Classification_Decision_Trees_drug_py_v1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Classification_Decision-Trees-drug-py-v1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.6"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Blackman9t/Machine_Learning/blob/master/Classification_Decision_Trees_drug_py_v1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "DIeo60vxD3DH",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://www.bigdatauniversity.com\"><img src=\"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width=\"400\" align=\"center\"></a>\n",
        "\n",
        "<h1><center>Decision Trees</center></h1>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "Wa8Kmk4HD3DK",
        "colab_type": "text"
      },
      "source": [
        "In this lab exercise, you will learn a popular machine learning algorithm, Decision Tree. You will use this classification algorithm to build a model from historical data of patients, and their response to different medications. Then you use the trained decision tree to predict the class of a unknown patient, or to find a proper drug for a new patient."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dq0vtV5_D3DL",
        "colab_type": "text"
      },
      "source": [
        "<h1>Table of contents</h1>\n",
        "\n",
        "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
        "    <ol>\n",
        "        <li><a href=\"#about_dataset\">About the dataset</a></li>\n",
        "        <li><a href=\"#downloading_data\">Downloading the Data</a></li>\n",
        "        <li><a href=\"#pre-processing\">Pre-processing</a></li>\n",
        "        <li><a href=\"#setting_up_tree\">Setting up the Decision Tree</a></li>\n",
        "        <li><a href=\"#modeling\">Modeling</a></li>\n",
        "        <li><a href=\"#prediction\">Prediction</a></li>\n",
        "        <li><a href=\"#evaluation\">Evaluation</a></li>\n",
        "        <li><a href=\"#visualization\">Visualization</a></li>\n",
        "    </ol>\n",
        "</div>\n",
        "<br>\n",
        "<hr>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "VYKwWwRoD3DN",
        "colab_type": "text"
      },
      "source": [
        "Import the Following Libraries:\n",
        "<ul>\n",
        "    <li> <b>numpy (as np)</b> </li>\n",
        "    <li> <b>pandas</b> </li>\n",
        "    <li> <b>DecisionTreeClassifier</b> from <b>sklearn.tree</b> </li>\n",
        "</ul>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "n9wz31OXD3DP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np \n",
        "import pandas as pd\n",
        "from sklearn.tree import DecisionTreeClassifier"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "lKrKURT-D3DV",
        "colab_type": "text"
      },
      "source": [
        "<div id=\"about_dataset\">\n",
        "    <h2>About the dataset</h2>\n",
        "    Imagine that you are a medical researcher compiling data for a study. You have collected data about a set of patients, all of whom suffered from the same illness. During their course of treatment, each patient responded to one of 5 medications, Drug A, Drug B, Drug c, Drug x and y. \n",
        "    <br>\n",
        "    <br>\n",
        "    Part of your job is to build a model to find out which drug might be appropriate for a future patient with the same illness. The feature sets of this dataset are Age, Sex, Blood Pressure, and Cholesterol of patients, and the target is the drug that each patient responded to.\n",
        "    <br>\n",
        "    <br>\n",
        "    It is a sample of binary classifier, and you can use the training part of the dataset \n",
        "    to build a decision tree, and then use it to predict the class of a unknown patient, or to prescribe it to a new patient.\n",
        "</div>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "CHntTnndD3DW",
        "colab_type": "text"
      },
      "source": [
        "<div id=\"downloading_data\"> \n",
        "    <h2>Downloading the Data</h2>\n",
        "    To download the data, we will use !wget to download it from IBM Object Storage.\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "931z04o-D3DX",
        "colab_type": "code",
        "outputId": "43a2f8b7-c2d1-433f-f126-f804bde807d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "!wget -O drug200.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/drug200.csv"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-02-18 12:15:33--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/drug200.csv\n",
            "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n",
            "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 6027 (5.9K) [text/csv]\n",
            "Saving to: ‘drug200.csv’\n",
            "\n",
