07_SpaceX_Machine_Learning_Prediction_Part_5.jupyterlite.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\">\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": [
    "## Import Libraries and Define Auxiliary Functions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import piplite\n",
    "#await piplite.install(['numpy'])\n",
    "#await piplite.install(['pandas'])\n",
    "#await piplite.install(['seaborn'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will import the following libraries for the lab\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "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']) \n",
    "    plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the dataframe\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from js import fetch\n",
    "#import io\n",
    "\n",
    "URL1 = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\"\n",
    "#resp1 = await fetch(URL1)\n",
    "#text1 = io.BytesIO((await resp1.arrayBuffer()).to_py())\n",
    "data = pd.read_csv('dataset_part_2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>Date</th>\n",
       "      <th>BoosterVersion</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Orbit</th>\n",
       "      <th>LaunchSite</th>\n",
       "      <th>Outcome</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>LandingPad</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Serial</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-06-04</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0003</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2012-05-22</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0005</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>ISS</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0007</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2013-09-29</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>PO</td>\n",
       "      <td>VAFB SLC 4E</td>\n",
       "      <td>False Ocean</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1003</td>\n",
       "      <td>-120.610829</td>\n",
       "      <td>34.632093</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2013-12-03</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>GTO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1004</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#URL2 = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_3.csv'\n",
    "#resp2 = await fetch(URL2)\n",
    "#text2 = io.BytesIO((await resp2.arrayBuffer()).to_py())\n",
    "X = pd.read_csv('dataset_part_3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Flights</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Orbit_ES-L1</th>\n",
       "      <th>Orbit_GEO</th>\n",
       "      <th>Orbit_GTO</th>\n",
       "      <th>Orbit_HEO</th>\n",
       "      <th>Orbit_ISS</th>\n",
       "      <th>...</th>\n",
       "      <th>Serial_B1058</th>\n",
       "      <th>Serial_B1059</th>\n",
       "      <th>Serial_B1060</th>\n",
       "      <th>Serial_B1062</th>\n",
       "      <th>GridFins_False</th>\n",
       "      <th>GridFins_True</th>\n",
       "      <th>Reused_False</th>\n",
       "      <th>Reused_True</th>\n",
       "      <th>Legs_False</th>\n",
       "      <th>Legs_True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 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",
       "\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",
       "\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",
       "\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",
       "\n",
       "[10 rows x 83 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(10)"
   ]
  },
  {
   "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = data['Class'].to_numpy()"
   ]
  },
  {
   "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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# students get this \n",
    "transform = preprocessing.StandardScaler()\n",
    "X = transform.fit_transform(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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.2, random_state=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can see we only have 18 test samples.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters ={'C':[0.01,0.1,1],\n",
    "             'penalty':['l2'],\n",
