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\" 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": [
    "## Import Libraries and Define Auxiliary Functions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'piplite'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpiplite\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m piplite\u001b[38;5;241m.\u001b[39minstall([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumpy\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m piplite\u001b[38;5;241m.\u001b[39minstall([\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpandas\u001b[39m\u001b[38;5;124m'\u001b[39m])\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'piplite'"
     ]
    }
   ],
   "source": [
    "import piplite\n",
    "await piplite.install(['numpy'])\n",
    "await piplite.install(['pandas'])\n",
    "await piplite.install(['seaborn'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import seaborn as sns"
   ]
  },
  {
   "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: js in c:\\users\\p\\anaconda 3\\envs\\new_env_name\\lib\\site-packages (1.0)\n",
      "Requirement already satisfied: fanstatic in c:\\users\\p\\anaconda 3\\envs\\new_env_name\\lib\\site-packages (from js) (1.4)\n",
      "Requirement already satisfied: setuptools in c:\\users\\p\\anaconda 3\\envs\\new_env_name\\lib\\site-packages (from js) (69.5.1)\n",
      "Requirement already satisfied: WebOb>=1.2 in c:\\users\\p\\anaconda 3\\envs\\new_env_name\\lib\\site-packages (from fanstatic->js) (1.8.7)\n"
     ]
    }
   ],
   "source": [
    "!pip install js"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'fetch' from 'js' (C:\\Users\\P\\anaconda 3\\envs\\NEW_ENV_NAME\\Lib\\site-packages\\js\\__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[5], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mjs\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m fetch\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mio\u001b[39;00m\n\u001b[0;32m      4\u001b[0m URL1 \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "\u001b[1;31mImportError\u001b[0m: cannot import name 'fetch' from 'js' (C:\\Users\\P\\anaconda 3\\envs\\NEW_ENV_NAME\\Lib\\site-packages\\js\\__init__.py)"
     ]
    }
   ],
   "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(text1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "URL1 = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\"\n",
    "URL1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"dataset_part_2.csv\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
       "      <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",
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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",
       "      <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",
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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",
       "      <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",
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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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'fetch' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[15], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m URL2 \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhttps://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_3.csv\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m----> 2\u001b[0m resp2 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[43mfetch\u001b[49m(URL2)\n\u001b[0;32m      3\u001b[0m text2 \u001b[38;5;241m=\u001b[39m io\u001b[38;5;241m.\u001b[39mBytesIO((\u001b[38;5;28;01mawait\u001b[39;00m resp2\u001b[38;5;241m.\u001b[39marrayBuffer())\u001b[38;5;241m.\u001b[39mto_py())\n\u001b[0;32m      4\u001b[0m X \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(text2)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'fetch' is not defined"
     ]
    }
   ],
   "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(text2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_3.csv'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "URL2 = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_3.csv'\n",
    "URL2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.read_csv(\"dataset_part_3.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Flights</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Orbit_ES-L1</th>\n",
       "      <th>Orbit_GEO</th>\n",
       "      <th>Orbit_GTO</th>\n",
       "      <th>Orbit_HEO</th>\n",
       "      <th>Orbit_ISS</th>\n",
       "      <th>...</th>\n",
       "      <th>Serial_B1058</th>\n",
       "      <th>Serial_B1059</th>\n",
       "      <th>Serial_B1060</th>\n",
       "      <th>Serial_B1062</th>\n",
       "      <th>GridFins_False</th>\n",
       "      <th>GridFins_True</th>\n",
       "      <th>Reused_False</th>\n",
       "      <th>Reused_True</th>\n",
       "      <th>Legs_False</th>\n",
       "      <th>Legs_True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>86.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>87.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>88.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>89.0</td>\n",
       "      <td>15400.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>90.0</td>\n",
       "      <td>3681.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 83 columns</p>\n",
       "</div>"
      ],
      "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",
       "..           ...           ...      ...    ...          ...          ...   \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",
       "..        ...        ...        ...        ...  ...           ...   \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",
