Crime Rate Analysis And Predictor Using Linear Regression.ipynb

{"nbformat":4,"nbformat_minor":0,"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":"3.8.5"},"colab":{"name":"Crime Rate Analysis And Predictor Using Linear Regression.ipynb","provenance":[]}},"cells":[{"cell_type":"code","metadata":{"id":"VmcuoqWYwk7E"},"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":773},"id":"S4DrsQUTwk7G","executionInfo":{"status":"ok","timestamp":1626261926891,"user_tz":-330,"elapsed":714,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"36c5cb0d-d331-4ff3-e80b-c9cce675a24c"},"source":["df=pd.read_csv(\"32_Murder_victim_age_sex.csv\")\n","df"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>Area_Name</th>\n","      <th>Year</th>\n","      <th>Group_Name</th>\n","      <th>Sub_Group_Name</th>\n","      <th>Victims_Above_50_Yrs</th>\n","      <th>Victims_Total</th>\n","      <th>Victims_Upto_10_15_Yrs</th>\n","      <th>Victims_Upto_10_Yrs</th>\n","      <th>Victims_Upto_15_18_Yrs</th>\n","      <th>Victims_Upto_18_30_Yrs</th>\n","      <th>Victims_Upto_30_50_Yrs</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Andaman &amp; Nicobar Islands</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>2. Female Victims</td>\n","      <td>NaN</td>\n","      <td>6</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>4.0</td>\n","      <td>2.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Andhra Pradesh</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>2. Female Victims</td>\n","      <td>67.0</td>\n","      <td>607</td>\n","      <td>15.0</td>\n","      <td>38.0</td>\n","      <td>43.0</td>\n","      <td>269.0</td>\n","      <td>175.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Arunachal Pradesh</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>2. Female Victims</td>\n","      <td>2.0</td>\n","      <td>16</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>10.0</td>\n","      <td>4.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Assam</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>2. Female Victims</td>\n","      <td>11.0</td>\n","      <td>128</td>\n","      <td>8.0</td>\n","      <td>4.0</td>\n","      <td>23.0</td>\n","      <td>45.0</td>\n","      <td>37.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Bihar</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>2. Female Victims</td>\n","      <td>12.0</td>\n","      <td>366</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>40.0</td>\n","      <td>191.0</td>\n","      <td>123.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1013</th>\n","      <td>Tamil Nadu</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>3. Total</td>\n","      <td>327.0</td>\n","      <td>1908</td>\n","      <td>13.0</td>\n","      <td>63.0</td>\n","      <td>16.0</td>\n","      <td>650.0</td>\n","      <td>839.0</td>\n","    </tr>\n","    <tr>\n","      <th>1014</th>\n","      <td>Tripura</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>3. Total</td>\n","      <td>24.0</td>\n","      <td>159</td>\n","      <td>2.0</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>60.0</td>\n","      <td>73.0</td>\n","    </tr>\n","    <tr>\n","      <th>1015</th>\n","      <td>Uttar Pradesh</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>3. Total</td>\n","      <td>344.0</td>\n","      <td>4456</td>\n","      <td>82.0</td>\n","      <td>138.0</td>\n","      <td>126.0</td>\n","      <td>2358.0</td>\n","      <td>1408.0</td>\n","    </tr>\n","    <tr>\n","      <th>1016</th>\n","      <td>Uttarakhand</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>3. Total</td>\n","      <td>19.0</td>\n","      <td>176</td>\n","      <td>1.0</td>\n","      <td>NaN</td>\n","      <td>2.0</td>\n","      <td>91.0</td>\n","      <td>63.0</td>\n","    </tr>\n","    <tr>\n","      <th>1017</th>\n","      <td>West Bengal</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>3. Total</td>\n","      <td>120.0</td>\n","      <td>2415</td>\n","      <td>8.0</td>\n","      <td>2.0</td>\n","      <td>6.0</td>\n","      <td>1177.0</td>\n","      <td>1102.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1018 rows × 11 columns</p>\n","</div>"],"text/plain":["                      Area_Name  ...  Victims_Upto_30_50_Yrs\n","0     Andaman & Nicobar Islands  ...                     2.0\n","1                Andhra Pradesh  ...                   175.0\n","2             Arunachal Pradesh  ...                     4.0\n","3                         Assam  ...                    37.0\n","4                         Bihar  ...                   123.0\n","...                         ...  ...                     ...\n","1013                 Tamil Nadu  ...                   839.0\n","1014                    Tripura  ...                    73.0\n","1015              Uttar Pradesh  ...                  1408.0\n","1016                Uttarakhand  ...                    63.0\n","1017                West Bengal  ...                  1102.0\n","\n","[1018 rows x 11 columns]"]},"metadata":{"tags":[]},"execution_count":2}]},{"cell_type":"markdown","metadata":{"id":"JXvPSsBywk7I"},"source":["## OUT OF THIS DATASET WE WILL EXTRACT THE NECESSARY COLUMNS OF THE AGE GROUPS ##"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":602},"id":"i6Q00FEtwk7I","executionInfo":{"status":"ok","timestamp":1626262033908,"user_tz":-330,"elapsed":432,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"f86e6819-ec75-40dd-cad8-bb8d511c9e89"},"source":["df=pd.DataFrame({\n","    \"State\":df[\"Area_Name\"],\n","    \"Year\":df[\"Year\"],\n","    \"Gender Group\":df[\"Group_Name\"],\n","    \"Number of victims( For age group of 18-30 years)\":df[\"Victims_Upto_18_30_Yrs\"],\n","    \"Number of victims( For age group of 30-50 years)\":df[\"Victims_Upto_30_50_Yrs\"],\n","    \"Number of victims( For age group above 50 years)\":df[\"Victims_Above_50_Yrs\"],\n","    \"Total Number Of Victims\":df[\"Victims_Upto_18_30_Yrs\"]+df[\"Victims_Upto_30_50_Yrs\"]+df[\"Victims_Above_50_Yrs\"]\n","})\n","df"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>State</th>\n","      <th>Year</th>\n","      <th>Gender Group</th>\n","      <th>Number of victims( For age group of 18-30 years)</th>\n","      <th>Number of victims( For age group of 30-50 years)</th>\n","      <th>Number of victims( For age group above 50 years)</th>\n","      <th>Total Number Of Victims</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Andaman &amp; Nicobar Islands</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>4.0</td>\n","      <td>2.0</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Andhra Pradesh</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>269.0</td>\n","      <td>175.0</td>\n","      <td>67.0</td>\n","      <td>511.0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Arunachal Pradesh</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>10.0</td>\n","      <td>4.0</td>\n","      <td>2.0</td>\n","      <td>16.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Assam</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>45.0</td>\n","      <td>37.0</td>\n","      <td>11.0</td>\n","      <td>93.0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Bihar</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>191.0</td>\n","      <td>123.0</td>\n","      <td>12.0</td>\n","      <td>326.0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1013</th>\n","      <td>Tamil Nadu</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>650.0</td>\n","      <td>839.0</td>\n","      <td>327.0</td>\n","      <td>1816.0</td>\n","    </tr>\n","    <tr>\n","      <th>1014</th>\n","      <td>Tripura</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>60.0</td>\n","      <td>73.0</td>\n","      <td>24.0</td>\n","      <td>157.0</td>\n","    </tr>\n","    <tr>\n","      <th>1015</th>\n","      <td>Uttar Pradesh</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>2358.0</td>\n","      <td>1408.0</td>\n","      <td>344.0</td>\n","      <td>4110.0</td>\n","    </tr>\n","    <tr>\n","      <th>1016</th>\n","      <td>Uttarakhand</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>91.0</td>\n","      <td>63.0</td>\n","      <td>19.0</td>\n","      <td>173.0</td>\n","    </tr>\n","    <tr>\n","      <th>1017</th>\n","      <td>West Bengal</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>1177.0</td>\n","      <td>1102.0</td>\n","      <td>120.0</td>\n","      <td>2399.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1018 rows × 7 columns</p>\n","</div>"],"text/plain":["                          State  ...  Total Number Of Victims\n","0     Andaman & Nicobar Islands  ...                      NaN\n","1                Andhra Pradesh  ...                    511.0\n","2             Arunachal Pradesh  ...                     16.0\n","3                         Assam  ...                     93.0\n","4                         Bihar  ...                    326.0\n","...                         ...  ...                      ...\n","1013                 Tamil Nadu  ...                   1816.0\n","1014                    Tripura  ...                    157.0\n","1015              Uttar Pradesh  ...                   4110.0\n","1016                Uttarakhand  ...                    173.0\n","1017                West Bengal  ...                   2399.0\n","\n","[1018 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TUnvaKhSwk7K","executionInfo":{"status":"ok","timestamp":1626262039538,"user_tz":-330,"elapsed":446,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"a9f1853d-841b-4df7-8e33-417eb095b345"},"source":["df[\"State\"].unique()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['Andaman & Nicobar Islands', 'Andhra Pradesh', 'Arunachal Pradesh',\n","       'Assam', 'Bihar', 'Chandigarh', 'Chhattisgarh', 'Delhi', 'Goa',\n","       'Gujarat', 'Haryana', 'Himachal Pradesh', 'Jammu & Kashmir',\n","       'Jharkhand', 'Karnataka', 'Kerala', 'Lakshadweep',\n","       'Madhya Pradesh', 'Maharashtra', 'Manipur', 'Meghalaya', 'Mizoram',\n","       'Nagaland', 'Odisha', 'Puducherry', 'Punjab', 'Rajasthan',\n","       'Sikkim', 'Tamil Nadu', 'Tripura', 'Uttar Pradesh', 'Uttarakhand',\n","       'West Bengal', 'Dadra & Nagar Haveli', 'Daman & Diu'], dtype=object)"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"dyQtPvapwk7M"},"source":["df_states={\n","    \"Andaman & Nicobar Islands\":0,\n","    \"Andhra Pradesh\":1,\n","    \"Arunachal Pradesh\":2,\n","    \"Assam\":3,\n","    \"Bihar\":4,\n","    \"Chandigarh\":5,\n","    \"Chattisgrah\":6,\n","    \"Delhi\":7,\n","    \"Goa\":8,\n","    \"Gujarat\":9,\n","    \"Haryana\":10,\n","    \"Himachal Pradesh\":11,\n","    \"Jammu & Kashmir\":12,\n","    \"Jharkhand\":13,\n","    \"Karnataka\":14,\n","    \"Kerala\":15,\n","    \"Lakshadweep\":16,\n","    \"Madhya Pradesh\":17,\n","    \"Maharashtra\":18,\n","    \"Manipur\":19,\n","    \"Meghalaya\":20,\n","    \"Mizoram\":21,\n","    \"Nagaland\":22,\n","    \"Odisha\":23,\n","    \"Puducherry\":24,\n","    \"Punjab\":25,\n","    \"Rajasthan\":26,\n","    \"Sikkim\":27,\n","    \"Tamil Nadu\":28,\n","    \"Tripura\":29,\n","    \"Uttar Pradesh\":30,\n","    \"Uttarakhand\":31,\n","    \"West Bengal\":32,\n","    \"Dadra & Nagar Haveli\":33,\n","    \"Daman & Diu\":34\n","}"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"t1qi2BD5wk7N","executionInfo":{"status":"ok","timestamp":1626262063211,"user_tz":-330,"elapsed":566,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"985a1f00-4371-4656-e9ec-c5be9f660651"},"source":["df_states"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'Andaman & Nicobar Islands': 0,\n"," 'Andhra Pradesh': 1,\n"," 'Arunachal Pradesh': 2,\n"," 'Assam': 3,\n"," 'Bihar': 4,\n"," 'Chandigarh': 5,\n"," 'Chattisgrah': 6,\n"," 'Dadra & Nagar Haveli': 33,\n"," 'Daman & Diu': 34,\n"," 'Delhi': 7,\n"," 'Goa': 8,\n"," 'Gujarat': 9,\n"," 'Haryana': 10,\n"," 'Himachal Pradesh': 11,\n"," 'Jammu & Kashmir': 12,\n"," 'Jharkhand': 13,\n"," 'Karnataka': 14,\n"," 'Kerala': 15,\n"," 'Lakshadweep': 16,\n"," 'Madhya Pradesh': 17,\n"," 'Maharashtra': 18,\n"," 'Manipur': 19,\n"," 'Meghalaya': 20,\n"," 'Mizoram': 21,\n"," 'Nagaland': 22,\n"," 'Odisha': 23,\n"," 'Puducherry': 24,\n"," 'Punjab': 25,\n"," 'Rajasthan': 26,\n"," 'Sikkim': 27,\n"," 'Tamil Nadu': 28,\n"," 'Tripura': 29,\n"," 'Uttar Pradesh': 30,\n"," 'Uttarakhand': 31,\n"," 'West Bengal': 32}"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5T9_siddwk7O","executionInfo":{"status":"ok","timestamp":1626262069369,"user_tz":-330,"elapsed":438,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"6dcb3738-8084-440a-b0cd-8565749f6d65"},"source":["df[\"Gender Group\"].unique()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['Murder - Female Victims', 'Murder - Male Victims',\n","       'Murder - Total Victims'], dtype=object)"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"id":"_yYpgPyvwk7R"},"source":["df_gender_group={\n","    \"Murder - Female Victims\":0,\n","    \"Murder - Male Victims\":1,\n","    \"Murder - Total Victims\":2\n","}"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"opN4rURKwk7R","executionInfo":{"status":"ok","timestamp":1626262074898,"user_tz":-330,"elapsed":6,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"ad179ac4-b6f3-4366-cbd3-d196738b78cc"},"source":["df_gender_group"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'Murder - Female Victims': 0,\n"," 'Murder - Male Victims': 1,\n"," 'Murder - Total Victims': 2}"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4PrpOVJvwk7R","executionInfo":{"status":"ok","timestamp":1626262081449,"user_tz":-330,"elapsed":640,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"69a1670e-d36d-4177-e840-18aa1202927e"},"source":["df.info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 1018 entries, 0 to 1017\n","Data columns (total 7 columns):\n"," #   Column                                            Non-Null Count  Dtype  \n","---  ------                                            --------------  -----  \n"," 0   State                                             1018 non-null   object \n"," 1   Year                                              1018 non-null   int64  \n"," 2   Gender Group                                      1018 non-null   object \n"," 3   Number of victims( For age group of 18-30 years)  1009 non-null   float64\n"," 4   Number of victims( For age group of 30-50 years)  1007 non-null   float64\n"," 5   Number of victims( For age group above 50 years)  962 non-null    float64\n"," 6   Total Number Of Victims                           958 non-null    float64\n","dtypes: float64(4), int64(1), object(2)\n","memory usage: 55.8+ KB\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"07GhfmUXwk7S"},"source":["## CHECKING THE OUTLIERS IN THE TARGET VARIABLE ##"]},{"cell_type":"code","metadata":{"id":"HSb9iLkvwk7S"},"source":["# In our case the number of victims of all age groups is the target variable which our model has to predict"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"_O6JvEirwk7S"},"source":["# Any data point below lower_limit and any data point above upper_limit will be treated as an outlier.