MTT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "47840ad4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "947752ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"D:\\Crime-Against-Women-PP22-C623-APACHE-TEAM-master\\crimes_against_women_2001-2014.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0806b6c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sr_no</th>\n",
       "      <th>state/ut</th>\n",
       "      <th>district</th>\n",
       "      <th>year</th>\n",
       "      <th>rape</th>\n",
       "      <th>kidnapping_and_abduction</th>\n",
       "      <th>dowry_deaths</th>\n",
       "      <th>assault_on_women</th>\n",
       "      <th>insult_to_modesty</th>\n",
       "      <th>cruelty_by_husband_or_relatives</th>\n",
       "      <th>importation_of_girls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>ADILABAD</td>\n",
       "      <td>2001</td>\n",
       "      <td>50</td>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "      <td>149</td>\n",
       "      <td>34</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>ANANTAPUR</td>\n",
       "      <td>2001</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>7</td>\n",
       "      <td>118</td>\n",
       "      <td>24</td>\n",
       "      <td>154</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>CHITTOOR</td>\n",
       "      <td>2001</td>\n",
       "      <td>27</td>\n",
       "      <td>34</td>\n",
       "      <td>14</td>\n",
       "      <td>112</td>\n",
       "      <td>83</td>\n",
       "      <td>186</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>CUDDAPAH</td>\n",
       "      <td>2001</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>126</td>\n",
       "      <td>38</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>EAST GODAVARI</td>\n",
       "      <td>2001</td>\n",
       "      <td>23</td>\n",
       "      <td>26</td>\n",
       "      <td>12</td>\n",
       "      <td>109</td>\n",
       "      <td>58</td>\n",
       "      <td>247</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10181</th>\n",
       "      <td>829</td>\n",
       "      <td>DEHLI UT</td>\n",
       "      <td>VIGILANCE</td>\n",
       "      <td>2014</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10182</th>\n",
       "      <td>830</td>\n",
       "      <td>DEHLI UT</td>\n",
       "      <td>WEST</td>\n",
       "      <td>2014</td>\n",
       "      <td>251</td>\n",
       "      <td>546</td>\n",
       "      <td>13</td>\n",
       "      <td>484</td>\n",
       "      <td>63</td>\n",
       "      <td>416</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10183</th>\n",
       "      <td>832</td>\n",
       "      <td>LAKSHADWEEP</td>\n",
       "      <td>LAKSHADWEEP</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10184</th>\n",
       "      <td>834</td>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>KARAIKAL</td>\n",
       "      <td>2014</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10185</th>\n",
       "      <td>835</td>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10186 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       sr_no        state/ut       district  year  rape  \\\n",
       "0          0  ANDHRA PRADESH       ADILABAD  2001    50   \n",
       "1          1  ANDHRA PRADESH      ANANTAPUR  2001    23   \n",
       "2          2  ANDHRA PRADESH       CHITTOOR  2001    27   \n",
       "3          3  ANDHRA PRADESH       CUDDAPAH  2001    20   \n",
       "4          4  ANDHRA PRADESH  EAST GODAVARI  2001    23   \n",
       "...      ...             ...            ...   ...   ...   \n",
       "10181    829        DEHLI UT      VIGILANCE  2014     0   \n",
       "10182    830        DEHLI UT           WEST  2014   251   \n",
       "10183    832     LAKSHADWEEP    LAKSHADWEEP  2014     1   \n",
       "10184    834      PUDUCHERRY       KARAIKAL  2014     3   \n",
       "10185    835      PUDUCHERRY     PUDUCHERRY  2014     7   \n",
       "\n",
       "       kidnapping_and_abduction  dowry_deaths  assault_on_women  \\\n",
       "0                            30            16               149   \n",
       "1                            30             7               118   \n",
       "2                            34            14               112   \n",
       "3                            20            17               126   \n",
       "4                            26            12               109   \n",
       "...                         ...           ...               ...   \n",
       "10181                         0             0                 0   \n",
       "10182                       546            13               484   \n",
       "10183                         0             0                 1   \n",
       "10184                         1             0                12   \n",
       "10185                         6             1                20   \n",
       "\n",
       "       insult_to_modesty  cruelty_by_husband_or_relatives  \\\n",
       "0                     34                              175   \n",
       "1                     24                              154   \n",
       "2                     83                              186   \n",
       "3                     38                               57   \n",
       "4                     58                              247   \n",
