6_S3_Decision-Trees-drug.ipynb

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    "<center>\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n",
    "\n",
    "# Decision Trees\n",
    "\n",
    "Estimated time needed: **15** minutes\n",
    "\n",
    "## Objectives\n",
    "\n",
    "After completing this lab you will be able to:\n",
    "\n",
    "*   Develop a classification model using Decision Tree Algorithm\n"
   ]
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    "In this lab exercise, you will learn a popular machine learning algorithm, Decision Trees. You will use this classification algorithm to build a model from the historical data of patients, and their response to different medications. Then you will use the trained decision tree to predict the class of an unknown patient, or to find a proper drug for a new patient.\n"
   ]
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   "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=\"https://#about_dataset\">About the dataset</a></li>\n",
    "        <li><a href=\"https://#downloading_data\">Downloading the Data</a></li>\n",
    "        <li><a href=\"https://#pre-processing\">Pre-processing</a></li>\n",
    "        <li><a href=\"https://#setting_up_tree\">Setting up the Decision Tree</a></li>\n",
    "        <li><a href=\"https://#modeling\">Modeling</a></li>\n",
    "        <li><a href=\"https://#prediction\">Prediction</a></li>\n",
    "        <li><a href=\"https://#evaluation\">Evaluation</a></li>\n",
    "        <li><a href=\"https://#visualization\">Visualization</a></li>\n",
    "    </ol>\n",
    "</div>\n",
    "<br>\n",
    "<hr>\n"
   ]
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   "source": [
    "Import the Following Libraries:\n",
    "\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>\n"
   ]
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   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
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    "<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 features of this dataset are Age, Sex, Blood Pressure, and the Cholesterol of the patients, and the target is the drug that each patient responded to.\n",
    "    <br>\n",
    "    <br>\n",
    "    It is a sample of multiclass 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 an unknown patient, or to prescribe a drug to a new patient.\n",
    "</div>\n"
   ]
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   "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>\n"
   ]
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     "text": [
      "--2022-01-26 13:17:44--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/drug200.csv\n",
      "Résolution de cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)… 169.63.118.104\n",
      "Connexion à cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443… connecté.\n",
      "requête HTTP transmise, en attente de la réponse… 200 OK\n",
      "Taille : 5827 (5,7K) [text/csv]\n",
      "Sauvegarde en : « drug200.csv »\n",
      "\n",
      "drug200.csv         100%[===================>]   5,69K  --.-KB/s    ds 0s      \n",
      "\n",
      "2022-01-26 13:17:45 (2,71 GB/s) — « drug200.csv » sauvegardé [5827/5827]\n",
      "\n"
     ]
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   "source": [
    "!wget -O drug200.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/drug200.csv"
   ]
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   "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)\n"
   ]
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   "source": [
    "Now, read the data using pandas dataframe:\n"
   ]
  },
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>"
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      "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"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data = pd.read_csv(\"drug200.csv\", delimiter=\",\")\n",
    "my_data[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "<div id=\"practice\"> \n",
    "    <h3>Practice</h3> \n",
    "    What is the size of data? \n",
    "</div>\n"
   ]
  },
  {
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    {
     "data": {
      "text/plain": [
       "(200, 6)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# write your code here\n",
    "\n",
    "my_data.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "my_data.shape\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div href=\"pre-processing\">\n",
    "    <h2>Pre-processing</h2>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "new_sheet": false,
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   },
   "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 </b> (target) </li>\n",
    "</ul>\n"
   ]
  },
  {
   "cell_type": "markdown",
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    "new_sheet": false,
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   },
   "source": [
    "Remove the column containing the target name since it doesn't contain numeric values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[23, 'F', 'HIGH', 'HIGH', 25.355],\n",
       "       [47, 'M', 'LOW', 'HIGH', 13.093],\n",
       "       [47, 'M', 'LOW', 'HIGH', 10.114],\n",
