SpaceX_Machine Learning Prediction_Part_5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <img src=\"https://gitlab.com/ibm/skills-network/courses/placeholder101/-/raw/master/labs/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\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": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pandas is a software library written for the Python programming language for data manipulation and analysis.\n",
    "import pandas as pd\n",
    "# NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\n",
    "import numpy as np\n",
    "# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\n",
    "import matplotlib.pyplot as plt\n",
    "#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\n",
    "import seaborn as sns\n",
    "# Preprocessing allows us to standarsize our data\n",
    "from sklearn import preprocessing\n",
    "# Allows us to split our data into training and testing data\n",
    "from sklearn.model_selection import train_test_split\n",
    "# Allows us to test parameters of classification algorithms and find the best one\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "# Logistic Regression classification algorithm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "# Support Vector Machine classification algorithm\n",
    "from sklearn.svm import SVC\n",
    "# Decision Tree classification algorithm\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "# K Nearest Neighbors classification algorithm\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function is to plot the confusion matrix.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y,y_predict):\n",
    "    \"this function plots the confusion matrix\"\n",
    "    from sklearn.metrics import confusion_matrix\n",
    "\n",
    "    cm = confusion_matrix(y, y_predict)\n",
    "    ax= plt.subplot()\n",
    "    sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells\n",
    "    ax.set_xlabel('Predicted labels')\n",
    "    ax.set_ylabel('True labels')\n",
    "    ax.set_title('Confusion Matrix'); \n",
    "    ax.xaxis.set_ticklabels(['did not land', 'land']); ax.yaxis.set_ticklabels(['did not land', 'landed'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the dataframe\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>Date</th>\n",
       "      <th>BoosterVersion</th>\n",
       "      <th>PayloadMass</th>\n",
       "      <th>Orbit</th>\n",
       "      <th>LaunchSite</th>\n",
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       "      <th>Flights</th>\n",
       "      <th>GridFins</th>\n",
       "      <th>Reused</th>\n",
       "      <th>Legs</th>\n",
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       "      <th>0</th>\n",
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       "      <td>2010-06-04</td>\n",
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       "      <td>LEO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2012-05-22</td>\n",
       "      <td>Falcon 9</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2013-03-01</td>\n",
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       "      <td>CCAFS SLC 40</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2013-09-29</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>PO</td>\n",
       "      <td>VAFB SLC 4E</td>\n",
       "      <td>False Ocean</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>B1003</td>\n",
       "      <td>-120.610829</td>\n",
       "      <td>34.632093</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2013-12-03</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>3170.000000</td>\n",
       "      <td>GTO</td>\n",
       "      <td>CCAFS SLC 40</td>\n",
       "      <td>None None</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
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      "text/plain": [
       "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n",
       "0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n",
       "1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n",
       "2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n",
       "3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n",
       "4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n",
       "\n",
       "       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n",
       "0    None None        1     False   False  False        NaN    1.0   \n",
       "1    None None        1     False   False  False        NaN    1.0   \n",
       "2    None None        1     False   False  False        NaN    1.0   \n",
       "3  False Ocean        1     False   False  False        NaN    1.0   \n",
       "4    None None        1     False   False  False        NaN    1.0   \n",
       "\n",
       "   ReusedCount Serial   Longitude   Latitude  Class  \n",
       "0            0  B0003  -80.577366  28.561857      0  \n",
       "1            0  B0005  -80.577366  28.561857      0  \n",
       "2            0  B0007  -80.577366  28.561857      0  \n",
       "3            0  B1003 -120.610829  34.632093      0  \n",
       "4            0  B1004  -80.577366  28.561857      0  "
      ]
     },
     "execution_count": 4,
     "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": [
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       "      <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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = data['Class'].to_numpy()\n",
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standardize the data in <code>X</code> then reassign it to the variable  <code>X</code> using the transform provided below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# students get this \n",
    "X= preprocessing.StandardScaler().fit(X).transform(X)"
   ]
  },
  {
   "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",
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       "        -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",
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       "        -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": [
    "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": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (72, 83) (72,)\n",
      "Test set: (18, 83) (18,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size=0.2, random_state=2)\n",
    "print ('Train set:', X_train.shape,  Y_train.shape)\n",
    "print ('Test set:', X_test.shape,  Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can see we only have 18 test samples.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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']}\n",
    "\n",
    "lr=LogisticRegression()\n",
    "grid_search = GridSearchCV(lr, parameters, cv=10)\n",
    "logreg_cv = grid_search.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 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": "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": 28,
   "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg_cv.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets look at the confusion matrix:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat=logreg_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examining the confusion matrix, we see that logistic regression can distinguish between the different classes.  We see that the major problem is false positives.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  6\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a support vector machine object then  create a  <code>GridSearchCV</code> object  <code>svm_cv</code> with cv - 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(svm, parameters, cv=10)\n",
    "svm_cv = grid_search.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_cv.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat=svm_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  8\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a decision tree classifier object then  create a  <code>GridSearchCV</code> object  <code>tree_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(tree, parameters, cv=10)\n",
    "tree_cv = grid_search.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 10, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 10, 'splitter': 'random'}\n",
      "accuracy : 0.8875\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": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7222222222222222"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_cv.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat = svm_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  10\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a k nearest neighbors object then  create a  <code>GridSearchCV</code> object  <code>knn_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(KNN, parameters, cv=10)\n",
    "knn_cv = grid_search.fit(X_train, Y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_cv.score(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat = knn_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  12\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the method performs best:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for Logistics Regression method: 0.8333333333333334\n",
      "Accuracy for Support Vector Machine method: 0.8333333333333334\n",
      "Accuracy for Decision tree method: 0.7222222222222222\n",
      "Accuracy for K nearsdt neighbors method: 0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "print('Accuracy for Logistics Regression method:', logreg_cv.score(X_test, Y_test))\n",
    "print( 'Accuracy for Support Vector Machine method:', svm_cv.score(X_test, Y_test))\n",
    "print('Accuracy for Decision tree method:', tree_cv.score(X_test, Y_test))\n",
    "print('Accuracy for K nearsdt neighbors method:', knn_cv.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "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": "3.8.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}