47. Drug Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b3377d46",
   "metadata": {},
   "source": [
    "# Drug Classification\n",
    "\n",
    "## Getting dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cb069733",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "26b49369",
   "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": [
    "df = pd.read_csv(\"data/proj47/drug200.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e512e5ba",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ec223c8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age            0\n",
       "Sex            0\n",
       "BP             0\n",
       "Cholesterol    0\n",
       "Na_to_K        0\n",
       "Drug           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eecd26b7",
   "metadata": {},
   "source": [
    "## Data Exploration "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5725aae0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5de22fab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Count'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAKNRZgAAgNEoMwAAwGiUGQAAYDTKDAAAMBplBgAAGI0yAwAAjEaZAQAARqPMAAAAo1FmAACA0SgzAADAaJQZAABgNMoMAAAwGmUGAAAYjTIDAACMRpkBAABGo8wAAACjUWYAAIDRKDMAAMBolBkAAGA0ygwAADAaZQYAABiNMgMAAIxGmQEAAEajzAAAAKNRZgAAgNEoMwAAwGiUGQAAYDTKDAAAMBplBgAAGI0yAwAAjEaZAQAARqPMAAAAo1FmAACA0SgzAADAaJQZAABgNMoMAAAwGmUGAAAYjTIDAACMRpkBAABGo8wAAACjUWYAAIDRvOwOAHtkZmbaHaFGAgMDFR4ebncMAIAbosx4mJwTRXJIio2NtTtKjfj5+Sozcz+FBgBQBWXGw5w8fUaWpKUTYjSoZ6TdcaolM/u4YhPeVn5+PmUGAFAFZcZDdQptob6d29gdAwCAy8YJwAAAwGiUGQAAYDTKDAAAMBplBgAAGI0yAwAAjEaZAQAARqPMAAAAo1FmAACA0SgzAADAaLaXmaNHjyo2NlatWrWSn5+fevfurZ07dzrXW5al+Ph4hYaGytfXV0OHDtW+fftsTAwAANyJrWWmoKBAgwcPlre3tz788EN98803Wrx4sZo3b+7cZtGiRUpKStLSpUuVnp6u4OBgDR8+XEVFRfYFBwAAbsPWZzMtXLhQYWFhWr16tXOsffv2zn+3LEvJycmaPXu2Ro4cKUlau3atgoKCtH79eo0fP76hIwMAADdj65GZjRs3qn///rr33nvVunVr9enTR6+88opzfVZWlnJzcxUTE+Mc8/Hx0ZAhQ5SWlnbefZaWlqqwsNBlAQAAVy5by8z333+v5cuXKzIyUps2bdKjjz6qyZMn6/XXX5ck5ebmSpKCgoJc3hcUFORc91uJiYkKCAhwLmFhYfX7QwAAAFvZWmYqKirUt29fJSQkqE+fPho/frweeeQRLV++3GU7h8Ph8tqyrCpjlWbNmqVTp045lyNHjtRbfgAAYD9by0xISIi6devmMhYVFaXs7GxJUnBwsCRVOQqTl5dX5WhNJR8fHzVr1sxlAQAAVy5by8zgwYN14MABl7Fvv/1W7dq1kyRFREQoODhYKSkpzvVlZWVKTU1VdHR0g2YFAADuydarmf76178qOjpaCQkJ+vOf/6zt27dr5cqVWrlypaRzXy9NnTpVCQkJioyMVGRkpBISEuTn56dRo0bZGR0AALgJW8vMgAED9M4772jWrFmaP3++IiIilJycrNGjRzu3mT59ukpKSjRhwgQVFBRo4MCB2rx5s/z9/W1MDgAA3IWtZUaS7rjjDt1xxx0XXO9wOBQfH6/4+PiGCwUAAIxh++MMAAAALgdlBgAAGI0yAwAAjEaZAQAARqPMAAAAo1FmAACA0SgzAADAaJQZAABgNMoMAAAwGmUGAAAYjTIDAACMRpkBAABGo8wAAACjUWYAAIDRKDMAAMBolBkAAGA0ygwAADAaZQYAABiNMgMAAIxGmQEAAEajzAAAAKNRZgAAgNEoMwAAwGiUGQAAYDTKDAAAMBplBgAAGI0yAwAAjFarMtOhQwf9+OOPVcZPnjypDh06XHYoAACA6qpVmTl06JDKy8urjJeWluro0aOXHQoAAKC6vGqy8caNG53/vmnTJgUEBDhfl5eXa8uWLWrfvn2dhQMAALiUGpWZu+66S5LkcDg0ZswYl3Xe3t5q3769Fi9eXGfhAAAALqVGZaaiokKSFBERofT0dAUGBtZLKAAAgOqqUZmplJWVVdc5AAAAaqVWZUaStmzZoi1btigvL895xKbSa6+9dtnBAAAAqqNWZWbevHmaP3+++vfvr5CQEDkcjrrOBQAAUC21KjMrVqzQmjVr9OCDD9Z1HgAAgBqp1X1mysrKFB0dXddZAAAAaqxWZWbcuHFav359XWcBAACosVp9zXTmzBmtXLlSH3/8sXr27Clvb2+X9UlJSXUSDgAA4FJqVWZ2796t3r17S5L27t3rso6TgQEAQEOqVZn55JNP6joHAABArdTqnBkAAAB3UasjMzfddNNFv07aunVrrQMBAADURK3KTOX5MpV+/vlnZWRkaO/evVUeQAkAAFCfalVmXnzxxfOOx8fH6/Tp05cVCAAAoCbq9JyZ2NhYnssEAAAaVJ2WmS+++EJNmjSpy10CAABcVK2+Zho5cqTLa8uylJOTox07dmjOnDl1EgwAAKA6alVmAgICXF5fddVV6tKli+bPn6+YmJg6CQYAAFAdtSozq1evruscAAAAtVKrMlNp586dyszMlMPhULdu3dSnT5+6ygUAAFAttSozeXl5uv/++7Vt2zY1b95clmXp1KlTuummm/Tmm2/qmmuuqeucAAAA51Wrq5kmTZqkwsJC7du3TydOnFBBQYH27t2rwsJCTZ48ua4zAgAAXFCtjsx89NFH+vjjjxUVFeUc69atm5YtW8YJwAAAoEHV6shMRUWFvL29q4x7e3uroqLiskMBAABUV63KzM0336wpU6bo2LFjzrGjR4/qr3/9q2655ZY6CwcAAHAptSozS5cuVVFRkdq3b6+OHTuqU6dOioiIUFFRkZYsWVLXGQEAAC6oVufMhIWF6auvvlJKSor2798vy7LUrVs3DRs2rK7zAQAAXFSNjsxs3bpV3bp1U2FhoSRp+PDhmjRpkiZPnqwBAwaoe/fu+uyzz+olKAAAwPnUqMwkJyfrkUceUbNmzaqsCwgI0Pjx45WUlFRn4QAAAC6lRmXm66+/1q233nrB9TExMdq5c+dlhwIAAKiuGpWZH3744byXZFfy8vLS8ePHLzsUAABAddWozLRp00Z79uy54Prdu3crJCTkskMBAABUV43KzO9//3s988wzOnPmTJV1JSUlmjt3ru644446CwcAAHApNbo0++mnn9aGDRvUuXNnxcXFqUuXLnI4HMrMzNSyZctUXl6u2bNn11dWAACAKmpUZoKCgpSWlqbHHntMs2bNkmVZkiSHw6ERI0bo5ZdfVlBQUL0EBQAAOJ8a3zSvXbt2+uCDD1RQUKDvvvtOlmUpMjJSLVq0qI98AAAAF1WrOwBLUosWLTRgwIC6zAIAAFBjtXo2U31ITEyUw+HQ1KlTnWOWZSk+Pl6hoaHy9fXV0KFDtW/fPvtCAgAAt+MWZSY9PV0rV65Uz549XcYXLVqkpKQkLV26VOnp6QoODtbw4cNVVFRkU1IAAOBubC8zp0+f1ujRo/XKK6+4nHdjWZaSk5M1e/ZsjRw5Uj169NDatWtVXFys9evX25gYAAC4E9vLzMSJE3X77bdXeeJ2VlaWcnNzFRMT4xzz8fHRkCFDlJaWdsH9lZaWqrCw0GUBAABXrlqfAFwX3nzzTX311VdKT0+vsi43N1eSqlzqHRQUpMOHD19wn4mJiZo3b17dBgUAAG7LtiMzR44c0ZQpU/TGG2+oSZMmF9zO4XC4vLYsq8rYr82aNUunTp1yLkeOHKmzzAAAwP3YdmRm586dysvLU79+/Zxj5eXl+vTTT7V06VIdOHBA0rkjNL9+3lNeXt5Fb8zn4+MjHx+f+gsOAADcim1HZm655Rbt2bNHGRkZzqV///4aPXq0MjIy1KFDBwUHByslJcX5nrKyMqWmpio6Otqu2AAAwM3YdmTG399fPXr0cBlr2rSpWrVq5RyfOnWqEhISFBkZqcjISCUkJMjPz0+jRo2yIzIAAHBDtp4AfCnTp09XSUmJJkyYoIKCAg0cOFCbN2+Wv7+/3dEAAICbcKsys23bNpfXDodD8fHxio+PtyUPAABwf7bfZwYAAOByUGYAAIDRKDMAAMBolBkAAGA0ygwAADAaZQYAABiNMgMAAIxGmQEAAEajzAAAAKNRZgAAgNEoMwAAwGiUGQAAYDTKDAAAMBplBgAAGI0yAwAAjEaZAQAARqPMAAAAo1FmAACA0SgzAADAaJQZAABgNMoMAAAwGmUGAAAYjTIDAACMRpkBAABGo8wAAACjUWYAAIDRKDMAAMBolBkAAGA0L7sDAICnyc7OVn5+vt0xaiQwMFDh4eF2xwDOizIDAA0oOztbXaOiVFJcbHeUGvH189P+zEwKDdwSZQYAGlB+fr5Kios1esYLCgrvaHecavkh+z9at/BJ5efnU2bgligzAGCDoPCOahvZ3e4YwBWBE4ABAIDRKDMAAMBolBkAAGA0ygwAADAaZQYAABiNMgMAAIxGmQEAAEajzAAAAKNx0zwATjwzCICJKDMAJPHMIADmoswAkMQzgwCYizIDwAXPDAJgGk4ABgAARqPMAAAAo1FmAACA0ThnBsbIzMy0O0KNcMkwADQMygzcXs6JIklSbGyszUlqhkuGAaBhUGbg9k6ePiNJun38bHXp2c/mNNXDJcMA0HAoMzBGq9B2XDIMAKiCMgNjFJw8qZycHLtjVItpjwQAAJNRZuD28k/9JEn6ZOtWfbo9w94w1VR++oQkGVO+AMBklBm4vaKSMknS4O5t1fNaM75m2n/goN77Wjp58qTdUQDgikeZgTEC/BorpFUzu2NUyw9XN7E7AgB4DMoMAKBauNcT3BVlBgBwUYUnjkviXk9wX5QZAMBFlZwulMS9nuC+KDMAgGrhXk9wVzxoEgAAGI0yAwAAjEaZAQAARqPMAAAAo1FmAACA0SgzAADAaJQZAABgNMoMAAAwGjfNA+pRVlaWvvrqK7tjVItpz90BgEq2lpnExERt2LBB+/fvl6+vr6Kjo7Vw4UJ16dLFuY1lWZo3b55WrlypgoICDRw4UMuWLVP37tyFEu7rdNG527/PmTNHc+bMsTlNzZw+fdruCABQI7aWmdTUVE2cOFEDBgzQ2bNnNXv2bMXExOibb75R06ZNJUmLFi1SUlKS1qxZo86dO+u5557T8OHDdeDAAfn7+9sZH7ig0pISSdKNoyar/+Ch9oappsztqfpw7Us6c+aM3VEAoEZsLTMfffSRy+vVq1erdevW2rlzp2688UZZlqXk5GTNnj1bI0eOlCStXbtWQUFBWr9+vcaPH29HbKDaAoLaGvMsmx+y/2N3BACoFbc6Z+bUqVOSpJYtW0o6d75Bbm6uYmJinNv4+PhoyJAhSktLO2+ZKS0tVWlpqfN1YWFhPacGALgr084FKy0tlY+Pj90xaiQwMND2J5O7TZmxLEvTpk3T9ddfrx49ekiScnNzJUlBQUEu2wYFBenw4cPn3U9iYqLmzZtXv2EBAG6t8MRxSVJsbKzNSWrKIcmyO0SN+Pr5aX9mpq2Fxm3KTFxcnHbv3q3PP/+8yjqHw+Hy2rKsKmOVZs2apWnTpjlfFxYWKiwsrG7DAgDcWsnpc0flbx8/W1169rM5TfVUnrdmUuYfsv+jdQufVH5+PmVm0qRJ2rhxoz799FO1bdvWOR4cHCzp3BGakJAQ53heXl6VozWVfHx8jDtEBwCoH61C2xl33ppJmd2FrTfNsyxLcXFx2rBhg7Zu3aqIiAiX9REREQoODlZKSopzrKysTKmpqYqOjm7ouAAAwA3ZemRm4sSJWr9+vf75z3/K39/feY5MQECAfH195XA4NHXqVCUkJCgyMlKRkZFKSEiQn5+fRo0aZWd0AADgJmwtM8uXL5ckDR061GV89erVGjt2rCRp+vTpKikp0YQJE5w3zdu8eTP3mAEAAJJsLjOWdekzth0Oh+Lj4xUfH1//gQAAgHHc4gRgALgcJt1LxKSsgCkoMwCMZe69RHgGFlCXKDMAjGXyvUR4BhZQdygzAIxn0n05eAYWUPdsvc8MAADA5aLMAAAAo1FmAACA0SgzAADAaJQZAABgNMoMAAAwGmUGAAAYjTIDAACMxk3zgHpUVFionJwcu2NUy8mTJ+2OAAC1QpkB6sGZn8slSTt27NCu/d/bnKZ6yvKyJEnFxcU2JwGAmqHMAPWg7Oy5MtOrY2tFD+hjc5rq+TLtZ312UCotK7M7CgDUCGUGqEdNm3grpFUzu2NUSzPfxnZHAIBa4QRgAABgNMoMAAAwGmUGAAAYjTIDAACMRpkBAABGo8wAAACjUWYAAIDRKDMAAMBo3DTPQx08dkLXfHvU7hjVciy/0O4IAAA3RpnxMPmnfpIkTXo5RVKKvWFqqKTsrN0RAABuiDLjYYpKzj13Z9hd96pn9yib01TPJ5+maddnH6v0/z/vCACAX6PMeKiWgdeobbt2dseolqubZ9odAQDgxjgBGAAAGI0yAwAAjEaZAQAARqPMAAAAo1FmAACA0SgzAADAaJQZAABgNMoMAAAwGmUGAAAYjTIDAACMRpkBAABGo8wAAACjUWYAAIDRKDMAAMBolBkAAGA0ygwAADCal90BALiXosJC5eTk2B2jWk6ePGl3BABugDIDQJJ05udySdKOHTu0a//3NqepnrK8LElScXGxzUkA2IkyA0CSVHb2XJnp1bG1ogf0