Machine Learning Prediction.ipynb

{
    "cells": [
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "# **Space X  Falcon 9 First Stage Landing Prediction**\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Assignment:  Machine Learning Prediction\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Estimated time needed: **60** minutes\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing\\_1.gif)\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Several examples of an unsuccessful landing are shown here:\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Most unsuccessful landings are planed. Space X; performs a controlled landing in the oceans.\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Objectives\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": ""
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "***\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Import Libraries and Define Auxiliary Functions\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "We will import the following libraries for the lab\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# Pandas is a software library written for the Python programming language for data manipulation and analysis.\nimport 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\nimport 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.\nimport 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\nimport seaborn as sns\n# Preprocessing allows us to standarsize our data\nfrom sklearn import preprocessing\n# Allows us to split our data into training and testing data\nfrom sklearn.model_selection import train_test_split\n# Allows us to test parameters of classification algorithms and find the best one\nfrom sklearn.model_selection import GridSearchCV\n# Logistic Regression classification algorithm\nfrom sklearn.linear_model import LogisticRegression\n# Support Vector Machine classification algorithm\nfrom sklearn.svm import SVC\n# Decision Tree classification algorithm\nfrom sklearn.tree import DecisionTreeClassifier\n# K Nearest Neighbors classification algorithm\nfrom sklearn.neighbors import KNeighborsClassifier",
            "execution_count": 1,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "This function is to plot the confusion matrix.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "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'])",
            "execution_count": 2,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Load the dataframe\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Load the data\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "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\ndata = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_2.csv')\n\ndata.head()",
            "execution_count": 3,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 3,
                    "data": {
                        "text/plain": "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n\n       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n0    None None        1     False   False  False        NaN    1.0   \n1    None None        1     False   False  False        NaN    1.0   \n2    None None        1     False   False  False        NaN    1.0   \n3  False Ocean        1     False   False  False        NaN    1.0   \n4    None None        1     False   False  False        NaN    1.0   \n\n   ReusedCount Serial   Longitude   Latitude  Class  \n0            0  B0003  -80.577366  28.561857      0  \n1            0  B0005  -80.577366  28.561857      0  \n2            0  B0007  -80.577366  28.561857      0  \n3            0  B1003 -120.610829  34.632093      0  \n4            0  B1004  -80.577366  28.561857      0  ",
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>Date</th>\n      <th>BoosterVersion</th>\n      <th>PayloadMass</th>\n      <th>Orbit</th>\n      <th>LaunchSite</th>\n      <th>Outcome</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>LandingPad</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2010-06-04</td>\n      <td>Falcon 9</td>\n      <td>6104.959412</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0003</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2012-05-22</td>\n      <td>Falcon 9</td>\n      <td>525.000000</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0005</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>2013-03-01</td>\n      <td>Falcon 9</td>\n      <td>677.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0007</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>2013-09-29</td>\n      <td>Falcon 9</td>\n      <td>500.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>False Ocean</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1003</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>2013-12-03</td>\n      <td>Falcon 9</td>\n      <td>3170.000000</td>\n      <td>GTO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1004</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "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\nX = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_3.csv')\n\nX.head(100)",
