NSSsurveyDataAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np #linear algebra\n",
    "import pandas as pd # a data processing and CSV I/O library\n",
    "\n",
    "import warnings # current version of seaborn generates a bunch of warnings that will be ignore\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Data Visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "sns.set(style='white', color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading NSS data...........\n",
    "nss = pd.read_csv('KLU NSS.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Village</th>\n",
       "      <th>Mode of Wages/ Income</th>\n",
       "      <th>How many in the family can use the mobile?</th>\n",
       "      <th>Type of TV</th>\n",
       "      <th>Number of people in the House</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prathuru</td>\n",
       "      <td>Bank</td>\n",
       "      <td>4</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prathuru</td>\n",
       "      <td>Bank</td>\n",
       "      <td>4</td>\n",
       "      <td>Smart</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nuthakki</td>\n",
       "      <td>Cash</td>\n",
       "      <td>4</td>\n",
       "      <td>Normal</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Nuthakki</td>\n",
       "      <td>Cash</td>\n",
       "      <td>0</td>\n",
       "      <td>NO TV</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Nuthakki</td>\n",
       "      <td>Cash</td>\n",
       "      <td>2</td>\n",
       "      <td>NO TV</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Nuthakki</td>\n",
       "      <td>Bank</td>\n",
       "      <td>2</td>\n",
       "      <td>Normal</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Nuthakki</td>\n",
       "      <td>Cash</td>\n",
       "      <td>4</td>\n",
       "      <td>Normal</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Nuthakki</td>\n",
       "      <td>Cash</td>\n",
       "      <td>4</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Mellempudi</td>\n",
       "      <td>Bank</td>\n",
       "      <td>2</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mellempudi</td>\n",
       "      <td>Cash</td>\n",
       "      <td>2</td>\n",
       "      <td>Smart</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Mellempudi</td>\n",
       "      <td>Cash</td>\n",
       "      <td>2</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Ippatam</td>\n",
       "      <td>Cash</td>\n",
       "      <td>3</td>\n",
       "      <td>Smart</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Mellempudi</td>\n",
       "      <td>Cash</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Peddapalem</td>\n",
       "      <td>Cash</td>\n",
       "      <td>5</td>\n",
       "      <td>Normal</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Ippatam</td>\n",
       "      <td>Cash</td>\n",
       "      <td>2</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Peddapalem</td>\n",
       "      <td>Cash</td>\n",
       "      <td>2</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Peddapalem</td>\n",
       "      <td>Cash</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Peddapalem</td>\n",
       "      <td>Cash</td>\n",
       "      <td>4</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Peddapalem</td>\n",
       "      <td>Bank</td>\n",
       "      <td>2</td>\n",
       "      <td>Normal</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Ippatam</td>\n",
       "      <td>Cash</td>\n",
       "      <td>2</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Ippatam</td>\n",
       "      <td>Cash</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Ippatam</td>\n",
       "      <td>Cash</td>\n",
       "      <td>0</td>\n",
       "      <td>NO TV</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Ippatam</td>\n",
       "      <td>Bank</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Athmakuru</td>\n",
       "      <td>Cash</td>\n",
       "      <td>4</td>\n",
       "      <td>Smart</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Athmakuru</td>\n",
       "      <td>Cash</td>\n",
       "      <td>3</td>\n",
       "      <td>Smart</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Athmakuru</td>\n",
       "      <td>Cash</td>\n",
       "      <td>2</td>\n",
       "      <td>Normal</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Athmakuru</td>\n",
       "      <td>Cash</td>\n",
       "      <td>2</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Mellempudi</td>\n",
       "      <td>Cash</td>\n",
       "      <td>2</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Mellempudi</td>\n",
       "      <td>Bank</td>\n",
       "      <td>3</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Mellempudi</td>\n",
       "      <td>Bank</td>\n",
       "      <td>4</td>\n",
       "      <td>Smart</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Mellempudi</td>\n",
       "      <td>Cash</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Mellempudi</td>\n",
       "      <td>Cash</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Mellempudi</td>\n",
