8-Machine Learning Prediction .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": 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": [
    {
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       "      <th>FlightNumber</th>\n",
       "      <th>Date</th>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2013-09-29</td>\n",
       "      <td>Falcon 9</td>\n",
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       "      <td>PO</td>\n",
       "      <td>VAFB SLC 4E</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2013-12-03</td>\n",
       "      <td>Falcon 9</td>\n",
       "      <td>3170.000000</td>\n",
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       "      <td>CCAFS SLC 40</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": 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": [
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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": 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"
   ]
  },
  {
   "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": [
    {
     "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": 5,
     "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.71291154e+00, -1.94814463e-16, -6.53912840e-01,\n",
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       "        -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",
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       "        -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",
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       "        -7.97724035e-01, -1.50755672e-01, -3.92232270e-01,\n",
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       "        -1.05999788e-01, -1.05999788e-01, -1.05999788e-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, -1.50755672e-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",
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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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# students get this\n",
    "X= preprocessing.StandardScaler().fit(X).transform(X)\n",
    "X[0:5]\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": 7,
   "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": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18,)"
      ]
     },
     "execution_count": 8,
     "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
       "             param_grid={'C': [0.01, 0.1, 1], 'penalty': ['l2'],\n",
       "                         'solver': ['lbfgs']})"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = {\"C\":[0.01, 0.1, 1], 'penalty':['l2'], 'solver':['lbfgs']} # l1 lasso l2 ridge\n",
    "lr = LogisticRegression()\n",
    "logreg_cv = GridSearchCV(lr, parameters, cv=10)\n",
    "logreg_cv.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We output the <code>GridSearchCV</code> object for logistic regression. We display the best parameters using the data attribute <code>best_params\\_</code> and the accuracy on the validation data using the data attribute <code>best_score\\_</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hyperparameters :(best parameters)  {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "Train set accuracy : 0.8464285714285713\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hyperparameters :(best parameters) \",logreg_cv.best_params_)\n",
    "print(\"Train set 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": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Accuracy: 0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "print('Test Set Accuracy:',logreg_cv.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets look at the confusion matrix:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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_LR=logreg_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat_LR)"
   ]
  },
  {
   "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": 13,
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=SVC(),\n",
       "             param_grid={'C': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         'gamma': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n",
       "       1.00000000e+03]),\n",
       "                         'kernel': ('linear', 'rbf', 'poly', 'rbf', 'sigmoid')})"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_cv = GridSearchCV(svm, parameters, cv=10)\n",
    "svm_cv.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hyperparameters :(best parameters)  {'C': 1.0, 'gamma': 0.03162277660168379, 'kernel': 'sigmoid'}\n",
      "accuracy : 0.8482142857142856\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hyperparameters :(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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Accuracy: 0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "print('Test Set Accuracy:',svm_cv.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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=svm_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat_SVM)"
   ]
  },
  {
   "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": 18,
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [2, 4, 6, 8, 10, 12, 14, 16, 18],\n",
       "                         'max_features': ['auto', 'sqrt'],\n",
       "                         'min_samples_leaf': [1, 2, 4],\n",
       "                         'min_samples_split': [2, 5, 10],\n",
       "                         'splitter': ['best', 'random']})"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_cv = GridSearchCV(tree, parameters, cv=10)\n",
    "tree_cv.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 12, 'max_features': 'auto', 'min_samples_leaf': 1, '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": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Accuracy: 0.7222222222222222\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Set Accuracy:\", tree_cv.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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_TREE = tree_cv.predict(X_test) #beware the typo in the provided code\n",
