Lab8-SpaceX_Machine Learning Prediction_Part_5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Space X  Falcon 9 First Stage Landing Prediction**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assignment:  Machine Learning Prediction\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **60** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Space X advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is because Space X can reuse the first stage. Therefore if we can determine if the first stage will land, we can determine the cost of a launch. This information can be used if an alternate company wants to bid against space X for a rocket launch.   In this lab, you will create a machine learning pipeline  to predict if the first stage will land given the data from the preceding labs.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing\\_1.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several examples of an unsuccessful landing are shown here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most unsuccessful landings are planed. Space X; performs a controlled landing in the oceans.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform exploratory  Data Analysis and determine Training Labels\n",
    "\n",
    "*   create a column for the class\n",
    "*   Standardize the data\n",
    "*   Split into training data and test data\n",
    "\n",
    "\\-Find best Hyperparameter for SVM, Classification Trees and Logistic Regression\n",
    "\n",
    "*   Find the method performs best using test data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries and Define Auxiliary Functions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will import the following libraries for the lab\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pandas is a software library written for the Python programming language for data manipulation and analysis.\n",
    "import pandas as pd\n",
    "# NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\n",
    "import numpy as np\n",
    "# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\n",
    "import matplotlib.pyplot as plt\n",
    "#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\n",
    "import seaborn as sns\n",
    "# Preprocessing allows us to standarsize our data\n",
    "from sklearn import preprocessing\n",
    "# Allows us to split our data into training and testing data\n",
    "from sklearn.model_selection import train_test_split\n",
    "# Allows us to test parameters of classification algorithms and find the best one\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "# Logistic Regression classification algorithm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "# Support Vector Machine classification algorithm\n",
    "from sklearn.svm import SVC\n",
    "# Decision Tree classification algorithm\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "# K Nearest Neighbors classification algorithm\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function is to plot the confusion matrix.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y,y_predict):\n",
    "    \"this function plots the confusion matrix\"\n",
    "    from sklearn.metrics import confusion_matrix\n",
    "\n",
    "    cm = confusion_matrix(y, y_predict)\n",
    "    ax= plt.subplot()\n",
    "    sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells\n",
    "    ax.set_xlabel('Predicted labels')\n",
    "    ax.set_ylabel('True labels')\n",
    "    ax.set_title('Confusion Matrix'); \n",
    "    ax.xaxis.set_ticklabels(['did not land', 'land']); ax.yaxis.set_ticklabels(['did not land', 'landed'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the dataframe\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "  </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",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
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       "    <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",
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       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
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       "    <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",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>B0007</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
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       "    <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",
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       "    <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",
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       "      <td>B1004</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
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      "text/plain": [
       "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n",
       "0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n",
       "1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n",
       "2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n",
       "3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n",
       "4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n",
       "\n",
       "       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n",
       "0    None None        1     False   False  False        NaN    1.0   \n",
       "1    None None        1     False   False  False        NaN    1.0   \n",
       "2    None None        1     False   False  False        NaN    1.0   \n",
       "3  False Ocean        1     False   False  False        NaN    1.0   \n",
       "4    None None        1     False   False  False        NaN    1.0   \n",
       "\n",
       "   ReusedCount Serial   Longitude   Latitude  Class  \n",
       "0            0  B0003  -80.577366  28.561857      0  \n",
       "1            0  B0005  -80.577366  28.561857      0  \n",
       "2            0  B0007  -80.577366  28.561857      0  \n",
       "3            0  B1003 -120.610829  34.632093      0  \n",
