SpaceX_Machine Learning Prediction_Part_5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Space X  Falcon 9 First Stage Landing Prediction**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assignment:  Machine Learning Prediction\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimated time needed: **60** minutes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Space X advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is because Space X can reuse the first stage. Therefore if we can determine if the first stage will land, we can determine the cost of a launch. This information can be used if an alternate company wants to bid against space X for a rocket launch.   In this lab, you will create a machine learning pipeline  to predict if the first stage will land given the data from the preceding labs.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing\\_1.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several examples of an unsuccessful landing are shown here:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most unsuccessful landings are planed. Space X; performs a controlled landing in the oceans.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objectives\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform exploratory  Data Analysis and determine Training Labels\n",
    "\n",
    "*   create a column for the class\n",
    "*   Standardize the data\n",
    "*   Split into training data and test data\n",
    "\n",
    "\\-Find best Hyperparameter for SVM, Classification Trees and Logistic Regression\n",
    "\n",
    "*   Find the method performs best using test data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Libraries and Define Auxiliary Functions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will import the following libraries for the lab\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pandas is a software library written for the Python programming language for data manipulation and analysis.\n",
    "import pandas as pd\n",
    "# NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\n",
    "import numpy as np\n",
    "# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\n",
    "import matplotlib.pyplot as plt\n",
    "#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\n",
    "import seaborn as sns\n",
    "# Preprocessing allows us to standarsize our data\n",
    "from sklearn import preprocessing\n",
    "# Allows us to split our data into training and testing data\n",
    "from sklearn.model_selection import train_test_split\n",
    "# Allows us to test parameters of classification algorithms and find the best one\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "# Logistic Regression classification algorithm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "# Support Vector Machine classification algorithm\n",
    "from sklearn.svm import SVC\n",
    "# Decision Tree classification algorithm\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "# K Nearest Neighbors classification algorithm\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function is to plot the confusion matrix.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y,y_predict):\n",
    "    \"this function plots the confusion matrix\"\n",
    "    from sklearn.metrics import confusion_matrix\n",
    "\n",
    "    cm = confusion_matrix(y, y_predict)\n",
    "    ax= plt.subplot()\n",
    "    sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells\n",
    "    ax.set_xlabel('Predicted labels')\n",
    "    ax.set_ylabel('True labels')\n",
    "    ax.set_title('Confusion Matrix'); \n",
    "    ax.xaxis.set_ticklabels(['did not land', 'land']); ax.yaxis.set_ticklabels(['did not land', 'landed'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the dataframe\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\")\n",
    "\n",
    "# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n",
    "\n",
    "# data = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_2.csv')\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['FlightNumber', 'PayloadMass', 'Flights', 'Block', 'ReusedCount',\n",
       "       'Orbit_ES-L1', 'Orbit_GEO', 'Orbit_GTO', 'Orbit_HEO', 'Orbit_ISS',\n",
       "       'Orbit_LEO', 'Orbit_MEO', 'Orbit_PO', 'Orbit_SO', 'Orbit_SSO',\n",
       "       'Orbit_VLEO', 'LaunchSite_CCAFS SLC 40', 'LaunchSite_KSC LC 39A',\n",
       "       'LaunchSite_VAFB SLC 4E', 'LandingPad_5e9e3032383ecb267a34e7c7',\n",
       "       'LandingPad_5e9e3032383ecb554034e7c9',\n",
       "       'LandingPad_5e9e3032383ecb6bb234e7ca',\n",
       "       'LandingPad_5e9e3032383ecb761634e7cb',\n",
       "       'LandingPad_5e9e3033383ecbb9e534e7cc', 'Serial_B0003', 'Serial_B0005',\n",
       "       'Serial_B0007', 'Serial_B1003', 'Serial_B1004', 'Serial_B1005',\n",
       "       'Serial_B1006', 'Serial_B1007', 'Serial_B1008', 'Serial_B1010',\n",
       "       'Serial_B1011', 'Serial_B1012', 'Serial_B1013', 'Serial_B1015',\n",
       "       'Serial_B1016', 'Serial_B1017', 'Serial_B1018', 'Serial_B1019',\n",
       "       'Serial_B1020', 'Serial_B1021', 'Serial_B1022', 'Serial_B1023',\n",