            "execution_count": 4,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 4,
                    "data": {
                        "text/plain": "    FlightNumber   PayloadMass  Flights  Block  ReusedCount  Orbit_ES-L1  \\\n0            1.0   6104.959412      1.0    1.0          0.0          0.0   \n1            2.0    525.000000      1.0    1.0          0.0          0.0   \n2            3.0    677.000000      1.0    1.0          0.0          0.0   \n3            4.0    500.000000      1.0    1.0          0.0          0.0   \n4            5.0   3170.000000      1.0    1.0          0.0          0.0   \n..           ...           ...      ...    ...          ...          ...   \n85          86.0  15400.000000      2.0    5.0          2.0          0.0   \n86          87.0  15400.000000      3.0    5.0          2.0          0.0   \n87          88.0  15400.000000      6.0    5.0          5.0          0.0   \n88          89.0  15400.000000      3.0    5.0          2.0          0.0   \n89          90.0   3681.000000      1.0    5.0          0.0          0.0   \n\n    Orbit_GEO  Orbit_GTO  Orbit_HEO  Orbit_ISS  ...  Serial_B1058  \\\n0         0.0        0.0        0.0        0.0  ...           0.0   \n1         0.0        0.0        0.0        0.0  ...           0.0   \n2         0.0        0.0        0.0        1.0  ...           0.0   \n3         0.0        0.0        0.0        0.0  ...           0.0   \n4         0.0        1.0        0.0        0.0  ...           0.0   \n..        ...        ...        ...        ...  ...           ...   \n85        0.0        0.0        0.0        0.0  ...           0.0   \n86        0.0        0.0        0.0        0.0  ...           1.0   \n87        0.0        0.0        0.0        0.0  ...           0.0   \n88        0.0        0.0        0.0        0.0  ...           0.0   \n89        0.0        0.0        0.0        0.0  ...           0.0   \n\n    Serial_B1059  Serial_B1060  Serial_B1062  GridFins_False  GridFins_True  \\\n0            0.0           0.0           0.0             1.0            0.0   \n1            0.0           0.0           0.0             1.0            0.0   \n2            0.0           0.0           0.0             1.0            0.0   \n3            0.0           0.0           0.0             1.0            0.0   \n4            0.0           0.0           0.0             1.0            0.0   \n..           ...           ...           ...             ...            ...   \n85           0.0           1.0           0.0             0.0            1.0   \n86           0.0           0.0           0.0             0.0            1.0   \n87           0.0           0.0           0.0             0.0            1.0   \n88           0.0           1.0           0.0             0.0            1.0   \n89           0.0           0.0           1.0             0.0            1.0   \n\n    Reused_False  Reused_True  Legs_False  Legs_True  \n0            1.0          0.0         1.0        0.0  \n1            1.0          0.0         1.0        0.0  \n2            1.0          0.0         1.0        0.0  \n3            1.0          0.0         1.0        0.0  \n4            1.0          0.0         1.0        0.0  \n..           ...          ...         ...        ...  \n85           0.0          1.0         0.0        1.0  \n86           0.0          1.0         0.0        1.0  \n87           0.0          1.0         0.0        1.0  \n88           0.0          1.0         0.0        1.0  \n89           1.0          0.0         0.0        1.0  \n\n[90 rows x 83 columns]",
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Flights</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Orbit_ES-L1</th>\n      <th>Orbit_GEO</th>\n      <th>Orbit_GTO</th>\n      <th>Orbit_HEO</th>\n      <th>Orbit_ISS</th>\n      <th>...</th>\n      <th>Serial_B1058</th>\n      <th>Serial_B1059</th>\n      <th>Serial_B1060</th>\n      <th>Serial_B1062</th>\n      <th>GridFins_False</th>\n      <th>GridFins_True</th>\n      <th>Reused_False</th>\n      <th>Reused_True</th>\n      <th>Legs_False</th>\n      <th>Legs_True</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>6104.959412</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.0</td>\n      <td>525.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.0</td>\n      <td>677.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>500.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3170.