K means Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K means clustering with Elbow method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "import datetime as dt\n",
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The data set is available in the 'Data' folder.  File name:llama_movements.7z.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1=pd.read_csv('llama_movements.csv')\n",
    "#df2=pd.read_csv('historical_weather.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TRIP ID</th>\n",
       "      <th>START TIME</th>\n",
       "      <th>STOP TIME</th>\n",
       "      <th>LLAMA ID</th>\n",
       "      <th>TRIP DURATION (SECONDS)</th>\n",
       "      <th>FROM HABITAT NAME</th>\n",
       "      <th>TO HABITAT NAME</th>\n",
       "      <th>FROM HABITAT ID</th>\n",
       "      <th>FROM LATITUDE</th>\n",
       "      <th>FROM LONGITUDE</th>\n",
       "      <th>TO HABITAT ID</th>\n",
       "      <th>TO LATITUDE</th>\n",
       "      <th>TO LONGITUDE</th>\n",
       "      <th>start hour</th>\n",
       "      <th>stop hour</th>\n",
       "      <th>START DAY</th>\n",
       "      <th>STOP DAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20941785</td>\n",
       "      <td>9/27/2018 17:36</td>\n",
       "      <td>9/27/2018 18:02</td>\n",
       "      <td>1420</td>\n",
       "      <td>1536</td>\n",
       "      <td>Gradients Descent</td>\n",
       "      <td>Peachtree</td>\n",
       "      <td>23</td>\n",
       "      <td>-9.205515</td>\n",
       "      <td>-74.973485</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.217429</td>\n",
       "      <td>-74.996096</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>2018-09-27</td>\n",
       "      <td>2018-09-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21032144</td>\n",
       "      <td>10/3/2018 17:35</td>\n",
       "      <td>10/3/2018 18:01</td>\n",
       "      <td>4539</td>\n",
       "      <td>1555</td>\n",
       "      <td>Gradients Descent</td>\n",
       "      <td>Peachtree</td>\n",
       "      <td>23</td>\n",
       "      <td>-9.205515</td>\n",
       "      <td>-74.973485</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.217429</td>\n",
       "      <td>-74.996096</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>2018-10-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21203053</td>\n",
       "      <td>10/16/2018 17:25</td>\n",
       "      <td>10/16/2018 17:52</td>\n",
       "      <td>4261</td>\n",
       "      <td>1642</td>\n",
       "      <td>Gradients Descent</td>\n",
       "      <td>Peachtree</td>\n",
       "      <td>23</td>\n",
       "      <td>-9.205515</td>\n",
       "      <td>-74.973485</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.217429</td>\n",
       "      <td>-74.996096</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>2018-10-16</td>\n",
       "      <td>2018-10-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19973129</td>\n",
       "      <td>8/5/2018 19:01</td>\n",
       "      <td>8/5/2018 19:38</td>\n",
       "      <td>5865</td>\n",
       "      <td>2220</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>Peachtree</td>\n",
       "      <td>3</td>\n",
       "      <td>-9.190730</td>\n",
       "      <td>-74.955617</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.217429</td>\n",
       "      <td>-74.996096</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>2018-08-05</td>\n",
       "      <td>2018-08-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18241036</td>\n",
       "      <td>4/30/2018 23:06</td>\n",
       "      <td>4/30/2018 23:34</td>\n",
       "      <td>6047</td>\n",
       "      <td>1721</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>Peachtree</td>\n",
       "      <td>3</td>\n",
       "      <td>-9.190730</td>\n",
       "      <td>-74.955617</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.217429</td>\n",
       "      <td>-74.996096</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>2018-04-30</td>\n",
       "      <td>2018-04-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    TRIP ID        START TIME         STOP TIME  LLAMA ID  \\\n",
       "0  20941785   9/27/2018 17:36   9/27/2018 18:02      1420   \n",
       "1  21032144   10/3/2018 17:35   10/3/2018 18:01      4539   \n",
       "2  21203053  10/16/2018 17:25  10/16/2018 17:52      4261   \n",
       "3  19973129    8/5/2018 19:01    8/5/2018 19:38      5865   \n",
       "4  18241036   4/30/2018 23:06   4/30/2018 23:34      6047   \n",
       "\n",
       "   TRIP DURATION (SECONDS)  FROM HABITAT NAME TO HABITAT NAME  \\\n",
       "0                     1536  Gradients Descent       Peachtree   \n",