       "4            0  B1004  -80.577366  28.561857      0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Orbit_ES-L1</th>\n",
       "      <th>Orbit_GEO</th>\n",
       "      <th>Orbit_GTO</th>\n",
       "      <th>Orbit_HEO</th>\n",
       "      <th>Orbit_ISS</th>\n",
       "      <th>...</th>\n",
       "      <th>Serial_B1058</th>\n",
       "      <th>Serial_B1059</th>\n",
       "      <th>Serial_B1060</th>\n",
       "      <th>Serial_B1062</th>\n",
       "      <th>GridFins_False</th>\n",
       "      <th>GridFins_True</th>\n",
       "      <th>Reused_False</th>\n",
       "      <th>Reused_True</th>\n",
       "      <th>Legs_False</th>\n",
       "      <th>Legs_True</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3325.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "      <td>2296.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "      <td>1316.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "      <td>4535.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>4428.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.0</td>\n",
       "      <td>2216.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.0</td>\n",
       "      <td>2395.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.0</td>\n",
       "      <td>570.000000</td>\n",
       "      <td>1.0</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.0</td>\n",
       "      <td>1898.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15.0</td>\n",
       "      <td>4707.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16.0</td>\n",
       "      <td>2477.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17.0</td>\n",
       "      <td>2034.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.0</td>\n",
       "      <td>553.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19.0</td>\n",
       "      <td>5271.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20.0</td>\n",
       "      <td>3136.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</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>...</td>\n",
       "      <td>0.0</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>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21.0</td>\n",
       "      <td>4696.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22.0</td>\n",
       "      <td>3100.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23.0</td>\n",
       "      <td>2257.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</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>...</td>\n",
       "      <td>0.0</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>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24.0</td>\n",
       "      <td>4600.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25.0</td>\n",
       "      <td>5500.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26.0</td>\n",
       "      <td>9600.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</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>...</td>\n",
       "      <td>0.0</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>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27.0</td>\n",
       "      <td>2490.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</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>...</td>\n",
       "      <td>0.0</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>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28.0</td>\n",
       "      <td>5600.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29.0</td>\n",
       "      <td>5300.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</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>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30.0</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</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>...</td>\n",
       "      <td>0.0</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>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>61.0</td>\n",
       "      <td>4400.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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>61</th>\n",
       "      <td>62.0</td>\n",
       "      <td>9600.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>63.0</td>\n",
       "      <td>12259.000000</td>\n",
       "      <td>1.0</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>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>64.0</td>\n",
       "      <td>2482.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>65.0</td>\n",
       "      <td>13620.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>66.0</td>\n",
       "      <td>1425.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>67.0</td>\n",
       "      <td>2227.700000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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>67</th>\n",
       "      <td>68.0</td>\n",
       "      <td>6500.000000</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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>69.0</td>\n",
       "      <td>15600.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>70.0</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>71.0</td>\n",
       "      <td>6800.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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>71</th>\n",
       "      <td>72.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>73.0</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>74.0</td>\n",
       "      <td>15600.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>76.0</td>\n",
       "      <td>1977.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77.0</td>\n",
       "      <td>15600.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79.0</td>\n",
       "      <td>9525.000000</td>\n",
       "      <td>1.0</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>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>80.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>81.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</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>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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>82.0</td>\n",
