8. SpaceX_Machine_Learning_Prediction_Part_5.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ztnAeYMp9tgm"
      },
      "source": [
        "<center>\n",
        "    <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
        "</center>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NJYMmuic9tgy"
      },
      "source": [
        "# **Space X  Falcon 9 First Stage Landing Prediction**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YtSD3pZr9tgz"
      },
      "source": [
        "## Assignment:  Machine Learning Prediction\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eD6_X6rU9tg0"
      },
      "source": [
        "Estimated time needed: **60** minutes\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "14xjr7Io9tg1"
      },
      "source": [
        "Space X advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is because Space X can reuse the first stage. Therefore if we can determine if the first stage will land, we can determine the cost of a launch. This information can be used if an alternate company wants to bid against space X for a rocket launch.   In this lab, you will create a machine learning pipeline  to predict if the first stage will land given the data from the preceding labs.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qWTBog1f9tg3"
      },
      "source": [
        "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing\\_1.gif)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JPHgG-4-9tg4"
      },
      "source": [
        "Several examples of an unsuccessful landing are shown here:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3indiKhh9tg5"
      },
      "source": [
        "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kCMwEg8Z9tg6"
      },
      "source": [
        "Most unsuccessful landings are planed. Space X; performs a controlled landing in the oceans.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YO8wDKpE9tg7"
      },
      "source": [
        "## Objectives\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8koVjV_i9tg8"
      },
      "source": [
        "Perform exploratory  Data Analysis and determine Training Labels\n",
        "\n",
        "*   create a column for the class\n",
        "*   Standardize the data\n",
        "*   Split into training data and test data\n",
        "\n",
        "\\-Find best Hyperparameter for SVM, Classification Trees and Logistic Regression\n",
        "\n",
        "*   Find the method performs best using test data\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gLMhHLTt9tg9"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6SgTvxbI9tg9"
      },
      "source": [
        "***\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E9EE9QGs9tg-"
      },
      "source": [
        "## Import Libraries and Define Auxiliary Functions\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xcXypKzp9tg-"
      },
      "source": [
        "We will import the following libraries for the lab\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3Rhiur369tg-"
      },
      "outputs": [],
      "source": [
        "# Pandas is a software library written for the Python programming language for data manipulation and analysis.\n",
        "import pandas as pd\n",
        "# NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\n",
        "import numpy as np\n",
        "# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\n",
        "import matplotlib.pyplot as plt\n",
        "#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\n",
        "import seaborn as sns\n",
        "# Preprocessing allows us to standarsize our data\n",
        "from sklearn import preprocessing\n",
        "# Allows us to split our data into training and testing data\n",
        "from sklearn.model_selection import train_test_split\n",
        "# Allows us to test parameters of classification algorithms and find the best one\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "# Logistic Regression classification algorithm\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "# Support Vector Machine classification algorithm\n",
        "from sklearn.svm import SVC\n",
        "# Decision Tree classification algorithm\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "# K Nearest Neighbors classification algorithm\n",
        "from sklearn.neighbors import KNeighborsClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CZfTjUE19thA"
      },
      "source": [
        "This function is to plot the confusion matrix.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jKvPTesQ9thB"
      },
      "outputs": [],
      "source": [
        "def plot_confusion_matrix(y,y_predict):\n",
        "    \"this function plots the confusion matrix\"\n",
        "    from sklearn.metrics import confusion_matrix\n",
        "\n",
        "    cm = confusion_matrix(y, y_predict)\n",
        "    ax= plt.subplot()\n",
        "    sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells\n",
        "    ax.set_xlabel('Predicted labels')\n",
        "    ax.set_ylabel('True labels')\n",
        "    ax.set_title('Confusion Matrix'); \n",
