W4_SpaceX_Machine_Learning_Prediction.ipynb

{
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        "name": "python",
        "version": 3
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    },
    "kernelspec": {
      "name": "python",
      "display_name": "Pyolite",
      "language": "python"
    }
  },
  "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\" target=\"_blank\">\n    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n    </a>\n</p>\n",
      "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": 32,
      "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": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": "This function is to plot the confusion matrix.\n",
      "metadata": {}
    },
    {
      "cell_type": "code",
      "source": "from sklearn.metrics import confusion_matrix\n\ndef plot_confusion_matrix(y,y_predict):\n    \n    cm = confusion_matrix(y, y_predict)\n    \n    ax= plt.subplot()\n    \n    sns.heatmap(cm, annot=True, ax = ax); #annot=True to annotate cells\n    \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', 'landed']); ax.yaxis.set_ticklabels(['did not land', 'landed'])\n    \n    plt.show()\n    ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 54,
      "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\"\n\nresp1 = await fetch(URL1)\ntext1 = io.BytesIO((await resp1.arrayBuffer()).to_py())\ndata = pd.read_csv(text1)\n\nprint(data.shape)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 35,
      "outputs": [
        {
          "name": "stdout",
          "text": "(90, 18)\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "data.head()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 36,
      "outputs": [
        {
          "execution_count": 36,
          "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'\n\nresp2 = await fetch(URL2)\ntext2 = io.BytesIO((await resp2.arrayBuffer()).to_py())\nX = pd.read_csv(text2)\n\nprint(X.shape)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 37,
      "outputs": [
        {
          "name": "stdout",
          "text": "(90, 83)\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "X.head()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 38,
      "outputs": [
        {
          "execution_count": 38,
          "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\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\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\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\n[5 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  </tbody>\n</table>\n<p>5 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()\n\nprint(Y) ",
      "metadata": {
        "trusted": true
      },
      "execution_count": 39,
      "outputs": [
        {
          "name": "stdout",
          "text": "[0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 1 0 0 1 1 1 1 1 0 1 1 0 1 1 0 1 1 1 0 1 1\n 1 1 1 1 1 1 1 1 0 0 0 1 1 0 0 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 0 1\n 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "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": "from sklearn.preprocessing import StandardScaler\n\nscaler = StandardScaler()\nscaler.fit(X) \nX = scaler.transform(X)\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 40,
      "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=0.2, random_state=2)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 41,
      "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": 42,
      "outputs": [
        {
          "execution_count": 42,
          "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": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "parameters = {\"C\":[0.01,0.1,1],'penalty':['l2'], 'solver':['lbfgs']} # l1 lasso l2 ridge\n\nlr=LogisticRegression()\n\nlogreg_cv = GridSearchCV(lr, parameters, cv=10)   # cv: for cross-validation splitting \n\nlogreg_cv.fit(X_train, Y_train)\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 44,
      "outputs": [
        {
          "execution_count": 44,
          "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": 45,
      "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": "logreg_cv.score(X_test, Y_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 50,
      "outputs": [
        {
          "execution_count": 50,
          "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": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x6649e78>",
            "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": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "svm_cv = GridSearchCV(svm, parameters, cv=10)   # cv: for cross-validation splitting \n\nsvm_cv.fit(X_train, Y_train)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 57,
      "outputs": [
        {
          "execution_count": 57,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=10, estimator=SVC(),\n             param_grid={'C': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n       1.00000000e+03]),\n                         'gamma': array([1.00000000e-03, 3.16227766e-02, 1.00000000e+00, 3.16227766e+01,\n       1.00000000e+03]),\n                         'kernel': ('linear', 'rbf', 'poly', 'rbf', 'sigmoid')})"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "print(\"tuned hpyerparameters :(best parameters) \",svm_cv.best_params_)\nprint(\"accuracy :\",svm_cv.best_score_)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 58,
      "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_cv.score(X_test, Y_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 59,
      "outputs": [
        {
          "execution_count": 59,
          "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": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x54f9458>",
            "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': ['auto', 'sqrt'],\n     'min_samples_leaf': [1, 2, 4],\n     'min_samples_split': [2, 5, 10]}\n\ntree = DecisionTreeClassifier()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "tree_cv = GridSearchCV(tree, parameters)\ntree_cv.fit(X_train, Y_train)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 63,
      "outputs": [
        {
          "execution_count": 63,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(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": 64,
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'criterion': 'entropy', 'max_depth': 6, 'max_features': 'auto', 'min_samples_leaf': 1, 'min_samples_split': 5, 'splitter': 'random'}\naccuracy : 0.9028571428571428\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": "tree_cv.score(X_test, Y_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 65,
      "outputs": [
        {
          "execution_count": 65,
          "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": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x670bc20>",
            "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": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "knn_cv = GridSearchCV(KNN, parameters)\nknn_cv.fit(X_train, Y_train)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 69,
      "outputs": [
        {
          "execution_count": 69,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(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": 70,
      "outputs": [
        {
          "name": "stdout",
          "text": "tuned hpyerparameters :(best parameters)  {'algorithm': 'auto', 'n_neighbors': 8, 'p': 1}\naccuracy : 0.8609523809523811\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_cv.score(X_test, Y_test)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 71,
      "outputs": [
        {
          "execution_count": 71,
          "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": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<pyolite.display.Image at 0x5f94870>",
            "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": "classifiers = [logreg_cv, svm_cv, tree_cv, knn_cv]\n\nfor cv in classifiers: \n    \n    print(cv.best_estimator_)\n    print('Best score on training data:', cv.best_score_)    \n    print('Score on test data:', cv.score(X_test, Y_test), '\\n')\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 81,
      "outputs": [
        {
          "name": "stdout",
          "text": "LogisticRegression(C=0.01)\nBest score on training data: 0.8464285714285713\nScore on test data: 0.8333333333333334 \n\nSVC(gamma=0.03162277660168379, kernel='sigmoid')\nBest score on training data: 0.8482142857142856\nScore on test data: 0.8333333333333334 \n\nDecisionTreeClassifier(criterion='entropy', max_depth=6, max_features='auto',\n                       min_samples_split=5, splitter='random')\nBest score on training data: 0.9028571428571428\nScore on test data: 0.8333333333333334 \n\nKNeighborsClassifier(n_neighbors=8, p=1)\nBest score on training data: 0.8609523809523811\nScore on test data: 0.8333333333333334 \n\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "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-SkillsNetworkCoursesIBMDS0321ENSkillsNetwork865-2022-01-01)\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": {}
    }
  ]
}