\n","def treat_outlier(value):\n","    if value<lower_limit:\n","        return lower_limit\n","    elif value>upper_limit:\n","        return upper_limit\n","    else:\n","        return value"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"uYhEZPgpwk7T"},"source":["### FOR THE AGE GROUP OF 18-30 YEARS ###"]},{"cell_type":"code","metadata":{"id":"DTOIZqQ-xvfO"},"source":["import seaborn as sns"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":350},"id":"Kj9hzWpawk7T","executionInfo":{"status":"ok","timestamp":1626262162164,"user_tz":-330,"elapsed":755,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"442b0188-5920-4375-8607-001c82849cf6"},"source":["sns.boxplot(df[\"Number of victims( For age group of 18-30 years)\"])"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n","  FutureWarning\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f9faf35d510>"]},"metadata":{"tags":[]},"execution_count":14},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"yiX_KNzUwk7T","outputId":"6ceefd40-585d-4d71-ac20-2afa590f6273"},"source":["\"\"\"\n","From the above figure we can see that there are a bunch of outliers.\n","So, we need to treat them and adjust them before handing our dataset to the model \n","\n","\"\"\" "],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'\\nFrom the above figure we can see that there are a bunch of outliers.\\nSo, we need to treat them and adjust them before handing our dataset to the model \\n\\n'"]},"metadata":{"tags":[]},"execution_count":161}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UkSPMHLCwk7T","executionInfo":{"status":"ok","timestamp":1626262180901,"user_tz":-330,"elapsed":432,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"f1da2e66-b4b2-4684-e874-7f30d43d47af"},"source":["df[\"Number of victims( For age group of 18-30 years)\"].describe()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["count    1009.000000\n","mean      314.739346\n","std       483.745420\n","min         0.000000\n","25%        13.000000\n","50%       116.000000\n","75%       421.000000\n","max      3724.000000\n","Name: Number of victims( For age group of 18-30 years), dtype: float64"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SPUGFGoZwk7T","executionInfo":{"status":"ok","timestamp":1626262186088,"user_tz":-330,"elapsed":458,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"8348c15a-d933-4364-f981-d61b3104dbc7"},"source":["q1=df[\"Number of victims( For age group of 18-30 years)\"].quantile(0.25)\n","q1"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["13.0"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2jZVRmP3wk7U","executionInfo":{"status":"ok","timestamp":1626262189193,"user_tz":-330,"elapsed":544,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"5d25f955-7f69-44a7-dc5f-088ae42315e5"},"source":["q3=df[\"Number of victims( For age group of 18-30 years)\"].quantile(0.75)\n","q3"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["421.0"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sAnkhoZlwk7U","executionInfo":{"status":"ok","timestamp":1626262191932,"user_tz":-330,"elapsed":678,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"ab91bad4-0462-4194-d42f-20adea633be5"},"source":["iqr=q3-q1\n","iqr"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["408.0"]},"metadata":{"tags":[]},"execution_count":18}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nxFpKL4lwk7U","executionInfo":{"status":"ok","timestamp":1626262194554,"user_tz":-330,"elapsed":708,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"7fc49b00-fe02-4bb7-f762-5102695693b9"},"source":["upper_limit=q3+(iqr*1.5)\n","upper_limit"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1033.0"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"code","metadata":{"id":"v9QyhIdcwk7U","outputId":"36dd339b-b5c4-46a2-c8b0-70229c539649"},"source":["\"\"\"\n","Since, upper_limit<df[\"Number of victims( For age group of 18-30 years)\"].max()\n","So, upper_limit=upper_limit\n","\n","\"\"\""],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'\\nSince, upper_limit<df[\"Number of victims( For age group of 18-30 years)\"].max()\\nSo, upper_limit=upper_limit\\n\\n'"]},"metadata":{"tags":[]},"execution_count":167}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LbehCDHmwk7V","executionInfo":{"status":"ok","timestamp":1626262199528,"user_tz":-330,"elapsed":717,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"25260161-5027-41a2-8cca-44ca77910a87"},"source":["lower_limit=q1-(1.5*iqr)\n","lower_limit"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["-599.0"]},"metadata":{"tags":[]},"execution_count":20}]},{"cell_type":"code","metadata":{"id":"e2k8z2Anwk7V","outputId":"33f62396-e379-43a4-ccfa-cf67b35c1ac2"},"source":["\"\"\"\n","Since, lower_limit<df[\"Number of victims( For age group of 18-30 years)\"].min()\n","So, lower_limit=df[\"Number of victims( For age group of 18-30 years)\"].min()\n","\n","\"\"\""],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'\\nSince, lower_limit<df[\"Number of victims( For age group of 18-30 years)\"].min()\\nSo, lower_limit=df[\"Number of victims( For age group of 18-30 years)\"].min()\\n\\n'"]},"metadata":{"tags":[]},"execution_count":169}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"07jl1uaLwk7V","executionInfo":{"status":"ok","timestamp":1626262203484,"user_tz":-330,"elapsed":435,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"e73c5081-2531-4539-ee0d-ba147c2723c7"},"source":["lower_limit=df[\"Number of victims( For age group of 18-30 years)\"].min()\n","lower_limit"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.0"]},"metadata":{"tags":[]},"execution_count":21}]},{"cell_type":"code","metadata":{"id":"aZGGO4DUwk7V"},"source":["df[\"Number of victims( For age group of 18-30 years)\"]=df[\"Number of victims( For age group of 18-30 years)\"].apply(treat_outlier)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"viLij1v1wk7V","executionInfo":{"status":"ok","timestamp":1626262208488,"user_tz":-330,"elapsed":2,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"083a10dc-ca8b-4292-d51e-4881907461ad"},"source":["df[\"Number of victims( For age group of 18-30 years)\"].describe()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["count    1009.000000\n","mean      266.431120\n","std       316.073868\n","min         0.000000\n","25%        13.000000\n","50%       116.000000\n","75%       421.000000\n","max      1033.000000\n","Name: Number of victims( For age group of 18-30 years), dtype: float64"]},"metadata":{"tags":[]},"execution_count":23}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":350},"id":"kMGi_2Nzwk7V","executionInfo":{"status":"ok","timestamp":1626262219570,"user_tz":-330,"elapsed":1542,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"ce1b5330-4334-4f61-a4cc-f7d6527b6332"},"source":["sns.boxplot(df[\"Number of victims( For age group of 18-30 years)\"])"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n","  FutureWarning\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f9fa0353410>"]},"metadata":{"tags":[]},"execution_count":24},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"KQlnM54fwk7W"},"source":["# So, from the above figure we can see that there is no outliers"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"NRyw0gy4wk7W"},"source":["### FOR THE AGE GROUP OF 30-50 YEARS ###"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":350},"id":"-gXd8BfMwk7W","executionInfo":{"status":"ok","timestamp":1626262236648,"user_tz":-330,"elapsed":995,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"32a482f1-c348-4f31-a286-d470f6e22d7c"},"source":["sns.boxplot(df[\"Number of victims( For age group of 30-50 years)\"])"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n","  FutureWarning\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f9fa02b2cd0>"]},"metadata":{"tags":[]},"execution_count":25},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"cqItw3HIwk7W","outputId":"fc9e581c-e8d4-42cf-d347-ebf0a6258a9d"},"source":["\"\"\"\n","From the above figure we can see that there are a bunch of outliers.\n","So, we need to treat them and adjust them before handing our dataset to the model \n","\n","\"\"\" "],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'\\nFrom the above figure we can see that there are a bunch of outliers.\\nSo, we need to treat them and adjust them before handing our dataset to the model \\n\\n'"]},"metadata":{"tags":[]},"execution_count":176}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SmYMBYGZwk7W","executionInfo":{"status":"ok","timestamp":1626262245607,"user_tz":-330,"elapsed":428,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"ceb56236-9c18-4cbc-e4db-8a6d9009eb15"},"source":["df[\"Number of victims( For age group of 30-50 years)\"].describe()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["count    1007.000000\n","mean      274.230387\n","std       396.206952\n","min         0.000000\n","25%        13.000000\n","50%       102.000000\n","75%       416.500000\n","max      3143.000000\n","Name: Number of victims( For age group of 30-50 years), dtype: float64"]},"metadata":{"tags":[]},"execution_count":26}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CzD-cEfawk7W","executionInfo":{"status":"ok","timestamp":1626262275377,"user_tz":-330,"elapsed":429,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"13c1e274-e6f5-41e9-f685-470f9996f442"},"source":["q1=df[\"Number of victims( For age group of 30-50 years)\"].quantile(0.25)\n","q1"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["13.0"]},"metadata":{"tags":[]},"execution_count":27}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gusWTchGwk7W","executionInfo":{"status":"ok","timestamp":1626262322393,"user_tz":-330,"elapsed":426,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"b1af15e2-9ad4-4c7f-8be9-b1d58687d354"},"source":["q3=df[\"Number of victims( For age group of 30-50 years)\"].quantile(0.75)\n","q3"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["416.5"]},"metadata":{"tags":[]},"execution_count":28}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"U5jvS61Ywk7X","executionInfo":{"status":"ok","timestamp":1626262326879,"user_tz":-330,"elapsed":412,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"7bcc7556-ffd0-4e9f-9704-0672a5c8219d"},"source":["iqr=q3-q1\n","iqr"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["403.5"]},"metadata":{"tags":[]},"execution_count":29}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"407HbK9Owk7X","executionInfo":{"status":"ok","timestamp":1626262329446,"user_tz":-330,"elapsed":3,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"440363dd-3171-4a8c-f9b1-eb20c662437c"},"source":["upper_limit=q3+(iqr*1.5)\n","upper_limit"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1021.75"]},"metadata":{"tags":[]},"execution_count":30}]},{"cell_type":"code","metadata":{"id":"_d8Y5-JUwk7X","outputId":"09ce4ba6-dfbd-4d4c-8d07-b4595935bc07"},"source":["\"\"\"\n","Since, upper_limit<df[\"Number of victims( For age group of 30-50 years)\"].max()\n","So, upper_limit=upper_limit\n","\n","\"\"\""],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'\\nSince, upper_limit<df[\"Number of victims( For age group of 30-50 years)\"].max()\\nSo, upper_limit=upper_limit\\n\\n'"]},"metadata":{"tags":[]},"execution_count":182}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rGyie1Tswk7X","executionInfo":{"status":"ok","timestamp":1626262334107,"user_tz":-330,"elapsed":422,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"46204a9d-c541-42c8-db7c-35a2e75259c0"},"source":["lower_limit=q1-(1.5*iqr)\n","lower_limit"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["-592.25"]},"metadata":{"tags":[]},"execution_count":31}]},{"cell_type":"code","metadata":{"id":"Ll7e2rlawk7X","outputId":"414b2aad-ee70-4374-97b6-40a70f4a5f92"},"source":["\"\"\"\n","Since, lower_limit<df[\"Number of victims( For age group of 30-50 years)\"].min()\n","So, lower_limit=df[\"Number of victims( For age group of 30-50 years)\"].min()\n","\n","\"\"\""],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'\\nSince, lower_limit<df[\"Number of victims( For age group of 30-50 years)\"].min()\\nSo, lower_limit=df[\"Number of victims( For age group of 30-50 years)\"].min()\\n\\n'"]},"metadata":{"tags":[]},"execution_count":184}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nc8l34mewk7X","executionInfo":{"status":"ok","timestamp":1626262340006,"user_tz":-330,"elapsed":438,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"531433dd-f051-4042-d1c6-cb47378063c9"},"source":["lower_limit=df[\"Number of victims( For age group of 30-50 years)\"].min()\n","lower_limit"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.0"]},"metadata":{"tags":[]},"execution_count":32}]},{"cell_type":"code","metadata":{"id":"2rVoIDFgwk7X"},"source":["df[\"Number of victims( For age group of 30-50 years)\"]=df[\"Number of victims( For age group of 30-50 years)\"].apply(treat_outlier)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6lYbOQ_Jwk7Y","executionInfo":{"status":"ok","timestamp":1626262348133,"user_tz":-330,"elapsed":417,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"2323ca72-236a-4538-bb73-9c1d1a45c9a6"},"source":["df[\"Number of victims( For age group of 30-50 years)\"].describe()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["count    1007.000000\n","mean      249.374131\n","std       305.815076\n","min         0.000000\n","25%        13.000000\n","50%       102.000000\n","75%       416.500000\n","max      1021.750000\n","Name: Number of victims( For age group of 30-50 years), dtype: float64"]},"metadata":{"tags":[]},"execution_count":34}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":350},"id":"6EsxXjEHwk7Y","executionInfo":{"status":"ok","timestamp":1626262356372,"user_tz":-330,"elapsed":948,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"3260b2ee-7078-4cd9-945f-0d007a2eccb1"},"source":["sns.boxplot(df[\"Number of victims( For age group of 30-50 years)\"])"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n","  