SpaceX_Machine Learning Prediction_Part_5.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<p style=\"text-align:center\">\n    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\" target=\"_blank\">\n    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n    </a>\n</p>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# **Space X  Falcon 9 First Stage Landing Prediction**\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Assignment:  Machine Learning Prediction\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Estimated time needed: **60** minutes\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Space X advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is because Space X can reuse the first stage. Therefore if we can determine if the first stage will land, we can determine the cost of a launch. This information can be used if an alternate company wants to bid against space X for a rocket launch.   In this lab, you will create a machine learning pipeline  to predict if the first stage will land given the data from the preceding labs.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing\\_1.gif)\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Several examples of an unsuccessful landing are shown here:\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Most unsuccessful landings are planed. Space X; performs a controlled landing in the oceans.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Objectives\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Perform exploratory  Data Analysis and determine Training Labels\n\n*   create a column for the class\n*   Standardize the data\n*   Split into training data and test data\n\n\\-Find best Hyperparameter for SVM, Classification Trees and Logistic Regression\n\n*   Find the method performs best using test data\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "***\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": 65,
            "metadata": {},
            "outputs": [],
            "source": "import warnings\nwarnings.filterwarnings('ignore',category=FutureWarning)"
        },
        {
            "cell_type": "code",
            "execution_count": 66,
            "metadata": {},
            "outputs": [],
            "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"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "This function is to plot the confusion matrix.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 67,
            "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": 68,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>Date</th>\n      <th>BoosterVersion</th>\n      <th>PayloadMass</th>\n      <th>Orbit</th>\n      <th>LaunchSite</th>\n      <th>Outcome</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>LandingPad</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2010-06-04</td>\n      <td>Falcon 9</td>\n      <td>6104.959412</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0003</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2012-05-22</td>\n      <td>Falcon 9</td>\n      <td>525.000000</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0005</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>2013-03-01</td>\n      <td>Falcon 9</td>\n      <td>677.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0007</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>2013-09-29</td>\n      <td>Falcon 9</td>\n      <td>500.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>False Ocean</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1003</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>2013-12-03</td>\n      <td>Falcon 9</td>\n      <td>3170.000000</td>\n      <td>GTO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1004</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\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  "
                    },
                    "execution_count": 68,
                    "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\ndata.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 69,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Flights</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Orbit_ES-L1</th>\n      <th>Orbit_GEO</th>\n      <th>Orbit_GTO</th>\n      <th>Orbit_HEO</th>\n      <th>Orbit_ISS</th>\n      <th>...</th>\n      <th>Serial_B1058</th>\n      <th>Serial_B1059</th>\n      <th>Serial_B1060</th>\n      <th>Serial_B1062</th>\n      <th>GridFins_False</th>\n      <th>GridFins_True</th>\n      <th>Reused_False</th>\n      <th>Reused_True</th>\n      <th>Legs_False</th>\n      <th>Legs_True</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>6104.959412</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.0</td>\n      <td>525.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.0</td>\n      <td>677.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>500.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3170.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>86.0</td>\n      <td>15400.000000</td>\n      <td>2.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>87.0</td>\n      <td>15400.000000</td>\n      <td>3.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>88.0</td>\n      <td>15400.000000</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>89.0</td>\n      <td>15400.000000</td>\n      <td>3.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>90.0</td>\n      <td>3681.000000</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>90 rows \u00d7 83 columns</p>\n</div>",
                        "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]"
                    },
                    "execution_count": 69,
                    "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\nX.head(100)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## TASK  1\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Create a NumPy array from the column <code>Class</code> in <code>data</code>, by applying the method <code>to_numpy()</code>  then\nassign 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": 70,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "numpy.ndarray"
