO  Orbit_HEO  Orbit_ISS  ...  Serial_B1058  \\\n0         0.0        0.0        0.0        0.0  ...           0.0   \n1         0.0        0.0        0.0        0.0  ...           0.0   \n2         0.0        0.0        0.0        1.0  ...           0.0   \n3         0.0        0.0        0.0        0.0  ...           0.0   \n4         0.0        1.0        0.0        0.0  ...           0.0   \n..        ...        ...        ...        ...  ...           ...   \n85        0.0        0.0        0.0        0.0  ...           0.0   \n86        0.0        0.0        0.0        0.0  ...           1.0   \n87        0.0        0.0        0.0        0.0  ...           0.0   \n88        0.0        0.0        0.0        0.0  ...           0.0   \n89        0.0        0.0        0.0        0.0  ...           0.0   \n\n    Serial_B1059  Serial_B1060  Serial_B1062  GridFins_False  GridFins_True  \\\n0            0.0           0.0           0.0             1.0            0.0   \n1            0.0           0.0           0.0             1.0            0.0   \n2            0.0           0.0           0.0             1.0            0.0   \n3            0.0           0.0           0.0             1.0            0.0   \n4            0.0           0.0           0.0             1.0            0.0   \n..           ...           ...           ...             ...            ...   \n85           0.0           1.0           0.0             0.0            1.0   \n86           0.0           0.0           0.0             0.0            1.0   \n87           0.0           0.0           0.0             0.0            1.0   \n88           0.0           1.0           0.0             0.0            1.0   \n89           0.0           0.0           1.0             0.0            1.0   \n\n    Reused_False  Reused_True  Legs_False  Legs_True  \n0            1.0          0.0         1.0        0.0  \n1            1.0          0.0         1.0        0.0  \n2            1.0          0.0         1.0        0.0  \n3            1.0          0.0         1.0        0.0  \n4            1.0          0.0         1.0        0.0  \n..           ...          ...         ...        ...  \n85           0.0          1.0         0.0        1.0  \n86           0.0          1.0         0.0        1.0  \n87           0.0          1.0         0.0        1.0  \n88           0.0          1.0         0.0        1.0  \n89           1.0          0.0         0.0        1.0  \n\n[90 rows x 83 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Flights</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Orbit_ES-L1</th>\n      <th>Orbit_GEO</th>\n      <th>Orbit_GTO</th>\n      <th>Orbit_HEO</th>\n      <th>Orbit_ISS</th>\n      <th>...</th>\n      <th>Serial_B1058</th>\n      <th>Serial_B1059</th>\n      <th>Serial_B1060</th>\n      <th>Serial_B1062</th>\n      <th>GridFins_False</th>\n      <th>GridFins_True</th>\n      <th>Reused_False</th>\n      <th>Reused_True</th>\n      <th>Legs_False</th>\n      <th>Legs_True</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>6104.959412</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.0</td>\n      <td>525.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.0</td>\n      <td>677.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>500.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3170.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>86.0</td>\n      <td>15400.000000</td>\n      <td>2.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>87.0</td>\n      <td>15400.000000</td>\n      <td>3.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>88.0</td>\n      <td>15400.000000</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>89.0</td>\n      <td>15400.000000</td>\n      <td>3.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>90.0</td>\n      <td>3681.000000</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>90 rows × 83 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 7
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  1\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a NumPy array from the column <code>Class</code> in <code>data</code>, by applying the method <code>to_numpy()</code>  then\nassign it  to the variable <code>Y</code>,make sure the output is a  Pandas series (only one bracket df\\['name of  column']).\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "Y = data['Class'].to_numpy()",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 8
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  2\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Standardize the data in <code>X</code> then reassign it to the variable  <code>X</code> using the transform provided below.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# students get this \ntransform = preprocessing.StandardScaler()\nX = transform.fit_transform(X)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 9
    },
    {
      "cell_type": "markdown",
      "source": "We split the data into training and testing data using the  function  <code>train_test_split</code>.   The training data is divided into validation data, a second set used for training  data; then the models are trained and hyperparameters are selected using the function <code>GridSearchCV</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  3\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Use the function train_test_split to split the data X and Y into training and test data. Set the parameter test_size to  0.2 and random_state to 2. The training data and test data should be assigned to the following labels.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<code>X_train, X_test, Y_train, Y_test</code>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 10
    },
    {
      "cell_type": "markdown",
      "source": "we can see we only have 18 test samples.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "Y_test.shape",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 11,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(18,)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 11
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  4\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a logistic regression object  then create a  GridSearchCV object  <code>logreg_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "parameters ={'C':[0.01,0.1,1],\n             'penalty':['l2'],\n             'solver':['lbfgs']}",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 12
    },
    {
      "cell_type": "code",
      "source": "parameters ={\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge\n# Create a logistic regression object\nlr=LogisticRegression()\n# Create a GridSearchCV object with logistic regression and parameter grid\nlogreg_cv = GridSearchCV(lr, parameters, cv=10, scoring='accuracy')\n# Fit the GridSearchCV object to your data\nlogreg_cv.fit(X_test, Y_test)\n",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stderr",
          "text": "/lib/python3.11/site-packages/sklearn/model_selection/_split.py:737: UserWarning: The least populated class in y has only 6 members, which is less than n_splits=10.\n  warnings.warn(\n",
          "output_type": "stream"
        },
        {
          "execution_count": 13,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=10, estimator=LogisticRegression(),\n             param_grid={'C': [0.01, 0.1, 1], 'penalty': ['l2'],\n                         'solver': ['lbfgs']},\n             scoring='accuracy')",
            "text/html": "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=LogisticRegression(),\n             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1], &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;]},\n             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=LogisticRegression(),\n             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1], &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;]},\n             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 13
    },
    {
      "cell_type": "markdown",
      "source": "We output the <code>GridSearchCV</code> object for logistic regression. We display the best parameters using the data attribute <code>best_params\\_</code> and the accuracy on the validation data using the data attribute <code>best_score\\_</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",logreg_cv.best_params_)\nprint(\"accuracy :\",logreg_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'C': 1, 'penalty': 'l2', 'solver': 'lbfgs'}\naccuracy : 0.85\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 14
