4-1 SpaceX_Machine_Learning_Prediction_Part_5.ipynb

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      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    },
    "kernelspec": {
      "name": "python",
      "display_name": "Python (Pyodide)",
      "language": "python"
    }
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  "nbformat_minor": 4,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "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\">\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",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "# **Space X  Falcon 9 First Stage Landing Prediction**\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Assignment:  Machine Learning Prediction\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Estimated time needed: **60** minutes\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Space X advertises Falcon 9 rocket launches on its website with a cost of 62 million dollars; other providers cost upward of 165 million dollars each, much of the savings is because Space X can reuse the first stage. Therefore if we can determine if the first stage will land, we can determine the cost of a launch. This information can be used if an alternate company wants to bid against space X for a rocket launch.   In this lab, you will create a machine learning pipeline  to predict if the first stage will land given the data from the preceding labs.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/landing_1.gif)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Several examples of an unsuccessful landing are shown here:\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "![](https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DS0701EN-SkillsNetwork/api/Images/crash.gif)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Most unsuccessful landings are planed. Space X; performs a controlled landing in the oceans.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Objectives\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Perform exploratory  Data Analysis and determine Training Labels\n\n*   create a column for the class\n*   Standardize the data\n*   Split into training data and test data\n\n\\-Find best Hyperparameter for SVM, Classification Trees and Logistic Regression\n\n*   Find the method performs best using test data\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Import Libraries and Define Auxiliary Functions\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "import piplite\nawait piplite.install(['numpy'])\nawait piplite.install(['pandas'])\nawait piplite.install(['seaborn'])",
      "metadata": {
        "trusted": true
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "We will import the following libraries for the lab\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# Pandas is a software library written for the Python programming language for data manipulation and analysis.\nimport pandas as pd\n# NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays\nimport numpy as np\n# Matplotlib is a plotting library for python and pyplot gives us a MatLab like plotting framework. We will use this in our plotter function to plot data.\nimport matplotlib.pyplot as plt\n#Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics\nimport seaborn as sns\n# Preprocessing allows us to standarsize our data\nfrom sklearn import preprocessing\n# Allows us to split our data into training and testing data\nfrom sklearn.model_selection import train_test_split\n# Allows us to test parameters of classification algorithms and find the best one\nfrom sklearn.model_selection import GridSearchCV\n# Logistic Regression classification algorithm\nfrom sklearn.linear_model import LogisticRegression\n# Support Vector Machine classification algorithm\nfrom sklearn.svm import SVC\n# Decision Tree classification algorithm\nfrom sklearn.tree import DecisionTreeClassifier\n# K Nearest Neighbors classification algorithm\nfrom sklearn.neighbors import KNeighborsClassifier",
      "metadata": {
        "trusted": true
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "This function is to plot the confusion matrix.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "def plot_confusion_matrix(y,y_predict):\n    \"this function plots the confusion matrix\"\n    from sklearn.metrics import confusion_matrix\n\n    cm = confusion_matrix(y, y_predict)\n    ax= plt.subplot()\n    sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells\n    ax.set_xlabel('Predicted labels')\n    ax.set_ylabel('True labels')\n    ax.set_title('Confusion Matrix'); \n    ax.xaxis.set_ticklabels(['did not land', 'land']); ax.yaxis.set_ticklabels(['did not land', 'landed']) \n    plt.show() ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "## Load the dataframe\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Load the data\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from js import fetch\nimport io\n\nURL1 = \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_2.csv\"\nresp1 = await fetch(URL1)\ntext1 = io.BytesIO((await resp1.arrayBuffer()).to_py())\ndata = pd.read_csv(text1)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "data.head()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 5,
