8) IBM_SpaceX_Machine_Learning_Prediction.ipynb

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      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    },
    "kernelspec": {
      "name": "python",
      "display_name": "Pyolite",
      "language": "python"
    }
  },
  "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\">\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
      },
      "execution_count": 143,
      "outputs": []
    },
    {
      "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
      },
      "execution_count": 144,
      "outputs": []
    },
    {
      "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
      },
      "execution_count": 145,
      "outputs": []
    },
    {
      "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
      },
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "data.head()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 147,
      "outputs": [
        {
          "execution_count": 147,
          "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": {}
        }
      ]
    },
    {
      "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
      },
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "X.head(100)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 149,
      "outputs": [
        {
          "execution_count": 149,
          "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": {}
        }
      ]
    },
    {
      "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()\n\nY",
      "metadata": {
        "trusted": true
      },
      "execution_count": 150,
      "outputs": [
        {
          "execution_count": 150,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n       1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,\n       1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 1], dtype=int64)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  2\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Standardize the data in <code>X</code> then reassign it to the variable  <code>X</code> using the transform provided below.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# students get this \ntransform = preprocessing.StandardScaler()\n\nX = transform.fit(X).transform(X)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 151,
      "outputs": []
    },
    {
      "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
      },
      "execution_count": 152,
      "outputs": []
    },
    {
      "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
      },
      "execution_count": 153,
      "outputs": [
        {
          "execution_count": 153,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(18,)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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']}  # #limited-memory BFGS algorithm",
      "metadata": {
        "trusted": true
      },
      "execution_count": 154,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "parameters ={\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge\nlr=LogisticRegression()\n\n\ngridsearch_cv_lr = GridSearchCV(lr, parameters, scoring='accuracy', cv=10)\nlogreg_cv = gridsearch_cv_lr.fit(X_train, Y_train)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 155,
      "outputs": []
    },
    {
      "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_)\n\nlr_best_score = logreg_cv.best_score_\n\nprint(\"accuracy :\",logreg_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 156,
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\naccuracy : 0.8464285714285713\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  5\n",
      "metadata": {
        "tags": []
      }
    },
    {
      "cell_type": "markdown",
      "source": "Calculate the accuracy on the test data using the method <code>score</code>:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "lr_score = logreg_cv.score(X_test, Y_test)\n\nprint(f\"Logistic Regression - Accuracy using method score: {lr_score}\")",
      "metadata": {
        "trusted": true
      },
      "execution_count": 157,
      "outputs": [
        {
          "name": "stdout",
          "text": "Logistic Regression - Accuracy using method score: 0.8333333333333334\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "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
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x61e3f10>",
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Mnjw5q6yySrbaaqscddRRadu27SLfv3///rniiity3nnn5bzzzkuzZs2y5ZZb5sc//vESl9XLwuGHH55PP/00d911V+6999706NEjl156ac4555x61zVv3jxnnHFGfv3rX+ekk07K3Llzc/rppy/RZ5g+fXpOOOGE9OjRI4cffnjd8S222CLDhg3LVVddlR122CG9e/eu1McDAAC+IGpKS7LLFAAAAAAAsFDWcAcAAAAAgAqwpAwAAAAAjc6ECRPyu9/9Ls8991xeffXVdOvWLXfffXfd+Xnz5uXKK6/MQw89lH/9618plUrp3r17jjnmmGyxxRYFJgeqmQl3AAAAABqdV199NQ8//HDWXXfdbLDBBgucnzlzZi677LL07Nkzv/rVr3L22Wenffv2GTZsWJ544okCEgMrAmu4AwAAANDo1NbWpkmT+bOow4cPzz//+c8FJtynT5+e9u3b1zs2ePDgrLvuurnkkkuWe2ag+plwBwAAAKDR+axsX5SmTZvWK9s/O9a9e/e8//77yzIasAJTuAMAAABAA8ydOzfPPfdcunXrVnQUoErZNBUAAACAFdKgQYMWe37MmDEVvd8VV1yRiRMn5sADD6zo+wJfHAp3GpWD1tur6AgAAAA0IleO/0PREZaLOR++XnSEZe6xxx7L+eefn+9///v58pe/XHQcoEop3AEAAABYIVV6gn1RXnjhhRx11FEZPHhwjjzyyOVyT2DFpHAHAAAAoDy184pOsMxMmDAhhxxySDbbbLOceuqpRccBqpxNUwEAAABgId5///0cdNBBWWONNXLeeeelefPmRUcCqpwJdwAAAAAanU8//TQPP/xwkuTtt9/O9OnTM3r06CTJVlttlZVWWimHHHJIJk+enBNOOCGvvvpq3WtbtGiRHj16FJIbqG41pVKpVHQIWF5smgoAAMDy1Gg2TZ34ciH3bb5a96V+7VtvvZVBgwYt9Ny1116brl27LvJ8165d8+CDDy71vYEvLhPuAAAAADQ6a621Vl5+efF/UPB55wH+m8IdAAAAgPLU1hadAKAq2DQVAAAAAAAqQOEOAAAAAAAVYEkZAAAAAMpSKllSBiAx4Q4AAAAAABVhwh0AAACA8tg0FSCJCXcAAAAAAKgIE+4AAAAAlMca7gBJTLgDAAAAAEBFKNwBAAAAAKACLCkDAAAAQHlq5xWdAKAqmHAHAAAAAIAKMOEOAAAAQHlsmgqQxIQ7AAAAAABUhMIdAAAAAAAqwJIyAAAAAJSn1pIyAIkJdwAAAAAAqAgT7gAAAACUpWTTVIAkJtwBAAAAAKAiTLgDAAAAUB5ruAMkMeEOAAAAAAAVoXAHAAAAAIAKsKQMAAAAAOWxaSpAEhPuAAAAAABQESbcAQAAAChP7byiEwBUBRPuAAAAAABQAQp3AAAAAACoAEvKAAAAAFAem6YCJDHhDgAAAAAAFWHCHQAAAIDy1JpwB0hMuAMAAAAAQEUo3AEAAAAAoAIsKQMAAABAeWyaCpDEhDsAAAAAAFSECXcAAAAAymPTVIAkJtwBAAAAAKAiTLgDAAAAUJZSaV7REQCqggl3AAAAAACoAIU7AAAAAABUgCVlAAAAAChPyaapAIkJdwAAAAAAqAgT7gAAAACUp9aEO0Biwh0AAAAAACpC4Q4AAAAAABVgSRkAAAAAymPTVIAkJtwBAAAAAKAiTLgDAAAAUJ7aeUUnAKgKJtwBAAAAAKACTLgDAAAAUB5ruAMkMeEOAAAAAAAVoXAHAAAAAIAKsKQMAAAAAOWptaQMQGLCHQAAAAAAKsKEOwAAAADlsWkqQBIT7gAAAAAAUBEKdwAAAAAAqABLygAAAABQHpumAiQx4Q4AAAAAABVhwh0AAACA8phwB0hiwh0AAAAAACrChDsAAAAAZSmV5hUdAaAqmHAHAAAAAIAKULgDAAAAAEAFWFIGAAAAgPLYNBUgiQl3AAAAAACoCBPuAAAAAJSnZMIdIDHhDgAAAAAAFaFwBwAAAACACrCkDAAAAADlsWkqQBIT7gAAAAAAUBEm3AEAAAAoj01TAZKYcAcAAAAAgIow4Q4AAABAeazhDpDEhDsAAAAAAFSEwh0AAAAAACrAkjIAAAAAlMemqQBJTLgDAAAAAEBFmHAHAAAAoDw2TQVIYsIdAAAAAAAqQuEOAAAAAAAVYEkZAAAAAMpjSRmAJCbcAQAAAACgIky4AwAAAFCekgl3gMSEOwAAAAAAVIQJdwAAAADKYw13gCQm3AEAAAAAoCIU7gAAAAAAUAGWlAEAAACgPDZNBUhiwh0AAAAAACrChDsAAAAA5bFpKkASE+4AAAAANEITJkzIyJEjs9tuu6VHjx4ZPHjwQq978MEH841vfCObbLJJdtxxx9x6663LOSmwIlG4AwAAANDovPrqq3n44Yez7rrrZoMNNljoNU899VSOPPLI9O7dO5dffnl23nnnnHDCCRk9evRyTgusKCwpAwAAAEB5VsBNUwcOHJjtt98+STJ8+PD885//XOCaiy++OL169copp5ySJNl6663z5ptv5rzzzstOO+20XPMCKwYT7gAAAAA0Ok2aLL4Wmz17dsaOHbtAsb7LLrvktddey1tvvbUs4wErKBPuAAAAAJTnC7hp6htvvJE5c+akW7du9Y5/tvzM66+/nrXWWquIaEAVU7gDAAAAsEIaNGjQYs+PGTNmqd97ypQpSZJ27drVO/7Z88/OA/wnhTsAAAAA5fkCTrgDLA2FOwAAAAArpHIm2D9P+/btkyTTpk2rd3zq1Kn1zgP8J5umAgAAAMB/WWedddK8efO8/vrr9Y5/9vy/13YHSBTuAAAAAJSrVCrmsQy1aNEiffv2zX333Vfv+L333psNNtjAhqnAQllSBgAAAIBG59NPP83DDz+cJHn77bczffr0jB49Okmy1VZbpWPHjvne976XYcOG5aSTTsrOO++csWPH5u67785vfvObIqMDVaymVFrGfxwIVeSg9fYqOgIAAACNyJXj/1B0hOXi01E/L+S+rfc9ealf+9Zbb2XQoEELPXfttdemb9++SeavE3/uuefm3//+d9Zcc80ceuih2Wsv/QKwcCbcAQAAAGh01lprrbz88sufe92gQYMWWcwD/DeFO4V45513luj6NddccxklAQAAAACoDIU7hRg4cGBqamoafP24ceOWYRoAAACgLLW1RScAqAoKdwpxwQUX1P08Y8aMnHPOOVlnnXWyww47pFOnTvnwww9z//33580338xxxx1XYFIAAAAAgIZRuFOI7bffvu7nE088Mf3798/pp59e75phw4ZlxIgRefzxxzNkyJDlHREAAABoqJIJd4AkaVJ0ABg9enQGDx680HO77rprHnjggeWcCAAAAABgySncKVzTpk3z4osvLvTciy++mCZN/McUAAAAAKh+lpShcN/4xjdy3nnnZdasWdl+++3TsWPHfPTRR/nTn/6Uyy67LPvss0/REQEAAIDFsWkqQBKFO1Xg+OOPT7NmzXLFFVfkwgsvrDvesmXLfOc738mPfvSjAtMBAAAAADSMwp3CNWvWLMcff3wOP/zwvPLKK/nggw/SpUuXbLjhhmnfvn3R8QAAAIDPUyoVnQCgKijcqRrt27fPlltuWXQMAAAAAIClonCnKkyZMiWPPPJI3nvvvcyaNaveuZqamhxxxBEFJQMAAAA+lzXcAZIo3KkCf/nLX3L00UdnxowZadWqVZo3b17vvMIdAAAAAFgRKNwp3K9+9atssskmOe2009K1a9ei4wAAAAAALBWFO4V78803M3z4cGU7AAAArKgsKQOQJGlSdADo0aNH3n333aJjAAAAAACUReFO4U466aRcd911efTRRzN37tyi4wAAAABLqlRbzAOgylhShsINHTo0c+fOzaGHHpomTZqkZcuW9c7X1NTk6aefLigdAAAAAEDDKNwp3EEHHZSampqiYwAAAAAAlEXhTuGOOuqooiMAAAAAZSjVloqOAFAVrOEOAAAAAAAVYMKdqjBhwoTcdtttGT9+fGbNmrXA+UsuuaSAVAAAAECD1NrAFCBRuFMFnn/++ey///5Zc801M378+HTv3j3Tpk3L22+/ndVXXz3rrLNO0REBAAAAAD6XJWUo3FlnnZWdd945d999d0