8.5 Trial and Error ML model.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: (69, 83) (69,)\nTest set: (21, 83) (21,)\n"
                }
            ],
            "source": "X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size=0.225, 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": "(21,)"
                    },
                    "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': 0.1, 'penalty': 'l2', 'solver': 'lbfgs'}\naccuracy : 0.8261904761904761\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": "0.8571428571428571"
                    },
                    "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': 0.03162277660168379, 'gamma': 0.001, 'kernel': 'linear'}\naccuracy : 0.8119047619047619\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": "0.8571428571428571"
                    },
                    "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': 1, 'min_samples_split': 10, 'splitter': 'random'}\naccuracy : 0.8833333333333334\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": "0.9523809523809523"
                    },
                    "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': 9, 'p': 1}\naccuracy : 0.8404761904761905\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": "0.8571428571428571"
                    },
                    "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: 0.8571428571428571\nAccuracy for Support Vector Machine method: 0.8571428571428571\nAccuracy for Decision tree method: 0.9523809523809523\nAccuracy for K-nearest neighbour method: 0.8571428571428571\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': [0.8571428571428571,\n  0.8571428571428571,\n  0.9523809523809523,\n  0.8571428571428571]}"
                    },
                    "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| 0.857                | 0.857                  |      0.952       |       0.857             |\n\n\n## manage to imrpove the results for decision tree by trial and error on the test dataset size. The best performing model thus far is when the test dataset size is 0.225\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
}