      "outputs": [
        {
          "execution_count": 5,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   FlightNumber        Date BoosterVersion  PayloadMass Orbit    LaunchSite  \\\n0             1  2010-06-04       Falcon 9  6104.959412   LEO  CCAFS SLC 40   \n1             2  2012-05-22       Falcon 9   525.000000   LEO  CCAFS SLC 40   \n2             3  2013-03-01       Falcon 9   677.000000   ISS  CCAFS SLC 40   \n3             4  2013-09-29       Falcon 9   500.000000    PO   VAFB SLC 4E   \n4             5  2013-12-03       Falcon 9  3170.000000   GTO  CCAFS SLC 40   \n\n       Outcome  Flights  GridFins  Reused   Legs LandingPad  Block  \\\n0    None None        1     False   False  False        NaN    1.0   \n1    None None        1     False   False  False        NaN    1.0   \n2    None None        1     False   False  False        NaN    1.0   \n3  False Ocean        1     False   False  False        NaN    1.0   \n4    None None        1     False   False  False        NaN    1.0   \n\n   ReusedCount Serial   Longitude   Latitude  Class  \n0            0  B0003  -80.577366  28.561857      0  \n1            0  B0005  -80.577366  28.561857      0  \n2            0  B0007  -80.577366  28.561857      0  \n3            0  B1003 -120.610829  34.632093      0  \n4            0  B1004  -80.577366  28.561857      0  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>Date</th>\n      <th>BoosterVersion</th>\n      <th>PayloadMass</th>\n      <th>Orbit</th>\n      <th>LaunchSite</th>\n      <th>Outcome</th>\n      <th>Flights</th>\n      <th>GridFins</th>\n      <th>Reused</th>\n      <th>Legs</th>\n      <th>LandingPad</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Serial</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n      <th>Class</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2010-06-04</td>\n      <td>Falcon 9</td>\n      <td>6104.959412</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0003</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2012-05-22</td>\n      <td>Falcon 9</td>\n      <td>525.000000</td>\n      <td>LEO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0005</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>2013-03-01</td>\n      <td>Falcon 9</td>\n      <td>677.000000</td>\n      <td>ISS</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B0007</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>2013-09-29</td>\n      <td>Falcon 9</td>\n      <td>500.000000</td>\n      <td>PO</td>\n      <td>VAFB SLC 4E</td>\n      <td>False Ocean</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1003</td>\n      <td>-120.610829</td>\n      <td>34.632093</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>2013-12-03</td>\n      <td>Falcon 9</td>\n      <td>3170.000000</td>\n      <td>GTO</td>\n      <td>CCAFS SLC 40</td>\n      <td>None None</td>\n      <td>1</td>\n      <td>False</td>\n      <td>False</td>\n      <td>False</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>0</td>\n      <td>B1004</td>\n      <td>-80.577366</td>\n      <td>28.561857</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "URL2 = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBM-DS0321EN-SkillsNetwork/datasets/dataset_part_3.csv'\nresp2 = await fetch(URL2)\ntext2 = io.BytesIO((await resp2.arrayBuffer()).to_py())\nX = pd.read_csv(text2)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "X.head(100)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 7,
      "outputs": [
        {
          "execution_count": 7,
          "output_type": "execute_result",
          "data": {
            "text/plain": "    FlightNumber   PayloadMass  Flights  Block  ReusedCount  Orbit_ES-L1  \\\n0            1.0   6104.959412      1.0    1.0          0.0          0.0   \n1            2.0    525.000000      1.0    1.0          0.0          0.0   \n2            3.0    677.000000      1.0    1.0          0.0          0.0   \n3            4.0    500.000000      1.0    1.0          0.0          0.0   \n4            5.0   3170.000000      1.0    1.0          0.0          0.0   \n..           ...           ...      ...    ...          ...          ...   \n85          86.0  15400.000000      2.0    5.0          2.0          0.0   \n86          87.0  15400.000000      3.0    5.0          2.0          0.0   \n87          88.0  15400.000000      6.0    5.0          5.0          0.0   \n88          89.0  15400.000000      3.0    5.0          2.0          0.0   \n89          90.0   3681.000000      1.0    5.0          0.0          0.0   \n\n    Orbit_GEO  Orbit_GTO  Orbit_HEO  Orbit_ISS  ...  Serial_B1058  \\\n0         0.0        0.0        0.0        0.0  ...           0.0   \n1         0.0        0.0        0.0        0.0  ...           0.0   \n2         0.0        0.0        0.0        1.0  ...           0.0   \n3         0.0        0.0        0.0        0.0  ...           0.0   \n4         0.0        1.0        0.0        0.0  ...           0.0   \n..        ...        ...        ...        ...  ...           ...   \n85        0.0        0.0        0.0        0.0  ...           0.0   \n86        0.0        0.0        0.0        0.0  ...           1.0   \n87        0.0        0.0        0.0        0.0  ...           0.0   \n88        0.0        0.0        0.0        0.0  ...           0.0   \n89        0.0        0.0        0.0        0.0  ...           0.0   \n\n    Serial_B1059  Serial_B1060  Serial_B1062  GridFins_False  GridFins_True  \\\n0            0.0           0.0           0.0             1.0            0.0   \n1            0.0           0.0           0.0             1.0            0.0   \n2            0.0           0.0           0.0             1.0            0.0   \n3            0.0           0.0           0.0             1.0            0.0   \n4            0.0           0.0           0.0             1.0            0.0   \n..           ...           ...           ...             ...            ...   \n85           0.0           1.0           0.0             0.0            1.0   \n86           0.0           0.0           0.0             0.0            1.0   \n87           0.0           0.0           0.0             0.0            1.0   \n88           0.0           1.0           0.0             0.0            1.0   \n89           0.0           0.0           1.0             0.0            1.0   \n\n    Reused_False  Reused_True  Legs_False  Legs_True  \n0            1.0          0.0         1.0        0.0  \n1            1.0          0.0         1.0        0.0  \n2            1.0          0.0         1.0        0.0  \n3            1.0          0.0         1.0        0.0  \n4            1.0          0.0         1.0        0.0  \n..           ...          ...         ...        ...  \n85           0.0          1.0         0.0        1.0  \n86           0.0          1.0         0.0        1.0  \n87           0.0          1.0         0.0        1.0  \n88           0.0          1.0         0.0        1.0  \n89           1.0          0.0         0.0        1.0  \n\n[90 rows x 83 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>FlightNumber</th>\n      <th>PayloadMass</th>\n      <th>Flights</th>\n      <th>Block</th>\n      <th>ReusedCount</th>\n      <th>Orbit_ES-L1</th>\n      <th>Orbit_GEO</th>\n      <th>Orbit_GTO</th>\n      <th>Orbit_HEO</th>\n      <th>Orbit_ISS</th>\n      <th>...</th>\n      <th>Serial_B1058</th>\n      <th>Serial_B1059</th>\n      <th>Serial_B1060</th>\n      <th>Serial_B1062</th>\n      <th>GridFins_False</th>\n      <th>GridFins_True</th>\n      <th>Reused_False</th>\n      <th>Reused_True</th>\n      <th>Legs_False</th>\n      <th>Legs_True</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>6104.959412</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.0</td>\n      <td>525.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.0</td>\n      <td>677.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>500.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5.0</td>\n      <td>3170.000000</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>85</th>\n      <td>86.0</td>\n      <td>15400.000000</td>\n      <td>2.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>87.0</td>\n      <td>15400.000000</td>\n      <td>3.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>88.0</td>\n      <td>15400.000000</td>\n      <td>6.0</td>\n      <td>5.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>89.0</td>\n      <td>15400.000000</td>\n      <td>3.0</td>\n      <td>5.0</td>\n      <td>2.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>90.0</td>\n      <td>3681.000000</td>\n      <td>1.0</td>\n      <td>5.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>90 rows × 83 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  1\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a NumPy array from the column <code>Class</code> in <code>data</code>, by applying the method <code>to_numpy()</code>  then\nassign it  to the variable <code>Y</code>,make sure the output is a  Pandas series (only one bracket df\\['name of  column']).\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "Y = (data['Class']).to_numpy()\nY",