       "1                     1555  Gradients Descent       Peachtree   \n",
       "2                     1642  Gradients Descent       Peachtree   \n",
       "3                     2220      Random Forest       Peachtree   \n",
       "4                     1721      Random Forest       Peachtree   \n",
       "\n",
       "   FROM HABITAT ID  FROM LATITUDE  FROM LONGITUDE  TO HABITAT ID  TO LATITUDE  \\\n",
       "0               23      -9.205515      -74.973485              0    -9.217429   \n",
       "1               23      -9.205515      -74.973485              0    -9.217429   \n",
       "2               23      -9.205515      -74.973485              0    -9.217429   \n",
       "3                3      -9.190730      -74.955617              0    -9.217429   \n",
       "4                3      -9.190730      -74.955617              0    -9.217429   \n",
       "\n",
       "   TO LONGITUDE  start hour  stop hour  START DAY   STOP DAY  \n",
       "0    -74.996096          17         18 2018-09-27 2018-09-27  \n",
       "1    -74.996096          17         18 2018-10-03 2018-10-03  \n",
       "2    -74.996096          17         17 2018-10-16 2018-10-16  \n",
       "3    -74.996096          19         19 2018-08-05 2018-08-05  \n",
       "4    -74.996096          23         23 2018-04-30 2018-04-30  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TRIP ID</th>\n",
       "      <th>START TIME</th>\n",
       "      <th>STOP TIME</th>\n",
       "      <th>LLAMA ID</th>\n",
       "      <th>TRIP DURATION (SECONDS)</th>\n",
       "      <th>FROM HABITAT NAME</th>\n",
       "      <th>TO HABITAT NAME</th>\n",
       "      <th>FROM HABITAT ID</th>\n",
       "      <th>FROM LATITUDE</th>\n",
       "      <th>FROM LONGITUDE</th>\n",
       "      <th>TO HABITAT ID</th>\n",
       "      <th>TO LATITUDE</th>\n",
       "      <th>TO LONGITUDE</th>\n",
       "      <th>start hour</th>\n",
       "      <th>stop hour</th>\n",
       "      <th>START DAY</th>\n",
       "      <th>STOP DAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20941785</td>\n",
       "      <td>9/27/2018 17:36</td>\n",
       "      <td>9/27/2018 18:02</td>\n",
       "      <td>1420</td>\n",
       "      <td>1536</td>\n",
       "      <td>Gradients Descent</td>\n",
       "      <td>Peachtree</td>\n",
       "      <td>23</td>\n",
       "      <td>-9.205515</td>\n",
       "      <td>-74.973485</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.217429</td>\n",
       "      <td>-74.996096</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>2018-09-27</td>\n",
       "      <td>2018-09-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21032144</td>\n",
       "      <td>10/3/2018 17:35</td>\n",
       "      <td>10/3/2018 18:01</td>\n",
       "      <td>4539</td>\n",
       "      <td>1555</td>\n",
       "      <td>Gradients Descent</td>\n",
       "      <td>Peachtree</td>\n",
       "      <td>23</td>\n",
       "      <td>-9.205515</td>\n",
       "      <td>-74.973485</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.217429</td>\n",
       "      <td>-74.996096</td>\n",
       "      <td>17</td>\n",
       "      <td>18</td>\n",
       "      <td>2018-10-03</td>\n",
       "      <td>2018-10-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21203053</td>\n",
       "      <td>10/16/2018 17:25</td>\n",
       "      <td>10/16/2018 17:52</td>\n",
       "      <td>4261</td>\n",
       "      <td>1642</td>\n",
       "      <td>Gradients Descent</td>\n",
       "      <td>Peachtree</td>\n",
       "      <td>23</td>\n",
       "      <td>-9.205515</td>\n",
       "      <td>-74.973485</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.217429</td>\n",
       "      <td>-74.996096</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>2018-10-16</td>\n",
       "      <td>2018-10-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19973129</td>\n",
       "      <td>8/5/2018 19:01</td>\n",
       "      <td>8/5/2018 19:38</td>\n",
       "      <td>5865</td>\n",
       "      <td>2220</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>Peachtree</td>\n",
       "      <td>3</td>\n",
       "      <td>-9.190730</td>\n",
       "      <td>-74.955617</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.217429</td>\n",
       "      <td>-74.996096</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>2018-08-05</td>\n",
       "      <td>2018-08-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18241036</td>\n",
       "      <td>4/30/2018 23:06</td>\n",
       "      <td>4/30/2018 23:34</td>\n",
       "      <td>6047</td>\n",