    "             'solver':['lbfgs']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1], &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n",
       "                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1], &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n",
       "                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
       "             param_grid={'C': [0.01, 0.1, 1], 'penalty': ['l2'],\n",
       "                         'solver': ['lbfgs']})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters ={\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge\n",
    "lr=LogisticRegression()\n",
    "logreg_cv = GridSearchCV(lr, param_grid=parameters, cv=10)\n",
    "logreg_cv.fit(X_train, Y_train)"
   ]
  },
  {
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "accuracy : 0.8464285714285713\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",logreg_cv.best_params_)\n",
    "print(\"accuracy :\",logreg_cv.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for the test set is: 0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy for the test set is:', logreg_cv.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets look at the confusion matrix:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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": 17,
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=SVC(),\n",
       "             param_grid={&#x27;C&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         &#x27;gamma&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         &#x27;kernel&#x27;: (&#x27;linear&#x27;, &#x27;rbf&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=SVC(),\n",
       "             param_grid={&#x27;C&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         &#x27;gamma&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         &#x27;kernel&#x27;: (&#x27;linear&#x27;, &#x27;rbf&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;)})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=SVC(),\n",
       "             param_grid={'C': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         'gamma': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         'kernel': ('linear', 'rbf', 'poly', 'rbf', 'sigmoid')})"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_cv = GridSearchCV(svm, parameters, cv=10)\n",
    "svm_cv.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'C': 1.0, 'gamma': 0.03162277660168379, 'kernel': 'sigmoid'}\n",
      "accuracy : 0.8482142857142856\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",svm_cv.best_params_)\n",
    "print(\"accuracy :\",svm_cv.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Accuracy for the test set is: 0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "print('The Accuracy for the test set is:', svm_cv.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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": 22,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_cv = GridSearchCV(tree, parameters, cv = 10)\n",
    "tree_cv.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'criterion': 'entropy', 'max_depth': 8, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 5, 'splitter': 'best'}\n",
      "accuracy : 0.8875000000000002\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": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Accuracy for the Decision Tree is: 0.8888888888888888\n"
     ]
    }
   ],
   "source": [
    "print('The Accuracy for the Decision Tree is:', tree_cv.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat = tree_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": 27,
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;algorithm&#x27;: [&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;],\n",
       "                         &#x27;n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         &#x27;p&#x27;: [1, 2]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;algorithm&#x27;: [&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;],\n",
       "                         &#x27;n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         &#x27;p&#x27;: [1, 2]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