        "    ax.xaxis.set_ticklabels(['did not land', 'land']); ax.yaxis.set_ticklabels(['did not land', 'landed'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gNfB5Xxh9thC"
      },
      "source": [
        "## Load the dataframe\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a_K1CEBI9thD"
      },
      "source": [
        "Load the data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "SomOgSFI9thE",
        "outputId": "686f78a5-a7e0-46b1-b3c5-57ef808d28ed"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-6ee7ec70-a40e-4828-8dcf-dbc0d7927a5d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6ee7ec70-a40e-4828-8dcf-dbc0d7927a5d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6ee7ec70-a40e-4828-8dcf-dbc0d7927a5d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6ee7ec70-a40e-4828-8dcf-dbc0d7927a5d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   FlightNumber        Date BoosterVersion  ...   Longitude   Latitude Class\n",
              "0             1  2010-06-04       Falcon 9  ...  -80.577366  28.561857     0\n",
              "1             2  2012-05-22       Falcon 9  ...  -80.577366  28.561857     0\n",
              "2             3  2013-03-01       Falcon 9  ...  -80.577366  28.561857     0\n",
              "3             4  2013-09-29       Falcon 9  ... -120.610829  34.632093     0\n",
              "4             5  2013-12-03       Falcon 9  ...  -80.577366  28.561857     0\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# data = pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\")\n",
        "\n",
        "# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n",
        "\n",
        "data = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_2.csv')\n",
        "\n",
        "data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "F6NnErre9thF",
        "outputId": "29bb9440-93a2-492e-9121-d49bbb51fc38"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-55f2fc33-379c-4ba7-95f5-a71d812c44d4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Orbit_LEO</th>\n",
              "      <th>Orbit_MEO</th>\n",
              "      <th>Orbit_PO</th>\n",
              "      <th>Orbit_SO</th>\n",
              "      <th>Orbit_SSO</th>\n",
              "      <th>Orbit_VLEO</th>\n",
              "      <th>LaunchSite_CCAFS SLC 40</th>\n",
              "      <th>LaunchSite_KSC LC 39A</th>\n",
              "      <th>LaunchSite_VAFB SLC 4E</th>\n",
              "      <th>LandingPad_5e9e3032383ecb267a34e7c7</th>\n",
              "      <th>LandingPad_5e9e3032383ecb554034e7c9</th>\n",
              "      <th>LandingPad_5e9e3032383ecb6bb234e7ca</th>\n",
              "      <th>LandingPad_5e9e3032383ecb761634e7cb</th>\n",
              "      <th>LandingPad_5e9e3033383ecbb9e534e7cc</th>\n",
              "      <th>Serial_B0003</th>\n",
              "      <th>Serial_B0005</th>\n",
              "      <th>Serial_B0007</th>\n",
              "      <th>Serial_B1003</th>\n",
              "      <th>Serial_B1004</th>\n",
              "      <th>Serial_B1005</th>\n",
              "      <th>Serial_B1006</th>\n",
              "      <th>Serial_B1007</th>\n",
              "      <th>Serial_B1008</th>\n",
              "      <th>Serial_B1010</th>\n",
              "      <th>Serial_B1011</th>\n",
              "      <th>Serial_B1012</th>\n",
              "      <th>Serial_B1013</th>\n",
              "      <th>Serial_B1015</th>\n",
              "      <th>Serial_B1016</th>\n",
              "      <th>Serial_B1017</th>\n",
              "      <th>...</th>\n",
              "      <th>Serial_B1021</th>\n",
              "      <th>Serial_B1022</th>\n",
              "      <th>Serial_B1023</th>\n",
              "      <th>Serial_B1025</th>\n",
              "      <th>Serial_B1026</th>\n",
              "      <th>Serial_B1028</th>\n",
              "      <th>Serial_B1029</th>\n",
              "      <th>Serial_B1030</th>\n",
              "      <th>Serial_B1031</th>\n",
              "      <th>Serial_B1032</th>\n",
              "      <th>Serial_B1034</th>\n",
              "      <th>Serial_B1035</th>\n",
              "      <th>Serial_B1036</th>\n",
              "      <th>Serial_B1037</th>\n",
              "      <th>Serial_B1038</th>\n",
              "      <th>Serial_B1039</th>\n",
              "      <th>Serial_B1040</th>\n",
              "      <th>Serial_B1041</th>\n",
              "      <th>Serial_B1042</th>\n",
              "      <th>Serial_B1043</th>\n",
              "      <th>Serial_B1044</th>\n",
              "      <th>Serial_B1045</th>\n",
              "      <th>Serial_B1046</th>\n",
              "      <th>Serial_B1047</th>\n",
              "      <th>Serial_B1048</th>\n",
              "      <th>Serial_B1049</th>\n",
              "      <th>Serial_B1050</th>\n",
              "      <th>Serial_B1051</th>\n",
              "      <th>Serial_B1054</th>\n",
