Notebooks/7. SpaceX_Machine_Learning_Prediction_Part_5.jupyterlite.ipynb

{
  "metadata": {
    "kernelspec": {
      "name": "python",
      "display_name": "Python (Pyodide)",
      "language": "python"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    }
  },
  "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": 135,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "import warnings\nwarnings.filterwarnings(\"ignore\")",
      "metadata": {
        "trusted": true
      },
      "execution_count": 136,
      "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\n# evaluation metrics\nfrom sklearn.metrics import f1_score, precision_score, jaccard_score, accuracy_score, recall_score ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 137,
      "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": 138,
      "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": 139,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "data.head()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 140,
      "outputs": [
        {
          "execution_count": 140,
          "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": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "X.head(100)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 142,
      "outputs": [
        {
          "execution_count": 142,
          "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": "data.columns",
      "metadata": {
        "trusted": true
      },
      "execution_count": 143,
      "outputs": [
        {
          "execution_count": 143,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Index(['FlightNumber', 'Date', 'BoosterVersion', 'PayloadMass', 'Orbit',\n       'LaunchSite', 'Outcome', 'Flights', 'GridFins', 'Reused', 'Legs',\n       'LandingPad', 'Block', 'ReusedCount', 'Serial', 'Longitude', 'Latitude',\n       'Class'],\n      dtype='object')"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "Y = data['Class'].to_numpy()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 144,
      "outputs": []
    },
    {
      "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()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 145,
      "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": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "# fit transform after \nfrom sklearn.preprocessing import StandardScaler\nscaler = StandardScaler()\nX_train= scaler.fit_transform(X_train)\nX_test = scaler.transform(X_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 147,
      "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": 148,
      "outputs": [
        {
          "execution_count": 148,
          "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']}",
      "metadata": {
        "trusted": true
      },
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "parameters ={\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge\nlr=LogisticRegression()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "# instantiate the model\nlogreg_cv = GridSearchCV(lr, parameters,cv = 10)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "# fit the gridSearchCv model\nlogreg_cv.fit(X_train,Y_train)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 153,
      "outputs": [
        {
          "execution_count": 153,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=10, estimator=LogisticRegression(),\n             param_grid={'C': [0.01, 0.1, 1], 'penalty': ['l2'],\n                         'solver': ['lbfgs']})",
            "text/html": "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=LogisticRegression(),\n             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1], &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=LogisticRegression(),\n             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1], &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We output the <code>GridSearchCV</code> object for logistic regression. We display the best parameters using the data attribute <code>best_params\\_</code> and the accuracy on the validation data using the data attribute <code>best_score\\_</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",logreg_cv.best_params_)\nprint(\"accuracy :\",logreg_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 154,
      "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": {}
    },
    {
      "cell_type": "markdown",
      "source": "Calculate the accuracy on the test data using the method <code>score</code>:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "logreg_cv.score(X_test,Y_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 185,
      "outputs": [
        {
          "execution_count": 185,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.8333333333333334"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Lets look at the confusion matrix:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "logyhat=logreg_cv.predict(X_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 186,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "print(\"Accuracy: %.4f\" % accuracy_score(Y_test,logyhat))\nprint(\"Jaccard score: %.4f\" % jaccard_score(Y_test,logyhat))\nprint(\"Precision: %.4f\" % precision_score(Y_test,logyhat))\nprint(\"Recall: %.4f\" % recall_score(Y_test,logyhat))\nprint(\"Avg F1-score: %.4f\" % f1_score(Y_test,logyhat, average='weighted'))",
