SpaceX_Machine_Learning_Prediction_Part_5.jupyterlite (1).ipynb

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      "name": "python",
      "nbconvert_exporter": "python",
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      "version": "3.8"
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      "name": "python",
      "display_name": "Pyolite",
      "language": "python"
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  "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\n\nfrom sklearn.metrics import accuracy_score",
      "metadata": {
        "trusted": true
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {},
      "execution_count": null,
      "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()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "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()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "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=.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": "# import pickle\n\n# with open('./x.pkl', 'wb') as file:\n#     pickle.dump(X, file)\n\n# with open('./y.pkl', 'wb') as file:\n#     pickle.dump(Y, file)\n\nX_test",
      "metadata": {
        "trusted": true
      },
      "execution_count": 12,
      "outputs": [
        {
          "execution_count": 12,
          "output_type": "execute_result",
          "data": {
            "text/plain": "    FlightNumber   PayloadMass  Flights  Block  ReusedCount  Orbit_ES-L1  \\\n48          49.0    350.000000      1.0    4.0          1.0          0.0   \n77          78.0  15400.000000      4.0    5.0          5.0          0.0   \n81          82.0   3880.000000      1.0    5.0          2.0          0.0   \n79          80.0  15400.000000      5.0    5.0          5.0          0.0   \n36          37.0    475.000000      1.0    3.0          1.0          0.0   \n84          85.0   1600.000000      4.0    5.0          3.0          0.0   \n62          63.0  12259.000000      1.0    5.0          5.0          0.0   \n14          15.0   4707.000000      1.0    1.0          0.0          0.0   \n74          75.0  15400.000000      4.0    5.0          3.0          0.0   \n0            1.0   6104.959412      1.0    1.0          0.0          0.0   \n3            4.0    500.000000      1.0    1.0          0.0          0.0   \n13          14.0   1898.000000      1.0    1.0          0.0          0.0   \n21          22.0   3100.000000      1.0    2.0          1.0          0.0   \n87          88.0  15400.000000      6.0    5.0          5.0          0.0   \n25          26.0   9600.000000      1.0    3.0          1.0          0.0   \n28          29.0   5300.000000      2.0    2.0          1.0          0.0   \n11          12.0   2395.000000      1.0    1.0          0.0          0.0   \n23          24.0   4600.000000      1.0    2.0          0.0          0.0   \n\n    Orbit_GEO  Orbit_GTO  Orbit_HEO  Orbit_ISS  ...  Serial_B1058  \\\n48        0.0        0.0        1.0        0.0  ...           0.0   \n77        0.0        0.0        0.0        0.0  ...           0.0   \n81        0.0        0.0        0.0        0.0  ...           0.0   \n79        0.0        0.0        0.0        0.0  ...           0.0   \n36        0.0        0.0        0.0        0.0  ...           0.0   \n84        0.0        0.0        0.0        0.0  ...           0.0   \n62        0.0        0.0        0.0        1.0  ...           0.0   \n14        0.0        1.0        0.0        0.0  ...           0.0   \n74        0.0        0.0        0.0        0.0  ...           0.0   \n0         0.0        0.0        0.0        0.0  ...           0.0   \n3         0.0        0.0        0.0        0.0  ...           0.0   \n13        0.0        0.0        0.0        1.0  ...           0.0   \n21        0.0        1.0        0.0        0.0  ...           0.0   \n87        0.0        0.0        0.0        0.0  ...           0.0   \n25        0.0        0.0        0.0        0.0  ...           0.0   \n28        0.0        1.0        0.0        0.0  ...           0.0   \n11        0.0        0.0        0.0        1.0  ...           0.0   \n23        0.0        1.0        0.0        0.0  ...           0.0   \n\n    Serial_B1059  Serial_B1060  Serial_B1062  GridFins_False  GridFins_True  \\\n48           0.0           0.0           0.0             0.0            1.0   \n77           0.0           0.0           0.0             0.0            1.0   \n81           0.0           1.0           0.0             0.0            1.0   \n79           0.0           0.0           0.0             0.0            1.0   \n36           0.0           0.0           0.0             0.0            1.0   \n84           1.0           0.0           0.0             0.0            1.0   \n62           0.0           0.0           0.0             0.0            1.0   \n14           0.0           0.0           0.0             1.0            0.0   \n74           0.0           0.0           0.0             0.0            1.0   \n0            0.0           0.0           0.0             1.0            0.0   \n3            0.0           0.0           0.0             1.0            0.0   \n13           0.0           0.0           0.0             0.0            1.0   \n21           0.0           0.0           0.0             0.0            1.0   \n87           0.0           0.0           0.0             0.0            1.0   \n25           0.0           0.0           0.0             0.0            1.0   \n28           0.0           0.0           0.0             0.0            1.0   \n11           0.0           0.0           0.0             0.0            1.0   \n23           0.0           0.0           0.0             0.0            1.0   \n\n    Reused_False  Reused_True  Legs_False  Legs_True  \n48           1.0          0.0         0.0        1.0  \n77           0.0          1.0         0.0        1.0  \n81           1.0          0.0         0.0        1.0  \n79           0.0          1.0         0.0        1.0  \n36           1.0          0.0         0.0        1.0  \n84           0.0          1.0         0.0        1.0  \n62           1.0          0.0         0.0        1.0  \n14           1.0          0.0         1.0        0.0  \n74           0.0          1.0         0.0        1.0  \n0            1.0          0.0         1.0        0.0  \n3            1.0          0.0         1.0        0.0  \n13           1.0          0.0         0.0        1.0  \n21           1.0          0.0         0.0        1.0  \n87           0.0          1.0         0.0        1.0  \n25           1.0          0.0         0.0        1.0  \n28           0.0          1.0         0.0        1.0  \n11           1.0          0.0         0.0        1.0  \n23           1.0          0.0         0.0        1.0  \n\n[18 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>48</th>\n      <td>49.0</td>\n      <td>350.000000</td>\n      <td>1.0</td>\n      <td>4.0</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>...</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>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>77</th>\n      <td>78.0</td>\n      <td>15400.000000</td>\n      <td>4.0</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>81</th>\n      <td>82.0</td>\n      <td>3880.000000</td>\n      <td>1.0</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>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>79</th>\n      <td>80.0</td>\n      <td>15400.000000</td>\n      <td>5.0</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>36</th>\n      <td>37.0</td>\n      <td>475.000000</td>\n      <td>1.0</td>\n      <td>3.0</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>...</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>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>84</th>\n      <td>85.0</td>\n      <td>1600.000000</td>\n      <td>4.0</td>\n      <td>5.0</td>\n      <td>3.0</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>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>1.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>63.0</td>\n      <td>12259.000000</td>\n      <td>1.0</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>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>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    <tr>\n      <th>14</th>\n      <td>15.0</td>\n      <td>4707.000000</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>74</th>\n      <td>75.0</td>\n      <td>15400.000000</td>\n      <td>4.0</td>\n      <td>5.0</td>\n      <td>3.0</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>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>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>13</th>\n      <td>14.0</td>\n      <td>1898.000000</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>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    <tr>\n      <th>21</th>\n      <td>22.0</td>\n      <td>3100.000000</td>\n      <td>1.0</td>\n      <td>2.0</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>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>1.0</td>\n      <td>0.0</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>25</th>\n      <td>26.0</td>\n      <td>9600.000000</td>\n      <td>1.0</td>\n      <td>3.0</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>...</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>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>29.0</td>\n      <td>5300.000000</td>\n      <td>2.0</td>\n      <td>2.0</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>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>11</th>\n      <td>12.0</td>\n      <td>2395.000000</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>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    <tr>\n      <th>23</th>\n      <td>24.0</td>\n      <td>4600.000000</td>\n      <td>1.0</td>\n      <td>2.0</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>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>18 rows × 83 columns</p>\n</div>"
          },
          "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": 13,
      "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, param_grid=parameters, cv=10)\nlogreg_cv.fit(X_train, Y_train)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 15,
      "outputs": [
        {
          "name": "stderr",
          "text": "/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  n_iter_i = _check_optimize_result(\n/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  n_iter_i = _check_optimize_result(\n/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  n_iter_i = _check_optimize_result(\n/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  n_iter_i = _check_optimize_result(\n/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  n_iter_i = _check_optimize_result(\n/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  n_iter_i = _check_optimize_result(\n/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  n_iter_i = _check_optimize_result(\n/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  n_iter_i = _check_optimize_result(\n/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  n_iter_i = _check_optimize_result(\n/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  n_iter_i = _check_optimize_result(\n/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  n_iter_i = _check_optimize_result(\n/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  n_iter_i = _check_optimize_result(\n/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  n_iter_i = _check_optimize_result(\n/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  n_iter_i = _check_optimize_result(\n/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  n_iter_i = _check_optimize_result(\n/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  n_iter_i = _check_optimize_result(\n/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n\nIncrease the number of iterations (max_iter) or scale the data as shown in:\n    https://scikit-learn.org/stable/modules/preprocessing.html\nPlease also refer to the documentation for alternative solver options:\n    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n  n_iter_i = _check_optimize_result(\n",