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  5\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Calculate the accuracy on the test data using the method <code>score</code>:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Get the best estimator\n#best_logreg = logreg_cv.best_estimator_\n\n# Calculate the accuracy on the test data\naccuracy = logreg_cv.score(X_test, Y_test)\n\nprint(\"Accuracy on test data: \", accuracy)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Accuracy on test data:  1.0\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 15
    },
    {
      "cell_type": "markdown",
      "source": "Lets look at the confusion matrix:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "yhat=logreg_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhIAAAHHCAYAAADqJrG+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAABFB0lEQVR4nO3deZyN9f//8ecZY86MMTPGPiPLWGJkK1uyf8hSZKms1RD1KYQsybcwQzVFIUqiQlLRgqhkTyJhDLJlGUu2kX2dGXOu3x/9nE/HzGTmuI7rOD3u3a7bbc77ep/3+3XNLXn13i6bYRiGAAAA3OBndQAAAOD2RSIBAADcRiIBAADcRiIBAADcRiIBAADcRiIBAADcRiIBAADcRiIBAADcRiIBAADcRiIBeNDu3bvVrFkzhYWFyWazad68eaa2v3//ftlsNk2fPt3Udm9njRo1UqNGjawOA/jXIJGAz9u7d6/++9//qnTp0goMDFRoaKjq1q2rt99+W5cvX/Zo3zExMdq6dateffVVzZw5UzVq1PBof7dSt27dZLPZFBoamunvcffu3bLZbLLZbHrzzTdz3P6RI0cUGxurxMREE6IF4Cn+VgcAeNK3336rRx99VHa7XU888YQqVaqk1NRUrV69WoMHD9a2bds0ZcoUj/R9+fJlrV27Vi+99JL69OnjkT5Kliypy5cvK3fu3B5p/0b8/f116dIlLViwQB06dHC5N2vWLAUGBurKlStutX3kyBHFxcWpVKlSqlatWra/t3jxYrf6A+AeEgn4rKSkJHXq1EklS5bU8uXLFRER4bzXu3dv7dmzR99++63H+j9x4oQkKV++fB7rw2azKTAw0GPt34jdblfdunX12WefZUgkPv30Uz344IP66quvbkksly5dUp48eRQQEHBL+gPwF6Y24LNGjx6tCxcu6MMPP3RJIq4pW7as+vXr5/x89epVjRo1SmXKlJHdblepUqX0f//3f0pJSXH5XqlSpdSqVSutXr1atWrVUmBgoEqXLq2PP/7YWSc2NlYlS5aUJA0ePFg2m02lSpWS9NeUwLWf/y42NlY2m82lbMmSJapXr57y5cunvHnzqnz58vq///s/5/2s1kgsX75c9evXV3BwsPLly6c2bdpox44dmfa3Z88edevWTfny5VNYWJi6d++uS5cuZf2LvU6XLl30/fff68yZM86y9evXa/fu3erSpUuG+qdOndKgQYNUuXJl5c2bV6GhoWrZsqU2b97srLNy5UrVrFlTktS9e3fnFMm152zUqJEqVaqkjRs3qkGDBsqTJ4/z93L9GomYmBgFBgZmeP7mzZsrPDxcR44cyfazAsiIRAI+a8GCBSpdurTuu+++bNXv2bOnhg8frnvuuUfjxo1Tw4YNFR8fr06dOmWou2fPHj3yyCO6//779dZbbyk8PFzdunXTtm3bJEnt27fXuHHjJEmdO3fWzJkzNX78+BzFv23bNrVq1UopKSkaOXKk3nrrLT300EP6+eef//F7S5cuVfPmzZWcnKzY2FgNGDBAa9asUd26dbV///4M9Tt06KDz588rPj5eHTp00PTp0xUXF5ftONu3by+bzaavv/7aWfbpp5+qQoUKuueeezLU37dvn+bNm6dWrVpp7NixGjx4sLZu3aqGDRs6/1KPjo7WyJEjJUlPP/20Zs6cqZkzZ6pBgwbOdk6ePKmWLVuqWrVqGj9+vBo3bpxpfG+//bYKFSqkmJgYpaenS5Lef/99LV68WBMnTlRkZGS2nxVAJgzAB509e9aQZLRp0yZb9RMTEw1JRs+ePV3KBw0aZEgyli9f7iwrWbKkIclYtWqVsyw5Odmw2+3GwIEDnWVJSUmGJGPMmDEubcbExBglS5bMEMOIESOMv/+RHDdunCHJOHHiRJZxX+tj2rRpzrJq1aoZhQsXNk6ePOks27x5s+Hn52c88cQTGfp78sknXdps166dUaBAgSz7/PtzBAcHG4ZhGI888ojRpEkTwzAMIz093ShatKgRFxeX6e/gypUrRnp6eobnsNvtxsiRI51l69evz/Bs1zRs2NCQZEyePDnTew0bNnQp++GHHwxJxiuvvGLs27fPyJs3r9G2bdsbPiOAG2NEAj7p3LlzkqSQkJBs1f/uu+8kSQMGDHApHzhwoCRlWEtRsWJF1a9f3/m5UKFCKl++vPbt2+d2zNe7trZi/vz5cjgc2frO0aNHlZiYqG7duil//vzO8ipVquj+++93PuffPfPMMy6f69evr5MnTzp/h9nRpUsXrVy5UseOHdPy5ct17NixTKc1pL/WVfj5/fWfnvT0dJ08edI5bZOQkJDtPu12u7p3756tus2aNdN///tfjRw5Uu3bt1dgYKDef//9bPcFIGskEvBJoaGhkqTz589nq/6BAwfk5+ensmXLupQXLVpU+fLl04EDB1zKS5QokaGN8PBwnT592s2IM+rYsaPq1q2rnj17qkiRIurUqZPmzJnzj0nFtTjLly+f4V50dLT+/PNPXbx40aX8+mcJDw+XpBw9ywMPPKCQkBDNnj1bs2bNUs2aNTP8Lq9xOBwaN26cypUrJ7vdroIFC6pQoULasmWLzp49m+0+ixUrlqOFlW+++aby58+vxMRETZgwQYULF872dwFkjUQCPik0NFSRkZH67bffcvS96xc7ZiVXrlyZlhuG4XYf1+bvrwkKCtKqVau0dOlSPf7449qyZYs6duyo+++/P0Pdm3Ezz3KN3W5X+/btNWPGDM2dOzfL0QhJeu211zRgwAA1aNBAn3zyiX744QctWbJEd911V7ZHXqS/fj85sWnTJiUnJ0uStm7dmqPvAsgaiQR8VqtWrbR3716tXbv2hnVLliwph8Oh3bt3u5QfP35cZ86cce7AMEN4eLjLDodrrh/1kCQ/Pz81adJEY8eO1fbt2/Xqq69q+fLlWrFiRaZtX4tz165dGe7t3LlTBQsWVHBw8M09QBa6dOmiTZs26fz585kuUL3myy+/VOPGjfXhhx+qU6dOatasmZo2bZrhd5LdpC47Ll68qO7du6tixYp6+umnNXr0aK1fv9609oF/MxIJ+KwXXnhBwcHB6tmzp44fP57h/t69e/X2229L+mtoXlKGnRVjx46VJD344IOmxVWmTBmdPXtWW7ZscZYdPXpUc+fOdal36tSpDN+9djDT9VtSr4mIiFC1atU0Y8YMl7+Yf/vtNy1evNj5nJ7QuHFjjRo1Su+8846KFi2aZb1cuXJlGO344osvdPjwYZeyawlPZklXTg0ZMkQHDx7UjBkzNHbsWJUqVUoxMTFZ/h4BZB8HUsFnlSlTRp9++qk6duyo6Ohol5Mt16xZoy+++ELdunWTJFWtWlUxMTGaMmWKzpw5o4YNG+rXX3/VjBkz1LZt2yy3FrqjU6dOGjJkiNq1a6e+ffvq0qVLeu+993TnnXe6LDYcOXKkVq1apQcffFAlS5ZUcnKyJk2apDvuuEP16tXLsv0xY8aoZcuWqlOnjnr06KHLly9r4sSJCgsLU2xsrGnPcT0/Pz+9/PLLN6zXqlUrjRw5Ut27d9d9992nrVu3atasWSpdurRLvTJlyihfvnyaPHmyQkJCFBwcrNq1aysqKipHcS1fvlyTJk3SiBEjnNtRp02bpkaNGmnYsGEaPXp0jtoDcB2Ld40AHvf7778bTz31lFGqVCkjICDACAkJMerWrWtMnDjRuHLlirNeWlqaERcXZ0RFRRm5c+c2ihcvbgwdOtSljmH8tf3zwQcfzNDP9dsOs9r+aRiGsXjxYqNSpUpGQECAUb58eeOTTz7JsP1z2bJlRps2bYzIyEgjICDAiIyMNDp37mz8/vvvGfq4fovk0qVLjbp16xpBQUFGaGio0bp1a2P79u0uda71d/320mnTphmSjKSkpCx/p4bhuv0zK1lt/xw4cKARERFhBAUFGXXr1jXWrl2b6bbN+fPnGxUrVjT8/f1dnrNhw4bGXXfdlWmff2/n3LlzRsmSJY177rnHSEtLc6n3/PPPG35+fsbatWv/8RkA/DObYeRgRRUAAMDfsEYCAAC4jUQCAAC4jUQCAAC4jUQCAAAftWrVKrVu3VqRkZGy2WyaN2+e815aWpqGDBmiypUrKzg4WJGRkXriiSdy/EZcEgkAAHzUxYsXVbVqVb377rsZ7l26dEkJCQkaNmyYEhIS9PXXX2vXrl166KGHctQHuzYAAPgXsNlsmjt3rtq2bZtlnfXr16tWrVo6cOBApu8UygwHUgEAcJtISUnJcCKr3W6X3W43pf2zZ8/KZrM53z6cHT6ZSGy4o63VIQBe6d5k3i8BXO9q6uEbV7pJaX/uM6Wd+Hc+VlxcnEvZiBEjTDm19sqVKxoyZIg6d+7sfINydvhkIgEAgC8aOnSoBgwY4FJmxmhEWlqaOnToIMMw9N577+XouyQSAAB4miPdlGbMnMa45loSceDAAS1fvjxHoxESiQQAAJ5nOKyOIFPXkojdu3drxYoVKlCgQI7bIJEAAMDTHNYkEhcuXNCePXucn5OSkpSYmKj8+fMrIiJCjzzyiBISErRw4UKlp6fr2LFjkqT8+fMrICAgW3345PZPFlsCmWOxJZDRLVlseXSHKe3kjojOUf2VK1eqcePGGcpjYmIUGxurqKioTL+3YsUKNWrUKFt9MCIBAICHGRZNbTRq1Ej/NF5gxlgCiQQAAJ5m0dTGrcAR2QAAwG2MSAAA4GleumvDDCQSAAB4mknnSHgjpjYAAIDbGJEAAMDTmNoAAABuY9cGAABARoxIAADgYVYdSHUrkEgAAOBpPjy1QSIBAICn+fCIBGskAACA2xiRAADA03z4QCoSCQAAPI2pDQAAgIwYkQAAwNPYtQEAANzG1AYAAEBGjEgAAOBpTG0AAAB3GYbvbv9kagMAALiNEQkAADzNhxdbkkgAAOBprJEAAABu8+ERCdZIAAAAtzEiAQCAp/HSLgAA4DamNgAAADJiRAIAAE9j1wYAAHAbUxsAAAAZMSIBAICnMbUBAADc5sOJBFMbAADAbYxIAADgYb78GnESCQAAPM2HpzZIJAAA8DS2fwIAAGTEiAQAAJ7G1AYAAHAbUxsAAAAZMSIBAICnMbUBAADcxtQGAABARoxIAADgaUxtAAAAt5FImCs8PFw2my1bdU+dOuXhaAAAgLssSSTGjx/v/PnkyZN65ZVX1Lx5c9WpU0eStHbtWv3www8aNmyYFeEBAGAuH15saTMMw7AygIcffliNGzdWnz59XMrfeecdLV26VPPmzctxmxvuaGtOcICPuTd5vdUhAF7nauphj/dx+Zs3TWkn6KFBprRjJst3bfzwww9q0aJFhvIWLVpo6dKlFkQEAIDJDIc5lxeyPJEoUKCA5s+fn6F8/vz5KlCggAURAQDgG1atWqXWrVsrMjJSNpstwyi/YRgaPny4IiIiFBQUpKZNm2r37t056sPyXRtxcXHq2bOnVq5cqdq1a0uS1q1bp0WLFmnq1KkWRwcAgAks2rVx8eJFVa1aVU8++aTat2+f4f7o0aM1YcIEzZgxQ1FRURo2bJiaN2+u7du3KzAwMFt9WJ5IdOvWTdHR0ZowYYK+/vprSVJ0dLRWr17tTCwAALitWTQt0bJlS7Vs2TLTe4ZhaPz48Xr55ZfVpk0bSdLHH3+sIkWKaN68eerUqVO2+rA8kZCk2rVra9asWVaHAQDAv0ZSUpKOHTumpk2bOsvCwsJUu3ZtrV279vZKJBwOh/bs2aPk5GQ5rhv+adCggUVRAQBgEpOmNlJSUpSSkuJSZrfbZbfbc9zWsWPHJElFihRxKS9SpIjzXnZYnkj88ssv6tKliw4cOKDrd6LabDalp6dbFBkAACYxKZGIj49XXFycS9mIESMUGxtrSvvusDyReOaZZ1SjRg19++23ioiIyPaJlwAA/NsMHTpUAwYMcClzZzRCkooWLSpJOn78uCIiIpzlx48fV7Vq1bLdjuWJxO7du/Xll1+qbNmyVocCAIBnmHT2o7vTGJmJiopS0aJFtWzZMmficO7cOa1bt07PPvtsttuxPJGoXbu29uzZQyIBAPBdFm3/vHDhgvbs2eP8nJSUpMTEROXPn18lSpRQ//799corr6hcuXLO7Z+RkZFq27ZttvuwPJF47rnnNHDgQB07dkyVK1dW7ty5Xe5XqVLFosgAALi9bdiwQY0bN3Z+vjYtEhMTo+nTp+uFF17QxYsX9fTTT+vMmTOqV6+eFi1alO0zJCQveNeGn1/GwzVtNpsMw3B7sSXv2gAyx7s2gIxuybs2ZpnzEsqgrqNMacdMlo9IJCUlWR0CAACe5aXvyTCD5YlEyZIlrQ4BAADPsmiNxK1geSJxzfbt23Xw4EGlpqa6lD/00EMWRQQAAG7E8kRi3759ateunbZu3epcGyHJeZ4EB1IBAG571i5H9CjLXyPer18/RUVFKTk5WXny5NG2bdu0atUq1ahRQytXrrQ6PAAAbp7DYc7lhSwfkVi7dq2WL1+uggULys/PT35+fqpXr57i4+PVt29fbdq0yeoQAQBAFiwfkUhPT1dISIgkqWDBgjpy5IikvxZh7tq1y8rQAAAwByMSnlOpUiVt3rxZUVFRql27tkaPHq2AgABNmTJFpUuXtjo8AABuHts/Pefll1/WxYsXJUkjR45Uq1atVL9+fRUoUECzZ8+2ODoAAPBPLE8kmjdv7vy5bNmy2rlzp06dOqXw8HDeBAoA8AmGw3d3bVieSGQmf/78VocAAIB5vHR9gxksSSTat2+f7bpff/21ByMBAAA3w5JEIiwszIpuAQCwBostzTVt2jQrugUAwBqskQAAAG7z4TUSlh9IBQAAbl+MSAAA4Gk+PCJBIgEAgKfx9k/P+fjjj5WSkpKhPDU1VR9//LEFEQEAgOyyPJHo3r27zp49m6H8/Pnz6t69uwURwQy5i+ZX1IT+qrb1Y92zZ7YqLn1beaqUsToswHLPPhOjPb//ogvn9mrN6gWqWaOa1SHhVuClXZ5jGEamR2H/8ccfnDdxm8oVFqwKc1/X+TVbtfvxUUo7eVaBUZFKP3vR6tAASz366EN6c8wI9er9on5dv0l9n+up776dpYqVGujEiZNWhwdPYvun+e6++27ZbDbZbDY1adJE/v7/CyU9PV1JSUlq0aKFVeHhJhTt1V6pR/7U/oETnWWph5ItjAjwDs/3e0offPipZnw8R5LUq/eLeqBlE3Xv1kmjx7xrcXSAeyxLJNq2bStJSkxMVPPmzZU3b17nvYCAAJUqVUoPP/ywRdHhZuS7v5bO/bhJpScPVsi9dynt2Cklf/y9/vx0idWhAZbJnTu37rmnil4f/Y6zzDAMLVu+WvfeW93CyHBLcLKl+UaMGCFJKlWqlDp27KjAwECrQoHJ7CWKqNDjLXR86jc6OvFLBVcrpxIje8pIvaqTX66wOjzAEgUL5pe/v7+Sj//pUp6cfEIVyrN+yOcxteE5MTExkqSNGzdqx44dkqS77rpLd999d7a+n5KSkmHXR6qRrgBbLnMDRfb52XRpy14dfuMTSdLlbUkKKl9ChR5vTiIBAD7G8l0bycnJ+s9//qOaNWuqb9++6tu3r6pXr64mTZroxIkTN/x+fHy8wsLCXK7p53ffgsiRlbTk07q8+5BL2ZXdfyigWCGLIgKs9+efp3T16lUVLlLQpbxw4UI6dvzG/63D7c1wOEy5vJHlicRzzz2n8+fPa9u2bTp16pROnTql3377TefOnVPfvn1v+P2hQ4fq7NmzLle3kHK3IHJk5cKGnQosXcylLLB0pFL/4D+W+PdKS0tTQsIW/adxPWeZzWbTfxrX0y+/bLQwMtwSDsOcywtZPrWxaNEiLV26VNHR0c6yihUr6t1331WzZs1u+H273S673e5SxrSGtY5P/UYV5r2uon0e0emFqxVc7U4V7NpMB4ZMsjo0wFLj3p6qaR+O08aELVq/fpP6PveUgoODNH3GbKtDg6ex2NJzHA6HcufOnaE8d+7ccnjpMA7+2aXNe7S35+sqNvRxRfbvoJRDx3Uo9kOdmrvK6tAAS33xxTcqVDC/YocPUtGihbR58zY92OoxJSf/eeMvA17KZhjWHgDepk0bnTlzRp999pkiIyMlSYcPH1bXrl0VHh6uuXPn5rjNDXe0NTlKwDfcm7ze6hAAr3M19bDH+7g4sqsp7QQPn2VKO2ayfI3EO++8o3PnzqlUqVIqU6aMypQpo6ioKJ07d04TJ068cQMAAHg7jsj2nOLFiyshIUFLly7Vzp07JUnR0dFq2rSpxZEBAIAbsTyRkP5auXz//ffr/vvvtzoUAADM56U7LszgFYnEsmXLtGzZMiUnJ2dYYPnRRx9ZFBUAACZh14bnxMXFaeTIkapRo4YiIiIyfRMoAADwTpYnEpMnT9b06dP1+OOPWx0KAACewdSG56Smpuq+++6zOgwAADzGW4+3NoPl2z979uypTz/91OowAACAGywfkbhy5YqmTJmipUuXqkqVKhlOuRw7dqxFkQEAYBKmNjxny5YtqlatmiTpt99+c7nHwksAgE8gkfCcFStWWB0CAACe5cPbPy1fIwEAAG5flo9IAADg85jaAAAA7jJ8OJFgagMAALiNEQkAADzNh0ckLEkkvvnmm2zXfeihhzwYCQAAt4APn2xpSSLRtm1bl882m02GYbh8viY9Pf1WhQUAAHLIkjUSDofDeS1evFjVqlXT999/rzNnzujMmTP67rvvdM8992jRokVWhAcAgLkchjmXF7J8jUT//v01efJk1atXz1nWvHlz5cmTR08//bR27NhhYXQAAJjAS5MAM1i+a2Pv3r3Kly9fhvKwsDDt37//lscDAACyz/JEombNmhowYICOHz/uLDt+/LgGDx6sWrVqWRgZAADmMAzDlCsn0tPTNWzYMEVFRSkoKEhlypTRqFGjctzOjVg+tfHRRx+pXbt2KlGihIoXLy5JOnTokMqVK6d58+ZZGxwAAGawYGrjjTfe0HvvvacZM2borrvu0oYNG9S9e3eFhYWpb9++pvVjeSJRtmxZbdmyRUuWLNHOnTslSdHR0WratClv/wQA+AYLEok1a9aoTZs2evDBByVJpUqV0meffaZff/3V1H4sTySkv7Z7NmvWTM2aNbM6FAAAvFZKSopSUlJcyux2u+x2e4a69913n6ZMmaLff/9dd955pzZv3qzVq1dr7NixpsZkSSIxYcIEPf300woMDNSECRP+sa6Zwy8AAFjBrHdtxMfHKy4uzqVsxIgRio2NzVD3xRdf1Llz51ShQgXlypVL6enpevXVV9W1a1dTYrnGZpi96iIboqKitGHDBhUoUEBRUVFZ1rPZbNq3b1+O299wR9ubiA7wXfcmr7c6BMDrXE097PE+zsY0MaWdwCnfZXtE4vPPP9fgwYM1ZswY3XXXXUpMTFT//v01duxYxcTEmBKPZNGIRFJSUqY/AwCArGWVNGRm8ODBevHFF9WpUydJUuXKlXXgwAHFx8ff/okEAAD/Kha8auPSpUvy83M95SFXrlxymPzeD0sSiQEDBmS7rtmLQgAAuNXMWiORE61bt9arr76qEiVK6K677tKmTZs0duxYPfnkk6b2Y0kisWnTJpfPCQkJunr1qsqXLy9J+v3335UrVy5Vr17divAAALjtTZw4UcOGDVOvXr2UnJysyMhI/fe//9Xw4cNN7ceSRGLFihXOn8eOHauQkBDNmDFD4eHhkqTTp0+re/fuql+/vhXhAQBgLgtGJEJCQjR+/HiNHz/eo/1Ysmvj74oVK6bFixfrrrvucin/7bff1KxZMx05ciTHbbJrA8gcuzaAjG7Fro0zHRub0k6+2StuXOkWs/xdG+fOndOJEycylJ84cULnz5+3ICIAAJBdlu/aaNeunbp376633nrL+ZKudevWafDgwWrfvr3F0QEAcPOsWGx5q1ieSEyePFmDBg1Sly5dlJaWJkny9/dXjx49NGbMGIujAwDABBZs/7xVLF8jcc3Fixe1d+9eSVKZMmUUHBzsdluskQAyxxoJIKNbsUbiVLuGprSTf+6PprRjJstHJK4JDg5WlSpVrA4DAADkgNckEgAA+CwfntogkQAAwMMMH04kLN/+CQAAbl+MSAAA4Gk+PCJBIgEAgIcxtQEAAJAJRiQAAPA0Hx6RIJEAAMDDfHlqg0QCAAAP8+VEgjUSAADAbYxIAADgYb48IkEiAQCApxk2qyPwGKY2AACA20wZkThz5ozy5ctnRlMAAPgcX57ayPGIxBtvvKHZs2c7P3fo0EEFChRQsWLFtHnzZlODAwDAFxgOmymXN8pxIjF58mQVL15ckrRkyRItWbJE33//vVq2bKnBgwebHiAAAPBeOZ7aOHbsmDORWLhwoTp06KBmzZqpVKlSql27tukBAgBwu2Nq42/Cw8N16NAhSdKiRYvUtGlTSZJhGEpPTzc3OgAAfIBh2Ey5vFGORyTat2+vLl26qFy5cjp58qRatmwpSdq0aZPKli1reoAAAMB75TiRGDdunEqVKqVDhw5p9OjRyps3ryTp6NGj6tWrl+kBAgBwu/PlqQ2bYRiG1UGYbcMdba0OAfBK9yavtzoEwOtcTT3s8T4O1WxiSjvF1y8zpR0zZWtE4ptvvsl2gw899JDbwQAA4It873/Z/ydbiUTbtm2z1ZjNZmPBJQAA/yLZSiQcDh+e3AEAwMO89TApM9zUEdlXrlxRYGCgWbEAAOCTfDmRyPE5Eunp6Ro1apSKFSumvHnzat++fZKkYcOG6cMPPzQ9QAAA4L1ynEi8+uqrmj59ukaPHq2AgABneaVKlfTBBx+YGhwAAL7AMMy5vFGOE4mPP/5YU6ZMUdeuXZUrVy5nedWqVbVz505TgwMAwBfw0q6/OXz4cKYnWDocDqWlpZkSFAAAuD3kOJGoWLGifvrppwzlX375pe6++25TggIAwJfwro2/GT58uGJiYnT48GE5HA59/fXX2rVrlz7++GMtXLjQEzECAHBb8+UjsnM8ItGmTRstWLBAS5cuVXBwsIYPH64dO3ZowYIFuv/++z0RIwAA8FJunSNRv359LVmyxOxYAADwSQ4vnZYwg9sHUm3YsEE7duyQ9Ne6ierVq5sWFAAAvsRb1zeYIceJxB9//KHOnTvr559/Vr58+SRJZ86c0X333afPP/9cd9xxh9kxAgBwW/PWrZtmyPEaiZ49eyotLU07duzQqVOndOrUKe3YsUMOh0M9e/b0RIwAAMBL5XhE4scff9SaNWtUvnx5Z1n58uU1ceJE1a9f39TgAADwBd56KqUZcpxIFC9ePNODp9LT0xUZGWlKUAAA+BKmNv5mzJgxeu6557RhwwZn2YYNG9SvXz+9+eabpgYHAAC8W7ZGJMLDw2Wz/S+bunjxomrXri1//7++fvXqVfn7++vJJ59U27ZtPRIoAAC3q3/99s/x48d7OAwAAHzXv377Z0xMjKfjAAAAtyG3D6SSpCtXrig1NdWlLDQ09KYCAgDA17Br428uXryoIUOGaM6cOTp58mSG++np6aYEBgCAr/DlNRI53rXxwgsvaPny5Xrvvfdkt9v1wQcfKC4uTpGRkfr44489ESMAAPBSOU4kFixYoEmTJunhhx+Wv7+/6tevr5dfflmvvfaaZs2a5YkYAQC4rRmGzZQrpw4fPqzHHntMBQoUUFBQkCpXruxyfIMZcjy1cerUKZUuXVrSX+shTp06JUmqV6+enn32WVODAwDAF1ixRuL06dOqW7euGjdurO+//16FChXS7t27FR4ebmo/OU4kSpcuraSkJJUoUUIVKlTQnDlzVKtWLS1YsMD5Ei8AAPA/VqyReOONN1S8eHFNmzbNWRYVFWV6Pzme2ujevbs2b94sSXrxxRf17rvvKjAwUM8//7wGDx5seoAAAOAvKSkpOnfunMuVkpKSad1vvvlGNWrU0KOPPqrChQvr7rvv1tSpU02PyWYYNzfgcuDAAW3cuFFly5ZVlSpVzIrrpvgHFLM6BMArXT7yk9UhAF4nd8HSHu9jfbF2prTz7VNVFRcX51I2YsQIxcbGZqgbGBgoSRowYIAeffRRrV+/Xv369dPkyZNNPR/qphMJb0QiAWSORALI6FYkEusi25vSTrWkzzKMQNjtdtnt9gx1AwICVKNGDa1Zs8ZZ1rdvX61fv15r1641JR4pm2skJkyYkO0G+/bt63YwAAAga1klDZmJiIhQxYoVXcqio6P11VdfmRpTthKJcePGZasxm81GIgEAwHWsGPqvW7eudu3a5VL2+++/q2TJkqb2k61EIikpydROAQD4N7Fi18bzzz+v++67T6+99po6dOigX3/9VVOmTNGUKVNM7SfHuzYAAID3q1mzpubOnavPPvtMlSpV0qhRozR+/Hh17drV1H5u6qVdAADgxqx6jXirVq3UqlUrj/ZBIgEAgIc5rA7Ag5jaAAAAbmNEAgAADzPEa8Rd/PTTT3rsscdUp04dHT58WJI0c+ZMrV692tTgAADwBQ7DnMsb5TiR+Oqrr9S8eXMFBQVp06ZNzhO2zp49q9dee830AAEAuN05ZDPl8kY5TiReeeUVTZ48WVOnTlXu3Lmd5XXr1lVCQoKpwQEAAO+W4zUSu3btUoMGDTKUh4WF6cyZM2bEBACAT2GNxN8ULVpUe/bsyVC+evVqlS7t+RefAABwu3GYdHmjHCcSTz31lPr166d169bJZrPpyJEjmjVrlgYNGqRnn33WEzECAAAvleOpjRdffFEOh0NNmjTRpUuX1KBBA9ntdg0aNEjPPfecJ2IEAOC25stTGzbDMNzaUJKamqo9e/bowoULqlixovLmzWt2bG7zDyhmdQiAV7p85CerQwC8Tu6Cnp+WX1SkkynttDj+uSntmMntA6kCAgIyvOccAAD8u+Q4kWjcuLFstqyHaJYvX35TAQEA4Gu8daGkGXKcSFSrVs3lc1pamhITE/Xbb78pJibGrLgAAPAZvrxGIseJxLhx4zItj42N1YULF246IAAAcPsw7e2fjz32mD766COzmgMAwGc4bOZc3si0t3+uXbtWgYGBZjUHAIDP8Nb3ZJghx4lE+/btXT4bhqGjR49qw4YNGjZsmGmBAQDgK7z0xZ2myHEiERYW5vLZz89P5cuX18iRI9WsWTPTAgMAAN4vR4lEenq6unfvrsqVKys8PNxTMQEA4FN8eftnjhZb5sqVS82aNeMtnwAA5IDDZjPl8kY53rVRqVIl7du3zxOxAACA20yOE4lXXnlFgwYN0sKFC3X06FGdO3fO5QIAAK4Mky5vlO01EiNHjtTAgQP1wAMPSJIeeughl6OyDcOQzWZTenq6+VECAHAb8+U1EtlOJOLi4vTMM89oxYoVnowHAADcRrKdSFx723jDhg09FgwAAL7IW0+lNEOOtn/+01s/AQBA5jjZ8v+78847b5hMnDp16qYCAgAAt48cJRJxcXEZTrYEAAD/zFt3XJghR4lEp06dVLhwYU/FAgCAT2KNhFgfAQCAu3x5+2e2D6S6tmsDAADgmmyPSDgcvpxPAQDgOb78v+I5fo04AADIGV9eI5Hjd20AAABcw4gEAAAe5suLA0gkAADwMF9OJJjaAAAAbmNEAgAADzN8eLEliQQAAB7G1AYAAEAmGJEAAMDDfHlEgkQCAAAP42RLAADgNk62BAAAyAQjEgAAeBhrJAAAgNt8OZFgagMAALiNEQkAADyMXRsAAMBt7NoAAAC3tddff102m039+/c3tV1GJAAA8DCrF1uuX79e77//vqpUqWJ624xIAADgYYZJlzsuXLigrl27aurUqQoPD7+Zx8gUiQQAAD6sd+/eevDBB9W0aVOPtM/UBgAAHuYwad9GSkqKUlJSXMrsdrvsdnum9T///HMlJCRo/fr1pvSfGUYkAADwMIdJV3x8vMLCwlyu+Pj4TPs8dOiQ+vXrp1mzZikwMNBjz2YzDMPntrf6BxSzOgTAK10+8pPVIQBeJ3fB0h7vY2TJrqa0M+T3j7I9IjFv3jy1a9dOuXLlcpalp6fLZrPJz89PKSkpLvfcxdQGAAC3iX+axrhekyZNtHXrVpey7t27q0KFChoyZIgpSYREIgEAgMdZsf0zJCRElSpVcikLDg5WgQIFMpTfDBIJAAA8zJdPtiSRAADgX2LlypWmt0kiAQCAh5m1/dMbkUgAAOBhvptGcI4EAAC4CYxIAADgYVa/tMuTLEsktmzZku26nnhbGQAAtwprJDygWrVqstlsMgxDNts/74tJT0+/RVEBAICcsGyNRFJSkvbt26ekpCR99dVXioqK0qRJk7Rp0yZt2rRJkyZNUpkyZfTVV19ZFSIAAKaw8jXinmbZiETJkiWdPz/66KOaMGGCHnjgAWdZlSpVVLx4cQ0bNkxt27a1IEIAAMzBGgkP27p1q6KiojKUR0VFafv27RZEBACAeXx5jYRXbP+Mjo5WfHy8UlNTnWWpqamKj49XdHS0hZEBAIB/4hUjEpMnT1br1q11xx13OHdobNmyRTabTQsWLLA4OgAAbo7vjkd4SSJRq1Yt7du3T7NmzdLOnTslSR07dlSXLl0UHBxscXQAANwc1kjcAsHBwXr66aetDgMAAOSAV6yRkKSZM2eqXr16ioyM1IEDByRJ48aN0/z58y2ODACAm2OY9I838opE4r333tOAAQPUsmVLnT592nkAVXh4uMaPH29tcAAA3CSHSZc38opEYuLEiZo6dapeeukl+fv/b7alRo0a2rp1q4WRAQCAf+IVaySSkpJ09913Zyi32+26ePGiBREBAGAezpHwsKioKCUmJmYoX7RoEedIAABuexyR7WEDBgxQ7969deXKFRmGoV9//VWfffaZ4uPj9cEHH1gdHgAAyIJXJBI9e/ZUUFCQXn75ZV26dEldunRRZGSk3n77bXXq1Mnq8OCmZ5+J0cABz6po0ULasmW7+vUfpvUbEq0OC7hlNiRu1bRPv9T2nXt04uQpvR0/TE0a3CdJSrt6VROnzNBPazfojyNHlTc4WPfWvFvPP9NdhQsVsDhymI2pjVuga9eu2r17ty5cuKBjx47pjz/+UI8ePawOC2569NGH9OaYERr1yljVrN1Cm7ds13ffzlIh/gOJf5HLl6+ofNnSemlgrwz3rlxJ0fZde/Xfbp0156N3NP61l7X/4B/qMyTOgkjhab68a8NmGIbPpUn+AcWsDuFfb83qBVq/YbP69X9ZkmSz2bR/33q9O2maRo951+Lo/r0uH/nJ6hD+tSrVbekyIpGZrTt2qXPP/lry1QxFFC18C6P7d8tdsLTH++hZ6hFT2vlg/5emtGMmy6Y27r77btlstmzVTUhI8HA0MFPu3Ll1zz1V9Prod5xlhmFo2fLVuvfe6hZGBni3CxcuyWazKSSEVwPg9mFZItG2bVvnz1euXNGkSZNUsWJF1alTR5L0yy+/aNu2berVK+OQ4N+lpKQoJSXFpcwwjGwnKTBfwYL55e/vr+Tjf7qUJyefUIXyZSyKCvBuKSmpGvfeR3qgaUPl5R1DPsdbpyXMYFkiMWLECOfPPXv2VN++fTVq1KgMdQ4dOvSP7cTHxysuznVO0eaXV7ZcoeYFCwAelHb1qgYOe02GYWjY4D5WhwMP8Nbjrc3gFYstv/jiCz3xxBMZyh977DF99dVX//jdoUOH6uzZsy6XzS/EU6EiG/7885SuXr2qwkUKupQXLlxIx46fsCgqwDtdSyKOHE/W1PGvMRqB245XJBJBQUH6+eefM5T//PPPCgwM/Mfv2u12