sTlN9XyZ9rM+OyiVlpXZHQWAjSgzAFw0beKtkFbN7I5RLc18G9sdAYAb4ARgAABgNMoMAAAwGmUGAAAYjTIDAACMRpkBAABGo8wAAACjUWYAAIDRKDMAAMBolBkAAGA0ygwAADAaZQYAABiNMgMAAIxGmQEAAEajzAAAAKNRZgAAgNEoMwAAwGiUGQAAYDTKDAAAMBplBgAAGI0yAwAAjGZEmXn55ZcVERGhJk2aqF+/fvrss8/sjgQAANyE25eZt956S1OnTtXs2bO1a9cu3XDDDbrtttuUnZ1tdzQAAOAG3L7MJCUl6eGHH9a4ceMUFRWl5ORkhYWFafny5XZHAwAAbsDL7gAXU1ZWpp07d2rmzJku4zExMUpLSzvve0pLS1VaWup8ferUKUlSYWFhnec7ffq0JGnD599o58Fjdb7/+rBj/38lSVmHDuuqqxw2p6meH46dm9tjR45o+46vbE5TPWRuGEeOHD33zwN7tL2Jj81pqufQN19Lkr7bu1NlZ0psTlM9ZG4YJmY+kXtE0rm/D+v679nK/VmWdemNLTd29OhRS5L1r3/9y2V8wYIFVufOnc/7nrlz51qSWFhYWFhYWK6A5ciRI5fsC259ZKaSw+F6BMGyrCpjlWbNmqVp06Y5X1dUVOjEiRNq1arVBd/jrgoLCxUWFqYjR46oWbNmdsexFXPxC+biHObhF8zFOczDL66EubAsS0VFRQoNDb3ktm5dZgIDA9WoUSPl5ua6jOfl5SkoKOi87/Hx8ZGPj+vh5ubNm9dXxAbRrFkzY38Z6xpz8Qvm4hzm4RfMxTnMwy9Mn4uAgIBqbefWJwA3btxY/fr1U0pKist4SkqKoqOjbUoFAADciVsfmZGkadOm6cEHH1T//v01aNAgrVy5UtnZ2Xr00UftjgYAANyA25eZ++67Tz/++KPmz5+vnJwc9ejRQx988IHatWtnd7R65+Pjo7lz51b52swTMRe/YC7OYR5+wVycwzz8wtPmwmFZ1bnmCQAAwD259TkzAAAAl0KZAQAARqPMAAAAo1FmAACA0SgzbuDTTz/VH/7wB4WGhsrhcOjdd991WW9ZluLj4xUaGipfX18NHTpU+/btsydsPUpMTNSAAQPk7++v1q1b66677tKBAwdctvGUuVi+fLl69uzpvOHVoEGD9OGHHzrXe8o8/FZiYqIcDoemTp3qHPOUuYiPj5fD4XBZgoODnes9ZR4k6ejRo4qNjVWrVq3k5+en3r17a+fOnc71njIX7du3r/I74XA4NHHiREmeMw8SZcYt/PTTT+rVq5eWLl163vWLFi1SUlKSli5dqvT0dAUHB2v48OEqKipq4KT1KzU1VRMnTtSXX36plJQUnT17VjExMfrpp5+c23jKXLRt21bPP/+8duzYoR07dujmm2/WnXfe6fwPkafMw6+lp6dr5cqV6tmzp8u4J81F9+7dlZOT41z27NnjXOcp81BQUKDBgwfL29tbH374ob755hstXrzY5U7vnjIX6enpLr8PlTeYvffeeyV5zjxIkls/aNITSbLeeecd5+uKigorODjYev75551jZ86csQICAqwVK1bYkLDh5OXlWZKs1NRUy7I8ey4sy7JatGhhvfrqqx45D0VFRVZkZKSVkpJiDRkyxJoyZYplWZ71OzF37lyrV69e513nSfMwY8YM6/rrr7/gek+ai9+aMmWK1bFjR6uiosLj5oEjM24uKytLubm5iomJcY75+PhoyJAhSktLszFZ/Tt16pQkqWXLlpI8dy7Ky8v15ptv6qefftKgQYM8ch4mTpyo22+/XcOGDXMZ97S5OHjwoEJDQxUREaH7779f33//vSTPmoeNGzeqf//+uvfee9W6dWv16dNHr7zyinO9J83Fr5WVlemNN97QQw89JIfD4XHzQJlxc5UP2fztgzWDgoKqPIDzSmJZlqZNm6brr79ePXr0kOR5c7Fnzx5dffXV8vHx0aOPPqp33nlH3bp187h5ePPNN/XVV18pMTGxyjpPmouBAwfq9ddf16ZNm/TKK68oNzdX0dHR+vHHHz1qHr7//nstX75ckZGR2rRpkx599FFNnjxZr7/+uiTP+p34tXfffVcnT57U2LFjJXnePLj94wxwjsPhcHltWVaVsStJXFycdu/erc8//7zKOk+Ziy5duigjI0MnT57UP/7xD40ZM0apqanO9Z4wD0eOHNGUKVO0efNmNWnS5ILbecJc3Hbbbc5/v/baazVo0CB17NhRa9eu1XXXXSfJM+ahoqJC/fv3V0JCgiSpT58+2rdvn5YvX67/+Z//cW7nCXPxa6tWrdJtt92m0NBQl3FPmQeOzLi5yqsVftuk8/LyqjTuK8WkSZO0ceNGffLJJ2rbtq1z3NPmonHjxurUqZP69++vxMRE9erVSy+99JJHzcPOnTuVl5enfv36ycvLS15eXkpNTdXf/vY3eXl5OX9eT5iL32ratKmuvfZaHTx40KN+J0JCQtStWzeXsaioKGVnZ0vyvP9OSNLhw4f18ccfa9y4cc4xT5sHyoybi4iIUHBwsPMsdencd6OpqamKjo62MVndsyxLcXFx2rBhg7Zu3aqIiAiX9Z40F+djWZZKS0s9ah5uueUW7dmzRxkZGc6lf//+Gj16tDIyMtShQwePmYvfKi0tVWZmpkJCQjzqd2Lw4MFVbtnw7bffOh8+7ElzUWn16tVq3bq1br/9dueYx82DXWce4xdFRUXWrl27rF27dlmSrKSkJGvXrl3W4cOHLcuyrOeff94KCAiwNmzYYO3Zs8d64IEHrJCQEKuwsNDm5HXrscceswICAqxt27ZZOTk5zqW4uNi5jafMxaxZs6xPP/3UysrKsnbv3m099dRT1lVXXWVt3rzZsizPmYfz+fXVTJblOXPx+OOPW9u2bbO+//5768svv7TuuOMOy9/f3zp06JBlWZ4zD9u3b7e8vLysBQsWWAcPHrTWrVtn+fn5WW+88YZzG0+ZC8uyrPLycis8PNyaMWNGlXWeNA+UGTfwySefWJKqLGPGjLEs69ylhnPnzrWCg4MtHx8f68Ybb7T27Nljb+h6cL45kGStXr3auY2nzMVDDz1ktWvXzmrcuLF1zTXXWLfccouzyFiW58zD+fy2zHjKXNx3331WSEiI5e3tbYWGhlojR4609u3b51zvKfNgWZb13nvvWT169LB8fHysrl27WitXrnRZ70lzsWnTJkuSdeDAgSrrPGkeHJZlWbYcEgIAAKgDnDMDAACMRpkBAABGo8wAAACjUWYAAIDRKDMAAMBolBkAAGA0ygwAADAaZQYAABiNMgMAAIxGmQEAAEajzAAAAKNRZgAAgNH+H8W1OlFYAANJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a0dccf0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['HIGH', 'LOW', 'NORMAL'], dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"BP\"].value_counts().keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "922fe6d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 3 artists>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "ax.bar(x=df[\"BP\"].value_counts().keys(), height=df[\"BP\"].value_counts().values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86c513e9",
   "metadata": {},
   "source": [
    "## Data Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "913d2901",
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6bc6debd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------\n",
      "Sex\n",
      "['F' 'M']\n",
      "----------\n",
      "----------\n",
      "BP\n",
      "['HIGH' 'LOW' 'NORMAL']\n",
      "----------\n",
      "----------\n",
      "Cholesterol\n",
      "['HIGH' 'NORMAL']\n",
      "----------\n",
      "----------\n",
      "Drug\n",
      "['DrugY' 'drugA' 'drugB' 'drugC' 'drugX']\n",
      "----------\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "for stuff in df.keys():\n",
    "    if pd.api.types.is_object_dtype(df[stuff]) == True:\n",
    "        le = LabelEncoder()\n",
    "        df[stuff] = le.fit_transform(df[stuff])\n",
    "        print(\"-\" * 10)\n",
    "        print(stuff)\n",
    "        print(le.classes_)\n",
    "        print(\"-\" * 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bc18411a",