          "output_type": "stream"
        },
        {
          "execution_count": 15,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=10, estimator=LogisticRegression(),\n             param_grid={'C': [0.01, 0.1, 1], 'penalty': ['l2'],\n                         'solver': ['lbfgs']})"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": "We output the <code>GridSearchCV</code> object for logistic regression. We display the best parameters using the data attribute <code>best_params\\_</code> and the accuracy on the validation data using the data attribute <code>best_score\\_</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",logreg_cv.best_params_)\nprint(\"accuracy :\",logreg_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 16,
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'C': 0.1, 'penalty': 'l2', 'solver': 'lbfgs'}\naccuracy : 0.8196428571428571\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": "l = logreg_cv.best_estimator_\n\naccuracy_score(l.predict(X_test), Y_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 17,
      "outputs": [
        {
          "execution_count": 17,
          "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": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x5e09de0>",
            "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()\nsvm_cv = GridSearchCV(svm, parameters, cv=10)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "svm_cv.fit(X_train, Y_train)",
      "metadata": {
        "trusted": true
      },
      "execution_count": null,
      "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": null,
      "outputs": []
    },
    {
      "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": "accuracy_score(svm_cv.predict(X_test), Y_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "## TASK  8\n",
      "metadata": {}
    },
    {
      "cell_type": "markdown",
      "source": "Create a decision tree classifier object then  create a  <code>GridSearchCV</code> object  <code>tree_cv</code> with cv = 10.  Fit the object to find the best parameters from the dictionary <code>parameters</code>.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "parameters = {'criterion': ['gini', 'entropy'],\n     'splitter': ['best', 'random'],\n     'max_depth': [2*n for n in range(1,10)],\n     'max_features': ['auto', 'sqrt'],\n     'min_samples_leaf': [1, 2, 4],\n     'min_samples_split': [2, 5, 10]}\n\ntree = DecisionTreeClassifier()\ntree_cv = GridSearchCV(tree, parameters, cv=10)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "tree_cv.fit(X_train, Y_train)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 16,
      "outputs": [
        {
          "execution_count": 16,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=10, estimator=DecisionTreeClassifier(),\n             param_grid={'criterion': ['gini', 'entropy'],\n                         'max_depth': [2, 4, 6, 8, 10, 12, 14, 16, 18],\n                         'max_features': ['auto', 'sqrt'],\n                         'min_samples_leaf': [1, 2, 4],\n                         'min_samples_split': [2, 5, 10],\n                         'splitter': ['best', 'random']})"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",tree_cv.best_params_)\nprint(\"accuracy :\",tree_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 17,
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'criterion': 'gini', 'max_depth': 4, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 2, 'splitter': 'random'}\naccuracy : 0.875\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": "accuracy_score(tree_cv.predict(X_test), Y_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 18,
      "outputs": [
        {
          "execution_count": 18,
          "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 = tree_cv.predict(X_test)\nplot_confusion_matrix(Y_test,yhat)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x55c49f0>",
            "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()\nknn_cv = GridSearchCV(KNN, parameters, cv=10)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "knn_cv.fit(X_train, Y_train)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 21,
      "outputs": [
        {
          "execution_count": 21,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=10, estimator=KNeighborsClassifier(),\n             param_grid={'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n                         'p': [1, 2]})"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",knn_cv.best_params_)\nprint(\"accuracy :\",knn_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 22,
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 3, 'p': 1}\naccuracy : 0.6642857142857143\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": "accuracy_score(knn_cv.predict(X_test), Y_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 24,
      "outputs": [
        {
          "execution_count": 24,
          "output_type": "execute_result",
          "data": {
            "text/plain": "0.6111111111111112"
          },
          "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": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x640b118>",
            "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": "print('''\nLR:{}\nCSM:{}\nTREE:{}\nKNN:{}\n'''.format(accuracy_score(logreg_cv.predict(X_test), Y_test),\n          accuracy_score(svm_cv.predict(X_test), Y_test)\n          accuracy_score(tree_cv.predict(X_test), Y_test)\n          accuracy_score(knn_cv.predict(X_test), Y_test)))",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    },
    {
      "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": {}
    }
  ]
}