              "      <th>Serial_B1056</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>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>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>0.0</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>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>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>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>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>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>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>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",
              "      <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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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",
              "      <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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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",
              "      <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",
              "      <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",
              "      <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",
              "      <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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>0.0</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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>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>0.0</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>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>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>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>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>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>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>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>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>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>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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-55f2fc33-379c-4ba7-95f5-a71d812c44d4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-55f2fc33-379c-4ba7-95f5-a71d812c44d4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-55f2fc33-379c-4ba7-95f5-a71d812c44d4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    FlightNumber   PayloadMass  Flights  ...  Reused_True  Legs_False  Legs_True\n",
              "0            1.0   6104.959412      1.0  ...          0.0         1.0        0.0\n",
              "1            2.0    525.000000      1.0  ...          0.0         1.0        0.0\n",
              "2            3.0    677.000000      1.0  ...          0.0         1.0        0.0\n",
              "3            4.0    500.000000      1.0  ...          0.0         1.0        0.0\n",
              "4            5.0   3170.000000      1.0  ...          0.0         1.0        0.0\n",
              "..           ...           ...      ...  ...          ...         ...        ...\n",
              "85          86.0  15400.000000      2.0  ...          1.0         0.0        1.0\n",
              "86          87.0  15400.000000      3.0  ...          1.0         0.0        1.0\n",
              "87          88.0  15400.000000      6.0  ...          1.0         0.0        1.0\n",
              "88          89.0  15400.000000      3.0  ...          1.0         0.0        1.0\n",
              "89          90.0   3681.000000      1.0  ...          0.0         0.0        1.0\n",
              "\n",
              "[90 rows x 83 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# X = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_3.csv')\n",
        "\n",
        "# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n",
        "\n",
        "X = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_3.csv')\n",
        "\n",
        "X.head(100)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0JDoOD8f9thF"
      },
      "source": [
        "## TASK  1\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "po7N2hWh9thG"
      },
      "source": [
        "Create a NumPy array from the column <code>Class</code> in <code>data</code>, by applying the method <code>to_numpy()</code>  then\n",
        "assign it  to the variable <code>Y</code>,make sure the output is a  Pandas series (only one bracket df\\['name of  column']).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6gJ3f30x9thG",
        "outputId": "dde2a819-c239-4e62-c53e-019eb4f32820"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n",
              "       1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,\n",
              "       1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "Y = data['Class'].to_numpy()\n",
        "Y"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VqsUGB_j9thH"
      },
      "source": [
        "## TASK  2\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4OFb15Eh9thH"
      },
      "source": [
        "Standardize the data in <code>X</code> then reassign it to the variable  <code>X</code> using the transform provided below.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HD9kGogF9thH"
      },
      "outputs": [],
      "source": [
        "# students get this \n",
        "transform = preprocessing.StandardScaler()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Ot7ztYD9thI",
