clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering of Patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from matplotlib import rcParams\n",
    "from tqdm import tqdm\n",
    "from collections import Counter\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "plt.style.use('seaborn')\n",
    "rcParams.update({'figure.autolayout': True})\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   Age Bracket         Gender Detected District Detected State Current Status  \\\n0         20.0              F          Thrissur         Kerala      Recovered   \n1         -1.0  Not Available         Alappuzha         Kerala      Recovered   \n2         -1.0  Not Available         Kasaragod         Kerala      Recovered   \n3         45.0              M        East Delhi          Delhi      Recovered   \n4         24.0              M         Hyderabad      Telangana      Recovered   \n\n  Nationality Type of transmission  Days Affected  \n0       India             Imported           15.0  \n1       India             Imported           12.0  \n2       India             Imported           11.0  \n3       India             Imported           13.0  \n4       India             Imported            0.0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Age Bracket</th>\n      <th>Gender</th>\n      <th>Detected District</th>\n      <th>Detected State</th>\n      <th>Current Status</th>\n      <th>Nationality</th>\n      <th>Type of transmission</th>\n      <th>Days Affected</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>20.0</td>\n      <td>F</td>\n      <td>Thrissur</td>\n      <td>Kerala</td>\n      <td>Recovered</td>\n      <td>India</td>\n      <td>Imported</td>\n      <td>15.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-1.0</td>\n      <td>Not Available</td>\n      <td>Alappuzha</td>\n      <td>Kerala</td>\n      <td>Recovered</td>\n      <td>India</td>\n      <td>Imported</td>\n      <td>12.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-1.0</td>\n      <td>Not Available</td>\n      <td>Kasaragod</td>\n      <td>Kerala</td>\n      <td>Recovered</td>\n      <td>India</td>\n      <td>Imported</td>\n      <td>11.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>45.0</td>\n      <td>M</td>\n      <td>East Delhi</td>\n      <td>Delhi</td>\n      <td>Recovered</td>\n      <td>India</td>\n      <td>Imported</td>\n      <td>13.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>24.0</td>\n      <td>M</td>\n      <td>Hyderabad</td>\n      <td>Telangana</td>\n      <td>Recovered</td>\n      <td>India</td>\n      <td>Imported</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "df = pd.read_csv(\"Data\\\\raw_data.csv\")\n",
    "\n",
    "unwanted_columns = [\n",
    "    'Patient Number',\n",
    "    'State Patient Number',\n",
    "    'Estimated Onset Date',\n",
    "    'Detected City',\n",
    "    'State code',\n",
    "    'Notes',\n",
    "    'Contracted from which Patient (Suspected)',\n",
    "    'Source_1',\n",
    "    'Source_2',\n",
    "    'Source_3',\n",
    "    'Backup Notes',\n",
    "]\n",
    "\n",
    "df.drop(unwanted_columns, axis=1, inplace=True)\n",
    "\n",
    "df['Date Announced'] = pd.to_datetime(df['Date Announced'], infer_datetime_format=True)\n",
    "df['Status Change Date'] = pd.to_datetime(df['Status Change Date'], infer_datetime_format=True)\n",
    "df['Age Bracket'] = pd.to_numeric(df['Age Bracket'], errors='coerce')\n",
    "\n",
    "df['Days Affected'] = df['Status Change Date'] - df['Date Announced']\n",
    "df['Days Affected'] = df['Days Affected'].map(lambda x: x.days)\n",
    "df.drop(['Status Change Date', 'Date Announced'], inplace=True, axis=1)\n",
    "df.replace('Imported ', 'Imported', inplace=True)\n",
    "\n",
    "nan_dict = {\n",
    "    'Age Bracket': -1,\n",
    "    'Gender': 'Not Available',\n",
    "    'Detected District': 'Not Available',\n",
    "    'Detected State': 'Not Available',\n",
    "    'Current Status': 'Not Available',\n",
    "    'Nationality': 'Not Available',\n",
    "    'Type of transmission': 'Not Available',\n",
    "    'Days Affected': -1\n",
    "}\n",
    "\n",
    "df.fillna(nan_dict, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scale data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   Age Bracket         Gender Detected District Detected State Current Status  \\\n0     1.468770              F          Thrissur         Kerala      Recovered   \n1    -0.275400  Not Available         Alappuzha         Kerala      Recovered   \n2    -0.275400  Not Available         Kasaragod         Kerala      Recovered   \n3     3.545163              M        East Delhi          Delhi      Recovered   \n4     1.800993              M         Hyderabad      Telangana      Recovered   \n\n  Nationality Type of transmission  Days Affected  \n0       India             Imported      13.675621  \n1       India             Imported      10.927722  \n2       India             Imported      10.011755  \n3       India             Imported      11.843688  \n4       India             Imported      -0.063876  ",