       "                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         'p': [1, 2]})"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_cv = GridSearchCV(KNN, parameters, cv = 10)\n",
    "knn_cv.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 10, 'p': 1}\n",
      "accuracy : 0.8482142857142858\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\n",
    "print(\"accuracy :\",knn_cv.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  11\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the accuracy of knn_cv on the test data using the method <code>score</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Accuracy for kNN is: 0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "print('The Accuracy for kNN is:', knn_cv.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhIAAAHFCAYAAACn7hC1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy88F64QAAAACXBIWXMAAA9hAAAPYQGoP6dpAABDK0lEQVR4nO3de3zP9f//8ft7s4NhY2OMjCFsjmOIHDOnJDoh+eQsERbJx0c1UoY+IQpRDp8USZEOFuXQydlEDKU5VOQYtTE7vH5/+Hl/e7fR9vZ6e733drt2eV0uez9fr9fz+Xjt4p2H5+llMwzDEAAAgBO8rA4AAAAUXCQSAADAaSQSAADAaSQSAADAaSQSAADAaSQSAADAaSQSAADAaSQSAADAaSQSAADAaSQS8Gi7d+9Wnz59FBERIX9/fxUtWlT16tXTlClTdPbsWZe2nZSUpBYtWigoKEg2m03Tp083vQ2bzaZx48aZXu8/WbhwoWw2m2w2mzZs2JDjvGEYqlKlimw2m1q2bOlUG7NmzdLChQvzdc+GDRuuGRMA1yhkdQCAq8ybN0+DBw9WtWrVNGrUKEVFRSkjI0Pbt2/XnDlztGnTJq1YscJl7fft21epqalaunSpSpQooYoVK5rexqZNm3TbbbeZXm9eFStWTG+++WaOZGHjxo06dOiQihUr5nTds2bNUsmSJdW7d+8831OvXj1t2rRJUVFRTrcLIH9IJOCRNm3apMcff1xt2rTRypUr5efnZz/Xpk0bjRw5UomJiS6N4fvvv9eAAQPUoUMHl7Vxxx13uKzuvOjWrZvefvttvfbaawoMDLSXv/nmm2rcuLEuXLhwU+LIyMiQzWZTYGCg5b8T4FbD0AY80sSJE2Wz2TR37lyHJOIqX19f3XvvvfbP2dnZmjJliqpXry4/Pz+Fhobq0Ucf1c8//+xwX8uWLVWzZk1t27ZNzZo1U0BAgCpVqqRJkyYpOztb0v91+2dmZmr27Nn2IQBJGjdunP3nv7p6z+HDh+1l69atU8uWLRUSEqLChQsrPDxcDzzwgNLS0uzX5Da08f3336tz584qUaKE/P39VbduXS1atMjhmqtDAEuWLNHYsWNVtmxZBQYGKjY2VgcOHMjbL1nSww8/LElasmSJvez8+fN6//331bdv31zvGT9+vBo1aqTg4GAFBgaqXr16evPNN/XX9wdWrFhRe/fu1caNG+2/v6s9Oldjf+uttzRy5EiVK1dOfn5++vHHH3MMbZw+fVrly5dXkyZNlJGRYa9/3759KlKkiP71r3/l+VkB5I5EAh4nKytL69atU/369VW+fPk83fP4449r9OjRatOmjVatWqUJEyYoMTFRTZo00enTpx2uPXHihB555BH17NlTq1atUocOHTRmzBgtXrxYktSxY0dt2rRJkvTggw9q06ZN9s95dfjwYXXs2FG+vr6aP3++EhMTNWnSJBUpUkSXL1++5n0HDhxQkyZNtHfvXs2YMUMffPCBoqKi1Lt3b02ZMiXH9f/5z3905MgRvfHGG5o7d65++OEHderUSVlZWXmKMzAwUA8++KDmz59vL1uyZIm8vLzUrVu3az7bY489pmXLlumDDz7Q/fffr6FDh2rChAn2a1asWKFKlSopOjra/vv7+zDUmDFjdPToUc2ZM0cfffSRQkNDc7RVsmRJLV26VNu2bdPo0aMlSWlpaXrooYcUHh6uOXPm5Ok5AVyHAXiYEydOGJKM7t275+n65ORkQ5IxePBgh/ItW7YYkoz//Oc/9rIWLVoYkowtW7Y4XBsVFWW0a9fOoUySMWTIEIey+Ph4I7ev3YIFCwxJRkpKimEYhrF8+XJDkrFr167rxi7JiI+Pt3/u3r274efnZxw9etThug4dOhgBAQHG77//bhiGYaxfv96QZNx9990O1y1btsyQZGzatOm67V6Nd9u2bfa6vv/+e8MwDKNBgwZG7969DcMwjBo1ahgtWrS4Zj1ZWVlGRkaG8fzzzxshISFGdna2/dy17r3aXvPmza95bv369Q7lkydPNiQZK1asMHr16mUULlzY2L1793WfEUDe0COBW9769eslKcekvoYNGyoyMlJffPGFQ3mZMmXUsGFDh7LatWvryJEjpsVUt25d+fr6auDAgVq0aJF++umnPN23bt06tW7dOkdPTO/evZWWlpajZ+SvwzvSleeQlK9nadGihSpXrqz58+drz5492rZt2zWHNa7GGBsbq6CgIHl7e8vHx0fPPfeczpw5o5MnT+a53QceeCDP144aNUodO3bUww8/rEWLFmnmzJmqVatWnu8HcG0kEvA4JUuWVEBAgFJSUvJ0/ZkzZyRJYWFhOc6VLVvWfv6qkJCQHNf5+fnp4sWLTkSbu8qVK+vzzz9XaGiohgwZosqVK6ty5cp65ZVXrnvfmTNnrvkcV8//1d+f5ep8kvw8i81mU58+fbR48WLNmTNHVatWVbNmzXK9duvWrWrbtq2kK6tqvvnmG23btk1jx47Nd7u5Pef1Yuzdu7cuXbqkMmXKMDcCMBGJBDyOt7e3WrdurR07duSYLJmbq3+ZHj9+PMe5X3/9VSVLljQtNn9/f0lSenq6Q/nf52FIUrNmzfTRRx/p/Pnz2rx5sxo3bqy4uDgtXbr0mvWHhIRc8zkkmfosf9W7d2+dPn1ac+bMUZ8+fa553dKlS+Xj46OPP/5YXbt2VZMmTRQTE+NUm7lNWr2W48ePa8iQIapbt67OnDmjp556yqk2AeREIgGPNGbMGBmGoQEDBuQ6OTEjI0MfffSRJOmuu+6SJPtkyau2bdum5ORktW7d2rS4rq482L17t0P51Vhy4+3trUaNGum1116TJO3cufOa17Zu3Vrr1q2zJw5X/e9//1NAQIDLlkaWK1dOo0aNUqdOndSrV69rXmez2VSoUCF5e3vbyy5evKi33norx7Vm9fJkZWXp4Ycfls1m0+rVq5WQkKCZM2fqgw8+uOG6AbCPBDxU48aNNXv2bA0ePFj169fX448/rho1aigjI0NJSUmaO3euatasqU6dOqlatWoaOHCgZs6cKS8vL3Xo0EGHDx/Ws88+q/Lly+vJJ580La67775bwcHB6tevn55//nkVKlRICxcu1LFjxxyumzNnjtatW6eOHTsqPDxcly5dsq+MiI2NvWb98fHx+vjjj9WqVSs999xzCg4O1ttvv61PPvlEU6ZMUVBQkGnP8neTJk36x2s6duyoqVOnqkePHho4cKDOnDmj//73v7ku0a1Vq5aWLl2qd999V5UqVZK/v79T8xri4+P11Vdfac2aNSpTpoxGjhypjRs3ql+/foqOjlZERES+6wTwf0gk4LEGDBighg0batq0aZo8ebJOnDghHx8fVa1aVT169NATTzxhv3b27NmqXLmy3nzzTb322msKCgpS+/btlZCQkOucCGcFBgYqMTFRcXFx6tmzp4oXL67+/furQ4cO6t+/v/26unXras2aNYqPj9eJEydUtGhR1axZU6tWrbLPMchNtWrV9O233+o///mPhgwZoosXLyoyMlILFizI1w6RrnLXXXdp/vz5mjx5sjp16qRy5cppwIABCg0NVb9+/RyuHT9+vI4fP64BAwbojz/+UIUKFRz22ciLtWvXKiEhQc8++6xDz9LChQsVHR2tbt266euvv5avr68ZjwfckmyG8ZddYAAAAPKBORIAAMBpJBIAAMBpJBIAAMBpJBIAAHioL7/8Up06dVLZsmVls9m0cuVK+7mMjAyNHj1atWrVUpEiRVS2bFk9+uijOZaP/xMSCQAAPFRqaqrq1KmjV199Nce5tLQ07dy5U88++6x27typDz74QAcPHsyxdf4/YdUGAAC3AJvNphUrVqhLly7XvGbbtm1q2LChjhw5ovDw8DzVyz4SAAAUEOnp6Tm22Pfz88t1UzdnnD9/XjabTcWLF8/zPR6ZSPSt+KDVIQAACoj5h5e7vI2M03l7g+8/SXj1fxo/frxDWXx8vMaNG3fDdV+6dEn//ve/1aNHDwUGBub5Po9MJAAA8ERjxozRiBEjHMrM6I3IyMhQ9+7dlZ2drVmzZuXrXhIJAABcLTvLlGrMHMa4KiMjQ127dlVKSorWrVuXr94IiUQCAADXM7KtjiBXV5OIH374QevXr3fq3UIkEgAAuFq2NYnEn3/+qR9//NH+OSUlRbt27VJwcLDKli2rBx98UDt37tTHH3+srKwsnThxQpIUHByc55fZeeTyTyZbAgDy6qZMtjyebEo9PmGR+bp+w4YNatWqVY7yXr16ady4cYqIiMj1vvXr16tly5Z5aoMeCQAAXMywaGijZcuWul5/gRl9CSQSAAC4mkVDGzcDW2QDAACn0SMBAICruemqDTOQSAAA4Gom7SPhjhjaAAAATqNHAgAAV2NoAwAAOI1VGwAAADnRIwEAgItZtSHVzUAiAQCAq3nw0AaJBAAArubBPRLMkQAAAE6jRwIAAFfz4A2pSCQAAHA1hjYAAAByokcCAABXY9UGAABwGkMbAAAAOdEjAQCAqzG0AQAAnGUYnrv8k6ENAADgNHokAABwNQ+ebEkiAQCAqzFHAgAAOM2DeySYIwEAAJxGjwQAAK7GS7sAAIDTGNoAAADIiR4JAABcjVUbAADAaQxtAAAA5ESPBAAArsbQBgAAcJoHJxIMbQAAAKfRIwEAgIt58mvESSQAAHA1Dx7aIJEAAMDVWP4JAACQEz0SAAC4GkMbAADAaQxtAAAA5ESPBAAArsbQBgAAcBpDGwAAADnRIwEAgKsxtAEAAJxGImGuEiVKyGaz5enas2fPujgaAADgLEsSienTp9t/PnPmjF544QW1a9dOjRs3liRt2rRJn332mZ599lkrwgMAwFwePNnSZhiGYWUADzzwgFq1aqUnnnjCofzVV1/V559/rpUrV+a7zr4VHzQpOgCAp5t/eLnL27i46r+m1FP43qdMqcdMlq/a+Oyzz9S+ffsc5e3atdPnn39uQUQAAJjMyDbncEOWJxIhISFasWJFjvKVK1cqJCTEgogAAPAMX375pTp16qSyZcvKZrPl6OU3DEPjxo1T2bJlVbhwYbVs2VJ79+7NVxuWr9oYP368+vXrpw0bNtjnSGzevFmJiYl64403LI4OAAATWLRqIzU1VXXq1FGfPn30wAMP5Dg/ZcoUTZ06VQsXLlTVqlX1wgsvqE2bNjpw4ICKFSuWpzYsTyR69+6tyMhIzZgxQx988IEMw1BUVJS++eYbNWrUyOrwAAC4cRYNS3To0EEdOnTI9ZxhGJo+fbrGjh2r+++/X5K0aNEilS5dWu+8844ee+yxPLVheSIhSY0aNdLbb79tdRgAANwyUlJSdOLECbVt29Ze5ufnpxYtWujbb78tWIlEdna2fvzxR508eVLZf+v+ad68uUVRAQBgEpOGNtLT05Wenu5Q5ufnJz8/v3zXdeLECUlS6dKlHcpLly6tI0eO5LkeyxOJzZs3q0ePHjpy5Ij+vhLVZrMpKyvLosgAADCJSYlEQkKCxo8f71AWHx+vcePGOV3n3zeINAwjz5tGSm6QSAwaNEgxMTH65JNPFBYWlq/gAQC4lYwZM0YjRoxwKHOmN0KSypQpI+lKz0RYWJi9/OTJkzl6Ka7H8kTihx9+0PLly1WlShWrQwEAwDVM2vvR2WGM3ERERKhMmTJau3atoqOjJUmXL1/Wxo0bNXny5DzXY3ki0ahRI/34448kEgAAz2XR8s8///xTP/74o/1zSkqKdu3apeDgYIWHhysuLk4TJ07U7bffrttvv10TJ05UQECAevTokec2LE8khg4dqpEjR+rEiROqVauWfHx8HM7Xrl3bosgAACjYtm/frlatWtk/Xx0W6dWrlxYuXKinn35aFy9e1ODBg3Xu3Dk1atRIa9asyfMeEpIbvGvDyyvn5po2m80+2cOZyZa8awMAkFc35V0bb5vzEsrCj0wwpR4zWd4jkZKSYnUIAAC4lpu+J8MMlicSFSpUsDoEAABcy6I5EjeD5YnEVfv27dPRo0d1+fJlh/J7773XoogAAMA/sTyR+Omnn3Tfffdpz5499rkR0v9tkMGGVACAAs/a6YguZflrxIcPH66IiAj99ttvCggI0N69e/Xll18qJiZGGzZssDo8AABuXHa2OYcbsrxHYtOmTVq3bp1KlSolLy8veXl5qWnTpkpISNCwYcOUlJRkdYgAAOAaLO+RyMrKUtGiRSVJJUuW1K+//irpyiTMAwcOWBkaAADmoEfCdWrWrKndu3erUqVKatSokaZMmSJfX1/NnTtXlSpVsjo8AABuHMs/XeeZZ55RamqqJOmFF17QPffco2bNmikkJETvvvuuxdEBAIDrsTyRaNeunf3nSpUqad++fTp79qxKlCjBm0ABAB7ByPbcVRuWJxK5CQ4OtjoEAADM46bzG8xgSSJx//335/naDz74wIWRAACAG2FJIhEUFGRFswAAWIPJluZasGCBFc0CAGAN5kgAAACnefAcCcs3pAIAAAUXPRIAALiaB/dIkEgAAOBqvP3Tdf73v/8pPT09R/nly5f1v//9z4KIAABAXtkMw9o0ydvbW8ePH1doaKhD+ZkzZxQaGqqsrKx819m34oNmhQcntOzZVq0eaaeSt5WSJP3ywzF9NGO59mzgTa64tfHdcE/zDy93eRtpUweYUk/AiHmm1GMmy4c2DMPIdSvsn3/+mf0mCqhzx89o+eTFOnnkhCTpzgdaaujcpzWu4yj9+sPPFkcHWIfvxi2M5Z/mi46Ols1mk81mU+vWrVWo0P+FkpWVpZSUFLVv396q8HADvvtih8PnD/67RC17tlXl6Kr8zxK3NL4b8ESWJRJdunSRJO3atUvt2rVT0aJF7ed8fX1VsWJFPfDAAxZFB7PYvLzUoGNj+RX216GdB60OB3AbfDduMexsab74+HhJUsWKFdWtWzf5+/tbFQpcoFy1cI394EX5+PkqPe2SXn1sin79kX9xAXw3blEePLRh+WTLq3bs2KHk5GTZbDZFRUUpOjo6T/elp6fnWPUxtFYvedu8XREm8sjbp5BCypZUQGAR1e/QSM27tdbkbvH8DxO3PL4b7uemTLac3MeUegJGu98rJiyfbHny5El1795dGzZsUPHixWUYhs6fP69WrVpp6dKlKlWq1HXvT0hI0Pjx4x3K6gZFKrp4lCvDxj/Iysi0Tyg7vOeQImpXUWzfu/W//8y1ODLAWnw3bk2GB29IZfk+EkOHDtWFCxe0d+9enT17VufOndP333+vCxcuaNiwYf94/5gxY3T+/HmHo3ZQtZsQOfLFZlMhXx+rowDcD9+NW0O2Yc7hhizvkUhMTNTnn3+uyMhIe1lUVJRee+01tW3b9h/v9/Pzk5+fn0MZwxrWun9UD+3ZkKSzx0/Lv0hhNep0p6rfEaWpvV60OjTAUnw3bmFMtnSd7Oxs+fjkzMZ9fHyU7cFdQZ4sqGSQBkwbqqBSJXTxjzT9vP+IpvZ6Ufu+3m11aICl+G7AE1meSNx1110aPny4lixZorJly0qSfvnlFz355JNq3bq1xdHBGQtGz7Y6BMAt8d24hbnpsIQZLJ8j8eqrr+qPP/5QxYoVVblyZVWpUkURERH6448/NHPmTKvDAwDgxmVnm3O4Ict7JMqXL6+dO3dq7dq12r9/vwzDUFRUlGJjY60ODQAA/APLE4mr2rRpozZt2lgdBgAA5vPgoQ23SCS++OILffHFFzp58mSOCZbz58+3KCoAAEzCqg3XGT9+vJ5//nnFxMQoLCws1zeBAgAA92R5IjFnzhwtXLhQ//rXv6wOBQAA12Bow3UuX76sJk2aWB0GAAAuwxbZLtS/f3+98847VocBAACcYHmPxKVLlzR37lx9/vnnql