       "      <td>Cash</td>\n",
       "      <td>2</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Athmakuru</td>\n",
       "      <td>Bank</td>\n",
       "      <td>3</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Athmakuru</td>\n",
       "      <td>Cash</td>\n",
       "      <td>4</td>\n",
       "      <td>Normal</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Athmakuru</td>\n",
       "      <td>Cash</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Athmakuru</td>\n",
       "      <td>Cash</td>\n",
       "      <td>2</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Athmakuru</td>\n",
       "      <td>Cash</td>\n",
       "      <td>3</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Athmakuru</td>\n",
       "      <td>Cash</td>\n",
       "      <td>3</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Athmakuru</td>\n",
       "      <td>Bank</td>\n",
       "      <td>2</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Athmakuru</td>\n",
       "      <td>Cash</td>\n",
       "      <td>4</td>\n",
       "      <td>NO TV</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Athmakuru</td>\n",
       "      <td>Cash</td>\n",
       "      <td>0</td>\n",
       "      <td>NO TV</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Athmakuru</td>\n",
       "      <td>Cash</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Athmakuru</td>\n",
       "      <td>Bank</td>\n",
       "      <td>4</td>\n",
       "      <td>Smart</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Athmakuru</td>\n",
       "      <td>Bank</td>\n",
       "      <td>3</td>\n",
       "      <td>Normal</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Athmakuru</td>\n",
       "      <td>Cash</td>\n",
       "      <td>2</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Ippatam</td>\n",
       "      <td>Cash</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Peddapalem</td>\n",
       "      <td>Cash</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Peddapalem</td>\n",
       "      <td>Cash</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Peddapalem</td>\n",
       "      <td>Cash</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Village Mode of Wages/ Income  \\\n",
       "0     Prathuru                  Bank   \n",
       "1     Prathuru                  Bank   \n",
       "2     Nuthakki                  Cash   \n",
       "3     Nuthakki                  Cash   \n",
       "4     Nuthakki                  Cash   \n",
       "5     Nuthakki                  Bank   \n",
       "6     Nuthakki                  Cash   \n",
       "7     Nuthakki                  Cash   \n",
       "8   Mellempudi                  Bank   \n",
       "9   Mellempudi                  Cash   \n",
       "10  Mellempudi                  Cash   \n",
       "11     Ippatam                  Cash   \n",
       "12  Mellempudi                  Cash   \n",
       "13  Peddapalem                  Cash   \n",
       "14     Ippatam                  Cash   \n",
       "15  Peddapalem                  Cash   \n",
       "16  Peddapalem                  Cash   \n",
       "17  Peddapalem                  Cash   \n",
       "18  Peddapalem                  Bank   \n",
       "19     Ippatam                  Cash   \n",
       "20     Ippatam                  Cash   \n",
       "21     Ippatam                  Cash   \n",
       "22     Ippatam                  Bank   \n",
       "23   Athmakuru                  Cash   \n",
       "24   Athmakuru                  Cash   \n",
       "25   Athmakuru                  Cash   \n",
       "26   Athmakuru                  Cash   \n",
       "27  Mellempudi                  Cash   \n",
       "28  Mellempudi                  Bank   \n",
       "29  Mellempudi                  Bank   \n",
       "30  Mellempudi                  Cash   \n",
       "31  Mellempudi                  Cash   \n",
       "32  Mellempudi                  Cash   \n",
       "33   Athmakuru                  Bank   \n",
       "34   Athmakuru                  Cash   \n",
       "35   Athmakuru                  Cash   \n",
       "36   Athmakuru                  Cash   \n",
       "37   Athmakuru                  Cash   \n",
       "38   Athmakuru                  Cash   \n",
       "39   Athmakuru                  Bank   \n",
       "40   Athmakuru                  Cash   \n",
       "41   Athmakuru                  Cash   \n",
       "42   Athmakuru                  Cash   \n",
       "43   Athmakuru                  Bank   \n",
       "44   Athmakuru                  Bank   \n",
       "45   Athmakuru                  Cash   \n",
       "46     Ippatam                  Cash   \n",
       "47  Peddapalem                  Cash   \n",
       "48  Peddapalem                  Cash   \n",
       "49  Peddapalem                  Cash   \n",
       "\n",
       "    How many in the family can use the mobile? Type of TV   \\\n",
       "0                                            4      Normal   \n",
       "1                                            4       Smart   \n",
       "2                                            4      Normal   \n",
       "3                                            0       NO TV   \n",