hYaGulxMa1grLS1NCQlb9J/G9ZxlNptN/2lcT7/8stHCyADvci2JOHjoiD4Y/5ryhTGS6qt8edeGV5wj0b9/fz377LNKSEhQrVq1JEnr1q3TRx99pGHDhlkcHdwx7u2pmvbhOG1M2KL16zep73NPKTg4SNNnzLY6NOCWuXTpsg7+ccT5+fCR49r5+16FhYaoYMH8GvDSq9r++x69OzpODodDf548JUkKCw1R7ty5rQobHuDwvQ2STl6z/XPOnDl6++23tWPHDklSdHS0+vXrpw4dOuS4LbZ/eodez3ZzHki1efM29X9+uH5dv8nqsP7V2P55a/2asEVPPjckQ3mblk3Vq8djav5It0y/99HEN1Trnioejg7X3Irtn4+XbG9KOzMPfG1KO2bymkTCTCQSQOZIJICMbkUi8ZhJicQnXphIeMXUxjWpqalKTk6Ww+E6E1SiRAmLIgIA4Ob58hHZXpFI7N69W08++aTWrFnjUn7tPIj09HSLIgMAAP/EKxKJbt26yd/fXwsXLlRERAS7LgAAPsWXz5HwikQiMTFRGzduVIUKFawOBQAA03nr1k0zeEUiUbFiRf355583rggAwG3Il9dIeMWBVG+88YZeeOEFrVy5UidPntS5c+dcLgAA4J28YkSiadOmkqQmTZq4lLPYEgDgC1gj4WErVqywOgQAADyGNRIe1rBhQ6tDAAAAbvCKROKaS5cu6eDBg0pNTXUpr1KFo2IBALcvHzxE2skrEokTJ06oe/fu+v777zO9zxoJAMDtjF0bHta/f3+dOXNG69atU1BQkBYtWqQZM2aoXLly+uabb6wODwAAZMErRiSWL1+u+fPnq0aNGvLz81PJkiV1//33KzQ0VPHx8XrwwQetDhEAALf58mJLrxiRuHjxogoXLixJCg8P14kTJyRJlStXVkJCgpWhAQBw0wyT/vFGXpFIlC9fXrt27ZIkVa1aVe+//74OHz6syZMnKyIiwuLoAABAVrxiaqNfv346evSoJGnEiBFq0aKFPvnkEwUEBGjGjBkWRwcAwM3x5cWWXpFIPPbYY86fq1evrgMHDmjnzp0qUaKEChYsaGFkAADcPLZ/esCAAQOyXXfs2LEejAQAAM/y5cWWliUSmzZtylY9m83m4UgAAIC7LEskeL8GAODfwlt3XJjBK9ZIAADgy3x5saVXbP8EAADmio+PV82aNRUSEqLChQurbdu2zqMWzEQiAQCAhxmGYcqVEz/++KN69+6tX375RUuWLFFaWpqaNWumixcvmvpsTG0AAOBhVkxtLFq0yOXz9OnTVbhwYW3cuFENGjQwrR8SCQAAbhMpKSlKSUlxKbPb7bLb7Tf87tmzZyVJ+fPnNzUmpjYAAPAws961ER8fr7CwMJcrPj7+hv07HA71799fdevWVaVKlUx9NkYkAADwMIdJJ1sOHTo0w4GO2RmN6N27t3777TetXr3alDj+jkQCAIDbRHanMf6uT58+WrhwoVatWqU77rjD9JhIJAAA8DArTpEwDEPPPfec5s6dq5UrVyoqKsoj/ZBIAADgYVbs2ujdu7c+/fRTzZ8/XyEhITp27JgkKSwsTEFBQab1YzN88JVk/gHFrA4B8EqXj/xkdQiA18ldsLTH+6hTrLEp7aw9nP3XS2T1rqpp06apW7dupsQjMSIBAIBPulXjBCQSAAB4mA8O/juRSAAA4GG8tAsAACATjEgAAOBhhg+PSJBIAADgYb68RoKpDQAA4DZGJAAA8DBfXmxJIgEAgIcxtQEAAJAJRiQAAPAwpjYAAIDb2P4JAADc5mCNBAAAQEaMSAAA4GFMbQAAALcxtQEAAJAJRiQAAPAwpjYAAIDbmNoAAADIBCMSAAB4GFMbAADAbUxtAAAAZIIRCQAAPIypDQAA4DbDcFgdgseQSAAA4GG+/Bpx1kgAAAC3MSIBAICHGT68a4NEAgAAD2NqAwAAIBOMSAAA4GFMbQAAALdxsiUAAEAmGJEAAMDDONkSAAC4zZfXSDC1AQAA3MaIBAAAHubL50iQSAAA4GG+PLVBIgEAgIex/RMAACATjEgAAOBhTG0AAAC3+fJiS6Y2AACA2xiRAADAw5jaAAAAbmPXBgAAQCYYkQAAwMN4aRcAAHAbUxsAAACZYEQCAAAPY9cGAABwmy+vkWBqAwAADzMMw5TLHe+++65KlSqlwMBA1a5dW7/++qupz0YiAQCAj5o9e7YGDBigESNGKCEhQVWrVlXz5s2VnJxsWh82wwcnbvwDilkdAuCVLh/5yeoQAK+Tu2Bpz/dh0t9LaamHc1S/du3aqlmzpt555x1JksPhUPHixfXcc8/pxRdfNCUmRiQAAPAww6QrJ1JTU7Vx40Y1bdrUWebn56emTZtq7dq1N/U8f8diSwAAbhMpKSlKSUlxKbPb7bLb7Rnq/vnnn0pPT1eRIkVcyosUKaKdO3eaFpNPJhJXczj0A89ISUlRfHy8hg4dmum/5MC/FX82/n3M+nspNjZWcXFxLmUjRoxQbGysKe27wyfXSMA7nDt3TmFhYTp79qxCQ0OtDgfwGvzZgLtyMiKRmpqqPHny6Msvv1Tbtm2d5TExMTpz5ozmz59vSkyskQAA4DZht9sVGhrqcmU1qhUQEKDq1atr2bJlzjKHw6Fly5apTp06psXkk1MbAABAGjBggGJiYlSjRg3VqlVL48eP18WLF9W9e3fT+iCRAADAR3Xs2FEnTpzQ8OHDdezYMVWrVk2LFi3KsADzZpBIwGPsdrtGjBjBYjLgOvzZwK3Up08f9enTx2Pts9gSAAC4jcWWAADAbSQSAADAbSQSAADAbSQSPqhRo0bq37+/83OpUqU0fvz4f/yOzWbTvHnzPBpXdnXr1s3l8BRPmT59uvLly+fxfuDbrv/z5gmxsbGqVq2aR/sA3MWujX+B9evXKzg42OowFBsbq3nz5ikxMdHqUAAAJiGR+BcoVKiQ1SEAAHwUUxu3uYsXL+qJJ55Q3rx5FRERobfeeitDneunNnbv3q0GDRooMDBQFStW1JIlS27YT6NGjdS3b1+98MILyp8/v4oWLZrhJTEHDx5UmzZtlDdvXoWGhqpDhw46fvy4pL+mEeLi4rR582bZbDbZbDZNnz49W8+4aNEi1atXT/ny5VOBAgXUqlUr7d2713l///79stls+vrrr9W4cWPlyZNHVatWzfCa3OnTp6tEiRLKkyeP2rVrp5MnT2arfyC7Zs6cqRo1aigkJERFixZVly5dlJyc7Ly/cuVK2Ww2LVu2TDVq1FCePHl03333adeuXS7tvP766ypSpIhCQkLUo0cPXbly5VY/CpBtJBK3ucGDB+vHH3/U/PnztXjxYq1cuVIJCQlZ1nc4HGrfvr0CAgK0bt06TZ48WUOGDMlWXzNmzFBwcLDWrVun0aNHa+TIkc4kxOFwqE2bNjp16pR+/PFHLVmyRPv27VPHjh0l/XW62sCBA3XXXXfp6NGjOnr0qPPejVy8eFEDBgzQhg0btGzZMvn5+aldu3ZyOBwu9V566SUNGjRIiYmJuvPOO9W5c2ddvXpVkrRu3Tr16NFDffr0UWJioho3bqxXXnklW/0D2ZWWlqZRo0Zp8+bNmjdvnvbv369u3bplqPfSSy/prbfe0oYNG+Tv768nn3zSeW/OnDmKjY3Va6+9pg0bNigiIkKTJk26hU8B5JCB29b58+eNgIAAY86cOc6ykydPGkFBQUa/fv2cZSVLljTGjRtnGIZh/PDDD4a/v79x+PBh5/3vv//ekGTMnTs3y74aNmxo1KtXz6WsZs2axpAhQwzDMIzFixcbuXLlMg4ePOi8v23bNkOS8euvvxqGYRgjRowwqlatesPniomJMdq0aZPl/RMnThiSjK1btxqGYRhJSUmGJOODDz7I0PeOHTsMwzCMzp07Gw888IBLOx07djTCwsJuGA/wTxo2bOjy5+3v1q9fb0gyzp8/bxiGYaxYscKQZCxdutRZ59tvvzUkGZcvXzYMwzDq1Klj9OrVy6Wd2rVrZ+vPDmAFRiRuY3v37lVqaqpq167tLMufP7/Kly+f5Xd27Nih4sWLKzIy0lmW3bfAValSxeVzRESEc9j2WrvFixd33q9YsaLy5cunHTt2ZKv9rOzevVudO3dW6dKlFRoaqlKlSkn6ayolq/giIiIkySW+v/+epOw/N5BdGzduVOvWrVWiRAmFhISoYcOGkvh3Fb6NRALZljt3bpfPNpstw/SCJ7Ru3VqnTp3S1KlTtW7dOq1bt06SlJqammV8NptNkm5JfID01xRc8+bNFRoaqlmzZmn9+vWaO3euJP5dhW8jkbiNlSlTRrlz53b+xSpJp0+f1u+//57ld6Kjo3Xo0CEdPXrUWfbLL7/cdCzX2j106JCzbPv27Tpz5owqVqwoSQoICFB6enqO2j158qR27dqll19+WU2aNFF0dLROnz7tVnx//z1J5jw3cM3OnTt18uRJvf7666pfv74qVKjgstAyu/h3Fbcbtn/exvLmzasePXpo8ODBKlCggAoXLqyXXnpJfn5Z54dNmzbVnXfeqZiYGI0ZM0bnzp3TSy+9dNOxNG3aVJUrV1bXrl01fvx4Xb16Vb169VLDhg1Vo0YNSX/tHklKSlJiYqLuuOMOhYSE3PDth+Hh4SpQoICmTJmiiIgIHTx4UC+++GKO4+vbt6/q1q2rN998U23atNEPP/ygRYsWufWsQGZKlCihgIAATZw4Uc8884x+++03jRo1Ksft9OvXT926dVONGjVUt25dzZo1S9u2bVPp0qU9EDVw8xiRuM2NGTNG9evXV+vWrdW0aVPVq1dP1atXz7K+n5+f5s6dq8uXL6tWrVrq2bOnXn311ZuOw2azaf78+QoPD1eDBg3UtGlTlS5dWrNnz3bWefjhh9WiRQs1btxYhQoV0meffXbDdv38/PT5559r48aNqlSpkp5//nmNGTMmx/Hde++9mjp1qt5++21VrVpVixcv1ssvv5zjdoCsFCpUSNOnT9cXX3yhihUr6vXXX9ebb76Z43Y6duyoYcOG6YUXXlD16tV14MABPfvssx6IGDAHrxEHAABuY0QCAAC4jUQCAAC4jUQCAAC4jUQCAAC4jUQCAAC4jUQCAAC4jUQCAAC4jUQCsFC3bt3Utm1b5+dGjRqpf//+tzyOlStXymaz6cyZM1nWsdlsmjdvXrbbjI2NVbVq1W4qrv3798tmsykxMfGm2gHgOSQSwHW6desmm80mm82mgIAAlS1bViNHjtTVq1c93vfXX3+d7WOVs/OXPwB4Gu/aADLRokULTZs2TSkpKfruu+/Uu3dv5c6dW0OHDs1QNzU1VQEBAab0mz9/flPaAYBbhREJIBN2u11FixZVyZIl9eyzz6pp06b65ptvJP1vOuLVV19VZGSkypcvL0k6dOiQOnTooHz58il//vxq06aN9u/f72wzPT1dAwYMUL58+VSgQAG98MILuv6E+uunNlJSUjRkyBAVL15cdrtdZcuW1Ycffqj9+/ercePGkv56sZnNZlO3bt0k/fU66vj4eEVFRSkoKEhVq1bVl19+6dLPd999pzvvvFNBQUFq3LixS5zZNWTIEN15553KkyePSpcurWHDhiktLS1Dvffff1/FixdXnjx51KFDB509e9bl/gcffKDo6GgFBgaqQoUKmjRpUpZ9nj59Wl27dlWhQoUUFBSkcuXKadq0aTmOHYB5GJEAsiEoKEgnT550fl62bJlCQ0O1ZMkSSVJaWpqaN2+uOnXq6KeffpK/v79eeeUVtWjRQlu2bFFAQIDeeustTZ8+XR999JGio6P11ltvae7cufrPf/6TZb9PPPGE1q5dqwkTJqhq1apKSkrSn3/+qeLFi+urr77Sww8/rF27dik0NFRBQUGSpPj4eH3yySeaPHmyypUrp1WrVumxxx5ToUKF1LBhQx06dEjt27dX79699fTTT2vDhg0aOHBgjn8nISEhmj59uiIjI7V161Y99dRTCgkJ0QsvvOCss2fPHs2ZM0cLFizQuXPn1KNHD/Xq1UuzZs2SJM2aNUvDhw/XO++8o7vvvlubNm3SU089peDgYMXExGToc9iwYdq+fbu+//57FSxYUHv27NHly5dzHDsAExkAXMTExBht2rQxDMMwHA6HsWTJEsNutxuDBg1y3i9SpIiRkpLi/M7MmTON8uXLGw6Hw1mWkpJiBAUFGT/88INhGIYRERFhjB492nk/LS3NuOOOO5x9GYZhNGzY0OjXr59hGIaxa9cuQ5KxZMmSTONcsWKFIck4ffq0s+zKlStGnjx5jDVr1rjU7dGjh9G5c2fDMAxj6NChRsWKFV3uDxkyJENb15NkzJ07N8v7Y8aMMapXr+78PGLECCNXrlzGH3/84Sz7/vvvDT8/P+Po0aOGYRhGmTJljE8//dSlnVGjRhl16tQxDMMwkpKSDEnGpk2bDMMwjNatWxvdu3fPMgYAtx4jEkAmFi5cqLx58yotLU0Oh0NdunRRbGys837lypVd1kVs3rxZe/bsUUhIiEs7V65c0d69e3X27FkdPXpUtWvXdt7z9/dXjRo1MkxvXJOYmKhcuXKpYcOG2Y57z549unTpku6//36X8tTUVN19992SpB07drjEIUl16tTJdh/XzJ49WxMmTNDevXt14cIFXb16VaGhoS51SpQooWLFirn043A4tGvXLoWEhGjv3r3q0aOHnnrqKWedq1evKiwsLNM+n332WT388MNKSEhQs2bN1LZtW9133305jh2AeUgkgEw0btxY7733ngICAhQZGSl/f9c/KsHBwS6fL1y4oOrVqzuH7P+uUKFCbsVwbaoiJy5cuCBJ+vbbb13+Apf+WvdhlrVr16pr166Ki4tT8+bNFRYWps8//1xvvfVWjmOdOnVqhsQmV65cmX6nZcuWOnDggL777jstWbJETZo0Ue/evfXmm2+6/zAAbgqJBJCJ4OBglS1bNtv177nnHs2ePVuFCxfO8H/l10RERGjdunVq0KCBpL/+z3vjxo265557Mq1fuXJlORwO/fjjj2ratGmG+9dGRNLT051lFStWlN1u18GDB7McyYiOjnYuHL3ml19+ufFD/s2aNWtUsmRJvfTSS86yAwcOZKh38OBBHTlyRJGRkc5+/Pz8VL58eRUpUkSRkZHat2+funbtmu2+CxUqpJiYGMXExKh+/foaPHgwiQRgIXZtACbo2rWrChYsqDZt2uinn35SUlKSVq5cqb59++qPP/6QJPXr10+vv/665s2bp507d6pXr17/eAZEqVKlFBMToyeffFLz5s1ztjlnzhxJUsmSJWWz2bRw4UKdOHFCFy5cUEhIiAYNGqTnn39eM2bM0N69e5WQkKCJEydqxowZkqRnnnlGu3fv1uDBg7Vr1y59+umnmj59eo6et1y5cjp48KA+//xz7d27VxMmTNDcuXMz1AsMDFRMTIw2b96sn376SX379lWHDh1UtGhRSVJcXJzi4+M1YcIE/f7779q6daumTZumsWPHZtrv8OHDNX/+fO3Zs0fbtm3TwoULFR0dnaPYAZiLRAIwQZ48ebRq1SqVKFFC7du3V3R0tHr06KErV644RygGDhyoxx9/XDExMapTp45CQkLUrl27f2z3vffe0yOPPKJevXqpQoUKeuqpp3Tx4kVJUrFixRQXF6cXX3xRRYoUUZ8+fSRJo0aN0rBhwxQfH6/o6Gi1aNFC3377raKioiT9tW7hq6++0rx581S1alVNnjxZr732Wo6e96GHHtLzzz+vPn36qFq1alqzZo2GDRuWoV7ZsmXVvn17PfDAA2rWrJmqVKnisr2zZ8+e+uCDDzRt2jRVrlxZDRs21PTp052xXi8gIEBDhw5VlSpV1KBBA+XKlUuff/55jmIHYC6bkdVKLwAAgBtgRAIAALiNRAIAALiNRAIAALiNRAIAALiNRAIAALiNRAIAALiNRAIAALiNRAIAALiNRAIAALiNRAIAALiNRAIAALiNRAIAALjt/wEf/+qMAWmx5gAAAABJRU5ErkJggg=="
          },
          "metadata": {}
        }
      ],
      "execution_count": 16
    },
    {
      "cell_type": "markdown",