    "plot_confusion_matrix(Y_test,yhat_TREE)"
   ]
  },
  {
   "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": 23,
   "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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
       "                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
       "                         'p': [1, 2]})"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_cv = GridSearchCV(KNN, parameters, cv=10)\n",
    "knn_cv.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 10, 'p': 1}\n",
      "accuracy : 0.8482142857142858\n"
     ]
    }
   ],
   "source": [
    "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\n",
    "print(\"accuracy :\",knn_cv.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  11\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the accuracy of knn_cv on the test data using the method <code>score</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Set Accuracy: 0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "print(\"Test Set Accuracy:\", knn_cv.score(X_test, Y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the confusion matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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 = knn_cv.predict(X_test)\n",
    "plot_confusion_matrix(Y_test,yhat_KNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TASK  12\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the method performs best:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import jaccard_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import log_loss\n",
    "\n",
    "tbldata=[['KNN',0.0,0.0,'NA'], ['Decision Tree',0.0,0.0,'NA'], ['SVM',0.0,0.0,'NA'], ['Logistic Regression',0.0,0.0,0.0]]\n",
    "results_df = pd.DataFrame(tbldata, columns=['Algorithm','Jaccard','F1-score','LogLoss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>Jaccard</th>\n",
       "      <th>F1-score</th>\n",
       "      <th>LogLoss</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algorithm</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.814815</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.715152</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.814815</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.814815</td>\n",
       "      <td>0.478667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Jaccard  F1-score   LogLoss\n",
       "Algorithm                                        \n",
       "KNN                  0.800000  0.814815        NA\n",
       "Decision Tree        0.666667  0.715152        NA\n",
       "SVM                  0.800000  0.814815        NA\n",
       "Logistic Regression  0.800000  0.814815  0.478667"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#KNN\n",
    "results_df.at[0,'Jaccard'] = jaccard_score(Y_test, yhat_KNN)\n",
    "results_df.at[0,'F1-score'] = f1_score(Y_test, yhat_KNN, average='weighted')\n",
    "\n",
    "#Decision Trees\n",
    "results_df.at[1,'Jaccard'] = jaccard_score(Y_test, yhat_TREE)\n",
    "results_df.at[1,'F1-score'] = f1_score(Y_test, yhat_TREE, average='weighted')\n",
    "\n",
    "#SVM\n",
    "results_df.at[2,'Jaccard'] = jaccard_score(Y_test, yhat_SVM)\n",
    "results_df.at[2,'F1-score'] = f1_score(Y_test, yhat_SVM, average='weighted')\n",
    "\n",
    "#LR\n",
    "\n",
    "lr_yhat_prob = logreg_cv.predict_proba(X_test)\n",
    "results_df.at[3,'Jaccard'] = jaccard_score(Y_test, yhat_LR)\n",
    "results_df.at[3,'F1-score'] = f1_score(Y_test, yhat_LR, average='weighted')\n",
    "results_df.at[3,'LogLoss'] = log_loss(Y_test, lr_yhat_prob)\n",
    "\n",
    "results_df.set_index('Algorithm', inplace=True)\n",
    "\n",
    "results_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jaccard     KNN\n",
       "F1-score    KNN\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the best method score within the results from above\n",
    "\n",
    "results_df[['Jaccard','F1-score']].idxmax(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_df.plot(kind='barh', figsize=(10, 7))\n",
    "\n",
    "plt.xlabel('Score') # add to x-label to the plot\n",
    "plt.ylabel('Algorithm') # add y-label to the plot\n",
    "plt.title('Best algorithm based on test set') # add title to the plot\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algorithm</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.848214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Decision Tree</th>\n",
       "      <td>0.889286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.848214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regression</th>\n",
       "      <td>0.846429</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Accuracy\n",
       "Algorithm                    \n",
       "KNN                  0.848214\n",
       "Decision Tree        0.889286\n",
       "SVM                  0.848214\n",
       "Logistic Regression  0.846429"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the most accurate model based on train set\n",
    "tbldata2=[['KNN',0.0], ['Decision Tree',0.0], ['SVM',0.0], ['Logistic Regression',0.0]]\n",
    "accur_df = pd.DataFrame(tbldata2, columns=['Algorithm','Accuracy'])\n",
    "#KNN\n",
    "accur_df.at[0,'Accuracy'] = knn_cv.best_score_\n",
    "\n",
    "#Decision Trees\n",
    "accur_df.at[1,'Accuracy'] = tree_cv.best_score_\n",
    "\n",
    "#SVM\n",
    "accur_df.at[2,'Accuracy'] = svm_cv.best_score_\n",
    "\n",
    "#LR\n",
    "accur_df.at[3,'Accuracy'] = logreg_cv.best_score_\n",
    "\n",
    "accur_df.set_index('Algorithm', inplace=True)\n",
    "\n",
    "accur_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accur_df.plot(kind='barh', figsize=(8, 8))\n",
    "\n",
    "plt.xlabel('Accuracy') # add to x-label to the plot\n",
    "plt.ylabel('Algorithm') # add y-label to the plot\n",
    "plt.title('Most accurate model based on train set') # add title to the plot\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}