       "       [28, 'F', 'NORMAL', 'HIGH', 7.798],\n",
       "       [61, 'F', 'LOW', 'HIGH', 18.043]], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = my_data[['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K']].values\n",
    "X[0:5]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you may figure out, some features in this dataset are categorical, such as **Sex** or **BP**. Unfortunately, Sklearn Decision Trees does not handle categorical variables. We can still convert these features to numerical values using **pandas.get_dummies()**\n",
    "to convert the categorical variable into dummy/indicator variables.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[23, 0, 0, 0, 25.355],\n",
       "       [47, 1, 1, 0, 13.093],\n",
       "       [47, 1, 1, 0, 10.114],\n",
       "       [28, 0, 2, 0, 7.798],\n",
       "       [61, 0, 1, 0, 18.043]], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can fill the target variable.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    drugY\n",
       "1    drugC\n",
       "2    drugC\n",
       "3    drugX\n",
       "4    drugY\n",
       "Name: Drug, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = my_data[\"Drug\"]\n",
    "y[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "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.cross_validation</b>.\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
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   "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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "<h3>Practice</h3>\n",
    "Print the shape of X_trainset and y_trainset. Ensure that the dimensions match.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X training set (140, 5) &  Size of Y training set (140,)\n"
     ]
    }
   ],
   "source": [
    "# your code\n",
    "\n",
    "print('Shape of X training set {}'.format(X_trainset.shape),'&',' Size of Y training set {}'.format(y_trainset.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "print('Shape of X training set {}'.format(X_trainset.shape),'&',' Size of Y training set {}'.format(y_trainset.shape))\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Print the shape of X_testset and y_testset. Ensure that the dimensions match.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X training set (60, 5) &  Size of Y training set (60,)\n"
     ]
    }
   ],
   "source": [
    "# your code\n",
    "\n",
    "print('Shape of X training set {}'.format(X_testset.shape),'&',' Size of Y training set {}'.format(y_testset.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "print('Shape of X training set {}'.format(X_testset.shape),'&',' Size of Y training set {}'.format(y_testset.shape))\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
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   "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>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=4)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drugTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\n",
    "drugTree # it shows the default parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Next, we will fit the data with the training feature matrix <b> X_trainset </b> and training  response vector <b> y_trainset </b>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=4)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drugTree.fit(X_trainset,y_trainset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "predTree = drugTree.predict(X_testset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "You can print out <b>predTree</b> and <b>y_testset</b> if you want to visually compare the predictions to the actual values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['drugY' 'drugX' 'drugX' 'drugX' 'drugX']\n",
      "40     drugY\n",
      "51     drugX\n",
      "139    drugX\n",
      "197    drugX\n",
      "170    drugX\n",
      "Name: Drug, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print (predTree [0:5])\n",
    "print (y_testset [0:5])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTrees's Accuracy:  0.9833333333333333\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "print(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_testset, predTree))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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 matches with the true set of labels, then the subset accuracy is 1.0; otherwise it is 0.0.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "<div id=\"visualization\">\n",
    "    <h2>Visualization</h2>\n",
    "\n",
    "Let's visualize the tree\n",
    "\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /Users/jennyferwan/opt/anaconda3\n",
      "\n",
      "  added / updated specs:\n",
      "    - pydotplus\n",
      "\n",
      "\n",
      "The following packages will be SUPERSEDED by a higher-priority channel:\n",
      "\n",
      "  conda              pkgs/main::conda-4.11.0-py39hecd8cb5_0 --> conda-forge::conda-4.11.0-py39h6e9494a_0\n",
      "\n",
      "\n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: done\n",
      "Collecting package metadata (current_repodata.json): done\n",
      "Solving environment: failed with initial frozen solve. Retrying with flexible solve.\n",