       "       'Serial_B1025', 'Serial_B1026', 'Serial_B1028', 'Serial_B1029',\n",
       "       'Serial_B1030', 'Serial_B1031', 'Serial_B1032', 'Serial_B1034',\n",
       "       'Serial_B1035', 'Serial_B1036', 'Serial_B1037', 'Serial_B1038',\n",
       "       'Serial_B1039', 'Serial_B1040', 'Serial_B1041', 'Serial_B1042',\n",
       "       'Serial_B1043', 'Serial_B1044', 'Serial_B1045', 'Serial_B1046',\n",
       "       'Serial_B1047', 'Serial_B1048', 'Serial_B1049', 'Serial_B1050',\n",
       "       'Serial_B1051', 'Serial_B1054', 'Serial_B1056', 'Serial_B1058',\n",
       "       'Serial_B1059', 'Serial_B1060', 'Serial_B1062', 'GridFins_False',\n",
       "       'GridFins_True', 'Reused_False', 'Reused_True', 'Legs_False',\n",
       "       'Legs_True'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_3.csv')\n",
    "\n",
    "# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n",
    "\n",
    "# X = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_3.csv')\n",
    "\n",
    "X.head(100)\n",
    "X.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a NumPy array from the column <code>Class</code> in <code>data</code>, by applying the method <code>to_numpy()</code>  then\n",
    "assign it  to the variable <code>Y</code>,make sure the output is a  Pandas series (only one bracket df\\['name of  column']).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = data['Class'].to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standardize the data in <code>X</code> then reassign it to the variable  <code>X</code> using the transform provided below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# students get this \n",
    "transform = preprocessing.StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = transform.fit_transform(X)\n"
   ]
  },
  {
   "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": 8,
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18,)"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters ={'C':[0.01,0.1,1],\n",
    "             'penalty':['l2'],\n",
    "             'solver':['lbfgs']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1], &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n",
       "                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;]},\n",
       "             return_train_score=True, scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
       "             param_grid={&#x27;C&#x27;: [0.01, 0.1, 1], &#x27;penalty&#x27;: [&#x27;l2&#x27;],\n",
       "                         &#x27;solver&#x27;: [&#x27;lbfgs&#x27;]},\n",
       "             return_train_score=True, scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
       "             param_grid={'C': [0.01, 0.1, 1], 'penalty': ['l2'],\n",
       "                         'solver': ['lbfgs']},\n",
       "             return_train_score=True, scoring='accuracy')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters ={\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge\n",
    "lr=LogisticRegression()\n",
    "logreg_cv = GridSearchCV(estimator=lr, param_grid=parameters, cv=10, scoring='accuracy', return_train_score=True)\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": 12,
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg_score = logreg_cv.score(X_test, Y_test)\n",
    "logreg_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets look at the confusion matrix:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat=logreg_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examining the confusion matrix, we see that logistic regression can distinguish between the different classes.  We see that the major problem is false positives.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  6\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a support vector machine object then  create a  <code>GridSearchCV</code> object  <code>svm_cv</code> with cv - 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=SVC(),\n",
       "             param_grid={&#x27;C&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         &#x27;gamma&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         &#x27;kernel&#x27;: (&#x27;linear&#x27;, &#x27;rbf&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;)},\n",
       "             return_train_score=True, scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=SVC(),\n",
       "             param_grid={&#x27;C&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         &#x27;gamma&#x27;: array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         &#x27;kernel&#x27;: (&#x27;linear&#x27;, &#x27;rbf&#x27;, &#x27;poly&#x27;, &#x27;rbf&#x27;, &#x27;sigmoid&#x27;)},\n",
       "             return_train_score=True, scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=SVC(),\n",
       "             param_grid={'C': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         'gamma': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         'kernel': ('linear', 'rbf', 'poly', 'rbf', 'sigmoid')},\n",
       "             return_train_score=True, scoring='accuracy')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_cv = GridSearchCV(estimator=svm, param_grid=parameters, cv=10, scoring='accuracy', return_train_score=True)\n",
