0.7.Machine_Learning_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** **\n",
    "\n",
    "* **The project workflow and most of the code in the notebooks were authored by <a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Joseph Santarcangelo</a>, <a href=\"https://www.linkedin.com/in/yan-luo-96288783/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Yan Luo</a>, <a href=\"https://www.linkedin.com/in/nayefaboutayoun/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Nayef Abou Tayoun</a>, and Lakshmi Holla for the IBM Data Science Certification Capstone Project.** \n",
    "\n",
    "* The project was organized into labs, with each lab corresponding to a step in a typical data science project lifecycle. The program participant was responsible for completing tasks, conducting analysis, and writing the final report.\n",
    "\n",
    "* You can read my comprehensive 67-page project report, which combines both technical and non-technical styles to describe each project step and discuss numerous insights from the data analysis, by following this link: <a href=\"https://github.com/Al-1n/IBM_SpaceX_Capstone/blob/master/SpaceX_Final_Report.pdf\">Full report</a>. \n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/alin-airinei/\">Alin Airinei</a>"
   ]
  },
  {
   "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": [
    "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 which performs best using test data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries and Define Auxiliary Functions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 81,
   "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",
    "    plt.figure(figsize=(10, 7))\n",
    "    plt.xticks(fontsize=16)\n",
    "    plt.yticks(fontsize=16)\n",
    "    ax= plt.subplot()\n",
    "    sns.set(font_scale=2.0)\n",
    "    sns.heatmap(cm, annot=True, ax = ax, cmap = 'Spectral', linewidths=2.0); #annot=True to annotate cells\n",
    "    ax.set_xlabel('Predicted labels', fontsize = 20, weight = \"bold\", color = 'dimgray')\n",
    "    ax.set_ylabel('True labels', fontsize = 20, weight = \"bold\", color = 'dimgray')\n",
    "    ax.set_title('Confusion Matrix', fontsize = 24, weight = \"bold\", color = 'dimgray'); \n",
    "    ax.xaxis.set_ticklabels(['did not land', 'landed']); ax.yaxis.set_ticklabels(['did not land', 'landed'])\n",
    "    plt.savefig(\"Confusion\", dpi=300, bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the dataframe\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>Date</th>\n",
       "      <th>BoosterVersion</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Orbit</th>\n",
       "      <th>LaunchSite</th>\n",
       "      <th>Outcome</th>\n",
       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