        "outputId": "1b659516-b9b6-4fa4-eae0-af93c755b4e1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.71291154e+00, -1.94814463e-16, -6.53912840e-01, ...,\n",
              "        -8.35531692e-01,  1.93309133e+00, -1.93309133e+00],\n",
              "       [-1.67441914e+00, -1.19523159e+00, -6.53912840e-01, ...,\n",
              "        -8.35531692e-01,  1.93309133e+00, -1.93309133e+00],\n",
              "       [-1.63592675e+00, -1.16267307e+00, -6.53912840e-01, ...,\n",
              "        -8.35531692e-01,  1.93309133e+00, -1.93309133e+00],\n",
              "       ...,\n",
              "       [ 1.63592675e+00,  1.99100483e+00,  3.49060516e+00, ...,\n",
              "         1.19684269e+00, -5.17306132e-01,  5.17306132e-01],\n",
              "       [ 1.67441914e+00,  1.99100483e+00,  1.00389436e+00, ...,\n",
              "         1.19684269e+00, -5.17306132e-01,  5.17306132e-01],\n",
              "       [ 1.71291154e+00, -5.19213966e-01, -6.53912840e-01, ...,\n",
              "        -8.35531692e-01, -5.17306132e-01,  5.17306132e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "X = transform.fit_transform(X)\n",
        "X"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z3yCnyVy9thI"
      },
      "source": [
        "We split the data into training and testing data using the  function  <code>train_test_split</code>.   The training data is divided into validation data, a second set used for training  data; then the models are trained and hyperparameters are selected using the function <code>GridSearchCV</code>.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "27wh7kJK9thJ"
      },
      "source": [
        "## TASK  3\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8DcADMbo9thJ"
      },
      "source": [
        "Use the function train_test_split to split the data X and Y into training and test data. Set the parameter test_size to  0.2 and random_state to 2. The training data and test data should be assigned to the following labels.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_8Xwsgiw9thJ"
      },
      "source": [
        "<code>X_train, X_test, Y_train, Y_test</code>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YchqRDSj9thK",
        "outputId": "b48817b5-e205-459a-d6e3-9064b45ee5f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set: (72, 83) (72,)\n",
            "Test set: (18, 83) (18,)\n"
          ]
        }
      ],
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)\n",
        "print ('Train set:', X_train.shape,  Y_train.shape)\n",
        "print ('Test set:', X_test.shape,  Y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6EskVVic9thK"
      },
      "source": [
        "we can see we only have 18 test samples.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EiTA2ZaI9thL",
        "outputId": "3a6a12e9-5d9f-44e0-c017-34ab3d936802"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(18,)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "Y_test.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w7UcBh6B9thL"
      },
      "source": [
        "## TASK  4\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "smOt6YZ39thL"
      },
      "source": [
        "Create a logistic regression object  then create a  GridSearchCV object  <code>logreg_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ON5HC9GD9thM"
      },
      "outputs": [],
      "source": [
        "parameters ={'C':[0.01,0.1,1],\n",
        "             'penalty':['l2'],\n",
        "             'solver':['lbfgs']}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VM_LviLC9thM",
        "outputId": "8f657b15-35c4-4b12-a121-e7f59d7c81db"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "parameters ={\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge\n",
        "lr=LogisticRegression()\n",
        "logreg_cv = GridSearchCV(lr, param_grid=parameters,scoring='accuracy', cv=10)\n",
        "logreg_cv.fit(X_train, Y_train)\n",
        "logreg_cv.best_params_\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nsH1J_r89thN"
      },
      "source": [
        "We output the <code>GridSearchCV</code> object for logistic regression. We display the best parameters using the data attribute <code>best_params\\_</code> and the accuracy on the validation data using the data attribute <code>best_score\\_</code>.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LsX1Jy1s9thN",
        "outputId": "870e0855-71d6-46fe-8e82-61ba14ffbd3b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tuned hpyerparameters :(best parameters)  {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
            "accuracy : 0.8464285714285713\n"
          ]
        }
      ],
      "source": [
        "print(\"tuned hpyerparameters :(best parameters) \",logreg_cv.best_params_)\n",
        "print(\"accuracy :\",logreg_cv.best_score_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gSGWOkwN9thN"
      },
      "source": [
        "## TASK  5\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_POHJ7qp9thO"
      },
      "source": [
        "Calculate the accuracy on the test data using the method <code>score</code>:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xnnZ9qCR9thO",