    "svm_cv.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_score = svm_cv.score(X_test, Y_test)\n",
    "svm_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat=svm_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  8\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a decision tree classifier object then  create a  <code>GridSearchCV</code> object  <code>tree_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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': ['sqrt'],\n",
    "     'min_samples_leaf': [1, 2, 4],\n",
    "     'min_samples_split': [2, 5, 10]}\n",
    "\n",
    "tree = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [2, 4, 6, 8, 10, 12, 14, 16, 18],\n",
       "                         &#x27;max_features&#x27;: [&#x27;sqrt&#x27;],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]},\n",
       "             return_train_score=True, scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [2, 4, 6, 8, 10, 12, 14, 16, 18],\n",
       "                         &#x27;max_features&#x27;: [&#x27;sqrt&#x27;],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]},\n",
       "             return_train_score=True, scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "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': ['sqrt'],\n",
       "                         'min_samples_leaf': [1, 2, 4],\n",
       "                         'min_samples_split': [2, 5, 10],\n",
       "                         'splitter': ['best', 'random']},\n",
       "             return_train_score=True, scoring='accuracy')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_cv = GridSearchCV(estimator=tree, param_grid=parameters, cv=10, scoring='accuracy', return_train_score=True)\n",
    "tree_cv.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'criterion': 'entropy', 'max_depth': 12, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 10, 'splitter': 'random'}\n",
      "accuracy : 0.8892857142857145\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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7777777777777778"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_score = tree_cv.score(X_test, Y_test)\n",
    "tree_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat = tree_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  10\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a k nearest neighbors object then  create a  <code>GridSearchCV</code> object  <code>knn_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;algorithm&#x27;: [&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;],\n",
       "                         &#x27;n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         &#x27;p&#x27;: [1, 2]},\n",
       "             return_train_score=True, scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
       "             param_grid={&#x27;algorithm&#x27;: [&#x27;auto&#x27;, &#x27;ball_tree&#x27;, &#x27;kd_tree&#x27;, &#x27;brute&#x27;],\n",
       "                         &#x27;n_neighbors&#x27;: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         &#x27;p&#x27;: [1, 2]},\n",
       "             return_train_score=True, scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
       "                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         'p': [1, 2]},\n",
       "             return_train_score=True, scoring='accuracy')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_cv = GridSearchCV(estimator=KNN, param_grid=parameters, cv=10, scoring='accuracy', return_train_score=True)\n",
    "knn_cv.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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 tree_cv on the test data using the method <code>score</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8611111111111112"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_score = knn_cv.score(X_train, Y_train)\n",
    "knn_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat = knn_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  12\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the method performs best:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ML Model', ylabel='Prediction Power'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = {'ML Model':['Logistic Regression', 'SVM', 'Decision Tree', 'K-Nearest Neighbour'],\n",
    "         'Model Score':[logreg_score, svm_score, tree_score, knn_score]}\n",
    "df_scores = pd.DataFrame(scores)\n",
    "df_scores.plot.bar(x='ML Model', y='Model Score', color='orange', legend=False, ylabel='Prediction Power')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Authors\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description      |\n",
    "| ----------------- | ------- | ------------- | ----------------------- |\n",
    "| 2021-08-31        | 1.1     | Lakshmi Holla | Modified markdown       |\n",
    "| 2020-09-20        | 1.0     | Joseph        | Modified Multiple Areas |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright © 2020 IBM Corporation. All rights reserved.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}