       "4                                            2       NO TV   \n",
       "5                                            2      Normal   \n",
       "6                                            4      Normal   \n",
       "7                                            4      Normal   \n",
       "8                                            2      Normal   \n",
       "9                                            2       Smart   \n",
       "10                                           2      Normal   \n",
       "11                                           3       Smart   \n",
       "12                                           1      Normal   \n",
       "13                                           5      Normal   \n",
       "14                                           2      Normal   \n",
       "15                                           2      Normal   \n",
       "16                                           0      Normal   \n",
       "17                                           4      Normal   \n",
       "18                                           2      Normal   \n",
       "19                                           2      Normal   \n",
       "20                                           1      Normal   \n",
       "21                                           0       NO TV   \n",
       "22                                           0      Normal   \n",
       "23                                           4       Smart   \n",
       "24                                           3       Smart   \n",
       "25                                           2      Normal   \n",
       "26                                           2      Normal   \n",
       "27                                           2      Normal   \n",
       "28                                           3      Normal   \n",
       "29                                           4       Smart   \n",
       "30                                           1      Normal   \n",
       "31                                           1      Normal   \n",
       "32                                           2      Normal   \n",
       "33                                           3      Normal   \n",
       "34                                           4      Normal   \n",
       "35                                           1      Normal   \n",
       "36                                           2      Normal   \n",
       "37                                           3      Normal   \n",
       "38                                           3      Normal   \n",
       "39                                           2      Normal   \n",
       "40                                           4       NO TV   \n",
       "41                                           0       NO TV   \n",
       "42                                           1      Normal   \n",
       "43                                           4       Smart   \n",
       "44                                           3      Normal   \n",
       "45                                           2      Normal   \n",
       "46                                           1      Normal   \n",
       "47                                           1      Normal   \n",
       "48                                           1      Normal   \n",
       "49                                           1      Normal   \n",
       "\n",
       "    Number of people in the House  \n",
       "0                               4  \n",
       "1                               6  \n",
       "2                               8  \n",
       "3                               1  \n",
       "4                               5  \n",
       "5                               5  \n",
       "6                               6  \n",
       "7                               3  \n",
       "8                               2  \n",
       "9                               8  \n",
       "10                              3  \n",
       "11                              3  \n",
       "12                              1  \n",
       "13                              7  \n",
       "14                              2  \n",
       "15                              2  \n",
       "16                              5  \n",
       "17                              4  \n",
       "18                              6  \n",
       "19                              4  \n",
       "20                              4  \n",
       "21                              2  \n",
       "22                              1  \n",
       "23                              4  \n",
       "24                              4  \n",
       "25                              5  \n",
       "26                              2  \n",
       "27                              2  \n",
       "28                              4  \n",
       "29                              5  \n",
       "30                              1  \n",
       "31                              2  \n",
       "32                              4  \n",
       "33                              3  \n",
       "34                             10  \n",
       "35                              3  \n",