       "...                  ...                              ...   \n",
       "10181                  0                                0   \n",
       "10182                 63                              416   \n",
       "10183                  2                                0   \n",
       "10184                  1                                1   \n",
       "10185                  7                                3   \n",
       "\n",
       "       importation_of_girls  \n",
       "0                         0  \n",
       "1                         0  \n",
       "2                         0  \n",
       "3                         0  \n",
       "4                         0  \n",
       "...                     ...  \n",
       "10181                     0  \n",
       "10182                     0  \n",
       "10183                     0  \n",
       "10184                     0  \n",
       "10185                     0  \n",
       "\n",
       "[10186 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fc29013c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10186 entries, 0 to 10185\n",
      "Data columns (total 11 columns):\n",
      " #   Column                           Non-Null Count  Dtype \n",
      "---  ------                           --------------  ----- \n",
      " 0   sr_no                            10186 non-null  int64 \n",
      " 1   state/ut                         10186 non-null  object\n",
      " 2   district                         10186 non-null  object\n",
      " 3   year                             10186 non-null  int64 \n",
      " 4   rape                             10186 non-null  int64 \n",
      " 5   kidnapping_and_abduction         10186 non-null  int64 \n",
      " 6   dowry_deaths                     10186 non-null  int64 \n",
      " 7   assault_on_women                 10186 non-null  int64 \n",
      " 8   insult_to_modesty                10186 non-null  int64 \n",
      " 9   cruelty_by_husband_or_relatives  10186 non-null  int64 \n",
      " 10  importation_of_girls             10186 non-null  int64 \n",
      "dtypes: int64(9), object(2)\n",
      "memory usage: 875.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5fe57bad",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = df.sort_values([\"year\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "711f34a4",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
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       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>ADILABAD</td>\n",
       "      <td>2001</td>\n",
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       "      <th>451</th>\n",
       "      <td>471</td>\n",
       "      <td>PUNJAB</td>\n",
       "      <td>AMRITSAR</td>\n",
       "      <td>2001</td>\n",
       "      <td>9</td>\n",
       "      <td>26</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
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       "      <td>77</td>\n",
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       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>472</td>\n",
       "      <td>PUNJAB</td>\n",
       "      <td>BARNALA</td>\n",
       "      <td>2001</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
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       "      <td>24</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>473</td>\n",
       "      <td>PUNJAB</td>\n",
       "      <td>BATALA</td>\n",
       "      <td>2001</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>474</td>\n",
       "      <td>PUNJAB</td>\n",
       "      <td>BHATINDA</td>\n",
       "      <td>2001</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>9652</th>\n",
       "      <td>277</td>\n",
       "      <td>JHARKHAND</td>\n",
       "      <td>JAMTARA</td>\n",
       "      <td>2014</td>\n",
       "      <td>27</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>9653</th>\n",
       "      <td>278</td>\n",
       "      <td>JHARKHAND</td>\n",
       "      <td>KHUNTI</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>9654</th>\n",
       "      <td>279</td>\n",
       "      <td>JHARKHAND</td>\n",
       "      <td>KODERMA</td>\n",
       "      <td>2014</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9644</th>\n",
       "      <td>269</td>\n",
       "      <td>JHARKHAND</td>\n",
       "      <td>DUMKA</td>\n",
       "      <td>2014</td>\n",
       "      <td>39</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10185</th>\n",
       "      <td>835</td>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>2014</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10186 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       sr_no        state/ut    district  year  rape  \\\n",
       "0          0  ANDHRA PRADESH    ADILABAD  2001    50   \n",
       "451      471          PUNJAB    AMRITSAR  2001     9   \n",
       "452      472          PUNJAB     BARNALA  2001    12   \n",
       "453      473          PUNJAB      BATALA  2001     8   \n",
       "454      474          PUNJAB    BHATINDA  2001    13   \n",
       "...      ...             ...         ...   ...   ...   \n",
       "9652     277       JHARKHAND     JAMTARA  2014    27   \n",
       "9653     278       JHARKHAND      KHUNTI  2014     7   \n",
       "9654     279       JHARKHAND     KODERMA  2014    15   \n",
       "9644     269       JHARKHAND       DUMKA  2014    39   \n",