27do5dLqdOnWpRZAAAmIShDdfZvXu36tatK0n6/vvvHc4x8RIA4BFIJFxn/fr1VocAAIBrefDyT8vnSAAAgILL8h4JAAA8HkMbAADAWYYHJxIMbQAAAKfRIwEAgKt5cI+EJYnEqlWr8nztvffe68JIAAC4CTx4Z0tLEokuXbo4fLbZbDIMw+HzVVlZWTcrLAAAkE+WzJHIzs62H2vWrFHdunW1evVq/f777zp//rw+/fRT1atXT4mJiVaEBwCAubINcw43ZPkcibi4OM2ZM0dNmza1l7Vr104BAQEaOHCgkpOTLYwOAAATuGkSYAbLV20cOnRIQUFBOcqDgoJ0+PDhmx8QAADIM8sTiQYNGiguLk7Hjx+3l504cUIjR45Uw4YNLYwMAABzGIZhypEfmZmZeuaZZxQREaHChQurUqVKev7555Vt8sRPy4c25s+fr/vuu08VKlRQeHi4JOno0aOqWrWqVq5caW1wAACYwYKhjcmTJ2vOnDlatGiRatSooe3bt6tPnz4KCgrS8OHDTWvH8kSiSpUq2r17t9auXav9+/fLMAxFRUUpNjaWt38CADyDBYnEpk2b1LlzZ3Xs2FGSVLFiRS1ZskTbt283tR3LEwnpynLPtm3bqm3btlaHAgCA20pPT1d6erpDmZ+fn/z8/HJc27RpU82ZM0cHDx5U1apV9d133+nrr7/W9OnTTY3JkkRixowZGjhwoPz9/TVjxozrXjts2LCbFBUAAK5h1rs2EhISNH78eIey+Ph4jRs3Lse1o0eP1vnz51W9enV5e3srKytLL774oh5++GFTYrnKZuR39oYJIiIitH37doWEhCgiIuKa19lsNv3000/5rr9vxQdvJDwAwC1k/uHlLm/jfK/WptTjP/fTPPdILF26VKNGjdJLL72kGjVqaNeuXYqLi9PUqVPVq1cvU+KRLOqRSElJyfVnAABwbddKGnIzatQo/fvf/1b37t0lSbVq1dKRI0eUkJBQ8BMJAABuKRa8aiMtLU1eXo67PHh7e3vG8s8RI0bk+dqpU6e6MBIAAFzPrDkS+dGpUye9+OKLCg8PV40aNZSUlKSpU6eqb9++prZjSSKRlJTk8HnHjh3KyspStWrVJEkHDx6Ut7e36tevb0V4AAAUeDNnztSzzz6rwYMH6+TJkypbtqwee+wxPffcc6a2Y0kisX79evvPU6dOVbFixbRo0SKVKFFCknTu3Dn16dNHzZo1syI8AADMZUGPRLFixTR9+nTTl3v+neVbZL/88stKSEiwJxGSVKJECb3wwgt6+eWXLYwMAACTZJt0uCHLE4kLFy7ot99+y1F+8uRJ/fHHHxZEBAAA8sryVRv33Xef+vTpo5dffll33HGHJGnz5s0aNWqU7r//foujAwDgxlkx2fJmsTyRmDNnjp566in17NlTGRkZkqRChQqpX79+eumllyyODgAAE7jpsIQZLE8kAgICNGvWLL300ks6dOiQDMNQlSpVVKRIEatDAwDAFPRI3ARFihRR7dq1rQ4DAADkg9skEgAAeCyGNgAAgLMMD04kLF/+CQAACi56JAAAcDUP7pEgkQAAwMUY2gAAAMgFPRIAALiaB/dIkEgAAOBinjy0QSIBAICLeXIiwRwJAADgNHokAABwMU/ukSCRAADA1Qyb1RG4DEMbAADAaTecSGRlZWnXrl06d+6cGfEAAOBxjGxzDneU70QiLi5Ob775pqQrSUSLFi1Ur149lS9fXhs2bDA7PgAACjwj22bK4Y7ynUgsX75cderUkSR99NFHSklJ0f79+xUXF6exY8eaHiAAAHBf+U4kTp8+rTJlykiSPv30Uz300EOqWrWq+vXrpz179pgeIAAABR1DG39RunRp7du3T1lZWUpMTFRsbKwkKS0tTd7e3qYHCABAQWcYNlMOd5Tv5Z99+vRR165dFRYWJpvNpjZt2kiStmzZourVq5seIAAAcF/5TiTGjRunmjVr6tixY3rooYfk5+cnSfL29ta///1v0wMEAKCgc9dhCTM4tSHVgw8+mKOsV69eNxwMAACeyF1XXJghT4nEjBkz8lzhsGHDnA4GAABPZBhWR+A6eUokpk2blqfKbDYbiQQAALeQPCUSKSkpro4DAACP5clDG05vkX358mUdOHBAmZmZZsYDAIDHYWfLv0hLS1O/fv0UEBCgGjVq6OjRo5KuzI2YNGmS6QECAAD3le9EYsyYMfruu++0YcMG+fv728tjY2P17rvvmhocAACewDDMOdxRvpd/rly5Uu+++67uuOMO2Wz/180SFRWlQ4cOmRocAACewF2HJcyQ7x6JU6dOKTQ0NEd5amqqQ2IBAAA8X74TiQYNGuiTTz6xf76aPMybN0+NGzc2LzIAADwE79r4i4SEBLVv31779u1TZmamXnnlFe3du1ebNm3Sxo0bXREjAAAFmidvkZ3vHokmTZrom2++UVpamipXrqw1a9aodOnS2rRpk+rXr++KGAEAgJty6l0btWrV0qJFi8yOBQAAj5TtpsMSZnAqkcjKytKKFSuUnJwsm82myMhIde7cWYUKOVUdAAAezV3nN5gh33/zf//99+rcubNOnDihatWqSZIOHjyoUqVKadWqVapVq5bpQQIAUJCx/PMv+vfvrxo1aujnn3/Wzp07tXPnTh07dky1a9fWwIEDXREjAABwU/nukfjuu++0fft2lShRwl5WokQJvfjii2rQoIGpwQEA4AncdVdKM+S7R6JatWr67bffcpSfPHlSVapUMSUoAAA8yS3/0q4LFy7Yj4kTJ2rYsGFavny5fv75Z/38889avny54uLiNHnyZFfHCwAA3EiehjaKFy/usP21YRjq2rWrvcz4/302nTp1UlZWlgvCBACg4Lrll3+uX7/e1XEAAOCxbvnlny1atHB1HAAAoAByegeptLQ0HT16VJcvX3Yor1279g0HBQCAJ/HkVRv5TiROnTqlPn36aPXq1bmeZ44EAACOPHmORL6Xf8bFxencuXPavHmzChcurMTERC1atEi33367Vq1a5YoYAQCAm8p3IrFu3TpNmzZNDRo0kJeXlypUqKCePXtqypQpSkhIcEWMAAAUaIZhM+XIr19++UU9e/ZUSEiIAgICVLduXe3YscPUZ8v30EZqaqpCQ0MlScHBwTp16pSqVq2qWrVqaefOnaYGBwCAJ7BijsS5c+d05513qlWrVlq9erVCQ0N16NAhFS9e3NR28p1IVKtWTQcOHFDFihVVt25dvf7666pYsaLmzJmjsLAwU4MDAMATWDFHYvLkySpfvrwWLFhgL6tYsaLp7Tg1R+L48eOSpPj4eCUmJio8PFwzZszQxIkTTQ8QAABckZ6e7rDb9IULF5Senp7rtatWrVJMTIweeughhYaGKjo6WvPmzTM9Jpth3FiHS1pamvbv36/w8HCVLFnSrLhuSCHfclaHALili79+ZXUIgNvxKVnJ5W1sK3efKfV8MqCOxo8f71AWHx+vcePG5bjW399fkjRixAg99NBD2rp1q+Li4vT666/r0UcfNSUeyYREwh2RSAC5I5EAcroZicSWsvebUk/dlCU5eiD8/Pzk5+eX41pfX1/FxMTo22+/tZcNGzZM27Zt06ZNm0yJR8rjHIkRI0bkucKpU6c6HQwAALi2ayUNuQkLC1NUVJRDWWRkpN5//31TY8pTIpGUlJSnyv76Yi8AAHCFFV3/d955pw4cOOBQdvDgQVWoUMHUdnhpFwAALmbFqo0nn3xSTZo00cSJE9W1a1dt3bpVc+fO1dy5c01tJ9+rNgAAgPtr0KCBVqxYoSVLlqhmzZqaMGGCpk+frkceecTUdpx+aRcAAMgbq14jfs899+iee+5xaRskEgAAuFi21QG4EEMbAADAafRIAADgYoY8d1WjUz0Sb731lu68806VLVtWR44ckSRNnz5dH374oanBAQDgCbINcw53lO9EYvbs2RoxYoTuvvtu/f7778rKypIkFS9eXNOnTzc7PgAACrxs2Uw53FG+E4mZM2dq3rx5Gjt2rLy9ve3lMTEx2rNnj6nBAQAA95bvORIpKSmKjo7OUe7n56fU1FRTggIAwJMwR+IvIiIitGvXrhzlq1evzrGnNwAAuLL804zDHeW7R2LUqFEaMmSILl26JMMwtHXrVi1ZskQJCQl64403XBEjAABwU/lOJPr06aPMzEw9/fTTSktLU48ePVSuXDm98sor6t69uytiBACgQPPkoQ2bYRhOLyg5ffq0srOzFRoaamZMN6yQbzmrQwDc0sVfv7I6BMDt+JSs5PI2Ekub8w