                    },
                    "execution_count": 70,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "Y = data['Class'].to_numpy()\ntype(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": 71,
            "metadata": {},
            "outputs": [],
            "source": "# students get this \ntransform = preprocessing.StandardScaler()"
        },
        {
            "cell_type": "code",
            "execution_count": 72,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "array([[-1.71291154e+00, -1.94814463e-16, -6.53912840e-01,\n        -1.57589457e+00, -9.73440458e-01, -1.05999788e-01,\n        -1.05999788e-01, -6.54653671e-01, -1.05999788e-01,\n        -5.51677284e-01,  3.44342023e+00, -1.85695338e-01,\n        -3.33333333e-01, -1.05999788e-01, -2.42535625e-01,\n        -4.29197538e-01,  7.97724035e-01, -5.68796459e-01,\n        -4.10890702e-01, -4.10890702e-01, -1.50755672e-01,\n        -7.97724035e-01, -1.50755672e-01, -3.92232270e-01,\n         9.43398113e+00, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.50755672e-01,\n        -1.50755672e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -2.15665546e-01,\n        -1.85695338e-01, -2.15665546e-01, -2.67261242e-01,\n        -1.05999788e-01, -2.42535625e-01, -1.05999788e-01,\n        -2.15665546e-01, -1.85695338e-01, -2.15665546e-01,\n        -1.85695338e-01, -1.05999788e-01,  1.87082869e+00,\n        -1.87082869e+00,  8.35531692e-01, -8.35531692e-01,\n         1.93309133e+00, -1.93309133e+00],\n       [-1.67441914e+00, -1.19523159e+00, -6.53912840e-01,\n        -1.57589457e+00, -9.73440458e-01, -1.05999788e-01,\n        -1.05999788e-01, -6.54653671e-01, -1.05999788e-01,\n        -5.51677284e-01,  3.44342023e+00, -1.85695338e-01,\n        -3.33333333e-01, -1.05999788e-01, -2.42535625e-01,\n        -4.29197538e-01,  7.97724035e-01, -5.68796459e-01,\n        -4.10890702e-01, -4.10890702e-01, -1.50755672e-01,\n        -7.97724035e-01, -1.50755672e-01, -3.92232270e-01,\n        -1.05999788e-01,  9.43398113e+00, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.50755672e-01,\n        -1.50755672e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -2.15665546e-01,\n        -1.85695338e-01, -2.15665546e-01, -2.67261242e-01,\n        -1.05999788e-01, -2.42535625e-01, -1.05999788e-01,\n        -2.15665546e-01, -1.85695338e-01, -2.15665546e-01,\n        -1.85695338e-01, -1.05999788e-01,  1.87082869e+00,\n        -1.87082869e+00,  8.35531692e-01, -8.35531692e-01,\n         1.93309133e+00, -1.93309133e+00],\n       [-1.63592675e+00, -1.16267307e+00, -6.53912840e-01,\n        -1.57589457e+00, -9.73440458e-01, -1.05999788e-01,\n        -1.05999788e-01, -6.54653671e-01, -1.05999788e-01,\n         1.81265393e+00, -2.90408935e-01, -1.85695338e-01,\n        -3.33333333e-01, -1.05999788e-01, -2.42535625e-01,\n        -4.29197538e-01,  7.97724035e-01, -5.68796459e-01,\n        -4.10890702e-01, -4.10890702e-01, -1.50755672e-01,\n        -7.97724035e-01, -1.50755672e-01, -3.92232270e-01,\n        -1.05999788e-01, -1.05999788e-01,  9.43398113e+00,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.50755672e-01,\n        -1.50755672e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -2.15665546e-01,\n        -1.85695338e-01, -2.15665546e-01, -2.67261242e-01,\n        -1.05999788e-01, -2.42535625e-01, -1.05999788e-01,\n        -2.15665546e-01, -1.85695338e-01, -2.15665546e-01,\n        -1.85695338e-01, -1.05999788e-01,  1.87082869e+00,\n        -1.87082869e+00,  8.35531692e-01, -8.35531692e-01,\n         1.93309133e+00, -1.93309133e+00],\n       [-1.59743435e+00, -1.20058661e+00, -6.53912840e-01,\n        -1.57589457e+00, -9.73440458e-01, -1.05999788e-01,\n        -1.05999788e-01, -6.54653671e-01, -1.05999788e-01,\n        -5.51677284e-01, -2.90408935e-01, -1.85695338e-01,\n         3.00000000e+00, -1.05999788e-01, -2.42535625e-01,\n        -4.29197538e-01, -1.25356634e+00, -5.68796459e-01,\n         2.43373723e+00, -4.10890702e-01, -1.50755672e-01,\n        -7.97724035e-01, -1.50755672e-01, -3.92232270e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n         9.43398113e+00, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.50755672e-01,\n        -1.50755672e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -2.15665546e-01,\n        -1.85695338e-01, -2.15665546e-01, -2.67261242e-01,\n        -1.05999788e-01, -2.42535625e-01, -1.05999788e-01,\n        -2.15665546e-01, -1.85695338e-01, -2.15665546e-01,\n        -1.85695338e-01, -1.05999788e-01,  1.87082869e+00,\n        -1.87082869e+00,  8.35531692e-01, -8.35531692e-01,\n         1.93309133e+00, -1.93309133e+00],\n       [-1.55894196e+00, -6.28670558e-01, -6.53912840e-01,\n        -1.57589457e+00, -9.73440458e-01, -1.05999788e-01,\n        -1.05999788e-01,  1.52752523e+00, -1.05999788e-01,\n        -5.51677284e-01, -2.90408935e-01, -1.85695338e-01,\n        -3.33333333e-01, -1.05999788e-01, -2.42535625e-01,\n        -4.29197538e-01,  7.97724035e-01, -5.68796459e-01,\n        -4.10890702e-01, -4.10890702e-01, -1.50755672e-01,\n        -7.97724035e-01, -1.50755672e-01, -3.92232270e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01,  9.43398113e+00, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.50755672e-01, -1.50755672e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -1.50755672e-01,\n        -1.50755672e-01, -1.05999788e-01, -1.05999788e-01,\n        -1.05999788e-01, -1.50755672e-01, -2.15665546e-01,\n        -1.85695338e-01, -2.15665546e-01, -2.67261242e-01,\n        -1.05999788e-01, -2.42535625e-01, -1.05999788e-01,\n        -2.15665546e-01, -1.85695338e-01, -2.15665546e-01,\n        -1.85695338e-01, -1.05999788e-01,  1.87082869e+00,\n        -1.87082869e+00,  8.35531692e-01, -8.35531692e-01,\n         1.93309133e+00, -1.93309133e+00]])"