qlUn75y19mzJgxufHGG1NTU5NDDjmk6IgAAADA4pRqi3kAVBmFO4V7+eWXs+uuu6ZJk/n/cfxsSZk+ffrkyCOPzDnnnFNkPAAAAACABlG4U7iampo0b948NTU16dSpU9555526c6uvvnrGjx9fXDgAAAAAgAayhjuF22CDDfLmm29m6623Tu/evXPllVdmww03TLNmzXLZZZdl7bXXLjoiAAAAsDi1paITAFQFhTuF23vvveum2n/4wx/moIMOym677ZYkad26dc4777wi4wEAAAAANIjCncLtvvvudT9vsMEGuffee/Pss89m5syZ6d27dzp16lRcOAAAAODz1drAFCBRuFOFVl555WyzzTZFxwAAAAAAWCIKdwpx//33L9H1O+ywwzJKAgAAAABQGQp3CnH00Uc3+NqampqMGzduGaYBAAAAymJJGYAkCncKMmbMmKIjAAAAAABUlMKdQnTt2rXoCAAAAECllEpFJwCoCk2KDgAAAAAAAF8EJtwBAAAAKI813AGSmHAHAAAAAICKMOEOQCHW/J+1stsPhma9TbqlXZcOmf3prLzz6lsZfdkf89yYp4uOBwBVy3coAED1MuFO4Z588sl88sknCz33ySef5Mknn1zOiYDloVPXLmnVplUeu/WhjDr5qtx1/h+SJMf8bkQG7Lt9wekAoHr5DgWqUm2pmAdAlakplWwjTbE23njj3HzzzenVq9cC5/75z3/mW9/6VsaNG1eRex203l4VeR9g2ahp0iQ/v/tXad6yRU4YdEzRcQBgheE7FKrXleP/UHSE5WLG2QcXct+VjruikPsCLIoJdwq3uD/z+fTTT9OqVavlmAYoUqm2Nh+9MykrtVup6CgAsELxHQoUrlRbzAOgyljDnUI8++yzeeaZZ+qe33XXXXn66frrTc6aNStjxoxJt27dlnc8YDlq0bplWrRqkdZtV0rvr2+ZTbbbLH+7+/GiYwFA1fMdCgBQfRTuFOIvf/lLLrjggiRJTU1NrrvuugWuadasWTbYYIP8/Oc/X97xgOVo6IkH5Gvf2SFJUjtvXp4e/bfcMNJfCwWAz+M7FACg+ijcKcSRRx6ZI488Mkmy0UYb5ZZbblnoGu7AF9+frrw7T9/7RDqs1jFb7to/TZo2SbMWvp4A4PP4DgWqig1MAZLYNJVGxqapUP1+eO3PslK7lXLq7iOKjgIAKxTfoVCdGs2mqb/630Luu9LxVxVyX4BFMf5AVZgxY0Zuv/32PP3005kyZUrat2+fzTffPHvssUdWWsnGT9CYPP1/T+SA0w/P6t3WzHuvv1N0HABYYfgOBYpUqrWBKUCSNCk6ALz77rv5xje+kVNPPTX//ve/U1NTk3//+9/55S9/md122y3vvvtu0RGB5ah5qxZJktZt/WEbACwJ36EAAMUz4U7hTj/99CTJPffck27dutUdf/3113P44YfnjDPOyG9/+9ui4gHLSNtO7TJt0tR6x5o2a5r+ew7IrE9n5Z1X3yooGQBUN9+hQFWyhjtAEoU7VeDxxx/PKaecUq9sT5Ju3brlmGOOyc9//vOCkgHL0gGnHZZWbVbKK397MZPf+yjtu3TI1rt/NWt+aa3c9IurM2vGzKIjAkBV8h0KAFC9FO4Ubt68eWnZsuVCz7Vs2TLz5s1bzomA5eFvdz+er+49MF/bb4es3KFtZn7yaSb84/X84Yzr8+wDTxUdDwCqlu9QAIDqpXCncH369MnFF1+crbbaKm3btq07Pm3atFxyySXp06dPgemAZeVvdz2Wv931WNExAGCF4zsUqEolm6YCJAp3qsDxxx+f/fbbLwMGDMjWW2+dzp07Z9KkSXniiSfSvHnznHbaaUVHBAAAAAD4XAp3CrfhhhvmzjvvzFVXXZWnn346//rXv9K+ffvsvffeOfDAA7P66qsXHREAAABYHJumAiRRuFMlVl999YwYMaLoGAAAAAAAS61J0QEAAAAAAOCLwIQ7hZs5c2Yuuuii3HfffXnvvfcye/bsBa4ZN25cAckAAACABqm1aSpAonCnCpx88sm5++67M3jw4GywwQZp3rx50ZEAAAAAAJaYwp3C/fnPf87xxx+f/fbbr+goAAAAwNKwaSpAEmu4UwWaNm2a9dZbr+gYAAAAAABlUbhTuH333Td//OMfi44BAAAALK1SbTEPgCpjSRkK16pVqzz99NPZZ5990q9fv7Rr167e+Zqamhx44IHFhAMAAAAAaCCFO4U7++yzkyTvvPNOnn322QXOK9wBAAAAgBWBwp3CvfTSS0VHAAAAAMph01SAJNZwBwAAAACAijDhDgAAAEBZSrU2MAVITLgDAAAAAEBFKNwBAAAAAKACLCkDAAAAQHlsmgqQxIQ7AAAAAABUhAl3CnHHHXcs0fW77777MskBAAAAVIAJd4AkCncKMnz48HrPa2pqkiSlUmmBY4nCHQAAAACofgp3CvHkk0/W/TxhwoQcc8wx2W233bLjjjumc+fO+fDDDzN69OjceeedOffcc4sLCgAAAHy+Um3RCQCqgsKdQrRt27bu53POOSdDhw7NoYceWnesU6dO6d69e1q1apWzzz4711xzTRExAQAAAAAazKapFO6ZZ55Jz549F3quZ8+eee6555ZzIgAAAACAJadwp3AdO3bMvffeu9Bz99xzTzp27LicEwEAAABLpLZUzAOgylhShsIdfvjhGTlyZN54441sv/326dSpUyZNmpQHHnggTz75ZE455ZSiIwIAAAAAfC6FO4Xbe++906VLl1xyySU566yzMnfu3DRr1iw9evTIRRddlIEDBxYdEQAAAFiMkmlzgCQKd6rE1772tXzta19LbW1tPvroo3Ts2DFNmljxCAAAAABYcSjcqSpNmjRJ586di44BAAAAALDEFO4U4tRTT81BBx2UNddcM6eeeurnXn/iiScuh1QAAADAUrGkDEAShTsFefDBB7PXXntlzTXXzIMPPrjYa2tqahTuAAAAAEDVU7hTiP8s2T+vcAcAAACqXG1t0QkAqoJdKQEAAAAAoAJMuFOIO+64Y4mu33333ZdJDgAAAACASlG4U4jhw4fXe15TU5MkKZVKCxxLFO4AAABQ1WyaCpBE4U5BnnzyybqfJ0yYkGOOOSa77bZbdtxxx3Tu3DkffvhhRo8enTvvvDPnnntucUEBAAAAABpI4U4h2rZtW/fzOeeck6FDh+bQQw+tO9apU6d07949rVq1ytlnn51rrrmmiJgAAABAQ5hwB0hi01SqwDPPPJOePXsu9FzPnj3z3HPPLedEAAAAAABLTuFO4Tp27Jh77713oefuueeedOzYcTknAgAAAJZEqVQq5AFQbSwpQ+EOP/zwjBw5Mm+88Ua23377dOrUKZMmTcoDDzyQJ598MqecckrREQEAAAAAPpfCncLtvffe6dKlSy655JKcddZZmTt3bpo1a5YePXrkoosuysCBA4uOCAAAAHwBjRkzJpdcckn+9a9/ZeWVV87mm2+e4447LmuvvXbR0YAVVE3J37+hitTW1uajjz5Kx44d06RJ5Vc8Omi9vSr+ngAAALAoV47/Q9ERlouph+xQyH3bXX7/Ur927NixOfDAA7P77rtnyJAh+fjjj/Pb3/42tbW1ueuuu9KqVasKJgUaCxPuVJUmTZqkc+fORccAAAAAvuDuueeerLnmmjnttNNSU1OTZP4+cwcccED++c9/Zosttig4IbAiUrgDAAAAUJ7aFW8Bhblz52bllVeuK9uTpG3btkliQ1ZgqVV+zQ4AAAAAqHJ77rlnXnvttdxwww2ZNm1a3nzzzfz6179Ojx490qdPn6LjASsoa7jTqFjDHQAAgOWp0azh/t2vF3LfPcbXLvb8mDFjFnv+z3/+c370ox/lk08+SZJsvPHGueKKKyx3Cyw1E+4AAAAAlKVUWyrkUY6///3v+clPfpK9994711xzTd2GqYceemhmzpxZod8M0NhYwx0AAACAFdLnTbAvzqmnnpqtt946w4cPrzvWu3fvbLfddvnjH/+YoUOHViIi0Mgo3AEAAAAozwq4aeprr72WQYMG1Tu2+uqrZ5VVVskbb7xRUCpgRWdJGQAAAAAanTXXXDMvvvhivWNvv/12Jk+enK5duxaUCljRmXAHAAAAoDyL37u0Ku2zzz457bTTcuqpp2bgwIH5+OOPc/HFF6dTp07Zeeedi44HrKAU7gAAAAA0OsOGDUuLFi0yatSo3HrrrVl55ZXTu3fvnHvuuVlllVWKjgesoBTuAAAAADQ6NTU12XfffbPvvvsWHQX4AlG4AwAAAFCW0gq4aSrAsmDTVAAAAAAAqAAT7gAAAACUx4Q7QBIT7gAAAAAAUBEKdwAAAAAAqABLygAAAABQntqiAwBUBxPuAAAAAABQASbcAQAAAChLyaapAElMuAMAAAAAQEWYcAcAAACgPNZwB0hiwh0AAAAAACpC4Q4AAAAAABVgSRkAAAAAymLTVID5TLgDAAAAAEAFmHAHAAAAoDw2TQVIYsIdAAAAAAAqQuEOAAAAAAAVYEkZAAAAAMpSsqQMQBIT7gAAAAAAUBEm3AEAAAAojwl3gCQm3AEAAAAAoCJMuAMAAABQFmu4A8xnwh0AAAAAACpA4Q4AAAAAABVgSRkAAAAAymNJGYAkJtwBAAAAAKAiTLgDAAAAUBabpgLMZ8IdAAAAAAAqQOEOAAAAAAAVYEkZAAAAAMpiSRmA+Uy4AwAAAABABZhwBwAAAKAsJtwB5jPhDgAAAAAAFWDCHQAAAIDylGqKTgBQFUy4AwAAAABABSjcAQAAAACgAiwpAwAAAEBZbJoKMJ8JdwAAAAAAqAAT7gAAAACUpVRr01SAxIQ7AAAAAABUhMIdAAAAAAAqwJIyAAAAAJTFpqkA85lwBwAAAACACjDhDgAAAEBZSiWbpgIkJtwBAAAAAKAiTLgDAAAAUBZruAPMZ8IdAAAAAAAqQOEOAAAAAAAVYEkZAAAAAMpSqrVpKkBiwh0AAAAAACrChDsAAAAAZSmVik4AUB1MuAMAAAAAQAUo3AEAAAAAoAIsKUODTZ8+PdOmTcsaa6xRd2zixIm56aabMnv27Oy4447p1atXgQkBAACAItg0FWA+hTsNNnLkyLz11lu55ZZbkswv4IcOHZr33nsvTZo0ybXXXpsrrrgiffv2LTgpAAAAAMDyZ0kZGuzpp5/OdtttV/f8j3/8Y95///3cdNNN+dvf/pbu3bvn4osvLi4gAAAAUIhSbU0hD4Bqo3CnwSZPnpzVVlut7vmDDz6YzTffPL17906bNm2y++6756WXXiowIQAAAABAcRTuNFi7du3y4YcfJklmzpyZp59+Ottss03d+aZNm2bmzJlFxQMAAAAAKJQ13GmwzTbbLDfeeGO6deuWRx99NLNmzcqgQYPqzo8fP77eBDwAAADQOJRKRScAqA4m3Gmw4447Ls2aNctRRx2VW265JQceeGD+53/+J0kyb968jB49OltuuWXBKQEAAAAAimHCnQZbd911M3r06Lz22mtp06ZN1lprrbpzn376aX72s59lo402KjAhAAAAUAQbmALMp3BniTRv3nyhpXqbNm2y/fbbF5AIAAAAAKA6KNxZpCeffHKpXmdZGQAAAGhcSiUT7gCJwp3F2H///VNT0/AvzFKplJqamowbN24ZpgIAAAAAqE4Kdxbp2muvLToCAAAAAMAKQ+HOIm211VZFRwAAAABWAKXaohMAVIcmRQdgxfT+++/npZdeyowZM4qOAgAAAABQFRTuLJEHHnggO+20UwYMGJA99tgjzz33XJLko48+yu67754HHnig4IQAAADA8lZbqinkAVBtFO402IMPPpijjjoqq6yySo444oiUSqW6cx07dsxqq62WW2+9tcCEAAAAAADFUbjTYBdeeGG22GKLjBo1Kt/5zncWON+7d++MGzeugGQAAAAAAMVTuNNgr776anbeeedFnu/cuXMmTZq0HBMBAAAA1aBUqinkAVBtFO40WOvWrfPpp58u8vybb76ZDh06LL9AAAAAAABVROFOg/Xt2zd33HFH5s6du8C5Dz74ILfccku+8pWvFJAMAAAAKFKptqaQB0C1UbjTYD/4wQ/y3nvvZa+99srNN9+cmpqa/OUvf8lvfvObDBkyJKVSKUcccUTRMQEAAAAACqFwp8G6deuWG2+8MR06dMhvf/vblEql/O53v8ull16aDTfcMDfeeGPWWmutomMCAAAAy1mpVMwDoNo0KzoAK5b/+Z//ydVXX50pU6ZkwoQJKZVKWXvttdOxY8eiowEAAAAAFErhzlJp3759evXqVXQMAAAAAICqoXBniXz00Ue5/PLL8/DDD+ftt99OknTt2jUDBgzId7/73XTu3LnghAAAAMDyZgNTgPms4U6DvfrqqxkyZEiuuuqqtG3bNjvttFN22mmntG3bNldddVW+8Y1v5JVXXik6JgAAAABAIUy402CnnHJK5s2bl1tuuWWB5WSef/75HHLIIfnFL36R6667rqCEAAAAQBFqSybcARIT7iyB559/PsOGDVvo2u29evXKsGHD8vzzzxeQDAAAAACgeAp3GqxTp05p2bLlIs+3bNkynTp1Wo6JAAAAAACqh8KdBhs2bFhGjRqVDz74YIFzEydOzKhRozJs2LACkgEAAABFKpVqCnkAVBtruLNIV1111QLHVlpppeywww7Zfvvts+666yZJxo8fnzFjxmSdddZZ3hEBAAAAAKpGTalUKhUdguq00UYbLfFrampqMm7cuGWQpjIOWm+voiMAAADQiFw5/g9FR1gunl9vSCH37TX+rkLuC7AoJtxZpDFjxhQdAQAAAABghaFwZ5G6du1adAQAAABgBVBrPXWAJDZNBQAAAACAijDhzhJ56aWXcv311+fFF1/MtGnTUltbW+98TU1NHnjggYLSAQAAAAAUx4Q7DTZ27Nh861vfykMPPZRVV101b775ZtZee+2suuqqeeedd7LSSitlyy23LDomAAAAsJyVSjWFPACqjcKdBjvvvPOy9tprZ/To0TnttNOSJIcddlhGjRqVm266KRMnTsxOO+1UcEoAAACAhrv99tuz++67Z5NNNknfvn1z8MEHZ+bMmUXHAlZQlpShwV588cUcddRRadOmTaZMmZIkdUvKbLrpphk6dGh++9vfZsCAAUXGBAAAAJazUqnoBEvn4osvzuWXX57DDz88vXv3zuTJk/PEE09k3rx5RUcDVlAKdxqsadOmWXnllZMk7dq1S7NmzTJp0qS682uvvXZee+21ouIBAAAANNjrr7+eCy64IBdddFG94cEdd9yxwFTAis6SMjTYOuusk/HjxyeZvzlqt27d6m2Q+tBDD6Vz584FpQMAAABouNtuuy1rrbWWv6kPVJTCnQYbMGBA7rnnnsydOzdJ8r//+7+5//77s8MOO2SHHXbIgw8+mKFDhxacEgAAAFjeaks1hTzK8dxzz2XDDTfMRRddlH79+uXLX/5y9tlnnzz33HMV+q0AjVFNqbSirrLF8jZnzpxMnz49HTp0SE3N/C+1P/7xj7n//vvTtGnTbLfddtlzzz0LTrl4B623V9ERAAAAaESuHP+HoiMsF0+ttXsh9z2++7TFnh8zZswiz+20006ZOHFiVl111Rx77LFp3bp1Lrnkkrzyyiu5//7706lTp0rHBRoBa7jTYM2bN88qq6xS79huu+2W3XbbraBES+7ad54oOgIArJA+fefRoiMAAFWsVOa0eRFKpVJmzJiR3/72t9loo42SJJtuumkGDhyY66+/Psccc0zBCYEVkcIdAAAAgBXS4ibYP0+7du3SoUOHurI9STp06JAePXrkX//6VyXiAY2Qwp1FGjZs2BK/pqamJtdcc80ySAMAAABUq3LXUy/Cl770pbzxxhsLPTdr1qzlnAb4orBpKotUKpWW+FFbW1t0bAAAAIDP9bWvfS0ff/xxxo0bV3ds8uTJeeGFF9KzZ88CkwErMpum0qg0a9G16AgAsEKyhjsALJ3mnbsVHWG5GLvmnoXct+87ty31a2tra7P33ntnypQpOfbYY9OyZctcdtllGT9+fO6+++506dKlgkmBxsKSMgAAAACUZUWc5mzSpEkuu+yynH766Rk5cmTmzJmTLbbYIjfccIOyHVhqCncAAAAAGqWOHTvmrLPOKjoG8AWicAcAAACgLCvipqkAy4JNUwEAAAAAoAIU7gAAAAAAUAGWlAEAAACgLCVLygAkUbizFCZOnJgnn3wykyZNyo477pjVV1898+bNy7Rp09K2bds0bdq06IgAAAAAAMudwp0GK5VKOeOMM3LDDTdk7ty5qampyYYbbpjVV189M2bMyMCBA3P00UfnwAMPLDoqAAAAsBzVFh0AoEpYw50Gu+KKK3LttdfmoIMOylVXXZVSqVR3rm3bttlhhx1y//33F5gQAAAAAKA4JtxpsN///vfZfffd88Mf/jCTJ09e4Hz37t3zyCOPFJAMAAAAKFIp1nAHSEy4swTefffdbLbZZos837p160yfPn05JgIAAAAAqB4KdxqsU6dOeffddxd5/oUXXsgaa6yxHBMBAAAAAFQPhTsN9vWvfz033XRT3nzzzbpjNTXz/8rYX/7yl9x+++3ZaaediooHAAAAFKS2VMwDoNrUlP5z50tYjGnTpuU73/lO3nrrrWyxxRZ59NFH079//8yYMSPPPvtsNt5449xwww1p3bp10VEXqVmLrkVHAIAV0qfvPFp0BABYITXv3K3oCMvFQ6t9q5D7bjfx94XcF2BRTLjTYG3bts0tt9ySgw8+OBMnTkzLli3z5JNPZtq0aTniiCNy4403VnXZDgAAACwbtakp5AFQbUy406iYcAeApWPCHQCWTmOZcH9wtb0Lue/AibcUcl+ARTHhDgAAAAAAFdCs6ACsOEaMGPG519TU1OS0005bDmkAAACAalGyvAtAEoU7S2Ds2LELHKutrc0HH3yQefPmpWPHjtZwBwAAAAAaLYU7Dfbggw8u9PicOXNy880355prrsmVV165nFMBAAAARastOgBAlbCGO2Vr3rx59ttvv2yzzTb5xS9+UXQcAAAAAIBCKNypmI022ihPPvlk0TEAAACA5ayUmkIeANVG4U7FPP7449ZwBwAAAAAaLWu402AXXHDBQo9PmzYtTz75ZF588cUceuihyzkVAAAAAEB1ULjTYIsq3Nu3b5+11147J598cvbee+/lnAoAAAAomk1TAeZTuNNgL730UtERAAAAAACqljXcaZCZM2fm9NNPz4MPPlh0FAAAAKDK1Bb0AKg2CncapFWrVrn55pszadKkoqMAAAAAAFQlhTsN1rNnz7zyyitFxwAAAAAAqEoKdxrspz/9ae699978/ve/z9y5c4uOAwAAAFSJUmoKeQBUm5pSqVQqOgTV68knn8wGG2yQjh07ZsiQIZk8eXImTZqUFi1aZLXVVkvLli3rXV9TU5M777yzoLSfr1mLrkVHAIAV0qfvPFp0BABYITXv3K3oCMvFPavtW8h9d504qpD7AixKs6IDUN2GDRuWs846K4MHD06HDh3SoUOHrL/++kXHAgAAAKpIrWFzgCQKdz5HqVTKZ38J4rrrris4DQAAAABA9bKGOwAAAAAAVIAJdz5XTY2/FwYAAAAsWq0NTAGSKNxpgB//+Mf58Y9/3KBra2pq8uKLLy7jRAAAAAAA1Ufhzufq379/1ltvvaJjAAAAAFWqVHQAgCqhcOdz7b777hkyZEjRMQAAAAAAqprCHQAAAICy1BYdAKBKNCk6AAAAAAAAfBEo3AEAAAAAoAIsKcNivfTSS0VHAAAAAKpcbU1N0REAqoIJdwAAAAAAqAAT7gAAAACUpVR0AIAqYcIdAAAAAAAqQOEOAAAAAAAVYEkZAAAAAMpSW3QAgCphwh0AAAAAACrAhDsAAAAAZamtKToBQHUw4Q4AAAAAABVgwh0AAACAstTGiDtAYsIdAAAAAAAqQuEOAAAAAAAVYEkZAAAAAMpSKjoAQJUw4Q4AAAAAABVgwh0AAACAstTaMxUgiQl3AAAAAACoCIU7AAAAAABUgCVlAAAAAChLbdEBAKqECXcAAAAAAKgAE+4AAAAAlKVUdACAKmHCHQAAAAAAKsCEOwAAAABlqa0pOgFAdTDhDgAAAAAAFaBwBwAAAACACrCkDAAAAABlqS06AECVMOEOAAAAAAAVYMIdAAAAgLKYcAeYz4Q7AAAAAABUgMIdAAAAAAAqwJIyAAAAAJSlVFN0AoDqYMIdAAAAAAAqwIQ7AAAAAGWxaSrAfCbcAQAAAACgAky4AwAAAFAWE+4A85lwBwAAAACAClC4AwAAAABABVhSBgAAAICylIoOAFAlTLgDAAAAAEAFmHAHAAAAoCy1NUUnAKgOJtwBAAAAAKACFO4AAAAAAFABlpQBAAAAoCy1RQcAqBIm3AEAAAAAoAJMuAMAAABQFhPuAPOZcAcAAAAAgAow4Q4AAABAWUpFBwCoEibcAQAAAACgAhTuAAAAAABQAZaUAQAAAKAstTVFJwCoDibcAQAAAACgAky4AwAAAFCW2qIDAFQJE+4AAAAANGqffPJJtt1223Tv3j3/+Mc/io4DrMAU7gAAAAA0ahdddFHmzZtXdAzgC0DhDgAAAEBZSgU9KuG1117LjTfemKOOOqpC7wg0Zgp3AAAAABqtU089Nfvss0/WX3/9oqMAXwAKdwAAAADKUptSIY9yjR49Oq+88kqOOOKICvwWAJJmRQcAAAAAgKUxaNCgxZ4fM2bMIs99+umnOeOMM3LsscemTZs2lY4GNFIKdwAAAADKUlt0gKVw8cUXp1OnTvnmN79ZdBTgC0ThDgAAAMAKaXET7Ivz9ttv58orr8yFF16YadOmJUlmzJhR96+ffPJJVl555YrlBBoPhTsAAAAAjcpbb72VOXPm5NBDD13g3LBhw7LpppvmlltuKSAZsKJTuAMAAABQlvK3L12+Nt5441x77bX1jo0bNy6nn356Tj755GyyySYFJQNWdAp3AAAAABqVdu3apW/fvgs917Nnz/Ts2XM5JwK+KBTuAAAAAJRlRdw0FWBZULgDAAAA0Oj17ds3L7/8ctExgBVck6IDAAAAAADAF4EJdwAAAADKUltTdAKA6mDCHQAAAAAAKsCEOwAAAABlqU2p6AgAVcGEOwAAAAAAVIDCHQAAAAAAKsCSMgAAAACUxYIyAPOZcAcAAAAAgAow4Q4AAABAWWqLDgBQJUy4AwAAAABABZhwBwAAAKAstVZxB0hiwh0AAAAAACpC4Q4AAAAAABVgSRkAAAAAymJBGYD5TLgDAAAAAEAFmHAHAAAAoCy1RQcAqBIm3AEAAAAAoAIU7gAAAAAAUAGWlAEAAACgLLW2TQVIYsIdAAAAAAAqwoQ7AAAAAGUx3w4wnwl3AAAAAACoABPuFGrgwIGpqalp8PVjxoxZhmkAAACApVFbdACAKqFwp1CDBg2qV7jfd999mT59evr3759OnTpl0qRJefzxx9O2bdvsuOOOBSYFAAAAAFg8hTuFOuGEE+p+vuKKK7LGGmvkiiuuSJs2beqOT5s2LYccckg6depUREQAAAAAgAaxhjtV47rrrsuhhx5ar2xPkrZt2+aQQw7J9ddfX1AyAAAAYHFKBf0DUG0U7lSNKVOmZNq0aQs9N23atEydOnU5JwIAAAAAaDiFO1Vj6623ztlnn52//e1v9Y6PHTs255xzTrbeeuuCkgEAAACLU1vQA6DaWMOdqnHKKafke9/7Xg444IC0bds2q6yySiZPnpxp06Zl4403zsknn1x0RAAAAACARVK4UzVWXXXV3HrrrXnkkUfy/PPP54MPPkiXLl3Sq1evbLvttkXHAwAAAABYLIU7VWfbbbdVsAMAAMAKpNYGpgBJFO5UoUceeST/+Mc/8t577+V73/te1lxzzTz55JNZZ511stpqqxUdDwAAAABgoRTuVI2PPvoo3//+9/Pcc89ljTXWyLvvvpt99tkna665Zm699da0bt06P//5z4uOCQAAAPwX8+0A8zUpOgB85pe//GUmT56cu+++O/fff39Kpf//67pfv3554oknCkwHAAAAALB4JtypGg8//HB+8YtfZIMNNsi8efPqnVtjjTUyceLEgpIBAAAAi2MNd4D5TLhTNebNm5eVVlppoeemTp2a5s2bL+dEAAAAAAANp3CnavTq1Su33nrrQs/dc8896dOnz3JOBCxrLVq0yOmn/TRvjH8606b8K4//5a5sP+irRccCgKoxY8anueCK63LYD09M/52+lS9vs3PuuOdP9a6pra3NHff8KUf+5KQM2mP/bDlo9+y+3+G59OpRmTVrdkHJAQAaJ4U7VeMHP/hB/vznP+c73/lObrjhhtTU1OSBBx7I0UcfnQcffDBHHXVU0RGBCrvyd7/JD445NKNG3Z5jf/jzzJtXm7vuvC7b9N+y6GgAUBUmT5maS666Ma+PfzPdv9RtodfMnDkrJ57260z+eEr23n2XHH/MYflyjw1z4e+uz+E/+lm9vZEAlpXagh4A1aam5H99UUWeeeaZnHPOOXnmmWcyb9681NTUpHfv3vnJT36SzTbbrOz3b9aiawVSApWw5Ra988Tj9+Qnx5+SX//m0iRJy5Yt89wzY/LBB5Py1QG7FZwQ+E+fvvNo0RGgUZo9e3amTpuezp065p/jXsk+Bx+TU3/6w+y+69frrpkzZ07++dKr2WyTHvVee/GVN+TC312fy889Lf22LP9/SwNLp3nnhf9h2RfNIet9q5D7Xj7+94XcF2BRbJpKVdlss81y/fXXZ+bMmZkyZUratWuX1q1bFx0LWAa++c1dM3fu3Fx+xQ11x2bNmpWrrr4pvzx1RNZaa8289dY7BSYEgOK1aNEinTt1XOw1zZs3X6BsT5JBA/rnwt9dn9fHv6FwB5a5kk1TAZJYUoYq1apVq6y22mrKdvgC673pl/PKq69n2rTp9Y4/+eSz/+98zwJSAcAXx4eTJidJVunQvuAkAACNhwl3CjVixIgluv70009fRkmA5W31NVbNe+++v8Dxd9+bmCRZY43VlnckAPhCufKGP6TNyivlK1tvUXQUAIBGQ+FOocaNG1fv+cSJEzN58uS0b98+nTp1yqRJkzJlypSsssoqWX311QtKCSwLrVu1yqxZsxY4PnPm/GOtW7da3pEA4Avjsmtuyl+feiYnHndE2rVtU3QcoBGwgSnAfAp3CnXHHXfU/fzII4/kpJNOym9+85tsvfXWdcefeOKJnHDCCfnBD36w/AMCy8ynM2emZcuWCxxv1Wr+sU8/nbm8IwHAF8L/PfBwzr/82uw5eMfss8fgouMAADQq1nCnapx11lk5+uij65XtSdKvX78cddRROeusswpKBiwL7737flZfY9UFjq+x+vylZN59d+LyjgQAK7zH//b3/PTUs7Nt/y0z8sdHFR0HaERKBf0DUG0U7lSNCRMmpEOHDgs91759+7zxxhvLNxCwTD333AvZ8H+6pe1//TX3rbbaLEny7HMvFBELAFZYz7/wUo756S/Sc6MNc84vfppmzZoWHQkAoNFRuFM1vvSlL+Wyyy7LJ598Uu/49OnTc9lll+VLX/pSQcmAZeHW2+5Js2bNcsjB36k71qJFixwwbGjGjv173nrrnQLTAcCK5bXxb+T7P/55uq6+Wi4886S0WsiybQDLUm1BD4BqYw13qsaJJ56Ygw8+OAMGDEjfvn3rNk0dO3Zs5s2blyuuuKLoiEAF/e3JZ/L7P9yVX546Iquu2jn/+tf4DNv/W1lvvbVy6GE/KjoeAFSNG/9wZ6ZN/yTvfzgpSfLQY2Mz8YMPkyTf3usbaVJTk8N+eGKmTpue//32N/PIE0/We/3aXddI7y9vvNxzAwA0RjWlUsmCV1SNDz/8MFdffXWef/75fPDBB+nSpUt69eqVAw44IF26dCn7/Zu16FqBlECltGzZMqec9ON8+9t7ZpVV2ucf/xiXn590Vu7/08NFRwP+y6fvPFp0BGi0dvjmAXnnvfcXeu6+P1ydJNlxrwMX+frddt4+vzzRH2ZDUZp37lZ0hOXigPW+Wch9rxl/ayH3BVgUhTuNisIdAJaOwh0Alk5jKdz3X3fPQu573YTbCrkvwKJYwx0AAAAAACrAGu5UjZkzZ+aiiy7Kfffdl/feey+zZ89e4Jpx48YVkAwAAABYHMsnAMyncKdqnHzyybn77rszePDgbLDBBmnevHnRkQAAAAAAGkzhTtX485//nOOPPz777bdf0VEAAAAAAJaYwp2q0bRp06y33npFxwAAAACWUK1FZQCS2DSVKrLvvvvmj3/8Y9ExAAAAAACWigl3qkarVq3y9NNPZ5999km/fv3Srl27eudrampy4IEHFhMOAAAAWKSSCXeAJElNqVTy34hUhY022mix52tqajJu3Liy7tGsRdeyXg8AjdWn7zxadAQAWCE179yt6AjLxb7r7l7IfUdNuKOQ+wIsigl3qsZLL71UdAQAAABgKdQWHQCgSljDHQAAAAAAKsCEO1Vn1qxZefPNNzNr1qwFzvXs2bOARAAAAAAAn0/hTtWYPXt2TjrppNx5552ZN2/eQq8pdw13AAAAoPJqbZoKkMSSMlSRCy+8MI899ljOOOOMlEql/OxnP8vpp5+efv36pWvXrrnkkkuKjggAAAAAsEgKd6rG6NGjc+SRR2bnnXdOkvTq1Su77757rrzyymy++eZ58MEHC04IAAAALEypoH8Aqo3Cnarx3nvvZf3110/Tpk3TsmXLTJ06te7cN77xjYwePbrAdAAAAAAAi6dwp2p06dKlrmRfa621Mnbs2Lpz48ePLygVAAAAAEDD2DSVqrHVVlvlqaeeysCBA/Otb30rZ555Zl5//fU0b948DzzwQAYPHlx0RAAAAGAhaosOAFAlFO5UjWOPPTaTJ09Okhx44IFJ5q/rPmvWrOy///454ogjCkwHAAAAALB4NaVSyQ4TNBrNWnQtOgIArJA+fefRoiMAwAqpeeduRUdYLvZYZ0gh9739jbsKuS/Aophwp1BDhjT8C7mmpiZ33nnnMkwDAAAAALD0FO4UqmfPnqmpqSk6BgAAAFCG2lhAASBRuFOwM844o+gIAAAAAAAV0aToAAAAAAAA8EVgwh0AAACAstQWHQCgSphwBwAAAACACjDhDgAAAEBZSjZNBUhiwh0AAAAAACpC4Q4AAAAAABVgSRkAAAAAylJrSRmAJCbcAQAAAACgIky4AwAAAFCWUsmEO0Biwh0AAAAAACpC4Q4AAAAAABVgSRkAAAAAylJbdACAKmHCHQAAAAAAKsCEOwAAAABlKcWmqQCJCXcAAAAAAKgIE+4AAAAAlKXWhDtAEhPuAAAAAABQESbcAQAAAGh0/u///i933nlnXnjhhUydOjXrrrtu9t9//3zzm99MTU1N0fGAFZTCHQAAAICylEor3pIyV199dbp27Zrhw4dnlVVWyeOPP56f/exnee+993LkkUcWHQ9YQSncAQAAAGh0Lr744nTs2LHueb9+/fLxxx/nqquuyve///00aWIlZmDJ+W8OAAAAAMpSm1Ihj3L8Z9n+mY033jjTp0/PjBkzynpvoPFSuAMAAABAkqeffjqrrbZa2rRpU3QUYAVlSRkAAAAAVkiDBg1a7PkxY8Y0+L2eeuqp3HvvvTn++OPLjQU0Ygp3AAAAAMpSKnN5l6K99957OfbYY9O3b98MGzas6DjACkzhDgAAAMAKaUkm2Bdl6tSpOeSQQ9KhQ4ecf/75NksFyqJwBwAAAKAstaUVc8J95syZOeywwzJt2rTcfPPNadu2bdGRgBWcwh0AAACARmfu3Ln5wQ9+kNdffz033HBDVltttaIjAV8ACncAAAAAyrIizreffPLJ+fOf/5zhw4dn+vTpefbZZ+vO9ejRIy1atCguHLDCUrgDAAAA0Og89thjSZIzzjhjgXNjxozJWmuttbwjAV8ACncAAAAAGp0HH3yw6AjAF5DCHQAAAICy1K6Qi8oAVF6TogMAAAAAAMAXgQl3AAAAAMpiwh1gPhPuAAAAAABQAQp3AAAAAACoAEvKAAAAAFCWUsmSMgCJCXcAAAAAAKgIE+4AAAAAlMWmqQDzmXAHAAAAAIAKMOEOAAAAQFlKJtwBkphwBwAAAACAilC4AwAAAABABVhSBgAAAICylEqWlAFITLgDAAAAAEBFmHAHAAAAoCy1Nk0FSGLCHQAAAAAAKkLhDgAAAAAAFWBJGQAAAADKYtNUgPlMuAMAAAAAQAWYcAcAAACgLDZNBZjPhDsAAAAAAFSACXcAAAAAylIy4Q6QxIQ7AAAAAABUhMIdAAAAAAAqwJIyAAAAAJSltmRJGYDEhDsAAAAAAFSECXcAAAAAymLTVID5TLgDAAAAAEAFKNwBAAAAAKACLCkDAAAAQFlsmgownwl3AAAAAACoABPuAAAAAJTFpqkA85lwBwAAAACACjDhDgAAAEBZrOEOMJ8JdwAAAAAAqACFOwAAAAAAVIAlZQAAAAAoi01TAeYz4Q4AAAAAABVgwh0AAACAstg0FWA+E+4AAAAAAFABCncAAAAAAKgAS8oAAAAAUBabpgLMZ8IdAAAAAAAqwIQ7AAAAAGUplWqLjgBQFUy4AwAAAABABZhwBwAAAKAstdZwB0hiwh0AAAAAACpC4Q4AAAAAABVgSRkAAAAAylIqWVIGIDHhDgAAAAAAFWHCHQAAAICy2DQVYD4T7gAAAAAAUAEKdwAAAAAAqABLygAAAABQFpumAsxnwh0AAAAAACrAhDsAAAAAZak14Q6QxIQ7AAAAAABUhMIdAAAAAAAqwJIyAAAAAJSlFEvKACQm3AEAAAAAoCJMuAMAAABQlpJNUwGSmHAHAAAAAICKMOEOAAAAQFlqreEOkMSEOwAAAAAAVITCHQAAAAAAKsCSMgAAAACUxaapAPOZcAcAAAAAgAow4Q4AAABAWWpNuAMkMeEOAAAAAAAVoXAHAAAAAIAKsKQMAAAAAGWxaSrAfCbcAQAAAACgAky4AwAAAFCW2phwB0hMuAMAAAAAQEWYcAcAAACgLNZwB5jPhDsAAAAAAFSAwh0AAAAAACrAkjIAAAAAlKXWkjIASUy4AwAAAABARZhwBwAAAKAspZhwB0hMuAMAAAAAQEUo3AEAAAAAoAIsKQMAAABAWWyaCjCfCXcAAAAAAKgAE+4AAAAAlKVkwh0giQl3AAAAAACoCBPuAAAAAJSlFBPuAIkJdwAAAAAAqAiFOwAAAAAAVIAlZQAAAAAoi01TAeYz4Q4AAABAo/Taa6/lf//3f9O7d+9ss802OfPMMzN79uyiYwErMBPuAAAAAJRlRZxwnzJlSg444ICst956Of/88zNx4sScccYZmTlzZkaOHFl0PGAFpXAHAAAAoNG56aab8sknn+SCCy5Ihw4dkiTz5s3LySefnMMOOyyrrbZasQGBFZIlZQAAAABodB555JH069evrmxPkp133jm1tbV57LHHigsGrNAU7gAAAACUpVTQoxyvv/56unXrVu9Yu3bt0qVLl7z++utlvjvQWFlSBgAAAIAV0qBBgxZ7fsyYMYs8N3Xq1LRr126B4+3bt8+UKVPKzgY0Tgp3GpW5s98uOgIAAAB84RT1/7c/r3AHWN4U7gAAAACskBY3wf552rVrl2nTpi1wfMqUKWnfvn05sYBGzBruAAAAADQ63bp1W2Ct9mnTpuWDDz5YYG13gIZSuAMAAADQ6Gy77bZ5/PHHM3Xq1Lpjo0ePTpMmTbLNNtsUmAxYkdWUSqVyN3UGAAAAgBXKlClTsuuuu2b99dfPYYcdlokTJ+aMM87IkCFDMnLkyKLjASsohTsAAAAAjdJrr72WX/ziF3nmmWey8sorZ7fddsuxxx6bFi1aFB0NWEEp3AEAAAAAoAKs4Q4AAAAAABWgcAcAAAAAgApQuAMAAAAAQAUo3AEAAAAAoAIU7gAAAAAAUAEKdwAAAAAAqACFO0AjMXXq1HTv3j233XZb3bGBAwfmlFNOWeLXLWvnn39+/v73v3/udW+99Va6d++e0aNHL4dU8z3wwAPp3r173nrrreV2TwC++IYPH57Bgwcvt/vttttuGT58+HK7HwBAY9Gs6AAAFOeCCy5Iu3btio6xgAsuuCArrbRS+vTpU3QUAAAAgAZTuAM0Yj169Cg6AgAAAMAXhiVlAL6gbrnllgwcODCbbrppDjjggEyYMGGBaxa2pExDXrcw3bt3z+WXX57zzz8//fv3T9++fTNixIjMmDGj3nUvv/xyvvvd76Z3797ZfPPNc/TRR+edd96p9z5JcuaZZ6Z79+7p3r17xo4d2+DPfccdd2TffffNVlttlS233DL7779/nn/++XrXnH/++dlss83y8ssvZ999982mm26awYMH59FHH6133Zw5c/LLX/4yW221VTbffPP89Kc/zSeffNLgLACwNN5///2MGDEigwYNSq9evbLDDjvk17/+dWbPnl3vuoZ+9/7973/PnnvumU022SSDBw/Oww8/vDw/DgBAo2LCHeAL6M9//nN+9rOfZc8998wuu+ySF154Icccc8wye91nbrjhhmy++eY544wzMn78+Jx55pnp1KlTjjvuuCTJu+++m/322y9rr712zjrrrMyaNSu/+c1vst9+++XOO+9MmzZtcvPNN2fo0KHZf//969ay/dKXvtTgDG+99VZ23333rLPOOpk9e3buueeefOc738mdd96Z9ddfv+66OXPm5LjjjsuwYcPy/e9/P5dffnmOPvroPPjgg1lllVWSJL/+9a8zatSoHHXUUenRo0fuueeenHPOOQ3OAgBLY/LkyenQoUNGjBiRdu3aZfz48Tn//PPzwQcf5PTTT6937ed9937wwQf57ne/m+7du+fcc8/N1KlTc/LJJ2fGjBnZeOONi/h4AABfaAp3gC+giy++OFtssUXd/yn/6le/mlmzZuWiiy5aJq/7TJcuXeoK6W233TYvvvhi7rvvvrr/03/11Vdn7ty5ufLKK9OhQ4ckycYbb5xdd901t99+e/bff//07t07SbLGGmvU/bwkjjzyyLqfa2trs8022+T555/P7bffnh/+8Id15z4r3AcMGJAkWX/99TNo0KA88sgj2W233fLxxx/nxhtvzCGHHJLDDjus7vex3377ZeLEiUucCwAaqnv37jn++OPrnvfp0yetW7fO8OHDM3LkyLRu3bru3Od9915zzTWpqanJ5ZdfnrZt2yZJVl999Rx44IHL7wMBADQilpQB+IKZN29eXnjhhXz961+vd3zHHXdcJq/7T/3796/3fIMNNsh7771X9/ypp55K375968r2z67ZaKON8vTTTzf4Povz2muv5Ygjjkj//v2z8cYbp2fPnvn3v/+d8ePH17uuSZMm6devX93ztdZaK61ataor01955ZXMnDlzgd/HDjvsUJGcALAopVIpV199dXbZZZf06tUrPXv2zHHHHZe5c+fmzTffrHft5333Pvfcc+nbt29d2Z4k/fr1q/ddDABA5ZhwB/iC+eijjzJ37tx07Nix3vHOnTsvk9f9p3bt2tV73rx583rrzU6dOnWhf329U6dOmTJlSoPvsyjTp0/PQQcdlI4dO2b48OFZc80107Jly5x44omZNWtWvWtbtWqVFi1aLJD3s+s++OCDumz/aUl+HwCwNK655pr86le/ysEHH5y+ffumXbt2+cc//pFTTjllge+zz/vu/eCDD7LuuusucI///r4HAKAyFO4AXzAdO3ZMs2bN8tFHH9U7/uGHHy6T1y2J9u3bZ9KkSQscnzRpUtZbb72y3//ZZ5/Ne++9l0svvTQbbbRR3fFp06Zl9dVXX6L36tKlS1221VZbre54JX8fALAwo0ePzsCBA/OjH/2o7thrr722VO/VpUuXhX73/vf3PQAAlWFJGYAvmKZNm6ZHjx7505/+VO/4fffdt0xetyQ233zz/PWvf603zf7666/n5Zdfzuabb1537D8nzZfEzJkz617/mb///e95++23l/i9Ntxww7Rq1WqB38f999+/xO8FAEti5syZ9b7LkuSuu+5aqvfq1atXxo4dm2nTptUde+KJJ/Lxxx+XExEAgEUw4Q7wBXT44Yfn+9//fkaMGJFddtklL7zwQv74xz8us9c11IEHHpjbbrstBx10UL73ve9l1qxZOffcc7PGGmtkjz32qLuuW7duGTNmTLbYYou0bt0666+/ftq0afO579+7d++stNJKOfnkk3PooYdm4sSJOf/88+tNqDdUhw4dss8+++Tyyy9Pq1at0qNHj9xzzz154403lvi9AGBJ9O/fP9dee22uv/76rLfeernzzjszYcKEpXqvAw44oG4T8EMOOSRTp07N+eefbw13AIBlxIQ7wBfQoEGDcvLJJ+eJJ57IEUcckcceeyznnnvuMntdQ62xxhq57rrr0r59+xx33HH52c9+lo022ijXXXddvUJ95MiRKZVKOeSQQ7LXXnvlhRdeaND7d+7cOb/97W/z0Ucf5fvf/36uueaanHzyyQtdu7YhfvSjH2WfffbJFVdckR/84Ad1xwBgWTriiCMyZMiQnHfeefnhD39Ytx/J0lh11VVz+eWXZ+bMmTnmmGNy+eWXZ+TIkUu81BoAAA1TUyqVSkWHAAAAAACAFZ0JdwAAAAAAqACFOwAAAAAAVIDCHQAAAAAAKkDhDgAAAAAAFaBwBwAAAACAClC4AwAAAABABSjcAQAAAACgAhTuAAAAAABQAQp3AIAqN3DgwAwfPrzu+dixY9O9e/eMHTu2wFT1/XfGRenevXvOP//8JX7/2267Ld27d88//vGPpYm3UOeff366d+9esfcDAABQuAMALMZnRe9nj0022SQ77rhjTjnllHz44YdFx1siDz/88FKV3QAAADRMs6IDAACsCI4++uistdZamT17dp5++umMGjUqDz/8cO6+++60bt16uWbZcsst8/zzz6d58+ZL9LqHH344N9xwQ4466qhllAwAAKBxU7gDADTAtttum0022SRJ8q1vfSsdOnTIVVddlTFjxmTw4MELfc2MGTOy0korVTxLkyZN0rJly4q/LwAAAOWxpAwAwFLYeuutkyRvvfVWkmT48OHZbLPN8sYbb+SQQw7JZpttluOOOy5JUltbm6uvvjq77rprNtlkk/Tv3z8jR47MlClT6r1nqVTKRRddlG233Tabbrpp9t9//7z66qsL3HtRa7g/99xzOeSQQ7Llllumd+/eGTJkSK655pq6fDfccEOS1Fsi5zOVzthQb7/9dk466aTsuOOO6dWrV/r27Zujjz667vf632bOnJmRI0emb9++6dOnT37yk58skDGZP83/7W9/O717985mm22WQw89tEE5H3vssey7777ZYoststlmm2XHHXfMr3/966X+fAAAQONiwh0AYCm88cYbSZIOHTrUHZs7d26++93vZvPNN8/xxx+fVq1aJUlGjhyZ22+/PXvuuWf233//vPXWW7nhhhvy4osvZtSoUXVLw/z2t7/NxRdfnAEDBmTAgAF54YUXctBBB2XOnDmfm+exxx7LYYcdllVXXTXDhg1L586d89prr+Whhx7KAQcckKFDh+b999/PY489ljPPPHOB1y+PjAvzj3/8I88880x23XXXrL766nn77bczatSoDBs2LPfcc88Cy/WccsopadeuXY488sj8+9//zqhRo/LOO+/kuuuuS01NTZLkjjvuyPDhw/OVr3wlxx13XD799NOMGjUq3/72t3P77bdnrbXWWmiWV199NYcddli6d++eo48+Oi1atMiECRPy97//fak+GwAA0Pgo3AEAGmD69On56KOPMnv27Pz973/PhRdemFatWuVrX/ta3TWzZ8/OTjvtlB/96Ed1x5566qn8/ve/z9lnn50hQ4bUHe/bt28OPvjgjB49OkOGDMlHH32UK664Itttt10uueSSuvL4N7/5TS655JLFZps3b15GjhyZVVddNXfccUfatWtXd65UKiVJNttss6y33np57LHHsttuu9V7/fLIuCjbbbdddtppp3rHvva1r2Xo0KG57777svvuu9c717x581x99dV1fwCw5ppr5qyzzsqDDz6YQYMG5ZNPPskvf/nLfOtb38ovfvGLutftscce2WmnnXLppZfWO/6fHnvsscyZMyeXX355OnbsuFSfBwAAaNwsKQMA0AAHHnhg+vXrlwEDBuTYY4/NyiuvnAsuuCCrrbZavev23Xffes9Hjx6dtm3bZptttslHH31U9+jZs2dWWmmlumVhHn/88cyZMyf77bdfXZGdJAcccMDnZnvxxRfz1ltvZdiwYfXK9iT13mtRlkfGRfnsbwEkyZw5czJ58uSss846adeuXV588cUFrh86dGi9zWL33XffNGvWLA8//HBdxqlTp2bXXXet91maNGmSTTfddIFleP7TZ7+7MWPGpLa2dqk/EwAA0HiZcAcAaICRI0dm/fXXT9OmTdO5c+esv/76adKk/uxCs2bNsvrqq9c7NmHChEybNi39+vVb6PtOmjQpSfLOO+8kSdZbb7165zt27Jj27dsvNtubb76ZJNlwww0b/HmWd8ZFmTlzZi699NLcdtttmThxYt1EfpJMmzZtgevXXXfdes9XXnnldOnSJW+//XaSZPz48UkW/YcAbdq0WWSWXXbZJb///e9z4okn5pxzzkm/fv3y9a9/PTvttNMC/14DAAAsjMIdAKABevXqlU022WSx17Ro0WKBYra2tjadOnXK2WefvdDXVMPSJUVm/MUvfpHbbrstBxxwQHr37p22bdumpqYmxx57bL3yvaE+e82ZZ56ZLl26LHC+adOmi3xtq1atcsMNN2Ts2LF56KGH8uijj+bee+/NzTffnCuvvHKxrwUAAEgU7gAAy9Q666yTJ554In369Km3fMp/W3PNNZPMn9Bee+21645/9NFHmTJlymLv8dn1r7zySvr377/I6xa1vMzyyLgon63TPnz48Lpjs2bNWuh0ezJ/Gn/rrbeue/7JJ5/kgw8+yLbbbpvk//9ddOrUabG/i0Vp0qRJ+vXrl379+mXEiBG55JJL8pvf/CZjx45dqvcDAAAaF383FgBgGdp5550zb968XHTRRQucmzt3bqZOnZok6d+/f5o3b57rr7++3mT3Nddc87n36NmzZ9Zaa61ce+21de/3mf98r9atWyfJAtcsj4yLsrCp8euuuy7z5s1b6PU333xz5syZU/d81KhRmTt3bl3h/tWvfjVt2rTJpZdeWu+6z3z00UeLzPLxxx8vcGzjjTdOMn9DXAAAgM9jwh0AYBnaaqutMnTo0Fx66aUZN25cttlmmzRv3jzjx4/P6NGjc8IJJ2SnnXZKx44dc9BBB+XSSy/NYYcdlgEDBuTFF1/MI488klVWWWWx92jSpElOOumkfO9738vuu++ePffcM126dMnrr7+ef/3rX/nd736XZH4xnySnnnpqvvKVr6Rp06bZddddl0vGRdluu+3yxz/+MW3atMmXvvSlPPvss3n88cfToUOHhV4/Z86cHHjggdl5553z73//OzfeeGM233zzDBo0KMn8NdpPOumk/OQnP8mee+6ZXXbZJR07dsw777yThx9+OH369MnIkSMX+t4XXnhhnnrqqQwYMCBdu3bNpEmTcuONN2b11VfP5ptvvlSfDwAAaFwU7gAAy9gpp5ySL3/5y7npppvym9/8Jk2bNk3Xrl3zjW98I3369Km77gc/+EFatGiRm266KWPHjk2vXr1y5ZVX5rDDDvvce3z1q1/NNddckwsvvDBXXnllSqVS1l577ey999511+ywww7Zf//9c8899+TOO+9MqVTKrrvuutwyLswJJ5yQJk2a5K677sqsWbPSp0+fXHXVVTn44IMXev3IkSNz11135bzzzsucOXOy66675sQTT6y3XM6QIUOy6qqr5rLLLsvvfve7zJ49O6uttlq22GKL7LnnnovMMnDgwLz99tu59dZbM3ny5KyyyirZaqutctRRR6Vt27ZL9fkAAIDGpaa0NLtR8f+1c4cEAAAADML6t76+R24xEAAAAAAAwPFwBwAAAACAgOAOAAAAAAABwR0AAAAAAAKCOwAAAAAABAR3AAAAAAAICO4AAAAAABAQ3AEAAAAAICC4AwAAAABAQHAHAAAAAICA4A4AAAAAAAHBHQAAAAAAAoI7AAAAAAAEBHcAAAAAAAgI7gAAAAAAEBDcAQAAAAAgILgDAAAAAEBAcAcAAAAAgIDgDgAAAAAAAcEdAAAAAAACgjsAAAAAAAQEdwAAAAAACAjuAAAAAAAQENwBAAAAACAguAMAAAAAQEBwBwAAAACAgOAOAAAAAAABwR0AAAAAAAKCOwAAAAAABAR3AAAAAAAICO4AAAAAABAQ3AEAAAAAICC4AwAAAABAQHAHAAAAAICA4A4AAAAAAAHBHQAAAAAAAoI7AAAAAAAEBHcAAAAAAAgI7gAAAAAAEBDcAQAAAAAgMKbSkInPcZsDAAAAAElFTkSuQmCC"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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
      },
      "execution_count": 159,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "gridsearch_cv_svm = GridSearchCV(svm, parameters, scoring='accuracy', cv=10)\nsvm_cv = gridsearch_cv_svm.fit(X_train, Y_train)\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hyperparameters :(best parameters) \",svm_cv.best_params_)\nsvm_best_score = svm_cv.best_score_\nprint(\"accuracy :\",svm_best_score)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 161,
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hyperparameters :(best parameters)  {'C': 1.0, 'gamma': 0.03162277660168379, 'kernel': 'sigmoid'}\naccuracy : 0.8482142857142856\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "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": "svm_score = svm_cv.score(X_test, Y_test)\n\nprint(f\"SVM - Accuracy using method score: {svm_score}\")",
      "metadata": {
        "trusted": true
      },
      "execution_count": 162,
      "outputs": [
        {
          "name": "stdout",
          "text": "SVM - Accuracy using method score: 0.8333333333333334\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "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