   "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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.355</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.093</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10.114</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7.798</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18.043</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Sex  BP  Cholesterol  Na_to_K  Drug\n",
       "0   23    0   0            0   25.355     0\n",
       "1   47    1   1            0   13.093     3\n",
       "2   47    1   1            0   10.114     3\n",
       "3   28    0   2            0    7.798     4\n",
       "4   61    0   1            0   18.043     0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ec0d167f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b2daaa79",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(\"Drug\", axis=1)\n",
    "y = df[\"Drug\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f28cbfba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.75"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Neighbours\n",
    "\n",
    "knc = KNeighborsClassifier()\n",
    "knc.fit(X_train, y_train)\n",
    "knc.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "81f89660",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.775"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SVC\n",
    "\n",
    "svclf = SVC().fit(X_train, y_train)\n",
    "svclf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3c07bafb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest\n",
    "\n",
    "rclf = RandomForestClassifier()\n",
    "rclf.fit(X_train, y_train)\n",
    "rclf.score(X_test, y_test) # Got 100% Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f71d424d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# XGB \n",
    "\n",
    "xgbclg = XGBClassifier()\n",
    "xgbclg.fit(X_train.values, y_train.values)\n",
    "xgbclg.score(X_test.values, y_test.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb12fe92",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b71a39d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/djoe/.local/lib/python3.10/site-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rclf.predict([[23, 1, 1, 1, 43.2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "87692396",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgbclg.predict([[23, 1, 1, 1, 43.2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94f514b2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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