      "metadata": {
        "trusted": true
      },
      "execution_count": 8,
      "outputs": [
        {
          "execution_count": 8,
          "output_type": "execute_result",
          "data": {
            "text/plain": "array([0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1,\n       1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,\n       1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n       1, 1], dtype=int64)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  2\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Standardize the data in <code>X</code> then reassign it to the variable  <code>X</code> using the transform provided below.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "# students get this \ntransform = preprocessing.StandardScaler()\nX = transform.fit_transform(X)\nX = pd.DataFrame(X)\nX",
      "metadata": {
        "trusted": true
      },
      "execution_count": 9,
      "outputs": [
        {
          "execution_count": 9,
          "output_type": "execute_result",
          "data": {
            "text/plain": "          0             1         2         3         4      5      6   \\\n0  -1.712912 -1.948145e-16 -0.653913 -1.575895 -0.973440 -0.106 -0.106   \n1  -1.674419 -1.195232e+00 -0.653913 -1.575895 -0.973440 -0.106 -0.106   \n2  -1.635927 -1.162673e+00 -0.653913 -1.575895 -0.973440 -0.106 -0.106   \n3  -1.597434 -1.200587e+00 -0.653913 -1.575895 -0.973440 -0.106 -0.106   \n4  -1.558942 -6.286706e-01 -0.653913 -1.575895 -0.973440 -0.106 -0.106   \n..       ...           ...       ...       ...       ...    ...    ...   \n85  1.558942  1.991005e+00  0.174991  0.945537  0.202528 -0.106 -0.106   \n86  1.597434  1.991005e+00  1.003894  0.945537  0.202528 -0.106 -0.106   \n87  1.635927  1.991005e+00  3.490605  0.945537  1.966480 -0.106 -0.106   \n88  1.674419  1.991005e+00  1.003894  0.945537  0.202528 -0.106 -0.106   \n89  1.712912 -5.192140e-01 -0.653913  0.945537 -0.973440 -0.106 -0.106   \n\n          7      8         9   ...        73        74        75        76  \\\n0  -0.654654 -0.106 -0.551677  ... -0.185695 -0.215666 -0.185695 -0.106000   \n1  -0.654654 -0.106 -0.551677  ... -0.185695 -0.215666 -0.185695 -0.106000   \n2  -0.654654 -0.106  1.812654  ... -0.185695 -0.215666 -0.185695 -0.106000   \n3  -0.654654 -0.106 -0.551677  ... -0.185695 -0.215666 -0.185695 -0.106000   \n4   1.527525 -0.106 -0.551677  ... -0.185695 -0.215666 -0.185695 -0.106000   \n..       ...    ...       ...  ...       ...       ...       ...       ...   \n85 -0.654654 -0.106 -0.551677  ... -0.185695 -0.215666  5.385165 -0.106000   \n86 -0.654654 -0.106 -0.551677  ...  5.385165 -0.215666 -0.185695 -0.106000   \n87 -0.654654 -0.106 -0.551677  ... -0.185695 -0.215666 -0.185695 -0.106000   \n88 -0.654654 -0.106 -0.551677  ... -0.185695 -0.215666  5.385165 -0.106000   \n89 -0.654654 -0.106 -0.551677  ... -0.185695 -0.215666 -0.185695  9.433981   \n\n          77        78        79        80        81        82  \n0   1.870829 -1.870829  0.835532 -0.835532  1.933091 -1.933091  \n1   1.870829 -1.870829  0.835532 -0.835532  1.933091 -1.933091  \n2   1.870829 -1.870829  0.835532 -0.835532  1.933091 -1.933091  \n3   1.870829 -1.870829  0.835532 -0.835532  1.933091 -1.933091  \n4   1.870829 -1.870829  0.835532 -0.835532  1.933091 -1.933091  \n..       ...       ...       ...       ...       ...       ...  \n85 -0.534522  0.534522 -1.196843  1.196843 -0.517306  0.517306  \n86 -0.534522  0.534522 -1.196843  1.196843 -0.517306  0.517306  \n87 -0.534522  0.534522 -1.196843  1.196843 -0.517306  0.517306  \n88 -0.534522  0.534522 -1.196843  1.196843 -0.517306  0.517306  \n89 -0.534522  0.534522  0.835532 -0.835532 -0.517306  0.517306  \n\n[90 rows x 83 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>...</th>\n      <th>73</th>\n      <th>74</th>\n      <th>75</th>\n      <th>76</th>\n      <th>77</th>\n      <th>78</th>\n      <th>79</th>\n      <th>80</th>\n      <th>81</th>\n      <th>82</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>-1.712912</td>\n      <td>-1.948145e-16</td>\n      <td>-0.653913</td>\n      <td>-1.575895</td>\n      <td>-0.973440</td>\n      <td>-0.106</td>\n      <td>-0.106</td>\n      <td>-0.654654</td>\n      <td>-0.106</td>\n      <td>-0.551677</td>\n      <td>...</td>\n      <td>-0.185695</td>\n      <td>-0.215666</td>\n      <td>-0.185695</td>\n      <td>-0.106000</td>\n      <td>1.870829</td>\n      <td>-1.870829</td>\n      <td>0.835532</td>\n      <td>-0.835532</td>\n      <td>1.933091</td>\n      <td>-1.933091</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-1.674419</td>\n      <td>-1.195232e+00</td>\n      <td>-0.653913</td>\n      <td>-1.575895</td>\n      <td>-0.973440</td>\n      <td>-0.106</td>\n      <td>-0.106</td>\n      <td>-0.654654</td>\n      <td>-0.106</td>\n      <td>-0.551677</td>\n      <td>...