                    },
                    "execution_count": 72,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "X = preprocessing.StandardScaler().fit(X).transform(X.astype(float))\nX[0:5]"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We split the data into training and testing data using the  function  <code>train_test_split</code>.   The training data is divided into validation data, a second set used for training  data; then the models are trained and hyperparameters are selected using the function <code>GridSearchCV</code>.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## TASK  3\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Use the function train_test_split to split the data X and Y into training and test data. Set the parameter test_size to  0.2 and random_state to 2. The training data and test data should be assigned to the following labels.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<code>X_train, X_test, Y_train, Y_test</code>\n"
        },
        {
            "cell_type": "code",
            "execution_count": 73,
            "metadata": {},
            "outputs": [],
            "source": "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "we can see we only have 18 test samples.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 74,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "(18,)"
                    },
                    "execution_count": 74,
                    "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": 77,
            "metadata": {},
            "outputs": [],
            "source": "parameters ={'C':[0.01,0.1,1],\n             'penalty':['l2'],\n             'solver':['lbfgs']}\nwarnings.filterwarnings('ignore')"
        },
        {
            "cell_type": "code",
            "execution_count": 78,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "GridSearchCV(cv=10, error_score='raise-deprecating',\n       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n          intercept_scaling=1, max_iter=100, multi_class='warn',\n          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n          tol=0.0001, verbose=0, warm_start=False),\n       fit_params=None, iid='warn', n_jobs=None,\n       param_grid={'C': [0.01, 0.1, 1], 'penalty': ['l2'], 'solver': ['lbfgs']},\n       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n       scoring=None, verbose=0)"
                    },
                    "execution_count": 78,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "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)"
        },
        {
            "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": 79,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "tuned hpyerparameters :(best parameters)  {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\naccuracy : 0.8472222222222222\n"
                }
            ],
            "source": "print(\"tuned hpyerparameters :(best parameters) \",logreg_cv.best_params_)\nprint(\"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": 80,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "accuracy : 0.8333333333333334\n"
                }
            ],
            "source": "print(\"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": 81,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "yhat=logreg_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Examining the confusion matrix, we see that logistic regression can distinguish between the different classes.  We see that the major problem is false positives.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## TASK  6\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Create a support vector machine object then  create a  <code>GridSearchCV</code> object  <code>svm_cv</code> with cv - 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 82,
            "metadata": {},
            "outputs": [],
            "source": "parameters = {'kernel':('linear', 'rbf','poly','rbf', 'sigmoid'),\n              'C': np.logspace(-3, 3, 5),\n              'gamma':np.logspace(-3, 3, 5)}\nsvm = SVC()"
        },
        {
            "cell_type": "code",
            "execution_count": 83,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "GridSearchCV(cv=10, error_score='raise-deprecating',\n       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n  shrinking=True, tol=0.001, verbose=False),\n       fit_params=None, iid='warn', n_jobs=None,\n       param_grid={'kernel': ('linear', 'rbf', 'poly', 'rbf', 'sigmoid'), 'C': array([1.00000e-03, 3.16228e-02, 1.00000e+00, 3.16228e+01, 1.00000e+03]), 'gamma': array([1.00000e-03, 3.16228e-02, 1.00000e+00, 3.16228e+01, 1.00000e+03])},\n       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n       scoring=None, verbose=0)"
                    },
                    "execution_count": 83,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "svm_cv = GridSearchCV(estimator=svm, cv=10, param_grid=parameters)\nsvm_cv.fit(X_train, Y_train)"
        },
        {
            "cell_type": "code",
            "execution_count": 84,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "tuned hpyerparameters :(best parameters)  {'C': 1.0, 'gamma': 0.03162277660168379, 'kernel': 'sigmoid'}\naccuracy : 0.8472222222222222\n"
                }
            ],
            "source": "print(\"tuned hpyerparameters :(best parameters) \",svm_cv.best_params_)\nprint(\"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": 85,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "accuracy : 0.8333333333333334\n"
                }
            ],
            "source": "print(\"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": 86,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "yhat=svm_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## TASK  8\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Create a decision tree classifier object then  create a  <code>GridSearchCV</code> object  <code>tree_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 87,