            "\rdrug200.csv           0%[                    ]       0  --.-KB/s               \rdrug200.csv         100%[===================>]   5.89K  --.-KB/s    in 0s      \n",
            "\n",
            "2020-02-18 12:15:33 (1.67 GB/s) - ‘drug200.csv’ saved [6027/6027]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zuy7at25D3Db",
        "colab_type": "text"
      },
      "source": [
        "__Did you know?__ When it comes to Machine Learning, you will likely be working with large datasets. As a business, where can you host your data? IBM is offering a unique opportunity for businesses, with 10 Tb of IBM Cloud Object Storage: [Sign up now for free](http://cocl.us/ML0101EN-IBM-Offer-CC)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dLq9OHa8D3Dc",
        "colab_type": "text"
      },
      "source": [
        "now, read data using pandas dataframe:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p_egMuNcOh5w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "387f04a0-a409-4fad-aa92-4a775ad8d8fa"
      },
      "source": [
        "my_data = pd.read_csv('drug200.csv', delimiter=\",\")\n",
        "my_data[:5]"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>BP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>Na_to_K</th>\n",
              "      <th>Drug</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>23</td>\n",
              "      <td>F</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>25.355</td>\n",
              "      <td>drugY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>47</td>\n",
              "      <td>M</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>13.093</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>47</td>\n",
              "      <td>M</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>10.114</td>\n",
              "      <td>drugC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>28</td>\n",
              "      <td>F</td>\n",
              "      <td>NORMAL</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>7.798</td>\n",
              "      <td>drugX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61</td>\n",
              "      <td>F</td>\n",
              "      <td>LOW</td>\n",
              "      <td>HIGH</td>\n",
              "      <td>18.043</td>\n",
              "      <td>drugY</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Age Sex      BP Cholesterol  Na_to_K   Drug\n",
              "0   23   F    HIGH        HIGH   25.355  drugY\n",
              "1   47   M     LOW        HIGH   13.093  drugC\n",
              "2   47   M     LOW        HIGH   10.114  drugC\n",
              "3   28   F  NORMAL        HIGH    7.798  drugX\n",
              "4   61   F     LOW        HIGH   18.043  drugY"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "LQbEGETHD3Di",
        "colab_type": "text"
      },
      "source": [
        "<div id=\"practice\"> \n",
        "    <h3>Practice</h3> \n",
        "    What is the size of data? \n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "UbVeK5jJD3Dj",
        "colab_type": "code",
        "outputId": "88c7f65f-cdda-40dd-e1ae-bfd75f51237b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# write your code here\n",
        "my_data.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sSm9aaZwD3Dn",
        "colab_type": "text"
      },
      "source": [
        "<div href=\"pre-processing\">\n",
        "    <h2>Pre-processing</h2>\n",
        "</div>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "ULWVy3gtD3Do",
        "colab_type": "text"
      },
      "source": [
        "Using <b>my_data</b> as the Drug.csv data read by pandas, declare the following variables: <br>\n",
        "\n",
        "<ul>\n",
        "    <li> <b> X </b> as the <b> Feature Matrix </b> (data of my_data) </li>\n",
        "    <li> <b> y </b> as the <b> response vector (target) </b> </li>\n",
        "</ul>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "OgZmiCf3D3Dp",
        "colab_type": "text"
      },
      "source": [
        "Remove the column containing the target name since it doesn't contain numeric values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a6_rdoLYD3Dq",
        "colab_type": "code",
        "outputId": "8db375e9-00d3-4431-bf8a-a827d88cfd7e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "X = my_data[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values\n",
        "X[0:5]"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[23, 'F', 'HIGH', 'HIGH', 25.355],\n",
              "       [47, 'M', 'LOW', 'HIGH', 13.093],\n",
              "       [47, 'M', 'LOW', 'HIGH', 10.113999999999999],\n",
              "       [28, 'F', 'NORMAL', 'HIGH', 7.797999999999999],\n",
              "       [61, 'F', 'LOW', 'HIGH', 18.043]], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MD3eeEQFD3Dw",
        "colab_type": "text"
      },
      "source": [