</td>\n      <td>-0.185695</td>\n      <td>-0.215666</td>\n      <td>-0.185695</td>\n      <td>-0.106000</td>\n      <td>1.870829</td>\n      <td>-1.870829</td>\n      <td>0.835532</td>\n      <td>-0.835532</td>\n      <td>1.933091</td>\n      <td>-1.933091</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-1.635927</td>\n      <td>-1.162673e+00</td>\n      <td>-0.653913</td>\n      <td>-1.575895</td>\n      <td>-0.973440</td>\n      <td>-0.106</td>\n      <td>-0.106</td>\n      <td>-0.654654</td>\n      <td>-0.106</td>\n      <td>1.812654</td>\n      <td>...</td>\n      <td>-0.185695</td>\n      <td>-0.215666</td>\n      <td>-0.185695</td>\n      <td>-0.106000</td>\n      <td>1.870829</td>\n      <td>-1.870829</td>\n      <td>0.835532</td>\n      <td>-0.835532</td>\n      <td>1.933091</td>\n      <td>-1.933091</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-1.597434</td>\n      <td>-1.200587e+00</td>\n      <td>-0.653913</td>\n      <td>-1.575895</td>\n      <td>-0.973440</td>\n      <td>-0.106</td>\n      <td>-0.106</td>\n      <td>-0.654654</td>\n      <td>-0.106</td>\n      <td>-0.551677</td>\n      <td>...</td>\n      <td>-0.185695</td>\n      <td>-0.215666</td>\n      <td>-0.185695</td>\n      <td>-0.106000</td>\n      <td>1.870829</td>\n      <td>-1.870829</td>\n      <td>0.835532</td>\n      <td>-0.835532</td>\n      <td>1.933091</td>\n      <td>-1.933091</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-1.558942</td>\n      <td>-6.286706e-01</td>\n      <td>-0.653913</td>\n      <td>-1.575895</td>\n      <td>-0.973440</td>\n      <td>-0.106</td>\n      <td>-0.106</td>\n      <td>1.527525</td>\n      <td>-0.106</td>\n      <td>-0.551677</td>\n      <td>...</td>\n      <td>-0.185695</td>\n      <td>-0.215666</td>\n      <td>-0.185695</td>\n      <td>-0.106000</td>\n      <td>1.870829</td>\n      <td>-1.870829</td>\n      <td>0.835532</td>\n      <td>-0.835532</td>\n      <td>1.933091</td>\n      <td>-1.933091</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>1.558942</td>\n      <td>1.991005e+00</td>\n      <td>0.174991</td>\n      <td>0.945537</td>\n      <td>0.202528</td>\n      <td>-0.106</td>\n      <td>-0.106</td>\n      <td>-0.654654</td>\n      <td>-0.106</td>\n      <td>-0.551677</td>\n      <td>...</td>\n      <td>-0.185695</td>\n      <td>-0.215666</td>\n      <td>5.385165</td>\n      <td>-0.106000</td>\n      <td>-0.534522</td>\n      <td>0.534522</td>\n      <td>-1.196843</td>\n      <td>1.196843</td>\n      <td>-0.517306</td>\n      <td>0.517306</td>\n    </tr>\n    <tr>\n      <th>86</th>\n      <td>1.597434</td>\n      <td>1.991005e+00</td>\n      <td>1.003894</td>\n      <td>0.945537</td>\n      <td>0.202528</td>\n      <td>-0.106</td>\n      <td>-0.106</td>\n      <td>-0.654654</td>\n      <td>-0.106</td>\n      <td>-0.551677</td>\n      <td>...</td>\n      <td>5.385165</td>\n      <td>-0.215666</td>\n      <td>-0.185695</td>\n      <td>-0.106000</td>\n      <td>-0.534522</td>\n      <td>0.534522</td>\n      <td>-1.196843</td>\n      <td>1.196843</td>\n      <td>-0.517306</td>\n      <td>0.517306</td>\n    </tr>\n    <tr>\n      <th>87</th>\n      <td>1.635927</td>\n      <td>1.991005e+00</td>\n      <td>3.490605</td>\n      <td>0.945537</td>\n      <td>1.966480</td>\n      <td>-0.106</td>\n      <td>-0.106</td>\n      <td>-0.654654</td>\n      <td>-0.106</td>\n      <td>-0.551677</td>\n      <td>...</td>\n      <td>-0.185695</td>\n      <td>-0.215666</td>\n      <td>-0.185695</td>\n      <td>-0.106000</td>\n      <td>-0.534522</td>\n      <td>0.534522</td>\n      <td>-1.196843</td>\n      <td>1.196843</td>\n      <td>-0.517306</td>\n      <td>0.517306</td>\n    </tr>\n    <tr>\n      <th>88</th>\n      <td>1.674419</td>\n      <td>1.991005e+00</td>\n      <td>1.003894</td>\n      <td>0.945537</td>\n      <td>0.202528</td>\n      <td>-0.106</td>\n      <td>-0.106</td>\n      <td>-0.654654</td>\n      <td>-0.106</td>\n      <td>-0.551677</td>\n      <td>...</td>\n      <td>-0.185695</td>\n      <td>-0.215666</td>\n      <td>5.385165</td>\n      <td>-0.106000</td>\n      <td>-0.534522</td>\n      <td>0.534522</td>\n      <td>-1.196843</td>\n      <td>1.196843</td>\n      <td>-0.517306</td>\n      <td>0.517306</td>\n    </tr>\n    <tr>\n      <th>89</th>\n      <td>1.712912</td>\n      <td>-5.192140e-01</td>\n      <td>-0.653913</td>\n      <td>0.945537</td>\n      <td>-0.973440</td>\n      <td>-0.106</td>\n      <td>-0.106</td>\n      <td>-0.654654</td>\n      <td>-0.106</td>\n      <td>-0.551677</td>\n      <td>...