      "metadata": {
        "trusted": true
      },
      "execution_count": 189,
      "outputs": [
        {
          "name": "stdout",
          "text": "Accuracy: 0.8333\nJaccard score: 0.8000\nPrecision: 0.8000\nRecall: 1.0000\nAvg F1-score: 0.8148\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "plot_confusion_matrix(Y_test,yhat)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": "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"
          },
          "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": "# instantiate the model\nsvm_cv = GridSearchCV(svm, parameters,cv = 10)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "svm_cv.fit(X_train,Y_train)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 161,
      "outputs": [
        {
          "execution_count": 161,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=10, estimator=SVC(),\n             param_grid={'C': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n       1.00000000e+03]),\n                         'gamma': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n       1.00000000e+03]),\n                         'kernel': ('linear', 'rbf', 'poly', 'rbf', 'sigmoid')})",
            "text/html": "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=SVC(),\n             param_grid={&#x27;C&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n       1.00000000e+03]),\n                         &#x27;gamma&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n       1.00000000e+03]),\n                         &#x27;kernel&#x27;: (&#x27;linear&#x27;, &#x27;rbf&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=SVC(),\n             param_grid={&#x27;C&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n       1.00000000e+03]),\n                         &#x27;gamma&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n       1.00000000e+03]),\n                         &#x27;kernel&#x27;: (&#x27;linear&#x27;, &#x27;rbf&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;)})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",svm_cv.best_params_)\nprint(\"accuracy :\",svm_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 162,
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(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_cv.score(X_test,Y_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 163,
      "outputs": [
        {
          "execution_count": 163,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.8333333333333334"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can plot the confusion matrix\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "svmyhat=svm_cv.predict(X_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 190,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "print(\"Accuracy: %.4f\" % accuracy_score(Y_test,svmyhat))\nprint(\"Jaccard score: %.4f\" % jaccard_score(Y_test,svmyhat))\nprint(\"Precision: %.4f\" % precision_score(Y_test,svmyhat))\nprint(\"Recall: %.4f\" % recall_score(Y_test,svmyhat))\nprint(\"Avg F1-score: %.4f\" % f1_score(Y_test,svmyhat, average='weighted'))",
      "metadata": {
        "trusted": true
      },
      "execution_count": 191,
      "outputs": [
        {
          "name": "stdout",
          "text": "Accuracy: 0.8333\nJaccard score: 0.8000\nPrecision: 0.8000\nRecall: 1.0000\nAvg F1-score: 0.8148\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "plot_confusion_matrix(Y_test,yhat)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhIAAAHHCAYAAADqJrG+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAABD30lEQVR4nO3dd3RU1fr/8c+EkEkISQg9USABFIJUQbh0uESKghSV6jU0vQpIL3IVSACNghRBEUEFRBS5IiCoKFX0ggiEANJLKNKlSguQOb8//DFfxySaDGc4k+H9cs1amX327P2cLAcedjs2wzAMAQAAuMHP6gAAAEDORSIBAADcRiIBAADcRiIBAADcRiIBAADcRiIBAADcRiIBAADcRiIBAADcRiIBAADcRiIBeNDevXvVuHFjhYWFyWazaeHChaa2f/DgQdlsNs2cOdPUdnOyBg0aqEGDBlaHAdw1SCTg8/bv369///vfKlmypAIDAxUaGqratWvrzTff1NWrVz3ad1xcnLZt26ZXXnlFs2fPVrVq1Tza353UuXNn2Ww2hYaGZvh73Lt3r2w2m2w2m954441st3/s2DHFx8crOTnZhGgBeIq/1QEAnvTll1/qySeflN1u19NPP63y5cvr+vXr+uGHHzRo0CBt375d06ZN80jfV69e1bp16/TSSy+pV69eHumjRIkSunr1qnLnzu2R9v+Ov7+/rly5osWLF6tt27Yu1+bMmaPAwEBdu3bNrbaPHTumhIQERUVFqXLlyln+3LfffutWfwDcQyIBn5WSkqL27durRIkSWrlypSIiIpzXevbsqX379unLL7/0WP+nT5+WJOXLl89jfdhsNgUGBnqs/b9jt9tVu3ZtffLJJ+kSiY8//liPPvqo5s+ff0diuXLlivLkyaOAgIA70h+A3zG1AZ81ZswYXbp0Se+//75LEnFL6dKl1adPH+f7mzdvatSoUSpVqpTsdruioqL0n//8R6mpqS6fi4qKUvPmzfXDDz+oevXqCgwMVMmSJfXhhx8668THx6tEiRKSpEGDBslmsykqKkrS71MCt37+o/j4eNlsNpeyZcuWqU6dOsqXL5/y5s2rMmXK6D//+Y/zemZrJFauXKm6desqODhY+fLlU8uWLbVz584M+9u3b586d+6sfPnyKSwsTF26dNGVK1cy/8X+SceOHfX111/r/PnzzrINGzZo79696tixY7r6Z8+e1cCBA1WhQgXlzZtXoaGhatasmbZs2eKss3r1aj300EOSpC5dujinSG7dZ4MGDVS+fHlt2rRJ9erVU548eZy/lz+vkYiLi1NgYGC6+2/SpInCw8N17NixLN8rgPRIJOCzFi9erJIlS6pWrVpZqt+9e3cNHz5cDz74oCZMmKD69esrMTFR7du3T1d33759euKJJ/Twww9r3LhxCg8PV+fOnbV9+3ZJUps2bTRhwgRJUocOHTR79mxNnDgxW/Fv375dzZs3V2pqqkaOHKlx48bpscce0//+97+//Nzy5cvVpEkTnTp1SvHx8erfv7/Wrl2r2rVr6+DBg+nqt23bVr/99psSExPVtm1bzZw