        "outputId": "6e4446d3-0268-4284-8ee9-f97a200d00a8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8333333333333334"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "logreg_cv.score(X_test, Y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R0mPKvO99thO"
      },
      "source": [
        "Lets look at the confusion matrix:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "FZP3eiDS9thP",
        "outputId": "684183b2-4993-4d8a-d850-80738f32c8f1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "yhat=logreg_cv.predict(X_test)\n",
        "plot_confusion_matrix(Y_test,yhat)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "os_6l0-O9thP"
      },
      "source": [
        "Examining the confusion matrix, we see that logistic regression can distinguish between the different classes.  We see that the major problem is false positives.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "645F9AYH9thP"
      },
      "source": [
        "## TASK  6\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4ovX9HgL9thQ"
      },
      "source": [
        "Create a support vector machine object then  create a  <code>GridSearchCV</code> object  <code>svm_cv</code> with cv - 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b0rX8yDc9thQ"
      },
      "outputs": [],
      "source": [
        "parameters = {'kernel':('linear', 'rbf','poly','rbf', 'sigmoid'),\n",
        "              'C': np.logspace(-3, 3, 5),\n",
        "              'gamma':np.logspace(-3, 3, 5)}\n",
        "svm = SVC()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iRiHXrjn9thQ",
        "outputId": "42b67a64-e040-44a4-ae12-e69752c07b7f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'C': 1.0, 'gamma': 0.03162277660168379, 'kernel': 'sigmoid'}"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "svm_cv = GridSearchCV(svm, param_grid=parameters,scoring='accuracy', cv=10)\n",
        "svm_cv.fit(X_train, Y_train)\n",
        "svm_cv.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bys3lRi59thR",
        "outputId": "8a48cd37-3472-4cc3-c193-7f430cca6a39"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tuned hpyerparameters :(best parameters)  {'C': 1.0, 'gamma': 0.03162277660168379, 'kernel': 'sigmoid'}\n",
            "accuracy : 0.8482142857142856\n"
          ]
        }
      ],
      "source": [
        "print(\"tuned hpyerparameters :(best parameters) \",svm_cv.best_params_)\n",
        "print(\"accuracy :\",svm_cv.best_score_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2UBSZLWC9thR"
      },
      "source": [
        "## TASK  7\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vRtwkCM99thR"
      },
      "source": [
        "Calculate the accuracy on the test data using the method <code>score</code>:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zyf-IXbH9thT",
        "outputId": "a410ed5c-e843-4650-cca4-0726db16d598"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8333333333333334"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "svm_cv.score(X_test, Y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CAmPD7wu9thT"
      },
      "source": [
        "We can plot the confusion matrix\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "0kTRoviw9thU",
        "outputId": "b0993e24-610c-40d7-89d4-70d3997bb8e6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "yhat=svm_cv.predict(X_test)\n",
        "plot_confusion_matrix(Y_test,yhat)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pGKuMi7y9thU"
      },
      "source": [
        "## TASK  8\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QKdhjmfm9thV"
      },
      "source": [
        "Create a decision tree classifier object then  create a  <code>GridSearchCV</code> object  <code>tree_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xF_C4UOg9thV"
      },
      "outputs": [],
      "source": [
        "parameters = {'criterion': ['gini', 'entropy'],\n",
        "     'splitter': ['best', 'random'],\n",
        "     'max_depth': [2*n for n in range(1,10)],\n",
        "     'max_features': ['auto', 'sqrt'],\n",
        "     'min_samples_leaf': [1, 2, 4],\n",
        "     'min_samples_split': [2, 5, 10]}\n",
        "\n",
        "tree = DecisionTreeClassifier()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OJIdFv-o9thW",
        "outputId": "99b287a5-8d4e-4eae-a258-b3d682d505af"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'criterion': 'gini',\n",
              " 'max_depth': 4,\n",
              " 'max_features': 'sqrt',\n",
              " 'min_samples_leaf': 1,\n",
              " 'min_samples_split': 2,\n",
              " 'splitter': 'random'}"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "tree_cv = GridSearchCV(tree, param_grid=parameters,scoring='accuracy', cv=10)\n",
        "tree_cv.fit(X_train, Y_train)\n",
        "tree_cv.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SfsY_vGo9thW",
        "outputId": "c6ac3b03-80b8-40ab-f28d-edf8851247f1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tuned hpyerparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 4, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'splitter': 'random'}\n",