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>86.0</td>\n      <td>15400.000000</td>\n      <td>2.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>87.0</td>\n      <td>15400.000000</td>\n      <td>3.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>88.0</td>\n      <td>15400.000000</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>89.0</td>\n      <td>15400.000000</td>\n      <td>3.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>90.0</td>\n      <td>3681.000000</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>90 rows \u00d7 83 columns</p>\n</div>"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## TASK  1\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Create a NumPy array from the column <code>Class</code> in <code>data</code>, by applying the method <code>to_numpy()</code>  then\nassign it  to the variable <code>Y</code>,make sure the output is a  Pandas series (only one bracket df\\['name of  column']).\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "Y = data['Class'].to_numpy()\ntype(Y)",
            "execution_count": 5,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 5,
                    "data": {
                        "text/plain": "numpy.ndarray"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## TASK  2\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Standardize the data in <code>X</code> then reassign it to the variable  <code>X</code> using the transform provided below.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "# students get this \ntransform = preprocessing.StandardScaler()",
            "execution_count": 6,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "X = preprocessing.StandardScaler().fit(X).transform(X.astype(float))\n#X = transform.fit(X).transform(X.astype(float))",
            "execution_count": 7,
            "outputs": []
        },
        {
            "metadata": {
                "scrolled": false
            },
            "cell_type": "code",
            "source": "X[0:5]",
            "execution_count": 8,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 8,
                    "data": {
                        "text/plain": "array([[-1.71291154e+00, -1.94814463e-16, -6.53912840e-01,\n        -1.57589457e+00, -9.73440458e-01, -1.05999788e-01,\n        -1.05999788e-01, -6.54653671e-01, -1.05999788e-01,\n        -5.51677284e-01,  3.44342023e+00, -1.85695338e-01,\n        -3.33333333e-01, -1.05999788e-01, -2.42535625e-01,\n        -4.29197538e-01,  7.97724035e-01, -5.68796459e-01,\n        -4.10890702e-01, -4.10890702e-01, -1.50755672e-01,\n        -7.97724035e-01, -1.50755672e-01, -3.92232270e-01,\n         9.43398113e+00, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.50755672e-01,\n        -1.50755672e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -2.15665546e-01,\n        -1.85695338e-01, -2.15665546e-01, -2.67261242e-01,\n        -1.05999788e-01, -2.42535625e-01, -1.05999788e-01,\n        -2.15665546e-01, -1.85695338e-01, -2.15665546e-01,\n        -1.85695338e-01, -1.05999788e-01,  1.87082869e+00,\n        -1.87082869e+00,  8.35531692e-01, -8.35531692e-01,\n         1.93309133e+00, -1.93309133e+00],\n       [-1.67441914e+00, -1.19523159e+00, -6.53912840e-01,\n        -1.57589457e+00, -9.73440458e-01, -1.05999788e-01,\n        -1.05999788e-01, -6.54653671e-01, -1.05999788e-01,\n        -5.51677284e-01,  3.44342023e+00, -1.85695338e-01,\n        -3.33333333e-01, -1.05999788e-01, -2.42535625e-01,\n        -4.29197538e-01,  7.97724035e-01, -5.68796459e-01,\n        -4.10890702e-01, -4.10890702e-01, -1.50755672e-01,\n        -7.97724035e-01, -1.50755672e-01, -3.92232270e-01,\n        -1.05999788e-01,  9.43398113e+00, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.50755672e-01,\n        -1.50755672e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -2.15665546e-01,\n        -1.85695338e-01, -2.15665546e-01, -2.67261242e-01,\n        -1.05999788e-01, -2.42535625e-01, -1.05999788e-01,\n        -2.15665546e-01, -1.85695338e-01, -2.15665546e-01,\n        -1.85695338e-01, -1.05999788e-01,  1.87082869e+00,\n        -1.87082869e+00,  8.35531692e-01, -8.35531692e-01,\n         1.93309133e+00, -1.93309133e+00],\n       [-1.63592675e+00, -1.16267307e+00, -6.53912840e-01,\n        -1.57589457e+00, -9.73440458e-01, -1.05999788e-01,\n        -1.05999788e-01, -6.54653671e-01, -1.05999788e-01,\n         1.81265393e+00, -2.90408935e-01, -1.85695338e-01,\n        -3.33333333e-01, -1.05999788e-01, -2.42535625e-01,\n        -4.29197538e-01,  7.97724035e-01, -5.68796459e-01,\n        -4.10890702e-01, -4.10890702e-01, -1.50755672e-01,\n        -7.97724035e-01, -1.50755672e-01, -3.92232270e-01,\n        -1.05999788e-01, -1.05999788e-01,  