       "10185    835      PUDUCHERRY  PUDUCHERRY  2014     7   \n",
       "\n",
       "       kidnapping_and_abduction  dowry_deaths  assault_on_women  \\\n",
       "0                            30            16               149   \n",
       "451                          26             7                 9   \n",
       "452                          13             6                13   \n",
       "453                           4             6                 1   \n",
       "454                           9             4                15   \n",
       "...                         ...           ...               ...   \n",
       "9652                         24            10                29   \n",
       "9653                          8             2                 3   \n",
       "9654                         15            16                 0   \n",
       "9644                         16            11                 9   \n",
       "10185                         6             1                20   \n",
       "\n",
       "       insult_to_modesty  cruelty_by_husband_or_relatives  \\\n",
       "0                     34                              175   \n",
       "451                    0                               77   \n",
       "452                    0                               24   \n",
       "453                    0                               19   \n",
       "454                    0                               57   \n",
       "...                  ...                              ...   \n",
       "9652                   0                               70   \n",
       "9653                   0                               11   \n",
       "9654                   0                               39   \n",
       "9644                   4                               55   \n",
       "10185                  7                                3   \n",
       "\n",
       "       importation_of_girls  \n",
       "0                         0  \n",
       "451                       0  \n",
       "452                       0  \n",
       "453                       0  \n",
       "454                       0  \n",
       "...                     ...  \n",
       "9652                      2  \n",
       "9653                      0  \n",
       "9654                      0  \n",
       "9644                      0  \n",
       "10185                     0  \n",
       "\n",
       "[10186 rows x 11 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "755b65f6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_kar = df[df[\"state/ut\"]==\"KARNATAKA\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dbfb4100",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bangalore = df_kar[df_kar[\"district\"]==\"BANGALORE COMMR.\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e3ed5394",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>sr_no</th>\n",
       "      <th>state/ut</th>\n",
       "      <th>district</th>\n",
       "      <th>year</th>\n",
       "      <th>rape</th>\n",
       "      <th>kidnapping_and_abduction</th>\n",
       "      <th>dowry_deaths</th>\n",
       "      <th>assault_on_women</th>\n",
       "      <th>insult_to_modesty</th>\n",
       "      <th>cruelty_by_husband_or_relatives</th>\n",
       "      <th>importation_of_girls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>255</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2001</td>\n",
       "      <td>40</td>\n",
       "      <td>56</td>\n",
       "      <td>40</td>\n",
       "      <td>223</td>\n",
       "      <td>51</td>\n",
       "      <td>229</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>972</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2002</td>\n",
       "      <td>32</td>\n",
       "      <td>58</td>\n",
       "      <td>33</td>\n",
       "      <td>237</td>\n",
       "      <td>62</td>\n",
       "      <td>242</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1614</th>\n",
       "      <td>1695</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2003</td>\n",
       "      <td>34</td>\n",
       "      <td>11</td>\n",
       "      <td>34</td>\n",
       "      <td>213</td>\n",
       "      <td>39</td>\n",
       "      <td>213</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2307</th>\n",
       "      <td>2423</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2004</td>\n",
       "      <td>32</td>\n",
       "      <td>61</td>\n",
       "      <td>44</td>\n",
       "      <td>151</td>\n",
       "      <td>35</td>\n",
       "      <td>227</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3006</th>\n",
       "      <td>3157</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2005</td>\n",
       "      <td>38</td>\n",
       "      <td>25</td>\n",
       "      <td>39</td>\n",
       "      <td>167</td>\n",
       "      <td>39</td>\n",
       "      <td>287</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3705</th>\n",
       "      <td>3891</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2006</td>\n",
       "      <td>33</td>\n",
       "      <td>29</td>\n",
       "      <td>50</td>\n",
       "      <td>143</td>\n",
       "      <td>2</td>\n",
       "      <td>290</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4412</th>\n",
       "      <td>4633</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2007</td>\n",
       "      <td>62</td>\n",