FutureWarning\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f9fa031cc50>"]},"metadata":{"tags":[]},"execution_count":35},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"lRqDI45xwk7Y"},"source":["# So, from the above figure we can see that there is no outliers"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"vYMgLBa0wk7Y"},"source":["### FOR THE AGE GROUP OF ABOVE 50 YEARS ###"]},{"cell_type":"code","metadata":{"id":"8nRG4Rtcwk7Y","outputId":"81a269c3-d4fa-477d-bbb1-876a4d48ae65"},"source":["sb.boxplot(df[\"Number of victims( For age group above 50 years)\"])"],"execution_count":null,"outputs":[{"output_type":"stream","text":["C:\\Users\\91910\\anaconda3_2\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n","  warnings.warn(\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["<AxesSubplot:xlabel='Number of victims( For age group above 50 years)'>"]},"metadata":{"tags":[]},"execution_count":190},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAXQklEQVR4nO3df5xVdZ3H8feHXzI0ajqUayM50MAKrmVltbZbgSEx1Gq7aeqD+CEKBo8A6VG7aWwLPtgtt/KR0W6buqm0Ymqaog9Awd/bDw0Mf6XWTcdy1oowKQvNgc/+8f3e4dzDvTN3GO79zsDr+XjMY875nu853+/5nnvfc+65954xdxcAoP4Gpe4AAByoCGAASIQABoBECGAASIQABoBEhvSm8siRI72lpaVGXQGA/dPmzZt/6+6vy5f3KoBbWlq0adOmfdcrADgAmNmz5cq5BAEAiRDAAJAIAQwAiRDAAJAIAQwAiRDAAJAIAQwAiRDAAJAIAQwAiRDAAJAIAQwAiRDAAJAIAQwAiRDAAJAIAQwAiRDAAJAIAQwAiRDAAJAIAQwAifTqf8L1xcqVK1UoFCou7+jokCQ1NzfXpP3W1lYtXLiwJtsGgL1RtwAuFAra8tgT2jni8LLLB/9puyTpV6/s+y4N/tML+3ybANBXdQtgSdo54nDtOGZa2WUNT66VpIrL+6K4bQDoT7gGDACJEMAAkAgBDACJEMAAkAgBDACJEMAAkAgBDACJEMAAkAgBDACJEMAAkAgBDACJEMAAkAgBDACJEMAAkAgBDACJEMAAkAgBDACJEMAAkAgBDACJEMAAkAgBDACJEMAAkAgBDACJEMAAkAgBDACJEMAAkAgBDACJEMAAkAgBDACJEMAAkAgBDACJEMAAkAgBDACJEMAAkAgBDACJEMAAkAgBDACJEMAAkAgBDACJEMAAkAgBDACJ1CWAV65cqY6Ojno0tV9YuXKlVq5cmbobAGpsSD0aKRQK2rFjhzTo4Ho0N+AVCoXUXQBQB1yCAIBECGAASIQABoBECGAASIQABoBECGAASIQABoBECGAASIQABoBECGAASIQABoBECGAASIQABoBECGAASIQABoBECGAASIQABoBECGAASIQABoBECGAASIQABoBECGAASIQABoBECGAASIQABoBECGAASIQABoBECGAASIQABoBECGAASIQABoBECGAASIQABoBECGAASIQABoBECGAASIQABoBECGAASIQA7ucKhYKmTp2qqVOnasaMGZoyZYomTpyoiRMnavr06Zo7d65mzpypSZMmafXq1Zo0aZKmT5+utrY2bd68WXPnzlVbW5tmzJihadOmqVAoSJJuueUWTZw4Uddee63mzJmjSZMmacqUKZoyZYqmTp2qOXPmaN68eZo/f77uuusunXTSSdq8eXNXf9ra2rRp0ybNnz9f8+bN09lnn622tjbNnj1b8+bN07nnnqsFCxaoUChowYIFmj9/fsn0tm3btG3bNs2fP18LFizoml+0aJEKhYIWLVqkbdu2dY1Dvm5Wcb18eXd1KrXVUx+yy8rVqbY/PfWzUr/2ZpsDSX/cz1r2ydy96sonnHCCb9q0qdeNLF68WIVCQdsHHawdx0wrW6fhybWSVHF5XzQ8uVZvH3OELr300n2+7VpYvHixJOnSSy/V7Nmz1d7evlfbaWxs1EsvvVRS1tLSoquuukqTJk1Stcd+yJAh6uzsVGNjo0aOHNnVn3Lbz2tpaemqn50+9dRT5e5as2ZNyfytt96qo48+Ws8++6xOOeUULVmyRJJ0ySWXlNQtlheX3XrrrSX18/J1ivP5tiqVZ7dRXFauTrX9qaSnfu3NNgeS/rif+6JPZrbZ3U/Il3MG3I8VCoW9Dl9JZcOxvb1dl19+edXhK0mdnZ1d28v2p6fwLbZXbnrt2rVat27dHvPurvb2drm71q9f33VmnK27bt26kjPD9evXl9TPy9cpFApd89m2KpUX+5Bflq9TbX8qKddGvl+93eZA0pexG6h9GrJPt1ZBR0eHduzYoUGDq3/S70uDXv69CoU/dJ1Z9neFQkENDQ1asWJFTbZ/zTXX1GS7vfHqq6/uMW9mJWU7d+7UqlWr5O5dfwSKdVetWqUlS5bo6quv1q5du0rq589S8nVWrFjRNZ9tq1J5sQ/5Zfk61fankuy6lfrV220OJH0Zu4Hapx7PgM1snpltMrNNW7du3WcNo2d9OfsdiPJn5Z2dndqwYYM2btxYsszdtWHDBknSxo0bu8K5WD8vX6e9vb0k0HsqL/Yhvyxfp9r+VFKujXy/ervNgaQvY1crte5Tj2fA7n6ZpMukcA14bxppbm7Wjh07tH3QwXuzep/tGn6IWgfgNeDGxsYDKoTNrCRohwwZopNPPrnr2nBxmZnp5JNPliRNnjxZa9euVWdnZ1f9vHydo446Ss8991xJ2HVXXuxDcRt52Xar6U8l2XUr9au32xxI+jJ2A7VPXAPux5YuXVqT7U6fPr0m2+2NoUOHaujQoSXzQ4aUng8MHjxYM2fO1KxZs0qWDR06VDNnzpQkzZo1S4MGDSqpn5evs3Tp0q75bFuVyot9yC/L16m2P5WUayPfr95ucyDpy9jVSq37RAD3Y62trWppadnr9RsbG/coa2lp0dy5c/e43tqdYvg1NjaW9Kfc9su1V2562rRpamtr22PezNTS0iIz09SpU9XU1KSmpqaSum1tbWpqapIkNTU1aerUqSX18/J1Wltbu+azbVUqL/Yhvyxfp9r+VFKujXy/ervNgaQvYzdQ+0QA93NLly7V8OHDNXz4cI0aNUrDhg3rWtbc3KyxY8fqjW98o8xM8+bNk5mpublZDQ0NWr58ucaOHauGhgaNGjVKI0aM6DqrPv/88yVJ5513nsaMGSMz07BhwzRs2DANHz5cY8aM0bhx4zR+/HhdeOGFGjRokJYvX97Vn4aGBi1btkzjx4/XuHHjNHr0aDU0NKilpUXjxo1Ta2urJkyYoKVLl2rChAkaP358yXTxrHL8+PGaMGFC1/xxxx2npUuX6rjjjis528jXzSqu193ZSb5OpbZ66kN2Wbk61fanp35W6ld/OCuspf64n7XsE58D7oeynwMGMPDxOWAA6GcIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIZEg9GmltbVVHR4e2v1KP1ga+1tbW1F0AUAd1CeCFCxeqUCjoV0//uh7NDXgLFy5M3QUAdcAlCABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgEQIYABIhAAGgESG1LOxwX96QQ1Prq2wbJskVVze13alI/b5dgGgL+oWwK2trd0u7+jolCQ1N9ciKI/osX0AqLe6BfDChQvr1RQADAhcAwaARAhgAEiEAAaARAhgAEiEAAaARAhgAEiEAAaARAhgAEiEAAaARAhgAEiEAAaARAhgAEiEAAaARAhgAEiEAAaARAhgAEiEAAaARAhgAEiEAAaARAhgAEjE3L36ymZbJT27l22NlPTbvVz3QMI4VYdxqg7j1LN6jNHR7v66fGGvArgvzGyTu59Ql8YGMMapOoxTdRinnqUcIy5BAEAiBDAAJFLPAL6sjm0NZIxTdRin6jBOPUs2RnW7BgwAKMUlCABIhAAGgERqHsBmNtXMnjKzgpl9ptbt9WdmNsrM7jazJ8zscTNbHMsPN7MNZvaz+PuwzDoXxLF7ysw+kK739Wdmg83sx2Z2W5xnnHLM7LVm9h0zezI+rk5knEqZ2ZL4fHvMzK41s+H9ZozcvWY/kgZL+rmkMZKGSXpY0oRattmffyQdKeltcfpgST+VNEHSv0v6TCz/jKSL4/SEOGYHSRodx3Jw6v2o43h9UtJqSbfFecZpzzG6WtK5cXqYpNcyTiXj0yzpGUkNcf56SbP7yxjV+gz4nZIK7v60u/9Z0rclnVrjNvstd3/e3R+K03+Q9ITCA+RUhSeS4u8Px+lTJX3b3V9x92ckFRTGdL9nZkdJ+qCkKzLFjFOGmR0i6b2S/luS3P3P7v6iGKe8IZIazGyIpBGS/k/9ZIxqHcDNkn6ZmX8ulh3wzKxF0lslPSDpCHd/XgohLen1sdqBPH5fkfSPknZlyhinUmMkbZV0ZbxUc4WZvUaMUxd375D0JUm/kPS8pO3ufof6yRjVOoCtTNkB/7k3M2uUdKOk8939991VLVO234+fmX1I0m/cfXO1q5Qp2+/HSeHM7m2Svu7ub5X0R4WX05UccOMUr+2eqnA54Q2SXmNmH+tulTJlNRujWgfwc5JGZeaPUjj9P2CZ2VCF8L3G3W+Kxb82syPj8iMl/SaWH6jj9zeSTjGzdoXLVieZ2f+Iccp7TtJz7v5AnP+OQiAzTrtNlvSMu29191cl3STp3eonY1TrAP6RpLFmNtrMhkk6U9KaGrfZb5mZKVyve8LdL8ksWiNpVpyeJemWTPmZZnaQmY2WNFbSg/XqbyrufoG7H+XuLQqPmbvc/WNinEq4+68k/dLM/jIWvV/ST8Q4Zf1C0l+b2Yj4/Hu/wnsv/WOM6vAu5DSFd/t/Lumzqd8VTfkj6W8VXs48ImlL/JkmqUnSnZJ+Fn8fnlnns3HsnpLUlnofEozZRO3+FATjtOf4HC9pU3xM3SzpMMZpjzFaLulJSY9J+pbCJxz6xRjxVWQASIRvwgFAIgQwACRCAANAIgQwACRCAANAIvtlAJuZm9mXM/OfMrNl+2jbV5nZaftiWz20c3q8u9XdVdY/wcy+2kOdC3Pz3+9LHzPbeauZXVGmfKKZbTezLfFn475o70BgZu1mNrJObeWP0+cyy/rV3QzNbJiZ3Rfv6zDg7ZcBLOkVSf9QrwdwtcxscC+qnyNpgbtPqqayu29y90U9VCsJYHd/dy/609N2V1ZYdr+7Hx9/Jle7wV6OVU3tL0/2HmSP00VS1zH4D0ltCncJO8vMJtSjM5XG3MNNve6UdEY9+lFr+2sAdyr8n6cl+QX5M1gzeyn+nmhm95rZ9Wb2UzP7gplNN7MHzexRM3tTZjOTzez+WO9Dcf3BZvZFM/uRmT1iZudltnu3ma2W9GiZ/pwVt/+YmV0cyz6n8KWN/zKzL+bqX2dm03L785HYTvG+uY1mdmXc7iNx+RcU7gi1xcyu2Zt9j2flj5nZw2Z2Xyw7WNKb3f3hag9OuX0u9sfMLjKzBySdmFtnbhzbh83sRjMbEcvfZGY/jMsuKu5TXPbpzPFYXqEv58R9vsfMLjezr2XG9ZL4CuRiMzs+tvOImX3X4v1j43onxOmRFr4+LTObbWa3mNn6eAb5LxXa/7qZbbJwv9p8Hz8dj8GDZtYa6x9tZnfGftxpZm80s0MtnDEPinVGmNkvzWxoHJ/1ZrY5PmaPqfY4qYq7GZrZwWb2jIWv2MvMDol9qdi2mf2dmT1g4QZCG83siFi+zMwuM7M7JK0ys2Pjvm+J+zs2NnuzpOm92I/+K/W3VGr0zZeXJB0iqV3SoZI+JWlZXHaVpNOydePviZJeVLhn70GSOiQtj8sWS/pKZv31Cn+8xip8d3y4pHmSlsY6Byl8O2l03O4fJY0u0883KHxV8nUKN1a5S9KH47J7JJ1QZp2/l3R1nB6mcOemBpV+Y+ziYn/j/GHZfe3Dvj8qqTlOvzb+niTpxgrHYaKk7dr9rb/P9rDPLumjFbbVlJleIWlhnL5N0llx+uOZfZqi8EfY4rG6TdJ7y4x/u6TDJQ2VdL+kr2WO822K94JV+KbZ++L0RZkx6TpOkkZKao/TsxXuvtUUj89jFY7n4fH34LitN8f5dsVvjkqamTm2t0qaFafnSLo5Tt8iaVKcPkPSFXH6Tklj4/S7FL7WXe44bVO4D+46ScfG8tOK24nzM4rjk1v/yswxnCfpy921rfBtveKXwM7N1F8mabN237t3paTpmcd6Q2astqbOmX3xs7+eAcvDXcZWSerpZXnWjzzcs/cVha8i3hHLH5XUkql3vbvvcvefSXpa0jEKT/iZZrZF4RaTTQoBLUkPeri3aN47JN3j4UYhnZKuUbi/a3fWKdyc5iCFl4b3ufuOXJ3JCi8dJUnu/rsetilVt+/fk3SVmc1VeBJIIbS3drPd7Evbf1X3+7xT4UZF5fxVPIt6VOHs59hYfqKkG+L06kz9KfHnx5IeUjhGY1XqnZLudfcXPNyo5Ybc8hvcfaeZHarwB+feWH61ej5OkrTB3bfF43OTwquavI+a2UOxn8cqvNQvujbzu/iK4MTMfn4rs83rtPtl+ZmSrrNw1713S7ohPi6/oXC88h6SdLS7v0Uh9G6O5dXeGewKSWfH6bMVbo/ZXdtHSbo9HstPa/exlKQ1mcfzDyRdaGb/FPu3Q5LcfaekP8dXXwPa/n5t6ysKD64rM2WdipdezMwU/rIWvZKZ3pWZ36XSsco/CF3hwbrQ3W/PLjCziQpnwOWUe4B3y91fNrN7JH1A4Ql3bZlqVqaPPelx393942b2LoUbpW8xs+Ml7VB4BVCt7vb55fjkKucqhbOsh81stsJZW0/tfN7dv7GXfZEqH7esrseT9hyHco+T3Y2Hm718StI73P13ZnZVbhtead0y5Wskfd7MDpf0doVXFq+R9KK7H9/dDnjmlqjuvtbM/tPC+ydV3RnM3b9nZi1m9j6FVwyPWbhZfKW2V0q6xN3XxOfHssyyrjF399XxctQHFQL7XHe/Ky4+SNLL3e3XQLDfngFLkru/oPAvSM7JFLcrPEClcD1r6F5s+nQzG2Th2ugYhZt23C5pfuZa2DgLN8fuzgOS3hevHQ6WdJake3tYRwrX4s6W9J7Ybt4dkj5RnLHd/+/q1WL/9oaZvcndH3D3z0n6rcKT8wlJrb3YzN7u88GSno/9z17/+6Gkj8TpMzPlt0uaE8/EZGbNZvZ6lXow9uUwC2/6fERluPt2Sb8zs/fEohmZPrdr9+Mp/+mYky3877EGhf+48L3c8kMUAmd7vA7allt+Rub3D+L09zP7OV3S/8Y+vhT351KFyxU7Y7A+Y2anxzEwM3tLfv/M7C/iyYjM7J0KubBNvbub4SqFk4ErY3+6a/tQhctc0u47ku3BzMZIetrdvxrbfXMsb1K4BPFqpXUHiv06gKMvK1ybK7pc4Un3oMJ1qWrOcvKeUngCrpP0cXd/WeFl2E8kPWRmjym85Or2FYaHO/FfIOluhetvD7n7Ld2tE92h8BJ4o4c3R/JWSDrM4htmCtdppXBN9BGLb8LthS9afPNM0n2SHnb3JyUdWu3LwT7s8z8rhPcGhTtbFZ0v6ZPxeB6pcM1ZHv7rwWpJP4gvdb+jEOLZvnRI+re43Y0Kx297hfZnKez/Iwp3ILsoln9J4Q/v91X6OJNCOH5L4fr3je6+Kdf+wwqXHh6X9E3tGdAHxTPAxdr9hvIiSWfHfsyIy4quk/Sx+LtouqRz4uPgcZX/l2CnSSo+Vr4q6UwPOhX+kN+u8If2end/vMz6UriUdJhKX5FVanuZwqWJ+xX+kFdyRuzXFoVLSKti+SRJa7tZb8DgbmjoMzNbIukP7r7HZ4Hr0PYISTvc3c3sTIU35MqFTKX1G939pXgG/F1J33T37+6Dfs1WeNPtEz3V3R9Y+GTRqe4+ow5t3STpAnd/qtZt1dr+fg0Y9fF1Sacnavvtkr4WX0K/qPDJgN5YZmaTFa693qHdb0ChSma2UuHyybSe6u6DtoYpfPJjwIevxBkwACRzIFwDBoB+iQAGgEQIYABIhAAGgEQIYABI5P8B6vQaG7gICX8AAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"281rZzNYwk7Y","outputId":"ee2fba22-0c04-45bc-97e6-a71ae4a90613"},"source":["\"\"\"\n","From the above figure we can see that there are a bunch of outliers.\n","So, we need to treat them and adjust them before handing our dataset to the model \n","\n","\"\"\" "],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'\\nFrom the above figure we can see that there are a bunch of outliers.\\nSo, we need to treat them and adjust them before handing our dataset to the model \\n\\n'"]},"metadata":{"tags":[]},"execution_count":191}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GbfVUU3rwk7Y","executionInfo":{"status":"ok","timestamp":1626262390203,"user_tz":-330,"elapsed":429,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"0917e2a0-c3ca-492e-977a-caeff45539fb"},"source":["df[\"Number of victims( For age group above 50 years)\"].describe()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["count    962.000000\n","mean      67.559252\n","std      100.650347\n","min        0.000000\n","25%        3.000000\n","50%       25.000000\n","75%       86.000000\n","max      824.000000\n","Name: Number of victims( For age group above 50 years), dtype: float64"]},"metadata":{"tags":[]},"execution_count":36}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0h_XexPIwk7Y","executionInfo":{"status":"ok","timestamp":1626262392801,"user_tz":-330,"elapsed":2,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"3ecafbad-8fee-4314-f7be-ade3db85f27e"},"source":["q1=df[\"Number of victims( For age group above 50 years)\"].quantile(0.25)\n","q1"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["3.0"]},"metadata":{"tags":[]},"execution_count":37}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hiBtjNeFwk7Z","executionInfo":{"status":"ok","timestamp":1626262395512,"user_tz":-330,"elapsed":6,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"7b9cc878-66e0-4a2f-a0bb-de244b14ec54"},"source":["q3=df[\"Number of victims( For age group above 50 years)\"].quantile(0.75)\n","q3"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["86.0"]},"metadata":{"tags":[]},"execution_count":38}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_5IrYW4Zwk7Z","executionInfo":{"status":"ok","timestamp":1626262398404,"user_tz":-330,"elapsed":420,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"e4467be8-e962-4caa-c945-e402f2c873dd"},"source":["iqr=q3-q1\n","iqr"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["83.0"]},"metadata":{"tags":[]},"execution_count":39}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"G13Lo17Zwk7Z","executionInfo":{"status":"ok","timestamp":1626262402982,"user_tz":-330,"elapsed":405,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"4ac099c8-f43e-40e6-b583-a13e34b199db"},"source":["upper_limit=q3+(iqr*1.5)\n","upper_limit"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["210.5"]},"metadata":{"tags":[]},"execution_count":40}]},{"cell_type":"code","metadata":{"id":"KPzho1BRwk7Z","outputId":"9c8dff33-e9bd-4f3d-e28e-db0c4a3ba9e8"},"source":["\"\"\"\n","Since, upper_limit<df[\"Number of victims( For age group above 50 years)\"].max()\n","So, upper_limit=upper_limit\n","\n","\"\"\""],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'\\nSince, upper_limit<df[\"Number of victims( For age group above 50 years)\"].max()\\nSo, upper_limit=upper_limit\\n\\n'"]},"metadata":{"tags":[]},"execution_count":197}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PxFrFtZzwk7Z","executionInfo":{"status":"ok","timestamp":1626262410162,"user_tz":-330,"elapsed":407,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"a13cccdb-5eaa-4db2-8f43-96f982ccaad1"},"source":["lower_limit=q1-(1.5*iqr)\n","lower_limit"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["-121.5"]},"metadata":{"tags":[]},"execution_count":41}]},{"cell_type":"code","metadata":{"id":"_u4oYjPBwk7Z","outputId":"52d08990-35c5-4e5f-d76e-3bbd377f12ee"},"source":["\"\"\"\n","Since, lower_limit<df[\"Number of victims( For age group above 50 years)\"].min()\n","So, lower_limit=df[\"Number of victims( For age group of above 50 years)\"].min()\n","\n","\"\"\""],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'\\nSince, lower_limit<df[\"Number of victims( For age group above 50 years)\"].min()\\nSo, lower_limit=df[\"Number of victims( For age group of above 50 years)\"].min()\\n\\n'"]},"metadata":{"tags":[]},"execution_count":199}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"W09b7rEYwk7Z","executionInfo":{"status":"ok","timestamp":1626262413891,"user_tz":-330,"elapsed":435,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"34283e1f-86c4-43c5-9521-a9a97d5a0719"},"source":["lower_limit=df[\"Number of victims( For age group above 50 years)\"].min()\n","lower_limit"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.0"]},"metadata":{"tags":[]},"execution_count":42}]},{"cell_type":"code","metadata":{"id":"74Vx1puDwk7a"},"source":["df[\"Number of victims( For age group above 50 years)\"]=df[\"Number of victims( For age group above 50 years)\"].apply(treat_outlier)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"d27GsWHWwk7a","executionInfo":{"status":"ok","timestamp":1626262431422,"user_tz":-330,"elapsed":416,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"fae8fc9c-3fba-47a1-89c6-e7a30b3d7a07"},"source":["df[\"Number of victims( For age group above 50 years)\"].describe()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["count    962.000000\n","mean      56.980249\n","std       68.410127\n","min        0.000000\n","25%        3.000000\n","50%       25.000000\n","75%       86.000000\n","max      210.500000\n","Name: Number of victims( For age group above 50 years), dtype: float64"]},"metadata":{"tags":[]},"execution_count":44}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":350},"id":"a8YGMMzzwk7a","executionInfo":{"status":"ok","timestamp":1626262439220,"user_tz":-330,"elapsed":1129,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"503caada-8d66-498c-d959-9cdf22a3e0c1"},"source":["sns.boxplot(df[\"Number of victims( For age group above 50 years)\"])"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n","  FutureWarning\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f9fa01a0110>"]},"metadata":{"tags":[]},"execution_count":45},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"Qv5KjDyqwk7a"},"source":["# So, from the above figure we can see that there is no outliers"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"bA3ROShawk7a"},"source":["### FOR TOTAL NUMBER OF VICTIMS ###"]},{"cell_type":"code","metadata":{"id":"2OJ2UPHxwk7a","outputId":"a6a45b7f-3d99-414c-f0f4-7b6a7707059e"},"source":["sb.boxplot(df[\"Total Number Of Victims\"])"],"execution_count":null,"outputs":[{"output_type":"stream","text":["C:\\Users\\91910\\anaconda3_2\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n","  warnings.warn(\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["<AxesSubplot:xlabel='Total Number Of Victims'>"]},"metadata":{"tags":[]},"execution_count":205},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"2g6riqwhwk7c","outputId":"d1b8be39-6ab7-4675-e668-98d82634f7c4"},"source":["\"\"\"\n","From the above figure we can see that there are a bunch of outliers.