      },
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x5e2fc18>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  8\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a decision tree classifier object then  create a  <code>GridSearchCV</code> object  <code>tree_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "parameters = {'criterion': ['gini', 'entropy'],\n     'splitter': ['best', 'random'],\n     'max_depth': [2*n for n in range(1,10)],\n     'max_features': ['auto', 'sqrt'],\n     'min_samples_leaf': [1, 2, 4],\n     'min_samples_split': [2, 5, 10]}\n\ntree = DecisionTreeClassifier()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 164,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "gridsearch_cv_tree = GridSearchCV(tree, parameters, scoring='accuracy', cv=10)\ntree_cv = gridsearch_cv_tree.fit(X_train, Y_train)\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hyperparameters :(best parameters) \",tree_cv.best_params_)\ntree_best_score = tree_cv.best_score_\nprint(\"accuracy :\",tree_best_score)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 166,
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hyperparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 4, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'splitter': 'best'}\naccuracy : 0.8767857142857143\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "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": "tree_score = tree_cv.score(X_test, Y_test)\n\n\nprint(f\"Decision Tree - Accuracy using method score: {tree_score}\")",
      "metadata": {
        "trusted": true
      },
      "execution_count": 167,
      "outputs": [
        {
          "name": "stdout",
          "text": "Decision Tree - Accuracy using method score: 0.6666666666666666\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "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
      },
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x5dde3a8>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "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
      },
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "gridsearch_cv_knn = GridSearchCV(KNN, parameters, scoring='accuracy', cv=10)\nknn_cv = gridsearch_cv_tree.fit(X_train, Y_train)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hyperparameters :(best parameters) \",knn_cv.best_params_)\nknn_best_score = knn_cv.best_score_\nprint(\"accuracy :\",knn_best_score)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 171,
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hyperparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 16, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'splitter': 'random'}\naccuracy : 0.8892857142857142\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "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": "knn_score = knn_cv.score(X_test, Y_test)\n\nprint(f\"KNN - Accuracy using method score: {knn_score}\")",
      "metadata": {
        "trusted": true
      },
      "execution_count": 172,
      "outputs": [
        {
          "name": "stdout",
          "text": "KNN - Accuracy using method score: 0.7222222222222222\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "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
      },
      "execution_count": 173,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x61ce730>",
            "image/png": 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2y1FHHZXWrVsv8vW32GKLXHnllTn//PNz/vnnZ7nllsumm26an/3sZ4tdVi8Jhx12WD7//PPce++9eeCBB9K1a9dcdtllOfvss+d7rkmTJjn99NNzzjnn5De/+U3mzJmT0047bbG+h2nTpuWXv/xlunbtmsMOO6z2eq9evTJkyJBcc8012W677dKjR49KfXsAAMA3RFVpcU6ZAgAAAAAAFsoOdwAAAAAAqACFOwAAAADLpJEjR2bPPffMxhtvnC233DLHHHNMxo8fX3QsoAGzUgYAAACAZc6oUaOy//77Z9ddd82AAQPyySef5LzzzktNTU3uvffeNG/evOiIQAPk0FQAAAAAljn3339/VllllZx66qmpqqpKkrRv3z777bdf/vnPf6ZXr14FJwQaIitlAAAAAFjmzJkzJ8svv3xt2Z4krVu3TpJYCAF8XQp3AAAAAJY5u+++e954443cdNNNmTp1asaPH59zzjknXbt2Tc+ePYuOBzRQdrgDAAAA0CD179//S++PHDnyS+//5S9/yU9/+tN89tlnSZINNtggV155ZTp27FixjMCyxQ53lilv9/ryX8QAwMKt/dIrRUcAgAZpzqx3i46wVMz++M2iIyy25557Lj//+c+z1157Zeutt84nn3ySiy++OIccckhuvvlmh6YCX4vCHQAAAIAG6asm2L/MySefnM033zxDhw6tvdajR49svfXW+eMf/5hBgwZVIiKwjFG4AwAAAFCemrlFJ1hsb7zxxgIraVZaaaWssMIKefvttwtKBTR0Dk0FAAAAYJmzyiqrZPTo0fNde/fddzN58uR06dKloFRAQ6dwBwAAAGCZs/fee+fhhx/OySefnCeffDIPPPBADjvssHTo0CE77rhj0fGABspKGQAAAADKU6opOsFiGzJkSJo2bZpbbrkld9xxR5Zffvn06NEj5557blZYYYWi4wENVFWpVCoVHQKWlrd79f/qhwCABaz90itFRwCABmnOrHeLjrBUzJ7wr0Let0nn6kLeF2BRTLgDAAAAUJ6ahjfhDrAk2OEOAAAAAAAVoHAHAAAAAIAKsFIGAAAAgLKUGuChqQBLggl3AAAAAACoABPuAAAAAJTHoakASUy4AwAAAABARZhwBwAAAKA8drgDJDHhDgAAAAAAFaFwBwAAAACACrBSBgAAAIDy1MwtOgFAvWDCHQAAAAAAKsCEOwAAAADlcWgqQBIT7gAAAAAAUBEKdwAAAAAAqAArZQAAAAAoT42VMgCJCXcAAAAAAKgIE+4AAAAAlKXk0FSAJCbcAQAAAACgIky4AwAAAFAeO9wBkphwBwAAAACAilC4AwAAAABABVgpAwAAAEB5HJoKkMSEOwAAAAAAVIQJdwAAAADKUzO36AQA9YIJdwAAAAAAqACFOwAAAAAAVICVMgAAAACUx6GpAElMuAMAAAAAQEWYcAcAAACgPDUm3AESE+4AAAAAAFARCncAAAAAAKgAK2UAAAAAKI9DUwGSmHAHAAAAAICKMOEOAAAAQHkcmgqQxIQ7AAAAAABUhAl3AAAAAMpSKs0tOgJAvWDCHQAAAAAAKkDhDgAAAAAAFWClDAAAAADlKTk0FSAx4Q4AAAAAABVhwh0AAACA8tSYcAdITLgDAAAAAEBFKNwBAAAAAKACrJQBAAAAoDwOTQVIYsIdAAAAAAAqwoQ7AAAAAOWpmVt0AoB6wYQ7AAAAAABUgAl3AAAAAMpjhztAEhPuAAAAAABQEQp3AAAAAACoACtlAAAAAChPjZUyAIkJdwAAAAAAqAgT7gAAAACUx6GpAElMuAMAAAAAQEUo3AEAAAAAoAKslAEAAACgPA5NBUhiwh0AAAAAACrChDsAAAAA5THhDpDEhDsAAAAAAFSECXcAAAAAylIqzS06AkC9YMIdAAAAAAAqQOEOAAAAAAAVYKUMAAAAAOVxaCpAEhPuAAAAAABQESbcAQAAAChPyYQ7QGLCHQAAAAAAKkLhDgAAAAAAFWClDAAAAADlcWgqQBIT7gAAAAAAUBEm3AEAAAAoj0NTAZKYcAcAAAAAgIow4Q4AAABAeexwB0hiwh0AAAAAACpC4Q4AAAAAABVgpQwAAAAA5WmAh6YOHjw4//u//7vQe+ecc0523nnnpZwI+CZQuAMAAACwzPn1r3+dadOmzXftuuuuy0MPPZQ+ffoUlApo6BTuAAAAAJSnAR6auu666y5w7ac//Wm+/e1vp3379gUkAr4J7HAHAAAAYJn33HPP5Z133smAAQOKjgI0YAp3AAAAAJZ59913X1q2bJn+/fsXHQVowKyUAQAAAKA8Ba2U+apyfOTIkXV6nTlz5uRPf/pT+vXrl5YtW1YiGrCMMuEOAAAAwDLtiSeeyKRJk7LLLrsUHQVo4Ey4AwAAAFCeUjET7nWdYP8q9913X9q1a5ctt9yyIq8HLLtMuAMAAACwzJoxY0Yefvjh7LDDDmnSpEnRcYAGzoQ7AAAAAOUpaId7JTzyyCOZPn16BgwYUHQU4BvAhDsAAAAAy6x77703q6yySjbZZJOiowDfAAp3AAAAAJZJn376af76179mp512SlVVVdFxgG8AK2UAAAAAKE9Bh6aWq23btvnnP/9ZdAzgG8SEOwAAAAAAVIAJdwAAAADK04APTQWoJBPuAAAAAABQAQp3AAAAAACoACtlAAAAAChPAz00FaDSTLgDAAAAAEAFmHAHAAAAoDwOTQVIYsIdAAAAAAAqwoQ7AAAAAOUx4Q6QxIQ7AAAAAABUhMIdAAAAAAAqwEoZAAAAAMpTKhWdAKBeMOEOAAAAAAAVYMIdAAAAgPI4NBUgiQl3AAAAAACoCBPuFOK9995brOdXWWWVJZQEAAAAAKAyFO4Uol+/fqmqqqrz82PGjFmCaQAAAICyWCkDkEThTkEuvPDC2j9Pnz49Z599dlZfffVst9126dChQz7++OM89NBDGT9+fI477rgCkwIAAAAA1I3CnUJsu+22tX8+4YQTssUWW+S0006b75khQ4Zk2LBhefLJJzNgwIClHREAAACoq5IJd4DEoanUAyNGjMguu+yy0Hs777xzHn744aWcCAAAAABg8SncKVzjxo0zevTohd4bPXp0GjXyH1MAAAAAoP6zUobCfe9738v555+fmTNnZtttt0379u0zadKk/PnPf87ll1+evffeu+iIAAAAwJdxaCpAEoU79cDxxx+f5ZZbLldeeWUuuuii2uvNmjXLD3/4w/z0pz8tMB0AAAAAQN0o3Cnccsstl+OPPz6HHXZYXn311Xz00Ufp1KlT1ltvvbRt27boeAAAAMBXKZWKTgBQLyjcqTfatm2bTTfdtOgYAAAAAABfi8KdeuHTTz/N448/ng8++CAzZ86c715VVVWOOOKIgpIBAAAAX8kOd4AkCnfqgb/97W85+uijM3369DRv3jxNmjSZ777CHQAAAABoCBTuFO53v/tdNtxww5x66qnp0qVL0XEAAAAAAL4WhTuFGz9+fIYOHapsBwAAgIbKShmAJEmjogNA165d8/777xcdAwAAAACgLAp3Cveb3/wmN9xwQ/76179mzpw5RccBAAAAFlepppgPgHrGShkKN2jQoMyZMyeHHHJIGjVqlGbNms13v6qqKs8++2xB6QAAAAAA6kbhTuEOOOCAVFVVFR0DAAAAAKAsCncKd9RRRxUdAQAAAChDqaZUdASAesEOdwAAAAAAqAAT7tQL48aNy5133pmxY8dm5syZC9y/9NJLC0gFAAAA1EmNA0wBEoU79cBLL72UwYMHZ5VVVsnYsWNTXV2dqVOn5t13381KK62U1VdfveiIAAAAAABfyUoZCnfmmWdmxx13zH333ZdSqZRTTjklI0eOzM0335yqqqocfPDBRUcEAAAAvkypppgPgHpG4U7h/vWvf2XnnXdOo0bz/uP4xUqZnj175sgjj8zZZ59dZDwAAAAAgDpRuFO4qqqqNGnSJFVVVenQoUPee++92nsrrbRSxo4dW1w4AAAAAIA6ssOdwq2zzjoZP358Nt988/To0SNXX3111ltvvSy33HK5/PLLs9pqqxUdEQAAAPgyNaWiEwDUCwp3CrfXXnvVTrX/5Cc/yQEHHJCBAwcmSVq0aJHzzz+/yHgAAAAAAHWicKdwu+66a+2f11lnnTzwwAN54YUXMmPGjPTo0SMdOnQoLhwAAADw1WocYAqQKNyph5Zffvl8+9vfLjoGAAAAAMBiUbhTiIceemixnt9uu+2WUBIAAAAAgMpQuFOIo48+us7PVlVVZcyYMUswDQAAAFAWK2UAkijcKcjIkSOLjgAAAAAAUFEKdwrRpUuXoiMAAAAAlVIqFZ0AoF5oVHQAAAAAAAD4JjDhDgAAAEB57HAHSGLCHQAAAAAAKsKEOwCFaLbJRul82TkLvffB/kdm1j/HLOVEANBwLL98yxz30x9ls003zqab9kj79ivkgAOPzfU33F50NACAZZrCncI9/fTT6dq1a5ZffvkF7n322WcZPXp0Nt100wKSAUvD1FvuzMzR/5rv2pzx7xaUBgAaho4d2+dXJ/wk48a9k5deGpOtt96i6EjAsq7GoakAicKdemDIkCG57bbb0r179wXuvfXWWxkyZEjGjDHpCt9UM174Rz4f+XjRMQCgQXn//Q/TZbUemTDho2zSs3tG/f1PRUcCACAKd+qBUmnR/xT8888/T/PmzZdiGqAIVS1bpDRzZjLXQUsAUBezZs3KhAkfFR0D4P+U/Hd5gEThTkFeeOGFPP/887Wf33vvvXn22Wfne2bmzJkZOXJk1l577aUdD1iKOgz/WRot3zKlOXMz84V/5JPzLsusMa8WHQsAAABgsSncKcTf/va3XHjhhUmSqqqq3HDDDQs8s9xyy2WdddbJr3/966UdD1gKSrNnZ/rIx/P5E6NS88mnabLWGmk9eK+seMW5mXDg0Zn9r9eLjggAAACwWBTuFOLII4/MkUcemSRZf/31c/vtty90hzvwzTXrpdH5+KUTaz///PGnMn3k41np1ivS7ogD89HRwwpMBwAALBaHpgIkUbhTD7zyyitFRwDqiTnvvJfPH3syLbfZMmnUKKmxBxIAAABoOBTu1AvTp0/PXXfdlWeffTaffvpp2rZtm0022SS77bZbWrZsWXQ8YCmaO+GjVDVtmqoWzVP6bHrRcQAAgDooGZYBSJI0KjoAvP/++/ne976Xk08+OW+99Vaqqqry1ltv5ZRTTsnAgQPz/vvvFx0RWIqW67JyambMTGn650VHAQAAAFgsJtwp3GmnnZYkuf/++7P22mvXXn/zzTdz2GGH5fTTT895551XVDxgCWnUrm1qPvl0vmtN/mvttNiqTz5/8n+Tkh2QAADQYNjhDpBE4U498OSTT+akk06ar2xPkrXXXjvHHHNMfv3rXxeUDFiSOp72q5RmzszMl17O3EmfpMnaa6TVbjunNGNmPrngyqLjAUC9d/iP9k+7dm2z8sqdkyS77LJtVl115STJhRddnSlTphYZDwBgmaRwp3Bz585Ns2bNFnqvWbNmmTt37lJOBCwN0x99Isvv2D+tf7BnGrVqmbmTP8nnf/lbPr38+sx5572i4wFAvfeTYw/LmmuuVvv57rvtnN132zlJctPNdyjcAQAKoHCncD179swll1ySzTbbLK1bt669PnXq1Fx66aXp2bNngemAJWXabXdl2m13FR0DABqsddfbvOgIAP+n5NBUgEThTj1w/PHHZ999903fvn2z+eabp2PHjpk4cWKeeuqpNGnSJKeeemrREQEAAAAAvpLCncKtt956ueeee3LNNdfk2Wefzeuvv562bdtmr732yv7775+VVlqp6IgAAADAl3FoKkAShTv1xEorrZRhw4YVHQMAAAAA4GtrVHQAAAAAAAD4JjDhTuFmzJiRiy++OA8++GA++OCDzJo1a4FnxowZU0AyAAAAoE5qHJoKkCjcqQdOPPHE3Hfffdlll12yzjrrpEmTJkVHAgAAAABYbAp3CveXv/wlxx9/fPbdd9+iowAAAABfh0NTAZLY4U490Lhx46y55ppFxwAAAAAAKIvCncLts88++eMf/1h0DAAAAODrKtUU8wFQz1gpQ+GaN2+eZ599NnvvvXf69OmTNm3azHe/qqoq+++/fzHhAAAAAADqSOFO4c4666wkyXvvvZcXXnhhgfsKdwAAAGBJueuuu3LdddfljTfeSMuWLbPhhhvmwgsvTPPmzYuOBjRACncK98orrxQdAQAAAChHAz009ZJLLskVV1yRww47LD169MjkyZPz1FNPZe7cuUVHAxoohTsAAAAAy5w333wzF154YS6++OL07du39vr2229fYCqgoVO4AwAAAFCWUk3DO8D0zjvvzKqrrjpf2Q5QrkZFBwAAAACApe3FF1/Meuutl4svvjh9+vTJt771rey999558cUXi44GNGAm3AEAAABokPr37/+l90eOHLnIex999FH++c9/5tVXX82vf/3rtGjRIpdeemkOOOCAPPTQQ+nQoUOl4wLLAIU7AAAAAOVpgIemlkqlTJ8+Peedd17WX3/9JMlGG22Ufv365cYbb8wxxxxTcEKgIVK4AwAAANAgfdkE+1dp06ZN2rVrV1u2J0m7du3StWvXvP7665WIByyDFO4U4u67716s53fdddclkgMAAACogAY44b7uuuvm7bffXui9mTNnLuU0wDeFwp1CDB06dL7Pq6qqksz761z/eS1RuAMAAACVtc022+TOO+/MmDFjssEGGyRJJk+enJdffjn7779/seGABkvhTiGefvrp2j+PGzcuxxxzTAYOHJjtt98+HTt2zMcff5wRI0bknnvuybnnnltcUAAAAOCrlWqKTrDYtt1222y44YY5+uijc+yxx6ZZs2a5/PLL07Rp0/zgBz8oOh7QQCncKUTr1q1r/3z22Wdn0KBBOeSQQ2qvdejQIdXV1WnevHnOOuusXHfddUXEBAAAAL6hGjVqlMsvvzynnXZahg8fntmzZ6dXr1656aab0qlTp6LjAQ2Uwp3CPf/88znooIMWeq9bt2655JJLlnIiAAAAYFnQvn37nHnmmUXHAL5BGhUdANq3b58HHnhgoffuv//+tG/ffiknAgAAABZLTamYD4B6xoQ7hTvssMMyfPjwvP3229l2223ToUOHTJw4MQ8//HCefvrpnHTSSUVHBAAAAAD4Sgp3CrfXXnulU6dOufTSS3PmmWdmzpw5WW655dK1a9dcfPHF6devX9ERAQAAgC9RMm0OkEThTj2xzTbbZJtttklNTU0mTZqU9u3bp1EjG48AAAAAgIZD4U690qhRo3Ts2LHoGAAAAAAAi03hTiFOPvnkHHDAAVlllVVy8sknf+XzJ5xwwlJIBQAAAHwtVsoAJFG4U5BHHnkke+yxR1ZZZZU88sgjX/psVVWVwh0AAAAAqPcU7hTi30v2ryrcAQAAgHqupqboBAD1glMpAQAAAACgAky4U4i77757sZ7fddddl0gOAAAAAIBKUbhTiKFDh873eVVVVZKkVCotcC1RuAMAAEC95tBUgCQKdwry9NNP1/553LhxOeaYYzJw4MBsv/326dixYz7++OOMGDEi99xzT84999ziggIAAAAA1JHCnUK0bt269s9nn312Bg0alEMOOaT2WocOHVJdXZ3mzZvnrLPOynXXXVdETAAAAKAuTLgDJHFoKvXA888/n27dui30Xrdu3fLiiy8u5UQAAAAAAItP4U7h2rdvnwceeGCh9+6///60b99+KScCAAAAFkepVCrkA6C+sVKGwh122GEZPnx43n777Wy77bbp0KFDJk6cmIcffjhPP/10TjrppKIjAgAAAAB8JYU7hdtrr73SqVOnXHrppTnzzDMzZ86cLLfccunatWsuvvji9OvXr+iIAAAAAABfSeFOvbDNNttkm222SU1NTSZNmpT27dunUSMbjwAAAKBBcGgqQBKFO/VMo0aN0rFjx6JjAAAAAAAsNoU7AAAAAOUx4Q6QJLGzAwAAAAAAKkDhDgAAAAAAFWClDAAAAABlKVkpA5DEhDsAAAAAAFSECXcAAAAAymPCHSCJCXcAAAAAAKgIE+4AAAAAlKem6AAA9YMJdwAAAAAAqACFOwAAAAAAVICVMgAAAACUpeTQVIAkJtwBAAAAAKAiTLgDAAAAUB4T7gBJTLgDAAAAAEBFKNwBAAAAAKACrJQBAAAAoDw1RQcAqB9MuAMAAAAAQAWYcAcAAACgLCWHpgIkMeEOAAAAAAAVYcIdAAAAgPLY4Q6QxIQ7AAAAAABUhMIdAAAAAAAqwEoZAAAAAMri0FSAeUy4AwAAAABABZhwBwAAAKA8Dk0FSGLCHQAAAAAAKkLhDgAAAAAAFWClDAAAAABlKVkpA5DEhDsAAAAAAFSECXcAAAAAymPCHSCJCXcAAAAAAKgIE+4AAAAAlMUOd4B5TLgDAAAAAEAFKNwBAAAAAKACrJQBAAAAoDxWygAkMeEOAAAAAAAVYcIdAAAAgLI4NBVgHhPuAAAAAABQAQp3AAAAAACoACtlAAAAACiLlTIA85hwBwAAAACACjDhDgAAAEBZTLgDzGPCHQAAAAAAKsCEOwAAAADlKVUVnQCgXjDhDgAAAAAAFaBwBwAAAACACrBSBgAAAICyODQVYB4T7gAAAAAAUAEm3AEAAAAoS6nGoakAiQl3AAAAAACoCIU7AAAAAABUgJUyAAAAAJTFoakA85hwBwAAAACACjDhDgAAAEBZSiWHpgIkJtwBAAAAAKAiTLgDAAAAUBY73AHmMeEOAAAAwDLnzjvvTHV19QIfZ511VtHRgAbMhDsAAAAAy6wrr7wyrVu3rv28c+fOBaYBGjqFOwAAAABlKdU03ENTu3Xrlvbt2xcdA/iGsFIGAAAAAAAqwIQ7AAAAAGUplYpO8PXtsssumTx5clZZZZXstddeOeigg9K4ceOiYwENlMIdAAAAgAapf//+X3p/5MiRi7zXqVOnHHXUUdloo41SVVWVRx55JOeee24mTJiQ4cOHVzoqsIxQuAMAAACwzPnOd76T73znO7Wfb7nllmnWrFmuu+66HHbYYVlxxRULTAc0VAp36mzatGmZOnVqVl555dprEyZMyK233ppZs2Zl++23T/fu3QtMCAAAABShqENTR458uKKvt+OOO+bqq6/OmDFjFO7A16Jwp86GDx+ed955J7fffnuSeQX8oEGD8sEHH6RRo0a5/vrrc+WVV6Z3794FJwUAAAAAWPoaFR2AhuPZZ5/N1ltvXfv5H//4x3z44Ye59dZb87//+7+prq7OJZdcUlxAAAAAoBClmqpCPirtgQceSOPGjdO1a9eKvzawbDDhTp1Nnjw5nTt3rv38kUceySabbJIePXokSXbddddceOGFBaUDAAAAqLsDDzwwvXv3TnV1dZJ5B6zefvvtGTJkSDp16lRwOqChUrhTZ23atMnHH3+cJJkxY0aeffbZHHbYYbX3GzdunBkzZhQVDwAAAKDO1lprrdxxxx354IMPUlNTkzXXXDO/+MUvMnjw4KKjAQ2Ywp0623jjjXPzzTdn7bXXzl//+tfMnDkz/fv3r70/duzY+SbgAQAAgGVDqVR0gsV3wgknFB0B+Aayw506O+6447LccsvlqKOOyu233579998///Vf/5UkmTt3bkaMGJFNN9204JQAAAAAAMUw4U6drbHGGhkxYkTeeOONtGrVKquuumrtvc8//zy/+tWvsv766xeYEAAAACjCkjjAFKAhUrizWJo0abLQUr1Vq1bZdtttC0gEAAAAAFA/KNxZpKeffvprfZ21MgAAALBsKZVMuAMkCne+xODBg1NVVfdfmKVSKVVVVRkzZswSTAUAAAAAUD8p3Fmk66+/vugIAAAAAAANhsKdRdpss82KjgAAAAA0AKWaohMA1A+Nig5Aw/Thhx/mlVdeyfTp04uOAgAAAABQLyjcWSwPP/xwdthhh/Tt2ze77bZbXnzxxSTJpEmTsuuuu+bhhx8uOCEAAACwtNWUqgr5AKhvFO7U2SOPPJKjjjoqK6ywQo444oiUSqXae+3bt0/nzp1zxx13FJgQAAAAAKA4Cnfq7KKLLkqvXr1yyy235Ic//OEC93v06JExY8YUkAwAAAAAoHgKd+rstddey4477rjI+x07dszEiROXYiIAAACgPiiVqgr5AKhvFO7UWYsWLfL5558v8v748ePTrl27pRcIAAAAAKAeUbhTZ717987dd9+dOXPmLHDvo48+yu23354tt9yygGQAAABAkUo1VYV8ANQ3Cnfq7Mc//nE++OCD7LHHHrnttttSVVWVv/3tb/n973+fAQMGpFQq5Ygjjig6JgAAAABAIRTu1Nnaa6+dm2++Oe3atct5552XUqmUq666KpdddlnWW2+93HzzzVl11VWLjgkAAAAsZaVSMR8A9c1yRQegYfmv//qvXHvttfn0008zbty4lEqlrLbaamnfvn3R0QAAAAAACqVw52tp27ZtunfvXnQMAAAAAIB6Q+HOYpk0aVKuuOKKPPbYY3n33XeTJF26dEnfvn1z4IEHpmPHjgUnBAAAAJY2B5gCzGOHO3X22muvZcCAAbnmmmvSunXr7LDDDtlhhx3SunXrXHPNNfne976XV199teiYAAAAAACFMOFOnZ100kmZO3dubr/99gXWybz00ks5+OCD89vf/jY33HBDQQkBAACAItSUTLgDJCbcWQwvvfRShgwZstDd7d27d8+QIUPy0ksvFZAMAAAAAKB4CnfqrEOHDmnWrNki7zdr1iwdOnRYiokAAAAAAOoPhTt1NmTIkNxyyy356KOPFrg3YcKE3HLLLRkyZEgByQAAAIAilUpVhXwA1Dd2uLNI11xzzQLXWrZsme222y7bbrtt1lhjjSTJ2LFjM3LkyKy++upLOyIAAAAAQL1RVSqVSkWHoH5af/31F/trqqqqMmbMmCWQpjLe7tW/6AgA0CCt/dIrRUcAgAZpzqx3i46wVLy05oBC3rf72HsLeV+ARTHhziKNHDmy6AgAAAAAAA2Gwp1F6tKlS9ERAAAAgAagxj51gCQOTQUAAAAAgIow4c5ieeWVV3LjjTdm9OjRmTp1ampqaua7X1VVlYcffrigdAAAAAAAxTHhTp2NGjUqe+65Zx599NGsuOKKGT9+fFZbbbWsuOKKee+999KyZctsuummRccEAAAAlrJSqaqQD4D6RuFOnZ1//vlZbbXVMmLEiJx66qlJkkMPPTS33HJLbr311kyYMCE77LBDwSkBAAAAAIqhcKfORo8enT322COtWrVK48aNk6R2pcxGG22UQYMG5bzzzisyIgAAAFCAUqmYD4D6RuFOnTVu3DjLL798kqRNmzZZbrnlMnHixNr7q622Wt54442i4gEAAAAAFErhTp2tvvrqGTt2bJJ5h6Ouvfba8x2Q+uijj6Zjx44FpQMAAAAAKJbCnTrr27dv7r///syZMydJ8t///d956KGHst1222W77bbLI488kkGDBhWcEgAAAFjaakpVhXwA1DdVpZKNV9TN7NmzM23atLRr1y5VVfN+qf3xj3/MQw89lMaNG2frrbfO7rvvXnDKL/d2r/5FRwCABmntl14pOgIANEhzZr1bdISl4plVdy3kfXu9c3ch7wuwKMsVHYCGo0mTJllhhRXmuzZw4MAMHDiwoESL75l3OhcdAQAapM/fu6LoCABAPVYybQ6QxEoZAAAAAACoCBPuLNKQIUMW+2uqqqpy3XXXLYE0AAAAQH1lnzrAPAp3FunrrPd3JAAAAAAAsKxSuLNIN9xwQ9ERAAAAAAAaDIU7AAAAAGXx990B5nFoKgAAAAAAVIAJdwAAAADK4tBUgHlMuAMAAAAAQAUo3AEAAAAAoAKslAEAAACgLCUrZQCSKNz5GiZMmJCnn346EydOzPbbb5+VVlopc+fOzdSpU9O6des0bty46IgAAAAAAEudwp06K5VKOf3003PTTTdlzpw5qaqqynrrrZeVVlop06dPT79+/XL00Udn//33LzoqAAAAsBTVFB0AoJ6ww506u/LKK3P99dfngAMOyDXXXJNSqVR7r3Xr1tluu+3y0EMPFZgQAAAAAKA4Jtypsz/84Q/Zdddd85Of/CSTJ09e4H51dXUef/zxApIBAAAARSrFDneAxIQ7i+H999/PxhtvvMj7LVq0yLRp05ZiIgAAAACA+kPhTp116NAh77///iLvv/zyy1l55ZWXYiIAAAAAgPpD4U6dffe7382tt96a8ePH116rqpr3V8b+9re/5a677soOO+xQVDwAAACgIDWlYj4A6hs73Kmzo48+OqNGjcrAgQPTq1evVFVV5Yorrsh5552XF154IRtssEEOO+ywomMCAAAAABTChDt11rp169x+++056KCDMmHChDRr1ixPP/10pk6dmiOOOCI333xzWrRoUXRMAAAAYCmrSVUhHwD1jQl3Fkvz5s1z+OGH5/DDDy86CgAAAABAvWLCHQAAAAAAKsCEO3U2bNiwr3ymqqoqp5566lJIAwAAANQXJetdAJIo3FkMo0aNWuBaTU1NPvroo8ydOzft27e3wx0AAAAAWGYp3KmzRx55ZKHXZ8+endtuuy3XXXddrr766qWcCgAAAChaTdEBAOoJO9wpW5MmTbLvvvvm29/+dn77298WHQcAAAAAoBAKdypm/fXXz9NPP110DAAAAGApK6WqkA+A+kbhTsU8+eSTdrgDAAAAAMssO9ypswsvvHCh16dOnZqnn346o0ePziGHHLKUUwEAAAAA1A8Kd+psUYV727Zts9pqq+XEE0/MXnvttZRTAQAAAEVzaCrAPAp36uyVV14pOgIAAAAAQL1lhzt1MmPGjJx22ml55JFHio4CAAAA1DM1BX0A1DcKd+qkefPmue222zJx4sSiowAAAAAA1EsKd+qsW7duefXVV4uOAQAAAABQLyncqbNf/OIXeeCBB/KHP/whc+bMKToOAAAAUE+UUlXIB0B949BUvtTTTz+dddZZJ+3bt8/QoUNTVVWV4cOH5+STT07nzp3TrFmz+Z6vqqrKPffcU1BaAAAAAIDiKNz5UkOGDMmZZ56ZXXbZJe3atUu7du2y1lprFR0LAAAAqEdqDJsDJFG48xVKpVJKpVKS5IYbbig4DQAAAABA/WWHOwAAAADLtM8++yxbbbVVqqur849//KPoOEADZsKdr1RV5e+FAQAAAItW08APML344oszd+7comMA3wAKd77Sz372s/zsZz+r07NVVVUZPXr0Ek4EAAAAUBlvvPFGbr755hx//PH59a9/XXQcoIFTuPOVtthii6y55ppFxwAAAADqqVLRAcpw8sknZ++9985aa61VdBTgG0DhzlfaddddM2DAgKJjAAAAAFTUiBEj8uqrr+aCCy7Iyy+/XHQc4BtA4Q4AAABAWWoKet/+/ft/6f2RI0cu8t7nn3+e008/Pccee2xatWpV6WjAMqpR0QEAAAAAYGm75JJL0qFDh3z/+98vOgrwDWLCHQAAAIAG6csm2L/Mu+++m6uvvjoXXXRRpk6dmiSZPn167f/97LPPsvzyy1csJ7DsULjzpV555ZWiIwAAAAD1XE1VVdERFss777yT2bNn55BDDlng3pAhQ7LRRhvl9ttvLyAZ0NAp3AEAAABYpmywwQa5/vrr57s2ZsyYnHbaaTnxxBOz4YYbFpQMaOgU7gAAAACUpVR