8. SpaceX_Machine Learning Prediction.ipynb

{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "<p style=\"text-align:center\">\n    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork26802033-2022-01-01\" target=\"_blank\">\n    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n    </a>\n</p>\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "# **Space X  Falcon 9 First Stage Landing Prediction**\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Assignment:  Machine Learning Prediction\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Estimated time needed: **60** minutes\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Space X advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is because Space X can reuse the first stage. Therefore if we can determine if the first stage will land, we can determine the cost of a launch. This information can be used if an alternate company wants to bid against space X for a rocket launch.   In this lab, you will create a machine learning pipeline  to predict if the first stage will land given the data from the preceding labs.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing\\_1.gif)\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Several examples of an unsuccessful landing are shown here:\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Most unsuccessful landings are planed. Space X; performs a controlled landing in the oceans.\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Objectives\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Perform exploratory  Data Analysis and determine Training Labels\n\n*   create a column for the class\n*   Standardize the data\n*   Split into training data and test data\n\n\\-Find best Hyperparameter for SVM, Classification Trees and Logistic Regression\n\n*   Find the method performs best using test data\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": ""
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "***\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Import Libraries and Define Auxiliary Functions\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We will import the following libraries for the lab\n"
        },
        {
            "cell_type": "code",
            "execution_count": 1,
            "metadata": {},
            "outputs": [],
            "source": "import warnings\nwarnings.filterwarnings(\"ignore\", category=DeprecationWarning) #run this cell twice to ignore warning\n# 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\n\nnp.random.seed(0)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "This function is to plot the confusion matrix.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [],
            "source": "def plot_confusion_matrix(y,y_predict):\n    \"this function plots the confusion matrix\"\n    from sklearn.metrics import confusion_matrix\n\n    cm = confusion_matrix(y, y_predict)\n    ax= plt.subplot()\n    sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells\n    ax.set_xlabel('Predicted labels')\n    ax.set_ylabel('True labels')\n    ax.set_title('Confusion Matrix'); \n    ax.xaxis.set_ticklabels(['did not land', 'land']); ax.yaxis.set_ticklabels(['did not land', 'landed'])"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "## Load the dataframe\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Load the data\n"
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>Date</th>\n      <th>BoosterVersion</th>\n      <th>PayloadMass</th>\n      <th>Orbit</th>\n      <th>LaunchSite</th>\n      <th>Outcome</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>LandingPad</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2010-06-04</td>\n      <td>Falcon 9</td>\n      <td>6104.959412</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0003</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2012-05-22</td>\n      <td>Falcon 9</td>\n      <td>525.000000</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0005</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>2013-03-01</td>\n      <td>Falcon 9</td>\n      <td>677.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0007</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>2013-09-29</td>\n      <td>Falcon 9</td>\n      <td>500.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>False Ocean</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1003</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>2013-12-03</td>\n      <td>Falcon 9</td>\n      <td>3170.000000</td>\n      <td>GTO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1004</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
                        "text/plain": "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\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": 3,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "data = pd.read_csv(\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\")\n\n# If you were unable to complete the previous lab correctly you can uncomment and load this csv\n\n# data = pd.read_csv('https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/dataset_part_2.csv')\n\ndata.head()"
        },
        {
            "cell_type": "code",
            "execution_count": 4,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "(90, 18)"
                    },
                    "execution_count": 4,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "data.shape"
        },
        {
            "cell_type": "code",
            "execution_count": 5,
            "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": 5,
                    "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": "code",
            "execution_count": 6,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "(90, 83)"
                    },
                    "execution_count": 6,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "X.shape"
        },
        {
            "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": 7,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "[0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0 1 1 1 1 1 0 1 1 0 1 1 0 1 1 1 0 1 1\n 1 1 1 1 1 1 1 1 0 0 0 1 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 0 1\n 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1]\n"
                }
            ],
            "source": "Y = data['Class'].to_numpy()\nprint(Y)"
        },
        {
            "cell_type": "code",
            "execution_count": 8,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "90"
                    },
                    "execution_count": 8,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "len(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": 9,
            "metadata": {},
            "outputs": [],
            "source": "# students get this \nX = preprocessing.StandardScaler().fit(X).transform(X)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "X_train, X_test, Y_train, Y_testWe 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": 10,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Train set: (72, 83) (72,)\nTest set: (18, 83) (18,)\n"
                }
            ],
            "source": "X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size=0.2, random_state=2)\nprint ('Train set:', X_train.shape,  Y_train.shape)\nprint ('Test set:', X_test.shape,  Y_test.shape)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "we can see we only have 18 test samples.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "(18,)"
                    },
                    "execution_count": 11,
                    "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": 12,
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": "parameters ={'C':[0.01,0.1,1],\n             'penalty':['l2'],\n             'solver':['lbfgs']}\n\nlr=LogisticRegression()\ngrid_search = GridSearchCV(lr, parameters, cv=10)\nlogreg_cv = grid_search.fit(X_train, Y_train)"
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {},
            "outputs": [],