9.43398113e+00,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.50755672e-01,\n        -1.50755672e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -2.15665546e-01,\n        -1.85695338e-01, -2.15665546e-01, -2.67261242e-01,\n        -1.05999788e-01, -2.42535625e-01, -1.05999788e-01,\n        -2.15665546e-01, -1.85695338e-01, -2.15665546e-01,\n        -1.85695338e-01, -1.05999788e-01,  1.87082869e+00,\n        -1.87082869e+00,  8.35531692e-01, -8.35531692e-01,\n         1.93309133e+00, -1.93309133e+00],\n       [-1.59743435e+00, -1.20058661e+00, -6.53912840e-01,\n        -1.57589457e+00, -9.73440458e-01, -1.05999788e-01,\n        -1.05999788e-01, -6.54653671e-01, -1.05999788e-01,\n        -5.51677284e-01, -2.90408935e-01, -1.85695338e-01,\n         3.00000000e+00, -1.05999788e-01, -2.42535625e-01,\n        -4.29197538e-01, -1.25356634e+00, -5.68796459e-01,\n         2.43373723e+00, -4.10890702e-01, -1.50755672e-01,\n        -7.97724035e-01, -1.50755672e-01, -3.92232270e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n         9.43398113e+00, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.50755672e-01,\n        -1.50755672e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -2.15665546e-01,\n        -1.85695338e-01, -2.15665546e-01, -2.67261242e-01,\n        -1.05999788e-01, -2.42535625e-01, -1.05999788e-01,\n        -2.15665546e-01, -1.85695338e-01, -2.15665546e-01,\n        -1.85695338e-01, -1.05999788e-01,  1.87082869e+00,\n        -1.87082869e+00,  8.35531692e-01, -8.35531692e-01,\n         1.93309133e+00, -1.93309133e+00],\n       [-1.55894196e+00, -6.28670558e-01, -6.53912840e-01,\n        -1.57589457e+00, -9.73440458e-01, -1.05999788e-01,\n        -1.05999788e-01,  1.52752523e+00, -1.05999788e-01,\n        -5.51677284e-01, -2.90408935e-01, -1.85695338e-01,\n        -3.33333333e-01, -1.05999788e-01, -2.42535625e-01,\n        -4.29197538e-01,  7.97724035e-01, -5.68796459e-01,\n        -4.10890702e-01, -4.10890702e-01, -1.50755672e-01,\n        -7.97724035e-01, -1.50755672e-01, -3.92232270e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01,  9.43398113e+00, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.50755672e-01,\n        -1.50755672e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -2.15665546e-01,\n        -1.85695338e-01, -2.15665546e-01, -2.67261242e-01,\n        -1.05999788e-01, -2.42535625e-01, -1.05999788e-01,\n        -2.15665546e-01, -1.85695338e-01, -2.15665546e-01,\n        -1.85695338e-01, -1.05999788e-01,  1.87082869e+00,\n        -1.87082869e+00,  8.35531692e-01, -8.35531692e-01,\n         1.93309133e+00, -1.93309133e+00]])"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## TASK  3\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "<code>X_train, X_test, Y_train, Y_test</code>\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)",
            "execution_count": 9,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "we can see we only have 18 test samples.\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "Y_test.shape",
            "execution_count": 10,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 10,
                    "data": {
                        "text/plain": "(18,)"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## TASK  4\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "parameters ={'C':[0.01,0.1,1],\n             'penalty':['l2'],\n             'solver':['lbfgs']}",
            "execution_count": 11,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "parameters ={\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge\nlr=LogisticRegression()\n\nlogreg_cv = GridSearchCV(estimator=lr, cv=10, param_grid=parameters)\nlogreg_cv.fit(X_train, Y_train)",
            "execution_count": 12,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 12,
                    "data": {
                        "text/plain": "GridSearchCV(cv=10, estimator=LogisticRegression(),\n             param_grid={'C': [0.01, 0.1, 1], 'penalty': ['l2'],\n                         'solver': ['lbfgs']})"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "print(\"tuned hpyerparameters :(best parameters) \",logreg_cv.best_params_)\nprint(\"accuracy :\",logreg_cv.best_score_)",
            "execution_count": 13,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "tuned hpyerparameters :(best parameters)  {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\naccuracy : 0.8464285714285713\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## TASK  5\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Calculate the accuracy on the test data using the method <code>score</code>:\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "print(\"accuracy :\",logreg_cv.score(X_test, Y_test))",