       "36                              4  \n",
       "37                              4  \n",
       "38                              4  \n",
       "39                              4  \n",
       "40                              4  \n",
       "41                              4  \n",
       "42                              2  \n",
       "43                              5  \n",
       "44                              6  \n",
       "45                              4  \n",
       "46                              2  \n",
       "47                              3  \n",
       "48                              4  \n",
       "49                              4  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nss.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Peddapalem       602\n",
       "Chirravuru       584\n",
       "Athmakuru        535\n",
       "Kolanukonda      332\n",
       "Ippatam          281\n",
       "Mellempudi       258\n",
       "Chinnapalem      251\n",
       "Gundimeda        225\n",
       "Nuthakki         221\n",
       "Revendrapadu     140\n",
       "Prathuru         135\n",
       "Vaddeswaram      132\n",
       "Kunchenapalli     50\n",
       "Seetanagaram       4\n",
       "Undavalli          2\n",
       "Name: Village, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nss['Village'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cash    2988\n",
       "Bank     764\n",
       "Name: Mode of Wages/ Income, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nss['Mode of Wages/ Income'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6ecd0320f0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KhQITJ07EyZMn5a6LiIhkIjnVY2JSeRd7e3skJSXByckJ2dnZshdGRETykAz+6dOno7Cw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\niKiNYfATEbUxDH4iojaGwU9E1Mb8P8dGwl+rN5mdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ecd01ec18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nss.plot(kind='scatter',x='Number of people in the House', y='How many in the family can use the mobile?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f6ec5bdd550>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cVWN85IB43T2qR73mTpm06rzOD2nlr+KTFbUeb4iMAADg3Ly9y1iV4Upwz549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\n/wEamdO+0lHkAAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ec5bdd160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='Number of people in the House', y='How many in the family can use the mobile?', data=nss, size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3752 entries, 0 to 3751\n",
      "Data columns (total 5 columns):\n",
      "Village                                       3752 non-null object\n",
      "Mode of Wages/ Income                         3752 non-null object\n",
      "How many in the family can use the mobile?    3752 non-null int64\n",
      "Type of TV                                    3687 non-null object\n",
      "Number of people in the House                 3752 non-null int64\n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 146.6+ KB\n"
     ]
    }
   ],
   "source": [
    "nss.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "# importing alll the necessary packages to use the various classification algorithms\n",
    "from sklearn.linear_model import LogisticRegression # for Logistic Regression Algorithm\n",
    "from sklearn.cross_validation import train_test_split # to split the dataset for training and testing \n",
    "from sklearn.neighbors import KNeighborsClassifier # KNN classifier\n",
    "from sklearn import svm # for suport vector machine algorithm\n",
    "from sklearn import metrics # for checking the model accuracy\n",
    "from sklearn.tree import DecisionTreeClassifier # for using DTA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3752, 5)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nss.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Kzc2VJG3atEmPPvqoBgwYoNGjR+v06dOSpG+//Vb9+/eXy+XSpEmTlJOTIykv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ec250a978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.heatmap(nss.corr(), annot=True, cmap='cubehelix_r') # draws heatmap with input as correlation matrix calculated by iris.corr() \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2626, 5)\n",
      "(1126, 5)\n"
     ]
    }
   ],
   "source": [
    "train, test = train_test_split(nss, test_size=0.3) # our main data split into train and test\n",
    "# the attribute test_size=0.3 splits the data into 70% and 30% ratio. train=70% and test=30%\n",
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X = train[['How many in the family can use the mobile?','Number of people in the House']] # taking the training data features\n",
    "train_y = train.Village # output of the training data\n",
    "\n",
    "test_X = test[['How many in the family can use the mobile?','Number of people in the House']] # taking test data feature\n",
    "test_y = test.Village # output value of the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>How many in the family can use the mobile?</th>\n",