       "..           ...           ...           ...             ...            ...   \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",
       "..           ...          ...         ...        ...  \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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = data[\"Class\"].to_numpy() \n",
    "Y"
   ]
  },
  {
   "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": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# students get this \n",
    "X = preprocessing.StandardScaler().fit(X).transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.71291154e+00, -1.94814463e-16, -6.53912840e-01,\n",
       "        -1.57589457e+00, -9.73440458e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -6.54653671e-01, -1.05999788e-01,\n",
       "        -5.51677284e-01,  3.44342023e+00, -1.85695338e-01,\n",
       "        -3.33333333e-01, -1.05999788e-01, -2.42535625e-01,\n",
       "        -4.29197538e-01,  7.97724035e-01, -5.68796459e-01,\n",
       "        -4.10890702e-01, -4.10890702e-01, -1.50755672e-01,\n",
       "        -7.97724035e-01, -1.50755672e-01, -3.92232270e-01,\n",
       "         9.43398113e+00, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n",
       "        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n",
       "        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.50755672e-01, -1.50755672e-01,\n",
       "        -1.50755672e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.50755672e-01, -2.15665546e-01,\n",
       "        -1.85695338e-01, -2.15665546e-01, -2.67261242e-01,\n",
       "        -1.05999788e-01, -2.42535625e-01, -1.05999788e-01,\n",
       "        -2.15665546e-01, -1.85695338e-01, -2.15665546e-01,\n",
       "        -1.85695338e-01, -1.05999788e-01,  1.87082869e+00,\n",
       "        -1.87082869e+00,  8.35531692e-01, -8.35531692e-01,\n",
       "         1.93309133e+00, -1.93309133e+00],\n",
       "       [-1.67441914e+00, -1.19523159e+00, -6.53912840e-01,\n",
       "        -1.57589457e+00, -9.73440458e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -6.54653671e-01, -1.05999788e-01,\n",
       "        -5.51677284e-01,  3.44342023e+00, -1.85695338e-01,\n",
       "        -3.33333333e-01, -1.05999788e-01, -2.42535625e-01,\n",
       "        -4.29197538e-01,  7.97724035e-01, -5.68796459e-01,\n",
       "        -4.10890702e-01, -4.10890702e-01, -1.50755672e-01,\n",
       "        -7.97724035e-01, -1.50755672e-01, -3.92232270e-01,\n",
       "        -1.05999788e-01,  9.43398113e+00, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n",
       "        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n",
       "        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.50755672e-01, -1.50755672e-01,\n",
       "        -1.50755672e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.50755672e-01, -2.15665546e-01,\n",
       "        -1.85695338e-01, -2.15665546e-01, -2.67261242e-01,\n",
       "        -1.05999788e-01, -2.42535625e-01, -1.05999788e-01,\n",
       "        -2.15665546e-01, -1.85695338e-01, -2.15665546e-01,\n",
       "        -1.85695338e-01, -1.05999788e-01,  1.87082869e+00,\n",
       "        -1.87082869e+00,  8.35531692e-01, -8.35531692e-01,\n",
       "         1.93309133e+00, -1.93309133e+00],\n",
       "       [-1.63592675e+00, -1.16267307e+00, -6.53912840e-01,\n",
       "        -1.57589457e+00, -9.73440458e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -6.54653671e-01, -1.05999788e-01,\n",
       "         1.81265393e+00, -2.90408935e-01, -1.85695338e-01,\n",
       "        -3.33333333e-01, -1.05999788e-01, -2.42535625e-01,\n",
       "        -4.29197538e-01,  7.97724035e-01, -5.68796459e-01,\n",
       "        -4.10890702e-01, -4.10890702e-01, -1.50755672e-01,\n",
       "        -7.97724035e-01, -1.50755672e-01, -3.92232270e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01,  9.43398113e+00,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n",
       "        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n",
       "        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.50755672e-01, -1.50755672e-01,\n",
       "        -1.50755672e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.50755672e-01, -2.15665546e-01,\n",
       "        -1.85695338e-01, -2.15665546e-01, -2.67261242e-01,\n",
       "        -1.05999788e-01, -2.42535625e-01, -1.05999788e-01,\n",
       "        -2.15665546e-01, -1.85695338e-01, -2.15665546e-01,\n",
       "        -1.85695338e-01, -1.05999788e-01,  1.87082869e+00,\n",
       "        -1.87082869e+00,  8.35531692e-01, -8.35531692e-01,\n",
       "         1.93309133e+00, -1.93309133e+00],\n",
       "       [-1.59743435e+00, -1.20058661e+00, -6.53912840e-01,\n",
       "        -1.57589457e+00, -9.73440458e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -6.54653671e-01, -1.05999788e-01,\n",
       "        -5.51677284e-01, -2.90408935e-01, -1.85695338e-01,\n",
       "         3.00000000e+00, -1.05999788e-01, -2.42535625e-01,\n",
       "        -4.29197538e-01, -1.25356634e+00, -5.68796459e-01,\n",
       "         2.43373723e+00, -4.10890702e-01, -1.50755672e-01,\n",
       "        -7.97724035e-01, -1.50755672e-01, -3.92232270e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "         9.43398113e+00, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n",
       "        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n",
       "        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.50755672e-01, -1.50755672e-01,\n",
       "        -1.50755672e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.50755672e-01, -2.15665546e-01,\n",
       "        -1.85695338e-01, -2.15665546e-01, -2.67261242e-01,\n",