/t9r8tNaUeM93QhlQlS5Y0Kw4AAFAA5TuRiIiIkM127S6an3766YYCAgDA07jrREkz5DuRiIuLc/ickZGhpKQkJSYmatSoUWbFBQCAx/DkORL5TiSGDx+ea/lrr72m7du333BAAACg4DDt7Z8dOnTQ+++/b1Z1AAB4jGybOYc7Mu3tn8uXL1dwcLBZ1QEA4DHc9T0ZZsh3IhEdHe0w2dIwDJ04cUKnTp3SrFmzTA0OAABP4KYv7jRFvhOJLl26OHz28vJSqVKl1LJlS1WvXt2suAAAQAGQr0QiMzNTFStWVLt27VSmTBlXxQQAgEfx5OWf+ZpsWahQIT3++ONKT093VTwAAHicbJvNlMMd5XvVRqNGjZSUlOSKWAAAQAGT7zkSgwcP1siRI/Xzzz+rfv36KlKkiMP52rVrmxYcAACegMmWkvr27avp06erW7dukqRhw4bZz9lsNhmGIZvNpqysLPOjBACgAPPkORJ5TiQWLVqkSZMmKSUlxZXxAACAAiTPicTVt41XqFDBZcEAAOCJ3HVXSjPka47E9d76CQAAcsfOlv9f1apV/zGZOHv27A0FBAAACo58JRLjx49XUFCQq2IBAMAjsWrj/+vevbtCQ0NdFQsAAB6JORJifgQAAM7y5OWfed7Z8uqqDQAAgKvy3CORne3J+RQAAK7jyf8Uz/cW2QAAIH88eY5Evl/aBQAAcBU9EgAAuJgnTw4gkQAAwMU8OZFgaAMAADiNHgkAAFzM8ODJliQSAAC4GEMbAAAAuaBHAgAAF/PkHgkSCQAAXIydLQEAgNPY2RIAACAX9EgAAOBizJEAAABO8+REgqENAADgNHokAABwMVZtAAAAp7FqAwAAFGgJCQmy2WyKi4sztV56JAAAcDGrJ1tu27ZNc+fOVe3atU2vmx4JAABczDDpcMaff/6pRx55RPPmzVOJEiVu5DFyRSIBAIAHGzJkiDp27KjY2FiX1M/QBgAALpZt0rqN9PR0paenO5T5+fnJz88v1+uXLl2qnTt3atu2baa0nxt6JAAAcLFsk46EhAQFBQU5HAkJCbm2eezYMQ0fPlyLFy+Wv7+/y57NZhiGxy1vLeRbzuoQALd08devrA4BcDs+JSu5vI3nKzxiSj2jD87Pc4/EypUrdd9998nb29telpWVJZvNJi8vL6WnpzuccxZDGwAAFBDXG8b4u9atW2vPnj0OZX369FH16tU1evRoU5IIiUQCAACXs2L5Z7FixVSzZk2HsiJFiigkJCRH+Y0gkQAAwMU8eWdLEgkAAG4RGzZsML1OEgkAAFzMrOWf7ohEAgAAF/PcNIJ9JAAAwA2gRwIAABez+qVdrmRZIrF79+48X+uKt5UBAHCzMEfCBerWrSubzSbDMGSzXX9dTFZW1k2KCgAA5IdlcyRSUlL0008/KSUlRe+//74iIiI0a9YsJSUlKSkpSbNmzVLlypX1/vvvWxUiAACmsPI14q5mWY9EhQoV7D8/9NBDmjFjhu6++257We3atVW+fHk9++yz6tKliwURAgBgDuZIuNiePXsUERGRozwiIkL79u2zICIAAMzjyXMk3GL5Z2RkpF544QVdunTJXpaenq4XXnhBkZGRFkYGAACuxy16JObMmaNOnTqpfPnyqlOnjiTpu+++k81m08cff2xxdAAA3BjP7Y9wk0SiYcOGSklJ0eLFi7V//34ZhqFu3bqpR48eKlKkiNXhAQBwQ5gjcRMEBARo4MCBVocBAADywS3mSEjSW2+9paZNm6ps2bI6cuSIJGnatGn68MMPLY4MAIAbY5j0nztyi0Ri9uzZGjFihDp06KBz587ZN6AqUaKEpk+fbm1wAADcoGyTDnfkFonEzJkzNW/ePI0dO1aFCv3faEtMTIz27NljYWQAAOB63GKOREpKiqKjo3OU+/n5KTU11YKIAAAwD/tIuFhERIR27dqVo3z16tWKioq6+QEBAGAitsh2sVGjRmnIkCG6dOmSDMPQ1q1btWTJEiUkJOiNN96wOjwAAHANbpFI9OnTR5mZmXr66aeVlpamHj16qFy5cnrllVfUvXt3q8ODkwY91ksjRwxSWFio9u47qJEj4/X1N1utDgu4abbv2qMF7yzXvv0/6tSZs3ol4Vm1bt5EkpSRmamZcxfpq03b9fOvx1W0SBHd0SBaTw7qo9BSIRZHDrMxtHETDBgwQEeOHNHJkyd14sQJHTt2TP369bM6LDjpoYfu1dSXxylh0gzFNGynr7/eqo8/Wqzy5ctaHRpw01y8eEnVqlTSf0YMznHu0qV07TtwSI/1fljL5r+q6ROf0ZGjP+uJ0eMtiBSu5smrNmyGYXhcmlTIt5zVIdzyvv36I+1M+l5PDB1jL9uze4NWrUrU2GcmWRjZre3ir19ZHcItq+adHRx6JHKzJ/mAHu4fp7XvL1JYmdCbGN2tzadkJZe30b/ig6bU88bh5abUYybLhjaio6Nls9nydO3OnTtdHA3M5OPjo3r1amvyS685lK9du1GN74ixKCrA/f35Z5psNpuKFePVACg4LEskunTpYv/50qVLmjVrlqKiotS4cWNJ0ubNm7V3714NHpyzS/Cv0tPTlZ6e7lBmGEaekxSYr2TJYBUqVEgnfzvtUH7y5GmV5l9ZQK7S0y9r2uwFurtNSxXlHUMex12HJcxgWSIRHx9v/7l///4aNmyYJkyYkOOaY8eOXbeehIQEjR/vOKZo8yoqm3egecHCKX8fNbPZbDnKAFyZeDkqfpIMI1vPPjXE6nDgAu66vbUZ3GKy5XvvvadHH300R3nPnj31/vvvX/feMWPG6Pz58w6HzauYq0JFHpw+fVaZmZkqXaaUQ3mpUiE6+dspi6IC3FNGZqZGPjtRPx8/oXnTJ9IbgQLHLRKJwoUL6+uvv85R/vXXX8vf3/+69/r5+SkwMNDhYFjDWhkZGdq5c7diWzd3KI+Nba5Nm7dbFBXgfq4mEUeP/ao3pk9U8SB6Uj2VJ6/acIt9JOLi4vT4449rx44duuOOOyRdmSMxf/58PffccxZHB2dMe2WeFi14RTt2fKfNW3ZoQL+eCi9fTq/Pfcvq0ICbJi3too7+/Kv98y+//qb9Bw8pKLCYSpUM0YixL2rfwR/12pTxys7O1ukzZyVJQYHF5OPjY1XYcIFsDx7WdZvln8uWLdMrr7yi5ORkSVJkZKSGDx+url275rsuln+6h0GP9dJTIx9XWFiovt97QE89NU5ffb3F6rBuaSz/vLm27tytvkNH5yjv3CFWg/v1VLsHe+d63/yZk9WwXm0XR4erbsbyz39VuN+Uet468oEp9ZjJbRIJM5FIALkjkQByuhmJRE+TEonFbphIuMXQxlWXL1/WyZMnlZ3tOBIUHh5uUUQAANw4T94i2y0SiR9++EF9+/bVt99+61B+dT+IrKwsiyIDAADX4xaJRO/evVWoUCF9/PHHCgsLY9UFAMCjePI+Em6RSOzatUs7duxQ9erVrQ4FAADTuevSTTO4RSIRFRWl06dP//OFAAAUQJ48R8ItNqSaPHmynn76aW3YsEFnzpzRhQsXHA4AAOCe3KJHIjY2VpLUunVrh3ImWwIAPAFzJFxs/fr1VocAAIDLMEfCxVq0aGF1CAAAwAlukUhclZaWpqNHj+ry5csO5bVrs1UsAKDg8sBNpO3cIpE4deqU+vTpo9WrV+d6njkSAICCjFUbLhYXF6dz585p8+bNKly4sBITE7Vo0SLdfvvtWrVqldXhAQCAa3CLHol169bpww8/VIMGDeTl5aUKFSqoTZs2CgwMVEJCgjp27Gh1iAAAOM2TJ1u6RY9EamqqQkNDJUnBwcE6deqUJKlWrVrauXOnlaEBAHDDDJP+c0dukUhUq1ZNBw4ckCTVrVtXr7/+un755RfNmTNHYWFhFkcHAACuxS2GNuLi4nT8+HFJUnx8vNq1a6fFixfL19dXixYtsjg6AABujCdPtnSLROKRRx6x/xwdHa3Dhw9r//79Cg8PV8mSJS2MDACAG8fyTxcYMWJEnq+dOnWqCyMBAMC1PHmypWWJRFJSUp6us9lsLo4EAAA4y7JEgvdrAABuFe664sIMbjFHAgAAT+bJky3dYvknAAAwV0JCgho0aKBixYopNDRUXbp0sW+1YCYSCQAAXMwwDFOO/Ni4caOGDBmizZs3a+3atcrMzFTbtm2Vmppq6rMxtAEAgItZMbSRmJjo8HnBggUKDQ3Vjh071Lx5c9PaIZEAAKCASE9PV3p6ukOZn5+f/Pz8/vHe8+fPS7ryKgozMbQBAICLmfWujYSEBAUFBTkcCQkJ/9y+YWjEiBFq2rSpatasaeqz0SMBAICLZZu0s+WYMWNybOiYl96IJ554Qrt379bXX39tShx/RSIBAEABkddhjL8aOnSoVq1apS+//FK33Xab6TGRSAAA4GJW7CJhGIaGDh2qFStWaMOGDYqIiHBJOyQSAAC4mBWrNoYMGaJ33nlHH374oYoVK6YTJ05IkoKCglS4cGHT2rEZHvhKskK+5awOAXBLF3/9yuoQALfjU7KSy9toXK6VKfVs+iXvr5e41ruqFixYoN69e5sSj0SPBAAAHulm9ROQSAAA4GIe2PlvRyIBAICL8dIuAACAXNAjAQCAixke3CNBIgEAgIt58hwJhjYAAIDT6JEAAMDFPHmyJYkEAAAuxtAGAABALuiRAADAxRjaAAAATmP5JwAAcFo2cyQAAAByokcCAAAXY2gDAAA4jaENAACAXNAjAQCAizG0AQAAnMbQBgAAQC7okQAAwMUY2gAAAE5jaAMAACAX9EgAAOBiDG0AAACnGUa21SG4DIkEAAAu5smvEWeOBAAAcBo9EgAAuJjhwas2SCQAAHAxhjYAAAByQY8EAAAuxtAGAABwGjtbAgAA5IIeCQAAXIydLQEAgNM8eY4EQxsAAMBp9EgAAOBinryPBIkEAAAu5slDGyQSAAC4GMs/AQAAckGPBAAALsbQBgAAcJonT7ZkaAMAADiNHgkAAFyMoQ0AAOA0Vm0AAADkgh4JAABcjJd2AQAApzG0AQAAkAt6JAAAcDFWbQAAAKd58hwJhjYAAHAxwzBMOZwxa9YsRUREyN/fX/Xr19dXX31l6rORSAAA4KHeffddxcXFaezYsUpKSlKzZs3UoUMHHT161LQ2bIYHDtwU8i1ndQiAW7r4q7n/EgE8gU/JSq5vw6S/lzIu/5Kv6xs1aqR69epp9uzZ9rLIyEh16dJFCQkJpsREjwQAAC5mmHTkx+XLl7Vjxw61bdvWobxt27b69ttvnX6Wv2OyJQAABUR6errS09Mdyvz8/OTn55fj2tOnTysrK0ulS5d2KC9durROnDhhWkwemUhk5rPrB66Rnp6uhIQEjRkzJtc/5MCtiu/Grcesv5fGjRun8ePHO5TFx8dr3Lhx17zHZrM5fDYMI0fZjfDIORJwDxcuXFBQUJDOnz+vwMBAq8MB3AbfDTgrPz0Sly9fVkBAgN577z3dd9999vLhw4dr165d2rhxoykxMUcCAIACws/PT4GBgQ7HtXq1fH19Vb9+fa1du9ahfO3atWrSpIlpMXnk0AYAAJBGjBihf/3rX4qJiVHjxo01d+5cHT16VIMGDTKtDRIJAAA8VLdu3XTmzBk9//zzOn78uGrWrKlPP/1UFSpUMK0NEgm4jJ+fn+Lj45lMBvwN3w3cTIMHD9bgwYNdVj+TLQEAgNOYbAkAAJxGIgEAAJxGIgEAAJxGIuGBWrZsqbi4OPvnihUravr06de9x2azaeXKlS6NK6969+6tLl26uLydhQsXqnjx4i5vB57t7983Vxg3bpzq1q3r0jYAZ7Fq4xawbds2FSlSxOowNG7cOK1cuVK7du2yOhQAgElIJG4BpUqVsjoEAICHYmijgEtNTdWjjz6qokWLKiwsTC+//HKOa/4+tPHDDz+oefPm8vf3V1RUVI7tU3PTsmVLDRs2TE8//bSCg4NVpkyZHC+JOXr0qDp37qyiRYsqMDBQXbt21W+//SbpyjDC+PHj9d1338lms8lms2nhwoV5esbExEQ1bdpUxYsXV0hIiO655x4dOnTIfv7w4cOy2Wz64IMP1KpVKwUEBKhOnTratGmTQz0LFy5UeHi4AgICdN999+nMmTN5ah/Iq8WLFysmJkbFihVTmTJl1KNHD508edJ+fsOGDbLZbPriiy8UExOjgIAANWnSRAcOHHCoZ9KkSSpdurSKFSumfv366dKlSzf7UYA8I5Eo4EaNGqX169drxYoVWrNmjTZs2KAdO3Zc8/rs7Gzdf//98vb21ubNmzVnzhyNHj06T20tWrRIRYoU0ZYtWzRlyhQ9//zz9iTEMAx16dJFZ8+e1caNG7V27VodOnRI3bp1k3Rld7WRI0eqRo0aOn78uI4fP24/909SU1M1YsQIbdu2TV988YW8vLx03333KTs72+G6sWPH6qmnntKuXbtUtWpVPfzww8rMzJQkbdmyRX379tXgwYO1a9cutWrVSi+88EKe2gfy6vLly5owYYK+++47rVy5UikpKerdu3eO68aOHauXX35Z27dvV6FChdS3b1/7uWXLlik+Pl4vvviitm/frrCwMM2aNesmPgWQTwYKrD/++MPw9fU1li5dai87c+aMUbhwYWP48OH2sgoVKhjTpk0zDMMwPvvsM8Pb29s4duyY/fzq1asNScaKFSuu2VaLFi2Mpk2bOpQ1aNDAGD16tGEYhrFmzRrD29vbOHr0qP383r17DUnG1q1bDcMwjPj4eKNOnTr/+Fy9evUyOnfufM3zJ0+eNCQZe/bsMQzDMFJSUgxJxhtvvJGj7eTkZMMwDOPhhx822rdv71BPt27djKCgoH+MB7ieFi1aOHzf/mrr1q2GJOOPP/4wDMMw1q9fb0gyPv/8c/s1n3zyiSHJuHjxomEYhtG4cWNj0KBBDvU0atQoT98dwAr0SBRghw4d0uXLl9W4cWN7WXBwsKpVq3bNe5KTkxUeHq7bbrvNXvbX+6+ndu3aDp/DwsLs3bbJyckqX768ypcvbz8fFRWl4sWLKzk5OU/1X8uhQ4fUo0cPVapUSYGBgYqIiJB0ZSjlWvGFhYVJkkN8f3/OvD43kFdJSUnq3LmzKlSooGLFiqlly5aS+LMKz0YiUYAZTuxunts9NpstT/f6+PjkuO/q8IJhGLnWc63y/OjUqZPOnDmjefPmacuWLdqyZYukK93I14rvapt/jQ9wpdTUVLVt21ZFixbV4sWLtW3bNq1YsUJS/v6sAgUNiUQBVqVKFfn4+Gjz5s32snPnzungwYPXvCcqKkpHjx7Vr7/+ai/7+6REZ1yt99ixY/ayffv26fz584qMjJQk+fr6KisrK1/1njlzRsnJyXrmmWfUunVrRUZG6ty5c07F99ffk6Qcn4EbsX//fp0+fVqTJk1Ss2bNVL16dYeJlnkVGRnJn1UUKCz/LMCKFi2qfv36adSoUQoJCVHp0qU1duxYeXldOz+MjY1VtWrV9Oijj+rll1/WhQsXNHbs2BuOJTY2VrVr19Yjjzyi6dOnKzMzU4MHD1aLFi0UExMj6crqkZSUFO3atUu33XabihUr9o9vPyxRooRCQkI0d+5chYWF6ejRo/r3v/+d7/iGDRumJk2aaMqUKerSpYvWrFmjxMREp54VyE14eLh8fX01c+ZMDRo0SN9//70mTJiQ73qGDx+uXr16KSYmRk2bNtXbb7+tvXv3qlKlSi6IGrhx9EgUcC+99JKaN2+ue++9V7GxsWratKnq169/zeu9vLy0YsUKpaenq2HDhurfv79efPHFG47j6s6YJUqUUPPmzRUbG6tKlSrp3XfftV/zwAMPqH379mrVqpVKlSqlJUuW/GO9Xl5eWrp0qXbs2KGaNWvqySef1EsvvZTv+O644w698cYbmjlzpurWras1a9bomWeeyXc9wLWUKlVKCxcu1HvvvaeoqChNmjRJ//3vf/NdT7du3fTcc89p9OjRql+/vo4cOaLHH3/cBRED5uA14gAAwGn0SAAAAKeRSAAAAKeRSAAAAKeRSAAAAKeRSAAAAKeRSAAAAKeRSAAAAKeRSAAWGjdunOrWrWv/3Lt3b3Xp0uWmx3H48GHZbDbt2rXrmtdUrFhR06dPz3OdCxcuVPHixW84tqubnQFwTyQSwN/07t1bNptNNptNPj4+qlSpkp566imlpqa6vO1XXnlFCxcuzNO1efnLHwBcjXdtALlo3769FixYoIyMDH311Vfq37+/UlNTNXv27BzXZmRk5HgzqrOCgoJMqQcAbhZ6JIBc+Pn5qUyZMipfvrx69OihRx55xN69fnU4Yv78+apUqZL8/PxkGIbOnz+vgQMHKjQ0VIGBgbrrrrv03XffOdQ7adIklS5dWsWKFVO/fv106dIlh/N/H9rIzs7W5MmTVaVKFfn5+Sk8PNz+bpSIiAhJUnR0tGw2m1q2bGm/b8GCBYqMjJS/v7+qV6+uWbNmObSzdetWRUdHy9/fXzExMUpKSsr372jq1KmqVauWihQpovLly2vw4MH6888/c1y3cuVKVa1aVf7+/mrTpo3DG2Il6aOPPlL9+vXl7++vSpUqafz48crMzMy