        "As you may figure out, some features in this dataset are categorical such as __Sex__ or __BP__. Unfortunately, Sklearn Decision Trees do not handle categorical variables. But still we can convert these features to numerical values. __pandas.get_dummies()__\n",
        "Convert categorical variable into dummy/indicator variables."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_5x-Nnt-D3Dy",
        "colab_type": "code",
        "outputId": "8a17f63e-537e-4715-d13f-009f72cdc9f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "from sklearn import preprocessing\n",
        "le_sex = preprocessing.LabelEncoder()\n",
        "le_sex.fit(['F','M'])\n",
        "X[:,1] = le_sex.transform(X[:,1]) \n",
        "\n",
        "\n",
        "le_BP = preprocessing.LabelEncoder()\n",
        "le_BP.fit([ 'LOW', 'NORMAL', 'HIGH'])\n",
        "X[:,2] = le_BP.transform(X[:,2])\n",
        "\n",
        "\n",
        "le_Chol = preprocessing.LabelEncoder()\n",
        "le_Chol.fit([ 'NORMAL', 'HIGH'])\n",
        "X[:,3] = le_Chol.transform(X[:,3]) \n",
        "\n",
        "X[0:5]\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[23, 0, 0, 0, 25.355],\n",
              "       [47, 1, 1, 0, 13.093],\n",
              "       [47, 1, 1, 0, 10.113999999999999],\n",
              "       [28, 0, 2, 0, 7.797999999999999],\n",
              "       [61, 0, 1, 0, 18.043]], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Hy0g48jD3D2",
        "colab_type": "text"
      },
      "source": [
        "Now we can fill the target variable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "jIZ5cHpqD3D3",
        "colab_type": "code",
        "outputId": "e7eee9f8-c09a-4cf1-f335-cb327ee0163f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "y = my_data[\"Drug\"]\n",
        "y[0:5]"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    drugY\n",
              "1    drugC\n",
              "2    drugC\n",
              "3    drugX\n",
              "4    drugY\n",
              "Name: Drug, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TrlmIOM_fh7C",
        "colab_type": "code",
        "outputId": "8de7db3f-3dc8-41fc-fbdf-c14477ff735a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "y.replace(to_replace=['drugA','drugB','drugC','drugX','drugY'], value=[0,1,2,3,4],inplace=True)\n",
        "y.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    4\n",
              "1    2\n",
              "2    2\n",
              "3    3\n",
              "4    4\n",
              "Name: Drug, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4pLaFdxgWhKO",
        "colab_type": "text"
      },
      "source": [
        "let's confirm the unique elements in the target or label 'Drug' column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T05vOclDWgNZ",
        "colab_type": "code",
        "outputId": "4cb14895-c972-4c42-bb70-fbef627692e8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y.unique()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 2, 3, 0, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Br7RhSi-XNbB",
        "colab_type": "text"
      },
      "source": [
        "Let's see the count of each drug type in total observations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p6W1WUkxXUzr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "e8bc027a-648e-4e47-cfa6-dba40221e040"
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(8,6))\n",
        "\n",
        "sns.countplot(my_data['Drug'])\n",
        "plt.title('Total count of each drug class', fontsize=14, color='w')\n",
        "plt.xlabel('Drug_Class',color='w', fontsize=13)\n",
        "plt.ylabel('Count',color='w',fontsize=13)\n",
        "plt.xticks(color='w',fontsize=12)\n",
        "plt.yticks(color='w',fontsize=12)\n",
        "plt.show()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfQAAAGKCAYAAAD6/rM7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "03Ib0lZ7ZP3y",
        "colab_type": "text"
      },
      "source": [
        "We can see that data for each drug class is not balanced. Class 4 has more data than 3 which is more than 0, 1 and 2.<br>The model learning on this data may tend to be biased, except we balance the data set. \n",
        "<br>\n",
        "We could write a research paper on the effect of balancing a classifier dataset,on a binary and multi-class classifier algorithm. See if it has any significant impact on the model prediction performance."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "Fb11cx9KD3D7",
        "colab_type": "text"
      },
      "source": [
        "<hr>\n",
        "\n",
        "<div id=\"setting_up_tree\">\n",
        "    <h2>Setting up the Decision Tree</h2>\n",
        "    We will be using <b>train/test split</b> on our <b>decision tree</b>. Let's import <b>train_test_split</b> from <b>sklearn.model_selection</b>.\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "TVe3c1gVD3D8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "C-93okkcD3EA",