       "      <td>3880.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>83.0</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</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>...</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>83</th>\n",
       "      <td>84.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>85.0</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</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>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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>86.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>87.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>88.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>90.0</td>\n",
       "      <td>3681.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    FlightNumber   PayloadMass  Flights  Block  ReusedCount  Orbit_ES-L1  \\\n",
       "0            1.0   6104.959412      1.0    1.0          0.0          0.0   \n",
       "1            2.0    525.000000      1.0    1.0          0.0          0.0   \n",
       "2            3.0    677.000000      1.0    1.0          0.0          0.0   \n",
       "3            4.0    500.000000      1.0    1.0          0.0          0.0   \n",
       "4            5.0   3170.000000      1.0    1.0          0.0          0.0   \n",
       "5            6.0   3325.000000      1.0    1.0          0.0          0.0   \n",
       "6            7.0   2296.000000      1.0    1.0          0.0          0.0   \n",
       "7            8.0   1316.000000      1.0    1.0          0.0          0.0   \n",
       "8            9.0   4535.000000      1.0    1.0          0.0          0.0   \n",
       "9           10.0   4428.000000      1.0    1.0          0.0          0.0   \n",
       "10          11.0   2216.000000      1.0    1.0          0.0          0.0   \n",
       "11          12.0   2395.000000      1.0    1.0          0.0          0.0   \n",
       "12          13.0    570.000000      1.0    1.0          0.0          1.0   \n",
       "13          14.0   1898.000000      1.0    1.0          0.0          0.0   \n",
       "14          15.0   4707.000000      1.0    1.0          0.0          0.0   \n",
       "15          16.0   2477.000000      1.0    1.0          0.0          0.0   \n",
       "16          17.0   2034.000000      1.0    1.0          0.0          0.0   \n",
       "17          18.0    553.000000      1.0    1.0          0.0          0.0   \n",
       "18          19.0   5271.000000      1.0    1.0          0.0          0.0   \n",
       "19          20.0   3136.000000      1.0    2.0          1.0          0.0   \n",
       "20          21.0   4696.000000      1.0    2.0          0.0          0.0   \n",
       "21          22.0   3100.000000      1.0    2.0          1.0          0.0   \n",
       "22          23.0   2257.000000      1.0    2.0          1.0          0.0   \n",
       "23          24.0   4600.000000      1.0    2.0          0.0          0.0   \n",
       "24          25.0   5500.000000      1.0    3.0          0.0          0.0   \n",
       "25          26.0   9600.000000      1.0    3.0          1.0          0.0   \n",
       "26          27.0   2490.000000      1.0    3.0          1.0          0.0   \n",
       "27          28.0   5600.000000      1.0    3.0          0.0          0.0   \n",
       "28          29.0   5300.000000      2.0    2.0          1.0          0.0   \n",
       "29          30.0   6104.959412      1.0    3.0          1.0          0.0   \n",
       "..           ...           ...      ...    ...          ...          ...   \n",
       "60          61.0   4400.000000      1.0    5.0          0.0          0.0   \n",
       "61          62.0   9600.000000      2.0    5.0          5.0          0.0   \n",
       "62          63.0  12259.000000      1.0    5.0          5.0          0.0   \n",
       "63          64.0   2482.000000      1.0    5.0          3.0          0.0   \n",
       "64          65.0  13620.000000      3.0    5.0          5.0          0.0   \n",
       "65          66.0   1425.000000      2.0    5.0          5.0          0.0   \n",
       "66          67.0   2227.700000      2.0    5.0          3.0          0.0   \n",
       "67          68.0   6500.000000      3.0    5.0          2.0          0.0   \n",
       "68          69.0  15600.000000      4.0    5.0          4.0          0.0   \n",
       "69          70.0   5000.000000      1.0    5.0          3.0          0.0   \n",
       "70          71.0   6800.000000      3.0    5.0          3.0          0.0   \n",
       "71          72.0  15400.000000      4.0    5.0          5.0          0.0   \n",
       "72          73.0   6104.959412      4.0    5.0          3.0          0.0   \n",
       "73          74.0  15600.000000      3.0    5.0          5.0          0.0   \n",
       "74          75.0  15400.000000      4.0    5.0          3.0          0.0   \n",
       "75          76.0   1977.000000      2.0    5.0          3.0          0.0   \n",
       "76          77.0  15600.000000      5.0    5.0          4.0          0.0   \n",
       "77          78.0  15400.000000      4.0    5.0          5.0          0.0   \n",
       "78          79.0   9525.000000      1.0    5.0          2.0          0.0   \n",
       "79          80.0  15400.000000      5.0    5.0          5.0          0.0   \n",
       "80          81.0  15400.000000      3.0    5.0          3.0          0.0   \n",
       "81          82.0   3880.000000      1.0    5.0          2.0          0.0   \n",
       "82          83.0   6104.959412      2.0    5.0          2.0          0.0   \n",
       "83          84.0  15400.000000      6.0    5.0          5.0          0.0   \n",
       "84          85.0   1600.000000      4.0    5.0          3.0          0.0   \n",
       "85          86.0  15400.000000      2.0    5.0          2.0          0.0   \n",