</td>\n      <td>-0.185695</td>\n      <td>-0.215666</td>\n      <td>-0.185695</td>\n      <td>9.433981</td>\n      <td>-0.534522</td>\n      <td>0.534522</td>\n      <td>0.835532</td>\n      <td>-0.835532</td>\n      <td>-0.517306</td>\n      <td>0.517306</td>\n    </tr>\n  </tbody>\n</table>\n<p>90 rows × 83 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We split the data into training and testing data using the  function  <code>train_test_split</code>.   The training data is divided into validation data, a second set used for training  data; then the models are trained and hyperparameters are selected using the function <code>GridSearchCV</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  3\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Use the function train_test_split to split the data X and Y into training and test data. Set the parameter test_size to  0.2 and random_state to 2. The training data and test data should be assigned to the following labels.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "<code>X_train, X_test, Y_train, Y_test</code>\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 2)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "we can see we only have 18 test samples.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "Y_test.shape",
      "metadata": {
        "trusted": true
      },
      "execution_count": 11,
      "outputs": [
        {
          "execution_count": 11,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(18,)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  4\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a logistic regression object  then create a  GridSearchCV object  <code>logreg_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "parameters ={'C':[0.01,0.1,1],\n             'penalty':['l2'],\n             'solver':['lbfgs']}",
      "metadata": {
        "trusted": true
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "parameters = {\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']}# l1 lasso l2 ridge\nlr = LogisticRegression()\nlogreg_cv = GridSearchCV(lr, parameters, cv = 10).fit(X_train, Y_train)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "We output the <code>GridSearchCV</code> object for logistic regression. We display the best parameters using the data attribute <code>best_params\\_</code> and the accuracy on the validation data using the data attribute <code>best_score\\_</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",logreg_cv.best_params_)\nprint(\"accuracy :\",logreg_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 14,
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\naccuracy : 0.8464285714285713\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  5\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Calculate the accuracy on the test data using the method <code>score</code>:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "lg = logreg_cv.score(X_test, Y_test)\nmethod = ['Logistic Regression']\naccuracy = [lg]\nlg",
      "metadata": {
        "trusted": true
      },
      "execution_count": 15,
      "outputs": [
        {
          "execution_count": 15,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.8333333333333334"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Lets look at the confusion matrix:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "yhat=logreg_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "Examining the confusion matrix, we see that logistic regression can distinguish between the different classes.  We see that the major problem is false positives.\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  6\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a support vector machine object then  create a  <code>GridSearchCV</code> object  <code>svm_cv</code> with cv - 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "parameters = {'kernel':('linear', 'rbf','poly','rbf', 'sigmoid'),\n              'C': np.logspace(-3, 3, 5),\n              'gamma':np.logspace(-3, 3, 5)}\nsvm = SVC()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "svm_cv = GridSearchCV(svm, parameters, cv = 10).fit(X_train, Y_train)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",svm_cv.best_params_)\nprint(\"accuracy :\",svm_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 19,
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'C': 1.0, 'gamma': 0.03162277660168379, 'kernel': 'sigmoid'}\naccuracy : 0.8482142857142856\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  7\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Calculate the accuracy on the test data using the method <code>score</code>:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "svm = svm_cv.score(X_test, Y_test)\nmethod.append('SVM')\naccuracy.append(svm)\nsvm",
      "metadata": {
        "trusted": true
      },
      "execution_count": 20,
      "outputs": [
        {