        "colab_type": "text"
      },
      "source": [
        "Now <b> train_test_split </b> will return 4 different parameters. We will name them:<br>\n",
        "X_trainset, X_testset, y_trainset, y_testset <br> <br>\n",
        "The <b> train_test_split </b> will need the parameters: <br>\n",
        "X, y, test_size=0.3, and random_state=3. <br> <br>\n",
        "The <b>X</b> and <b>y</b> are the arrays required before the split, the <b>test_size</b> represents the ratio of the testing dataset, and the <b>random_state</b> ensures that we obtain the same splits."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "tp13nnxVD3EB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "whl3cquLD3EE",
        "colab_type": "text"
      },
      "source": [
        "<h3>Practice</h3>\n",
        "Print the shape of X_trainset and y_trainset. Ensure that the dimensions match"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "5O0kDyYLD3EF",
        "colab_type": "code",
        "outputId": "3c89223f-c57f-49e7-e19f-994757d1bc71",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# your code\n",
        "\n",
        "print('X-trainset shape is',X_trainset.shape,'y-trainset shape is',y_trainset.shape)\n"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X-trainset shape is (140, 5) y-trainset shape is (140,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "kiP7DJpXD3EJ",
        "colab_type": "text"
      },
      "source": [
        "Print the shape of X_testset and y_testset. Ensure that the dimensions match"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "lpLKwzbxD3EK",
        "colab_type": "code",
        "outputId": "f22a2c10-4d37-4bf7-f851-9d129532a947",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# your code\n",
        "\n",
        "print('X-testset shape is',X_testset.shape,'y-testset shape is',y_testset.shape)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X-testset shape is (60, 5) y-testset shape is (60,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "fFnokmo1D3EP",
        "colab_type": "text"
      },
      "source": [
        "<hr>\n",
        "\n",
        "<div id=\"modeling\">\n",
        "    <h2>Modeling</h2>\n",
        "    We will first create an instance of the <b>DecisionTreeClassifier</b> called <b>drugTree</b>.<br>\n",
        "    Inside of the classifier, specify <i> criterion=\"entropy\" </i> so we can see the information gain of each node.\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "p7csjvB7D3EQ",
        "colab_type": "code",
        "outputId": "c54504ba-069d-4e97-b73d-2d07e6c1ede6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "drugTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\n",
        "drugTree # it shows the default parameters"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n",
              "                       max_depth=4, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "dVkr9GPVD3EU",
        "colab_type": "text"
      },
      "source": [
        "Next, we will fit the data with the training feature matrix <b> X_trainset </b> and training  response vector <b> y_trainset </b>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "18dvUQo-D3EV",
        "colab_type": "code",
        "outputId": "cbda6bc6-906f-4d57-c591-600c831192ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "drugTree.fit(X_trainset,y_trainset)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n",
              "                       max_depth=4, max_features=None, max_leaf_nodes=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
              "                       random_state=None, splitter='best')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "gTy16AkVD3Eb",
        "colab_type": "text"
      },
      "source": [
        "<hr>\n",
        "\n",
        "<div id=\"prediction\">\n",
        "    <h2>Prediction</h2>\n",
        "    Let's make some <b>predictions</b> on the testing dataset and store it into a variable called <b>predTree</b>.\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "ueMuv9KDD3Ed",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predTree = drugTree.predict(X_testset)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "MRMG-FKMD3Eh",
        "colab_type": "text"
      },
      "source": [
        "You can print out <b>predTree</b> and <b>y_testset</b> if you want to visually compare the prediction to the actual values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "scrolled": true,
        "id": "pwz_ZmcsD3Ek",
        "colab_type": "code",
        "outputId": "230a87f5-0649-4976-da1b-8384cd6dca1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "print (predTree [19:29])\n",