       "86          87.0  15400.000000      3.0    5.0          2.0          0.0   \n",
       "87          88.0  15400.000000      6.0    5.0          5.0          0.0   \n",
       "88          89.0  15400.000000      3.0    5.0          2.0          0.0   \n",
       "89          90.0   3681.000000      1.0    5.0          0.0          0.0   \n",
       "\n",
       "    Orbit_GEO  Orbit_GTO  Orbit_HEO  Orbit_ISS    ...      Serial_B1058  \\\n",
       "0         0.0        0.0        0.0        0.0    ...               0.0   \n",
       "1         0.0        0.0        0.0        0.0    ...               0.0   \n",
       "2         0.0        0.0        0.0        1.0    ...               0.0   \n",
       "3         0.0        0.0        0.0        0.0    ...               0.0   \n",
       "4         0.0        1.0        0.0        0.0    ...               0.0   \n",
       "5         0.0        1.0        0.0        0.0    ...               0.0   \n",
       "6         0.0        0.0        0.0        1.0    ...               0.0   \n",
       "7         0.0        0.0        0.0        0.0    ...               0.0   \n",
       "8         0.0        1.0        0.0        0.0    ...               0.0   \n",
       "9         0.0        1.0        0.0        0.0    ...               0.0   \n",
       "10        0.0        0.0        0.0        1.0    ...               0.0   \n",
       "11        0.0        0.0        0.0        1.0    ...               0.0   \n",
       "12        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "13        0.0        0.0        0.0        1.0    ...               0.0   \n",
       "14        0.0        1.0        0.0        0.0    ...               0.0   \n",
       "15        0.0        0.0        0.0        1.0    ...               0.0   \n",
       "16        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "17        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "18        0.0        1.0        0.0        0.0    ...               0.0   \n",
       "19        0.0        0.0        0.0        1.0    ...               0.0   \n",
       "20        0.0        1.0        0.0        0.0    ...               0.0   \n",
       "21        0.0        1.0        0.0        0.0    ...               0.0   \n",
       "22        0.0        0.0        0.0        1.0    ...               0.0   \n",
       "23        0.0        1.0        0.0        0.0    ...               0.0   \n",
       "24        0.0        1.0        0.0        0.0    ...               0.0   \n",
       "25        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "26        0.0        0.0        0.0        1.0    ...               0.0   \n",
       "27        0.0        1.0        0.0        0.0    ...               0.0   \n",
       "28        0.0        1.0        0.0        0.0    ...               0.0   \n",
       "29        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "..        ...        ...        ...        ...    ...               ...   \n",
       "60        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "61        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "62        0.0        0.0        0.0        1.0    ...               0.0   \n",
       "63        0.0        0.0        0.0        1.0    ...               0.0   \n",
       "64        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "65        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "66        0.0        0.0        0.0        1.0    ...               0.0   \n",
       "67        0.0        1.0        0.0        0.0    ...               0.0   \n",
       "68        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "69        0.0        0.0        0.0        1.0    ...               0.0   \n",
       "70        0.0        1.0        0.0        0.0    ...               0.0   \n",
       "71        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "72        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "73        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "74        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "75        0.0        0.0        0.0        1.0    ...               0.0   \n",
       "76        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "77        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "78        0.0        0.0        0.0        1.0    ...               1.0   \n",
       "79        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "80        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "81        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "82        1.0        0.0        0.0        0.0    ...               1.0   \n",
       "83        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "84        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "85        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "86        0.0        0.0        0.0        0.0    ...               1.0   \n",
       "87        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "88        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "89        0.0        0.0        0.0        0.0    ...               0.0   \n",
       "\n",
       "    Serial_B1059  Serial_B1060  Serial_B1062  GridFins_False  GridFins_True  \\\n",
       "0            0.0           0.0           0.0             1.0            0.0   \n",
       "1            0.0           0.0           0.0             1.0            0.0   \n",
       "2            0.0           0.0           0.0             1.0            0.0   \n",
       "3            0.0           0.0           0.0             1.0            0.0   \n",
       "4            0.0           0.0           0.0             1.0            0.0   \n",
       "5            0.0           0.0           0.0             1.0            0.0   \n",
       "6            0.0           0.0           0.0             1.0            0.0   \n",
       "7            0.0           0.0           0.0             1.0            0.0   \n",
       "8            0.0           0.0           0.0             1.0            0.0   \n",
       "9            0.0           0.0           0.0             1.0            0.0   \n",
       "10           0.0           0.0           0.0             1.0            0.0   \n",
       "11           0.0           0.0           0.0             0.0            1.0   \n",
       "12           0.0           0.0           0.0             0.0            1.0   \n",
       "13           0.0           0.0           0.0             0.0            1.0   \n",
       "14           0.0           0.0           0.0             1.0            0.0   \n",
       "15           0.0           0.0           0.0             0.0            1.0   \n",
       "16           0.0           0.0           0.0             0.0            1.0   \n",
       "17           0.0           0.0           0.0             0.0            1.0   \n",
       "18           0.0           0.0           0.0             0.0            1.0   \n",
       "19           0.0           0.0           0.0             0.0            1.0   \n",
       "20           0.0           0.0           0.0             0.0            1.0   \n",
       "21           0.0           0.0           0.0             0.0            1.0   \n",
       "22           0.0           0.0           0.0             0.0            1.0   \n",
       "23           0.0           0.0           0.0             0.0            1.0   \n",
       "24           0.0           0.0           0.0             0.0            1.0   \n",
       "25           0.0           0.0           0.0             0.0            1.0   \n",
       "26           0.0           0.0           0.0             0.0            1.0   \n",
       "27           0.0           0.0           0.0             1.0            0.0   \n",
       "28           0.0           0.0           0.0             0.0            1.0   \n",
       "29           0.0           0.0           0.0             0.0            1.0   \n",
       "..           ...           ...           ...             ...            ...   \n",
       "60           0.0           0.0           0.0             1.0            0.0   \n",
       "61           0.0           0.0           0.0             0.0            1.0   \n",
       "62           0.0           0.0           0.0             0.0            1.0   \n",
       "63           0.0           0.0           0.0             0.0            1.0   \n",
       "64           0.0           0.0           0.0             0.0            1.0   \n",
       "65           0.0           0.0           0.0             0.0            1.0   \n",
       "66           0.0           0.0           0.0             0.0            1.0   \n",
       "67           0.0           0.0           0.0             1.0            0.0   \n",
       "68           0.0           0.0           0.0             0.0            1.0   \n",
       "69           1.0           0.0           0.0             0.0            1.0   \n",
       "70           0.0           0.0           0.0             0.0            1.0   \n",
       "71           0.0           0.0           0.0             0.0            1.0   \n",
       "72           0.0           0.0           0.0             1.0            0.0   \n",
       "73           0.0           0.0           0.0             0.0            1.0   \n",
       "74           0.0           0.0           0.0             0.0            1.0   \n",
       "75           1.0           0.0           0.0             0.0            1.0   \n",
       "76           0.0           0.0           0.0             0.0            1.0   \n",
       "77           0.0           0.0           0.0             0.0            1.0   \n",
       "78           0.0           0.0           0.0             0.0            1.0   \n",
       "79           0.0           0.0           0.0             0.0            1.0   \n",
       "80           1.0           0.0           0.0             0.0            1.0   \n",
       "81           0.0           1.0           0.0             0.0            1.0   \n",
       "82           0.0           0.0           0.0             0.0            1.0   \n",
       "83           0.0           0.0           0.0             0.0            1.0   \n",
       "84           1.0           0.0           0.0             0.0            1.0   \n",
       "85           0.0           1.0           0.0             0.0            1.0   \n",
       "86           0.0           0.0           0.0             0.0            1.0   \n",
       "87           0.0           0.0           0.0             0.0            1.0   \n",
       "88           0.0           1.0           0.0             0.0            1.0   \n",
       "89           0.0           0.0           1.0             0.0            1.0   \n",
       "\n",
       "    Reused_False  Reused_True  Legs_False  Legs_True  \n",
       "0            1.0          0.0         1.0        0.0  \n",
       "1            1.0          0.0         1.0        0.0  \n",
       "2            1.0          0.0         1.0        0.0  \n",
       "3            1.0          0.0         1.0        0.0  \n",
       "4            1.0          0.0         1.0        0.0  \n",
       "5            1.0          0.0         1.0        0.0  \n",
       "6            1.0          0.0         0.0        1.0  \n",
       "7            1.0          0.0         0.0        1.0  \n",
       "8            1.0          0.0         1.0        0.0  \n",
       "9            1.0          0.0         1.0        0.0  \n",
       "10           1.0          0.0         1.0        0.0  \n",
       "11           1.0          0.0         0.0        1.0  \n",
       "12           1.0          0.0         0.0        1.0  \n",
       "13           1.0          0.0         0.0        1.0  \n",
       "14           1.0          0.0         1.0        0.0  \n",
       "15           1.0          0.0         0.0        1.0  \n",
       "16           1.0          0.0         0.0        1.0  \n",
       "17           1.0          0.0         0.0        1.0  \n",