       "      <th>LandingPad</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Serial</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2010-06-04</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0003</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2012-05-22</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0005</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2013-03-01</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>ISS</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B0007</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2013-09-29</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>PO</td>\n",
       "      <td>VAFB SLC 4E</td>\n",
       "      <td>False Ocean</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1003</td>\n",
       "      <td>-120.610829</td>\n",
       "      <td>34.632093</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2013-12-03</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>GTO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1004</td>\n",
       "      <td>-80.577366</td>\n",
       "      <td>28.561857</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n",
       "0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n",
       "1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n",
       "2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n",
       "3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n",
       "4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n",
       "\n",
       "       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n",
       "0    None None        1     False   False  False        NaN    1.0   \n",
       "1    None None        1     False   False  False        NaN    1.0   \n",
       "2    None None        1     False   False  False        NaN    1.0   \n",
       "3  False Ocean        1     False   False  False        NaN    1.0   \n",
       "4    None None        1     False   False  False        NaN    1.0   \n",
       "\n",
       "   ReusedCount Serial   Longitude   Latitude  Class  \n",
       "0            0  B0003  -80.577366  28.561857      0  \n",
       "1            0  B0005  -80.577366  28.561857      0  \n",
       "2            0  B0007  -80.577366  28.561857      0  \n",
       "3            0  B1003 -120.610829  34.632093      0  \n",
       "4            0  B1004  -80.577366  28.561857      0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\")\n",
    "\n",
    "# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n",
    "\n",
    "# data = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_2.csv')\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Flights</th>\n",
       "      <th>Block</th>\n",
       "      <th>ReusedCount</th>\n",
       "      <th>Orbit_ES-L1</th>\n",
       "      <th>Orbit_GEO</th>\n",
       "      <th>Orbit_GTO</th>\n",
       "      <th>Orbit_HEO</th>\n",
       "      <th>Orbit_ISS</th>\n",
       "      <th>...</th>\n",
       "      <th>Serial_B1058</th>\n",
       "      <th>Serial_B1059</th>\n",
       "      <th>Serial_B1060</th>\n",
       "      <th>Serial_B1062</th>\n",
       "      <th>GridFins_False</th>\n",
       "      <th>GridFins_True</th>\n",
       "      <th>Reused_False</th>\n",
       "      <th>Reused_True</th>\n",
       "      <th>Legs_False</th>\n",