       "      <td>35</td>\n",
       "      <td>54</td>\n",
       "      <td>187</td>\n",
       "      <td>2</td>\n",
       "      <td>290</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5124</th>\n",
       "      <td>5380</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2008</td>\n",
       "      <td>54</td>\n",
       "      <td>2</td>\n",
       "      <td>52</td>\n",
       "      <td>179</td>\n",
       "      <td>1</td>\n",
       "      <td>307</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5854</th>\n",
       "      <td>6145</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2009</td>\n",
       "      <td>65</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>251</td>\n",
       "      <td>35</td>\n",
       "      <td>367</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6593</th>\n",
       "      <td>6919</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2010</td>\n",
       "      <td>65</td>\n",
       "      <td>29</td>\n",
       "      <td>52</td>\n",
       "      <td>308</td>\n",
       "      <td>50</td>\n",
       "      <td>398</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7340</th>\n",
       "      <td>7701</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2011</td>\n",
       "      <td>97</td>\n",
       "      <td>206</td>\n",
       "      <td>53</td>\n",
       "      <td>250</td>\n",
       "      <td>40</td>\n",
       "      <td>458</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8106</th>\n",
       "      <td>8502</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2012</td>\n",
       "      <td>90</td>\n",
       "      <td>433</td>\n",
       "      <td>51</td>\n",
       "      <td>321</td>\n",
       "      <td>51</td>\n",
       "      <td>524</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8885</th>\n",
       "      <td>299</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE COMMR.</td>\n",
       "      <td>2013</td>\n",
       "      <td>80</td>\n",
       "      <td>538</td>\n",
       "      <td>52</td>\n",
       "      <td>378</td>\n",
       "      <td>73</td>\n",
       "      <td>482</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      sr_no   state/ut          district  year  rape  \\\n",
       "244     255  KARNATAKA  BANGALORE COMMR.  2001    40   \n",
       "926     972  KARNATAKA  BANGALORE COMMR.  2002    32   \n",
       "1614   1695  KARNATAKA  BANGALORE COMMR.  2003    34   \n",
       "2307   2423  KARNATAKA  BANGALORE COMMR.  2004    32   \n",
       "3006   3157  KARNATAKA  BANGALORE COMMR.  2005    38   \n",
       "3705   3891  KARNATAKA  BANGALORE COMMR.  2006    33   \n",
       "4412   4633  KARNATAKA  BANGALORE COMMR.  2007    62   \n",
       "5124   5380  KARNATAKA  BANGALORE COMMR.  2008    54   \n",
       "5854   6145  KARNATAKA  BANGALORE COMMR.  2009    65   \n",
       "6593   6919  KARNATAKA  BANGALORE COMMR.  2010    65   \n",
       "7340   7701  KARNATAKA  BANGALORE COMMR.  2011    97   \n",
       "8106   8502  KARNATAKA  BANGALORE COMMR.  2012    90   \n",
       "8885    299  KARNATAKA  BANGALORE COMMR.  2013    80   \n",
       "\n",
       "      kidnapping_and_abduction  dowry_deaths  assault_on_women  \\\n",
       "244                         56            40               223   \n",
       "926                         58            33               237   \n",
       "1614                        11            34               213   \n",
       "2307                        61            44               151   \n",
       "3006                        25            39               167   \n",
       "3705                        29            50               143   \n",
       "4412                        35            54               187   \n",
       "5124                         2            52               179   \n",
       "5854                         2            50               251   \n",
       "6593                        29            52               308   \n",
       "7340                       206            53               250   \n",
       "8106                       433            51               321   \n",
       "8885                       538            52               378   \n",
       "\n",
       "      insult_to_modesty  cruelty_by_husband_or_relatives  importation_of_girls  \n",
       "244                  51                              229                     0  \n",
       "926                  62                              242                     0  \n",
       "1614                 39                              213                     0  \n",
       "2307                 35                              227                     0  \n",
       "3006                 39                              287                     0  \n",
       "3705                  2                              290                     0  \n",
       "4412                  2                              290                     0  \n",
       "5124                  1                              307                     0  \n",
       "5854                 35                              367                     0  \n",
       "6593                 50                              398                     0  \n",
       "7340                 40                              458                     0  \n",
       "8106                 51                              524                     0  \n",