        "print (y_testset [19:29])\n"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[3 3 4 4 4 3 1 4 4 0]\n",
            "58     3\n",
            "14     3\n",
            "143    4\n",
            "12     4\n",
            "6      4\n",
            "182    3\n",
            "161    1\n",
            "128    4\n",
            "122    4\n",
            "101    0\n",
            "Name: Drug, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "z0h-9LK4D3En",
        "colab_type": "text"
      },
      "source": [
        "<hr>\n",
        "\n",
        "<div id=\"evaluation\">\n",
        "    <h2>Evaluation</h2>\n",
        "    Next, let's import <b>metrics</b> from sklearn and check the accuracy of our model.\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "SPzpKG0ED3Eo",
        "colab_type": "code",
        "outputId": "b5ff21a0-53ac-4c6e-ba0a-612cfb78c1e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn import metrics\n",
        "print(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_testset, predTree))  # sklearn.metrics.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None)[source]¶"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "DecisionTrees's Accuracy:  0.9833333333333333\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "HZGkaKlED3Er",
        "colab_type": "text"
      },
      "source": [
        "__Accuracy classification score__ computes subset accuracy: the set of labels predicted for a sample must exactly match the corresponding set of labels in y_true.  \n",
        "\n",
        "In multilabel classification, the function returns the subset accuracy. If the entire set of predicted labels for a sample strictly match with the true set of labels, then the subset accuracy is 1.0; otherwise it is 0.0.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "utB_29G3D3Es",
        "colab_type": "text"
      },
      "source": [
        "## Practice \n",
        "Can you calculate the accuracy score without sklearn ?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GQIPJqDoRuCb",
        "colab_type": "text"
      },
      "source": [
        "## Calculating the jaccard_index score manually without sklearn\n",
        "\n",
        "#### First let's create a data frame of the prediction values and the label values of the test data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "67gZeeVND3Eu",
        "colab_type": "code",
        "outputId": "3d824f46-ea55-4ef2-f85f-2565f6d73fb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "# your code here\n",
        "feature = {'feature':predTree, 'label':y_testset}\n",
        "frame = pd.DataFrame(feature)\n",
        "frame.head()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>feature</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     feature  label\n",
              "40         4      4\n",
              "51         3      3\n",
              "139        3      3\n",
              "197        3      3\n",
              "170        3      3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f0Zn0luuRpZ0",
        "colab_type": "text"
      },
      "source": [
        "#### Let's check the total lenghth of the frame DF"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2FFTR57PlNE",
        "colab_type": "code",
        "outputId": "d1be467b-b248-440b-b696-2dd97d5754af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "frame.shape"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Emgy-2TTR8bW",
        "colab_type": "text"
      },
      "source": [
        "#### Let's compute the intersection of the jaccard_index. This is how many features match the labels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wNabGR8dPvdr",
        "colab_type": "code",
        "outputId": "f7b4fe71-1ede-4367-b6b5-208c6394f650",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "intersection = len(frame[frame.feature==frame.label])\n",
        "intersection"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "59"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w0SANn0XSYQ4",
        "colab_type": "text"
      },
      "source": [
        "#### Next, let's compute the union. This is the total number of features + total number of labels - intersection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YIPm4XRqQUjA",
        "colab_type": "code",
        "outputId": "3a8c9ebd-e6a9-441f-a01e-aec059b4a484",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "union = (len(frame) * 2) - intersection\n",
        "union"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "61"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f1tveLJmSqCh",
        "colab_type": "text"
      },
      "source": [
        "#### Finally we can easily find the jaccard index by dividing the intersection by the union"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CaHhOFS5Q5oU",
        "colab_type": "code",