5UwkJCVmOs02bNrLZbPr888+dZR9//LHKli2rBx98MF39AwcOaOHChWrevLnGjx+vQYMGadu2bapfv77zL/WYmBiNHDlSkvTss89q9uzZmj17turVq+ds58yZM2rWrJkqV66siRMnqmHDhhnG9+abb6pQoUKKi4tTWlqaJOndd9/Vt99+q8mTJysyMjLL9wogAwbggy5cuGBIMlq2bJml+snJyYYko3v37i7lAwcONCQZK1eudJaVKFHCkGSsWbPGWXbq1CnDbrcbAwYMcJalpKQYkoyxY8e6tBkXF2eUKFEiXQwjRoww/viVnDBhgiHJOH36dKZx3+pjxowZzrLKlSsbhQsXNs6cOeMs27Jli+Hn52c8/fTT6frr2rWrS5utW7c2ChQokGmff7yP4OBgwzAM44knnjAaNWpkGIZhpKWlGUWLFjUSEhIy/B1cu3bNSEtLS3cfdrvdGDlypLNsw4YN6e7tlvr16xuSjKlTp2Z4rX79+i5l33zzjSHJGD16tHHgwAEjb968RqtWrf72HgH8PUYk4JMuXrwoSQoJCclS/a+++kqS1L9/f5fyAQMGSFK6tRTlypVT3bp1ne8LFSqkMmXK6MCBA27H/Ge31lYsWrRIDocjS585fvy4kpOT1blzZ+XPn99ZXrFiRT388MPO+/yj5557zuV93bp1debMGefvMCs6duyo1atX68SJE1q5cqVOnDiR4bSG9Pu6Cj+/3//oSUtL05kzZ5zTNklJSVnu0263q0uXLlmq27hxY/373//WyJEj1aZNGwUGBurdd9/Ncl8AMkciAZ8UGhoqSfrtt9+yVP/QoUPy8/NT6dKlXcqLFi2qfPny6dChQy7lxYsXT9dGeHi4zp0752bE6bVr1061a9dW9+7dVaRIEbVv317z5s37y6TiVpxlypRJdy0mJka//vqrLl++7FL+53sJDw+XpGzdyyOPPKKQkBB9+umnmjNnjh566KF0v8tbHA6HJkyYoPvuu092u10FCxZUoUKFtHXrVl24cCHLfd5zzz3ZWlj5xhtvKH/+/EpOTtakSZNUuHDhLH8WQOZIJOCTQkNDFRkZqZ9//jlbn/vzYsfM5MqVK8NywzDc7uPW/P0tQUFBWrNmjZYvX65//etf2rp1q9q1a6eHH344Xd3bcTv3covdblebNm00a9YsLViwINPRCEl69dVX1b9/f9WrV08fffSRvvnmGy1btkwPPPBAlkdepN9/P9mxefNmnTp1SpK0bdu2bH0WQOZIJOCzmjdvrv3792vdunV/W7dEiRJyOBzau3evS/nJkyd1/vx55w4MM4SHh7vscLjlz6MekuTn56dGjRpp/Pjx2rFjh1555RWtXLlSq1atyrDtW3Hu3r073bVdu3apYMGCCg4Ovr0byETHjh21efNm/fbbbxkuUL3ls88+U8OGDfX++++rffv2aty4sWJjY9P9TrKa1GXF5cuX1aVLF5UrV07PPvusxowZow0bNpjWPnA3I5GAzxo8eLCCg4PVvXt3nTx5Mt31/fv3680335T0+9C8pHQ7K8aPHy9JevTRR02Lq1SpUrpw4YK2bt3qLDt+/LgWLFjgUu/s2bPpPnvrYKY/b0m9JSIiQpUrV9asWbNc/mL++eef9e233zrv0xMaNmyoUaNG6a233lLRokUzrZcrV650ox3//e9/dfToUZeyWwlPRklXdg0ZMkSHDx/WrFmzNH78eEVFRSkuLi7T3yOArONAKvisUqVK6eOPP1a7du0UExPjcrLl2rVr9d///ledO3eWJFWqVElxcXGaNm2azp8/r/r16+unn37SrFmz1KpVq0y3Frqjffv2GjJkiFq3bq3evXvrypUreuedd3T//fe7LDYcOXKk1qxZo0cffVQlSpTQqVOnNGXKFN17772qU6dOpu2PHTtWzZo1U82aNdWtWzddvXpVkydPVlhYmOLj4027jz/z8/PTyy+//Lf1mjdvrpEjR6pLly6qVauWtm3bpjlz5qhkyZIu9UqVKqV8+fJp6tSpCgkJUXBwsGrUqKHo6OhsxbVy5UpNmTJFI0aMcG5HnTFjhho0aKBhw4ZpzJgx2WoPwJ9YvGsE8Lg9e/YYzzzzjBEVFWUEBAQYISEhRu3atY3Jkycb165dc9a7ceOGkZCQYERHRxu5c+c2ihUrZgwdOtSljmH8vv3z0UcfTdfPn7cdZrb90zAM49tvvzXKly9vBAQEGGXKlDE++uijdNs/V6xYYbRs2dKIjIw0AgICjMjISKNDhw7Gnj170vXx5y2Sy5cvN2rXrm0EBQUZoaGhRosWLYwdO3a41LnV35+3l86YMcOQZKSkpGT6OzUM1+2fmcls++eAAQOMiIgIIygoyKhdu7axbt26DLdtLlq0yChXrpzh7+/vcp/169c3HnjggQz7/GM7Fy9eNEqUKGE8+OCDxo0bN1zq9evXz/Dz8zPWrVv3l/cA4K/ZDCMbK6oAAAD+gDUSAADAbSQSAADAbSQSAADAbSQSAAD4qDVr1qhFixaKjIyUzWbTwoULnddu3LihIUOGqEKFCgoODlZkZKSefvrpbD8Rl0QCAAAfdfnyZVWqVElvv/12umtXrlxRUlKShg0bpqSkJH3++efavXu3HnvssWz1wa4NAADuAjabTQsWLFCrVq0yrbNhwwZVr15dhw4dyvCZQhnhQCoAAHKI1NTUdCey2u122e12U9q/cOGCbDab8+nDWeGTiUTXqCesDgEAkEN8cPAzj/dx49cDprST+NaHSkhIcCkbMWKEKafWXrt2TUOGDFGHDh2cT1DOCp9MJAAA8EVDhw5V//79XcrMGI24ceOG2rZtK8Mw9M4772TrsyQSAAB4miPNlGbMnMa45VYScejQIa1cuTJboxESiQQAAJ5nOKyOIEO3koi9e/dq1apVKlCgQLbbIJEAAMDTHNYkEpcuXdK+ffuc71NSUpScnKz8+fMrIiJCTzzxhJKSkrRkyRKlpaXpxIkTkqT8+fMrICAgS3345PZPFlsCALLqjiy2PL7TlHZyR8Rkq/7q1avVsGHDdOVxcXGKj49XdHR0hp9btWqVGjRokKU+GJEAAMDDDIumNho0aKC/Gi8wYyyBRAIAAE+zaGrjTuCIbAAA4DZGJAAA8DQv3bVhBhIJAAA8zaRzJLwRUxsAAMBtjEgAAOBpTG0AAAC3sWsDAAAgPUYkAADwMKsOpLoTSCQAAPA0H57aIJEAAMDTfHhEgjUSAADAbYxIAADgaT58IBWJBAAAnsbUBgAAQHqMSAAA4Gns2gAAAG5jagMAACA9RiQAAPA0pjYAAIC7DMN3t38ytQEAANzGiAQAAJ7mw4stSSQAAPA01kgAAAC3+fCIBGskAACA2xiRAADA03hoFwAAcBtTGwAAAOkxIgEAgKexawMAALiNqQ0AAID0GJEAAMDTmNoAAABu8+FEgqkNAADgNkYkAADwMF9+jDiJBAAAnubDUxskEgAAeBrbPwEAANJjRAIAAE9jagMAALiNqQ0AAID0GJEAAMDTmNoAAABuY2oDAAAgPUYkAADwNKY2AACA20gkzBUeHi6bzZalumfPnvVwNAAAwF2WJBITJ050/nzmzBmNHj1aTZo0Uc2aNSVJ69at0zfffKNhw4ZZER4AAOby4cWWNsMwDCsDePzxx9WwYUP16tXLpfytt97S8uXLtXDhwmy32TXqCZOiAwD4ug8OfubxPq5+8YYp7QQ9NtCUdsxk+a6Nb775Rk2bNk1X3rRpUy1fvtyCiAAAMJnhMOflhSxPJAoUKKBFixalK1+0aJEKFChgQUQAAPiGNWvWqEWLFoqMjJTNZks3ym8YhoYPH66IiAgFBQUpNjZWe/fuzVYflu/aSEhIUPfu3bV69WrVqFFDkrR+/XotXbpU06dPtzg6AABMYNGujcuXL6tSpUrq2rWr2rRpk+76mDFjNGnSJM2aNUvR0dEaNmyYmjRpoh07digwMDBLfVieSHTu3FkxMTGaNGmSPv/8c0lSTEyMfvjhB2diAQBAjmbRtESzZs3UrFmzDK8ZhqGJEyfq5ZdfVsuWLSVJH374oYoUKaKFCxeqffv2WerD8kRCkmrUqKE5c+ZYHQYAAHeNlJQUnThxQrGxsc6ysLAw1ahRQ+vWrctZiYTD4dC+fft06tQpOf40/FOvXj2LogIAwCQmTW2kpqYqNTXVpcxut8tut2e7rRMnTkiSihQp4lJepEgR57WssDyR+PHHH9WxY0cdOnRIf96JarPZlJaWZlFkAACYxKREIjExUQkJCS5lI0aMUHx8vCntu8PyROK5555TtWrV9OWXXyoiIiLLJ14CAHC3GTp0qPr37+9S5s5ohCQVLVpUknTy5ElFREQ4y0+ePKnKlStnuR3LE4m9e/fqs88+U+nSpa0OBQAAzzDp7Ed3pzEyEh0draJFi2rFihXOxOHixYtav369nn/++Sy3Y3kiUaNGDe3bt49EAgDguyza/nnp0iXt27fP+T4lJUXJycnKnz+/ihcvrr59+2r06NG67777nNs/IyMj1apVqyz3YXki8cILL2jAgAE6ceKEKlSooNy5c7tcr1ixokWRAQCQs23cuFENGzZ0vr81LRIXF6eZM2dq8ODBunz5sp599lmdP39ederU0dKlS7N8hoTkBc/a8PNLf7imzWaTYRhuL7bkWRsAgKy6I8/amGPOQyiDOo0ypR0zWT4ikZKSYnUIAAB4lpc+J8MMlicSJUqUsDoEAAA8y6I1EneC5YnELTt27NDhw4d1/fp1l/LHHnvMoogAAMDfsTyROHDggFq3bq1t27Y510ZIcp4nwYFUAIAcz9rliB5l+WPE+/Tpo+joaJ06dUp58uTR9u3btWbNGlWrVk2rV6+2OjwAAG6fw2HOywtZPiKxbt06rVy5UgULFpSfn5/8/PxUp04dJSYmqnfv3tq8ebPVIQIAgExYPiKRlpamkJAQSVLBggV17NgxSb8vwty9e7eVoQEAYA5GJDynfPny2rJli6Kjo1WjRg2NGTNGAQEBmjZtmkqWLGl1eAAA3D62f3rOyy+/rMuXL0uSRo4cqebNm6tu3boqUKCAPv30U4ujAwAAf8XyRKJJkybOn0uXLq1du3bp7NmzCg8P50mgAACfYDh8d9eG5YlERvLnz291CAAAmMdL1zeYwZJEok2bNlmu+/nnn3swEgAAcDssSSTCwsKs6BYAAGuw2NJcM2bMsKJbAACswRoJAADgNh9eI2H5gVQAACDnYkQCAABP8+ERCRIJAAA8jad/es6HH36o1NTUdOXXr1/Xhx9+aEFEAAAgq2yGYW2alCtXLh0/flyFCxd2KT9z5owKFy6stLS0bLfZNeoJs8KDGxo81VgNOzVRwXsLSZKO7j2ixZM+07bVPMkVdze+G97pg4OfebyPK+OfMaWdPP2nm9KOmSyf2jAMI8OjsH/55RfOm8ihzh0/o89e/0gnDx6XzWZT7ccb6IVpgxX/6CAd2/uL1eEBluG7cRdj+6f5qlSpIpvNJpvNpkaNGsnf//9CSUtLU0pKipo2bWpVeLgNW1Zscnn/+RufqMFTjVWqyv38YYm7Gt8N+CLLEolWrVpJkpKTk9WkSRPlzZvXeS0gIEBRUVF6/PHHLYoOZrH5+emhR2vKHhSo/Ul7rA4H8Bp8N+4ynGxpvhEjRkiSoqKi1K5dOwUGBloVCjzgnjLF9dLnryi3PUCpV67prX+P0bF9/IsL4Ltxl/LhqQ3LF1vesmnTJu3cuVOS9MADD6hKlSpZ+lxqamq6XR8vVIhTLlsu02NE1uXK7a8CkQUVFJJH1R75h+q1a6TX243gD0zc9fhueJ87stjy9S6mtJNniPc9YsLyxZanTp1S+/bttXr1auXLl0+SdP78eTVs2FBz585VoUKF/vLziYmJSkhIcCmrHBajKvnKeSpkZEHajZs6deiEJOnQzwcUXbG0Yrs+og//M83iyABr8d24Oxk+fCCV5edIvPDCC/rtt9+0fft2nT17VmfPntXPP/+sixcvqnfv3n/7+aFDh+rChQsur4phZe5A5MgOm59N/gG5rQ4D8Dp8N+4SDsOclxeyfERi6dKlWr58uWJiYpxl5cqV09tvv63GjRv/7eftdrvsdrtLGdMa1np8cEdtW71ZZ479qsDgIP2jZR2V+ccDGv/0aKtDAyzFd+MuxmJLz3E4HMqdO302njt3bjl8eCjIl4UWCFP38S8orFC4rv52Rb/sOqTxT4/Wjh+2Wh0aYCm+G/BFlicS//znP9WnTx998sknioyMlCQdPXpU/fr1U6NGjSyODu6YMeQdq0MAvBLfjbuYl05LmMHyNRJvvfWWLl68qKioKJUqVUqlSpVSdHS0Ll68qMmTJ1sdHgAAt8/hMOflhSwfkShWrJiSkpK0fPly7dq1S5IUExOj2NhYiyMDAAB/x/JEQpJsNpsefvhhPfzww1aHAgCA+Xx4asMrEokVK1ZoxYoVOnXqVLoFlh988IFFUQEAYBJ2bXhOQkKCRo4cqWrVqikiIiLDJ4ECAADvZHkiMXXqVM2cOVP/+te/rA4FAADPYGrDc65fv65atWpZHQYAAB7DEdke1L17d3388cdWhwEAANxg+YjEtWvXNG3aNC1fvlwVK1ZMd8rl+PHjLYoMAACTMLXhOVu3blXlypUlST///LPLNRZeAgB8AomE56xatcrqEAAA8Cwf3v5p+RoJAACQc1k+IgEAgM9jagMAALjL8OFEgqkNAADgNkYkAADwNB8ekbAkkfjiiy+yXPexxx7zYCQAANwBPnyypSWJRKtWrVze22w2GYbh8v6WtLS0OxUWAADIJkvWSDgcDufr22+/VeXKlfX111/r/PnzOn/+vL766is9+OCDWrp0qRXhAQBgLodhzssLWb5Gom/fvpo6darq1KnjLGvSpIny5MmjZ599Vjt37rQwOgAATOClSYAZLN+1sX//fuXLly9deVhYmA4ePHjH4wEAAFlneSLx0EMPqX///jp58qSz7OTJkxo0aJCqV69uYWQAAJjDMAxTXtmRlpamYcOGKTo6WkFBQSpVqpRGjRqV7Xb+juVTGx988IFat26t4sWLq1ixYpKkI0eO6L777tPChQutDQ4AADNYMLXx+uuv65133tGsWbP0wAMPaOPGjerSpYvCwsLUu3dv0/qxPJEoXbq0tm7dqmXLlmnXrl2SpJiYGMXGxvL0TwCAb7AgkVi7dq1atmypRx99VJIUFRWlTz75RD/99JOp/VieSEi/b/ds3LixGjdubHUoAAB4rdTUVKWmprqU2e122e32dHVr1aqladOmac+ePbr//vu1ZcsW/fDDDxo/frypMVmSSEyaNEnPPvusAgMDNWnSpL+sa+bwCwAAVjDrWRuJiYlKSEhwKRsxYoTi4+PT1X3xxRd18eJFlS1bVrly5VJaWppeeeUVderUyZRYbrEZZq+6yILo6Ght3LhRBQoUUHR0dKb1bDabDhw4kO32u0Y9cTvhAQDuIh8c/MzjfVyIa2RKO4HTvsryiMTcuXM1aNAgjR07Vg888ICSk5PVt29fjR8/XnFxcabEI1k0IpGSkpLhzwAAIHOZJQ0ZGTRokF588UW1b99eklShQgUdOnRIiYmJOT+RAADgrmLBozauXLkiPz/XUx5y5colh8nP/bAkkejfv3+W65q9KAQAgDvNrDUS2dGiRQu98sorKl68uB544AFt3rxZ48ePV9euXU3tx5JEYvPmzS7vk5KSdPPmTZUpU0aStGfPHuXKlUtVq1a1IjwAAHK8yZMna9iwYerRo4dOnTqlyMhI/fvf/9bw4cNN7ceSRGLVqlXOn8ePH6+QkBDNmjVL4eHhkqRz586pS5cuqlu3rhXhAQBgLgtGJEJCQjRx4kRNnDjRo/1YfkT2uHHjlJiY6EwiJCk8PFyjR4/WuHHjLIwMAACTOEx6eSHLE4mLFy/q9OnT6cpPnz6t3377zYKIAABAVlm+a6N169bq0qWLxo0b53xI1/r16zVo0CC1adPG4ugAALh9Viy2vFMsTySmTp2qgQMHqmPHjrpx44Ykyd/fX926ddPYsWMtjg4AABN46bSEGSxPJPLkyaMpU6Zo7Nix2r9/vySpVKlSCg4OtjgyAADMwYjEHRAcHKyKFStaHQYAAMgGr0kkAADwWUxtAAAAdxk+nEhYvv0TAADkXIxIAADgaT48IkEiAQCAhzG1AQAAkAFGJAAA8DQfHpEgkQAAwMN8eWqDRAIAAA/z5USCNRIAAMBtjEgAAOBhvjwiQSIBAICnGTarI/AYpjYAAIDbTBmROH/+vPLly2dGUwAA+BxfntrI9ojE66+/rk8//dT5vm3btipQoIDuuecebdmyxdTgAADwBYbDZsrLG2U7kZg6daqKFSsmSVq2bJmWLVumr7/+Ws2aNdOgQYNMDxAAAHivbE9tnDhxwplILFmyRG3btlXjxo0VFRWlGjVqmB4gAAA5HVMbfxAeHq4jR45IkpYuXarY2FhJkmEYSktLMzc6AAB8gGHYTHl5o2yPSLRp00YdO3bUfffdpzNnzqhZs2aSpM2bN6t06dKmBwgAALxXthOJCRMmKCoqSkeOHNGYMWOUN29eSdLx48fVo0cP0wMEACCn8+WpjWwnErlz59bAgQPTlffr18+UgAAA8DXeuuPCDFlKJL744ossN/jYY4+5HQwAAL7IMKyOwHOylEi0atUqS43ZbDYWXAIAcBfJUiLhcPjw5A4AAB52109tZObatWsKDAw0KxYAAHySLycS2T5HIi0tTaNGjdI999yjvHnz6sCBA5KkYcOG6f333zc9QAAA4L2ynUi88sormjlzpsaMGaOAgABnefny5fXee++ZGhwAAL7AMMx5eaNsJxIffvihpk2bpk6dOilXrlzO8kqVKmnXrl2mBgcAgC/goV1/cPTo0QxPsHQ4HLpx44YpQQEAgJwh24lEuXLl9P3336cr/+yzz1SlShVTggIAwJfwrI0/GD58uOLi4nT06FE5HA59/vnn2r17tz788EMtWbLEEzECAJCj+fIR2dkekWjZsqUWL16s5cuXKzg4WMOHD9fOnTu1ePFiPfzww56IEQAAeCm3zpGoW7euli1bZnYsAAD4JIeXTkuYwe0DqTZu3KidO3dK+n3dRNWqVU0LCgAAX+Kt6xvMkO1E4pdfflGHDh30v//9T/ny5ZMknT9/XrVq1dLcuXN17733mh0jAAA5mrdu3TRDttdIdO/eXTdu3NDOnTt19uxZnT17Vjt37pTD4VD37t09ESMAAPBS2R6R+O6777R27VqVKVPGWVamTBlNnjxZdevWNTU4AAB8gbeeSmmGbCcSxYoVy/DgqbS0NEVGRpoSFAAAvoSpjT8YO3asXnjhBW3cuNFZtnHjRvXp00dvvPGGqcEBAADvlqURifDwcNls/5dNXb58WTVq1JC//+8fv3nzpvz9/dW1a1e1atXKI4ECAJBT3fXbPydOnOjhMAAA8F13/fbPuLg4T8cBAAByILcPpJKka9eu6fr16y5loaGhtxUQAAC+hl0bf3D58mUNGTJE8+bN05kzZ9JdT0tLMyUwAAB8hS+vkcj2ro3Bgwdr5cqVeuedd2S32/Xee+8pISFBkZGR+vDDDz0RIwAA8FLZTiQWL16sKVOm6PHHH5e/v7/q1q2rl19+Wa+++qrmzJnjiRgBAMjRDMNmyiu7jh49qqeeekoFChRQUFCQKlSo4HJ8gxmyPbVx9uxZlSxZUtLv6yHOnj0rSapTp46ef/55U4MDAMAXWLFG4ty5c6pdu7YaNmyor7/+WoUKFdLevXsVHh5uaj/ZTiRKliyplJQUFS9eXGXLltW8efNUvXp1LV682PkQLwAA8H+sWCPx+uuvq1ixYpoxY4azLDo62vR+sj210aVLF23ZskWS9OKLL+rtt99WYGCg+vXrp0GDBpkeIAAA+F1qaqouXrzo8kpNTc2w7hdffKFq1arpySefVOHChVWlShVNnz7d9JhshnF7Ay6HDh3Spk2bVLp0aVWsWNGsuG6Lf8A9VocAeKWrx763OgTA6+QuWNLjfWy4p7Up7Xz5TCUlJCS4lI0YMULx8fHp6gYGBkqS+vfvryeffFIbNmxQnz59NHXqVFPPh7rtRMIbkUgAGSORANK7E4nE+sg2prRTOeWTdCMQdrtddrs9Xd2AgABVq1ZNa9eudZb17t1bGzZs0Lp160yJR8riGolJkyZlucHevXu7HQwAAMhcZklDRiIiIlSuXDmXspiYGM2fP9/UmLKUSEyYMCFLjdlsNhIJAAD+xIqh/9q1a2v37t0uZXv27FGJEiVM7SdLiURKSoqpnQIAcDexYtdGv379