       "      <th>Legs_True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6104.959412</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>677.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_3.csv')\n",
    "\n",
    "# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n",
    "\n",
    "# X = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_3.csv')\n",
    "\n",
    "X.head(100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a NumPy array from the column <code>Class</code> in <code>data</code>, by applying the method <code>to_numpy()</code>  then\n",
    "assign it  to the variable <code>Y</code>,make sure the output is a  Pandas series (only one bracket df\\['name of  column']).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "5    0\n",
       "6    1\n",
       "7    1\n",
       "8    0\n",
       "9    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outcome = data['Class'].to_numpy()\n",
    "Y = pd.Series(outcome, copy=False)\n",
    "Y.head(10)"
   ]
  },
  {
   "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": 23,
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# students get this \n",
    "X = preprocessing.StandardScaler().fit(X).transform(X)\n",
    "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": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can see we only have 18 test samples.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18,)"
      ]
     },
     "execution_count": 25,
     "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": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters ={'C':[0.01,0.1,1],\n",
    "             'penalty':['l2'],\n",
    "             'solver':['lbfgs']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters ={\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge\n",
    "lr=LogisticRegression()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
       "             param_grid={'C': [0.01, 0.1, 1], 'penalty': ['l2'],\n",
       "                         'solver': ['lbfgs']})"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg_cv=GridSearchCV(lr, parameters, cv=10)\n",
    "logreg_cv.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We output the <code>GridSearchCV</code> object for logistic regression. We display the best parameters using the data attribute <code>best_params\\_</code> and the accuracy on the validation data using the data attribute <code>best_score\\_</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression Accuracy:  0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "yhat = logreg_cv.predict(X_test)\n",
    "from sklearn import metrics\n",
    "print(\"Logistic Regression Accuracy: \", metrics.accuracy_score(Y_test, yhat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets look at the confusion matrix:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat=logreg_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examining the confusion matrix, we see that logistic regression can distinguish between the different classes.  We see that the major problem is false positives.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  6\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a support vector machine object then  create a  <code>GridSearchCV</code> object  <code>svm_cv</code> with cv - 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=SVC(),\n",