       "      <th>Number of people in the House</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3311</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3245</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1845</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2547</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      How many in the family can use the mobile?  \\\n",
       "3311                                           1   \n",
       "141                                            6   \n",
       "3245                                           1   \n",
       "1845                                           2   \n",
       "2547                                           1   \n",
       "\n",
       "      Number of people in the House  \n",
       "3311                              2  \n",
       "141                               9  \n",
       "3245                              4  \n",
       "1845                              2  \n",
       "2547                              2  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>How many in the family can use the mobile?</th>\n",
       "      <th>Number of people in the House</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3046</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      How many in the family can use the mobile?  \\\n",
       "309                                            1   \n",
       "296                                            4   \n",
       "352                                            1   \n",
       "3046                                           0   \n",
       "619                                            0   \n",
       "\n",
       "      Number of people in the House  \n",
       "309                               2  \n",
       "296                               6  \n",
       "352                               4  \n",
       "3046                              3  \n",
       "619                               1  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3311        Prathuru\n",
       "141       Mellempudi\n",
       "3245    Revendrapadu\n",
       "1845      Chirravuru\n",
       "2547      Peddapalem\n",
       "Name: Village, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of the SVM is:  0.180284191829\n"
     ]
    }
   ],
   "source": [
    "model = svm.SVC() # select the svm algorithm\n",
    "\n",
    "# we train the algorithm with training data and training output\n",
    "model.fit(train_X, train_y)\n",
    "\n",
    "# we pass the testing data to the stored algorithm to predict the outcome\n",
    "prediction = model.predict(test_X)\n",
    "print('The accuracy of the SVM is: ', metrics.accuracy_score(prediction, test_y)) # we check the accuracy of the algorithm\n",
    "#we pass the predicted output by the model and the actual output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Logistic Regression is:  0.16785079929\n"
     ]
    }
   ],
   "source": [
    "model = LogisticRegression()\n",
    "model.fit(train_X, train_y)\n",
    "prediction = model.predict(test_X)\n",
    "print('The accuracy of Logistic Regression is: ', metrics.accuracy_score(prediction, test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of Decision Tree is:  0.178507992895\n"
     ]
    }
   ],
   "source": [
    "model = DecisionTreeClassifier()\n",
    "model.fit(train_X, train_y)\n",
    "prediction = model.predict(test_X)\n",
    "print('The accuracy of Decision Tree is: ', metrics.accuracy_score(prediction, test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy of KNN is:  0.119005328597\n"
     ]
    }
   ],
   "source": [
    "model = KNeighborsClassifier(n_neighbors=3) # this examines 3 neighbors for putting the data into class\n",
    "model.fit(train_X, train_y)\n",
    "prediction = model.predict(test_X)\n",
    "print('The accuracy of KNN is: ', metrics.accuracy_score(prediction, test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7f6ec1904860>,\n",
       "  <matplotlib.axis.XTick at 0x7f6ec1904c88>,\n",
       "  <matplotlib.axis.XTick at 0x7f6ec1918748>,\n",
       "  <matplotlib.axis.XTick at 0x7f6ec14c0588>,\n",
       "  <matplotlib.axis.XTick at 0x7f6ec14c0cc0>,\n",
       "  <matplotlib.axis.XTick at 0x7f6ec14c4438>,\n",
       "  <matplotlib.axis.XTick at 0x7f6ec14c4b70>,\n",
       "  <matplotlib.axis.XTick at 0x7f6ec14c82e8>,\n",
       "  <matplotlib.axis.XTick at 0x7f6ec14c8a20>,\n",
       "  <matplotlib.axis.XTick at 0x7f6ec14cf198>],\n",
       " <a list of 10 Text xticklabel objects>)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9fedQWKqAm4CH5xcnYN59YQ79rb8vSgCEmIlWq8OW3cVoalXhmYVxGD/Gb8htRYcJcfVG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\nkdVZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6ec190c2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a_index = list(range(1,11))\n",
    "a = pd.Series()\n",
    "for i in list(range(1,11)):\n",
    "    model = KNeighborsClassifier(n_neighbors=i)\n",
    "    model.fit(train_X, train_y)\n",
    "    prediction = model.predict(test_X)\n",
    "    a = a.append(pd.Series(metrics.accuracy_score(prediction, test_y)))\n",
    "plt.plot(a_index, a)\n",
    "x = [1,2,3,4,5,6,7,8,9,10]\n",
    "plt.xticks(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}