            "accuracy : 0.9160714285714286\n"
          ]
        }
      ],
      "source": [
        "print(\"tuned hpyerparameters :(best parameters) \",tree_cv.best_params_)\n",
        "print(\"accuracy :\",tree_cv.best_score_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NTmNAVsf9thW"
      },
      "source": [
        "## TASK  9\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EdrC2Xjs9thX"
      },
      "source": [
        "Calculate the accuracy of tree_cv on the test data using the method <code>score</code>:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8hG0T-q9thX",
        "outputId": "4e66ba67-59f6-467d-cbc2-8cf276177e99"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7777777777777778"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "tree_cv.score(X_test, Y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Gp8fpMe9thY"
      },
      "source": [
        "We can plot the confusion matrix\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "E2Qs_17L9thY",
        "outputId": "7fd0605d-b290-49fd-9c77-4671d4c016ff"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "yhat = svm_cv.predict(X_test)\n",
        "plot_confusion_matrix(Y_test,yhat)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IHF5T5m59thY"
      },
      "source": [
        "## TASK  10\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GbjI-nK79thZ"
      },
      "source": [
        "Create a k nearest neighbors object then  create a  <code>GridSearchCV</code> object  <code>knn_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pzPD_pa79thZ"
      },
      "outputs": [],
      "source": [
        "parameters = {'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
        "              'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
        "              'p': [1,2]}\n",
        "\n",
        "KNN = KNeighborsClassifier()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c3PaEx_89tha",
        "outputId": "e8412548-a30c-4c63-de81-ef7a607466ad"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'algorithm': 'auto', 'n_neighbors': 10, 'p': 1}"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "\n",
        "knn_cv = GridSearchCV(KNN, param_grid=parameters,scoring='accuracy', cv=10)\n",
        "knn_cv.fit(X_train, Y_train)\n",
        "knn_cv.best_params_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dNzTvdTt9tha",
        "outputId": "89abdeb7-8a0e-4d07-e36c-ca8b2cf7af4b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 10, 'p': 1}\n",
            "accuracy : 0.8482142857142858\n"
          ]
        }
      ],
      "source": [
        "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\n",
        "print(\"accuracy :\",knn_cv.best_score_)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "scZCXCey9tha"
      },
      "source": [
        "## TASK  11\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gzBCRVgp9thb"
      },
      "source": [
        "Calculate the accuracy of tree_cv on the test data using the method <code>score</code>:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YBB5IANr9thb",
        "outputId": "3f996916-f2e4-4d6a-c83c-5a69c5abcd91"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8333333333333334"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "knn_cv.score(X_test, Y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GzvZ6tua9thb"
      },
      "source": [
        "We can plot the confusion matrix\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "QyXNdTbP9thc",
        "outputId": "2a5068eb-8d13-4046-9a8f-cc1d778aadee"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "yhat = knn_cv.predict(X_test)\n",
        "plot_confusion_matrix(Y_test,yhat)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZNmr4uhJ9thc"
      },
      "source": [
        "## TASK  12\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xfDmR9a69thc"
      },
      "source": [
        "Find the method performs best:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vApUJXb69thd"
      },
      "outputs": [],
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AtBJJqGl9thd"
      },
      "source": [
        "## Authors\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J_CWpsdU9thd"
      },
      "source": [
        "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FRFw8q3h9thd"
      },
      "source": [
        "## Change Log\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ti9_p9qW9the"
      },
      "source": [
        "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description      |\n",
        "| ----------------- | ------- | ------------- | ----------------------- |\n",
        "| 2021-08-31        | 1.1     | Lakshmi Holla | Modified markdown       |\n",
        "| 2020-09-20        | 1.0     | Joseph        | Modified Multiple Areas |\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6B_aAJKh9the"
      },
      "source": [
        "Copyright © 2020 IBM Corporation. All rights reserved.\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    },
    "colab": {
      "name": "SpaceX_Machine Learning Prediction_Part_5.ipynb",
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}