            "execution_count": 14,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "accuracy : 0.8333333333333334\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Lets look at the confusion matrix:\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "yhat=logreg_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)",
            "execution_count": 15,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 2 Axes>",
                        "image/png": 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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## TASK  6\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "parameters = {'kernel':('linear', 'rbf','poly','rbf', 'sigmoid'),\n              'C': np.logspace(-3, 3, 5),\n              'gamma':np.logspace(-3, 3, 5)}\nsvm = SVC()",
            "execution_count": 16,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "svm_cv = GridSearchCV(estimator=svm, cv=10, param_grid=parameters)\nsvm_cv.fit(X_train, Y_train)",
            "execution_count": 17,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 17,
                    "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')})"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "print(\"tuned hpyerparameters :(best parameters) \",svm_cv.best_params_)\nprint(\"accuracy :\",svm_cv.best_score_)",
            "execution_count": 18,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "tuned hpyerparameters :(best parameters)  {'C': 1.0, 'gamma': 0.03162277660168379, 'kernel': 'sigmoid'}\naccuracy : 0.8482142857142856\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## TASK  7\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Calculate the accuracy on the test data using the method <code>score</code>:\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "print(\"accuracy :\", svm_cv.score(X_test, Y_test))",
            "execution_count": 19,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "accuracy : 0.8333333333333334\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "We can plot the confusion matrix\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "yhat=svm_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)",
            "execution_count": 20,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 2 Axes>",
                        "image/png": 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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## TASK  8\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "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\ntree = DecisionTreeClassifier()",
            "execution_count": 21,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "tree_cv = GridSearchCV(estimator=tree, cv=10, param_grid=parameters)\ntree_cv.fit(X_train, Y_train)",
            "execution_count": 22,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 22,
                    "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']})"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "print(\"tuned hpyerparameters :(best parameters) \",tree_cv.best_params_)\nprint(\"accuracy :\",tree_cv.best_score_)",
            "execution_count": 23,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "tuned hpyerparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 4, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'splitter': 'random'}\naccuracy : 0.875\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## TASK  9\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Calculate the accuracy of tree_cv on the test data using the method <code>score</code>:\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "print(\"accuracy :\", tree_cv.score(X_test, Y_test))",
            "execution_count": 24,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "accuracy : 0.9444444444444444\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "We can plot the confusion matrix\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "yhat = tree_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)",
            "execution_count": 25,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 2 Axes>",
                        "image/png": 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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## TASK  10\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "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\nKNN = KNeighborsClassifier()",
            "execution_count": 26,
            "outputs": []
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "knn_cv = GridSearchCV(estimator=KNN, cv=10, param_grid=parameters)\nknn_cv.fit(X_train, Y_train)",
            "execution_count": 27,
            "outputs": [
                {
                    "output_type": "execute_result",
                    "execution_count": 27,
                    "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]})"
                    },