1zcuXL+uJJ55QWFiY/P39VbFiRSUkJOQ7dgDmoUcCyIPChQsrIyPD/vnHH3/UsmXL9P7778vb21uS1LFjRwUHB+vTTz9VUFCQXn/9dbVu3VoHDx5UcHCwli1bpvj4eL322mtq1qyZ3nrrLc2YMeO6b3UcM2aM5s2bp2nTpqlp06Y6fvy49u/fL+lKMtCwYUN9/vnnqlGjhnx9fSVJ8+bNU3x8vF599VVFR0crKSlJAwYMUJEiRdSrVy+lpqbqnnvu0V133aXFixcrJSVFw4cPz/fvxMvLSzNmzLC/1XXw4MF6+umnHZKWtLQ0vfjii1q0aJF8fX01ePBgde/eXd98840k6bPPPlPPnj01Y8YMNWvWTIcOHdLAgQMlSfHx8TnanDFjhlatWqVly5YpPDxcx44dy5GYALjJDAAOevXqZXTu3Nn+ecuWLUZISIjRtWtXwzAMIz4+3vDx8TFOnjxpv+aLL74wAgMDjUuXLjnUVblyZeP11183DMMwGjdubAwaNMjhfKNGjYw6derk2vaFCxcMPz8/Y968ebnGmZKSYkgykpKSHMrLly9vvPPOOw5lEyZMMBo3bmwYhmG8/vrrRnBwsJGammo/P3v27Fzr+qsKFSoY06ZNu+b5ZcuWGSEhIfbPCxYsMCQZmzdvtpclJycbkowtW7YYhmEYzZo1MyZOnOhQz1tvvWWEhYXZP0syVqxYYRiGYQwdOtS46667jOzs7GvGAeDmokcCyMXHH3+sokWLKjMzUxkZGercubNmzpxpP1+hQgWVKlXK/nnHjh36888/FRIS4lDPxYsXdejQIUlScnKyBg0a5HC+cePGWr9+fa4xJCcnKz09Xa1bt85z3KdOndKxY8fUr18/DRgwwF6emZlpn3+RnJysOnXqKCAgwCGO/Fq/fr0mTpyoffv26cKFC8rMzNSlS5eUmpqqIkWKSJIKFSqkmJgY+z3Vq1dX8eLFlZycrIYNG2rHjh3atm2bw6vss7KydOnSJaWlpTnEKF0Z+mnTpo2qVaum9u3b65577lHbtm3zHTsA85BIALlo1aqVZs+eLR8fH5UtWzbHZMqrf1FelZ2drbCwMG3YsCFHXc4ugSxcuHC+78nOzpZ0ZXijUaNGDueuDsEYhuFUPH915MgR3X333Ro0aJAmTJig4OBgff311+rXr5/DEJB0Zfnm310ty87O1vjx43X//ffnuMbf3z9HWb169ZSSkqLVq1fr888/V9euXRUbG6vly5ff8DMBcA6JBJCLIkWKqEqVKnm+vl69ejpx4oQKFSqkihUr5npNZGSkNm/erEcffdRetnnz5mvWefvtt6tw4cL64osv1L9//xznr86JyMrKspeVLl1a5cqV008//aRHHnkk13qjoqL01ltv6eLFi/Zk5Xpx5Gb79u3KzMzUyy+/LC+vK3O2ly1bluO6zMxMbd++XQ0bNpQkHThwQL///ruqV68u6crv7cCBA/n6XQcGBqpbt27q1q2bHnzwQbVv315nz55VcHBwvp4BgDlIJAATxMbGqnHjxurSpYsmT56satWq6ddff9Wnn36qLl26KCYmRsOHD1evXr0UExOjpk2b6u2339bevXuvOdnS399fo0eP1tNPPy1fX1/deeedOnXqlPbu3at+/fopNDRUhQsXVmJiom677Tb5+/srKChI48aN07BhwxQYGKgOHTooPT1d27dv17lz5zRixAj16NFDY8eOVb9+/fTMM8/o8OHD+u9//5uv561cubIyMzM1c+ZMderUSd98843mzJmT4zofHx8NHTpUM2bMkI+Pj5544gndcccd9sTiueee0z333KPy5cvroYcekpeXl3bv3q09e/bohRdeyFHftGnTFBYWprp168rLy0vvvfeeypQpY8rGVwCcw/JPwAQ2m02ffvqpmjdvrr59+6pq1arq3r27Dh8+rNKlS0uSunXrpueee06jR49W/fr1deTIET3++OPXrffZZ5/VyJEj9dxzzykyMlLdunXTyZMnJV2ZfzBjxgy9/vrrKlu2rDp37ixJ6t+/v9544w0tXLhQtWrVUosWLbRw4UL7ctGiRYvqo48+0r59+xQdHa2xY8dq8uTJ+XreunXraurUqZo8ebJq1qypt99+O9dlmAEBARo9erR69Oihxo0bq3Dhwlq6dKn9fLt27fTxxx9r7dq1atCgge644w5NnTpVFSpUyLXdokWLavLkyYqJiVGDBg10+PBhffrpp/ZeEQA3n80wY8AUAADckkjjAQCA00gkAACA00gkAACA00gkAACA00gkAACA00gkAACA00gkAACA00gkAACA00gkAACA00gkAACA00gkAACA00gkAACA0/4fSJA9PyZ/6KAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tree</td>\n",
       "      <td>0.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LogReg</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>kNN</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Model  Accuracy\n",
       "2    Tree  0.888889\n",
       "0  LogReg  0.833333\n",
       "1     SVM  0.833333\n",
       "3     kNN  0.833333"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc = {'Model': ['LogReg', 'SVM', 'Tree', 'kNN'],\n",
    "       'Accuracy': [logreg_cv.score(X_test, Y_test),\n",
    "                    svm_cv.score(X_test, Y_test), \n",
    "                    tree_cv.score(X_test, Y_test),\n",
    "                    knn_cv.score(X_test, Y_test)]}\n",
    "\n",
    "df = pd.DataFrame.from_dict(acc, orient = 'index').transpose().sort_values(by='Accuracy',ascending=False)\n",
    "df\n",
    "\n",
    "## It seems that LogReg, SVM and Tree have the same (Best) accuracy. The model that performs the worst is kNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot the accuracy as bar chart\n",
    "ax = sns.barplot(x='Model', y='Accuracy', data = df)\n",
    "ax.bar_label(ax.containers[0], fmt='%.2f')\n",
    "plt.xlabel('Model', fontsize=20)\n",
    "plt.ylabel('Accuracy', fontsize=20)\n",
    "plt.title('Accuracy on the Test Set for each model')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Pratiksha Verma](https://www.linkedin.com/in/pratiksha-verma-6487561b1/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By      | Change Description      |\n",
    "| ----------------- | ------- | -------------   | ----------------------- |\n",
    "| 2022-11-09        | 1.0     | Pratiksha Verma | Converted initial version to Jupyterlite|\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <h3 align=\"center\"> IBM Corporation 2022. All rights reserved. <h3/>\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}