       "        -1.05999788e-01, -2.42535625e-01, -1.05999788e-01,\n",
       "        -2.15665546e-01, -1.85695338e-01, -2.15665546e-01,\n",
       "        -1.85695338e-01, -1.05999788e-01,  1.87082869e+00,\n",
       "        -1.87082869e+00,  8.35531692e-01, -8.35531692e-01,\n",
       "         1.93309133e+00, -1.93309133e+00],\n",
       "       [-1.55894196e+00, -6.28670558e-01, -6.53912840e-01,\n",
       "        -1.57589457e+00, -9.73440458e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01,  1.52752523e+00, -1.05999788e-01,\n",
       "        -5.51677284e-01, -2.90408935e-01, -1.85695338e-01,\n",
       "        -3.33333333e-01, -1.05999788e-01, -2.42535625e-01,\n",
       "        -4.29197538e-01,  7.97724035e-01, -5.68796459e-01,\n",
       "        -4.10890702e-01, -4.10890702e-01, -1.50755672e-01,\n",
       "        -7.97724035e-01, -1.50755672e-01, -3.92232270e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01,  9.43398113e+00, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n",
       "        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n",
       "        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.50755672e-01, -1.50755672e-01,\n",
       "        -1.50755672e-01, -1.05999788e-01, -1.05999788e-01,\n",
       "        -1.05999788e-01, -1.50755672e-01, -2.15665546e-01,\n",
       "        -1.85695338e-01, -2.15665546e-01, -2.67261242e-01,\n",
       "        -1.05999788e-01, -2.42535625e-01, -1.05999788e-01,\n",
       "        -2.15665546e-01, -1.85695338e-01, -2.15665546e-01,\n",
       "        -1.85695338e-01, -1.05999788e-01,  1.87082869e+00,\n",
       "        -1.87082869e+00,  8.35531692e-01, -8.35531692e-01,\n",
       "         1.93309133e+00, -1.93309133e+00]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:5]"
   ]
  },
  {
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (72, 83) (72,)\n",
      "Test set: (18, 83) (18,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2) \n",
    "print(\"Train set:\", X_train.shape, Y_train.shape) \n",
    "print(\"Test set:\", X_test.shape, Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can see we only have 18 test samples.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18,)"
      ]
     },
     "execution_count": 16,
     "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": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters ={'C':[0.01,0.1,1],\n",
    "             'penalty':['l2'],\n",
    "             'solver':['lbfgs']} \n",
    "lr=LogisticRegression() \n",
    "grid_search = GridSearchCV(lr, parameters, cv=10)\n",
    "logreg_cv = grid_search.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters ={\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge\n",
    "lr=LogisticRegression()\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": 19,
   "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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WJxIhISFasWJFjvKVK1cqJCTEgogAAPAMX375pTp16qSyZcvKZrPl6OU3DEPjxo1T2bJlVbhwYbVs2VJ79+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=",
      "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": 22,
   "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": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(svm, parameters, cv=10)\n",
    "svm_cv = grid_search.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 25,
     "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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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": 27,
   "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': ['log2', 'sqrt'],\n",
    "     'min_samples_leaf': [1, 2, 4],\n",
    "     'min_samples_split': [2, 5, 10]}\n",
    "\n",
    "tree = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(tree, parameters, cv=10, error_score=\"raise\")\n",
    "tree_cv = grid_search.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 14, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 5, 'splitter': 'best'}\n",
      "accuracy : 0.8892857142857142\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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7777777777777778"
      ]
     },
     "execution_count": 30,
     "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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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": 32,
   "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": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(KNN, parameters, cv=10) \n",
    "knn_cv = grid_search.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 35,
     "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": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WJxIhISFasWJFjvKVK1cqJCTEgogAAPAMX375pTp16qSyZcvKZrPl6OU3DEPjxo1T2bJlVbhwYbVs2VJ79+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=",
      "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": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for Logistic Regression method: 0.8333333333333334\n",
      "Accuracy for Support Vector Machine method: 0.8333333333333334\n",
      "Accuracy for Decision tree method: 0.7777777777777778\n",
      "Accuracy for K Nearest neighbors method: 0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy for Logistic 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 Nearest neighbors method:\", knn_cv.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Models = [\"LR\", \"SVM\", \"DT\", \"KNN\"] \n",
    "Accuracy = [0.833, 0.833, 0.777, 0.833]  \n",
    "plt.bar(Models, Accuracy)\n",
    "plt.xlabel(\"Models\")\n",
    "plt.ylabel(\"Accuracy\") \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/)\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"
   ]
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