      "source": "Examining the confusion matrix, we see that logistic regression can distinguish between the different classes.  We see that the major problem is false positives.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  6\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a support vector machine object then  create a  <code>GridSearchCV</code> object  <code>svm_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "parameters = {'kernel':('linear', 'rbf','poly','rbf', 'sigmoid'),\n              'C': np.logspace(-3, 3, 5),\n              'gamma':np.logspace(-3, 3, 5)}\nsvm = SVC()",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 17
    },
    {
      "cell_type": "code",
      "source": "# Create a GridSearchCV object with SVM and parameter grid\nsvm_cv = GridSearchCV(svm, parameters, cv=10, scoring='accuracy')\n# Fit the GridSearchCV object to data\nsvm_cv.fit(X_train, Y_train)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "execution_count": 18,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=10, estimator=SVC(),\n             param_grid={'C': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n       1.00000000e+03]),\n                         'gamma': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n       1.00000000e+03]),\n                         'kernel': ('linear', 'rbf', 'poly', 'rbf', 'sigmoid')},\n             scoring='accuracy')",
            "text/html": "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=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;)},\n             scoring=&#x27;accuracy&#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;)},\n             scoring=&#x27;accuracy&#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>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 18
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",svm_cv.best_params_)\nprint(\"accuracy :\",svm_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'C': 1.0, 'gamma': 0.03162277660168379, 'kernel': 'sigmoid'}\naccuracy : 0.8482142857142856\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 19
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  7\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Calculate the accuracy on the test data using the method <code>score</code>:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Calculate the accuracy on the test data\naccuracy = svm_cv.score(X_test, Y_test)\n\nprint(\"Accuracy on test data: \", accuracy)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Accuracy on test data:  0.8333333333333334\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 20
    },
    {
      "cell_type": "markdown",
      "source": "We can plot the confusion matrix\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "yhat=svm_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhIAAAHHCAYAAADqJrG+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAABD30lEQVR4nO3dd3RU1fr/8c+EkEkISQg9USABFIJUQbh0uESKghSV6jU0vQpIL3IVSACNghRBEUEFRBS5IiCoKFX0ggiEANJLKNKlSguQOb8//DFfxySaDGc4k+H9cs1amX327P2cLAcedjs2wzAMAQAAuMHP6gAAAEDORSIBAADcRiIBAADcRiIBAADcRiIBAADcRiIBAADcRiIBAADcRiIBAADcRiIBAADcRiIBeNDevXvVuHFjhYWFyWazaeHChaa2f/DgQdlsNs2cOdPUdnOyBg0aqEGDBlaHAdw1SCTg8/bv369///vfKlmypAIDAxUaGqratWvrzTff1NWrVz3ad1xcnLZt26ZXXnlFs2fPVrVq1Tza353UuXNn2Ww2hYaGZvh73Lt3r2w2m2w2m954441st3/s2DHFx8crOTnZhGgBeIq/1QEAnvTll1/qySeflN1u19NPP63y5cvr+vXr+uGHHzRo0CBt375d06ZN80jfV69e1bp16/TSSy+pV69eHumjRIkSunr1qnLnzu2R9v+Ov7+/rly5osWLF6tt27Yu1+bMmaPAwEBdu3bNrbaPHTumhIQERUVFqXLlyln+3LfffutWfwDcQyIBn5WSkqL27durRIkSWrlypSIiIpzXevbsqX379unLL7/0WP+nT5+WJOXLl89jfdhsNgUGBnqs/b9jt9tVu3ZtffLJJ+kSiY8//liPPvqo5s+ff0diuXLlivLkyaOAgIA70h+A3zG1AZ81ZswYXbp0Se+//75LEnFL6dKl1adPH+f7mzdvatSoUSpVqpTsdruioqL0n//8R6mpqS6fi4qKUvPmzfXDDz+oevXqCgwMVMmSJfXhhx8668THx6tEiRKSpEGDBslmsykqKkrS71MCt37+o/j4eNlsNpeyZcuWqU6dOsqXL5/y5s2rMmXK6D//+Y/zemZrJFauXKm6desqODhY+fLlU8uWLbVz584M+9u3b586d+6sfPnyKSwsTF26dNGVK1cy/8X+SceOHfX111/r/PnzzrINGzZo79696tixY7r6Z8+e1cCBA1WhQgXlzZtXoaGhatasmbZs2eKss3r1aj300EOSpC5dujinSG7dZ4MGDVS+fHlt2rRJ9erVU548eZy/lz+vkYiLi1NgYGC6+2/SpInCw8N17NixLN8rgPRIJOCzFi9erJIlS6pWrVpZqt+9e3cNHz5cDz74oCZMmKD69esrMTFR7du3T1d33759euKJJ/Twww9r3LhxCg8PV+fOnbV9+3ZJUps2bTRhwgRJUocOHTR79mxNnDgxW/Fv375dzZs3V2pqqkaOHKlx48bpscce0//+97+//Nzy5cvVpEkTnTp1SvHx8erfv7/Wrl2r2rVr6+DBg+nqt23bVr/99psSExPVtm1bzZw5UwkJCVmOs02bNrLZbPr888+dZR9//LHKli2rBx98MF39AwcOaOHChWrevLnGjx+vQYMGadu2bapfv77zL/WYmBiNHDlSkvTss89q9uzZmj17turVq+ds58yZM2rWrJkqV66siRMnqmHDhhnG9+abb6pQoUKKi4tTWlqaJOndd9/Vt99+q8mTJysyMjLL9wogAwbggy5cuGBIMlq2bJml+snJyYYko3v37i7lAwcONCQZK1eudJaVKFHCkGSsWbPGWXbq1CnDbrcbAwYMcJalpKQYkoyxY8e6tBkXF2eUKFEiXQwjRoww/viVnDBhgiHJOH36dKZx3+pjxowZzrLKlSsbhQsXNs6cOeMs27Jli+Hn52c8/fTT6frr2rWrS5utW7c2ChQokGmff7yP4OBgwzAM44knnjAaNWpkGIZhpKWlGUWLFjUSEhIy/B1cu3bNSEtLS3cfdrvdGDlypLNsw4YN6e7tlvr16xuSjKlTp2Z4rX79+i5l33zzjSHJGD16tHHgwAEjb968RqtWrf72HgH8PUYk4JMuXrwoSQoJCclS/a+++kqS1L9/f5fyAQMGSFK6tRTlypVT3bp1ne8LFSqkMmXK6MCBA27H/Ge31lYsWrRIDocjS585fvy4kpOT1blzZ+XPn99ZXrFiRT388MPO+/yj5557zuV93bp1debMGefvMCs6duyo1atX68SJE1q5cqVOnDiR4bSG9Pu6Cj+/3//oSUtL05kzZ5zTNklJSVnu0263q0uXLlmq27hxY/373//WyJEj1aZNGwUGBurdd9/Ncl8AMkciAZ8UGhoqSfrtt9+yVP/QoUPy8/NT6dKlXcqLFi2qfPny6dChQy7lxYsXT9dGeHi4zp0752bE6bVr1061a9dW9+7dVaRIEbVv317z5s37y6TiVpxlypRJdy0mJka//vqrLl++7FL+53sJDw+XpGzdyyOPPKKQkBB9+umnmjNnjh566KF0v8tbHA6HJkyYoPvuu092u10FCxZUoUKFtHXrVl24cCHLfd5zzz3ZWlj5xhtvKH/+/EpOTtakSZNUuHDhLH8WQOZIJOCTQkNDFRkZqZ9//jlbn/vzYsfM5MqVK8NywzDc7uPW/P0tQUFBWrNmjZYvX65//etf2rp1q9q1a6eHH344Xd3bcTv3covdblebNm00a9YsLViwINPRCEl69dVX1b9/f9WrV08fffSRvvnmGy1btkwPPPBAlkdepN9/P9mxefNmnTp1SpK0bdu2bH0WQOZIJOCzmjdvrv3792vdunV/W7dEiRJyOBzau3evS/nJkyd1/vx55w4MM4SHh7vscLjlz6MekuTn56dGjRpp/Pjx2rFjh1555RWtXLlSq1atyrDtW3Hu3r073bVdu3apYMGCCg4Ovr0byETHjh21efNm/fbbbxkuUL3ls88+U8OGDfX++++rffv2aty4sWJjY9P9TrKa1GXF5cuX1aVLF5UrV07PPvusxowZow0bNpjWPnA3I5GAzxo8eLCCg4PVvXt3nTx5Mt31/fv3680335T0+9C8pHQ7K8aPHy9JevTRR02Lq1SpUrpw4YK2bt3qLDt+/LgWLFjgUu/s2bPpPnvrYKY/b0m9JSIiQpUrV9asWbNc/mL++eef9e233zrv0xMaNmyoUaNG6a233lLRokUzrZcrV650ox3//e9/dfToUZeyWwlPRklXdg0ZMkSHDx/WrFmzNH78eEVFRSkuLi7T3yOArONAKvisUqVK6eOPP1a7du0UExPjcrLl2rVr9d///ledO3eWJFWqVElxcXGaNm2azp8/r/r16+unn37SrFmz1KpVq0y3Frqjffv2GjJkiFq3bq3evXvrypUreuedd3T//fe7LDYcOXKk1qxZo0cffVQlSpTQqVOnNGXKFN17772qU6dOpu2PHTtWzZo1U82aNdWtWzddvXpVkydPVlhYmOLj4027jz/z8/PTyy+//Lf1mjdvrpEjR6pLly6qVauWtm3bpjlz5qhkyZIu9UqVKqV8+fJp6tSpCgkJUXBwsGrUqKHo6OhsxbVy5UpNmTJFI0aMcG5HnTFjhho0aKBhw4ZpzJgx2WoPwJ9YvGsE8Lg9e/YYzzzzjBEVFWUEBAQYISEhRu3atY3Jkycb165dc9a7ceOGkZCQYERHRxu5c+c2ihUrZgwdOtSljmH8vv3z0UcfTdfPn7cdZrb90zAM49tvvzXKly9vBAQEGGXKlDE++uijdNs/V6xYYbRs2dKIjIw0AgICjMjISKNDhw7Gnj170vXx5y2Sy5cvN2rXrm0EBQUZoaGhRosWLYwdO3a41LnV35+3l86YMcOQZKSkpGT6OzUM1+2fmcls++eAAQOMiIgIIygoyKhdu7axbt26DLdtLlq0yChXrpzh7+/vcp/169c3HnjggQz7/GM7Fy9eNEqUKGE8+OCDxo0bN1zq9evXz/Dz8zPWrVv3l/cA4K/ZDCMbK6oAAAD+gDUSAADAbSQSAADAbSQSAADAbSQSAAD4qDVr1qhFixaKjIyUzWbTwoULnddu3LihIUOGqEKFCgoODlZkZKSefvrpbD8Rl0QCAAAfdfnyZVWqVElvv/12umtXrlxRUlKShg0bpqSkJH3++efavXu3HnvssWz1wa4NAADuAjabTQsWLFCrVq0yrbNhwwZVr15dhw4dyvCZQhnhQCoAAHKI1NTUdCey2u122e12U9q/cOGCbDab8+nDWeGTiUTXqCesDgEAkEN8cPAzj/dx49cDprST+NaHSkhIcCkbMWKEKafWXrt2TUOGDFGHDh2cT1DOCp9MJAAA8EVDhw5V//79XcrMGI24ceOG2rZtK8Mw9M4772TrsyQSAAB4miPNlGbMnMa45VYScejQIa1cuTJboxESiQQAAJ5nOKyOIEO3koi9e/dq1apVKlCgQLbbIJEAAMDTHNYkEpcuXdK+ffuc71NSUpScnKz8+fMrIiJCTzzxhJKSkrRkyRKlpaXpxIkTkqT8+fMrICAgS3345PZPFlsCALLqjiy2PL7TlHZyR8Rkq/7q1avVsGHDdOVxcXGKj49XdHR0hp9btWqVGjRokKU+GJEAAMDDDIumNho0aKC/Gi8wYyyBRAIAAE+zaGrjTuCIbAAA4DZGJAAA8DQv3bVhBhIJAAA8zaRzJLwRUxsAAMBtjEgAAOBpTG0AAAC3sWsDAAAgPUYkAADwMKsOpLoTSCQAAPA0H57aIJEAAMDTfHhEgjUSAADAbYxIAADgaT58IBWJBAAAnsbUBgAAQHqMSAAA4Gns2gAAAG5jagMAACA9RiQAAPA0pjYAAIC7DMN3t38ytQEAANzGiAQAAJ7mw4stSSQAAPA01kgAAAC3+fCIBGskAACA2xiRAADA03hoFwAAcBtTGwAAAOkxIgEAgKexawMAALiNqQ0AAID0GJEAAMDTmNoAAABu8+FEgqkNAADgNkYkAADwMF9+jDiJBAAAnubDUxskEgAAeBrbPwEAANJjRAIAAE9jagMAALiNqQ0AAID0GJEAAMDTmNoAAABuY2oDAAAgPUYkAADwNKY2AACA20gkzBUeHi6bzZalumfPnvVwNAAAwF2WJBITJ050/nzmzBmNHj1aTZo0Uc2aNSVJ69at0zfffKNhw4ZZER4AAOby4cWWNsMwDCsDePzxx9WwYUP16tXLpfytt97S8uXLtXDhwmy32TXqCZOiAwD4ug8OfubxPq5+8YYp7QQ9NtCUdsxk+a6Nb775Rk2bNk1X3rRpUy1fvtyCiAAAMJnhMOflhSxPJAoUKKBFixalK1+0aJEKFChgQUQAAPiGNWvWqEWLFoqMjJTNZks3ym8YhoYPH66IiAgFBQUpNjZWe/fuzVYflu/aSEhIUPfu3bV69WrVqFFDkrR+/XotXbpU06dPtzg6AABMYNGujcuXL6tSpUrq2rWr2rRpk+76mDFjNGnSJM2aNUvR0dEaNmyYmjRpoh07digwMDBLfVieSHTu3FkxMTGaNGmSPv/8c0lSTEyMfvjhB2diAQBAjmbRtESzZs3UrFmzDK8ZhqGJEyfq5ZdfVsuWLSVJH374oYoUKaKFCxeqffv2WerD8kRCkmrUqKE5c+ZYHQYAAHeNlJQUnThxQrGxsc6ysLAw1ahRQ+vWrctZiYTD4dC+fft06tQpOf40/FOvXj2LogIAwCQmTW2kpqYqNTXVpcxut8tut2e7rRMnTkiSihQp4lJepEgR57WssDyR+PHHH9WxY0cdOnRIf96