      "Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.\n",
      "Collecting package metadata (repodata.json): done\n",
      "Solving environment: done\n",
      "\n",
      "## Package Plan ##\n",
      "\n",
      "  environment location: /Users/jennyferwan/opt/anaconda3\n",
      "\n",
      "  added / updated specs:\n",
      "    - python-graphviz\n",
      "\n",
      "\n",
      "The following packages will be downloaded:\n",
      "\n",
      "    package                    |            build\n",
      "    ---------------------------|-----------------\n",
      "    atk-1.0-2.36.0             |       he69c4ee_4         359 KB  conda-forge\n",
      "    cairo-1.16.0               |    h0ab9d94_1001         1.3 MB  conda-forge\n",
      "    expat-2.4.3                |       he49afe7_0         144 KB  conda-forge\n",
      "    font-ttf-dejavu-sans-mono-2.37|       hab24e00_0         388 KB  conda-forge\n",
      "    font-ttf-inconsolata-3.000 |       h77eed37_0          94 KB  conda-forge\n",
      "    font-ttf-source-code-pro-2.038|       h77eed37_0         684 KB  conda-forge\n",
      "    font-ttf-ubuntu-0.83       |       hab24e00_0         1.9 MB  conda-forge\n",
      "    fontconfig-2.13.94         |       h10f422b_0         285 KB  conda-forge\n",
      "    fonts-conda-ecosystem-1    |                0           4 KB  conda-forge\n",
      "    fonts-conda-forge-1        |                0           4 KB  conda-forge\n",
      "    fribidi-1.0.10             |       hbcb3906_0          64 KB  conda-forge\n",
      "    gdk-pixbuf-2.42.6          |       h2e6141f_0         579 KB  conda-forge\n",
      "    gettext-0.19.8.1           |    h7937167_1005         3.3 MB  conda-forge\n",
      "    glib-2.68.4                |       he49afe7_0         429 KB  conda-forge\n",
      "    glib-tools-2.68.4          |       he49afe7_0          81 KB  conda-forge\n",
      "    graphite2-1.3.13           |    h2e338ed_1001          85 KB  conda-forge\n",
      "    graphviz-2.47.1            |       hf6ef6b3_0         4.0 MB  conda-forge\n",
      "    gtk2-2.24.33               |       h55c0dba_0         7.0 MB  conda-forge\n",
      "    gts-0.7.6                  |       hccb3bdf_2         327 KB  conda-forge\n",
      "    harfbuzz-2.4.0             |       h92b87b8_1         1.2 MB  conda-forge\n",
      "    libgd-2.3.3                |       he7e2580_0         200 KB\n",
      "    libglib-2.68.4             |       hd556434_0         2.8 MB  conda-forge\n",
      "    librsvg-2.50.3             |       hd4300c0_1         4.3 MB  conda-forge\n",
      "    libtool-2.4.6              |    he49afe7_1008         516 KB  conda-forge\n",
      "    pango-1.42.4               |       haa940fe_4         509 KB  conda-forge\n",
      "    pcre-8.45                  |       he49afe7_0         220 KB  conda-forge\n",
      "    pixman-0.38.0              |    h01d97ff_1003         611 KB  conda-forge\n",
      "    python-graphviz-0.19.1     |     pyhaef67bd_0          34 KB  conda-forge\n",
      "    ------------------------------------------------------------\n",
      "                                           Total:        31.3 MB\n",
      "\n",
      "The following NEW packages will be INSTALLED:\n",
      "\n",
      "  atk-1.0            conda-forge/osx-64::atk-1.0-2.36.0-he69c4ee_4\n",
      "  cairo              conda-forge/osx-64::cairo-1.16.0-h0ab9d94_1001\n",
      "  expat              conda-forge/osx-64::expat-2.4.3-he49afe7_0\n",
      "  font-ttf-dejavu-s~ conda-forge/noarch::font-ttf-dejavu-sans-mono-2.37-hab24e00_0\n",
      "  font-ttf-inconsol~ conda-forge/noarch::font-ttf-inconsolata-3.000-h77eed37_0\n",
      "  font-ttf-source-c~ conda-forge/noarch::font-ttf-source-code-pro-2.038-h77eed37_0\n",
      "  font-ttf-ubuntu    conda-forge/noarch::font-ttf-ubuntu-0.83-hab24e00_0\n",
      "  fontconfig         conda-forge/osx-64::fontconfig-2.13.94-h10f422b_0\n",
      "  fonts-conda-ecosy~ conda-forge/noarch::fonts-conda-ecosystem-1-0\n",
      "  fonts-conda-forge  conda-forge/noarch::fonts-conda-forge-1-0\n",
      "  fribidi            conda-forge/osx-64::fribidi-1.0.10-hbcb3906_0\n",
      "  gdk-pixbuf         conda-forge/osx-64::gdk-pixbuf-2.42.6-h2e6141f_0\n",
      "  gettext            conda-forge/osx-64::gettext-0.19.8.1-h7937167_1005\n",
      "  glib               conda-forge/osx-64::glib-2.68.4-he49afe7_0\n",
      "  glib-tools         conda-forge/osx-64::glib-tools-2.68.4-he49afe7_0\n",
      "  graphite2          conda-forge/osx-64::graphite2-1.3.13-h2e338ed_1001\n",
      "  graphviz           conda-forge/osx-64::graphviz-2.47.1-hf6ef6b3_0\n",
      "  gtk2               conda-forge/osx-64::gtk2-2.24.33-h55c0dba_0\n",
      "  gts                conda-forge/osx-64::gts-0.7.6-hccb3bdf_2\n",
      "  harfbuzz           conda-forge/osx-64::harfbuzz-2.4.0-h92b87b8_1\n",
      "  libgd              pkgs/main/osx-64::libgd-2.3.3-he7e2580_0\n",
      "  libglib            conda-forge/osx-64::libglib-2.68.4-hd556434_0\n",
      "  librsvg            conda-forge/osx-64::librsvg-2.50.3-hd4300c0_1\n",
      "  libtool            conda-forge/osx-64::libtool-2.4.6-he49afe7_1008\n",
      "  pango              conda-forge/osx-64::pango-1.42.4-haa940fe_4\n",