VKtWLb366qtq27atfvrpJ02bNk3Tpk0ztZ9s79oAAADe76GHHtKCBQv0ySefqHz58ho1apQmTpyoTp06mdrPbT20CwAA/D2rHiPevHlzNW/e3KN9kEgAAOBhDqsD8CCmNgAAgNsYkQAAwMMM8RhxF99//72eeuop1axZU0ePHpUkzZ49Wz/88IOpwQEA4Aschjkvb5TtRGL+/Plq0qSJgoKCtHnzZucJWxcuXNCrr75qeoAAAOR0DtlMeXmjbCcSo0eP1tSpUzV9+nTlzp3bWV67dm0lJSWZGhwAAPBu2V4jsXv3btWrVy9deVhYmM6fP29GTAAA+BTWSPxB0aJFtW/fvnTlP/zwg0qW9PyDTwAAyGkcJr28UbYTiWeeeUZ9+vTR+vXrZbPZdOzYMc2ZM0cDBw7U888/74kYAQCAl8r21MaLL74oh8OhRo0a6cqVK6pXr57sdrsGDhyoF154wRMxAgCQo/ny1IbNMAy3NpRcv35d+/bt06VLl1SuXDnlzZvX7Njc5h9wj9UhAF7p6rHvrQ4B8Dq5C3p+Wn5pkfamtNP05FxT2jGT2wdSBQQEpHvOOQAAuLtkO5Fo2LChbLbMh2hWrlx5WwEBAOBrvHWhpBmynUhUrlzZ5f2NGzeUnJysn3/+WXFxcWbFBQCAz/DlNRLZTiQmTJiQYXl8fLwuXbp02wEBAICcw7Snfz711FP64IMPzGoOAACf4bCZ8/JGpj39c926dQoMDDSrOQAAfIa3PifDDNlOJNq0aePy3jAMHT9+XBs3btSwYcNMCwwAAF/hpQ/uNEW2E4mwsDCX935+fipTpoxGjhypxo0bmxYYAADwftlKJNLS0tSlSxdVqFBB4eHhnooJAACf4svbP7O12DJXrlxq3LgxT/kEACAbHDabKS9vlO1dG+XLl9eBAwc8EQsAAMhhsp1IjB49WgMHDtSSJUt0/PhxXbx40eUFAABcGSa9vFGW10iMHDlSAwYM0COPPCJJeuyxx1yOyjYMQzabTWlpaeZHCQBADubLaySynEgkJCToueee06pVqzwZDwAAyEGynEjcetp4/fr1PRYMAAC+yFtPpTRDtrZ//tVTPwEAQMY42fL/u//++/82mTh79uxtBQQAAHKObCUSCQkJ6U62BAAAf81bd1yYIVuJRPv27VW4cGFPxQIAgE9ijYRYHwEAgLt8eftnlg+kurVrAwAA4JYsj0g4HL6cTwEA4Dm+/E/xbD9GHAAAZI8vr5HI9rM2AAAAbmFEAgAAD/PlxQEkEgAAeJgvJxJMbQAAALcxIgEAgIcZPrzYkkQCAAAPY2oDAAAgA4xIAADgYb48IkEiAQCAh3GyJQAAcBsnWwIAAGSAEQkAADyMNRIAAMBtvpxIMLUBAADcxogEAAAexq4NAADgNnZtAACAHO21116TzWZT3759TW2XEQkAADzM6sWWGzZs0LvvvquKFSua3jYjEgAAeJhh0ssdly5dUqdOnTR9+nSFh4ffzm1kiEQCAAAf1rNnTz366KOKjY31SPtMbQAA4GEOk/ZtpKamKjU11aXMbrfLbrdnWH/u3LlKSkrShg0bTOk/I4xIAADgYQ6TXomJiQoLC3N5JSYmZtjnkSNH1KdPH82ZM0eBgYEeuzebYRg+t73VP+Aeq0MAvNLVY99bHQLgdXIXLOnxPkaW6GRKO0P2fJDlEYmFCxeqdevWypUrl7MsLS1NNptNfn5+Sk1NdbnmLqY2AADIIf5qGuPPGjVqpG3btrmUdenSRWXLltWQIUNMSSIkEgkAADzOiu2fISEhKl++vEtZcHCwChQokK78dpBIAADgYb58siWJBAAAd4nVq1eb3iaJBAAAHmbW9k9vRCIBAICH+W4awTkSAADgNjAiAQCAh1n90C5PsiyR2Lp1a5breuJpZQAA3CmskfCAypUry2azyTAM2Wx/vS8mLS3tDkUFAACyw7I1EikpKTpw4IBSUlI0f/58RUdHa8qUKdq8ebM2b96sKVOmqFSpUpo/f75VIQIAYAorHyPuaZaNSJQoUcL585NPPqlJkybpkUcecZZVrFhRxYoV07Bhw9SqVSsLIgQAwByskfCwbdu2KTo6Ol15dHS0duzYYUFEAACYx5fXSHjF9s+YmBglJibq+vXrzrLr168rMTFRMTExFkYGAAD+ileMSEydOlUtWrTQvffe69yhsXXrVtlsNi1evNji6AAAuD2+Ox7hJYlE9erVdeDAAc2ZM0e7du2SJLVr104dO3ZUcHCwxdEBAHB7WCNxBwQHB+vZZ5+1OgwAAJANXrFGQpJmz56tOnXqKDIyUocOHZIkTZgwQYsWLbI4MgAAbo9h0n/eyCsSiXfeeUf9+/dXs2bNdO7cOecBVOHh4Zo4caK1wQEAcJscJr28kVckEpMnT9b06dP10ksvyd///2ZbqlWrpm3btlkYGQAA+CtesUYiJSVFVapUSVdut9t1+fJlCyICAMA8nCPhYdHR0UpOTk5XvnTpUs6RAADkeByR7WH9+/dXz549de3aNRmGoZ9++kmffPKJEhMT9d5771kdHgAAyIRXJBLdu3dXUFCQXn75ZV25ckUdO3ZUZGSk3nzzTbVv397q8OCm55+L04D+z6to0ULaunWH+vQdpg0bk60OC7hjNiZv04yPP9OOXft0+sxZvZk4TI3q1ZIk3bh5U5OnzdL36zbql2PHlTc4WP94qIr6PddFhQsVsDhymI2pjTugU6dO2rt3ry5duqQTJ07ol19+Ubdu3awOC2568snH9MbYERo1erweqtFUW7bu0FdfzlEh/oDEXeTq1WsqU7qkXhrQI921a9dStWP3fv27cwfN++AtTXz1ZR08/It6DUmwIFJ4mi/v2rAZhuFzaZJ/wD1Wh3DXW/vDYm3YuEV9+r4sSbLZbDp4YIPenjJDY8a+bXF0d6+rx763OoS7VvnazVxGJDKybedudejeV8vmz1JE0cJ3MLq7W+6CJT3eR/eoJ0xp572Dn5nSjpksm9qoUqWKbDZbluomJSV5OBqYKXfu3HrwwYp6bcxbzjLDMLRi5Q/6xz+qWhgZ4N0uXboim82mkBAeDYCcw7JEolWrVs6fr127pilTpqhcuXKqWbOmJOnHH3/U9u3b1aNH+iHBP0pNTVVqaqpLmWEYWU5SYL6CBfPL399fp07+6lJ+6tRplS1TyqKoAO+WmnpdE975QI/E1ldenjHkc7x1WsIMliUSI0aMcP7cvXt39e7dW6NGjUpX58iRI3/ZTmJiohISXOcUbX55ZcsVal6wAOBBN27e1IBhr8owDA0b1MvqcOAB3nq8tRm8YrHlf//7Xz399NPpyp966inNnz//Lz87dOhQXbhwweVl8wvxVKjIgl9/PaubN2+qcJGCLuWFCxfSiZOnLYoK8E63kohjJ09p+sRXGY1AjuMViURQUJD+97//pSv/3//+p8DAwL/8rN1uV2hoqMuLaQ1r3bhxQ0lJW/XPhnWcZTabTf9sWEc//rjJwsgA73IriTh85Jjem/iq8oUxkuqrfHnXhlecI9G3b189//zzSkpKUvXq1SVJ69ev1wcffKBhw4ZZHB3cMeHN6Zrx/gRtStqqDRs2q/cLzyg4OEgzZ31qdWjAHXPlylUd/uWY8/3RYye1a89+hYWGqGDB/Or/0ivasWef3h6TIIfDoV/PnJUkhYWGKHfu3FaFDQ9w+N4GSSev2f45b948vfnmm9q5c6ckKSYmRn369FHbtm2z3RbbP71Dj+c7Ow+k2rJlu/r2G66fNmy2Oqy7Gts/76yfkraq6wtD0pW3bBarHt2eUpMnOmf4uQ8mv67qD1b0cHS45U5s//xXiTamtDP70OemtGMmr0kkzEQiAWSMRAJI704kEk+ZlEh85IWJhFdMbdxy/fp1nTp1Sg6H60xQ8eLFLYoIAIDb58tHZHtFIrF371517dpVa9eudSm/dR5EWlqaRZEBAIC/4hWJROfOneXv768lS5YoIiKCXRcAAJ/iy+dIeEUikZycrE2bNqls2bJWhwIAgOm8deumGbwikShXrpx+/fXXv68IAEAO5MtrJLziQKrXX39dgwcP1urVq3XmzBldvHjR5QUAALyTV4xIxMbGSpIaNWrkUs5iSwCAL2CNhIetWrXK6hAAAPAY1kh4WP369a0OAQAAuMErEolbrly5osOHD+v69esu5RUrclQsACDn8sFDpJ28IpE4ffq0unTpoq+//jrD66yRAADkZOza8LC+ffvq/PnzWr9+vYKCgrR06VLNmjVL9913n7744gurwwMAAJnwihGJlStXatGiRapWrZr8/PxUokQJPfzwwwoNDVViYqIeffRRq0MEAMBtvrzY0itGJC5fvqzChQtLksLDw3X69GlJUoUKFZSUlGRlaAAA3DbDpP+8kVckEmXKlNHu3bslSZUqVdK7776ro0ePaurUqYqIiLA4OgAAkBmvmNro06ePjh8/LkkaMWKEmjZtqo8++kgBAQGaNWuWxdEBAHB7fHmxpVckEk899ZTz56pVq+rQoUPatWuXihcvroIFC1oYGQAAt4/tnx7Qv3//LNcdP368ByMBAMCzfHmxpWWJxObNm7NUz2azeTgSAADgLssSCZ6vAQC4W3jrjgszeMUaCQAAfJkvL7b0iu2fAADAXImJiXrooYcUEhKiwoULq1WrVs6jFsxEIgEAgIcZhmHKKzu+++479ezZUz/++KOWLVumGzduqHHjxrp8+bKp98bUBgAAHmbF1MbSpUtd3s+cOVOFCxfWpk2bVK9ePdP6IZEAACCHSE1NVWpqqkuZ3W6X3W7/289euHBBkpQ/f35TY2JqAwAADzPrWRuJiYkKCwtzeSUmJv5t/w6HQ3379lXt2rVVvnx5U++NEQkAADzMYdLJlkOHDk13oGNWRiN69uypn3/+WT/88IMpcfwRiQQAADlEVqcx/qhXr15asmSJ1qxZo3vvvdf0mEgkAADwMCtOkTAMQy+88IIWLFig1atXKzo62iP9kEgAAOBhVuza6Nmzpz7++GMtWrRIISEhOnHihCQpLCxMQUFBpvVjM3zwkWT+AfdYHQLgla4e+97qEACvk7tgSY/3UfOehqa0s+5o1h8vkdmzqmbMmKHOnTubEo/EiAQAAD7pTo0TkEgAAOBhPjj470QiAQCAh/HQLgAAgAwwIgEAgIcZPjwiQSIBAICH+fIaCaY2AACA2xiRAADAw3x5sSWJBAAAHsbUBgAAQAYYkQAAwMOY2gAAAG5j+ycAAHCbgzUSAAAA6TEiAQCAhzG1AQAA3MbUBgAAQAYYkQAAwMOY2gAAAG5jagMAACADjEgAAOBhTG0AAAC3MbUBAACQAUYkAADwMKY2AACA2wzDYXUIHkMiAQCAh/nyY8RZIwEAANzGiAQAAB5m+PCuDRIJAAA8jKkNAACADDAiAQCAhzG1AQAA3MbJlgAAABlgRAIAAA/jZEsAAOA2X14jwdQGAABwGyMSAAB4mC+fI0EiAQCAh/ny1AaJBAAAHsb2TwAAgAwwIgEAgIcxtQEAANzmy4stmdoAAABuY0QCAAAPY2oDAAC4jV0bAAAAGWBEAgAAD+OhXQAAwG1MbQAAAGSAEQkAADyMXRsAAMBtvrxGgqkNAAA8zDAMU17uePvttxUVFaXAwEDVqFFDP/30k6n3RiIBAICP+vTTT9W/f3+NGDFCSUlJqlSpkpo0aaJTp06Z1ofN8MGJG/+Ae6wOAfBKV499b3UIgNfJXbCk5/sw6e+lG9ePZqt+jRo19NBDD+mtt96SJDkcDhUrVkwvvPCCXnzxRVNiYkQCAAAPM0x6Zcf169e1adMmxcbGOsv8/PwUGxurdevW3db9/BGLLQEAyCFSU1OVmprqUma322W329PV/fXXX5WWlqYiRYq4lBcpUkS7du0yLSafTCRuZnPoB56RmpqqxMREDR06NMP/yYG7Fd+Nu49Zfy/Fx8crISHBpWzEiBGKj483pX13+OQaCXiHixcvKiwsTBcuXFBoaKjV4QBeg+8G3JWdEYnr168rT548+uyzz9SqVStneVxcnM6fP69FixaZEhNrJAAAyCHsdrtCQ0NdXpmNagUEBKhq1apasWKFs8zhcGjFihWqWbOmaTH55NQGAACQ+vfvr7i4OFWrVk3Vq1fXxIkTdfnyZXXp0sW0PkgkAADwUe3atdPp06c1fPhwnThxQpUrV9bSpUvTLcC8HSQS8Bi73a4RI0awmAz4E74buJN69eqlXr16eax9FlsCAAC3sdgSAAC4jUQCAAC4jUQCAAC4jUTCBzVo0EB9+/Z1vo+KitLEiRP/8jM2m00LFy70aFxZ1blzZ5fDUzxl5syZypcvn8f7gW/78/fNE+Lj41W5cmWP9gG4i10bd4ENGzYoODjY6jAUHx+vhQsXKjk52epQAAAmIZG4CxQqVMjqEAAAPoqpjRzu8uXLevrpp5U3b15FRERo3Lhx6er8eWpj7969qlevngIDA1WuXDktW7bsb/tp0KCBevfurcGDByt//vwqWrRouofEHD58WC1btlTevHkVGhqqtm3b6uTJk5J+n0ZISEjQli1bZLPZZLPZNHPmzCzd49KlS1WnTh3ly5dPBQoUUPPmzbV//37n9YMHD8pms+nzzz9Xw4YNlSdPHlWqVCndY3Jnzpyp4sWLK0+ePGrdurXOnDmTpf6BrJo9e7aqVaumkJAQFS1aVB07dtSpU6ec11evXi2bzaYVK1aoWrVqypMnj2rVqqXdu3e7tPPaa6+pSJEiCgkJUbdu3XTt2rU7fStAlpFI5HCDBg3Sd999p0WLFunbb7/V6tWrlZSUlGl9h8OhNm3aKCAgQOvXr9fUqVM1ZMiQLPU1a9YsBQcHa/369RozZoxGjhzpTEIcDodatmyps2fP6rvvvtOyZct04MABtWvXTtLvp6sNGDBADzzwgI4fP67jx487r/2dy5cvq3///tq4caNWrFghPz8/tW7dWg6Hw6XeSy+9pIEDByo5OVn333+/OnTooJs3b0qS1q9fr27duqlXr15KTk5Ww4YNNXr06Cz1D2TVjRs3NGrUKG3ZskULFy7UwYMH1blz53T1XnrpJY0bN04bN26Uv7+/unbt6rw2b948xcfH69VXX9XGjRsVERGhKVOm3MG7ALLJQI7122+/GQEBAca8efOcZWfOnDGCgoKMPn36OMtKlChhTJgwwTAMw/jmm28Mf39/4+jRo87rX3/9tSHJWLBgQaZ91a9f36hTp45L2UMPPWQMGTLEMAzD+Pbbb41cuXIZhw8fdl7fvn27Icn46aefDMMwjBEjRhiVKlX62/uKi4szWrZsmen106dPG5KMbdu2GYZhGCkpKYYk47333kvX986dOw3DMIwOHToYjzzyiEs77dq1M8LCwv42HuCv1K9f3+X79kcbNmwwJBm//fabYRiGsWrVKkOSsXz5cmedL7/80pBkXL161TAMw6hZs6bRo0cPl3Zq1KiRpe8OYAVGJHKw/fv36/r166pRo4azLH/+/CpTpkymn9m5c6eKFSumyMhIZ1lWnwJXsWJFl/cRERHOYdtb7RYrVsx5vVy5csqXL5927tyZpfYzs3fvXnXo0EElS5ZUaGiooqKiJP0+lZJZfBEREZLkEt8ff09S1u8byKpNmzapRYsWKl68uEJCQlS/fn1J/L8K30YigSzLnTu3y3ubzZZuesETWrRoobNnz2r69Olav3691q9fL0m6fv16pvHZbDZJuiPxAdLvU3BNmjRRaGio5syZow0bNmjBggWS+H8Vvo1EIgcrVaqUcufO7fyLVZLOnTunPXv2ZPqZmJgYHTlyRMePH3eW/fjjj7cdy612jxw54izbsWOHzp8/r3LlykmSAgIClJaWlq12z5w5o927d+vll19Wo0aNFBMTo3PnzrkV3x9/T5I59w3csmvXLp05c0avvfaa6tatq7Jly7ostMwq/l9FTsP2zxwsb9686tatmwYNGqQCBQqocOHCeumll+Tnl3l+GBsbq/vvv19xcXEaO3asLl68qJdeeum2Y4mNjVWFChXUqVMnTZw4UTdv3lSPHj1Uv359VatWTdLvu0dSUlKUnJyse++9VyEhIX/79MPw8HAVKFBA06ZNU0REhA4fPqwXX3wx2/H17t1btWvX1htvvKGWLVvqm2++0dKlS926VyAjxYsXV0BAgCZPnqznnntOP//8s0aNGpXtdvr06aPOnTurWrVqql27tubMmaPt27erZMmSHogauH2MSORwY8eOVd26ddWiRQvFxsaqTp06qlq1aqb1/fz8tGDBAl29elXVq1dX9+7d9corr9x2HDabTYsWLVJ4eLjq1aun2NhYlSxZUp9++qmzzuOPP66mTZuqYcOGKlSokD755JO/bdfPz09z587Vpk2bVL58efXr109jx47Ndnz/+Mc/NH36dL355puqVKmSvv32W7388svZbgfITKFChTRz5kz997//Vbly5fTaa6/pjTfeyHY77dq107BhwzR48GBVrVpVhw4d0vPPP++BiAFz8BhxAADgNkYkAACA20gkAACA20gkAACA20gkAACA20gkAACA20gkAACA20gkAACA20gkAAt17txZrVq1cr5v0KCB+vbte8fjWL16tWw2m86fP59pHZvNpoULF2a5zfj4eFWuXPm24jp48KBsNpuSk5Nvqx0AnkMiAfxJ586dZbPZZLPZFBAQoNKlS2vkyJG6efOmx/v+/PPPs3ysclb+8gcAT+NZG0AGmjZtqhkzZig1NVVfffWVevbsqdy5c2vo0KHp6l6/fl0BAQGm9Js/f35T2gGAO4URCSADdrtdRYsWVYkSJfT8888rNjZWX3zxhaT/m4545ZVXFBkZqTJlykiSjhw5orZt2ypfvnzKnz+/WrZsqYMHDzrbTEtLU//+/ZUvXz4VKFBAgwcP1p9PqP/z1EZqaqqGDBmiYsWKyW63q3Tp0nr//fd18OBBNWzYUNLvDzaz2Wzq3LmzpN8fR52YmKjo6GgFBQWpUqVK+uyzz1z6+eqrr3T//fcrKChIDRs2dIkzq4YMGaL7779fefLkUcmSJTVs2DDduHEjXb13331XxYoVU548edS2bVtduHDB5fp7772nmJgYBQYGqmzZspoyZUqmfZ47d06dOnVSoUKFFBQUpPvuu08zZszIduwAzMOIBJAFQUFBOnPmjPP9ihUrFBoaqmXLlkmSbty4oSZNmqhmzZr6/vvv5e/vr9GjR6tp06baunWrAgICNG7cOM2cOVMffPCBYmJiNG7cOC1YsED//Oc/M+336aef1rp16zRp0iRVqlRJKSkp+vXXX1WsWDHNnz9fjz/+uHbv3q3Q0FAFBQVJkhITE/XRRx9p6tSpuu+++7RmzRo99dRTKlSokOrXr68jR46oTZs26tmzp5599llt3LhRAwYMyPbvJCQkRDNnzlRkZKS2bdumZ555RiEhIRo8eLCzzr59+zRv3jwtXrxYFy9eVLdu3dSjRw/NmTNHkjRnzhwNHz5cb731lqpUqaLNmzfrmWeeUXBwsOLi4tL1OWzYMO3YsUNff/21ChYsqH379unq1avZjh2AiQwALuLi4oyWLVsahmEYDofDWLZsmWG3242BAwc6rxcpUsRITU11fmb27NlGmTJlDIfD4SxLTU01goKCjG+++cYwDMOIiIgwxowZ47x+48YN495773X2ZRiGUb9+faNPnz6GYRjG7t27DUnGsmXLMoxz1apVhiTj3LlzzrJr164ZefLkMdauXetSt1u3bkaHDh0MwzCMoUOHGuXKlXO5PmTIkHRt/ZkkY8GCBZleHzt2rFG1alXn+xEjRhi5cuUyfvnlF2fZ119/bfj5+RnHjx83DMMwSpUqZXz88ccu7YwaNcqoWbOmYRiGkZKSYkgyNm/ebBiGYbRo0cLo0qVLpjEAuPMYkQAysGTJEuXNm1c3btyQw+FQx44dFR8f77xeoUIFl3URW7Zs0b59+xQSEuLSzrVr17R//35duHBBx48fV40aNZzX/P39Va1atXTTG7ckJycrV65cql+/fpbj3rdvn65cuaKHH37Ypfz69euqUqWKJGnnzp0ucUhSzZo1s9zHLZ9++qkmTZqk/fv369KlS7p586ZCQ0Nd6hQvXlz33HOPSz8Oh0O7d+9WSEiI9u/fr27duumZZ55x1rl586bCwsIy7PP555/X448/rqSkJDVu3FitWrVSrVq1sh07APOQSAAZaNiwod555x0FBAQoMjJS/v6uX5Xg4GCX95cuXVLVqlWdQ/Z/VKhQIbdiuDVVkR2XLl2SJH355Zcuf4FLv6/7MMu6devUqVMnJSQkqEmTJgoLC9PcuXM1bty4bMc6ffr0dIlNrly5MvxMs2bNdOjQIX311VdatmyZGjVqpJ49e+qNN95w/2YA3BYSCSADwcHBKl26dJbrP/jgg/r0009VuHDhdP8qvyUiIkLr169XvXr1JP3+L+9NmzbpwQcfzLB+hQoV5HA49N133yk2Njbd9VsjImlpac6ycuXKyW636/Dhw5mOZMTExDgXjt7y448//v1N/sHatWtVokQJvfTSS86yQ4cOpat3+PBhHTt2TJGRkc5+/Pz8VKZMGRUpUkSRkZE6cOCAOnXqlOW+CxUqpLi4OMXFxalu3boaNGgQiQRgIXZtACbo1KmTChYsqJYtW+r7779XSkqKVq9erd69e+uXX36RJPXp00evvfaaFi5cqF27dqlHjx5/eQZEVFSU4uLi1LVrVy1cuNDZ5rx58yRJJUqUkM1m05IlS3T69GldunRJISEhGjhwoPr166dZs2Zp//79SkpK0uTJkzVr1ixJ0nPPPae9e/dq0KBB2r17tz7++GPNnDkzW/d733336fDhw5o7d67279+vSZMmacGCBenqBQYGKi4uTlu2bNH333+v3r17q23btipatKgkKSEhQYmJiZo0aZL27Nmjbdu2acaMGRo/fnyG/Q4fPlyLFi3Svn37tH37di1ZskQxMTHZih2AuUgkABPkyZNHa9asUfHixdWmTRvFxMSoW7duunbtmnOEYsCAAfrXv/6luLg41axZUyEhIWrduvVftvvOO+/oiSeeUI8ePVS2bFk988wzunz5siTpnnvuUUJCgl588UUVKVJEvXr1kiSNGjVKw4YNU2JiomJiYtS0aVN9+eWXio6OlvT7uoX58+dr4cKFqlSpkqZOnapXX301W/f72GOPqV+/furVq5cqV66stWvXatiwYenqlS5dWm3atNEjjzyixo0bq2LFii7bO7t376733ntPM2bMUIUKFVS/fn3NnDnTGeufBQQEaOjQoapYsaLq1aunXLlyae7cudmKHYC5bEZmK70AAAD+BiMSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbSQSAADAbf8PkAtlXISM2hwAAAAASUVORK5CYII="
          },
          "metadata": {}
        }
      ]
    },
    {
      "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": 169,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "tree_cv = GridSearchCV(tree, parameters, cv=10)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "tree_cv.fit(X_train,Y_train)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 171,
      "outputs": [
        {
          "execution_count": 171,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n             param_grid={'criterion': ['gini', 'entropy'],\n                         'max_depth': [2, 4, 6, 8, 10, 12, 14, 16, 18],\n                         'max_features': ['auto', 'sqrt'],\n                         'min_samples_leaf': [1, 2, 4],\n                         'min_samples_split': [2, 5, 10],\n                         'splitter': ['best', 'random']})",
            "text/html": "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n                         &#x27;max_depth&#x27;: [2, 4, 6, 8, 10, 12, 14, 16, 18],\n                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n                         &#x27;max_depth&#x27;: [2, 4, 6, 8, 10, 12, 14, 16, 18],\n                         &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",tree_cv.best_params_)\nprint(\"accuracy :\",tree_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 172,
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'criterion': 'entropy', 'max_depth': 4, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'splitter': 'best'}\naccuracy : 0.875\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  9\n",
      "metadata": {
        "tags": []
      }
    },
    {
      "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_cv.score(X_test,Y_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 173,
      "outputs": [
        {
          "execution_count": 173,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.8888888888888888"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can plot the confusion matrix\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "tyhat = tree_cv.predict(X_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 174,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "print(\"Accuracy: %.4f\" % accuracy_score(Y_test,tyhat))\nprint(\"Jaccard score: %.4f\" % jaccard_score(Y_test,tyhat))\nprint(\"Precision: %.4f\" % precision_score(Y_test,tyhat))\nprint(\"Recall: %.4f\" % recall_score(Y_test,tyhat))\nprint(\"Avg F1-score: %.4f\" % f1_score(Y_test,tyhat, average='weighted'))",
      "metadata": {
        "trusted": true
      },
      "execution_count": 175,
      "outputs": [
        {
          "name": "stdout",
          "text": "Accuracy: 0.8889\nJaccard score: 0.8571\nPrecision: 0.8571\nRecall: 1.0000\nAvg F1-score: 0.8821\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "plot_confusion_matrix(Y_test,tyhat)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 176,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "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": 177,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "knn_cv = GridSearchCV(KNN, parameters, cv=10)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 178,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "knn_cv.fit(X_train,Y_train)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 179,
      "outputs": [
        {
          "execution_count": 179,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n             param_grid={'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                         'p': [1, 2]})",
            "text/html": "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n             param_grid={&#x27;algorithm&#x27;: [&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;],\n                         &#x27;n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                         &#x27;p&#x27;: [1, 2]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n             param_grid={&#x27;algorithm&#x27;: [&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;],\n                         &#x27;n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                         &#x27;p&#x27;: [1, 2]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\nprint(\"accuracy :\",knn_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 180,