       "18           1.0          0.0         0.0        1.0  \n",
       "19           1.0          0.0         0.0        1.0  \n",
       "20           1.0          0.0         0.0        1.0  \n",
       "21           1.0          0.0         0.0        1.0  \n",
       "22           1.0          0.0         0.0        1.0  \n",
       "23           1.0          0.0         0.0        1.0  \n",
       "24           1.0          0.0         0.0        1.0  \n",
       "25           1.0          0.0         0.0        1.0  \n",
       "26           1.0          0.0         0.0        1.0  \n",
       "27           1.0          0.0         1.0        0.0  \n",
       "28           0.0          1.0         0.0        1.0  \n",
       "29           1.0          0.0         0.0        1.0  \n",
       "..           ...          ...         ...        ...  \n",
       "60           1.0          0.0         1.0        0.0  \n",
       "61           0.0          1.0         0.0        1.0  \n",
       "62           1.0          0.0         0.0        1.0  \n",
       "63           1.0          0.0         0.0        1.0  \n",
       "64           0.0          1.0         0.0        1.0  \n",
       "65           0.0          1.0         0.0        1.0  \n",
       "66           0.0          1.0         0.0        1.0  \n",
       "67           0.0          1.0         1.0        0.0  \n",
       "68           0.0          1.0         0.0        1.0  \n",
       "69           1.0          0.0         0.0        1.0  \n",
       "70           0.0          1.0         0.0        1.0  \n",
       "71           0.0          1.0         0.0        1.0  \n",
       "72           0.0          1.0         1.0        0.0  \n",
       "73           0.0          1.0         0.0        1.0  \n",
       "74           0.0          1.0         0.0        1.0  \n",
       "75           0.0          1.0         0.0        1.0  \n",
       "76           0.0          1.0         0.0        1.0  \n",
       "77           0.0          1.0         0.0        1.0  \n",
       "78           1.0          0.0         0.0        1.0  \n",
       "79           0.0          1.0         0.0        1.0  \n",
       "80           0.0          1.0         0.0        1.0  \n",
       "81           1.0          0.0         0.0        1.0  \n",
       "82           0.0          1.0         0.0        1.0  \n",
       "83           0.0          1.0         0.0        1.0  \n",
       "84           0.0          1.0         0.0        1.0  \n",
       "85           0.0          1.0         0.0        1.0  \n",
       "86           0.0          1.0         0.0        1.0  \n",
       "87           0.0          1.0         0.0        1.0  \n",
       "88           0.0          1.0         0.0        1.0  \n",
       "89           1.0          0.0         0.0        1.0  \n",
       "\n",
       "[90 rows x 83 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": {},
   "source": [
    "## TASK  1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a NumPy array from the column <code>Class</code> in <code>data</code>, by applying the method <code>to_numpy()</code>  then\n",
    "assign it  to the variable <code>Y</code>,make sure the output is a  Pandas series (only one bracket df\\['name of  column']).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data['Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.23.4\n"
     ]
    }
   ],
   "source": [
    "print(pd.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = data['Class']\n",
    "type(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = data['Class'].values\n",
    "type(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(90,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standardize the data in <code>X</code> then reassign it to the variable  <code>X</code> using the transform provided below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# students get this \n",
    "transform = preprocessing.StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "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]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = transform.fit(X).transform(X)\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We split the data into training and testing data using the  function  <code>train_test_split</code>.   The training data is divided into validation data, a second set used for training  data; then the models are trained and hyperparameters are selected using the function <code>GridSearchCV</code>.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  3\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the function train_test_split to split the data X and Y into training and test data. Set the parameter test_size to  0.2 and random_state to 2. The training data and test data should be assigned to the following labels.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<code>X_train, X_test, Y_train, Y_test</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can see we only have 18 test samples.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18,)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train (72, 83) \n",
      "X_test (18, 83) \n",
      "Y_train (72,) \n",
      "Y_test (18,)\n"
     ]
    }
   ],
   "source": [
    "print('X_train', X_train.shape, '\\nX_test', X_test.shape, '\\nY_train', Y_train.shape, '\\nY_test', Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  4\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a logistic regression object  then create a  GridSearchCV object  <code>logreg_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters ={'C':[0.01,0.1,1],\n",
    "             'penalty':['l2'],\n",
    "             'solver':['lbfgs']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Xu Qiao Qi\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "parameters ={\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge\n",
    "lr=LogisticRegression()\n",
    "grid_search = GridSearchCV(lr, parameters, cv=10)\n",