JarPZlJaWZlFkAACYxKREIjExUQkJCS5lI0aMUHx8vCntu8PyROK5555TtWrV9OWXXyoiIiLLJ14CAHC3GTp0qPr37+9S5s5ohCQVLVpUknTy5ElFREQ4y0+ePKnKlStnuR3LE4m9e/fqs88+U+nSpa0OBQAAzzDp7Ed3pzEyEh0draJFi2rFihXOxOHixYtav369nn/++Sy3Y3kiUaNGDe3bt49EAgDguyza/nnp0iXt27fP+T4lJUXJycnKnz+/ihcvrr59+2r06NG67777nNs/IyMj1apVqyz3YXki8cILL2jAgAE6ceKEKlSooNy5c7tcr1ixokWRAQCQs23cuFENGzZ0vr81LRIXF6eZM2dq8ODBunz5sp599lmdP39ederU0dKlS7N8hoTkBc/a8PNLf7imzWaTYRhuL7bkWRsAgKy6I8/amGPOQyiDOo0ypR0zWT4ikZKSYnUIAAB4lpc+J8MMlicSJUqUsDoEAAA8y6I1EneC5YnELTt27NDhw4d1/fp1l/LHHnvMoogAAMDfsTyROHDggFq3bq1t27Y510ZIcp4nwYFUAIAcz9rliB5l+WPE+/Tpo+joaJ06dUp58uTR9u3btWbNGlWrVk2rV6+2OjwAAG6fw2HOywtZPiKxbt06rVy5UgULFpSfn5/8/PxUp04dJSYmqnfv3tq8ebPVIQIAgExYPiKRlpamkJAQSVLBggV17NgxSb8vwty9e7eVoQEAYA5GJDynfPny2rJli6Kjo1WjRg2NGTNGAQEBmjZtmkqWLGl1eAAA3D62f3rOyy+/rMuXL0uSRo4cqebNm6tu3boqUKCAPv30U4ujAwAAf8XyRKJJkybOn0uXLq1du3bp7NmzCg8P50mgAACfYDh8d9eG5YlERvLnz291CAAAmMdL1zeYwZJEok2bNlmu+/nnn3swEgAAcDssSSTCwsKs6BYAAGuw2NJcM2bMsKJbAACswRoJAADgNh9eI2H5gVQAACDnYkQCAABP8+ERCRIJAAA8jad/es6HH36o1NTUdOXXr1/Xhx9+aEFEAAAgq2yGYW2alCtXLh0/flyFCxd2KT9z5owKFy6stLS0bLfZNeoJs8KDGxo81VgNOzVRwXsLSZKO7j2ixZM+07bVPMkVdze+G97pg4OfebyPK+OfMaWdPP2nm9KOmSyf2jAMI8OjsH/55RfOm8ihzh0/o89e/0gnDx6XzWZT7ccb6IVpgxX/6CAd2/uL1eEBluG7cRdj+6f5qlSpIpvNJpvNpkaNGsnf//9CSUtLU0pKipo2bWpVeLgNW1Zscnn/+RufqMFTjVWqyv38YYm7Gt8N+CLLEolWrVpJkpKTk9WkSRPlzZvXeS0gIEBRUVF6/PHHLYoOZrH5+emhR2vKHhSo/Ul7rA4H8Bp8N+4ynGxpvhEjRkiSoqKi1K5dOwUGBloVCjzgnjLF9dLnryi3PUCpV67prX+P0bF9/IsL4Ltxl/LhqQ3LF1vesmnTJu3cuVOS9MADD6hKlSpZ+lxqamq6XR8vVIhTLlsu02NE1uXK7a8CkQUVFJJH1R75h+q1a6TX243gD0zc9fhueJ87stjy9S6mtJNniPc9YsLyxZanTp1S+/bttXr1auXLl0+SdP78eTVs2FBz585VoUKF/vLziYmJSkhIcCmrHBajKvnKeSpkZEHajZs6deiEJOnQzwcUXbG0Yrs+og//M83iyABr8d24Oxk+fCCV5edIvPDCC/rtt9+0fft2nT17VmfPntXPP/+sixcvqnfv3n/7+aFDh+rChQsur4phZe5A5MgOm59N/gG5rQ4D8Dp8N+4SDsOclxeyfERi6dKlWr58uWJiYpxl5cqV09tvv63GjRv/7eftdrvsdrtLGdMa1np8cEdtW71ZZ479qsDgIP2jZR2V+ccDGv/0aKtDAyzFd+MuxmJLz3E4HMqdO302njt3bjl8eCjIl4UWCFP38S8orFC4rv52Rb/sOqTxT4/Wjh+2Wh0aYCm+G/BFlicS//znP9WnTx998sknioyMlCQdPXpU/fr1U6NGjSyODu6YMeQdq0MAvBLfjbuYl05LmMHyNRJvvfWWLl68qKioKJUqVUqlSpVSdHS0Ll68qMmTJ1sdHgAAt8/hMOflhSwfkShWrJiSkpK0fPly7dq1S5IUExOj2NhYiyMDAAB/x/JEQpJsNpsefvhhPfzww1aHAgCA+Xx4asMrEokVK1ZoxYoVOnXqVLoFlh988IFFUQEAYBJ2bXhOQkKCRo4cqWrVqikiIiLDJ4ECAADvZHkiMXXqVM2cOVP/+te/rA4FAADPYGrDc65fv65atWpZHQYAAB7DEdke1L17d3388cdWhwEAANxg+YjEtWvXNG3aNC1fvlwVK1ZMd8rl+PHjLYoMAACTMLXhOVu3blXlypUlST///LPLNRZeAgB8AomE56xatcrqEAAA8Cwf3v5p+RoJAACQc1k+IgEAgM9jagMAALjL8OFEgqkNAADgNkYkAADwNB8ekbAkkfjiiy+yXPexxx7zYCQAANwBPnyypSWJRKtWrVze22w2GYbh8v6WtLS0OxUWAADIJkvWSDgcDufr22+/VeXKlfX111/r/PnzOn/+vL766is9+OCDWrp0qRXhAQBgLodhzssLWb5Gom/fvpo6darq1KnjLGvSpIny5MmjZ599Vjt37rQwOgAATOClSYAZLN+1sX//fuXLly9deVhYmA4ePHjH4wEAAFlneSLx0EMPqX///jp58qSz7OTJkxo0aJCqV69uYWQAAJjDMAxTXtmRlpamYcOGKTo6WkFBQSpVqpRGjRqV7Xb+juVTGx988IFat26t4sWLq1ixYpKkI0eO6L777tPChQutDQ4AADNYMLXx+uuv65133tGsWbP0wAMPaOPGjerSpYvCwsLUu3dv0/qxPJEoXbq0tm7dqmXLlmnXrl2SpJiYGMXGxvL0TwCAb7AgkVi7dq1atmypRx99VJIUFRWlTz75RD/99JOp/VieSEi/b/ds3LixGjdubHUoAAB4rdTUVKWmprqU2e122e32dHVr1aqladOmac+ePbr//vu1ZcsW/fDDDxo/frypMVmSSEyaNEnPPvusAgMDNWnSpL+sa+bwCwAAVjDrWRuJiYlKSEhwKRsxYoTi4+PT1X3xxRd18eJFlS1bVrly5VJaWppeeeUVderUyZRYbrEZZq+6yILo6Ght3LhRBQoUUHR0dKb1bDabDhw4kO32u0Y9cTvhAQDuIh8c/MzjfVyIa2RKO4HTvsryiMTcuXM1aNAgjR07Vg888ICSk5PVt29fjR8/XnFxcabEI1k0IpGSkpLhzwAAIHOZJQ0ZGTRokF588UW1b99eklShQgUdOnRIiYmJOT+RAADgrmLBozauXLkiPz/XUx5y5colh8nP/bAkkejfv3+W65q9KAQAgDvNrDUS2dGiRQu98sorKl68uB544AFt3rxZ48ePV9euXU3tx5JEYvPmzS7vk5KSdPPmTZUpU0aStGfPHuXKlUtVq1a1IjwAAHK8yZMna9iwYerRo4dOnTqlyMhI/fvf/9bw4cNN7ceSRGLVqlXOn8ePH6+QkBDNmjVL4eHhkqRz586pS5cuqlu3rhXhAQBgLgtGJEJCQjRx4kRNnDjRo/1YfkT2uHHjlJiY6EwiJCk8PFyjR4/WuHHjLIwMAACTOEx6eSHLE4mLFy/q9OnT6cpPnz6t3377zYKIAABAVlm+a6N169bq0qWLxo0b53xI1/r16zVo0CC1adPG4ugAALh9Viy2vFMsTySmTp2qgQMHqmPHjrpx44Ykyd/fX926ddPYsWMtjg4AABN46bSEGSxPJPLkyaMpU6Zo7Nix2r9/vySpVKlSCg4OtjgyAADMwYjEHRAcHKyKFStaHQYAAMgGr0kkAADwWUxtAAAAdxk+nEhYvv0TAADkXIxIAADgaT48IkEiAQCAhzG1AQAAkAFGJAAA8DQfHpEgkQAAwMN8eWqDRAIAAA/z5USCNRIAAMBtjEgAAOBhvjwiQSIBAICnGTarI/AYpjYAAIDbTBmROH/+vPLly2dGUwAA+BxfntrI9ojE66+/rk8//dT5vm3btipQoIDuuecebdmyxdTgAADwBYbDZsrLG2U7kZg6daqKFSsmSVq2bJmWLVumr7/+Ws2aNdOgQYNMDxAAAHivbE9tnDhxwplILFmyRG3btlXjxo0VFRWlGjVqmB4gAAA5HVMbfxAeHq4jR45IkpYuXarY2FhJkmEYSktLMzc6AAB8gGHYTHl5o2yPSLRp00YdO3bUfffdpzNnzqhZs2aSpM2bN6t06dKmBwgAALxXthOJCRMmKCoqSkeOHNGYMWOUN29eSdLx48fVo0cP0wMEACCn8+WpjWwnErlz59bAgQPTlffr18+UgAAA8DXeuuPCDFlKJL744ossN/jYY4+5HQwAAL7IMKyOwHOylEi0atUqS43ZbDYWXAIAcBfJUiLhcPjw5A4AAB52109tZObatWsKDAw0KxYAAHySLycS2T5HIi0tTaNGjdI999yjvHnz6sCBA5KkYcOG6f333zc9QAAA4L2ynUi88sormjlzpsaMGaOAgABnefny5fXee++ZGhwAAL7AMMx5eaNsJxIffvihpk2bpk6dOilXrlzO8kqVKmnXrl2mBgcAgC/goV1/cPTo0QxPsHQ4HLpx44YpQQEAgJwh24lEuXLl9P3336cr/+yzz1SlShVTggIAwJfwrI0/GD58uOLi4nT06FE5HA59/vnn2r17tz788EMtWbLEEzECAJCj+fIR2dkekWjZsqUWL16s5cuXKzg4WMOHD9fOnTu1ePFiPfzww56IEQAAeCm3zpGoW7euli1bZnYsAAD4JIeXTkuYwe0DqTZu3KidO3dK+n3dRNWqVU0LCgAAX+Kt6xvMkO1E4pdfflGHDh30v//9T/ny5ZMknT9/XrVq1dLcuXN17733mh0jAAA5mrdu3TRDttdIdO/eXTdu3NDOnTt19uxZnT17Vjt37pTD4VD37t09ESMAAPBS2R6R+O6777R27VqVKVPGWVamTBlNnjxZdevWNTU4AAB8gbeeSmmGbCcSxYoVy/DgqbS0NEVGRpoSFAAAvoSpjT8YO3asXnjhBW3cuNFZtnHjRvXp00dvvPGGqcEBAADvlqURifDwcNls/5dNXb58WTVq1JC//+8fv3nzpvz9/dW1a1e1atXKI4ECAJBT3fXbPydOnOjhMAAA8F13/fbPuLg4T8cBAAByILcPpJKka9eu6fr16y5loaGhtxUQAAC+hl0bf3D58mUNGTJE8+bN05kzZ9JdT0tLMyUwAAB8hS+vkcj2ro3Bgwdr5cqVeuedd2S32/Xee+8pISFBkZGR+vDDDz0RIwAA8FLZTiQWL16sKVOm6PHHH5e/v7/q1q2rl19+Wa+++qrmzJnjiRgBAMjRDMNmyiu7jh49qqeeekoFChRQUFCQKlSo4HJ8gxmyPbVx9uxZlSxZUtLv6yHOnj0rSapTp46ef/55U4MDAMAXWLFG4ty5c6pdu7YaNmyor7/+WoUKFdLevXsVHh5uaj/ZTiRKliyplJQUFS9eXGXLltW8efNUvXp1LV682PkQLwAA8H+sWCPx+uuvq1ixYpoxY4azLDo62vR+sj210aVLF23ZskWS9OKLL+rtt99WYGCg+vXrp0GDBpkeIAAA+F1qaqouXrzo8kpNTc2w7hdffKFq1arpySefVOHChVWlShVNnz7d9JhshnF7Ay6HDh3Spk2bVLp0aVWsWNGsuG6Lf8A9VocAeKWrx763OgTA6+QuWNLjfWy4p7Up7Xz5TCUlJCS4lI0YMULx8fHp6gYGBkqS+vfvryeffFIbNmxQnz59NHXqVFPPh7rtRMIbkUgAGSORANK7E4nE+sg2prRTOeWTdCMQdrtddrs9Xd2AgABVq1ZNa9eudZb17t1bGzZs0Lp160yJR8riGolJkyZlucHevXu7HQwAAMhcZklDRiIiIlSuXDmXspiYGM2fP9/UmLKUSEyYMCFLjdlsNhIJAAD+xIqh/9q1a2v37t0uZXv27FGJEiVM7SdLiURKSoqpnQIAcDexYtdGv379VKtWLb366qtq27atfvrpJ02bNk3Tpk0ztZ9s79oAAADe76GHHtKCBQv0ySefqHz58ho1apQmTpyoTp06mdrPbT20CwAA/D2rHiPevHlzNW/e3KN9kEgAAOBhDqsD8CCmNgAAgNsYkQAAwMMM8RhxF99//72eeuop1axZU0ePHpUkzZ49Wz/88IOpwQEA4Aschjkvb5TtRGL+/Plq0qSJgoKCtHnzZucJWxcuXNCrr75qeoAAAOR0DtlMeXmjbCcSo0eP1tSpUzV9+nTlzp3bWV67dm0lJSWZGhwAAPBu2V4jsXv3btWrVy9deVhYmM6fP29GTAAA+BTWSPxB0aJFtW/fvnTlP/zwg0qW9PyDTwAAyGkcJr28UbYTiWeeeUZ9+vTR+vXrZbPZdOzYMc2ZM0cDBw7U888/74kYAQCAl8r21MaLL74oh8OhRo0a6cqVK6pXr57sdrsGDhyoF154wRMxAgCQo/ny1IbNMAy3NpRcv35d+/bt06VLl1SuXDnlzZvX7Njc5h9wj9UhAF7p6rHvrQ4B8Dq5C3p+Wn5pkfamtNP05FxT2jGT2wdSBQQEpHvOOQAAuLtkO5Fo2LChbLbMh2hWrlx5WwEBAOBrvHWhpBmynUhUrlzZ5f2NGzeUnJysn3/+WXFxcWbFBQCAz/DlNRLZTiQmTJiQYXl8fLwuXbp02wEBAICcw7Snfz711FP64IMPzGoOAACf4bCZ8/JGpj39c926dQoMDDSrOQAAfIa3PifDDNlOJNq0aePy3jAMHT9+XBs3btSwYcNMCwwAAF/hpQ/uNEW2E4mwsDCX935+fipTpoxGjhypxo0bmxYYAADwftlKJNLS0tSlSxdVqFBB4eHhnooJAACf4svbP7O12DJXrlxq3LgxT/kEACAbHDabKS9vlO1dG+XLl9eBAwc8EQsAAMhhsp1IjB49WgMHDtSSJUt0/PhxXbx40eUFAABcGSa9vFGW10iMHDlSAwYM0COPPCJJeuyxx1yOyjYMQzabTWlpaeZHCQBADubLaySynEgkJCToueee06pVqzwZDwAAyEGynEjcetp4/fr1PRYMAAC+yFtPpTRDtrZ//tVTPwEAQMY42fL/u//++/82mTh79uxtBQQAAHKObCUSCQkJ6U62BAAAf81bd1yYIVuJRPv27VW4cGFPxQIAgE9ijYRYHwEAgLt8eftnlg+kurVrAwAA4JYsj0g4HL6cTwEA4Dm+/E/xbD9GHAAAZI8vr5HI9rM2AAAAbmFEAgAAD/PlxQEkEgAAeJgvJxJMbQAAALcxIgEAgIcZPrzYkkQCAAAPY2oDAAAgA4xIAADgYb48IkEiAQCAh3GyJQAAcBsnWwIAAGSAEQkAADyMNRIAAMBtvpxIMLUBAADcxogEAAAexq4NAADgNnZtAACAHO21116TzWZT3759TW2XEQkAADzM6sWWGzZs0LvvvquKFSua3jYjEgAAeJhh0ssdly5dUqdOnTR9+nSFh4ffzm1kiEQCAAAf1rNnTz366KOKjY31SPtMbQAA4GEOk/ZtpKamKjU11aXMbrfLbrdnWH/u3LlKSkrShg0bTOk/I4xIAADgYQ6TXomJiQoLC3N5JSYmZtjnkSNH1KdPH82ZM0eBgYEeuzebYRg+t73VP+Aeq0MAvNLVY99bHQLgdXIXLOnxPkaW6GRKO0P2fJDlEYmFCxeqdevWypUrl7MsLS1NNptNfn5+Sk1NdbnmLqY2AADIIf5qGuPPGjVqpG3btrmUdenSRWXLltWQIUNMSSIkEgkAADzOiu2fISEhKl++vEtZcHCwChQokK78dpBIAADgYb58siWJBAAAd4nVq1eb3iaJBAAAHmbW9k9vRCIBAICH+W4awTkSAADgNjAiAQCAh1n90C5PsiyR2Lp1a5breuJpZQAA3CmskfCAypUry2azyTAM2Wx/vS8mLS3tDkUFAACyw7I1EikpKTpw4IBSUlI0f/58RUdHa8qUKdq8ebM2b96sKVOmqFSpUpo/f75VIQIAYAorHyPuaZaNSJQoUcL585NPPqlJkybpkUcecZZVrFhRxYoV07Bhw9SqVSsLIgQAwByskfCwbdu2KTo6Ol15dHS0duzYYUFEAACYx5fXSHjF9s+YmBglJibq+vXrzrLr168rMTFRMTExFkYGAAD+ileMSEydOlUtWrTQvffe69yhsXXrVtlsNi1evNji6AAAuD2+Ox7hJYlE9erVdeDAAc2ZM0e7du2SJLVr104dO3ZUcHCwxdEBAHB7WCNxBwQHB+vZZ5+1OgwAAJANXrFGQpJmz56tOnXqKDIyUocOHZIkTZgwQYsWLbI4MgAAbo9h0n/eyCsSiXfeeUf9+/dXs2bNdO7cOecBVOHh4Zo4caK1wQEAcJscJr28kVckEpMnT9b06dP10ksvyd///2ZbqlWrpm3btlkYGQAA+CtesUYiJSVFVapUSVdut9t1+fJlCyICAMA8nCPhYdHR0UpOTk5XvnTpUs6RAADkeByR7WH9+/dXz549de3aNRmGoZ9++kmffPKJEhMT9d5771kdHgAAyIRXJBLdu3dXUFCQXn75ZV25ckUdO3ZUZGSk3nzzTbVv397q8OCm55+L04D+z6to0ULaunWH+vQdpg0bk60OC7hjNiZv04yPP9OOXft0+sxZvZk4TI3q1ZIk3bh5U5OnzdL36zbql2PHlTc4WP94qIr6PddFhQsVsDhymI2pjTugU6dO2rt3ry5duqQTJ07ol19+Ubdu3awOC2568snH9MbYERo1erweqtFUW7bu0FdfzlEh/oDEXeTq1WsqU7qkXhrQI921a9dStWP3fv27cwfN++AtTXz1ZR08/It6DUmwIFJ4mi/v2rAZhuFzaZJ/wD1Wh3DXW/vDYm3YuEV9+r4sSbLZbDp4YIPenjJDY8a+bXF0d6+rx763OoS7VvnazVxGJDKybedudejeV8vmz1JE0cJ3MLq7W+6CJT3eR/eoJ0xp572Dn5nSjpksm9qoUqWKbDZbluomJSV5OBqYKXfu3HrwwYp6bcxbzjLDMLRi5Q/6xz+qWhgZ4N0uXboim82mkBAeDYCcw7JEolWrVs6fr127pilTpqhcuXKqWbOmJOnHH3/U9u3b1aNH+iHBP0pNTVVqaqpLmWEYWU5SYL6CBfPL399fp07+6lJ+6tRplS1TyqKoAO+WmnpdE975QI/E1ldenjHkc7x1WsIMliUSI0aMcP7cvXt39e7dW6NGjUpX58iRI3/ZTmJiohISXOcUbX55ZcsVal6wAOBBN27e1IBhr8owDA0b1MvqcOAB3nq8tRm8YrHlf//7Xz399NPpyp966inNnz//Lz87dOhQXbhwweVl8wvxVKjIgl9/PaubN2+qcJGCLuWFCxfSiZOnLYoK8E63kohjJ09p+sRXGY1AjuMViURQUJD+97//pSv/3//+p8DAwL/8rN1uV2hoqMuLaQ1r3bhxQ0lJW/XPhnWcZTabTf9sWEc//rjJwsgA73IriTh85Jjem/iq8oUxkuqrfHnXhlecI9G3b189//zzSkpKUvXq1SVJ69ev1wcffKBhw4ZZHB3cMeHN6Zrx/gRtStqqDRs2q/cLzyg4OEgzZ31qdWjAHXPlylUd/uWY8/3RYye1a89+hYWGqGDB/Or/0ivasWef3h6TIIfDoV/PnJUkhYWGKHfu3FaFDQ9w+N4GSSev2f45b948vfnmm9q5c6ckKSYmRn369FHbtm2z3RbbP71Dj+c7Ow+k2rJlu/r2G66fNmy2Oqy7Gts/76yfkraq6wtD0pW3bBarHt2eUpMnOmf4uQ8mv67qD1b0cHS45U5s//xXiTamtDP70OemtGMmr0kkzEQiAWSMRAJI704kEk+ZlEh85IWJhFdMbdxy/fp1nTp1Sg6H60xQ8eLFLYoIAIDb58tHZHtFIrF371517dpVa9eudSm/dR5EWlqaRZEBAIC/4hWJROfOneXv768lS5YoIiKCXRcAAJ/iy+dIeEUikZycrE2bNqls2bJWhwIAgOm8deumGbwikShXrpx+/fXXv68IAEAO5MtrJLziQKrXX39dgwcP1urVq3XmzBldvHjR5QUAALyTV4xIxMbGSpIaNWrkUs5iSwCAL2CNhIetWrXK6hAAAPAY1kh4WP369a0OAQAAuMErEolbrly5osOHD+v69esu5RUrclQsACDn8sFDpJ28IpE4ffq0unTpoq+//jrD66yRAADkZOza8LC+ffvq/PnzWr9+vYKCgrR06VLNmjVL9913n7744gurwwMAAJnwihGJlStXatGiRapWrZr8/PxUokQJPfzwwwoNDVViYqIeffRRq0MEAMBtvrzY0itGJC5fvqzChQtLksLDw3X69GlJUoUKFZSUlGRlaAAA3DbDpP+8kVckEmXKlNHu3bslSZUqVdK7776ro0ePaurUqYqIiLA4OgAAkBmvmNro06ePjh8/LkkaMWKEmjZtqo8++kgBAQGaNWuWxdEBAHB7fHmxpVckEk899ZTz56pVq+rQoUPatWuXihcvroIFC1oYGQAAt4/tnx7Qv3//LNcdP368ByMBAMCzfHmxpWWJxObNm7NUz2azeTgSAADgLssSCZ6vAQC4W3jrjgszeMUaCQAAfJkvL7b0iu2fAADAXImJiXrooYcUEhKiwoULq1WrVs6jFsxEIgEAgIcZhmHKKzu+++479ezZUz/++KOWLVumGzduqHHjxrp8+bKp98bUBgAAHmbF1MbSpUtd3s+cOVOFCxfWpk2bVK9ePdP6IZEAACCHSE1NVWpqqkuZ3W6X3W7/289euHBBkpQ/f35TY2JqAwAADzPrWRuJiYkKCwtzeSUmJv5t/w6HQ3379lXt2rVVvnx5U++NEQkAADzMYdLJlkOHDk13oGNWRiN69uypn3/+WT/88IMpcfwRiQQAADlEVqcx/qhXr15asmSJ1qxZo3vvvdf0mEgkAADwMCtOkTAMQy+88IIWLFig1atXKzo62iP9kEgAAOBhVuza6Nmzpz7++GMtWrRIISEhOnHihCQpLCxMQUFBpvVjM3zwkWT+AfdYHQLgla4e+97qEACvk7tgSY/3UfOehqa0s+5o1h8vkdmzqmbMmKHOnTubEo/EiAQAAD7pTo0TkEgAAOBhPjj470QiAQCAh/HQLgAAgAwwIgEAgIcZPjwiQSIBAICH+fIaCaY2AACA2xiRAADAw3x5sSWJBAAAHsbUBgAAQAYYkQAAwMOY2gAAAG5j+ycAAHCbgzUSAAAA6TEiAQCAhzG1AQAA3MbUBgAAQAYYkQAAwMOY2gAAAG5jagMAACADjEgAAOBhTG0AAAC3MbUBAACQAUYkAADwMKY2AACA2wzDYXUIHkMiAQCAh/nyY8RZIwEAANzGiAQAAB5m+PCuDRIJAAA8jKkNAACADDAiAQCAhzG1AQAA3MbJlgAAABlgRAIAAA/jZEsAAOA2X14jwdQGAABwGyMSAAB4mC+fI0EiAQCAh/ny1AaJBAAAHsb2TwAAgAwwIgEAgIcxtQEAANzmy4stmdoAAABuY0QCAAAPY2oDAAC4jV0bAAAAGWBEAgAAD+OhXQAAwG1MbQAAAGSAEQkAADyMXRsAAMBtvrxGgqkNAAA8zDAMU17uePvttxUVFaXAwEDVqFFDP/30k6n3RiIBAICP+vTTT9W/f3+NGDFCSUlJqlSpkpo0aaJTp06Z1ofN8MGJG/+Ae6wOAfBKV499b3UIgNfJXbCk5/sw6e+lG9ePZqt+jRo19NBDD+mtt96SJDkcDhUrVkwvvPCCXnzxRVNiYkQCAAAPM0x6Zcf169e1adMmxcbGOsv8/PwUGxurdevW3db9/BGLLQEAyCFSU1OVmprqUma322W329PV/fXXX5WWlqYiRYq4lBcpUkS7du0yLSafTCRuZnPoB56RmpqqxMREDR06NMP/yYG7Fd+Nu49Zfy/Fx8crISHBpWzEiBGKj483pX13+OQaCXiHixcvKiwsTBcuXFBoaKjV4QBeg+8G3JWdEYnr168rT548+uyzz9SqVStneVxcnM6fP69FixaZEhNrJAAAyCHsdrtCQ0NdXpmNagUEBKhq1apasWKFs8zhcGjFihWqWbOmaTH55NQGAACQ+vfvr7i4OFWrVk3Vq1fXxIkTdfnyZXXp0sW0PkgkAADwUe3atdPp06c1fPhwnThxQpUrV9bSpUvTLcC8HSQS8Bi73a4RI0awmAz4E74buJN69eqlXr16eax9FlsCAAC3sdgSAAC4jUQCAAC4jUQCAAC4jUTCBzVo0EB9+/Z1vo+KitLEiRP/8jM2m00LFy70aFxZ1blzZ5fDUzxl5syZypcvn8f7gW/78/fNE+Lj41W5cmWP9gG4i10bd4ENGzYoODjY6jAUHx+vhQsXKjk52epQAAAmIZG4CxQqVMjqEAAAPoqpjRzu8uXLevrpp5U3b15FRERo3Lhx6er8eWpj7969qlevngIDA1WuXDktW7bsb/tp0KCBevfurcGDByt//vwqWrRouofEHD58WC1btlTevHkVGhqqtm3b6uTJk5J+n0ZISEjQli1bZLPZZLPZNHPmzCzd49KlS1WnTh3ly5dPBQoUUPPmzbV//37n9YMHD8pms+nzzz9Xw4YNlSdPHlWqVCndY3Jnzpyp4sWLK0+ePGrdurXOnDmTpf6BrJo9e7aqVaumkJAQFS1aVB07dtSpU6ec11evXi2bzaYVK1aoWrVqypMnj2rVqqXdu3e7tPPaa6+pSJEiCgkJUbdu3XTt2rU7fStAlpFI5HCDBg3Sd999p0WLFunbb7/V6tWrlZSUlGl9h8OhNm3aKCAgQOvXr9fUqVM1ZMiQLPU1a9YsBQcHa/369RozZoxGjhzpTEIcDodatmyps2fP6rvvvtOyZct04MABtWvXTtLvp6sNGDBADzzwgI4fP67jx487r/2dy5cvq3///tq4caNWrFghPz8/tW7dWg6Hw6XeSy+9pIEDByo5OVn333+/OnTooJs3b0qS1q9fr27duqlXr15KTk5Ww4YNNXr06Cz1D2TVjRs3NGrUKG3ZskULFy7UwYMH1blz53T1XnrpJY0bN04bN26Uv7+/unbt6rw2b948xcfH69VXX9XGjRsVERGhKVOm3MG7ALLJQI7122+/GQEBAca8efOcZWfOnDGCgoKMPn36OMtKlChhTJgwwTAMw/jmm28Mf39/4+jRo87rX3/9tSHJWLBgQaZ91a9f36hTp45L2UMPPWQMGTLEMAzD+Pbbb41cuXIZhw8fdl7fvn27Icn46aefDMMwjBEjRhiVKlX62/uKi4szWrZsmen106dPG5KMbdu2GYZhGCkpKYYk47333kvX986dOw3DMIwOHToYjzzyiEs77dq1M8LCwv42HuCv1K9f3+X79kcbNmwwJBm//fabYRiGsWrVKkOSsXz5cmedL7/80pBkXL161TAMw6hZs6bRo0cPl3Zq1KiRpe8OYAVGJHKw/fv36/r166pRo4azLH/+/CpTpkymn9m5c6eKFSumyMhIZ1lWnwJXsWJFl/cRERHOYdtb7RYrVsx5vVy5csqXL5927tyZpfYzs3fvXnXo0EElS5ZUaGiooqKiJP0+lZJZfBEREZLkEt8ff09S1u8byKpNmzapRYsWKl68uEJCQlS/fn1J/L8K30YigSzLnTu3y3ubzZZuesETWrRoobNnz2r69Olav3691q9fL0m6fv16pvHZbDZJuiPxAdLvU3BNmjRRaGio5syZow0bNmjBggWS+H8Vvo1EIgcrVaqUcufO7fyLVZLOnTunPXv2ZPqZmJgYHTlyRMePH3eW/fjjj7cdy612jxw54izbsWOHzp8/r3LlykmSAgIClJaWlq12z5w5o927d+vll19Wo0aNFBMTo3PnzrkV3x9/T5I59w3csmvXLp05c0avvfaa6tatq7Jly7ostMwq/l9FTsP2zxwsb9686tatmwYNGqQCBQqocOHCeumll+Tnl3l+GBsbq/vvv19xcXEaO3asLl68qJdeeum2Y4mNjVWFChXUqVMnTZw4UTdv3lSPHj1Uv359VatWTdLvu0dSUlKUnJyse++9VyEhIX/79MPw8HAVKFBA06ZNU0REhA4fPqwXX3wx2/H17t1btWvX1htvvKGWLVvqm2++0dKlS926VyAjxYsXV0BAgCZPnqznnntOP//8s0aNGpXtdvr06aPOnTurWrVqql27tubMmaPt27erZMmSHogauH2MSORwY8eOVd26ddWiRQvFxsaqTp06qlq1aqb1/fz8tGDBAl29elXVq1dX9+7d9corr9x2HDabTYsWLVJ4eLjq1aun2NhYlSxZUp9++qmzzuOPP66mTZuqYcOGKlSokD755JO/bdfPz09z587Vpk2bVL58efXr109jx47Ndnz/+Mc/NH36dL355puqVKmSvv32W7388svZbgfITKFChTRz5kz997//Vbly5fTaa6/pjTfeyHY77dq107BhwzR48GBVrVpVhw4d0vPPP++BiAFz8BhxAADgNkYkAACA20gkAACA20gkAACA20gkAACA20gkAACA20gkAACA20gkAACA20gkAAt17txZrVq1cr5v0KCB+vbte8fjWL16tWw2m86fP59pHZvNpoULF2a5zfj4eFWuXPm24jp48KBsNpuSk5Nvqx0AnkMiAfxJ586dZbPZZLPZFBAQoNKlS2vkyJG6efOmx/v+/PPPs3ysclb+8gcAT+NZG0AGmjZtqhkzZig1NVVfffWVevbsqdy5c2vo0KHp6l6/fl0BAQGm9Js/f35T2gGAO4URCSADdrtdRYsWVYkSJfT8888rNjZWX3zxhaT/m4545ZVXFBkZqTJlykiSjhw5orZt2ypfvnzKnz+/WrZsqYMHDzrbTEtLU//+/ZUvXz4VKFBAgwcP1p9PqP/z1EZqaqqGDBmiYsWKyW63q3Tp0nr//fd18OBBNWzYUNLvDzaz2Wzq3LmzpN8fR52YmKjo6GgFBQWpUqVK+uyzz1z6+eqrr3T//fcrKChIDRs2dIkzq4YMGaL7779fefLkUcmSJTVs2DDduHEjXb13331XxYoVU548edS2bVtduHDB5fp7772nmJgYBQYGqmzZspoyZUqmfZ47d06dOnVSoUKFFBQUpPvuu08zZszIduwAzMOIBJAFQUFBOnPmjPP9ihUrFBoaqmXLlkmSbty4oSZNmqhmzZr6/vvv5e/vr9GjR6tp06baunWrAgICNG7cOM2cOVMffPCBYmJiNG7cOC1YsED//Oc/M+336aef1rp16zRp0iRVqlRJKSkp+vXXX1WsWDHNnz9fjz/+uHbv3q3Q0FAFBQVJkhITE/XRRx9p6tSpuu+++7RmzRo99dRTKlSokOrXr68jR46oTZs26tmzp5599llt3LhRAwYMyPbvJCQkRDNnzlRkZKS2bdumZ555RiEhIRo8eLCzzr59+zRv3jwtXrxYFy9eVLdu3dSjRw/NmTNHkjRnzhwNHz5cb731lqpUqaLNmzfrmWeeUXBwsOLi4tL1OWzYMO3YsUNff/21ChYsqH379unq1avZjh2AiQwALuLi4oyWLVsahmEYDofDWLZsmWG3242BAwc6rxcpUsRITU11fmb27NlGmTJlDIfD4SxLTU01goKCjG+++cYwDMOIiIgwxowZ47x+48YN495773X2ZRiGUb9+faNPnz6GYRjG7t27DUnGsmXLMoxz1apVhiTj3LlzzrJr164ZefLkMdauXetSt1u3bkaHDh0MwzCMoUOHGuXKlXO5PmTIkHRt/ZkkY8GCBZleHzt2rFG1alXn+xEjRhi5cuUyfvnlF2fZ119/bfj5+RnHjx83DMMwSpUqZXz88ccu7YwaNcqoWbOmYRiGkZKSYkgyNm/ebBiGYbRo0cLo0qVLpjEAuPMYkQAysGTJEuXNm1c3btyQw+FQx44dFR8f77xeoUIFl3URW7Zs0b59+xQSEuLSzrVr17R//35duHBBx48fV40aNZzX/P39Va1atXTTG7ckJycrV65cql+/fpbj3rdvn65cuaKHH37Ypfz69euqUqWKJGnnzp0ucUhSzZo1s9zHLZ9++qkmTZqk/fv369KlS7p586ZCQ0Nd6hQvXlz33HOPSz8Oh0O7d+9WSEiI9u/fr27duumZZ55x1rl586bCwsIy7PP555/X448/rqSkJDVu3FitWrVSrVq1sh07APOQSAAZaNiwod555x0FBAQoMjJS/v6uX5Xg4GCX95cuXVLVqlWdQ/Z/VKhQIbdiuDVVkR2XLl2SJH355Zcuf4FLv6/7MMu6devUqVMnJSQkqEmTJgoLC9PcuXM1bty4bMc6ffr0dIlNrly5MvxMs2bNdOjQIX311VdatmyZGjVqpJ49e+qNN95w/2YA3BYSCSADwcHBKl26dJbrP/jgg/r0009VuHDhdP8qvyUiIkLr169XvXr1JP3+L+9NmzbpwQcfzLB+hQoV5HA49N133yk2Njbd9VsjImlpac6ycuXKyW636/Dhw5mOZMTExDgXjt7y448//v1N/sHatWtVokQJvfTSS86yQ4cOpat3+PBhHTt2TJGRkc5+/Pz8VKZMGRUpUkSRkZE6cOCAOnXqlOW+CxUqpLi4OMXFxalu3boaNGgQiQRgIXZtACbo1KmTChYsqJYtW+r7779XSkqKVq9erd69e+uXX36RJPXp00evvfaaFi5cqF27dqlHjx5/eQZEVFSU4uLi1LVrVy1cuNDZ5rx58yRJJUqUkM1m05IlS3T69GldunRJISEhGjhwoPr166dZs2Zp//79SkpK0uTJkzVr1ixJ0nPPPae9e/dq0KBB2r17tz7++GPNnDkzW/d733336fDhw5o7d67279+vSZMmacGCBenqBQYGKi4uTlu2bNH333+v3r17q23btipatKgkKSEhQYmJiZo0aZL27Nmjbdu2acaMGRo/fnyG/Q4fPlyLFi3Svn37tH37di1ZskQxMTHZih2AuUgkABPkyZNHa9asUfHixdWmTRvFxMSoW7duunbtmnOEYsCAAfrXv/6luLg41axZUyEhIWrduvVftvvOO+/oiSeeUI8ePVS2bFk988wzunz5siTpnnvuUUJCgl588UUVKVJEvXr1kiSNGjVKw4YNU2JiomJiYtS0aVN9+eWXio6OlvT7uoX58+dr4cKFqlSpkqZOnapXX301W/f72GOPqV+/furVq5cqV66stWvXatiwYenqlS5dWm3atNEjjzyixo0bq2LFii7bO7t376733ntPM2bMUIUKFVS/fn3NnDnTGeufBQQEaOjQoapYsaLq1aunXLlyae7cudmKHYC5bEZmK70AAAD+BiMSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbf8PkAtlXISM2hwAAAAASUVORK5CYII="