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 10, 'p': 1}\naccuracy : 0.8482142857142858\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_cv.score(X_test,Y_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 181,
      "outputs": [
        {
          "execution_count": 181,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.8333333333333334"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can plot the confusion matrix\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "knnyhat = knn_cv.predict(X_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 182,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "print(\"Accuracy: %.4f\" % accuracy_score(Y_test,knnyhat))\nprint(\"Jaccard score: %.4f\" % jaccard_score(Y_test,knnyhat))\nprint(\"Precision: %.4f\" % precision_score(Y_test,knnyhat))\nprint(\"Recall: %.4f\" % recall_score(Y_test,knnyhat))\nprint(\"Avg F1-score: %.4f\" % f1_score(Y_test,knnyhat, average='weighted'))",
      "metadata": {
        "trusted": true
      },
      "execution_count": 183,
      "outputs": [
        {
          "name": "stdout",
          "text": "Accuracy: 0.8333\nJaccard score: 0.8000\nPrecision: 0.8000\nRecall: 1.0000\nAvg F1-score: 0.8148\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "plot_confusion_matrix(Y_test,knnyhat)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 184,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  12\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Find the method performs best:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import pandas as pd\n  \n# intialise data of lists.\ndata = {'Models':['Logistic Regression', 'SVM', 'Decision Tree', 'KNN'],\n        'Accuracy':[accuracy_score(Y_test,logyhat), accuracy_score(Y_test,svmyhat), accuracy_score(Y_test,tyhat), accuracy_score(Y_test,knnyhat)]}\n  \n# Create DataFrame\ndf = pd.DataFrame(data)\n  \n# Print the output.\ndf",
      "metadata": {
        "trusted": true
      },
      "execution_count": 207,
      "outputs": [
        {
          "execution_count": 207,
          "output_type": "execute_result",
          "data": {
            "text/plain": "                Models  Accuracy\n0  Logistic Regression  0.833333\n1                  SVM  0.833333\n2        Decision Tree  0.888889\n3                  KNN  0.833333",
            "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>Models</th>\n      <th>Accuracy</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Logistic Regression</td>\n      <td>0.833333</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>SVM</td>\n      <td>0.833333</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Decision Tree</td>\n      <td>0.888889</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>KNN</td>\n      <td>0.833333</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "# plot the barchart\nax = sns.barplot(x = 'Models', y = 'Accuracy', data = df) \nax.bar_label(ax.containers[0], fmt='%.2f')\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "**Answer: Decision Tree Classifier score the best of all the models with an accuracy of 88.88% on the unseen test dataset.**",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### **F1-score:**",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import pandas as pd\n  \n# intialise data of lists.\ndata2 = {'Models':['Logistic Regression', 'SVM', 'Decision Tree', 'KNN'],\n        'F1-score':[f1_score(Y_test,logyhat, average='weighted'), f1_score(Y_test,svmyhat, average='weighted'), f1_score(Y_test,tyhat, average='weighted'), f1_score(Y_test,knnyhat, average='weighted')]}\n  \n# Create DataFrame\ndf2 = pd.DataFrame(data2)\n  \n# Print the output.\ndf2",
      "metadata": {
        "trusted": true
      },
      "execution_count": 209,
      "outputs": [
        {
          "execution_count": 209,
          "output_type": "execute_result",
          "data": {
            "text/plain": "                Models  F1-score\n0  Logistic Regression  0.814815\n1                  SVM  0.814815\n2        Decision Tree  0.882051\n3                  KNN  0.814815",
            "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>Models</th>\n      <th>F1-score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Logistic Regression</td>\n      <td>0.814815</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>SVM</td>\n      <td>0.814815</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Decision Tree</td>\n      <td>0.882051</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>KNN</td>\n      <td>0.814815</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "ax = sns.barplot(x = 'Models', y = 'F1-score', data = df2) \nax.bar_label(ax.containers[0], fmt='%.2f')\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 214,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "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": {}
    }
  ]
}