drug-predictor.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "69eec563",
   "metadata": {},
   "source": [
    "# Drug Predictor - Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8006cc88",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e073ba1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "import sklearn.tree as tree\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed9e3468",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eafb6418",
   "metadata": {},
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data = pd.read_csv('drug200.csv')\n",
    "my_data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5b0c43a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 6)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fab882b",
   "metadata": {},
   "source": [
    "## Pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "84b3eb54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Sex', 'BP', 'Cholesterol', 'Na_to_K', 'Drug'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "13410184",
   "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[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "60f0404f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['F', 'M', 'M', 'F', 'F'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:5,1] # Sex category before label encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "50a48588",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "le_sex = preprocessing.LabelEncoder()\n",
    "le_sex.fit(['F', 'M'])\n",
    "X[:,1] = le_sex.transform(X[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cff4bc16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 0], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:5,1] # After encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d1da3726",
   "metadata": {},
   "outputs": [],
   "source": [
    "le_BP = preprocessing.LabelEncoder()\n",
    "le_BP.fit(['LOW', 'NORMAL', \"HIGH\"])\n",
    "X[:,2] = le_BP.transform(X[:,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9f3e6a96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 2, 1], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:5,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8cc566df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['HIGH', 'NORMAL'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_data['Cholesterol'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9a9e901c",
   "metadata": {},
   "outputs": [],
   "source": [
    "le_chol = preprocessing.LabelEncoder()\n",
    "le_chol.fit(['NORMAL', 'HIGH'])\n",
    "X[:,3] = le_chol.transform(X[:,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d96755d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:10,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1cc0f0ed",
   "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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3c5ac46d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'drugA', 'drugB', 'drugC', 'drugX', 'drugY'}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = my_data[\"Drug\"]\n",
    "set(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7254f0ee",
   "metadata": {},
   "source": [
    "## Setting up the Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3dcfd0bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "56dbeaa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bbb33e79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X trainset dimensions: (140, 5)\n",
      "Y trainset dimensions: (140,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X trainset dimensions:\",X_trainset.shape)\n",
    "print(\"Y trainset dimensions:\",y_trainset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ff91902f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X testset dimensions: (60, 5)\n",
      "Y testset dimensions:  (60,)\n"
     ]
    }
   ],
   "source": [
    "print(\"X testset dimensions:\", X_testset.shape)\n",
    "print(\"Y testset dimensions: \", y_testset.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c61767c",
   "metadata": {},
   "source": [
    "## Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f89230af",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=4)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drugTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth=4)\n",
    "drugTree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4e591e92",
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   "outputs": [
    {
     "data": {
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       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=4)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', max_depth=4)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drugTree.fit(X_trainset, y_trainset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f576026d",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7f266b80",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictionTree = drugTree.predict(X_testset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1764e056",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted drugs: ['drugY' 'drugX' 'drugX' 'drugX' 'drugX']\n",
      "Actual drugs: ['drugY' 'drugX' 'drugX' 'drugX' 'drugX']\n"
     ]
    }
   ],
   "source": [
    "print(\"Predicted drugs:\",predictionTree[:5])\n",
    "print(\"Actual drugs:\",y_testset.iloc[:5].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cd8540e",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1476bfa1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTree's accuracy: 0.9833333333333333\n"
     ]
    }
   ],
   "source": [
    "print(\"DecisionTree's accuracy:\", metrics.accuracy_score(y_testset, predictionTree))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5c09a6a9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(drugTree)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}