      "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 Bracket</th>\n      <th>Gender</th>\n      <th>Detected District</th>\n      <th>Detected State</th>\n      <th>Current Status</th>\n      <th>Nationality</th>\n      <th>Type of transmission</th>\n      <th>Days Affected</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.468770</td>\n      <td>F</td>\n      <td>Thrissur</td>\n      <td>Kerala</td>\n      <td>Recovered</td>\n      <td>India</td>\n      <td>Imported</td>\n      <td>13.675621</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.275400</td>\n      <td>Not Available</td>\n      <td>Alappuzha</td>\n      <td>Kerala</td>\n      <td>Recovered</td>\n      <td>India</td>\n      <td>Imported</td>\n      <td>10.927722</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-0.275400</td>\n      <td>Not Available</td>\n      <td>Kasaragod</td>\n      <td>Kerala</td>\n      <td>Recovered</td>\n      <td>India</td>\n      <td>Imported</td>\n      <td>10.011755</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3.545163</td>\n      <td>M</td>\n      <td>East Delhi</td>\n      <td>Delhi</td>\n      <td>Recovered</td>\n      <td>India</td>\n      <td>Imported</td>\n      <td>11.843688</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.800993</td>\n      <td>M</td>\n      <td>Hyderabad</td>\n      <td>Telangana</td>\n      <td>Recovered</td>\n      <td>India</td>\n      <td>Imported</td>\n      <td>-0.063876</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "scale_cols = [\n",
    "    'Age Bracket',\n",
    "    'Days Affected',\n",
    "]\n",
    "\n",
    "df[scale_cols] = scaler.fit_transform(df[scale_cols])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encode Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   Age Bracket  Gender  Detected District  Detected State  Current Status  \\\n0     1.468770       0                410              15               3   \n1    -0.275400       2                  8              15               3   \n2    -0.275400       2                207              15               3   \n3     3.545163       1                110               7               3   \n4     1.800993       1                157              28               3   \n\n   Nationality  Type of transmission  Days Affected  \n0            1                     0      13.675621  \n1            1                     0      10.927722  \n2            1                     0      10.011755  \n3            1                     0      11.843688  \n4            1                     0      -0.063876  ",
      "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 Bracket</th>\n      <th>Gender</th>\n      <th>Detected District</th>\n      <th>Detected State</th>\n      <th>Current Status</th>\n      <th>Nationality</th>\n      <th>Type of transmission</th>\n      <th>Days Affected</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.468770</td>\n      <td>0</td>\n      <td>410</td>\n      <td>15</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>13.675621</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.275400</td>\n      <td>2</td>\n      <td>8</td>\n      <td>15</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>10.927722</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-0.275400</td>\n      <td>2</td>\n      <td>207</td>\n      <td>15</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>10.011755</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3.545163</td>\n      <td>1</td>\n      <td>110</td>\n      <td>7</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>11.843688</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.800993</td>\n      <td>1</td>\n      <td>157</td>\n      <td>28</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>-0.063876</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "encode_cols = [\n",
    "    'Gender',\n",
    "    'Detected District',\n",
    "    'Detected State',\n",
    "    'Current Status',\n",
    "    'Nationality',\n",
    "    'Type of transmission',\n",
    "]\n",
    "\n",
    "le_dict = {col: LabelEncoder() for col in encode_cols}\n",
    "for col in encode_cols:\n",
    "    df[col] = le_dict[col].fit_transform(df[col])\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Work with data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n       n_clusters=2, n_init=10, n_jobs=-1, precompute_distances='auto',\n       random_state=None, tol=0.0001, verbose=0)"
     },
     "metadata": {},
     "execution_count": 23
    }
   ],
   "source": [
    "data = df.values\n",
    "\n",
    "kmeans = KMeans(n_clusters=2, n_jobs=-1)\n",
    "kmeans.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "labels = kmeans.labels_\n",
    "counter = Counter(labels)\n",
    "\n",
    "plt.bar(counter.keys(), counter.values())\n",
    "plt.xticks(range(len(counter.keys())), counter.keys())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Label: 0\nNot Available\nAlappuzha\nKerala\nRecovered\nIndia\nImported\n"
    }
   ],
   "source": [
    "for i in range(len(labels)):\n",
    "    if labels[i] == 0:\n",
    "        print('Label: 0')\n",
    "        for col in encode_cols:\n",
    "            print(le_dict[col].inverse_transform(df[col])[i])\n",
    "        break\n"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python37664bitcc33fecb0cd5463a9828fed86f5243b3",
   "display_name": "Python 3.7.6 64-bit"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}