       "      <td>1721</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>Peachtree</td>\n",
       "      <td>3</td>\n",
       "      <td>-9.190730</td>\n",
       "      <td>-74.955617</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.217429</td>\n",
       "      <td>-74.996096</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>2018-04-30</td>\n",
       "      <td>2018-04-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    TRIP ID        START TIME         STOP TIME  LLAMA ID  \\\n",
       "0  20941785   9/27/2018 17:36   9/27/2018 18:02      1420   \n",
       "1  21032144   10/3/2018 17:35   10/3/2018 18:01      4539   \n",
       "2  21203053  10/16/2018 17:25  10/16/2018 17:52      4261   \n",
       "3  19973129    8/5/2018 19:01    8/5/2018 19:38      5865   \n",
       "4  18241036   4/30/2018 23:06   4/30/2018 23:34      6047   \n",
       "\n",
       "   TRIP DURATION (SECONDS)  FROM HABITAT NAME TO HABITAT NAME  \\\n",
       "0                     1536  Gradients Descent       Peachtree   \n",
       "1                     1555  Gradients Descent       Peachtree   \n",
       "2                     1642  Gradients Descent       Peachtree   \n",
       "3                     2220      Random Forest       Peachtree   \n",
       "4                     1721      Random Forest       Peachtree   \n",
       "\n",
       "   FROM HABITAT ID  FROM LATITUDE  FROM LONGITUDE  TO HABITAT ID  TO LATITUDE  \\\n",
       "0               23      -9.205515      -74.973485              0    -9.217429   \n",
       "1               23      -9.205515      -74.973485              0    -9.217429   \n",
       "2               23      -9.205515      -74.973485              0    -9.217429   \n",
       "3                3      -9.190730      -74.955617              0    -9.217429   \n",
       "4                3      -9.190730      -74.955617              0    -9.217429   \n",
       "\n",
       "   TO LONGITUDE  start hour  stop hour  START DAY   STOP DAY  \n",
       "0    -74.996096          17         18 2018-09-27 2018-09-27  \n",
       "1    -74.996096          17         18 2018-10-03 2018-10-03  \n",
       "2    -74.996096          17         17 2018-10-16 2018-10-16  \n",
       "3    -74.996096          19         19 2018-08-05 2018-08-05  \n",
       "4    -74.996096          23         23 2018-04-30 2018-04-30  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# splitting the datetime into date and time variable\n",
    "df1['start hour']=(df1['START TIME'].str.split(\" \", expand=True)[1].str.split(\":\", expand=True)[0]).astype(\"int\")\n",
    "df1['stop hour']=(df1['STOP TIME'].str.split(\" \", expand=True)[1].str.split(\":\", expand=True)[0]).astype(\"int\")\n",
    "df1['START DAY']=pd.to_datetime(df1['START TIME'].str.split(\" \", expand=True)[0])\n",
    "df1['STOP DAY']=pd.to_datetime(df1['STOP TIME'].str.split(\" \", expand=True)[0])\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering the habitats depending on the number of llamas' arriving and departing every hour to find out a geographic pattern in movements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>start hour</th>\n",
       "      <th>stop hour</th>\n",
       "      <th>FROM HABITAT ID</th>\n",
       "      <th>TO HABITAT ID</th>\n",
       "      <th>LLAMA COUNT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>107572</th>\n",
       "      <td>2018-07-18</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138348</th>\n",
       "      <td>2018-09-24</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117176</th>\n",
       "      <td>2018-08-06</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140864</th>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94759</th>\n",
       "      <td>2018-06-19</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Day  start hour  stop hour  FROM HABITAT ID  TO HABITAT ID  \\\n",
       "107572 2018-07-18           8          8               23              3   \n",
       "138348 2018-09-24          17         17                3             23   \n",
       "117176 2018-08-06          17         17                3             23   \n",
       "140864 2018-10-01          17         17                3             23   \n",
       "94759  2018-06-19          17         17                3             23   \n",
       "\n",
       "        LLAMA COUNT  \n",
       "107572           22  \n",
       "138348           22  \n",
       "117176           22  \n",
       "140864           21  \n",
       "94759            21  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In this problem also, we are considering the trips which started and ended in the same day.\n",