\n","So, we need to treat them and adjust them before handing our dataset to the model \n","\n","\"\"\" "],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'\\nFrom the above figure we can see that there are a bunch of outliers.\\nSo, we need to treat them and adjust them before handing our dataset to the model \\n\\n'"]},"metadata":{"tags":[]},"execution_count":206}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"CJZr4P3Nwk7c","executionInfo":{"status":"ok","timestamp":1626262465299,"user_tz":-330,"elapsed":6,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"0d48234c-99aa-44c7-bab4-193cb8720b70"},"source":["df[\"Total Number Of Victims\"].describe()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["count     958.000000\n","mean      687.015658\n","std       975.783413\n","min         0.000000\n","25%        45.000000\n","50%       279.000000\n","75%      1010.250000\n","max      7691.000000\n","Name: Total Number Of Victims, dtype: float64"]},"metadata":{"tags":[]},"execution_count":46}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PizZ4NEVwk7d","executionInfo":{"status":"ok","timestamp":1626262467633,"user_tz":-330,"elapsed":422,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"ece9ea4f-66d4-420a-ace7-9997e8d52211"},"source":["q1=df[\"Total Number Of Victims\"].quantile(0.25)\n","q1"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["45.0"]},"metadata":{"tags":[]},"execution_count":47}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PofX9el2wk7d","executionInfo":{"status":"ok","timestamp":1626262470308,"user_tz":-330,"elapsed":4,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"c3587132-58a4-4277-ea77-994880c6e0bf"},"source":["q3=df[\"Total Number Of Victims\"].quantile(0.75)\n","q3"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["1010.25"]},"metadata":{"tags":[]},"execution_count":48}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"T5RgrQj_wk7d","executionInfo":{"status":"ok","timestamp":1626262473278,"user_tz":-330,"elapsed":3,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"c9739f6f-8a58-45be-edc2-245a28a9f8de"},"source":["iqr=q3-q1\n","iqr"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["965.25"]},"metadata":{"tags":[]},"execution_count":49}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AsDJnyw6wk7d","executionInfo":{"status":"ok","timestamp":1626262476006,"user_tz":-330,"elapsed":2,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"5c16f6a0-188d-4871-912c-6ab6f97cc1c7"},"source":["upper_limit=q3+(iqr*1.5)\n","upper_limit"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["2458.125"]},"metadata":{"tags":[]},"execution_count":50}]},{"cell_type":"code","metadata":{"id":"Xn-Q9LLMwk7d","outputId":"dd7ebc7e-d14c-478f-89ab-67b84c526a81"},"source":["\"\"\"\n","Since, upper_limit<df[\"Total Number of Victims\"].max()\n","So, upper_limit=upper_limit\n","\n","\"\"\""],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'\\nSince, upper_limit<df[\"Total Number of Victims\"].max()\\nSo, upper_limit=upper_limit\\n\\n'"]},"metadata":{"tags":[]},"execution_count":212}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MESYueRUwk7d","executionInfo":{"status":"ok","timestamp":1626262479569,"user_tz":-330,"elapsed":538,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"533e66c2-3d40-4584-e042-82fb1a2eaead"},"source":["lower_limit=q1-(1.5*iqr)\n","lower_limit"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["-1402.875"]},"metadata":{"tags":[]},"execution_count":51}]},{"cell_type":"code","metadata":{"id":"8x7bDpVdwk7d","outputId":"e3132bb8-0e27-41b9-b782-e8b73938246a"},"source":["\"\"\"\n","Since, lower_limit<df[\"Total Number of Victims\"].min()\n","So, lower_limit=df[\"Total Number of Victims\"].min()\n","\n","\"\"\""],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'\\nSince, lower_limit<df[\"Total Number of Victims\"].min()\\nSo, lower_limit=df[\"Total Number of Victims\"].min()\\n\\n'"]},"metadata":{"tags":[]},"execution_count":218}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3oG9Zwldwk7e","executionInfo":{"status":"ok","timestamp":1626262485779,"user_tz":-330,"elapsed":404,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"5fb07f6e-5264-4694-d949-6614602316b2"},"source":["lower_limit=df[\"Total Number Of Victims\"].min()\n","lower_limit"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.0"]},"metadata":{"tags":[]},"execution_count":52}]},{"cell_type":"code","metadata":{"id":"XwDAIVGmwk7e"},"source":["df[\"Total Number Of Victims\"]=df[\"Total Number Of Victims\"].apply(treat_outlier)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JL1wRLrbwk7e","executionInfo":{"status":"ok","timestamp":1626262493422,"user_tz":-330,"elapsed":412,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"dc170373-0984-40fc-ce1b-38f622da6760"},"source":["df[\"Total Number Of Victims\"].describe()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["count     958.000000\n","mean      616.268398\n","std       722.445668\n","min         0.000000\n","25%        45.000000\n","50%       279.000000\n","75%      1010.250000\n","max      2458.125000\n","Name: Total Number Of Victims, dtype: float64"]},"metadata":{"tags":[]},"execution_count":54}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":350},"id":"4lL06kBFwk7e","executionInfo":{"status":"ok","timestamp":1626262501162,"user_tz":-330,"elapsed":690,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"f49960ad-b456-451f-9d7e-17bb4a2981a0"},"source":["sns.boxplot(df[\"Total Number Of Victims\"])"],"execution_count":null,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n","  FutureWarning\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 0x7f9fa081b710>"]},"metadata":{"tags":[]},"execution_count":55},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"_T0AYRmPwk7e"},"source":["# From the above figur we can see that there is no outliers"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"s2GZfLC8wk7e"},"source":["## FINDING AND TREATING MISSING VALUES IN OUR INDEPENDENT VARIABLES  ##"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PMA9o4LDwk7e","executionInfo":{"status":"ok","timestamp":1626262510291,"user_tz":-330,"elapsed":438,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"364699e4-3f6e-455e-a4c5-265eab088f45"},"source":["df.columns[df.isna().any()]   # There are missing values in \"Number of victims\" column."],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['Number of victims( For age group of 18-30 years)',\n","       'Number of victims( For age group of 30-50 years)',\n","       'Number of victims( For age group above 50 years)',\n","       'Total Number Of Victims'],\n","      dtype='object')"]},"metadata":{"tags":[]},"execution_count":56}]},{"cell_type":"code","metadata":{"id":"U6Avvx8ywk7e","outputId":"916d9c79-2928-4ead-ab3f-1dabb573eb92"},"source":["df"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>State</th>\n","      <th>Year</th>\n","      <th>Gender Group</th>\n","      <th>Number of victims( For age group of 18-30 years)</th>\n","      <th>Number of victims( For age group of 30-50 years)</th>\n","      <th>Number of victims( For age group above 50 years)</th>\n","      <th>Total Number Of Victims</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Andaman &amp; Nicobar Islands</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>4.0</td>\n","      <td>2.00</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Andhra Pradesh</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>269.0</td>\n","      <td>175.00</td>\n","      <td>67.0</td>\n","      <td>511.000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Arunachal Pradesh</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>10.0</td>\n","      <td>4.00</td>\n","      <td>2.0</td>\n","      <td>16.000</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Assam</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>45.0</td>\n","      <td>37.00</td>\n","      <td>11.0</td>\n","      <td>93.000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Bihar</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>191.0</td>\n","      <td>123.00</td>\n","      <td>12.0</td>\n","      <td>326.000</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1013</th>\n","      <td>Tamil Nadu</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>650.0</td>\n","      <td>839.00</td>\n","      <td>210.5</td>\n","      <td>1816.000</td>\n","    </tr>\n","    <tr>\n","      <th>1014</th>\n","      <td>Tripura</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>60.0</td>\n","      <td>73.00</td>\n","      <td>24.0</td>\n","      <td>157.000</td>\n","    </tr>\n","    <tr>\n","      <th>1015</th>\n","      <td>Uttar Pradesh</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>1033.0</td>\n","      <td>1021.75</td>\n","      <td>210.5</td>\n","      <td>2458.125</td>\n","    </tr>\n","    <tr>\n","      <th>1016</th>\n","      <td>Uttarakhand</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>91.0</td>\n","      <td>63.00</td>\n","      <td>19.0</td>\n","      <td>173.000</td>\n","    </tr>\n","    <tr>\n","      <th>1017</th>\n","      <td>West Bengal</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>1033.0</td>\n","      <td>1021.75</td>\n","      <td>120.0</td>\n","      <td>2399.000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1018 rows × 7 columns</p>\n","</div>"],"text/plain":["                          State  Year             Gender Group  \\\n","0     Andaman & Nicobar Islands  2001  Murder - Female Victims   \n","1                Andhra Pradesh  2001  Murder - Female Victims   \n","2             Arunachal Pradesh  2001  Murder - Female Victims   \n","3                         Assam  2001  Murder - Female Victims   \n","4                         Bihar  2001  Murder - Female Victims   \n","...                         ...   ...                      ...   \n","1013                 Tamil Nadu  2010   Murder - Total Victims   \n","1014                    Tripura  2010   Murder - Total Victims   \n","1015              Uttar Pradesh  2010   Murder - Total Victims   \n","1016                Uttarakhand  2010   Murder - Total Victims   \n","1017                West Bengal  2010   Murder - Total Victims   \n","\n","      Number of victims( For age group of 18-30 years)  \\\n","0                                                  4.0   \n","1                                                269.0   \n","2                                                 10.0   \n","3                                                 45.0   \n","4                                                191.0   \n","...                                                ...   \n","1013                                             650.0   \n","1014                                              60.0   \n","1015                                            1033.0   \n","1016                                              91.0   \n","1017                                            1033.0   \n","\n","      Number of victims( For age group of 30-50 years)  \\\n","0                                                 2.00   \n","1                                               175.00   \n","2                                                 4.00   \n","3                                                37.00   \n","4                                               123.00   \n","...                                                ...   \n","1013                                            839.00   \n","1014                                             73.00   \n","1015                                           1021.75   \n","1016                                             63.00   \n","1017                                           1021.75   \n","\n","      Number of victims( For age group above 50 years)  \\\n","0                                                  NaN   \n","1                                                 67.0   \n","2                                                  2.0   \n","3                                                 11.0   \n","4                                                 12.0   \n","...                                                ...   \n","1013                                             210.5   \n","1014                                              24.0   \n","1015                                             210.5   \n","1016                                              19.0   \n","1017                                             120.0   \n","\n","      Total Number Of Victims  \n","0                         NaN  \n","1                     511.000  \n","2                      16.000  \n","3                      93.000  \n","4                     326.000  \n","...                       ...  \n","1013                 1816.000  \n","1014                  157.000  \n","1015                 2458.125  \n","1016                  173.000  \n","1017                 2399.000  \n","\n","[1018 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":225}]},{"cell_type":"code","metadata":{"id":"zubjT0cSwk7e"},"source":["df[\"Number of victims( For age group of 18-30 years)\"]=df[\"Number of victims( For age group of 18-30 years)\"].fillna(method=\"ffill\")\n","df[\"Number of victims( For age group of 18-30 years)\"]=df[\"Number of victims( For age group of 18-30 years)\"].fillna(method=\"bfill\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Hk8WZmrAwk7f"},"source":["df[\"Number of victims( For age group of 30-50 years)\"]=df[\"Number of victims( For age group of 30-50 years)\"].fillna(method=\"ffill\")\n","df[\"Number of victims( For age group of 30-50 years)\"]=df[\"Number of victims( For age group of 30-50 years)\"].fillna(method=\"bfill\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Nd8eq8fVwk7f"},"source":["df[\"Number of victims( For age group above 50 years)\"]=df[\"Number of victims( For age group above 50 years)\"].fillna(method=\"ffill\")\n","df[\"Number of victims( For age group above 50 years)\"]=df[\"Number of victims( For age group above 50 years)\"].fillna(method=\"bfill\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"tXsG8shcwk7f"},"source":["df[\"Total Number Of Victims\"]=df[\"Total Number Of Victims\"].fillna(method=\"ffill\")\n","df[\"Total Number Of Victims\"]=df[\"Total Number Of Victims\"].fillna(method=\"bfill\")"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":602},"id":"X4ldEKJSwk7f","executionInfo":{"status":"ok","timestamp":1626262533882,"user_tz":-330,"elapsed":450,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"cdc402b1-e9f4-49c2-ae50-23ec51cde3d8"},"source":["df"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>State</th>\n","      <th>Year</th>\n","      <th>Gender Group</th>\n","      <th>Number of victims( For age group of 18-30 years)</th>\n","      <th>Number of victims( For age group of 30-50 years)</th>\n","      <th>Number of victims( For age group above 50 years)</th>\n","      <th>Total Number Of Victims</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Andaman &amp; Nicobar Islands</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>4.0</td>\n","      <td>2.00</td>\n","      <td>67.0</td>\n","      <td>511.000</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Andhra Pradesh</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>269.0</td>\n","      <td>175.00</td>\n","      <td>67.0</td>\n","      <td>511.000</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Arunachal Pradesh</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>10.0</td>\n","      <td>4.00</td>\n","      <td>2.0</td>\n","      <td>16.000</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Assam</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>45.0</td>\n","      <td>37.00</td>\n","      <td>11.0</td>\n","      <td>93.000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Bihar</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>191.0</td>\n","      <td>123.00</td>\n","      <td>12.0</td>\n","      <td>326.000</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1013</th>\n","      <td>Tamil Nadu</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>650.0</td>\n","      <td>839.00</td>\n","      <td>210.5</td>\n","      <td>1816.000</td>\n","    </tr>\n","    <tr>\n","      <th>1014</th>\n","      <td>Tripura</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>60.0</td>\n","      <td>73.00</td>\n","      <td>24.0</td>\n","      <td>157.000</td>\n","    </tr>\n","    <tr>\n","      <th>1015</th>\n","      <td>Uttar Pradesh</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>1033.0</td>\n","      <td>1021.75</td>\n","      <td>210.5</td>\n","      <td>2458.125</td>\n","    </tr>\n","    <tr>\n","      <th>1016</th>\n","      <td>Uttarakhand</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>91.0</td>\n","      <td>63.00</td>\n","      <td>19.0</td>\n","      <td>173.000</td>\n","    </tr>\n","    <tr>\n","      <th>1017</th>\n","      <td>West Bengal</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>1033.0</td>\n","      <td>1021.75</td>\n","      <td>120.0</td>\n","      <td>2399.000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1018 rows × 7 columns</p>\n","</div>"],"text/plain":["                          State  ...  Total Number Of Victims\n","0     Andaman & Nicobar Islands  ...                  511.000\n","1                Andhra Pradesh  ...                  511.000\n","2             Arunachal Pradesh  ...                   16.000\n","3                         Assam  ...                   93.000\n","4                         Bihar  ...                  326.000\n","...                         ...  ...                      ...\n","1013                 Tamil Nadu  ...                 1816.000\n","1014                    Tripura  ...                  157.000\n","1015              Uttar Pradesh  ...                 2458.125\n","1016                Uttarakhand  ...                  173.000\n","1017                West Bengal  ...                 2399.000\n","\n","[1018 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":61}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZlinBJ6Xwk7f","executionInfo":{"status":"ok","timestamp":1626262539267,"user_tz":-330,"elapsed":424,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"90f5b6bf-fdee-4c47-f922-e361d4c1bbab"},"source":["df.columns[df.isna().any()]  # There are no missing values in our dataset"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index([], dtype='object')"]},"metadata":{"tags":[]},"execution_count":62}]},{"cell_type":"code","metadata":{"id":"LkgWPirmwk7f"},"source":["df[\"Total Number Of Victims\"]=df[\"Number of victims( For age group of 18-30 years)\"]+df[\"Number of victims( For age group of 30-50 years)\"]+df[\"Number of victims( For age group above 50 years)\"]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":602},"id":"vJ0jS61bwk7f","executionInfo":{"status":"ok","timestamp":1626262544569,"user_tz":-330,"elapsed":5,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"a38c3661-29a0-4461-fc91-ba14f65c73ae"},"source":["df"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>State</th>\n","      <th>Year</th>\n","      <th>Gender Group</th>\n","      <th>Number of victims( For age group of 18-30 years)</th>\n","      <th>Number of victims( For age group of 30-50 years)</th>\n","      <th>Number of victims( For age group above 50 years)</th>\n","      <th>Total Number Of Victims</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Andaman &amp; Nicobar Islands</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>4.0</td>\n","      <td>2.00</td>\n","      <td>67.0</td>\n","      <td>73.00</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Andhra Pradesh</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>269.0</td>\n","      <td>175.00</td>\n","      <td>67.0</td>\n","      <td>511.00</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Arunachal Pradesh</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>10.0</td>\n","      <td>4.00</td>\n","      <td>2.0</td>\n","      <td>16.00</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Assam</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>45.0</td>\n","      <td>37.00</td>\n","      <td>11.0</td>\n","      <td>93.00</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Bihar</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>191.0</td>\n","      <td>123.00</td>\n","      <td>12.0</td>\n","      <td>326.00</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1013</th>\n","      <td>Tamil Nadu</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>650.0</td>\n","      <td>839.00</td>\n","      <td>210.5</td>\n","      <td>1699.50</td>\n","    </tr>\n","    <tr>\n","      <th>1014</th>\n","      <td>Tripura</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>60.0</td>\n","      <td>73.00</td>\n","      <td>24.0</td>\n","      <td>157.00</td>\n","    </tr>\n","    <tr>\n","      <th>1015</th>\n","      <td>Uttar Pradesh</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>1033.0</td>\n","      <td>1021.75</td>\n","      <td>210.5</td>\n","      <td>2265.25</td>\n","    </tr>\n","    <tr>\n","      <th>1016</th>\n","      <td>Uttarakhand</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>91.0</td>\n","      <td>63.00</td>\n","      <td>19.0</td>\n","      <td>173.00</td>\n","    </tr>\n","    <tr>\n","      <th>1017</th>\n","      <td>West Bengal</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>1033.0</td>\n","      <td>1021.75</td>\n","      <td>120.0</td>\n","      <td>2174.75</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1018 rows × 7 columns</p>\n","</div>"],"text/plain":["                          State  ...  Total Number Of Victims\n","0     Andaman & Nicobar Islands  ...                    73.00\n","1                Andhra Pradesh  ...                   511.00\n","2             Arunachal Pradesh  ...                    16.00\n","3                         Assam  ...                    93.00\n","4                         Bihar  ...                   326.00\n","...                         ...  ...                      ...\n","1013                 Tamil Nadu  ...                  1699.50\n","1014                    Tripura  ...                   157.00\n","1015              Uttar Pradesh  ...                  2265.25\n","1016                Uttarakhand  ...                   173.00\n","1017                West Bengal  ...                  2174.75\n","\n","[1018 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":64}]},{"cell_type":"code","metadata":{"id":"0dH7Uwwkwk7f"},"source":["df[\"Number of victims( For age group of 18-30 years)\"]=df[\"Number of victims( For age group of 18-30 years)\"].astype(int)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"FHWMSyhJwk7f"},"source":["df[\"Number of victims( For age group of 30-50 years)\"]=df[\"Number of victims( For age group of 30-50 years)\"].astype(int)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"bsBSocAuwk7g"},"source":["df[\"Number of victims( For age group above 50 years)\"]=df[\"Number of victims( For age group above 50 years)\"].astype(int)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"qkUWwO7Vwk7g"},"source":["df[\"Total Number Of Victims\"]=df[\"Total Number Of Victims\"].astype(int)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":602},"id":"M2K0zUgGwk7g","executionInfo":{"status":"ok","timestamp":1626262562574,"user_tz":-330,"elapsed":5,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"23ffa728-b7c4-4669-9fb5-7ed8f8690b65"},"source":["df"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>State</th>\n","      <th>Year</th>\n","      <th>Gender Group</th>\n","      <th>Number of victims( For age group of 18-30 years)</th>\n","      <th>Number of victims( For age group of 30-50 years)</th>\n","      <th>Number of victims( For age group above 50 years)</th>\n","      <th>Total Number Of Victims</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Andaman &amp; Nicobar Islands</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>67</td>\n","      <td>73</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Andhra Pradesh</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>269</td>\n","      <td>175</td>\n","      <td>67</td>\n","      <td>511</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Arunachal Pradesh</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>10</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>16</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Assam</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>45</td>\n","      <td>37</td>\n","      <td>11</td>\n","      <td>93</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Bihar</td>\n","      <td>2001</td>\n","      <td>Murder - Female Victims</td>\n","      <td>191</td>\n","      <td>123</td>\n","      <td>12</td>\n","      <td>326</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1013</th>\n","      <td>Tamil Nadu</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>650</td>\n","      <td>839</td>\n","      <td>210</td>\n","      <td>1699</td>\n","    </tr>\n","    <tr>\n","      <th>1014</th>\n","      <td>Tripura</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>60</td>\n","      <td>73</td>\n","      <td>24</td>\n","      <td>157</td>\n","    </tr>\n","    <tr>\n","      <th>1015</th>\n","      <td>Uttar Pradesh</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>1033</td>\n","      <td>1021</td>\n","      <td>210</td>\n","      <td>2265</td>\n","    </tr>\n","    <tr>\n","      <th>1016</th>\n","      <td>Uttarakhand</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>91</td>\n","      <td>63</td>\n","      <td>19</td>\n","      <td>173</td>\n","    </tr>\n","    <tr>\n","      <th>1017</th>\n","      <td>West Bengal</td>\n","      <td>2010</td>\n","      <td>Murder - Total Victims</td>\n","      <td>1033</td>\n","      <td>1021</td>\n","      <td>120</td>\n","      <td>2174</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1018 rows × 7 columns</p>\n","</div>"],"text/plain":["                          State  ...  Total Number Of Victims\n","0     Andaman & Nicobar Islands  ...                       73\n","1                Andhra Pradesh  ...                      511\n","2             Arunachal Pradesh  ...                       16\n","3                         Assam  ...                       93\n","4                         Bihar  ...                      326\n","...                         ...  ...                      ...\n","1013                 Tamil Nadu  ...                     1699\n","1014                    Tripura  ...                      157\n","1015              Uttar Pradesh  ...                     2265\n","1016                Uttarakhand  ...                      173\n","1017                West Bengal  ...                     