      "  pcre               conda-forge/osx-64::pcre-8.45-he49afe7_0\n",
      "  pixman             conda-forge/osx-64::pixman-0.38.0-h01d97ff_1003\n",
      "  python-graphviz    conda-forge/noarch::python-graphviz-0.19.1-pyhaef67bd_0\n",
      "\n",
      "\n",
      "\n",
      "Downloading and Extracting Packages\n",
      "atk-1.0-2.36.0       | 359 KB    | ##################################### | 100% \n",
      "fontconfig-2.13.94   | 285 KB    | ##################################### | 100% \n",
      "gdk-pixbuf-2.42.6    | 579 KB    | ##################################### | 100% \n",
      "fonts-conda-forge-1  | 4 KB      | ##################################### | 100% \n",
      "graphite2-1.3.13     | 85 KB     | ##################################### | 100% \n",
      "font-ttf-ubuntu-0.83 | 1.9 MB    | ##################################### | 100% \n",
      "librsvg-2.50.3       | 4.3 MB    | ##################################### | 100% \n",
      "pixman-0.38.0        | 611 KB    | ##################################### | 100% \n",
      "libglib-2.68.4       | 2.8 MB    | ##################################### | 100% \n",
      "font-ttf-dejavu-sans | 388 KB    | ##################################### | 100% \n",
      "libtool-2.4.6        | 516 KB    | ##################################### | 100% \n",
      "glib-tools-2.68.4    | 81 KB     | ##################################### | 100% \n",
      "cairo-1.16.0         | 1.3 MB    | ##################################### | 100% \n",
      "gts-0.7.6            | 327 KB    | ##################################### | 100% \n",
      "font-ttf-source-code | 684 KB    | ##################################### | 100% \n",
      "glib-2.68.4          | 429 KB    | ##################################### | 100% \n",
      "fonts-conda-ecosyste | 4 KB      | ##################################### | 100% \n",
      "graphviz-2.47.1      | 4.0 MB    | ##################################### | 100% \n",
      "font-ttf-inconsolata | 94 KB     | ##################################### | 100% \n",
      "fribidi-1.0.10       | 64 KB     | ##################################### | 100% \n",
      "python-graphviz-0.19 | 34 KB     | ##################################### | 100% \n",
      "pango-1.42.4         | 509 KB    | ##################################### | 100% \n",
      "gettext-0.19.8.1     | 3.3 MB    | ##################################### | 100% \n",
      "pcre-8.45            | 220 KB    | ##################################### | 100% \n",
      "harfbuzz-2.4.0       | 1.2 MB    | ##################################### | 100% \n",
      "libgd-2.3.3          | 200 KB    | ##################################### | 100% \n",
      "gtk2-2.24.33         | 7.0 MB    | ##################################### | 100% \n",
      "expat-2.4.3          | 144 KB    | ##################################### | 100% \n",
      "Preparing transaction: done\n",
      "Verifying transaction: done\n",
      "Executing transaction: | \n",
      "\\ \n",
      "done\n"
     ]
    }
   ],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "from  io import StringIO\n",
    "import pydotplus\n",
    "import matplotlib.image as mpimg\n",
    "from sklearn import tree\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fe2a82f27c0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 7200x14400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dot_data = StringIO()\n",
    "filename = \"drugtree.png\"\n",
    "featureNames = my_data.columns[0:5]\n",
    "out=tree.export_graphviz(drugTree,feature_names=featureNames, out_file=dot_data, class_names= 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=(100, 200))\n",
    "plt.imshow(img,interpolation='nearest')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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=\"https://www.ibm.com/analytics/spss-statistics-software?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2021-01-01\">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://www.ibm.com/cloud/watson-studio?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2021-01-01\">Watson Studio</a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thank you for completing this lab!\n",
    "\n",
    "## Author\n",
    "\n",
    "Saeed Aghabozorgi\n",
    "\n",
    "### Other Contributors\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2021-01-01\" target=\"_blank\">Joseph Santarcangelo</a>\n",
    "\n",
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By | Change Description                   |\n",
    "| ----------------- | ------- | ---------- | ------------------------------------ |\n",
    "| 2020-11-20        | 2.2     | Lakshmi    | Changed import statement of StringIO |\n",
    "| 2020-11-03        | 2.1     | Lakshmi    | Changed URL of the csv               |\n",
    "| 2020-08-27        | 2.0     | Lavanya    | Moved lab to course repo in GitLab   |\n",
    "|                   |         |            |                                      |\n",
    "|                   |         |            |                                      |\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
   ]
  }
 ],
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