            "source": "parameters ={\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge\nlr=LogisticRegression()\n"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We output the <code>GridSearchCV</code> object for logistic regression. We display the best parameters using the data attribute <code>best_params\\_</code> and the accuracy on the validation data using the data attribute <code>best_score\\_</code>.\n"
        },
        {
            "cell_type": "code",
            "execution_count": 14,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "tuned hpyerparameters :(best parameters)  {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\naccuracy : 0.8464285714285713\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": 15,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "0.8333333333333334"
                    },
                    "execution_count": 15,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "logreg_cv.score(X_test,Y_test)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "Lets look at the confusion matrix:\n"
        },
        {
            "cell_type": "code",
            "execution_count": 16,
            "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": 17,
            "metadata": {
                "tags": []
            },
            "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()\ngs_svm = GridSearchCV (svm, parameters, cv=10)\nsvm_cv = gs_svm.fit(X_train, Y_train)"
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "tuned hpyerparameters :(best parameters)  {'C': 1.0, 'gamma': 0.03162277660168379, 'kernel': 'sigmoid'}\naccuracy : 0.8482142857142856\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": 19,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "0.8333333333333334"
                    },
                    "execution_count": 19,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "svm_cv.score(X_test,Y_test)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We can plot the confusion matrix\n"
        },
        {
            "cell_type": "code",
            "execution_count": 20,
            "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": 21,
            "metadata": {},
            "outputs": [],
            "source": "parameters = {'criterion': ['gini', 'entropy'],\n     'splitter': ['best', 'random'],\n     'max_depth': [2*n for n in range(1,10)],\n     'max_features': ['auto', 'sqrt'],\n     'min_samples_leaf': [1, 2, 4],\n     'min_samples_split': [2, 5, 10]}\n\ntree = DecisionTreeClassifier()"
        },
        {
            "cell_type": "code",
            "execution_count": 22,
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": "gs_dt = GridSearchCV(tree, parameters, cv = 10)\ntree_cv = gs_dt.fit(X_train,Y_train)"
        },
        {
            "cell_type": "code",
            "execution_count": 23,
            "metadata": {
                "tags": []
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "tuned hpyerparameters :(best parameters)  {'criterion': 'entropy', 'max_depth': 12, 'max_features': 'sqrt', 'min_samples_leaf': 4, 'min_samples_split': 2, 'splitter': 'random'}\naccuracy : 0.8767857142857143\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": 24,
            "metadata": {
                "tags": []
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "0.8333333333333334"
                    },
                    "execution_count": 24,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "tree_cv.score(X_test,Y_test)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We can plot the confusion matrix\n"
        },
        {
            "cell_type": "code",
            "execution_count": 25,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "yhat = tree_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": 26,
            "metadata": {},
            "outputs": [],
            "source": "parameters = {'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n              'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n              'p': [1,2]}\n\nKNN = KNeighborsClassifier()"
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "metadata": {
                "tags": []
            },
            "outputs": [],
            "source": "gs_knn = GridSearchCV(KNN, parameters, cv=10)\nknn_cv = gs_knn.fit(X_train,Y_train)"
        },
        {
            "cell_type": "code",
            "execution_count": 28,
            "metadata": {},
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 10, 'p': 1}\naccuracy : 0.8482142857142858\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": 29,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": "0.8333333333333334"
                    },
                    "execution_count": 29,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "knn_cv.score(X_test,Y_test)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "We can plot the confusion matrix\n"
        },
        {
            "cell_type": "code",
            "execution_count": 30,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 432x288 with 2 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "yhat = 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": 31,
            "metadata": {
                "scrolled": false
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": "Accuracy for Logistics Regression method: 0.8333333333333334\nAccuracy for Support Vector Machine method: 0.8333333333333334\nAccuracy for Decision tree method: 0.8333333333333334\nAccuracy for K-nearest neighbour method: 0.8333333333333334\n"
                }
            ],
            "source": "print('Accuracy for Logistics Regression method:', logreg_cv.score(X_test, Y_test))\nprint( 'Accuracy for Support Vector Machine method:', svm_cv.score(X_test, Y_test))\nprint('Accuracy for Decision tree method:', tree_cv.score(X_test, Y_test))\nprint('Accuracy for K-nearest neighbour method:', knn_cv.score(X_test, Y_test))"
        },
        {
            "cell_type": "code",
            "execution_count": 32,
            "metadata": {},
            "outputs": [],
            "source": "accuracy_score = {'algorithm': ['Logistic Regression', 'SVM', 'Decision Tree', 'KNN'],\n        'score': [logreg_cv.score(X_test, Y_test), svm_cv.score(X_test, Y_test), tree_cv.score(X_test, Y_test), knn_cv.score(X_test, Y_test)]} "
        },
        {
            "cell_type": "code",
            "execution_count": 33,
            "metadata": {
                "scrolled": true
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": "{'algorithm': ['Logistic Regression', 'SVM', 'Decision Tree', 'KNN'],\n 'score': [0.8333333333333334,\n  0.8333333333333334,\n  0.8333333333333334,\n  0.8333333333333334]}"
                    },
                    "execution_count": 33,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": "accuracy_score"
        },
        {
            "cell_type": "code",
            "execution_count": 34,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "image/png": 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\n",
                        "text/plain": "<Figure size 576x576 with 1 Axes>"
                    },
                    "metadata": {
                        "needs_background": "light"
                    },
                    "output_type": "display_data"
                }
            ],
            "source": "f, ax=plt.subplots(figsize=(8,8))\nax= sns.barplot(x = 'algorithm', y = 'score', data=accuracy_score)\nax.set_title('Model Accuracy  on the Test Set', fontsize = 15)\nax.set_ylabel('Accuracy',fontsize = 15)\nax.set_xlabel('Model',fontsize = 15)\nfor i in ax.containers:\n    ax.bar_label(i,)"
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": "|             | Logistics Regression | Support Vector Machine | Decision tree    | K-nearest neighbour     |\n|-------------| -------------------- | ---------------------- | ---------------- | ----------------------- |\n|TEST ACCURACY| 0.833                | 0.833                  |      0.833       |       0.833             |\n\n\n## The results for all methods are the same due to the small dataset and even smaller test set\n"
        },
        {
            "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 3.9",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.9.13"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 4
}