2174\n","\n","[1018 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":69}]},{"cell_type":"markdown","metadata":{"id":"my-GmRwhwk7g"},"source":["## FINDING OUTLIERS IN INDEPENDENT VARIABLES ##"]},{"cell_type":"code","metadata":{"id":"fUUBiyNLwk7g","outputId":"2479a8f0-f153-4e8e-fca2-84193624abb3"},"source":["df.info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 1018 entries, 0 to 1017\n","Data columns (total 7 columns):\n"," #   Column                                            Non-Null Count  Dtype \n","---  ------                                            --------------  ----- \n"," 0   State                                             1018 non-null   object\n"," 1   Year                                              1018 non-null   int64 \n"," 2   Gender Group                                      1018 non-null   object\n"," 3   Number of victims( For age group of 18-30 years)  1018 non-null   int32 \n"," 4   Number of victims( For age group of 30-50 years)  1018 non-null   int32 \n"," 5   Number of victims( For age group above 50 years)  1018 non-null   int32 \n"," 6   Total Number Of Victims                           1018 non-null   int32 \n","dtypes: int32(4), int64(1), object(2)\n","memory usage: 39.9+ KB\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"s3DEPffqwk7g","outputId":"1aab84e5-a98c-4203-ee3b-ef40b5572a6d"},"source":["sb.boxplot(df[\"Year\"])  # No outliers in \"Year\" column"],"execution_count":null,"outputs":[{"output_type":"stream","text":["C:\\Users\\91910\\anaconda3_2\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n","  warnings.warn(\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["<AxesSubplot:xlabel='Year'>"]},"metadata":{"tags":[]},"execution_count":240},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"ETIpYnHNwk7g"},"source":["## PLOTTING THE GRAPH FOR THE STATE OF MAHARASHTRA ##"]},{"cell_type":"code","metadata":{"id":"O4JYtdE5wk7g","outputId":"3e8c679c-49ad-4fc8-8af3-15592f34d603"},"source":["# Graph against female victims for the state of Maharashtra\n","plt.bar(df[(df[\"State\"]==\"Maharashtra\") & (df[\"Gender Group\"]==\"Murder - Female Victims\")][\"Year\"], df[(df[\"State\"]==\"Maharashtra\") & (df[\"Gender Group\"]==\"Murder - Female Victims\")][\"Total Number Of Victims\"])\n","plt.xlabel(\"Year\")\n","plt.ylabel(\"Number of female victims\")\n","plt.title(\"Crime rate graph against female for the state of Maharashtra\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Crime rate graph against female for the state of Maharashtra')"]},"metadata":{"tags":[]},"execution_count":241},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"_vFZcqAvwk7h","outputId":"ee52fecc-6bf7-4613-ae81-052be59ed516"},"source":["# Graph against male victims for the state of Maharashtra\n","plt.bar(df[(df[\"State\"]==\"Maharashtra\") & (df[\"Gender Group\"]==\"Murder - Male Victims\")][\"Year\"], df[(df[\"State\"]==\"Maharashtra\") & (df[\"Gender Group\"]==\"Murder - Male Victims\")][\"Total Number Of Victims\"])\n","plt.xlabel(\"Year\")\n","plt.ylabel(\"Number of male victims\")\n","plt.title(\"Crime rate graph against male for the state of Maharashtra\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Crime rate graph against male for the state of Maharashtra')"]},"metadata":{"tags":[]},"execution_count":242},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"ekxBrSiPwk7h"},"source":["## PLOTTING THE GRAPH FOR THE STATE OF ASSAM ##"]},{"cell_type":"code","metadata":{"id":"6qxKDFIfwk7h","outputId":"9d15400d-3e9c-4b90-e629-e25d2413a446"},"source":["# Graph against female victims for the state of Assam\n","plt.bar(df[(df[\"State\"]==\"Assam\") & (df[\"Gender Group\"]==\"Murder - Female Victims\")][\"Year\"], df[(df[\"State\"]==\"Assam\") & (df[\"Gender Group\"]==\"Murder - Female Victims\")][\"Total Number Of Victims\"])\n","plt.xlabel(\"Year\")\n","plt.ylabel(\"Number of female victims\")\n","plt.title(\"Crime rate graph against female for the state of Assam\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Crime rate graph against female for the state of Assam')"]},"metadata":{"tags":[]},"execution_count":243},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAgTUlEQVR4nO3debwcVZn/8c8XEvYECIQYwhJUQKOyGRFwAQVFQEwGRVFhAsIgDigMuATHERRUXPCHDjKYATWOCgaQXQQmiAyKQNiXyJrIFpKAQCDs5Pn9cc4tKs29fev27eUu3/fr1a+urqqu85zqqnq6Tm2KCMzMzABW6HQAZmY2cDgpmJlZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFYZlUpD0VUmndTqOoUDSfEm7DIA4LpE0rQ3ljJN0laSnJZ3Y6vJqym54Xkv6nKSFkp6RtE4LYpsoKSSNaPa0BzJJx0t6TNKjnY6lWYZEUpD0KUlz8gK/IG8g3t3T+BHx7Yg4qJ0xViXpF5KO73Qcg01E7BYRM/szDUn7S7q6l9EOBh4DRkfEUf0pr10kjQR+CHwwItaIiMebMM2O/Bnoa7mtXJ8kbQgcBUyKiNfVGW8TScskndKKOJpt0CcFSUcCJwHfBsYBGwGnAFN6GL9j/2QG2r+ogRbPILExcGcMrqs+xwGrAHf09YtKBv12okU2Bh6PiEW9jPfPwBPAPpJWbn1Y/RQRg/YFrAk8A+xdZ5xjgbOBXwFLgINyv1/l4ROBAA4AHiT9eIcA7wBuBZ4ETq6Z5meAuXncS4GNeyi7a9oHAg8AV+X+ZwGPAk8BVwFvyf0PBl4CXsz1ujD3Xx84B1gMzAO+UKe+6wAX5rpeDxwPXF0aHsChwD3AvNzvR7nuS4AbgPd0M/9+CzwN3AhsWRo+H/hinldP5fFW6SG2NwBXAI+T/m3/GlirNHwb4KZczll5WsfnYWsDF+V58ETu3qD03SuBg3L3/sDVwA/yuPOA3Urj7g/cn8uZB3waeDPwPPBKnvdPdhP/L2p+n11If6ymA/fles0CxjSybFWYP/OBXXJ3j+XWxLwZsDTH8QxwRe6/Q14+nsrvO9TMy28BfwaeA95YM83/AZblYc8AXy7VdRppWX8M+PfSdyrFm8ddN/++TwL/AP4vf/815bZhfVoT+GUe9+/A13Isu+Q4luVp/6LONO4DPgcsBD5W6i/g/wGLcuy3Am/Nw3YH7iQtow8DX+zDenA88JeuOpO2Cb/m1W3CxLrb1XZtwFvxAj4EvAyMqDPOsXnBmJp/zFXpPimcSvo39UHSxuE8YD1gQv7RdszjTwXuJW1ERuSF5C89lN017V8CqwOr5v6fAUYBK5P2cm6u2fAcX7My3QB8HVgJeD1pg7ZrD2WemV+rAZNIG6PapHA5MKYUz755wRlB2h1+lLxhL82/jwEjSQlgHjCytKG6jrSijSEly0N6iO2NwAdyvceSVuCT8rCVSCvd4bmcvUgrc1dSWAf4aK7XKNKG4LyalaGcFF4C/gVYkbRCPkJaCVcnrRyb53HH8+pGZP/yvOqhDrW/zxHAX4ENcr1+CpzR4LLV4/wpzetdeiu3znI4In8eQ9qg7Jd/80/mz+uU5uUDwFvy8JHdTLOIpaaM/yatY1sCLwBvbiDe7+R5NjK/3gOou3LbsD79Ejg/T38icDdwYB62E/BQL8vLe/J8WBv4T+CC0rBdcyxrkZbNNwPj87AF5D9n+bvb9GE9uJf0B2NNUmK5m5TERuT6/LxuzM3YOHfqRfqH92gv4xxL/ode0682KUwoDX8c+ETp8znAEbn7kq6ForSQPUs3ewulab++Tnxr5XHW7GEhfifwQM13ju7uhyVtAF8ib/Byv+72FN7fyzx7grw3kOfVX2vqW15g5wP7loZ/Dzi14u83Fbgpd7+X9I9IpeFXl+dFzXe3Ap4ofb6S5ZPCvaVhq+V6v46UFJ7MK9aqNdPcn74nhbnAzqXP4/NvMKKvy1a9+VOa17v0Vm6d5bArKewHXFczzjXA/qV5+c1e5kMRS00Z5X+t1wH7NBDvN0kb4jf2Vm4b1qcXSMcMuvp9Frgyd+9E70nhNPJGG9g+13m9/Pn9pA32dsAKNd97IJc1upfpb8Vr14PyHtqJwCWlz3tSSprdvQZ7W+HjwLoV2sYfrDCthaXu57r5vEbu3hj4kaQnJT1J2r0V6V9fr+VLWlHSCZLuk7SEtJBD2mXuzsbA+l3l5TK/SmonrjWWtDEq17e7ui/XT9JRkuZKeipPf82aeIrxI2IZ8BBpz6BL+cyLZ3l1Xi1H0nqSzpT0cK77r0rlrA88HHnJrS1X0mqSfirp7/m7VwFrSVqxu7LKMUXEs7lzjYhYCnyC1IyzQNLFkt7UwzSq2Bg4t/TbzCU1QZV/n0rLVi/zp5Fye7I+aa+s7O8svwxXWWe609Oy0Jd4v0/6t3uZpPslTe+psBavT+vy6h5sl9r51CNJqwJ7k5puiIhrSBv7T+XPVwAnAz8BFkqaIWl0/vpHSU1If5f0J0nb52lWWQ+qbsu6NdiTwjWk3fGpvYwXvQzviweBz0bEWqXXqhHxl4rlf4p0EHwX0sZ3Yu6vHmJ9kNT2Xy5vVETs3k05i0nNaRuU+m1YLx5J7wG+AnwcWDsi1iK1b6o0/oal8VfI03+km+n25ju57C0iYjSp2aqrnAXABEndlktq1toceGf+7nu7QuprEBFxaUR8gPRv9W+kJg9obDl5kHS8ovz7rBIRDzcwrXrzp5nlPkLaOJZtRNpT69LbvOjrvKocb0Q8HRFHRcTrSf9sj5S0cw/ltnJ9eoz0z748r2rnUz3/BIwGTpH0aD5tdQLpwHNXXX8cEW8nNdVtBnwp978+IqaQmhnPIx2DgSauBz0Z1EkhIp4itQ3+RNLUnEVHStpN0vdaVOypwNGS3gIgaU1Je/fh+6NIu6SPk5o1vl0zfCGpnbPLdcASSV+RtGr+Z/RWSe+onXBEvAL8Djg2z4s3UVoA68TzMimhjJD0ddKCXPZ2SXvlPbIjcvx/rVDX7sp6BnhS0gTyCpBdQ/rneJikEZKmANvWfPe5/N0xwDENlN91ncFHJK2e6/FMLhfSvN9A0kp9mOSpwLckbZynPzbH3oh686eZ5f4e2EzpVO4Rkj5BOv50UR9irV1Oe1M5XkkflvTG/AdhCen3Kf9G5XJbvT7NynGPyrEfSdqDq2Ia8DPgbaRmnq2AdwFbSXqbpHdIeqfSKcNLySc6SFpJ0qclrRkRL5XmQVd9+70e1DOokwJARPyQ9EN9jbRhexA4jJRdW1HeucB3gTPz7tvtwG59mMQvSbugD5MOAtVuXE8HJuVd2/PygrknaYGaR/r3chrpX1F3DsvDHiWdrXEGaaXpyaWk4yR357ie57VNB+eTmlyeILVH75UX1r76BukMo6eAi0kJDICIeJF0cPlAUpv/vqSNVFfsJ5EOYD5Gmmd/aKB8SMv8UaR/y/8AdgT+NQ+7gnTa5qOSHqs4vR8BF5CaOp7Osb2zwdh6nD/NLDfSdQofJs2Hx0lnD304IqrWGdJezdfycvrFCuP3Jd5Ngf8lJchrgFMi4soeym31+vR50gb7ftIxrt+QNvR15aS+M+lEgUdLrxtIy+400p+v/yatV38n/RY/yJPYD5iftzGHkNYHaN560HPsyzfh2lAj6bvA6yJiWoPfP5Z0wG/f3sZtNknXkg5a/7zdZZsNV4N+T8GWJ+lNkrZQsi3pn/e5nY6rCkk7SnpdbtKYBmxBC/4JmVnPfEXr0DOK1GS0Pukc+BNJzT+DweakNtw1SBf8fCwiFnQ2JLPhxc1HZmZWcPORmZkVBnXz0brrrhsTJ07sdBhmZoPKDTfc8FhEjO1u2KBOChMnTmTOnDmdDsPMbFCRVHtFe8HNR2ZmVnBSMDOzgpOCmZkVnBTMzKzgpGBmZgUnBTMzKzgpmJlZwUnBzMwKTgpmZlYY1Fc0D2YTp1/c0unPP2GPlk7fzIYm7ymYmVnBScHMzApOCmZmVnBSMDOzgpOCmZkVnBTMzKzgpGBmZgUnBTMzKzgpmJlZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFZwUzMys4FtnD0Otvm03+NbdlnhZG3y8p2BmZoWWJQVJP5O0SNLtpX5jJF0u6Z78vnZp2NGS7pV0l6RdWxWXmZn1rJV7Cr8APlTTbzowOyI2BWbnz0iaBOwDvCV/5xRJK7YwNjMz60bLkkJEXAX8o6b3FGBm7p4JTC31PzMiXoiIecC9wLatis3MzLrX7mMK4yJiAUB+Xy/3nwA8WBrvodzvNSQdLGmOpDmLFy9uabBmZsPNQDnQrG76RXcjRsSMiJgcEZPHjh3b4rDMzIaXdieFhZLGA+T3Rbn/Q8CGpfE2AB5pc2xmZsNeu5PCBcC03D0NOL/Ufx9JK0vaBNgUuK7NsZmZDXstu3hN0hnATsC6kh4CjgFOAGZJOhB4ANgbICLukDQLuBN4GTg0Il5pVWxmZta9liWFiPhkD4N27mH8bwHfalU8ZmbWO9/mwsyGJN9iozED5ewjMzMbAJwUzMys4KRgZmYFJwUzMys4KZiZWcFJwczMCsP6lNRWn7I2FE9XM7OhzXsKZmZWcFIwM7NCr0lB0uGSRis5XdKNkj7YjuDMzKy9quwpfCYilgAfBMYCB5BubGdmZkNMlaTQ9QCc3YGfR8QtdP9QHDMzG+SqJIUbJF1GSgqXShoFLGttWGZm1glVTkk9ENgKuD8inpW0DqkJyczMhphek0JELJP0MvBeSeXxb21dWGZm1gm9JgVJPwO2AO7g1WajAH7XwrjMzKwDqjQfbRcRk1oeiZm1hB82Y31R5UDzNZKcFMzMhoEqewozSYnhUeAF0umoERFbtDQyMzNruypJ4WfAfsBt+FRUM7NeDeYmuypJ4YGIuKAlpZuZ2YBSJSn8TdJvgAtJzUcARITPPjIzG2KqJIVVScmgfBM8n5JqZjYEVbl4zVcvW9MM5rZWs+Ggx6Qg6csR8T1J/0naM1hORHyhpZGZmVnb1dtTmJvf57QjEDMz67wek0JEXJg7n42Is8rDJO3d0qjMzKwjqlzRfHTFfpVJ+jdJd0i6XdIZklaRNEbS5ZLuye9r96cMMzPru3rHFHYjPUNhgqQflwaNBl5utEBJE4AvAJMi4jlJs4B9gEnA7Ig4QdJ0YDrwlUbLMTOzvqu3p/AI6XjC88ANpdcFwK79LHcEsGq+FfdquawppFtqkN+n9rMMMzPro3rHFG4BbpF0LrA0Il4BkLQisHKjBUbEw5J+ADwAPAdcFhGXSRoXEQvyOAskrddoGWZm1pgqxxQuI13A1mVV4H8bLTAfK5gCbAKsD6wuad8+fP9gSXMkzVm8eHGjYZiZWTeqJIVVIuKZrg+5e7V+lLkLMC8iFkfES6Qro3cAFkoaD5DfF3X35YiYERGTI2Ly2LFj+xGGmZnVqpIUlkrapuuDpLeTmn0a9QCwnaTVJAnYmXRNxAXAtDzONOD8fpRhZmYNqHLvoyOAsyQ9kj+PBz7RaIERca2ks4EbSWcx3QTMANYAZkk6kJQ4fC2EmVmbVbn30fWS3gRsTnrAzt9ys0/DIuIY4Jia3i+Q9hrMzKxD6l2n8P6IuELSXjWDNpXkW2ebmQ1B9fYUdgSuAPbsZphvnW1mNgTVu06hq3nnoK5rFMysMb5luA0WVc4+midphqSd89lCZmY2RFVJCpuTLlY7lJQgTpb07taGZWZmndBrUoiI5yJiVkTsBWxNuiHen1oemZmZtV2VPQUk7SjpFNK1BasAH29pVGZm1hG9XqcgaR5wMzAL+FJELG11UGZm1hlVrmjeMiKWtDwSMzPruCrHFJwQzMyGiUrHFMzMbHhwUjAzs0KvSUHSOEmnS7okf56U72RqZmZDTJU9hV8Al5KekgZwN+l22mZmNsRUSQrrRsQsYBlARLwM+F5IZmZDUNUnr61DujMqkrYDnmppVGZm1hFVrlM4kvSozDdI+jMwFvhYS6MyM7OOqPLktRsl7cirT167q79PXjMzs4Gp3pPXap+41mUzP3nNzGxoqren0N0T17r4yWtmZkNQvSevHdDOQMzMrPOqHGhG0h7AW0i3zQYgIr7ZqqDMzKwzqlzRfCrwCeDzpAPNewMbtzguMzPrgCp7CjtExBaSbo2Ib0g6ER9PsEFo4vSLW17G/BP2aHkZZq1U5eK15/L7s5LWB14CNmldSGZm1ilV9hQukrQW8H3S4zgDOK2VQZmZWWdUuXjtuNx5jqSLgFUiwre5MDMbgqo8o3lFYA9gYtf4+eK1H7Y2NDMza7cqzUcXAs8Dt5HvlGpmZkNTlaSwQURs0cxC8zGK04C3ko5RfAa4C/gtaY9kPvDxiHiimeWamVl9Vc4+ukTSB5tc7o+AP0TEm4AtgbnAdGB2RGwKzM6fzcysjaokhb8C50p6TtISSU9LWtJogZJGA+8FTgeIiBcj4klgCjAzjzYTmNpoGWZm1pgqSeFEYHtgtYgYHRGjImJ0P8p8PbAY+LmkmySdJml1YFxELADI7+t192VJB0uaI2nO4sWL+xGGmZnVqpIU7gFuj4hoUpkjgG2A/4qIrYGl9KGpKCJmRMTkiJg8duzYJoVkZmZQ7UDzAuBKSZcAL3T17McpqQ8BD0XEtfnz2aSksFDS+IhYIGk8sKjB6ZuZWYOq7CnMIx34XQkYVXo1JCIeBR6UtHnutTNwJ+mRn9Nyv2nA+Y2WYWZmjalyRfM3ACStHhFLm1Tu54FfS1oJuB84gJSgZkk6EHiAdDdWMzNroypXNG9POlNoDWAjSVsCn42If2200Ii4GZjczaCdG52mmZn1X5Xmo5OAXYHHASLiFtIppWZmNsRUSQpExIM1vV5pQSxmZtZhVc4+elDSDkDkYwBfIF2BbGZmQ0yVPYVDgEOBCaTTSbfKn83MbIjpcU9B0ncj4ivA+yLi022MyczMOqTensLukkYCR7crGDMz66x6xxT+ADwGrJ5vgCfSba4FRD/vf2RmZgNQj3sKEfGliFgTuLh8I7wm3BDPzMwGqF4PNEfElHYEYmZmnVfpOgUzMxsenBTMzKzQY1KQNDu/f7d94ZiZWSfVO/tovKQdgY9IOpN01lEhIm5saWRmZtZ29ZLC10kPv9kAqH2gTgDvb1VQZmbWGT0mhYg4Gzhb0n9ExHFtjMnMzDqkykN2jpP0EV69XfaVEXFRa8MyM7NO6PXsI0nfAQ4nPTLzTuDw3M/MzIaYKrfO3gPYKiKWAUiaCdyE74lkZjbkVL1OYa1S95otiMPMzAaAKnsK3wFukvRH0mmp78V7CWZmQ1KVA81nSLoSeAcpKXwlIh5tdWBmZtZ+VfYUiIgFwAUtjsXMzDrM9z4yM7OCk4KZmRXqJgVJK0i6vV3BmJlZZ9VNCvnahFskbdSmeMzMrIOqHGgeD9wh6TpgaVfPiPhIy6IyM7OOqJIUvtHyKMzMbECo8ozmPwHzgZG5+3qg389SkLSipJskXZQ/j5F0uaR78vva/S3DzMz6psoN8f4FOBv4ae41ATivCWUfDswtfZ4OzI6ITYHZ+bOZmbVRlVNSDwXeBSwBiIh7gPX6U6ikDUg32jut1HsKMDN3zwSm9qcMMzPruypJ4YWIeLHrg6QRpCev9cdJwJeBZaV+4/KV011XUHebeCQdLGmOpDmLFy/uZxhmZlZWJSn8SdJXgVUlfQA4C7iw0QIlfRhYFBE3NPL9iJgREZMjYvLYsWMbDcPMzLpR5eyj6cCBwG3AZ4Hfs3yzT1+9C/iIpN2BVYDRkn4FLJQ0PiIWSBoPLOpHGWZm1oAqd0ldlh+scy2p2eiuiGi4+SgijibfelvSTsAXI2JfSd8HpgEn5PfzGy3DzMwa02tSkLQHcCpwH+nW2ZtI+mxEXNLkWE4AZkk6EHgA2LvJ0zczs15UaT46EXhfRNwLIOkNwMVAv5NCRFwJXJm7Hwd27u80zcyscVUONC/qSgjZ/bi938xsSOpxT0HSXrnzDkm/B2aRjinsTbqq2czMhph6zUd7lroXAjvm7sWAb0FhZjYE9ZgUIuKAdgZiZmadV+Xso02AzwMTy+P71tlmZkNPlbOPzgNOJ13FvKz+qGZmNphVSQrPR8SPWx6JmZl1XJWk8CNJxwCXAS909YyIfj9TwczMBpYqSeFtwH7A+3m1+SjyZzMzG0KqJIV/Al5fvn22mZkNTVWuaL4FWKvFcZiZ2QBQZU9hHPA3Sdez/DEFn5JqZjbEVEkKx7Q8CjMzGxCqPE/hT+0IxMzMOq/KFc1P8+ozmVcCRgJLI2J0KwMzM7P2q7KnMKr8WdJUYNtWBWRmZp1T5eyj5UTEefgaBTOzIalK89FepY8rAJN5tTnJzMyGkCpnH5Wfq/AyMB+Y0pJozMyso6ocU/BzFczMhol6j+P8ep3vRUQc14J4zMysg+rtKSztpt/qwIHAOoCTgpnZEFPvcZwndnVLGgUcDhwAnAmc2NP3zMxs8Kp7TEHSGOBI4NPATGCbiHiiHYGZmVn71Tum8H1gL2AG8LaIeKZtUZmZWUfUu3jtKGB94GvAI5KW5NfTkpa0JzwzM2unescU+ny1s5mZDW7e8JuZWaHtSUHShpL+KGmupDskHZ77j5F0uaR78vva7Y7NzGy468SewsvAURHxZmA74FBJk4DpwOyI2BSYnT+bmVkbtT0pRMSCiLgxdz8NzAUmkO6nNDOPNhOY2u7YzMyGu44eU5A0EdgauBYYFxELICUOYL0evnOwpDmS5ixevLhtsZqZDQcdSwqS1gDOAY6IiMqnuEbEjIiYHBGTx44d27oAzcyGoY4kBUkjSQnh1xHxu9x7oaTxefh4YFEnYjMzG846cfaRgNOBuRHxw9KgC4BpuXsacH67YzMzG+6qPGSn2d4F7AfcJunm3O+rwAnALEkHAg8Ae3cgNjOzYa3tSSEirgbUw+Cd2xmLmZktz1c0m5lZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFZwUzMys4KRgZmYFJwUzMys4KZiZWcFJwczMCk4KZmZWcFIwM7OCk4KZmRWcFMzMrOCkYGZmBScFMzMrOCmYmVnBScHMzApOCmZmVnBSMDOzgpOCmZkVnBTMzKzgpGBmZgUnBTMzKzgpmJlZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFQZcUpD0IUl3SbpX0vROx2NmNpwMqKQgaUXgJ8BuwCTgk5ImdTYqM7PhY0AlBWBb4N6IuD8iXgTOBKZ0OCYzs2FDEdHpGAqSPgZ8KCIOyp/3A94ZEYeVxjkYODh/3By4q+2Bdsa6wGOdDqIDXO/hxfVuj40jYmx3A0a0MYgq1E2/5bJWRMwAZrQnnIFD0pyImNzpONrN9R5eXO/OG2jNRw8BG5Y+bwA80qFYzMyGnYGWFK4HNpW0iaSVgH2ACzock5nZsDGgmo8i4mVJhwGXAisCP4uIOzoc1kAx7JrMMtd7eHG9O2xAHWg2M7POGmjNR2Zm1kFOCmZmVnBS6BBJG0r6o6S5ku6QdHjuP0bS5ZLuye9rl75zdL79x12Sds39VpN0saS/5emc0Kk6VdGsetdM8wJJt7ezHn3VzHpLWknSDEl359/9o52oUxVNrvcnJd0m6VZJf5C0bifqVEVf6y1pnTz+M5JOrpnW23O975X0Y0ndnbrfPBHhVwdewHhgm9w9CribdGuP7wHTc//pwHdz9yTgFmBlYBPgPtLB+NWA9+VxVgL+D9it0/Vrdb1L09sL+A1we6fr1q56A98Ajs/dKwDrdrp+ra436aSYRV11zd8/ttP1a2K9VwfeDRwCnFwzreuA7UnXcV3S6vXbewodEhELIuLG3P00MBeYQLqtx8w82kxgau6eApwZES9ExDzgXmDbiHg2Iv6Yp/MicCPp+o4BqVn1BpC0BnAkcHzbKtCgZtYb+AzwnTytZRExYK8AbmK9lV+r53/KoxnA1zD1td4RsTQirgaeL09H0nhgdERcEylD/JJX51VLOCkMAJImAlsD1wLjImIBpAULWC+PNgF4sPS1h3K/8nTWAvYEZrc24uZoQr2PA04Enm1HvM3Sn3rn3xjgOEk3SjpL0ri2BN5P/al3RLwEfA64jZQMJgGntyfy/qlY755MIM2DLq9Z75vNSaHD8r/dc4AjImJJvVG76VecTyxpBHAG8OOIuL+5UTZff+staSvgjRFxbivia5Um/N4jSHuCf46IbYBrgB80PdAma8LvPZKUFLYG1gduBY5ueqBN1od69ziJbvq19DoCJ4UOygv6OcCvI+J3uffCvMvYteu4KPfv7RYgM4B7IuKklgbdBE2q9/bA2yXNB64GNpN0Zeujb1yT6v04ac+oKxmeBWzT4tD7pUn13gogIu7LzSizgB1aH33j+ljvnjzE8s3BLb/1j5NCh+R20dOBuRHxw9KgC4BpuXsacH6p/z6SVpa0CbAp6QAUko4H1gSOaEPo/dKsekfEf0XE+hExkXSA7u6I2KkddWhEE+sdwIXATnm8nYE7Wxx+w5q4nD8MTJLUdWfPD5Da6QekBurdrdzE9LSk7fI0/7m37/Rbu47G+/WasxPeTdoNvBW4Ob92B9YhHRO4J7+PKX3n30lnY9xFPgOB9M8hSCtI13QO6nT9Wl3vmmlOZOCffdS0egMbA1flac0GNup0/dpU70Pycn4rKTGu0+n6Nbne84F/AM+Q9hAm5f6TgdvzPDmZfCeKVr18mwszMyu4+cjMzApOCmZmVnBSMDOzgpOCmZkVnBTMzKzgpGDWB0qulrRbqd/HJf2hk3GZNYtPSTXrI0lvJV1JvDXpDp43Ax+KiPsamNaKEfFKcyM0a5yTglkDJH0PWEq65fFS0gVlbyPdm+jYiDg/3wjtf/I4AIdFxF8k7QQcAywAtoqISe2N3qxnTgpmDZC0Ouk25S8CFwF3RMSv8l1MryPtRQSwLCKel7QpcEZETM5J4WLgrZFuD202YIzodABmg1FELJX0W9ItCT4O7Cnpi3nwKsBGpBuXnZzv6PoKsFlpEtc5IdhA5KRg1rhl+SXgoxFxV3mgpGOBhcCWpJM6yg9QWdqmGM36xGcfmfXfpcDnu56dK2nr3H9NYEFELAP2Ix2UNhvQnBTM+u84YCRwq6Tb82eAU4Bpkv5Kajry3oENeD7QbGZmBe8pmJlZwUnBzMwKTgpmZlZwUjAzs4KTgpmZFZwUzMys4KRgZmaF/w8w89N50G7lYgAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"4hkMmWZ7wk7h","outputId":"36f4437f-02b6-4392-bb0a-39584db5711d"},"source":["# Graph against male victims for the state of Assam\n","plt.bar(df[(df[\"State\"]==\"Assam\") & (df[\"Gender Group\"]==\"Murder - Male Victims\")][\"Year\"], df[(df[\"State\"]==\"Assam\") & (df[\"Gender Group\"]==\"Murder - Male Victims\")][\"Total Number Of Victims\"])\n","plt.xlabel(\"Year\")\n","plt.ylabel(\"Number of male victims\")\n","plt.title(\"Crime rate graph against male for the state of Assam\")"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Text(0.5, 