                    "metadata": {}
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\nprint(\"accuracy :\",knn_cv.best_score_)",
            "execution_count": 28,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 10, 'p': 1}\naccuracy : 0.8482142857142858\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## TASK  11\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Calculate the accuracy of tree_cv on the test data using the method <code>score</code>:\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "print(\"accuracy :\", knn_cv.score(X_test, Y_test))",
            "execution_count": 29,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "accuracy : 0.8333333333333334\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "We can plot the confusion matrix\n"
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "yhat = knn_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)",
            "execution_count": 30,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 2 Axes>",
                        "image/png": 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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## TASK  12\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Find the method performs best:\n"
        },
        {
            "metadata": {
                "scrolled": true
            },
            "cell_type": "code",
            "source": "print(\"Model\\t\\tAccuracy\\tTestAccuracy\")#,logreg_cv.best_score_)\nprint(\"LogReg\\t\\t{}\\t\\t{}\".format((logreg_cv.best_score_).round(5), logreg_cv.score(X_test, Y_test).round(5)))\nprint(\"SVM\\t\\t{}\\t\\t{}\".format((svm_cv.best_score_).round(5), svm_cv.score(X_test, Y_test).round(5)))\nprint(\"Tree\\t\\t{}\\t\\t{}\".format((tree_cv.best_score_).round(5), tree_cv.score(X_test, Y_test).round(5)))\nprint(\"KNN\\t\\t{}\\t\\t{}\".format((knn_cv.best_score_).round(5), knn_cv.score(X_test, Y_test).round(5)))\n\ncomparison = {}\n\ncomparison['LogReg'] = {'Accuracy': logreg_cv.best_score_.round(5), 'TestAccuracy': logreg_cv.score(X_test, Y_test).round(5)}\ncomparison['SVM'] = {'Accuracy': svm_cv.best_score_.round(5), 'TestAccuracy': svm_cv.score(X_test, Y_test).round(5)}\ncomparison['Tree'] = {'Accuracy': tree_cv.best_score_.round(5), 'TestAccuracy': tree_cv.score(X_test, Y_test).round(5)}\ncomparison['KNN'] = {'Accuracy': knn_cv.best_score_.round(5), 'TestAccuracy': knn_cv.score(X_test, Y_test).round(5)}\n",
            "execution_count": 32,
            "outputs": [
                {
                    "output_type": "stream",
                    "text": "Model\t\tAccuracy\tTestAccuracy\nLogReg\t\t0.84643\t\t0.83333\nSVM\t\t0.84821\t\t0.83333\nTree\t\t0.875\t\t0.94444\nKNN\t\t0.84821\t\t0.83333\n",
                    "name": "stdout"
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "code",
            "source": "x = []\ny1 = []\ny2 = []\nfor meth in comparison.keys():\n    x.append(meth)    \n    y1.append(comparison[meth]['Accuracy'])\n    y2.append(comparison[meth]['TestAccuracy'])\n    \n\nx_axis = np.arange(len(x))\n\nplt.bar(x_axis - 0.2, y1, 0.4, label = 'Accuracy')\nplt.bar(x_axis + 0.2, y2, 0.4, label = 'Test Accuracy')\n\nplt.ylim([0,1])\nplt.xticks(x_axis, x)\n\nplt.xlabel(\"Methods\")\nplt.ylabel(\"Accuracy\")\nplt.title(\"Accuracy of Each Method\")\nplt.legend(loc='lower left')\nplt.show()\n    ",
            "execution_count": 33,
            "outputs": [
                {
                    "output_type": "display_data",
                    "data": {
                        "text/plain": "<Figure size 432x288 with 1 Axes>",
                        "image/png": 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\n"
                    },
                    "metadata": {
                        "needs_background": "light"
                    }
                }
            ]
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Authors\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "## Change Log\n"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "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"
        },
        {
            "metadata": {},
            "cell_type": "markdown",
            "source": "Copyright \u00a9 2020 IBM Corporation. All rights reserved.\n"
        }
    ],
    "metadata": {
        "kernelspec": {
            "name": "python3",
            "display_name": "Python 3.7",
            "language": "python"
        },
        "language_info": {
            "name": "python",
            "version": "3.7.11",
            "mimetype": "text/x-python",
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "pygments_lexer": "ipython3",
            "nbconvert_exporter": "python",
            "file_extension": ".py"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 4
}