       "8885                 73                              482                     0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bangalore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b6342247",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ZH\\AppData\\Local\\Temp/ipykernel_7204/2224261929.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_bangalore[\"t\"] = np.arange(1,14)\n",
      "C:\\Users\\ZH\\AppData\\Local\\Temp/ipykernel_7204/2224261929.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_bangalore[\"t_square\"] = df_bangalore[\"t\"] * df_bangalore[\"t\"]\n",
      "C:\\Users\\ZH\\AppData\\Local\\Temp/ipykernel_7204/2224261929.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_bangalore[\"log_rape\"] = np.log(df_bangalore[\"rape\"])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['sr_no', 'state/ut', 'district', 'year', 'rape',\n",
       "       'kidnapping_and_abduction', 'dowry_deaths', 'assault_on_women',\n",
       "       'insult_to_modesty', 'cruelty_by_husband_or_relatives',\n",
       "       'importation_of_girls', 't', 't_square', 'log_rape'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "df_bangalore[\"t\"] = np.arange(1,14)\n",
    "\n",
    "df_bangalore[\"t_square\"] = df_bangalore[\"t\"] * df_bangalore[\"t\"]\n",
    "df_bangalore[\"log_rape\"] = np.log(df_bangalore[\"rape\"])\n",
    "df_bangalore.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "73e1eed7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Partition\n",
    "Train = df_bangalore.head(10)\n",
    "Test = df_bangalore.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6d8feec",
   "metadata": {},
   "source": [
    "Linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "cdf739c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.874949435319596"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf \n",
    "\n",
    "linear_model = smf.ols('rape ~ t', data = Train).fit()\n",
    "pred_linear =  pd.Series(linear_model.predict(pd.DataFrame(Test['t'])))\n",
    "rmse_linear = np.sqrt(np.mean((np.array(Test['rape']) - np.array(pred_linear))**2))\n",
    "rmse_linear\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2b018f7",
   "metadata": {},
   "source": [
    "Exponential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "454c0083",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.928533729978327"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Exp = smf.ols('log_rape ~ t', data = Train).fit()\n",
    "pred_Exp = pd.Series(Exp.predict(pd.DataFrame(Test['t'])))\n",
    "rmse_Exp = np.sqrt(np.mean((np.array(Test['rape']) - np.array(np.exp(pred_Exp)))**2))\n",
    "rmse_Exp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88a51cb8",
   "metadata": {},
   "source": [
    "Quadratic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e8b992b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.459506695306878"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Quad = smf.ols('rape ~ t + t_square', data = Train).fit()\n",
    "pred_Quad = pd.Series(Quad.predict(Test[[\"t\", \"t_square\"]]))\n",
    "rmse_Quad = np.sqrt(np.mean((np.array(Test['rape']) - np.array(pred_Quad))**2))\n",
    "rmse_Quad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "051e968e",
   "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>MODEL</th>\n",
       "      <th>RMSE_Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>rmse_linear</td>\n",
       "      <td>20.874949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rmse_Exp</td>\n",
       "      <td>18.928534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rmse_Quad</td>\n",
       "      <td>17.459507</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MODEL  RMSE_Values\n",
       "0  rmse_linear    20.874949\n",
       "1     rmse_Exp    18.928534\n",
       "2    rmse_Quad    17.459507"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {\"MODEL\":pd.Series([\"rmse_linear\",\"rmse_Exp\",\"rmse_Quad\"]),\"RMSE_Values\":pd.Series([rmse_linear,rmse_Exp,rmse_Quad])}\n",
    "table_rmse = pd.DataFrame(data)\n",
    "table_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a7fd30a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'rmse_quad' has the least value among the models prepared so far Predicting new values \n",
    "predict_data = pd.read_csv(r\"D:\\Crime-Against-Women-PP22-C623-APACHE-TEAM-master\\Bangalore_rape_test.csv\")\n",
    "\n",
    "model_full = smf.ols('rape ~ t+t_square', data=df_bangalore).fit()\n",
    "\n",
    "pred_new  = pd.Series(model_full.predict(predict_data))\n",
    "pred_new\n",
    "\n",
    "predict_data[\"predicted_rape\"] = pd.Series(pred_new)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "17478f3b",
   "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>year</th>\n",
       "      <th>t</th>\n",
       "      <th>t_square</th>\n",
       "      <th>predicted_rape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>14</td>\n",
       "      <td>196</td>\n",
       "      <td>105.069930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>15</td>\n",
       "      <td>225</td>\n",
       "      <td>115.792208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year   t  t_square  predicted_rape\n",
       "0  2014  14       196      105.069930\n",
       "1  2015  15       225      115.792208"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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