1.0, 'Crime rate graph against male for the state of Assam')"]},"metadata":{"tags":[]},"execution_count":244},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAju0lEQVR4nO3debwcVZn/8c+XBMIaICQwWQgJGtGAgphB3FF0ABeSwcGJoxARJ6IoKDhDov4EB6KIyyjDoOY3qEGWTNwgLgiIIsPIFtlDiAQSSSCQsAeQJeGZP85pU2n63qp7b/e9nfT3/Xr1q6tPnap6TnV1P111qqoVEZiZmXVns4EOwMzM2p+ThZmZlXKyMDOzUk4WZmZWysnCzMxKOVmYmVkpJ4sKJH1W0n8NdBybAknLJL29DeK4RNK0gY6jJySdIum8Xk67h6SbJK2RdFyzY8vLuFLSR1ox73Yl6Q2S7pL0pKQpAx1PK3VkspD0T5IW5Dd4Zf7ieGNX9SPiSxHRlh8CST+QdNpAx7GxiYhDImJOX+Yh6UOSrm5WTC32r8CVEbFdRJzZ15n1JXH153IlHSBpRQtD+jfgrIjYNiIu6iaOKyU9KmlIC2NpqY5LFpJOAL4JfAnYBRgLnA1M7qL+4H4Lro2W3Ui7xWM9shuwsDcT+n3vVul6lTQOeBMQwKH9EFNrRETHPIDtgSeBw7upcwrwY+A84AngI7nsvDx+HOlNPwpYDjwKHAP8LXAr8Bjpl0Zxnh8GFuW6lwK7dbHs2ryPBu4FrsrlPwIeAB4HrgL2zOXTgeeB53K7fp7LRwE/AVYDS4HjumnvTsDPc1tvAE4Dri6MD+BY4C5gaS77Vm77E8AfgTc1WH//DawBbgT2LoxfBnwmr6vHc70tu4jtJcBvgYeBh4DzgR0K4/cFbsrL+VGe12l53I7AL/I6eDQPjylMeyXwkTz8IeBq4Gu57lLgkELdDwH35OUsBT4AvAJ4BliX1/1jXbThyrxO/1B7j/I6P7+wzscV6pet2/MKr/fP830MuAU4oIsYfpvjfCbH8DLSZ+HcvH7+DHwe2KzQ3v8F/h14pLZOC/M7mLTNPZ/nd0uhrafmadcAlwHDexpvrnsScF+ez2LgwG6WexTp87Umv08fzeXbAH8BXsj1nyR9NjYDZgB3k7atecCwbmL5Z2BJXhfzgVG5/O4877/keQ/pYvov5HXyDeAXdePeCdyRY78P+EwuH07aZh/Ly/2fwvtTi31Nnvbv67bV2nv3WF4fr8/ly4FVwLRefX8240t4Y3nkjW0tMLibOqfkjXFK3qi2onGy+A6wJfB3pA/hRcDOwOj8hrwl15+SN7RXAINJH8o/dLHs2rzPzRv6Vrn8w8B2wBDSXtHNhWl+QOHDnGP+Y95AtwB2zxvMQV0sc25+bA1MzBtUfbK4HBhWiOeDpC+8wcCJpES2Zd36+wdgc1JiWApsnscvA64nfWiHkT7kx3QR20uBd+R2jyAlym/mcVuQvuSOz8s5jPRFUksWOwHvze3ajpRMLirM+0o2TBbPk74UBgEfA+4HlN+HJ4A9ct2RrE/WHyquqy7acGV+/19C+oK+A/gT8Pa8/s4Fvl+oX7Zua9vhaNIX3Tvze/6O/HpEN3F8pPD6XODivG7G5ZiOLrRrLfDJHMdWXXxOzmuwjLtJyWir/Pr0nsYL7EHaDmtfyuOAl3Sz3Hfl9SvgLcDTwL553AHAirr6nwKuBcaQtq3vAhd2sd7eRvqhsm+u+x/kH3GF7fntJdvAEuDjwGtI29kuhXEryT8ISD9wanF/mfQds3l+vAlQHnc465PePwJPASPr3rujSNvyaaQfnv+Z4/87UpLZtsffn7394t0YH6RfhA+U1DmluDHUb6Cs/0IfXRj/MPCPhdc/AT6Vhy8hfwjz683yxrxbg2XX5r17N/HtkOtsn1//gA2TxWuBe+ummUnhC6lQPihvvHsUyhrtWbytZJ09St57yOvq2rr2Fj8Qy4APFsafAXyn4vs3BbgpD7+Z9EtMhfFXU/cruDBuH+DRwusr2TBZLCmM2zq3+29IyeIxUuLZqm6eH6Jasvhc4fXXgUsKr99DIflXWLe17fAk4Id1dS+li1+Nde0dBDwLTCyM/yipT6PWrnu7iqk+lrplfL7w+uPAr3saL+lHwipSQt28bLkNpr8IOD4PH8CLk8Ui4MDC65Gkz8GLfkQC5wBnFF5vm+uOK2zPXSYL4I25/vD8+k7g04Xx9+Z1P7Ruun8jJfOXVvhc3AxMLrx3dxXGvTJvy8UE9TCwT9l86x+d1mfxMDC8wjHY5RXm9WBh+C8NXm+bh3cDviXpMUmPkXYpRfqlVbp8SYMknS7pbklPkDZOSLupjewGjKotLy/zs6T+mXojSL8ci+1t1PYNyiSdKGmRpMfz/Levi+ev9SPiBWAF6ZdQzQOF4adZv642IGlnSXMl3Zfbfl5hOaOA+yJv/fXLlbS1pO9K+nOe9ipgB0mDGi2rGFNEPJ0Ht42Ip0i/3o4BVkr6paSXdzGPrlTdVqqs25rdgMPr3uc3kr74ygxn/Z5ZzZ/ZcJus8hlopKv3tnK8EbGE9Ov/FGBV3gZG1derkXSIpGslPZLn+066/nzUYvlZIY5FpMN0jT4joyisp4h4kvQ90t3nt2gacFlEPJRfX5DLat6b4/2zpN9Lel0u/yppj+QySfdImlFo75GSbi7Ev1dde+u3LyKiy22uqk5LFteQDhlNKakXJeN7YjnpGOoOhcdWEfGHisv/J1Ln+9tJXxzjcrm6iHU5qW+huLztIuKdDZazmrTLOqZQtmt38Uh6E+lX4vuAHSNiB1Lfgwr1dy3U3yzP//4G8y3z5bzsV0XEUNIhmtpyVgKjJTVcLukQzh7Aa/O0b66F1NMgIuLSiHgH6YvtTuD/10b1dF7dqbhua5aTfqkX3+dtIuL0Cot6iPRrd7dC2VjSnlpNWdt62vYexRsRF0TEG3OMAXyl0XLz2UU/IfU37ZLX2a/o+vNRi+WQuli2jIj7GtS9n8J6krQN6TBho7obkLQV6b18i6QHJD0AfBrYW9LeuZ03RMRk0iHsi0j9J0TEmog4MSJ2J+19niDpQEm7kba/TwA75fbeTi+2657qqGQREY+TjuX/p6Qp+dfn5vmXyRktWux3gJmS9gSQtL2kw3sw/XakQwYPkw6PfKlu/IOkfoma64EnJJ0kaau8Z7KXpL+tn3FErAN+CpyS18XLgSMrxLOWlGgGS/oCMLSuzmskHZb34D6V47+2QlsbLetJ4DFJo4F/KYy7hvRr8BOSBkuaDOxXN+1f8rTDgJN7sXwk7SLp0Pwl8WyOZ10e/SAwRtIWvZl3A1XWbc15wHskHZTf4y3zaaJjuqj/V/l9nwfMkrRd/gI6Ic+zqgeBcfnHQBWV41W6JuRtORE8Q3ofi+u8uNwtSMfiVwNrJR1COi5fjHMnSdsXyr5DavtueXkj8vbTyAXAUZL2yfF8CbguIpZVaPOUHPdE0mHQfUh9l/8DHClpC0kfkLR9RDxP6htbl2N6t6SX5h9DtfJ1pMOikduLpKNIexYt11HJAiAivkH6YHyetMKXk7L0RS1a3s9Iv4rm5sMhtwOH9GAW55J2g+8jdY7Wf+meA0zMu6QX5S+C95A2zKWkX5H/RdoraeQTedwDwA+BC0lfil25lNQP86cc1zO8+JDFxaRDN48CRwCH5Q9DT32R1LH4OPBLUmIDICKeI3VqH03qU/gg6eyRWuzfJHWyPkRaZ7/uxfIhfUZOJP3CfITUgfrxPO63pNMmH5D0UOPJe6TKugUgIpaT9jg/y/rt+F+o/pn+JKlj9B5SX88FwPd6EOuP8vPDkm4sq9zDeIcAp5PeuwdIv7o/22i5EbEGOI6U/B4l7YnPLyz3TtI2fU/+jIwinXE2n3SIZw1p+3htF3FfAfw/0t7LSlJH+tSy9mbTSH2F90bEA7UHcBap/xTS52NZ/m44hrQdA0wAfkP6cXINcHZEXBkRd5D6va4hJcJXks5+arla77oZAJK+AvxNREwrrdx4+lNInXIfLKvbbJKuI3WWf7+/l222qeu4PQvbkKSXS3qVkv1Iv9R/NtBxVSHpLZL+Jh+Gmga8it7vQZhZN3xlpm1H2k0fRTpd8eukw0gbgz1Ihx+2JZ3f/w8RsXJgQzLbNPkwlJmZlfJhKDMzK7XJHoYaPnx4jBs3bqDDMDPbaAwfPpxLL7300og4uH7cJpssxo0bx4IFCwY6DDOzjYqkhle/+zCUmZmVcrIwM7NSThZmZlbKycLMzEo5WZiZWSknCzMzK+VkYWZmpZwszMyslJOFmZmV2mSv4Daz9jVuxi9bvoxlp7+r5cvoJN6zMDOzUk4WZmZWysnCzMxKOVmYmVkpJwszMyvls6HMOpTPSLKe8J6FmZmVcrIwM7NSThZmZlbKycLMzEo5WZiZWSknCzMzK+VkYWZmpZwszMyslC/KM7OO4osRe6dlexaSvidplaTbC2VflXSnpFsl/UzSDoVxMyUtkbRY0kGF8tdIui2PO1OSWhWzmZk11srDUD8ADq4ruxzYKyJeBfwJmAkgaSIwFdgzT3O2pEF5mm8D04EJ+VE/TzMza7GWJYuIuAp4pK7ssohYm19eC4zJw5OBuRHxbEQsBZYA+0kaCQyNiGsiIoBzgSmtitnMzBobyA7uDwOX5OHRwPLCuBW5bHQeri9vSNJ0SQskLVi9enWTwzUz61wDkiwkfQ5YC5xfK2pQLbopbygiZkfEpIiYNGLEiL4HamZmwACcDSVpGvBu4MB8aAnSHsOuhWpjgPtz+ZgG5WZm1o/6dc9C0sHAScChEfF0YdR8YKqkIZLGkzqyr4+IlcAaSfvns6COBC7uz5jNzKyFexaSLgQOAIZLWgGcTDr7aQhweT4D9tqIOCYiFkqaB9xBOjx1bESsy7P6GOnMqq1IfRyXYGZm/aplySIi3t+g+Jxu6s8CZjUoXwDs1cTQzMysh3y7DzMzK+VkYWZmpZwszMyslG8k2GZ8kzMza0feszAzs1Les7C24D0q6wQb83buZGE2gDbmLw/rLE4W1vH8hW1Wzn0WZmZWysnCzMxKOVmYmVkpJwszMyvlDm77K3f0mllXvGdhZmalnCzMzKxUj5KFpM0kDW1VMGZm1p5Kk4WkCyQNlbQN6Z/sFkv6l9aHZmZm7aLKnsXEiHgCmAL8ChgLHNHKoMzMrL1USRabS9qclCwujojngWhpVGZm1laqJIvvAsuAbYCrJO0GPNHKoMzMrL2UXmcREWcCZxaK/izpra0LyczM2k1pspC0A3AkMK6u/nGtCcnMzNpNlcNQvyIlituAPxYe3ZL0PUmrJN1eKBsm6XJJd+XnHQvjZkpaImmxpIMK5a+RdFsed6Yk9aB9ZmbWBFVu97FlRJzQi3n/ADgLOLdQNgO4IiJOlzQjvz5J0kRgKrAnMAr4jaSXRcQ64NvAdOBaUuI6GLikF/FU5ttemJltqMqexQ8l/bOkkXnPYJikYWUTRcRVwCN1xZOBOXl4DukMq1r53Ih4NiKWAkuA/SSNBIZGxDUREaTEMwUzM+tXVfYsngO+CnyO9afMBrB7L5a3S0SsBIiIlZJ2zuWjSXsONSty2fN5uL68IUnTSXshjB07thfhmZlZI1WSxQnASyPioRbG0agfIropbygiZgOzASZNmuRrQczMmqTKYaiFwNNNWt6D+dAS+XlVLl8B7FqoNwa4P5ePaVBuZmb9qEqyWAfcLOm7+WykMyWdWTpVY/OBaXl4GnBxoXyqpCGSxgMTgOvzIas1kvbPZ0EdWZjGzMz6SZXDUBflR49IuhA4ABguaQVwMnA6ME/S0cC9wOEAEbFQ0jzSjQrXAsfmM6EAPkY6s2or0llQLT0TyszMXqzKFdxzyup0Md37uxh1YBf1ZwGzGpQvAPbqTQxmZtYcXSYLSfMi4n2SbqNBp3JEvKqlkZmZWdvobs/i+Pz87v4IxMzM2leXHdy16yGAj0fEn4sP4OP9E56ZmbWDKmdDvaNB2SHNDsTMzNpXd30WHyPtQbxE0q2FUdsBf2h1YGZm1j6667O4gHSa6pdJN/yrWRMR9fd8MjOzTVh3fRaPR8Qy4FvAI4X+iuclvba/AjQzs4FXpc/i28CThddP5TIzM+sQVZKF8u3BAYiIF6h25beZmW0iqiSLeyQdJ2nz/DgeuKfVgZmZWfuokiyOAV4P3Ee6C+xryf8ZYWZmnaHKvaFWkf7y1MzMOlR311n8a0ScIek/aHxvqONaGpmZmbWN7vYsFuXnBf0RiJmZta8uk0VE/DwP3hoRN/VTPGZm1oaqdHB/Q9Kdkk6VtGfLIzIzs7ZTmiwi4q2kf7xbDcyWdJukz7c6MDMzax9V9iyIiAci4kzSabQ3A19oZVBmZtZeSpOFpFdIOkXS7cBZpDvOjml5ZGZm1jaq3Lbj+8CFwN9FxP0tjsfMzNpQlYvy9u+PQMzMrH1V6rMwM7PONiDJQtKnJS2UdLukCyVtKWmYpMsl3ZWfdyzUnylpiaTFkg4aiJjNzDpZ5WQhaZtmLFDSaOA4YFJE7AUMIt17agZwRURMAK7Ir5E0MY/fEzgYOFvSoGbEYmZm1VQ5G+r1ku4g3/5D0t6Szu7jcgcDW0kaDGwN3A9MBubk8XOAKXl4MjA3Ip6NiKXAEmC/Pi7fzMx6oMqexb8DBwEPA0TELcCbe7vAiLgP+BpwL7ASeDwiLgN2iYiVuc5KYOc8yWhgeWEWK3LZi0iaLmmBpAWrV6/ubYhmZlan6kV5y+uK1vV2gbkvYjIwHhgFbCPpg91N0iikRhUjYnZETIqISSNGjOhtiGZmVqdKslgu6fVASNpC0mdYf0fa3ng7sDQiVkfE88BPSX+u9KCkkQD5eVWuvwLYtTD9GNJhKzMz6ydV/ynvWNKhnxXAPvl1b90L7C9pa0kCDiQln/nAtFxnGnBxHp4PTJU0RNJ4YAJwfR+Wb2ZmPVTloryHgA80a4ERcZ2kHwM3AmuBm4DZwLbAPElHkxLK4bn+QknzgDty/WMjoteHwczMrOe6+6e8hv+QV9OXf8qLiJOBk+uKnyXtZTSqPwuY1dvlmZlZ33S3Z+F/yDMzM6D7f8qb09U4MzPrLKV9FpJGACcBE4Eta+UR8bYWxmVmZm2kytlQ55POVhoPfBFYBtzQwpjMzKzNVEkWO0XEOcDzEfH7iPgw4NuWm5l1kCp/fvR8fl4p6V2kC+L8T3lmZh2kSrI4TdL2wInAfwBDgU+3NCozM2srVS7K+0UefBx4a2vDMTOzdlTlbKjxwCeBccX6EXFo68IyM7N2UuUw1EXAOcDPgRdaGo2ZmbWlKsnimYg4s+WRmJlZ26qSLL4l6WTgMtL9mwCIiBtbFpWZmbWVKsnilcARwNtYfxgq8mszM+sAVZLF3wO7R8RzrQ7GzMzaU5UruG8BdmhxHGZm1saq7FnsAtwp6QY27LPwqbNmZh2iSrKo/5MiMzPrMFWu4P59fwRiZmbtq0qfhZmZdTgnCzMzK9VlspB0RX7+Sv+FY2Zm7ai7PouRkt4CHCppLqDiSF/BbWbWObpLFl8AZpD+6OgbdeN8BbeZWQfp8jBURPw4Ig4BzoiIt9Y9+pQoJO0g6ceS7pS0SNLrJA2TdLmku/LzjoX6MyUtkbRY0kF9WbaZmfVcaQd3RJwq6VBJX8uPdzdhud8Cfh0RLwf2BhaR9mKuiIgJwBX5NZImAlOBPYGDgbMlDWpCDGZmVlFpspD0ZeB44I78OD6X9YqkocCbSf+RQUQ8FxGPAZOBObnaHGBKHp4MzI2IZyNiKbAE2K+3yzczs56rcgX3u4B9IuIFAElzgJuAmb1c5u7AauD7kvYG/khKRrtExEqAiFgpaedcfzRwbWH6FbnsRSRNB6YDjB07tpfhmZlZvarXWexQGN6+j8scDOwLfDsiXg08RT7k1AU1KItGFSNidkRMiohJI0aM6GOYZmZWU2XP4svATZJ+R/rifjO936uAtGewIiKuy69/TEoWD0oamfcqRgKrCvV3LUw/Bri/D8s3M7MeqtLBfSGwP/DT/HhdRMzt7QIj4gFguaQ9ctGBpL6Q+cC0XDYNuDgPzwemShoiaTwwAbi+t8s3M7Oeq7JnQe5LmN/E5X4SOF/SFsA9wFGkxDVP0tHAvcDhedkLJc0jJZS1wLERsa6JsZiZWYlKyaLZIuJmYFKDUQd2UX8WMKuVMZmZWdd8I0EzMyvVbbKQtJmk2/srGDMza0/dJot8bcUtknzRgplZB6vSZzESWCjpetI1EYD/g9vMrJNUSRZfbHkUZmbW1ir9B7ek3YAJEfEbSVsDvpGfmVkHqXIjwX8mXWX93Vw0GriohTGZmVmbqXLq7LHAG4AnACLiLmDnbqcwM7NNSpVk8WxEPFd7IWkwXdzIz8zMNk1VksXvJX0W2ErSO4AfAT9vbVhmZtZOqiSLGaT/n7gN+CjwK+DzrQzKzMzaS5WzoV7If3h0Henw0+KI8GEoM7MOUposJL0L+A5wN+n/LMZL+mhEXNLq4MzMrD1UuSjv68BbI2IJgKSXAL8EnCzMzDpElT6LVbVEkd3D+n+xMzOzDtDlnoWkw/LgQkm/AuaR+iwOB27oh9jMzKxNdHcY6j2F4QeBt+Th1cCOLYvIzMzaTpfJIiKO6s9AzMysfVU5G2o86T+zxxXr+xblZmado8rZUBcB55Cu2n6hpdGYmVlbqpIsnomIM1seiZmZta0qyeJbkk4GLgOerRVGxI0ti8rMzNpKlWTxSuAI4G2sPwwV+XWvSRoELADui4h3SxoG/Depb2QZ8L6IeDTXnQkcDawDjouIS/uybDMz65kqyeLvgd2LtylvkuOBRcDQ/HoGcEVEnC5pRn59kqSJwFRgT2AU8BtJL4uIdU2Ox8zMulDlCu5bgB2auVBJY4B3Af9VKJ4MzMnDc4AphfK5EfFsRCwFlgD7NTMeMzPrXpU9i12AOyXdwIZ9Fn05dfabwL8C2xWXExEr87xXSqr9G99o4NpCvRW57EUkTQemA4wdO7YP4ZmZWVGVZHFyMxco6d2k+039UdIBVSZpUNbwFukRMRuYDTBp0iTfRt3MrEmq/J/F75u8zDcAh0p6J7AlMFTSecCDkkbmvYqRrL9Z4Qpg18L0Y4D7mxyTmZl1o7TPQtIaSU/kxzOS1kl6orcLjIiZETEmIsaROq5/GxEfBOYD03K1acDFeXg+MFXSkHw1+QTg+t4u38zMeq7KnkWxXwFJU2hNB/PpwDxJRwP3ku5uS0QslDQPuANYCxzrM6HMzPpXlT6LDUTERfnU1j6LiCuBK/Pww8CBXdSbBcxqxjLNzKznqtxI8LDCy82ASXTRwWxmZpumKnsWxf+1WEu6unpyS6IxM7O2VKXPwv9rYWbW4br7W9UvdDNdRMSpLYjHzMzaUHd7Fk81KNuGdEO/nQAnCzOzDtHd36p+vTYsaTvSjf+OAuYCX+9qOjMz2/R022eRbxt+AvAB0s399q3dNtzMzDpHd30WXwUOI91r6ZUR8WS/RWVmZm2lu9t9nEj6/4jPA/cXbvmxpi+3+zAzs41Pd30WVf7rwszMOoATgpmZlXKyMDOzUk4WZmZWysnCzMxKOVmYmVkpJwszMyvlZGFmZqWcLMzMrJSThZmZlXKyMDOzUk4WZmZWysnCzMxK9XuykLSrpN9JWiRpoaTjc/kwSZdLuis/71iYZqakJZIWSzqov2M2M+t0A7FnsRY4MSJeAewPHCtpIjADuCIiJgBX5NfkcVOBPYGDgbMlDRqAuM3MOla/J4uIWBkRN+bhNcAiYDQwmfRvfOTnKXl4MjA3Ip6NiKXAEmC/fg3azKzDDWifhaRxwKuB64BdImIlpIQC7JyrjQaWFyZbkcsazW+6pAWSFqxevbplcZuZdZoBSxaStgV+AnwqIrr75z01KItGFSNidkRMiohJI0aMaEaYZmbGACULSZuTEsX5EfHTXPygpJF5/EhgVS5fAexamHwMcH9/xWpmZgNzNpSAc4BFEfGNwqj5wLQ8PA24uFA+VdIQSeOBCcD1/RWvmZl18x/cLfQG4AjgNkk357LPAqcD8yQdDdwLHA4QEQslzQPuIJ1JdWxErOv3qM3MOli/J4uIuJrG/RAAB3YxzSxgVsuCMjOzbvkKbjMzK+VkYWZmpZwszMyslJOFmZmVcrIwM7NSThZmZlbKycLMzEo5WZiZWSknCzMzK+VkYWZmpZwszMyslJOFmZmVcrIwM7NSThZmZlbKycLMzEo5WZiZWSknCzMzK+VkYWZmpZwszMyslJOFmZmVcrIwM7NSThZmZlbKycLMzEptNMlC0sGSFktaImnGQMdjZtZJNopkIWkQ8J/AIcBE4P2SJg5sVGZmnWOjSBbAfsCSiLgnIp4D5gKTBzgmM7OOoYgY6BhKSfoH4OCI+Eh+fQTw2oj4RF296cD0/HIPYHG/BjowhgMPDXQQA6RT2+52d5b+bPdDABFxcP2Iwf0UQF+pQdmLslxEzAZmtz6c9iFpQURMGug4BkKntt3t7izt0u6N5TDUCmDXwusxwP0DFIuZWcfZWJLFDcAESeMlbQFMBeYPcExmZh1jozgMFRFrJX0CuBQYBHwvIhYOcFjtoqMOu9Xp1La73Z2lLdq9UXRwm5nZwNpYDkOZmdkAcrIwM7NSThZtRtKukn4naZGkhZKOz+XDJF0u6a78vGNhmpn5NiiLJR2Uy7aW9EtJd+b5nD5QbaqqWW2vm+d8Sbf3Zzt6qpntlrSFpNmS/pTf+/cORJuqaHK73y/pNkm3Svq1pOED0aYqetpuSTvl+k9KOqtuXq/J7V4i6UxJjS4zaI6I8KONHsBIYN88vB3wJ9ItTs4AZuTyGcBX8vBE4BZgCDAeuJt0EsDWwFtznS2A/wEOGej29UfbC/M7DLgAuH2g29Zf7Qa+CJyWhzcDhg90+1rdbtKJOqtqbc3TnzLQ7Wtiu7cB3ggcA5xVN6/rgdeRrkW7pJWfce9ZtJmIWBkRN+bhNcAiYDTp9iZzcrU5wJQ8PBmYGxHPRsRSYAmwX0Q8HRG/y/N5DriRdH1K22pW2wEkbQucAJzWbw3opWa2G/gw8OU8rxciom2veG5iu5Uf2+Rf1kNp4+uwetruiHgqIq4GninOR9JIYGhEXBMpc5zL+nXVdE4WbUzSOODVwHXALhGxEtLGBuycq40GlhcmW5HLivPZAXgPcEVrI26eJrT9VODrwNP9EW+z9KXd+X0GOFXSjZJ+JGmXfgm8j/rS7oh4HvgYcBspSUwEzumfyPumYru7Mpq0Dmpe9NlvJieLNpV/Gf8E+FREPNFd1QZlfz0fWtJg4ELgzIi4p7lRtkZf2y5pH+ClEfGzVsTXKk14zweT9h7/NyL2Ba4Bvtb0QJusCe/35qRk8WpgFHArMLPpgTZZD9rd5SwalLXsWggnizaUN/6fAOdHxE9z8YN5t7O2+7kql5fdCmU2cFdEfLOlQTdJk9r+OuA1kpYBVwMvk3Rl66PvvSa1+2HSnlQtSf4I2LfFofdJk9q9D0BE3J0Px8wDXt/66Huvh+3uygo2PLTc0tsgOVm0mXzM9RxgUUR8ozBqPjAtD08DLi6UT5U0RNJ4YAKp0wtJpwHbA5/qh9D7rFltj4hvR8SoiBhH6hj8U0Qc0B9t6I0mtjuAnwMH5HoHAne0OPxea+K2fh8wUdKIXO8dpH6AttSLdjeUD1WtkbR/nueRZdP0SX+eBeBHpTMl3kjalbwVuDk/3gnsROpzuCs/DytM8znSmSGLyWdDkH5lBOlDU5vPRwa6ff3R9rp5jqP9z4ZqWruB3YCr8ryuAMYOdPv6qd3H5G39VlLC3Gmg29fkdi8DHgGeJO1RTMzlk4Db8zo5i3xXjlY8fLsPMzMr5cNQZmZWysnCzMxKOVmYmVkpJwszMyvlZGFmZqWcLMyaRMnVkg4plL1P0q8HMi6zZvCps2ZNJGkv0pXTrybdEfVm4OCIuLsX8xoUEeuaG6FZ7zhZmDWZpDOAp0i3ln6KdKHcK0n3bjolIi7ON5D7Ya4D8ImI+IOkA4CTgZXAPhExsX+jN2vMycKsySRtQ7ol/HPAL4CFEXFevivs9aS9jgBeiIhnJE0ALoyISTlZ/BLYK9JtuM3awuCBDsBsUxMRT0n6b9KtGd4HvEfSZ/LoLYGxpBu+nZXvkLsOeFlhFtc7UVi7cbIwa40X8kPAeyNicXGkpFOAB4G9SSeaFP/Y5ql+itGsMp8NZdZalwKfrP03sqRX5/LtgZUR8QJwBKkz3KxtOVmYtdapwObArZJuz68BzgamSbqWdAjKexPW1tzBbWZmpbxnYWZmpZwszMyslJOFmZmVcrIwM7NSThZmZlbKycLMzEo5WZiZWan/A1QzZsHrYOnSAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"sJ7Pnwygwk7h","outputId":"d8ead8ee-fa64-44ad-e97a-6c5fce2a468d"},"source":["plt.hist(df[\"Total Number Of Victims\"])"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([489., 122.,  68.,  72.,  72.,  46.,  28.,  38.,   9.,  74.]),\n"," array([   0. ,  226.5,  453. ,  679.5,  906. , 1132.5, 1359. , 1585.5,\n","        1812. , 2038.5, 2265. ]),\n"," <BarContainer object of 10 