0gMXUpk2b9O7de6H3unXrlm7dui3lRMA3hUNTAQAAAACgAky4AwAAALDM6927d/71r38VHQNo4BTuAAAAAJSlpugAAPWElTIAAAAAAFABJtwBAAAAKEtNVdEJAOoHE+4AAAAAAFABJtwBAAAAKEtNjLgDJCbcAQAAAACgIhTuAAAAAABQAVbKAAAAAFCWUtEBAOoJE+4AAAAAAFABJtwBAAAAKEuNM1MBkphwBwAAAACAilC4AwAAAABABVgpAwAAAEBZaooOAFBPmHAHAAAAAIAKMOEOAAAAQFlKRQcAqCdMuAMAAAAAQAWYcAcAAACgLDVVRScAqB9MuAMAAAAAQAUo3AEAAAAAoAKslAEAAACgLDVFBwCoJ0y4AwAAAABABZhwBwAAAKAsJtwB5jHhDgAAAAAAFaBwBwAAAACACrBSBgAAAICylKqKTgBQP5hwBwAAAACACjDhDgAAAEBZHJoKMI8JdwAAAAAAqAAT7gAAAACUxYQ7wDwm3AEAAAAAoAIU7gAAAAAAUAFWygAAAABQllLRAQDqCRPuAAAAAABQASbcAQAAAChLTVXRCQDqBxPuAAAAAABQAQp3AAAAAACoACtlAAAAAChLTdEBAOoJE+4AAAAAAFABJtwBAAAAKIsJd4B5TLgDAAAAAEAFmHAHAAAAoCylogMA1BMm3AEAAAAAoAIU7gAAAAAAUAFWygAAAABQlpqqohMA1A8m3AEAAAAAoAJMuAMAAABQlpqiAwDUEybcAQAAAACgAhTuAAAAAABQAVbKAAAAAFCWUtEBAOoJE+4AAAAAAFABJtwBAAAAKEuNGXeAJCbcAQAAAACgIky4AwAAAFCWmqIDANQTJtwBAAAAAKACFO4AAAAAAFABVsoAAAAAUBZHpgLMY8IdAAAAAAAqwIQ7AAAAAGVxaCrAPCbcAQAAAACgAhTuAAAAAABQAVbKAAAAAFCWmqqiEwDUDybcAQAAAACgAky4AwAAAFCWmpSKjgBQL5hwBwAAAACAClC4AwAAAABABVgpAwAAAEBZLJQBmMeEOwAAAAAAVIAJdwAAAADKUlN0AIB6woQ7AAAAAABUgAl3AAAAAMpSY4s7QBIT7gAAAAAAUBEKdwAAAAAAqAArZQAAAAAoi4UyAPOYcAcAAAAAgAow4Q4AAABAWWqKDgBQTyjcAQAAAFjmPPbYY7niiivy+uuvZ9q0aencuXO23XbbHHnkkWndunXR8YAGSuEOAAAAwDLnk08+Sffu3TN48OC0a9cur732Wi644IK89tprufrqq4uOBzRQCncAAAAAylLTAI9NHThw4Hyf9+7dO02bNs2vfvWrTJgwIZ07dy4oGdCQOTQVAAAAAJK0a9cuSTJ79uxigwANlgl3AAAAAMrS8Obb/8/cuXMzZ86cvP7667nooovSr1+/rLrqqkXHAhoohTsAAAAADVL//v2/9P7IkSO/8jW22WabTJgwIUnyne98J2effXZFsgHLJoU7herXr1+qqqrq/HxdflECAAAAS1dN0QHKcPnll+fzzz/P66+/nksuuSSHHXZYrrnmmjRu3LjoaEADpHCnUP3795+vcH/wwQczbdq0bLHFFunQoUMmTpyYJ598Mq1bt872229fYFIAAACgvqnEYN7666+fJNl4442z4YYbZuDAgfnzn/+cHXbYoezXBpY9CncK9ctf/rL2z1deeWVWXnnlXHnllWnVqlXt9alTp+bggw9Ohw4diogIAAAALCOqq6vTpEmTvP3220VHARqoRkUHgC/ccMMNOeSQQ+Yr25OkdevWOfjgg3PjjTcWlAwAAAD4MqWC/lVpL774YmbPnu3QVOBrM+FOvfHpp59m6tSpC703derUTJkyZSknAgAAAL6pjjzyyHzrW99KdXV1mjdvnldeeSVXXXVVqqurs+222xYdD2igFO7UG5tvvnnOOuusrLzyytlss81qr48aNSpnn312Nt988wLTAQAAAIvSEA9N7d69ex544IFcfvnlKZVK6dKlS/bcc88ceOCBadq0adHxgAaqqlQqVf7v38DX8OGHH+ZHP/pRRo8endatW2eFFVbI5MmTM3Xq1GywwQa55JJL0rlz57Le486VflChtACwbBnwz5OLjgAADVKTjmsXHWGpOHLNQYW874VjbyvkfQEWxYQ79caKK66YO+64I48//nheeumlfPTRR+nUqVO6d++erbbaquh4AAAAAABfSuFOvbPVVlsp2AEAAKABqVkCB5gCNEQKd+qdxx9/PP/4xz/ywQcf5Ec/+lFWWWWVPP3001l99dXLXikDAAAAALCkKNypNyZNmpTDDz88L774YlZeeeW8//772XvvvbPKKqvkjjvuSIsWLfLrX/+66JgAAADAfzDfDjBPo6IDwBdOOeWUTJ48Offdd18eeuih/Pt5vn369MlTTz1VYDoAAAAAgC9nwp1647HHHstvf/vbrLPOOpk7d+5891ZeeeVMmDChoGQAAADAl7HDHWAeE+7UG3Pnzk3Lli0Xem/KlClp0qTJUk4EAAAAAFB3JtypN7p375477rgjffv2XeDe/fffn549exaQClhaqo8ZmG7DBuXTV8Zn5NbHFx0HAOq1cePfzQVXXJ/nX3o5n06ZlpU7d8pO3906+//g+2nRvHnR8QAAllkKd+qNH//4xxkyZEh++MMfZvvtt09VVVUefvjhXHbZZXnsscdy8803Fx0RWEJarNw+1ccMzJzPZhQdBQDqvfcnfJR9Dv5xWi3fMvt8/3tp26ZVXvznK7noqhsz+l+v54Lf/broiMAyqKboAAD1hMKdemPjjTfO9ddfn7PPPju/+93vUiqVcumll6ZHjx659tpr061bt6IjAkvIt379w0x69vVUNW6Upu1bFx0HAOq1e0eMzJSp03L9xWdl3bXXSJLsOXCn1NTU5J4RI/PplKlp28bvUwCAIijcqVc23njj3HjjjZkxY0Y+/fTTtGnTJi1atCg6FrAEddh8/XTZZbM8su0vstGp+xUdBwDqvc+mT0+SdGjfbr7rnTq2T6NGjZx9BBSi5NBUgCQOTaWeat68eTp37qxsh2+6RlXZ6JT9Mvamv2TKK+OLTgMADcKmG3dPkgw/7dy88uobeX/CR/nTw4/ltrvuzw/3+F5atrDDHQCgKCbcKdSwYcMW6/nTTjttCSUBirD2ftum5aod87e9Ti06CgA0GFtu3itHHTwkV1x/W/7yt7/XXj9kv71z9CH+thgAQJEU7hRqzJgx830+YcKETJ48OW3btk2HDh0yceLEfPrpp1lhhRWy0korFZQSWBKartAqG/xsj7zy+7sya+LUouMAQIOyysqds0mPb+W7W387bdu0yeNP/W+uuP62dGy/Qn6wx/eKjgcsgxyaCjCPwp1C3X333bV/fvzxx/Ob3/wmv//977P55pvXXn/qqafyy1/+Mj/+8Y+XfkBgiek6dK/M/uSzvHHVg0VHAYAG5YGHH82Jvzs/9916RVZasVOS5LtbfzulmlJ+f8nV2em7W6dd2zYFpwQAWDbZ4U69ceaZZ+boo4+er2xPkj59+uSoo47KmWeeWVAyoNKWX2ulrLVvv7x+1Yi0WGmFtFytY1qu1jGNmzVJo+Uap+VqHdOk3fJFxwSAeum2O+/P+uutU1u2f2HrLXvn8xkzM+bVNwpKBizLSgX9C6C+MeFOvTFu3Li0a9duoffatm2bt99+e+kGApaYFiuvkKrGjdLjlP2TU/Zf4P4OT5+f1y//U14afsNSzwYA9d3ESZPTpnWrBa7PmTM3STJ37tylHQkAgP9P4U69se666+byyy/PpptumuWX/7/J1mnTpuXyyy/PuuuuW2A6oJKmvPJOntr/nAWudx26Z5q0ap4XT7ghn42dUEAyAKj/1li9S5783+cy9u13subqq9Zef+DhR9OoUaOst85aBaYDllV2uAPMo3Cn3jjhhBNy0EEHpW/fvundu3ftoamjRo3K3Llzc+WVVxYdEaiQWZOm5v0Rzyxwfd1DdkiShd4DAOb57x/skb/9/ZkMOfxn+cH3B6Rd2zZ57IlR+evfn8n3B+yQFTt1KDoiAMAyS+FOvdGzZ8889NBDufbaa/PSSy/lzTffTKdOnbL33ntnv/32S6dOnb76RQAA4BuuV48Nc+Ol5+Tiq27MrXfdl08+nZpVV+6cow/ZLwf8cM+i4wEALNOqSqWSEyZYZty50g+KjgAADdKAf55cdAQAaJCadFy76AhLxeA1di/kfW8Yd2ch7wuwKI2KDgAAAAAAAN8EVspQb8yYMSMXX3xxHnzwwXzwwQeZNWvWAs+MGTOmgGQAAADAl7E+AWAehTv1xoknnpj77rsvu+yyS9ZZZ500adKk6EgAAAAAAHWmcKfe+Mtf/pLjjz8+++67b9FRAAAAAAAWm8KdeqNx48ZZc801i44BAAAALKYaS2UAkjg0lXpkn332yR//+MeiYwAAAAAAfC0m3Kk3mjdvnmeffTZ77713+vTpkzZt2sx3v6qqKvvvv38x4QAAAIBFKplwB0iicKceOeuss5Ik7733Xl544YUF7ivcAQAAAID6TOFOvfHKK68UHQEAAAD4GmqKDgBQT9jhDgAAAAAAFWDCnXpn5syZGT9+fGbOnLnAvW7duhWQCAAAAADgqyncqTdmzZqV3/zmN7nnnnsyd+7chT4zZsyYpZwKAAAA+Co1Dk0FSGKlDPXIRRddlCeeeCKnn356SqVSfvWrX+W0005Lnz590qVLl1x66aVFRwQAAAAAWCSFO/XGiBEjcuSRR2bHHXdMknTv3j277rprrr766myyySZ55JFHCk4IAAAALEypoH8B1DcKd+qNDz74IGuttVYaN26cZs2aZcqUKbX3vve972XEiBEFpgMAAAAA+HIKd+qNTp061Zbsq666akaNGlV7b+zYsQWlAgAAAACoG4emUm9sttlmeeaZZ9KvX7/sueeeOeOMM/Lmm2+mSZMmefjhh7PLLrsUHREAAABYiJqiAwDUEwp36o1jjz02kydPTpLsv//+SebtdZ85c2YGDx6cI444osB0AAAAAABfTuFOvdGpU6d06tSp9vP999+/tngHAAAA6q9SyQGmAInCnYINGDCgzs9WVVXlnnvuWYJpAAAAAAC+PoU7herWrVuqqqqKjgEAAACUoSYm3AEShTsFO/3004uOAAAAAABQEY2KDgAAAAAAAN8EJtwBAAAAKEtN0QEA6gkT7gAAAAAAUAEm3AEAAAAoS8mhqQBJTLgDAAAAAEBFKNwBAAAAAKACrJQBAAAAoCw1VsoAJDHhDgAAAAAAFWHCHQAAAICylEom3AESE+4AAAAAAFARCncAAAAAAKgAK2UAAAAAKEtN0QEA6gkT7gAAAAAAUAEm3AEAAAAoSykOTQVITLgDAAAAAEBFmHAHAAAAoCw1JtwBkphwBwAAAACAilC4AwAAAABABVgpAwAAAEBZSiUrZQASE+4AAAAAAFARJtwBAAAAKItDUwHmMeEOAAAAAAAVoHAHAAAAAIAKsFIGAAAAgLKUrJQBSGLCHQAAAAAAKsKEOwAAAABlqSmZcAdITLgDAAAAAEBFmHAHAAAAoCzm2wHmMeEOAAAAAAAVoHAHAAAAAIAKsFIGAAAAgLLUWCoDkMSEOwAAAAAAVIQJdwAAAADK0hAn3P/0pz/lnnvuycsvv5wpU6ZkjTXWyODBg/P9738/VVVVRccDGiiFOwAAAADLnGuvvTZdunTJ0KFDs8IKK+TJJ5/Mr371q3zwwQc58sgji44HNFAKdwAAAACWOZdccknat29f+3mfPn3yySef5Jprrsnhhx+eRo1sYgYWn//PAQAAAEBZSqVSIR/l+Pey/QsbbLBBpk2blunTp5f12sCyS+EOAAAAAEmeffbZdO7cOa1atSo6CtBAWSkDAAAAQFmKOjS1f//+X3p/5MiRdX6tZ555Jg888ECOP/74cmMByzAT7gAAAAAs0z744IMce+yx6d27d4YMGVJ0HKABM+EOAAAAQFlKBU24L84E+6JMmTIlBx98cNq1a5cLLrjAYalAWRTuAAAAACyTZsyYkUMPPTRTp07NbbfdltatWxcdCWjgFO4AAAAALHPmzJmTH//4x3nzzTdz0003pXPnzkVHAr4BFO4AAAAAlKVUKmalTDlOPPHE/OUvf8nQoUMzbdq0vPDCC7X3unbtmqZNmxYXDmiwFO4AAAAALHOeeOKJJMnpp5++wL2RI0dm1VVXXdqRgG8AhTsAAAAAZakp6NDUcjzyyCNFRwC+gRy7DAAAAAAAFaBwBwAAAACACrBSBgAAAICyNMRDUwGWBBPuAAAAAABQASbcAQAAAChLQzw0FWBJMOEOAAAAAAAVYMIdAAAAgLKUTLgDJDHhDgAAAAAAFaFwBwAAAACACrBSBgAAAICy1JSslAFITLgDAAAAAEBFmHAHAAAAoCwOTQWYx4Q7AAAAAABUgMIdAAAAAAAqwEoZAAAAAMri0FSAeUy4AwAAAABABZhwBwAAAKAsDk0FmMeEOwAAAAAAVIAJdwAAAADKYoc7wDwm3AEAAAAAoAIU7gAAAAAAUAFWygAAAABQFoemAsxjwh0AAAAAACrAhDsAAAAAZXFoKsA8JtwBAAAAAKACFO4AAAAAAFABVsoAAAAAUBaHpgLMY8IdAAAAAAAqwIQ7AAAAAGUplWqKjgBQL5hwBwAAAACACjDhDgAAAEBZauxwB0hiwh0AAAAAACpC4Q4AAAAAABVgpQwAAAAAZSmVrJQBSEy4AwAAAABARZhwBwAAAKAsDk0FmMeEOwAAAAAAVIDCHQAAAAAAKsBKGQAAAADK4tBUgHlMuAMAAAAAQAWYcAcAAACgLDUm3AGSmHAHAAAAAICKULgDAAAAAEAFWCkDAAAAQFlKsVIGIDHhDgAAAAAAFWHCHQAAAICylByaCpDEhDsAAAAAAFSECXcAAAAAylJjhztAEhPuAAAAAABQEQp3AAAAAACoACtlAAAAACiLQ1MB5jHhDgAAAAAAFWDCHQAAAICy1JhwB0hiwh0AAAAAACpC4Q4AAAAAABVgpQwAAAAAZXFoKsA8JtwBAAAAAKACTLgDAAAAUJaamHAHSEy4AwAAAABARZhwBwAAAKAsdrgDzGPCHQAAAAAAKkDhDgAAAAAAFWClDAAAAABlqbFSBiCJCXcAAAAAAKgIE+4AAAAAlKUUE+4AiQl3AAAAAACoCIU7AAAAAABUgJUyAAAAAJTFoakA85hwBwAAAACACjDhDgAAAEBZSibcAZKYcAcAAAAAgIow4Q4AAABAWUox4Q6QmHAHAAAAAICKULgDAAAAAEAFWCkDAAAAQFkcmgowj8IdAAAAgGXOuHHjctVVV+XFF1/Ma6+9lrXXXjv33Xdf0bGABk7hDgAAAEBZGuKE+2uvvZbHHnssG220UWpqahrk9wDUP3a4AwAAALDM6devXx577LGcf/756datW9FxgG8IhTsAAAAAy5xGjdRiQOVZKQMAAABAWSxjAZhH4Q4AAABAg9S/f/8vvT9y5MillARgHoU7y5TdP7i56AgAAADwjTNn1ruFvO9XFe4AS5vCHQAAAIAGyQQ7UN84HQIAAAAAACpA4Q4AAAAAABVgpQwAAAAAy5zPP/88jz32WJLk3XffzbRp0zJixIgkyWabbZb27dsXGQ9ooKpKpVKp6BAAAAAAsDS98847izx09frrr0/v3r2XciLgm0DhDgAAAAAAFWCHOwAAAAAAVIDCHQAAAAAAKkDhDgAAAAAAFaBwBwAAAACAClC4AwAAAABABSjcAQAAAACgAhTuAMuIKVOmpLq6OnfeeWfttX79+uWkk05a7K9b0i644II899xzX/ncO++8k+rq6owYMWIppJrn4YcfTnV1dd55552l9p4AfPMNHTo0u+yyy1J7v4EDB2bo0KFL7f0AAJYVyxUdAIDiXHjhhWnTpk3RMRZw4YUXpmXLlunZs2fRUQAAAADqTOEOsAzr2rVr0REAAAAAvjGslAH4hrr99tvTr1+/bLTRRtlvv/0ybty4BZ5Z2EqZunzdwlRXV+eKK67IBRdckC222CK9e/fOsGHDMn369Pme+9e//pUDDzwwPXr0yCabbJKjjz4677333nyvkyRnnHFGqqurU11dnVGjRtX5+7777ruzzz77ZLPNNsumm26awYMH56WXXprvmQsuuCAbb7xx/vWvf2WfffbJRhttlF122SV//etf53tu9uzZOeWUU7LZZptlk002yS9+8Yt89tlndc4CAF/Hhx9+mGHDhqV///7p3r17tttuu5xzzjmZNWvWfM/V9Xfvc889l9133z0bbrhhdtlllzz22GNL89sBAFimmHAH+Ab6y1/+kl/96lfZfffds9NOO+Xll1/OMcccs8S+7gs33XRTNtlkk5x++ukZO3ZszjjjjHTo0CHHHXdckuT999/Pvvvum9VWWy1nnnlmZs6cmd///vfZd999c88996RVq1a57bbbMmjQoAwePLh2l+26665b5wzvvPNOdt1116y++uqZNWtW7r///vzwhz/MPffck7XWWqv2udmzZ+e4447LkCFDcvjhh+eKK67I0UcfnUceeSQrrLBCkuScc87JLbfckqOOOipdu3bN/fffn7PPPrvOWQDg65g8eXLatWuXYcOGpU2bNhk7dmwuuOCCfPTRRznttNPme/arfvd+9NFHOfDAA1NdXZ1zzz03U6ZMyYknnpjp06dngw02KOLbAwD4RlO4A3wDXXLJJenVq1ft/yj/zne+k5kzZ+biiy9eIl/3hU6dOtUW0ltttVVGjx6dBx98sPZ/9F977bWZM2dOrr766rRr1y5JssEGG2TnnXfOXXfdlcGDB6dHjx5JkpVXXrn2z4vjyCOPrP1zTU1Nvv3tb+ell17KXXfdlZ/85Ce1974o3Pv27ZskWWuttdK/f/88/vjjGThwYD755JPcfPPNOfjgg3PooYfW/jz23XffTJgwYbFzAUBdVVdX5/jjj6/9vGfPnmnRokWGDh2a4cOHp0WLFrX3vup373XXXZeqqqpcccUVad26dZJkpZVWyv7777/0viEAgGWIlTIA3zBz587Nyy+/nO9+97vzXd9+++2XyNf9uy222GK+z9dZZ5188MEHtZ8/88wz6d27d23Z/sUz66+/fp599tk6v8+XeeONN3LEEUdkiy22yAYbbJBu3brlrbfeytixY+d7rlGjRunTp0/t56uuumqaN29eW6a/+uqrmTFjxgI/j+22264iOQFgUUqlUq699trstNNO6d69e7p165bjjjsuc+bMyfjx4+d79qt+97744ovp3bt3bdmeJH369JnvdzEAAJVjwh3gG2bSpEmZM2dO2rdvP9/1jh07LpGv+3dt2rSZ7/MmTZrMt292ypQpC/3r6x06dMinn35a5/dZlGnTpuWAAw5I+/btM3To0Kyyyipp1qxZTjjhhMycOXO+Z5s3b56mTZsukPeL5z766KPabP9ucX4eAPB1XHfddfnd736Xgw46KL17906bNm3yj3/8IyeddNICv8++6nfvRx99lDXWWGOB9/jP3/cAAFSGwh3gG6Z9+/ZZbrnlMmnSpPmuf/zxx0vk6xZH27ZtM3HixAWuT5w4MWuuuWbZr//CCy/kgw8+yGWXXZb111+/9vrUqVOz0korLdZrderUqTZb586da69X8ucBAAszYsSI9OvXLz/96U9rr73xxhtf67U6deq00N+9//n7HgCAyrBSBuAbpnHjxunatWv+/Oc/z3f9wQcfXCJftzg22WST/P3vf59vmv3NN9/Mv/71r2yyySa11/590nxxzJgxo/brv/Dcc8/l3XffXezXWm+99dK8efMFfh4PPfTQYr8WACyOGTNmzPe7LEnuvffer/Va3bt3z6hRozJ16tTaa0899VQ++eSTciICALAIJtwBvoEOO+ywHH744Rk2bFh22mmnvPzyy/njH/+4xL6urvbff//ceeedOeCAA/KjH/0oM2fOzLnnnpuVV145u+22W+1za6+9dkaOHJlevXqlRYsWWWuttdKqVauvfP0ePXqkZcuWOfHEE3PIIYdkwoQJueCCC+abUK+rdu3aZe+9984VV1yR5s2bp2vXrrn//vvz9ttvL/ZrAcDi2GKLLXL99dfnxhtvzJprrpl77rkn48aN+1qvtd9++9UeAn7wwQdnypQpueCCC+xwBwBYQky4A3wD9e/fPyeeeGKeeuqpHHHEEXniiSdy7rnnLrGvq6uVV145N9xwQ9q2bZvjjjsuv/rVr7L++uvnhhtumK9QHz58eEqlUg4++ODsscceefnll+v0+h07dsx5552XSZMm5fDDD891112XE088caG7a+vipz/9afbee+9ceeWV+fGPf1x7DQCWpCOOOCIDBgzI+eefn5/85Ce155F8HSuuuGKuuOKKzJgxI8ccc0yuuOKKDB8+fLFXrQEAUDdVpVKpVHQIAAAAAABo6Ey4AwAAAABABSjcAQAAAACgAhTuAAAAAABQAQp3AAAAAACoAIU7AAAAAABUgMIdAAAAAAAqQOEOAAAAAAAVoHAHAAAAAIAKULgDANRz/fr1y9ChQ2s/HzVqVKqrqzNq1KgCU83vPzMuSnV1dS644ILFfv0777wz1dXV+cc//vF14i3UBRdckOrq6oq9HgAAgMIdAOBLfFH0fvGx4YYbZvvtt89JJ52Ujz/+uOh4i+Wxxx77WmU3AAAAdbNc0QEAABqCo48+OquuumpmzZqVZ599Nrfccksee+yx3HfffWnRosVSzbLpppvmpZdeSpMmTRbr6x577LHcdNNNOeqoo5ZQMgAAgGWbwh0AoA622mqrbLjhhkmSPffcM+3atcs111yTkSNHZpdddlno10yfPj0tW7aseJZGjRqlWbNmFX9dAAAAymOlDADA17D55psnSd55550kydChQ7Pxxhvn7bffzsEHH5yNN944xx13XJKkpqYm1157bXbeeedsuOGG2WKLLTJ8+PB8+umn871mqVTKxRdfnK222iobbbRRBg8enNdee22B917UDvcXX3wxBx98cDbddNP06NEjAwYMyHXXXVeb76abbkqS+VbkfKHSGevq3XffzW9+85tsv/326d69e3r37p2jjz669uf6n2bMmJHhw4end+/e6dmzZ37+858vkDGZN83/gx/8ID169MjGG2+cQw45pE45n3jiieyzzz7p1atXNt5442y//fY555xzvvb3BwAALFtMuAMAfA1vv/12kqRdu3a11+bMmZMDDzwwm2yySY4//vg0b948STJ8+PDcdddd2X333TN48OC88847uemmmzJ69OjccssttathzjvvvFxyySXp27dv+vbtm5dffjkHHHBAZs+e/ZV5nnjiiRx66KFZccUVM2TIkHTs2DFvvPFGHn300ey3334ZNGhQPvzwwzzxxBM544wzFvj6pZFxYf7xj3/k+eefz84775yVVlop7777bm655ZYMGTIk999//wLrek466aS0adMmRx55ZN56663ccsstee+993LDDTekqqoqSXL33Xdn6NCh2XLLLXPcccfl888/zy233JIf/OAHueuuu7LqqqsuNMtrr72WQw89NNXV1Tn66KPTtGnTjBs3Ls8999zX+t4AAIBlj8IdAKAOpk2blkmTJmXWrFl57rnnctFFF6V58+bZZpttap+ZNWtWdthhh/z0pz+tvfbMM8/kD3/4Q84666wMGDCg9nrv3r1z0EEHZcSIERkwYEAmTZqUK6+8MltvvXUuvfTS2vL497//fS699NIvzTZ37twMHz48K664Yu6+++60adOm9l6pVEqSbLzxxllzzTXzxBNPZODAgfN9/dLIuChbb711dthhh/mubbPNNhk0aFAefPDB7LrrrvPda9KkSa699trafwCwyiqr5Mwzz8wjjzyS/v3757PPPsspp5ySPffcM7/97W9rv2633XbLDjvskMsuu2y+6//uiSeeyOzZs3PFFVekffv2X+v7AQAAlm1WygAA1MH++++fPn36pG/fvjn22GOz/PLL58ILL0znzp3ne26fffaZ7/MRI0akdevW+fa3v51JkybVfnTr1i0tW7asXQvz5JNPZvbs2dl3331ri+wk2W+//b4y2+jRo/POO+9kyJAh85XtSeZ7rUVZGhkX5Yu/BZAks2fPzuTJk7P66qunTZs2GT169ALPDxo0aL7DYvfZZ58st9xyeeyxx2ozTpkyJTvvvPN830ujRo2y0UYbLbCG59998bMbOXJkampqvvb3BAAALLtMuAMA1MHw4cOz1lprpXHjxunYsWPWWmutNGo0/+zCcsstl5VWWmm+a+PGjcvUqVPTp0+fhb7uxIkTkyTvvfdekmTNNdec73779u3Ttm3bL802fvz4JMl6661X5+9naWdclBkzZuSyyy7LnXfemQkTJtRO5CfJ1KlTF3h+jTXWmO/z5ZdfPp06dcq7776bJBk7dmySRf9DgFatWi0yy0477ZQ//OEPOeGEE3L22WenT58++e53v5sddthhgX+vAQAAFkbhDgBQB927d8+GG274pc80bdp0gWK2pqYmHTp0yFlnnbXQr6kPq0uKzPjb3/42d955Z/bbb7/06NEjrVu3TlVVVY499tj5yve6+uJrzjjjjHTq1GmB+40bN17k1zZv3jw33XRTRo0alUcffTR//etf88ADD+S2227L1Vdf/aVfCwAAkCjcAQCWqNVXXz1PPfVUevbsOd/6lP+0yiqrJJk3ob3aaqvVXp80aVI+/fTTL32PL55/9dVXs8UWWyzyuUWtl1kaGRfliz3tQ4cOrb02c+bMhU63J/Om8TfffPPazz/77LN89NFH2WqrrZL838+iQ4cOX/qzWJRGjRqlT58+6dOnT4YNG5ZLL700v//97zNq1Kiv9XoAAMCyxd+NBQBYgnbcccfMnTs3F1988QL35syZkylTpiRJtthiizRp0iQ33njjfJPd11133Ve+R7du3bLqqqvm+uuvr329L/z7a7Vo0SJJFnhmaWRclIVNjd9www2ZO3fuQp+/7bbbMnv27NrPb7nllsyZM6e2cP/Od76TVq1a5bLLLpvvuS9MmjRpkVk++eSTBa5tsMEGSeYdiAsAAPBVTLgDACxBm222WQYNGpTLLrssY8aMybe//e00adIkY8eOzYgRI/LLX/4yO+ywQ9q3b58DDjggl112WQ499ND07ds3o0ePzuOPP54VVljhS9+jUaNG+c1vfpMf/ehH2XXXXbP77runU6dOefPNN/P666/nqquuSjKvmE+Sk08+OVtuuWUaN26cnXfeealkXJStt946f/zjH9OqVausu+66eeGFF/Lkk0+mXbt2C31+9uzZ2X///bPjjjvmrbfeys0335xNNtkk/fv3TzJvR/tvfvOb/PznP8/uu++enXbaKe3bt897772Xxx57LD179szw4cMX+toXXXRRnnnmmfTt2zddunTJxIkTc/PNN2ellVbKJpts8rW+PwAAYNmicAcAWMJOOumkfOtb38qtt96a3//+92ncuHG6dOmS733ve+nZs2ftcz/+8Y/TtGnT3HrrrRk1alS6d++eq6++OoceeuhXvsd3vvOdXHfddbnoooty9dVXp1QqZbXVVstee+1V+8x2222XwYMH5/77788999yTUqmUnXfeeallXJhf/vKXadSoUe69997MnDkzPXv2zDXXXJODDjpooc8PHz489957b84///zMnj07O++8c0444YT51uUMGDAgK664Yi6//PJcddVVmTVrVjp37pxevXpl9913X2SWfv365d13380dd9yRyZMnZ4UVVshmm22Wo446Kq1bt/5a3x8AALBsqSp9ndOoAAAAAACA+djhDgAAAAAAFaBwBwAAAACAClC4AwAAAABABSjcAQAAAACgAhTuAAAAAABQAQp3AAAAAACoAIU7AAAAAABUgMIdAAAAAAAqQOEOAAAAAAAVoHAHAPh/7dixAAAAAMAgf+tJ7CyMAAAAYCDcAQAAAABgINwBAAAAAGAg3AEAAAAAYCDcAQAAAABgINwBAAAAAGAg3AEAAAAAYCDcAQAAAABgINwBAAAAAGAg3AEAAAAAYCDcAQAAAABgINwBAAAAAGAg3AEAAAAAYCDcAQAAAABgINwBAAAAAGAg3AEAAAAAYCDcAQAAAABgINwBAAAAAGAg3AEAAAAAYCDcAQAAAABgINwBAAAAAGAg3AEAAAAAYCDcAQAAAABgINwBAAAAAGAg3AEAAAAAYCDcAQAAAABgINwBAAAAAGAg3AEAAAAAYCDcAQAAAABgINwBAAAAAGAQuQzcOpwBctIAAAAASUVORK5CYII="
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  12\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Find the method performs best:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "algorithms = ['Logistic Regression', 'Support Vector Machine', 'Decision Tree', 'K Nearest Neighbours']\n\nscores = [lr_score, svm_score, tree_score, knn_score]\n\nbest_scores = [lr_best_score, svm_best_score, tree_best_score, knn_best_score]\n\ncolumn_names = ['Algorithm', 'Accuracy Score', 'Best Score']",
      "metadata": {
        "trusted": true
      },
      "execution_count": 174,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "df = pd.DataFrame(list(zip(algorithms, scores, best_scores)),columns = column_names)\n\ndf",
      "metadata": {
        "trusted": true
      },
      "execution_count": 175,
      "outputs": [
        {
          "execution_count": 175,
          "output_type": "execute_result",
          "data": {
            "text/plain": "                Algorithm  Accuracy Score  Best Score\n0     Logistic Regression        0.833333    0.846429\n1  Support Vector Machine        0.833333    0.848214\n2           Decision Tree        0.666667    0.876786\n3    K Nearest Neighbours        0.722222    0.889286",
            "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>Algorithm</th>\n      <th>Accuracy Score</th>\n      <th>Best Score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Logistic Regression</td>\n      <td>0.833333</td>\n      <td>0.846429</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Support Vector Machine</td>\n      <td>0.833333</td>\n      <td>0.848214</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Decision Tree</td>\n      <td>0.666667</td>\n      <td>0.876786</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>K Nearest Neighbours</td>\n      <td>0.722222</td>\n      <td>0.889286</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "sns.set(style=\"whitegrid\")\n\nplt.figure(figsize=(15,8))\nsns.barplot(x=algorithms, y=best_scores, palette=\"Blues\")\nplt.title(\"Determining the Best Performing Classification Algorithm\")\nplt.ylabel(\"Best Score\")\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x5d727c0>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "plt.figure(figsize=(15,8))\nsns.barplot(x=algorithms, y=scores, palette=\"Blues\")\nplt.title(\"Determining the Best Performing Classification Algorithm\")\nplt.ylabel(\"Accuracy Score\")\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x76fefc8>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "## Authors\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "[Pratiksha Verma](https://www.linkedin.com/in/pratiksha-verma-6487561b1/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01)\n",
      "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": {}
    }
  ]
}