       "             param_grid={'C': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         'gamma': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         'kernel': ('linear', 'rbf', 'poly', 'rbf', 'sigmoid')})"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_cv=GridSearchCV(svm, parameters, cv=10)\n",
    "svm_cv.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Support Vector Machine Accuracy:  0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "yhat = svm_cv.predict(X_test)\n",
    "print(\"Support Vector Machine Accuracy: \", metrics.accuracy_score(Y_test, yhat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWgAAAEWCAYAAABLzQ1kAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAf80lEQVR4nO3deZgcVbnH8e9vJiEJkIR9C2CCsgjIIjsIBBFlX1zYvQpo4AqCywVBuURARVxQvBeXiAiEEFkkIAQhCsQAsmQhBAjblbCEBBJQSCABkpn3/lHVoTNOZrp7urqrp3+f56kn3VXV55zp6bxz+tQ5bykiMDOz/GmpdwPMzKxzDtBmZjnlAG1mllMO0GZmOeUAbWaWUw7QZmY55QBtPSZpgKRbJb0p6YYelHOcpAnVbFs9SPqzpC/Uux3W+Bygm4ikYyVNkfSWpLlpIPlYFYr+LLAusGZEfK7SQiJiTER8sgrtWY6k4ZJC0k0d9m+b7p9YYjnflXRNd+dFxAERcVWFzTVbxgG6SUj6BvBz4AckwXRj4JfAYVUo/gPAMxGxtAplZWU+sLukNYv2fQF4ploVKOH/U1Y1/jA1AUmDgQuAUyPipoh4OyKWRMStEXFmek4/ST+XNCfdfi6pX3psuKTZkr4paV7a+z4hPXY+cB5wVNozP6ljT1PS0LSn2id9/kVJz0laKGmWpOOK9t9X9LrdJU1Oh04mS9q96NhESRdKuj8tZ4Kktbp4G94DbgaOTl/fChwJjOnwXl0q6SVJCyRNlbRnun9/4NtFP+ejRe34vqT7gUXAJum+L6XHfyXpxqLyL5Z0lySV+vuz5uUA3Rx2A/oD47o45zvArsB2wLbAzsC5RcfXAwYDQ4CTgMskrR4RI0l65ddFxKoR8buuGiJpFeAXwAERMRDYHZjeyXlrAOPTc9cELgHGd+gBHwucAKwDrAT8V1d1A1cD/5E+/hTwBDCnwzmTSd6DNYBrgRsk9Y+IOzr8nNsWvebzwAhgIPBCh/K+CWyT/vHZk+S9+0I4x4KVwAG6OawJvNbNEMRxwAURMS8i5gPnkwSegiXp8SURcTvwFrB5he1pB7aWNCAi5kbEE52ccxDwbESMjoilETEWeAo4pOic30fEMxGxGLieJLCuUET8HVhD0uYkgfrqTs65JiJeT+v8KdCP7n/OKyPiifQ1SzqUtwg4nuQPzDXAVyNidjflmQEO0M3idWCtwhDDCmzA8r2/F9J9y8roEOAXAauW25CIeBs4CjgFmCtpvKQtSmhPoU1Dip6/UkF7RgOnAfvQyTeKdBjnyXRY5Q2Sbw1dDZ0AvNTVwYh4GHgOEMkfErOSOEA3hweAd4DDuzhnDsnFvoKN+fev/6V6G1i56Pl6xQcj4s6I2A9Yn6RX/NsS2lNo08sVtqlgNPAV4Pa0d7tMOgTxLZKx6dUjYjXgTZLACrCiYYkuhysknUrSE58DnFVxy63pOEA3gYh4k+RC3mWSDpe0sqS+kg6Q9KP0tLHAuZLWTi+2nUfylbwS04G9JG2cXqA8p3BA0rqSDk3Hot8lGSpp66SM24HN0qmBfSQdBWwJ3FZhmwCIiFnA3iRj7h0NBJaSzPjoI+k8YFDR8VeBoeXM1JC0GfA9kmGOzwNnSdqustZbs3GAbhIRcQnwDZILf/NJvpafRjKzAZIgMgWYATwGTEv3VVLXX4Dr0rKmsnxQbSG5cDYH+CdJsPxKJ2W8Dhycnvs6Sc/z4Ih4rZI2dSj7vojo7NvBncCfSabevUDyraN4+KKwCOd1SdO6qycdUroGuDgiHo2IZ0lmgowuzJAx64p8MdnMLJ/cgzYzyykHaDOzKpN0Rbqo6/GifT+W9JSkGZLGSVqtu3IcoM3Mqu9KYP8O+/4CbB0R25Bc5zin44s6coA2M6uyiJhEchG8eN+EorUEDwIbdldOVwsX6urEoZ/11UszK8kVz9/Y49wmS157ruSYs9LaHzyZZHl/waiIGFVGdSeSzHTqUm4DtJlZTbV3Nh2/c2kwLicgLyPpOyTz7cd0d64DtJkZQLRnXoWSGzkcDOxbSsIsB2gzM4D2bAN0mrL2W8DeHdMMrIgDtJkZEFXsQUsaCwwnSVI2GxhJMmujH/CXNB34gxFxSlflOECbmQG0Ve+GQBFxTCe7u8yV3hkHaDMzKOsiYa04QJuZQU0uEpbLAdrMDDK/SFgJB2gzM6p7kbBaHKDNzMA9aDOz3Gpb0v05NeYAbWYGvkhoZpZbHuIwM8sp96DNzHLKPWgzs3yKdl8kNDPLJ/egzcxyymPQZmY55WRJZmY55R60mVlOeQzazCynqpiwv1ocoM3MwD1oM7O8ivBFQjOzfHIP2swsp5phFoekhUCs6HhEDKp2nWZmPdYMPeiIGAgg6QLgFWA0IOA4YGC16zMzq4omm8XxqYjYpej5ryQ9BPwowzrNzCqTwyGOlgzLbpN0nKRWSS2SjgPyd5nUzAySIY5StxrJMkAfCxwJvJpun0v3mZnlTw4DdGZDHBHxPHBYVuWbmVVVDoc4MgvQktYGvgwMLa4nIk7Mqk4zs4pV8SKhpCuAg4F5EbF1um8N4DqSmPg8cGRE/KurcrIc4rgFGAz8FRhftJmZ5U91hziuBPbvsO9s4K6I2BS4K33epSxncawcEd/KsHwzs+qp4hBHREySNLTD7sOA4enjq4CJQJcxMsse9G2SDsywfDOz6sn+IuG6ETEXIP13ne5ekGWAPoMkSC+WtEDSQkkLMqzPzKxyZQRoSSMkTSnaRmTRpCxncXjVoJk1jlhhhopOTo1RwKgya3hV0voRMVfS+sC87l6QabIkSasDmwL9C/siYlKWdZqZVWRp5ku9/wR8Afhh+u8t3b0gy2l2XyIZ5tgQmA7sCjwAfDyrOs3MKlbFi4SSxpJcEFxL0mxgJElgvl7SScCLJIv3upRlD/oMYCfgwYjYR9IWwPkZ1mdmVrkqrhCMiGNWcGjfcsrJMkC/ExHvSEJSv4h4StLmGdZnZla5MsagayXLAD1b0mrAzcBfJP0LmJNhfWZmlWuGfNAFEXFE+vC7ku4hWVV4R1b1mZn1SDME6HS9eUePpf+uCvyz2nWamfVUtOUvG3IWPeipJLe8UtG+wvMANsmgTjOznmmGHnREDKt2mWZmmWumdKNmZg2lvblmcZiZNY5mGOIwM2tITXKREABJoyPi893tsxXr068vZ193AX379aWltZUpf36AW352fb2bZXXmz0VGmqwHvVXxE0mtwA4Z1tfrLH13CT8+9nzeXfQOrX1aOefG7/HYxEd47pFn6900qyN/LjKSwzHoqueDlnSOpIXANkV5oBeSpNbrNnuTLe/dRe8A0NqnldY+rclERWt6/lxkINpL32oki2l2FwEXSbooIs6pdvnNRi0tjLztYtb5wHrcPfpOnpvuXpL5c5GJZuhBF0TEOZIOlfSTdDu4u9cU36Xg6YXPZdW0hhLt7Xz3wDP55m4nM2zbDzFks43q3STLAX8uqi/a20veaiWzAC3pIpKUozPT7Yx03wpFxKiI2DEidtx8oBccFlu8YBFPP/gEW++9fb2bYjniz0UVtbWVvtVIlvckPAjYLyKuiIgrSG5BflCG9fU6A9cYxIBBKwPQt99KbLnHNrzyj5fr3CqrN38uMtIepW81kvU86NV4PznS4Izr6nUGr7M6J/30NFpaWlCLmDz+7zx699R6N8vqzJ+LjDTZNLuLgEfSVKMC9gJ80bAMs596gfMPOrPezbCc8eciIzm8SJhlPuixkiaS3PZKwLci4pWs6jMz65EmTJbUAryW1rOZpM18V28zy6Vm6kFLuhg4CngCKPxpCsAB2sxyJ5Y2US4O4HBg84h4N8M6zMyqo5l60MBzQF/AAdrM8q/JxqAXAdMl3UVRkI6I0zOs08ysMk3Wg/5TupmZ5V40U4COiKuyKtvMrOqa7CKhmVnjaKYetJlZQ8lhgM4yWZKZWcOIiJK37kj6uqQnJD0uaayk/pW0qeo9aEm30sX9HSLi0GrXaWbWY1XqQUsaApwObBkRiyVdDxwNXFluWVkMcfwk/ffTwHrANenzY4DnM6jPzKznqjvE0QcYIGkJsDIwp9JCqioi/gYg6cKI2Kvo0K2SvMzbzHIplpa+UEXSCGBE0a5RETEKICJelvQT4EVgMTAhIiZU0qYsx6DXlrTstiiShgFrZ1ifmVnl2kvfiu/+lG6jCsVIWh04DBgGbACsIun4SpqU5SyOrwMTJRVuLjgUODnD+szMKlbFhSqfAGZFxHwASTcBu/P+cG/JslyocoekTYEt0l1POXGSmeVW9QL0i8CuklYmGeLYF5hSSUFZzOL4eETcLenTHQ59UBIRcVO16zQz67Eq5UqKiIck3QhMA5YCjwCjun5V57LoQe8N3A0c0smxABygzSx3qpmLIyJGAiN7Wk4WszhGpv+eUO2yzcyyEkvzt5IwiyGOb3R1PCIuqXadZmY9lr900JkMcQxM/92c5IaxhZSjh+DbXZlZTuUwX38mQxznA0iaAHw0Ihamz78L3FDt+szMqqIZAnSRjYH3ip6/RzIX2swsdxq+B52ukNkoImaUcPpo4GFJ40hmbxwBOIm/meVSLK13C/5dtwFa0kTg0PTc6cB8SX+LiO4uBn5f0p+BPdNdJ0TEIz1rrplZNhq1Bz04IhZI+hLw+4gYKamUHjQRMY1ksraZWa7lMUCXkiypj6T1gSOB2zJuj5lZfYRK32qklB70BcCdwH0RMTnNUPdsts0yM6utPPaguw3QEXEDRdPjIuI54DNZNsrMrNaivXY941KtMEBL+h+6vnXV6Zm0yMysDtrbGihAU2F6PDOzRtRQQxwRsdycZUmrRMTb2TfJzKz28jjE0e0sDkm7SZoJPJk+31bSLzNvmZlZDUWUvtVKKdPsfg58CngdICIeBfbq6gVmZo0m2lXyVislLfWOiJek5RrVlk1zzMzqo9EuEha8JGl3ICStBJxOOtxhZtZb5HEMupQAfQpwKTAEeJlk0cqpWTbKzKzWooYrBEtVykKV14DjatAWM7O6yeM0u1JmcWwi6VZJ8yXNk3RLutzbzKzXaA+VvNVKKbM4rgWuB9YHNiBZ9j02y0aZmdVahEreaqWUAK2IGB0RS9PtGrpYAm5m1oja21TyVitd5eJYI314j6SzgT+QBOajgPE1aJuZWc002iyOqSQBudDqk4uOBXBhVo0yM6u1Wo4tl6qrXBzDatkQM7N6ashpdgCStga2BPoX9kXE1Vk1ysys1mqZY6NUpdw0diQwnCRA3w4cANwHOECbWa9RzSEOSasBlwNbkwwJnxgRD5RbTimzOD4L7Au8EhEnANsC/cqtyMwsz9rbVfJWgkuBOyJiC5KYWVF6jFKGOBZHRLukpZIGAfMAL1Qxs16lWj3oNE7uBXwRICLeA96rpKxSAvSUtLv+W5KZHW8BD1dSWTmunlP2twFrAovn3FvvJlgvVc5FQkkjgBFFu0ZFxKj08SbAfOD3krYliZtnVHLDE0UZI+OShgKDImJGuRWVq89KQ3I4ZG/15gBtnem71iY97v4+tMGnS445u8y5aYX1SdoReBDYIyIeknQpsCAi/rvcNnW1UOWjXR2LiGnlVmZmlldV7BHOBmZHxEPp8xuBsyspqKshjp92cSyAj1dSoZlZHrW1lzJnonsR8YqklyRtHhFPk0yymFlJWV0tVNmn0gaamTWaKmcb/SowJr3JyXPACZUUUtJCFTOz3i6o3jzoiJgO7NjTchygzcyA9hxOS3CANjMD2qvYg66WUu6oIknHSzovfb6xpJ2zb5qZWe0EKnmrlVIuW/4S2A04Jn2+ELgssxaZmdVBGyp5q5VShjh2iYiPSnoEICL+lV6ZNDPrNXJ4z9iSAvQSSa2k87glrU0+fxYzs4rlMaiVMsTxC2AcsI6k75OkGv1Bpq0yM6uxPI5Bd9uDjogxkqaSrIYRcHhEVJQ6z8wsr3J4S8KSEvZvDCwCbi3eFxEvZtkwM7NayuM0u1LGoMfz/s1j+wPDgKeBrTJsl5lZTbXVuwGdKGWI4yPFz9Msdyev4HQzs4bUrsbsQS8nIqZJ2imLxpiZ1UsOV3qXNAb9jaKnLcBHSe4WYGbWa+Rxml0pPeiBRY+XkoxJ/zGb5piZ1UfDzeJIF6isGhFn1qg9ZmZ1Ucsl3KXq6pZXfSJiaVe3vjIz6y0arQf9MMl483RJfwJuAJbdlTYibsq4bWZmNdOoY9BrAK+T3IOwMB86AAdoM+s1Gm0WxzrpDI7HeT8wF+TxZzEzq1ijDXG0AqtCpyPnDtBm1qs02hDH3Ii4oGYtMTOro7YG60HnsLlmZtlotB70vjVrhZlZnTVUgI6If9ayIWZm9ZTHC2tlJ0syM+uNGm0Wh5lZ02ioIQ4zs2bSkAn7zcyaQbWHONJkc1OAlyPi4ErKcIA2MyOTIY4zgCeBQZUW0FK9tpiZNa4oY+uOpA2Bg4DLe9ImB2gzM6CdKHmTNELSlKJtRIfifg6cRQ875h7iMDOjvIuEETEKGNXZMUkHA/MiYqqk4T1pkwO0mRlVHYPeAzhU0oFAf2CQpGsi4vhyC/IQh5kZySyOUreuRMQ5EbFhRAwFjgburiQ4Q0Y9aEmf7uq478ZiZnnTnsPF3lkNcRyS/rsOsDtwd/p8H2AivhuLmeVMFuE5IiaSxLyKZBKgI+IEAEm3AVtGxNz0+frAZVnUaWbWE8241HtoITinXgU2y7hOM7OytTXREEfBREl3AmNJvkEcDdyTcZ1mZmVruh50RJwm6Qhgr3TXqIgYl2WdZmaVaKaLhMWmAQsj4q+SVpY0MCIW1qBeM7OS5S88ZzwPWtKXgRuB36S7hgA3Z1mnmVkl2svYaiXrHvSpwM7AQwAR8aykdTKu08ysbM14kfDdiHhPSpbeSOpDPr9JmFmTy+MYdNZLvf8m6dvAAEn7ATcAt2ZcZ6/yqU8O54nHJ/HUzPs468xT690cq5Nzf3AJex10NIcff8qyfT/538s55Jgvc8R//Cenn3MBCxa+VccWNr5qphutlqwD9NnAfOAx4GTgduDcjOvsNVpaWvjFpd/n4EOO5yPb7sNRRx3Ohz+8ab2bZXVw+IH78etLvrfcvt122p5xo3/NuKt/xdCNhnD56Ovq1LreoZx0o7WSaYCOiPaI+G1EfC4iPps+zt/3iJzaeaft+cc/nmfWrBdZsmQJ119/C4ce8ql6N8vqYMftPsLgQQOX27fHLjvQp08rANtstQWvznutHk3rNZrmIqGkx+jim0BEbJNFvb3NBkPW46XZc5Y9n/3yXHbeafs6tsjyatz4Cey/7971bkZDixyOQWd1kbBwg8TCoOno9N/jgEUrelF6V4IRAGodTEvLKhk1rzEULq4W8xcQ6+g3V42ltbWVgz+5T72b0tCaZhZHRLwAIGmPiNij6NDZku4HLljB65bdpaDPSkPy927V2Muz57LRhhsse77hkPWZO/fVOrbI8uaW2//CpPsf5vJfXNTpH3QrXR6Xemd9kXAVSR8rPJG0O9Dc3eIyTJ4ynQ99aBhDh25E3759OfLIw7j1tgn1bpblxH0PTuF3Y27gfy4eyYD+/evdnIbXHlHyVitZz4M+CbhC0uD0+RvAiRnX2Wu0tbVxxtfO5fbx19La0sKVV13HzJnP1LtZVgdnjvwhkx+ZwRtvLGDfw4/nKyd9nstHX8d7S5bw5a99B0guFI4866t1bmnjyuNXdtViTFPSoLSuN0t9jYc4rDOL59xb7yZYDvVda5Mej+8c+4EjSo45174wribjSZn2oCX1Az4DDAX6FMbIIqLTMWgzs3ppplkcBbcAbwJTgXczrsvMrGJLmzBAbxgR+2dch5lZj+WxB531LI6/S/pIxnWYmfVY06wkLPIx4IuSZpEMcQgIryQ0s7zJ4yKwrAP0ARmXb2ZWFXlMN5r1PQkLKwrXATyT3sxyK49LvbO+5dWhkp4FZgF/A54H/pxlnWZmlWi6dKPAhcCuwDMRMQzYF7g/4zrNzMoWESVvtZJ1gF4SEa8DLZJaIuIeYLuM6zQzK1szzuJ4Q9KqwCRgjKR5wNKM6zQzK1u15kFL2gi4GliPJJ6PiohLKykr6x70YcBi4OvAHcA/gEMyrtPMrGxVHINeCnwzIj5MMsR7qqQtK2lT1rM43i56elWWdZmZ9URbVGfwIiLmAnPTxwslPQkMAWaWW1ZWt7xaSOfZ+woLVQZlUa+ZWaWyWOotaSiwPfBQJa/P6o4qA7s/y8wsP8pJxF98e77UqPSOUMXnrAr8EfhaRCyopE1ZXyQ0M2sI5fSfi2/P1xlJfUmC85iIuKnSNjlAm5lRvaXeShLf/w54MiIu6UlZWc/iMDNrCFWcxbEH8Hng45Kmp9uBlbTJPWgzM6o6i+M+kgkRPeYAbWZGPhP2O0CbmdGc+aDNzBpC0+WDNjNrFO5Bm5nlVFtN89SVxgHazIzyVhLWigO0mRmexWFmllvuQZuZ5ZR70GZmOeUetJlZTlVrqXc1OUCbmeEhDjOz3Ar3oM3M8slLvc3McspLvc3Mcso9aDOznGpr9xi0mVkueRaHmVlOeQzazCynPAZtZpZT7kGbmeWULxKameWUhzjMzHLKQxxmZjnldKNmZjnledBmZjnlHrSZWU615zDdaEu9G2BmlgcRUfLWHUn7S3pa0v9JOrvSNrkHbWZG9WZxSGoFLgP2A2YDkyX9KSJmlluWe9BmZkCUsXVjZ+D/IuK5iHgP+ANwWCVtym0Peul7L6vebcgLSSMiYlS922H54s9FdZUTcySNAEYU7RpV9LsYArxUdGw2sEslbXIPujGM6P4Ua0L+XNRJRIyKiB2LtuI/lJ0F+orGTxygzcyqazawUdHzDYE5lRTkAG1mVl2TgU0lDZO0EnA08KdKCsrtGLQtx+OM1hl/LnIoIpZKOg24E2gFroiIJyopS3lMEGJmZh7iMDPLLQdoM7OccoDuAUnflfRf6eMLJH2ik3OGS7qtSvV9u4tjz0taq0r1vFWNcqwy1Xr/JQ2V9Hg1yrL6cICukog4LyL+mnE1KwzQZtb7OECXSdJ30iQofwU2L9p/paTPpo/3l/SUpPuAT6+gnC9KuknSHZKelfSjomPHSHpM0uOSLk73/RAYIGm6pDHdtPFmSVMlPZGueCrsf0vS9yU9KulBSeum+4dJekDSZEkX9uDtsSqStKqkuyRNSz8Ph6X7h0p6UtJv09/xBEkD0mM7pL/fB4BT6/oDWI85QJdB0g4kcxq3Jwm8O3VyTn/gt8AhwJ7Ael0UuR1wFPAR4ChJG0naALgY+Hh6fCdJh0fE2cDiiNguIo7rpqknRsQOwI7A6ZLWTPevAjwYEdsCk4Avp/svBX4VETsBr3RTttXOO8AREfFRYB/gp5IKq9Q2BS6LiK2AN4DPpPt/D5weEbvVurFWfQ7Q5dkTGBcRiyJiAZ1PPt8CmBURz0Yyh/GaLsq7KyLejIh3gJnAB0iC/sSImB8RS4ExwF5ltvN0SY8CD5KsaNo03f8eUBgPnwoMTR/vAYxNH48usy7LjoAfSJoB/JUkx8O66bFZETE9fTwVGCppMLBaRPwt3e/fZYPzQpXylTJxvNTJ5e8WPW4j+X30KEmUpOHAJ4DdImKRpIlA//Twknh/4nuhvgJPiM+f44C1gR0iYomk53n/d9nxszOA5LPj32Mv4h50eSYBR0gaIGkgyTBGR08BwyR9MH1+TJl1PATsLWmtNK/sMUChR7REUt9uXj8Y+FcanLcAdi2hzvtJhm4gCQqWD4OBeWlw3ofkG9YKRcQbwJuSPpbu8u+ywTlAlyEipgHXAdOBPwL3dnLOOyRZxsanFwlfKLOOucA5wD3Ao8C0iLglPTwKmNHNRcI7gD7p1+ILSYY5unMGcKqkySRBwfJhDLCjpCkkwfapEl5zAnBZepFwcZaNs+x5qbeZWU65B21mllMO0GZmOeUAbWaWUw7QZmY55QBtZpZTDtD2byS1pTk/Hpd0g6SVe1BWcY6SyyVt2cW5wyXtXkEdnWbyKyXDX7mZ44ozGJplzQHaOlPI+bE1yfLwU4oPpgtoyhYRX4qImV2cMhwoO0Cb9VYO0Nade4EPpb3beyRdCzwmqVXSj9MMeDMknQygxP9KmilpPLBOoSBJEyXtmD7eP83S9miasW0oyR+Cr6e99z0lrS3pj2kdkyXtkb52zTSD2yOSfkMJy+NXlOEvPfbTtC13SVo73fdBJZkGp0q6N12V2bHM09Ofc4akP1T4/pqtkHNx2ApJ6gMcQLI6EWBnYOuImJUGuTcjYidJ/YD7JU0gyfS3OUmGvnVJkkBd0aHctUky/u2VlrVGRPxT0q+BtyLiJ+l51wI/i4j7JG1MchPODwMjgfsi4gJJB5Gs3OzOiWkdA4DJkv4YEa+TZPibFhHflHReWvZpJKs2T4mIZyXtAvySJMNgsbOBYRHxrqTVSnlPzcrhAG2dGSBpevr4XuB3JEMPD0fErHT/J4FtCuPLJEvENyXJvDc2ItqAOZLu7qT8XYFJhbIi4p8raMcngC3fz7DJoDQHyl6kebYjYrykf5XwM50u6Yj0cSHD3+tAO8nyfUgyD94kadX0572hqO5+nZQ5Axgj6Wbg5hLaYFYWB2jrzOKI2K54Rxqo3i7eBXw1Iu7scN6BdJ9RrdSsay0kWfmWyymRtqXkHAXdZPjrKNJ63+j4HnTiIJI/FocC/y1pqzRFrFlVeAzaKnUn8J+F7HqSNpO0CknGv6PTMer1SRLNd/QASca+Yelr10j3LwQGFp03gWS4gfS87dKHk0gztUk6AFi9m7Z2leGvBSh8CziWZOhkATBL0ufSOiRp2+ICJbUAG0XEPcBZwGrAqt20w6ws7kFbpS4nSfg/TUmXdj5wODCOZKz2MeAZ3k+VukxEzE/HsG9KA908YD/gVuBGJbd2+ipwOklmthkkn9VJJBcSzwfGSpqWlv9iN229AzglLedpls/w9zawlaSpwJskd7iB5A/ArySdC/QF/kCSXbCgFbhGSZJ8kYyVv9FNO8zK4mx2ZmY55SEOM7OccoA2M8spB2gzs5xygDYzyykHaDOznHKANjPLKQdoM7Oc+n8LPO3nN5PTsQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat=svm_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  8\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a decision tree classifier object then  create a  <code>GridSearchCV</code> object  <code>tree_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'criterion': ['gini', 'entropy'],\n",
    "     'splitter': ['best', 'random'],\n",
    "     'max_depth': [2*n for n in range(1,10)],\n",
    "     'max_features': ['auto', 'sqrt'],\n",
    "     'min_samples_leaf': [1, 2, 4],\n",
    "     'min_samples_split': [2, 5, 10]}\n",