            "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\ntree = DecisionTreeClassifier()"
        },
        {
            "cell_type": "code",
            "execution_count": 88,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "GridSearchCV(cv=10, error_score='raise-deprecating',\n       estimator=DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n            max_features=None, max_leaf_nodes=None,\n            min_impurity_decrease=0.0, min_impurity_split=None,\n            min_samples_leaf=1, min_samples_split=2,\n            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n            splitter='best'),\n       fit_params=None, iid='warn', n_jobs=None,\n       param_grid={'criterion': ['gini', 'entropy'], 'splitter': ['best', 'random'], 'max_depth': [2, 4, 6, 8, 10, 12, 14, 16, 18], 'max_features': ['auto', 'sqrt'], 'min_samples_leaf': [1, 2, 4], 'min_samples_split': [2, 5, 10]},\n       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n       scoring=None, verbose=0)"
                    },
                    "execution_count": 88,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "tree_cv = GridSearchCV(estimator=tree, cv=10, param_grid=parameters)\ntree_cv.fit(X_train, Y_train)"
        },
        {
            "cell_type": "code",
            "execution_count": 89,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "tuned hpyerparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 2, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'splitter': 'random'}\naccuracy : 0.8888888888888888\n"
                }
            ],
            "source": "print(\"tuned hpyerparameters :(best parameters) \",tree_cv.best_params_)\nprint(\"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": 90,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "accuracy : 0.8333333333333334\n"
                }
            ],
            "source": "print(\"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": 91,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "yhat = svm_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## TASK  10\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Create a k nearest neighbors object then  create a  <code>GridSearchCV</code> object  <code>knn_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 92,
            "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\nKNN = KNeighborsClassifier()"
        },
        {
            "cell_type": "code",
            "execution_count": 93,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "GridSearchCV(cv=10, error_score='raise-deprecating',\n       estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n           weights='uniform'),\n       fit_params=None, iid='warn', n_jobs=None,\n       param_grid={'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'], 'p': [1, 2]},\n       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n       scoring=None, verbose=0)"
                    },
                    "execution_count": 93,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "knn_cv = GridSearchCV(estimator=KNN, cv=10, param_grid=parameters)\nknn_cv.fit(X_train, Y_train)"
        },
        {
            "cell_type": "code",
            "execution_count": 94,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 9, 'p': 1}\naccuracy : 0.8472222222222222\n"
                }
            ],
            "source": "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\nprint(\"accuracy :\",knn_cv.best_score_)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## TASK  11\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Calculate the accuracy of tree_cv on the test data using the method <code>score</code>:\n"
        },
        {
            "cell_type": "code",
            "execution_count": 95,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 9, 'p': 1}\naccuracy : 0.8472222222222222\n"
                }
            ],
            "source": "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\nprint(\"accuracy :\",knn_cv.best_score_)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We can plot the confusion matrix\n"
        },
        {
            "cell_type": "code",
            "execution_count": 96,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "yhat = knn_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## TASK  12\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Find the method performs best:\n"
        },
        {
            "cell_type": "code",
            "execution_count": 97,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Model\t\tAccuracy\tTestAccuracy\nLogReg\t\t0.84722\t\t0.83333\nSVM\t\t0.84722\t\t0.83333\nTree\t\t0.88889\t\t0.83333\nKNN\t\t0.84722\t\t0.83333\n"
                }
            ],
            "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)}"
        },
        {
            "cell_type": "code",
            "execution_count": 98,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "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()"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Authors\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Change Log\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description      |\n| ----------------- | ------- | ------------- | ----------------------- |\n| 2021-08-31        | 1.1     | Lakshmi Holla | Modified markdown       |\n| 2020-09-20        | 1.0     | Joseph        | Modified Multiple Areas |\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Copyright \u00a9 2020 IBM Corporation. All rights reserved.\n"
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python",
            "language": "python",
            "name": "conda-env-python-py"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.7.12"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 4
}