          "execution_count": 20,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.8333333333333334"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can plot the confusion matrix\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "yhat=svm_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  8\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a decision tree classifier object then  create a  <code>GridSearchCV</code> object  <code>tree_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "parameters = {'criterion': ['gini', 'entropy'],\n     'splitter': ['best', 'random'],\n     'max_depth': [2*n for n in range(1,10)],\n     'max_features': ['sqrt'],\n     'min_samples_leaf': [1, 2, 4],\n     'min_samples_split': [2, 5, 10]}\n\ntree = DecisionTreeClassifier()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "tree_cv = GridSearchCV(tree, parameters, cv = 10).fit(X_train, Y_train)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",tree_cv.best_params_)\nprint(\"accuracy :\",tree_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 24,
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 4, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'splitter': 'best'}\naccuracy : 0.8767857142857143\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  9\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Calculate the accuracy of tree_cv on the test data using the method <code>score</code>:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "tr = tree_cv.score(X_test, Y_test)\nmethod.append('Decision Tree Classifier')\naccuracy.append(tr)\ntr",
      "metadata": {
        "trusted": true
      },
      "execution_count": 25,
      "outputs": [
        {
          "execution_count": 25,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.7777777777777778"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can plot the confusion matrix\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "yhat = tree_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  10\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a k nearest neighbors object then  create a  <code>GridSearchCV</code> object  <code>knn_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "parameters = {'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n              'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n              'p': [1,2]}\n\nKNN = KNeighborsClassifier()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "knn_cv = GridSearchCV(KNN, parameters, cv = 10).fit(X_train, Y_train)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\nprint(\"accuracy :\",knn_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 29,
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 10, 'p': 1}\naccuracy : 0.8482142857142858\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  11\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Calculate the accuracy of knn_cv on the test data using the method <code>score</code>:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "knn = knn_cv.score(X_test, Y_test)\nmethod.append('K-Nearnest Neighnors')\naccuracy.append(knn)\nknn",
      "metadata": {
        "trusted": true
      },
      "execution_count": 30,
      "outputs": [
        {
          "execution_count": 30,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.8333333333333334"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We can plot the confusion matrix\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "yhat = knn_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 2 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  12\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Find the method performs best:\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "def addlabels(x,y):\n    for i in range(len(x)):\n        plt.text(i-.13, y[i]+.008, round(y[i], 3))",
      "metadata": {
        "trusted": true
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "plt.bar(method, accuracy, width = 0.5)\naddlabels(method, accuracy)\nplt.xlabel('Methods')\nplt.ylabel('Accuracy')\nplt.xticks(rotation = 90)\nplt.title(\"Performance of all methods\")\nplt.show()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "## Authors\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "[Pratiksha Verma](https://www.linkedin.com/in/pratiksha-verma-6487561b1/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2023-01-01)\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "## Change Log\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "| Date (YYYY-MM-DD) | Version | Changed By      | Change Description      |\n| ----------------- | ------- | -------------   | ----------------------- |\n| 2022-11-09        | 1.0     | Pratiksha Verma | Converted initial version to Jupyterlite|\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "### <h3 align=\"center\"> IBM Corporation 2022. All rights reserved. <h3/>\n",
      "metadata": {}
    }
  ]
}