    "\n",
    "tree = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [2, 4, 6, 8, 10, 12, 14, 16, 18],\n",
       "                         'max_features': ['auto', 'sqrt'],\n",
       "                         'min_samples_leaf': [1, 2, 4],\n",
       "                         'min_samples_split': [2, 5, 10],\n",
       "                         'splitter': ['best', 'random']})"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_cv=GridSearchCV(tree, parameters, cv=10)\n",
    "tree_cv.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 6, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 10, 'splitter': 'best'}\n",
      "accuracy : 0.8910714285714285\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": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Accuracy:  0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "yhat = tree_cv.predict(X_test)\n",
    "print(\"Decision Tree Accuracy: \", metrics.accuracy_score(Y_test, yhat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x504 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": 42,
   "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": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
       "                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         'p': [1, 2]})"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_cv=GridSearchCV(KNN, parameters, cv=10)\n",
    "knn_cv.fit(X_train, Y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K Nearest Neighbors Accuracy:  0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "yhat = knn_cv.predict(X_test)\n",
    "print(\"K Nearest Neighbors Accuracy: \", metrics.accuracy_score(Y_test, yhat))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat = knn_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "Model = [\"LogReg\", \"SVM\", \"Decision Tree\", \"KNN\"]\n",
    "Accuracy = [0.8464, 0.8482, 0.8911, 0.8482]\n",
    "\n",
    "\n",
    "sns.barplot(y = Accuracy, x= Model, palette = ('rainbow'))\n",
    "plt.xlabel(\"Model\",fontsize=20, color = 'dimgray', weight = \"bold\")\n",
    "plt.ylabel(\"Accuracy\",fontsize=20, color = 'dimgray', weight = \"bold\")\n",
    "plt.title(\"Accuracy of built classification models\", fontsize=20, color = 'dimgray', weight = \"bold\")\n",
    "plt.savefig(\"Model_accuracy.png\", dpi=300, bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  12\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the method which performs best:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All methods virtually perform the same"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2021-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description      |\n",
    "| ----------------- | ------- | ------------- | ----------------------- |\n",
    "| 2021-08-31        | 1.1     | Lakshmi Holla | Modified markdown       |\n",
    "| 2020-09-20        | 1.0     | Joseph        | Modified Multiple Areas |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2020 IBM Corporation. All rights reserved.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}