    "logreg_cv = grid_search.fit(X_train, Y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We output the <code>GridSearchCV</code> object for logistic regression. We display the best parameters using the data attribute <code>best_params\\_</code> and the accuracy on the validation data using the data attribute <code>best_score\\_</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "accuracy : 0.8472222222222222\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",logreg_cv.best_params_)\n",
    "print(\"accuracy :\",logreg_cv.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  5\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the accuracy on the test data using the method <code>score</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg_cv.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets look at the confusion matrix:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat=logreg_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examining the confusion matrix, we see that logistic regression can distinguish between the different classes.  We see that the major problem is false positives.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  6\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a support vector machine object then  create a  <code>GridSearchCV</code> object  <code>svm_cv</code> with cv - 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Xu Qiao Qi\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "grid_search = GridSearchCV(svm, parameters, cv=10)\n",
    "svm_cv = grid_search.fit(X_train, Y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'C': 1.0, 'gamma': 0.03162277660168379, 'kernel': 'sigmoid'}\n",
      "accuracy : 0.8472222222222222\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",svm_cv.best_params_)\n",
    "print(\"accuracy :\",svm_cv.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  7\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the accuracy on the test data using the method <code>score</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_cv.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat=svm_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  8\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a decision tree classifier object then  create a  <code>GridSearchCV</code> object  <code>tree_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'criterion': ['gini', 'entropy'],\n",
    "     'splitter': ['best', 'random'],\n",
    "     'max_depth': [2*n for n in range(1,10)],\n",
    "     'max_features': ['auto', 'sqrt'],\n",
    "     'min_samples_leaf': [1, 2, 4],\n",
    "     'min_samples_split': [2, 5, 10]}\n",
    "\n",
    "tree = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Xu Qiao Qi\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "grid_search = GridSearchCV(tree, parameters, cv=10)\n",
    "tree_cv = grid_search.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'criterion': 'entropy', 'max_depth': 4, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'splitter': 'best'}\n",
      "accuracy : 0.9027777777777778\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",tree_cv.best_params_)\n",
    "print(\"accuracy :\",tree_cv.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  9\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the accuracy of tree_cv on the test data using the method <code>score</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5555555555555556"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_cv.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat = svm_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  10\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a k nearest neighbors object then  create a  <code>GridSearchCV</code> object  <code>knn_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "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": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Xu Qiao Qi\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:841: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "grid_search = GridSearchCV(knn, parameters, cv=10)\n",
    "knn_cv = grid_search.fit(X_train, Y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 9, 'p': 1}\n",
      "accuracy : 0.8472222222222222\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\n",
    "print(\"accuracy :\",knn_cv.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  11\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the accuracy of tree_cv on the test data using the method <code>score</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_cv.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat = knn_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  12\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the method performs best:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for Logistics Regression method: 0.8333333333333334\n",
      "Accuracy for Support Vector Machine method: 0.8333333333333334\n",
      "Accuracy for Decision tree method: 0.5555555555555556\n",
      "Accuracy for K nearsdt neighbors method: 0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy for Logistics Regression method:', logreg_cv.score(X_test, Y_test))\n",
    "print( 'Accuracy for Support Vector Machine method:', svm_cv.score(X_test, Y_test))\n",
    "print('Accuracy for Decision tree method:', tree_cv.score(X_test, Y_test))\n",
    "print('Accuracy for K nearsdt neighbors method:', knn_cv.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "models=['Logistics Regression', 'SVM', 'Decision Tree', 'KNN']\n",
    "result=[logreg_cv.score(X_test, Y_test), svm_cv.score(X_test, Y_test), tree_cv.score(X_test, Y_test),knn_cv.score(X_test, Y_test)]\n",
    "sns.barplot(x=models, y=result)\n",
    "plt.xlabel(\"model category\",fontsize=20)\n",
    "plt.ylabel(\"Accuracy\",fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">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": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": {},
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}