          },
          "metadata": {}
        }
      ],
      "execution_count": 21
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  8\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a decision tree classifier object then  create a  <code>GridSearchCV</code> object  <code>tree_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "parameters = {'criterion': ['gini', 'entropy'],\n     'splitter': ['best', 'random'],\n     'max_depth': [2*n for n in range(1,10)],\n     'max_features': [None, 'sqrt', 'log2'],\n     'min_samples_leaf': [1, 2, 4],\n     'min_samples_split': [2, 5, 10]}\n\ntree = DecisionTreeClassifier()",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 43
    },
    {
      "cell_type": "code",
      "source": "tree_cv = GridSearchCV(tree, parameters, cv=10, scoring='accuracy', error_score='raise')\n\n# Fit the GridSearchCV object to data\ntry:\n    tree_cv.fit(X_train, Y_train)\nexcept ValueError as e:\n    print(f\"Error encountered: {e}\")",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 44
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",tree_cv.best_params_)\nprint(\"accuracy :\",tree_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 8, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'splitter': 'random'}\naccuracy : 0.8875\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 45
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  9\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Calculate the accuracy of tree_cv on the test data using the method <code>score</code>:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Calculate the accuracy on the test data\naccuracy = tree_cv.score(X_test, Y_test)\n\nprint(\"Accuracy on test data: \", accuracy)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Accuracy on test data:  0.7777777777777778\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 48
    },
    {
      "cell_type": "markdown",
      "source": "We can plot the confusion matrix\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "yhat = tree_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": "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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 49
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  10\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a k nearest neighbors object then  create a  <code>GridSearchCV</code> object  <code>knn_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "parameters = {'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n              'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n              'p': [1,2]}\n\nKNN = KNeighborsClassifier()",
      "metadata": {
        "trusted": true
      },
      "outputs": [],
      "execution_count": 28
    },
    {
      "cell_type": "code",
      "source": "knn_cv = GridSearchCV(KNN, parameters, cv=10, scoring='accuracy')\n\nknn_cv.fit(X_train, Y_train)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stderr",
          "text": "/lib/python3.11/site-packages/threadpoolctl.py:1019: RuntimeWarning: libc not found. The ctypes module in Python 3.11 is maybe too old for this OS.\n  warnings.warn(\n",
          "output_type": "stream"
        },
        {
          "execution_count": 29,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n             param_grid={'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                         'p': [1, 2]},\n             scoring='accuracy')",
            "text/html": "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 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-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 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-4 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-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 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-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 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-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 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-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" 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]},\n             scoring=&#x27;accuracy&#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-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=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]},\n             scoring=&#x27;accuracy&#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-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>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 29
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\nprint(\"accuracy :\",knn_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 10, 'p': 1}\naccuracy : 0.8482142857142858\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 30
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  11\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Calculate the accuracy of knn_cv on the test data using the method <code>score</code>:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Calculate the accuracy on the test data\naccuracy = knn_cv.score(X_test, Y_test)\n\nprint(\"Accuracy on test data: \", accuracy)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "Accuracy on test data:  0.8333333333333334\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 31
    },
    {
      "cell_type": "markdown",
      "source": "We can plot the confusion matrix\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "yhat = knn_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": "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"
          },
          "metadata": {}
        }
      ],
      "execution_count": 32
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  12\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Find the method performs best:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Extract the best scores for each model\nknn_best_score = knn_cv.best_score_\ntree_best_score = tree_cv.best_score_\nsvm_best_score = svm_cv.best_score_\nlogreg_best_score = logreg_cv.best_score_\n\n# Create a dictionary to hold the scores\nmodel_scores = {\n    'KNN': knn_best_score,\n    'Decision Tree': tree_best_score,\n    'SVM': svm_best_score,\n    'Logistic Regression': logreg_best_score\n}\n\n# Determine the best model\nbest_model = max(model_scores, key=model_scores.get)\nbest_score = model_scores[best_model]\n\nprint(f\"The best model is {best_model} with a score of {best_score:.4f}\")",
      "metadata": {
        "trusted": true
      },
      "outputs": [
        {
          "name": "stdout",
          "text": "The best model is Decision Tree with a score of 0.8875\n",
          "output_type": "stream"
        }
      ],
      "execution_count": 46
    },
    {
      "cell_type": "markdown",
      "source": "## Authors\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "[Pratiksha Verma](https://www.linkedin.com/in/pratiksha-verma-6487561b1/)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Change Log\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "| Date (YYYY-MM-DD) | Version | Changed By      | Change Description      |\n| ----------------- | ------- | -------------   | ----------------------- |\n| 2022-11-09        | 1.0     | Pratiksha Verma | Converted initial version to Jupyterlite|\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### <h3 align=\"center\"> IBM Corporation 2022. All rights reserved. <h3/>\n",
      "metadata": {}
    }
  ]
}