    "\n",
    "c=df1[(df1['START DAY']==df1['STOP DAY'])].groupby(['START DAY','start hour','stop hour','FROM HABITAT ID','TO HABITAT ID'])['LLAMA ID'].nunique().reset_index().sort_values(by='LLAMA ID',ascending=False).rename(columns={'LLAMA ID':'LLAMA COUNT','START DAY':'Day'})\n",
    "\n",
    "c.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp1=c.groupby(['Day','start hour','FROM HABITAT ID'])['LLAMA COUNT'].sum().reset_index().rename(columns={'start hour':'hour','FROM HABITAT ID':'ID','LLAMA COUNT':'LLAMA DEPARTED'})\n",
    "temp2=c.groupby(['Day','stop hour','TO HABITAT ID'])['LLAMA COUNT'].sum().reset_index().rename(columns={'stop hour':'hour','TO HABITAT ID':'ID','LLAMA COUNT':'LLAMA ARRIVED'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Day</th>\n",
       "      <th>hour</th>\n",
       "      <th>LLAMA DEPARTED</th>\n",
       "      <th>LLAMA ARRIVED</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Day  hour  LLAMA DEPARTED  LLAMA ARRIVED  ID\n",
       "0 2017-07-01     0               2              0   3\n",
       "1 2017-07-01     0               1              1  23\n",
       "2 2017-07-01     1               1              0   0\n",
       "3 2017-07-01     1               1              1   3\n",
       "4 2017-07-01     1               1              0  14"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cl=pd.merge(temp1,temp2,how='left',on=['Day','hour','ID']).fillna(0)\n",
    "df_cl['LLAMA ARRIVED']=df_cl['LLAMA ARRIVED'].astype('int64')\n",
    "df_cl=df_cl[['Day','hour','LLAMA DEPARTED','LLAMA ARRIVED','ID']]\n",
    "df_cl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df_cl.iloc[ :,[2,3]]\n",
    "#y=df_cl.iloc[ :, -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We are using Elbow method to find out the optimal number of clusters needed \n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "wcss=[]\n",
    "for i in range(1,11):\n",
    "    kmeans=KMeans(n_clusters=i, init='k-means++', random_state= 42)\n",
    "    kmeans.fit(x)\n",
    "    wcss.append(kmeans.inertia_)\n",
    "    \n",
    "plt.plot(range(1,11),wcss)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using k=5 because there is no improvement in WCSS after 5. We can use 4 also and see the result. For this illustration, I am using 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans=KMeans(n_clusters=5, init='k-means++', random_state= 42)\n",
    "y=kmeans.fit_predict(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Day</th>\n",
       "      <th>hour</th>\n",
       "      <th>LLAMA DEPARTED</th>\n",
       "      <th>LLAMA ARRIVED</th>\n",
       "      <th>ID</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Day  hour  LLAMA DEPARTED  LLAMA ARRIVED  ID  Cluster\n",
       "0 2017-07-01     0               2              0   3        3\n",
       "1 2017-07-01     0               1              1  23        3\n",
       "2 2017-07-01     1               1              0   0        3\n",
       "3 2017-07-01     1               1              1   3        3\n",
       "4 2017-07-01     1               1              0  14        3"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_predict=pd.DataFrame(y,columns=['Cluster'])\n",
    "df_final=pd.concat([df_cl,df_predict],axis=1)\n",
    "df_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2650b600bc8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here a visual representation is made to see the habitats based on the movements of Llamas.\n",
    "# We refer to the Dashboard for dynamic visualization which will show the geographic movments during different period of time.\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "sns.set_context('talk')\n",
    "plt.figure(figsize=(10,5))\n",
    "sns.scatterplot(df_final['LLAMA DEPARTED'][df_final['Cluster']==3],df_final['LLAMA ARRIVED'][df_final['Cluster']==3],label=3)\n",
    "sns.scatterplot(df_final['LLAMA DEPARTED'][df_final['Cluster']==2],df_final['LLAMA ARRIVED'][df_final['Cluster']==2],label=2)\n",
    "sns.scatterplot(df_final['LLAMA DEPARTED'][df_final['Cluster']==4],df_final['LLAMA ARRIVED'][df_final['Cluster']==4],label=4)\n",
    "sns.scatterplot(df_final['LLAMA DEPARTED'][df_final['Cluster']==1],df_final['LLAMA ARRIVED'][df_final['Cluster']==1],label=1)\n",
    "sns.scatterplot(df_final['LLAMA DEPARTED'][df_final['Cluster']==0],df_final['LLAMA ARRIVED'][df_final['Cluster']==0],label=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#b.to_csv('llama_group.csv',index=False)\n",
    "#df_final.to_csv('llama_cluster.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is the new change "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}