        "outputId": "71c64470-b368-4832-fd00-ee28a1929d03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "jaccard_index = intersection / union\n",
        "jaccard_index"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9672131147540983"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y4igvXLyTSY8",
        "colab_type": "text"
      },
      "source": [
        "### Calculating the F-1 Score or the confusion matrix score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nunNc3OnTY1y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import f1_score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mb0OBVlC06cv",
        "colab_type": "text"
      },
      "source": [
        "**The _F1_score_ can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. The relative contribution of precision and recall to the F1 score are equal. The formula for the F1 score is**:<br>\n",
        "\n",
        "### F1 = 2 * (precision * recall) / (precision + recall)\n",
        "\n",
        " #### sklearn.metrics.f1_score(y_true, y_pred, labels=None, pos_label=1, average=’binary’, sample_weight=None)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pFYxLfqkTqft",
        "colab_type": "code",
        "outputId": "c817238b-978d-41ef-f074-62edd9f5e1e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "f1_score(y_testset, predTree, average='weighted') "
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9833152664859981"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sRBIkCk3D3Ey",
        "colab_type": "text"
      },
      "source": [
        "<hr>\n",
        "\n",
        "<div id=\"visualization\">\n",
        "    <h2>Visualization</h2>\n",
        "    Lets visualize the tree\n",
        "</div>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bn4kYciRD3Ez",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Notice: You might need to uncomment and install the pydotplus and graphviz libraries if you have not installed these before\n",
        "# !conda install -c conda-forge pydotplus -y\n",
        "# !conda install -c conda-forge python-graphviz -y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "V-HXHvI4D3E2",
        "colab_type": "code",
        "outputId": "dc94df01-aef7-4e04-9fe0-df7e6ddfddca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        }
      },
      "source": [
        "from sklearn.externals.six import StringIO\n",
        "import pydotplus\n",
        "import matplotlib.image as mpimg\n",
        "from sklearn import tree\n",
        "%matplotlib inline "
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: FutureWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
            "  \"(https://pypi.org/project/six/).\", FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "q48Wk0NjD3E6",
        "colab_type": "code",
        "outputId": "f79ced4f-d132-45fe-8385-8be18879835e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 769
        }
      },
      "source": [
        "dot_data = StringIO()\n",
        "filename = \"drugtree.png\"\n",
        "featureNames = my_data.columns[0:5]\n",
        "targetNames = my_data[\"Drug\"].unique().tolist()\n",
        "out=tree.export_graphviz(drugTree,feature_names=featureNames, out_file=dot_data, class_names= str(np.unique(y_trainset)), filled=True,  special_characters=True,rotate=False)  \n",
        "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
        "graph.write_png(filename)\n",
        "img = mpimg.imread(filename)\n",
        "plt.figure(figsize=(15, 15))\n",
        "plt.imshow(img,interpolation='nearest')"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f824db90278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA20AAALfCAYAAADyhyoAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNgAAgDDRBgAAECbaAAAAwgYz9Nog1Z+5ggAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "deletable": true,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "Tb73Kmb9D3E9",
        "colab_type": "text"
      },
      "source": [
        "<h2>Want to learn more?</h2>\n",
        "\n",
        "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"http://cocl.us/ML0101EN-SPSSModeler\">SPSS Modeler</a>\n",
        "\n",
        "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://cocl.us/ML0101EN_DSX\">Watson Studio</a>\n",
        "\n",
        "<h3>Thanks for completing this lesson!</h3>\n",
        "\n",
        "<h4>Author:  <a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a></h4>\n",
        "<p><a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>\n",
        "\n",
        "<hr>\n",
        "\n",
        "<p>Copyright &copy; 2018 <a href=\"https://cocl.us/DX0108EN_CC\">Cognitive Class</a>. This notebook and its source code are released under the terms of the <a href=\"https://bigdatauniversity.com/mit-license/\">MIT License</a>.</p>"
      ]
    }
  ]
}