artists>)"]},"metadata":{"tags":[]},"execution_count":245},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAOmElEQVR4nO3dX4hc533G8e8TOVFK/lCrXgkhiUopuqhciJMuaopLaOsSK3ap3AuDAm11YdCNAgm0FKm5aHohcAoNpbQuqE3otk0jBImxSGgbocSEQrCyTv1PVlRtYtfeSkibhJLkRq2VXy/mVTWWdrWz2l2v993vB4bznnfeM+d3XkaPj8/MnE1VIUnqy1tWugBJ0tIz3CWpQ4a7JHXIcJekDhnuktShO1a6AIC77rqrtm/fvtJlSNKq8vTTT3+vqsZme+5NEe7bt29ncnJypcuQpFUlyX/O9ZyXZSSpQyOFe5KXkzyf5Jkkk61vQ5KTSc635Z1D4w8nmUpyLsn9y1W8JGl2Czlz/7Wquqeqxtv6IeBUVe0ETrV1kuwC9gF3A3uAx5KsW8KaJUnzWMxlmb3ARGtPAA8N9R+rqitV9RIwBexexH4kSQs0argX8JUkTyc50Po2VdVFgLbc2Pq3AK8ObTvd+l4nyYEkk0kmZ2Zmbq96SdKsRv22zL1VdSHJRuBkkm/fYmxm6bvp7mRVdRQ4CjA+Pu7dyyRpCY105l5VF9ryMvA4g8ssl5JsBmjLy234NLBtaPOtwIWlKliSNL95wz3JO5K861ob+BDwAnAC2N+G7QeeaO0TwL4k65PsAHYCp5e6cEnS3Ea5LLMJeDzJtfH/VFX/kuSbwPEkjwCvAA8DVNWZJMeBF4HXgINVdXVZqpckzWrecK+q7wLvnaX/+8B9c2xzBDiy6OpGtP3Ql9+oXb3Oy48+uCL7laT5+AtVSeqQ4S5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nq0MjhnmRdkn9P8qW2viHJySTn2/LOobGHk0wlOZfk/uUoXJI0t4WcuX8MODu0fgg4VVU7gVNtnSS7gH3A3cAe4LEk65amXEnSKEYK9yRbgQeBvx3q3gtMtPYE8NBQ/7GqulJVLwFTwO4lqVaSNJJRz9z/HPhD4CdDfZuq6iJAW25s/VuAV4fGTbe+10lyIMlkksmZmZmF1i1JuoV5wz3JbwKXq+rpEV8zs/TVTR1VR6tqvKrGx8bGRnxpSdIo7hhhzL3AbyV5AHg78O4k/whcSrK5qi4m2QxcbuOngW1D228FLixl0ZKkW5v3zL2qDlfV1qrazuCD0q9W1e8AJ4D9bdh+4InWPgHsS7I+yQ5gJ3B6ySuXJM1plDP3uTwKHE/yCPAK8DBAVZ1Jchx4EXgNOFhVVxddqSRpZAsK96p6Eniytb8P3DfHuCPAkUXWJkm6Tf5CVZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KH5g33JG9PcjrJs0nOJPmT1r8hyckk59vyzqFtDieZSnIuyf3LeQCSpJuNcuZ+Bfj1qnovcA+wJ8kHgEPAqaraCZxq6yTZBewD7gb2AI8lWbcMtUuS5jBvuNfAj9vqW9ujgL3AROufAB5q7b3Asaq6UlUvAVPA7qUsWpJ0ayNdc0+yLskzwGXgZFU9BWyqqosAbbmxDd8CvDq0+XTru/E1DySZTDI5MzOziEOQJN1opHCvqqtVdQ+wFdid5BduMTyzvcQsr3m0qsaranxsbGykYiVJo1nQt2Wq6r+BJxlcS7+UZDNAW15uw6aBbUObbQUuLLZQSdLoRvm2zFiSn27tnwJ+A/g2cALY34btB55o7RPAviTrk+wAdgKnl7huSdIt3DHCmM3ARPvGy1uA41X1pSTfAI4neQR4BXgYoKrOJDkOvAi8BhysqqvLU74kaTbzhntVPQe8b5b+7wP3zbHNEeDIoquTJN0Wf6EqSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdmjfck2xL8rUkZ5OcSfKx1r8hyckk59vyzqFtDieZSnIuyf3LeQCSpJuNcub+GvD7VfXzwAeAg0l2AYeAU1W1EzjV1mnP7QPuBvYAjyVZtxzFS5JmN2+4V9XFqvpWa/8IOAtsAfYCE23YBPBQa+8FjlXVlap6CZgCdi9x3ZKkW1jQNfck24H3AU8Bm6rqIgz+AwBsbMO2AK8ObTbd+m58rQNJJpNMzszM3EbpkqS5jBzuSd4JfAH4eFX98FZDZ+mrmzqqjlbVeFWNj42NjVqGJGkEI4V7krcyCPbPVdUXW/elJJvb85uBy61/Gtg2tPlW4MLSlCtJGsUo35YJ8BngbFV9euipE8D+1t4PPDHUvy/J+iQ7gJ3A6aUrWZI0nztGGHMv8LvA80meaX1/BDwKHE/yCPAK8DBAVZ1Jchx4kcE3bQ5W1dWlLlySNLd5w72q/o3Zr6MD3DfHNkeAI4uoS5K0CP5CVZI6ZLhLUocMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktQhw12SOmS4S1KHDHdJ6pDhLkkdMtwlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUocMd0nq0B0rXcBqtv3Ql1dkvy8/+uCK7FfS6uGZuyR1yHCXpA4Z7pLUIcNdkjpkuEtSh+YN9ySfTXI5yQtDfRuSnExyvi3vHHrucJKpJOeS3L9chUuS5jbKmfvfAXtu6DsEnKqqncCptk6SXcA+4O62zWNJ1i1ZtZKkkcwb7lX1deAHN3TvBSZaewJ4aKj/WFVdqaqXgClg99KUKkka1e1ec99UVRcB2nJj698CvDo0brr13STJgSSTSSZnZmZuswxJ0myW+gPVzNJXsw2sqqNVNV5V42NjY0tchiStbbcb7peSbAZoy8utfxrYNjRuK3Dh9suTJN2O2w33E8D+1t4PPDHUvy/J+iQ7gJ3A6cWVKElaqHlvHJbk88CvAnclmQb+GHgUOJ7kEeAV4GGAqjqT5DjwIvAacLCqri5T7ZKkOcwb7lX1kTmeum+O8UeAI4spSpK0OP5CVZI6ZLhLUocMd0nqkOEuSR0y3CWpQ/4NVUlr3kr9PWRYvr+JbLhrQVbyH8FK8Q+SazXysowkdcgz91VoLZ49S1oYz9wlqUOGuyR1yHCXpA4Z7pLUIcNdkjpkuEtShwx3SeqQ4S5JHfJHTNI8VupHY972QIvhmbskdchwl6QOGe6S1CGvuUu6iZ8zrH6euUtShwx3SeqQ4S5JHfKau/Qm5R9l0WJ45i5JHTLcJalDhrskdchwl6QOGe6S1CHDXZI6ZLhLUof8nrukNw2/2790lu3MPcmeJOeSTCU5tFz7kSTdbFnCPck64K+ADwO7gI8k2bUc+5Ik3Wy5ztx3A1NV9d2q+h/gGLB3mfYlSbrBcl1z3wK8OrQ+DfzS8IAkB4ADbfXHSc4tYn93Ad9bxPY9cS6ucy6ucy6ue1PNRT61qM1/dq4nlivcM0tfvW6l6ihwdEl2lkxW1fhSvNZq51xc51xc51xct1bmYrkuy0wD24bWtwIXlmlfkqQbLFe4fxPYmWRHkrcB+4ATy7QvSdINluWyTFW9luSjwL8C64DPVtWZ5dhXsySXdzrhXFznXFznXFy3JuYiVTX/KEnSquLtBySpQ4a7JHVoVYf7WrzFQZKXkzyf5Jkkk61vQ5KTSc635Z1D4w+3+TmX5P6Vq3zxknw2yeUkLwz1LfjYk/xim8OpJH+RZLav7r6pzTEXn0zyX+298UySB4ae63kutiX5WpKzSc4k+VjrX5Pvjf9XVavyweCD2u8A7wHeBjwL7Frput6A434ZuOuGvj8FDrX2IeBTrb2rzct6YEebr3UrfQyLOPYPAu8HXljMsQOngV9m8HuMfwY+vNLHtkRz8UngD2YZ2/tcbAbe39rvAv6jHfOafG9ce6zmM3dvcXDdXmCitSeAh4b6j1XVlap6CZhiMG+rUlV9HfjBDd0LOvYkm4F3V9U3avCv+e+Htlk15piLufQ+Fxer6lut/SPgLINfya/J98Y1qzncZ7vFwZYVquWNVMBXkjzdbuEAsKmqLsLgjQ5sbP1rYY4WeuxbWvvG/l58NMlz7bLNtcsQa2YukmwH3gc8xRp/b6zmcJ/3Fgedureq3s/gjpsHk3zwFmPX6hzB3Mfe85z8NfBzwD3AReDPWv+amIsk7wS+AHy8qn54q6Gz9HU3H6s53NfkLQ6q6kJbXgYeZ3CZ5VL7X0ra8nIbvhbmaKHHPt3aN/avelV1qaquVtVPgL/h+iW47uciyVsZBPvnquqLrXtNvzdWc7ivuVscJHlHknddawMfAl5gcNz727D9wBOtfQLYl2R9kh3ATgYfGPVkQcfe/vf8R0k+0L4J8XtD26xq14Ks+W0G7w3ofC5a7Z8BzlbVp4eeWtvvjZX+RHcxD+ABBp+Mfwf4xErX8wYc73sYfMr/LHDm2jEDPwOcAs635YahbT7R5uccq/iT/3Ysn2dwueF/GZxlPXI7xw6MMwi+7wB/Sful9mp6zDEX/wA8DzzHIMA2r5G5+BUGl0+eA55pjwfW6nvj2sPbD0hSh1bzZRlJ0hwMd0nqkOEuSR0y3CWpQ4a7JHXIcJekDhnuktSh/wMQ9CKiFoF/CgAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"n8jmiz9ewk7h"},"source":["## TREATING CATEGORICAL DATA ##"]},{"cell_type":"code","metadata":{"id":"Zfc4lBZmwk7h","outputId":"f3a15518-d989-4ebc-be31-eacb56625644"},"source":["\"\"\"\n","Categorical data can't be directly handled to the machine learning model.\n","So, before feeding our dataset in our model, we must convert categorical data into dummy variables, since regression model can work only on numerical data\n","\n","\"\"\""],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["\"\\nCategorical data can't be directly handled to the machine learning model.\\nSo, before feeding our dataset in our model, we must convert categorical data into dummy variables, since regression model can work only on numerical data\\n\\n\""]},"metadata":{"tags":[]},"execution_count":246}]},{"cell_type":"code","metadata":{"id":"c6sO4nXCwk7i"},"source":["from sklearn.preprocessing import LabelEncoder"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"hOZBDo5hwk7i"},"source":["le=LabelEncoder()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"sTFIIVq0wk7i"},"source":["df[\"State\"]=le.fit_transform(df[\"State\"])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"VWhLepS_wk7i"},"source":["df[\"Gender Group\"]=le.fit_transform(df[\"Gender Group\"])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":484},"id":"q7h8k4Iqwk7i","executionInfo":{"status":"ok","timestamp":1626262683642,"user_tz":-330,"elapsed":454,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"d1560024-0f7a-4579-a10c-a7b2340a0c24"},"source":["df"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>State</th>\n","      <th>Year</th>\n","      <th>Gender Group</th>\n","      <th>Number of victims( For age group of 18-30 years)</th>\n","      <th>Number of victims( For age group of 30-50 years)</th>\n","      <th>Number of victims( For age group above 50 years)</th>\n","      <th>Total Number Of Victims</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>2001</td>\n","      <td>0</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>67</td>\n","      <td>73</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>2001</td>\n","      <td>0</td>\n","      <td>269</td>\n","      <td>175</td>\n","      <td>67</td>\n","      <td>511</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>2001</td>\n","      <td>0</td>\n","      <td>10</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>16</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>2001</td>\n","      <td>0</td>\n","      <td>45</td>\n","      <td>37</td>\n","      <td>11</td>\n","      <td>93</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>2001</td>\n","      <td>0</td>\n","      <td>191</td>\n","      <td>123</td>\n","      <td>12</td>\n","      <td>326</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1013</th>\n","      <td>30</td>\n","      <td>2010</td>\n","      <td>2</td>\n","      <td>650</td>\n","      <td>839</td>\n","      <td>210</td>\n","      <td>1699</td>\n","    </tr>\n","    <tr>\n","      <th>1014</th>\n","      <td>31</td>\n","      <td>2010</td>\n","      <td>2</td>\n","      <td>60</td>\n","      <td>73</td>\n","      <td>24</td>\n","      <td>157</td>\n","    </tr>\n","    <tr>\n","      <th>1015</th>\n","      <td>32</td>\n","      <td>2010</td>\n","      <td>2</td>\n","      <td>1033</td>\n","      <td>1021</td>\n","      <td>210</td>\n","      <td>2265</td>\n","    </tr>\n","    <tr>\n","      <th>1016</th>\n","      <td>33</td>\n","      <td>2010</td>\n","      <td>2</td>\n","      <td>91</td>\n","      <td>63</td>\n","      <td>19</td>\n","      <td>173</td>\n","    </tr>\n","    <tr>\n","      <th>1017</th>\n","      <td>34</td>\n","      <td>2010</td>\n","      <td>2</td>\n","      <td>1033</td>\n","      <td>1021</td>\n","      <td>120</td>\n","      <td>2174</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1018 rows × 7 columns</p>\n","</div>"],"text/plain":["      State  ...  Total Number Of Victims\n","0         0  ...                       73\n","1         1  ...                      511\n","2         2  ...                       16\n","3         3  ...                       93\n","4         4  ...                      326\n","...     ...  ...                      ...\n","1013     30  ...                     1699\n","1014     31  ...                      157\n","1015     32  ...                     2265\n","1016     33  ...                      173\n","1017     34  ...                     2174\n","\n","[1018 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":74}]},{"cell_type":"code","metadata":{"id":"NKh9316_wk7i","outputId":"950cd2f9-2503-42b3-ac58-fc367f9739b8"},"source":["df.info()"],"execution_count":null,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 1018 entries, 0 to 1017\n","Data columns (total 7 columns):\n"," #   Column                                            Non-Null Count  Dtype\n","---  ------                                            --------------  -----\n"," 0   State                                             1018 non-null   int32\n"," 1   Year                                              1018 non-null   int64\n"," 2   Gender Group                                      1018 non-null   int32\n"," 3   Number of victims( For age group of 18-30 years)  1018 non-null   int32\n"," 4   Number of victims( For age group of 30-50 years)  1018 non-null   int32\n"," 5   Number of victims( For age group above 50 years)  1018 non-null   int32\n"," 6   Total Number Of Victims                           1018 non-null   int32\n","dtypes: int32(6), int64(1)\n","memory usage: 31.9 KB\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"u_KQdRvkwk7i"},"source":["# Now, our dataset is ready to feed in our model"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"b8u_hsjPwk7i","outputId":"430cde72-74c8-4b8d-f91a-e23bf0bb9546"},"source":["\"\"\"\n","Now, we divide our dataset into train and test data.\n","With the train data we train our model on how to predict based on input features.\n","With the test data we use the prediction case and measure the accuracy of our model.\n","After that our model is ready to predict the number of victims of crime in a particular state for a particular year.\n","\n","\"\"\""],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'\\nNow, we divide our dataset into train and test data.\\nWith the train data we train our model on how to predict based on input features.\\nWith the test data we use the prediction case and measure the accuracy of our model.\\nAfter that our model is ready to predict the number of victims of crime in a particular state for a particular year.\\n\\n'"]},"metadata":{"tags":[]},"execution_count":254}]},{"cell_type":"code","metadata":{"id":"EpcZ4-FPwk7j"},"source":["X=df.drop(columns=[\"Number of victims( For age group of 18-30 years)\",\"Number of victims( For age group of 30-50 years)\",\"Number of victims( For age group above 50 years)\",\"Total Number Of Victims\"])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":417},"id":"mT426Iyiwk7j","executionInfo":{"status":"ok","timestamp":1626262717206,"user_tz":-330,"elapsed":425,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"8e373286-632d-410b-b324-c247aa35f646"},"source":["X"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>State</th>\n","      <th>Year</th>\n","      <th>Gender Group</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>2001</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>2001</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>2001</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>2001</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>2001</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1013</th>\n","      <td>30</td>\n","      <td>2010</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>1014</th>\n","      <td>31</td>\n","      <td>2010</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>1015</th>\n","      <td>32</td>\n","      <td>2010</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>1016</th>\n","      <td>33</td>\n","      <td>2010</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>1017</th>\n","      <td>34</td>\n","      <td>2010</td>\n","      <td>2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1018 rows × 3 columns</p>\n","</div>"],"text/plain":["      State  Year  Gender Group\n","0         0  2001             0\n","1         1  2001             0\n","2         2  2001             0\n","3         3  2001             0\n","4         4  2001             0\n","...     ...   ...           ...\n","1013     30  2010             2\n","1014     31  2010             2\n","1015     32  2010             2\n","1016     33  2010             2\n","1017     34  2010             2\n","\n","[1018 rows x 3 columns]"]},"metadata":{"tags":[]},"execution_count":76}]},{"cell_type":"code","metadata":{"id":"x1G3vxORwk7j","outputId":"ff301313-bc44-4a8e-8c2c-21f9e06f17f3"},"source":["df"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>State</th>\n","      <th>Year</th>\n","      <th>Gender Group</th>\n","      <th>Number of victims( For age group of 18-30 years)</th>\n","      <th>Number of victims( For age group of 30-50 years)</th>\n","      <th>Number of victims( For age group above 50 years)</th>\n","      <th>Total Number Of Victims</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>2001</td>\n","      <td>0</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>67</td>\n","      <td>73</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>2001</td>\n","      <td>0</td>\n","      <td>269</td>\n","      <td>175</td>\n","      <td>67</td>\n","      <td>511</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>2001</td>\n","      <td>0</td>\n","      <td>10</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>16</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>2001</td>\n","      <td>0</td>\n","      <td>45</td>\n","      <td>37</td>\n","      <td>11</td>\n","      <td>93</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>2001</td>\n","      <td>0</td>\n","      <td>191</td>\n","      <td>123</td>\n","      <td>12</td>\n","      <td>326</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1013</th>\n","      <td>30</td>\n","      <td>2010</td>\n","      <td>2</td>\n","      <td>650</td>\n","      <td>839</td>\n","      <td>210</td>\n","      <td>1699</td>\n","    </tr>\n","    <tr>\n","      <th>1014</th>\n","      <td>31</td>\n","      <td>2010</td>\n","      <td>2</td>\n","      <td>60</td>\n","      <td>73</td>\n","      <td>24</td>\n","      <td>157</td>\n","    </tr>\n","    <tr>\n","      <th>1015</th>\n","      <td>32</td>\n","      <td>2010</td>\n","      <td>2</td>\n","      <td>1033</td>\n","      <td>1021</td>\n","      <td>210</td>\n","      <td>2265</td>\n","    </tr>\n","    <tr>\n","      <th>1016</th>\n","      <td>33</td>\n","      <td>2010</td>\n","      <td>2</td>\n","      <td>91</td>\n","      <td>63</td>\n","      <td>19</td>\n","      <td>173</td>\n","    </tr>\n","    <tr>\n","      <th>1017</th>\n","      <td>34</td>\n","      <td>2010</td>\n","      <td>2</td>\n","      <td>1033</td>\n","      <td>1021</td>\n","      <td>120</td>\n","      <td>2174</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1018 rows × 7 columns</p>\n","</div>"],"text/plain":["      State  Year  Gender Group  \\\n","0         0  2001             0   \n","1         1  2001             0   \n","2         2  2001             0   \n","3         3  2001             0   \n","4         4  2001             0   \n","...     ...   ...           ...   \n","1013     30  2010             2   \n","1014     31  2010             2   \n","1015     32  2010             2   \n","1016     33  2010             2   \n","1017     34  2010             2   \n","\n","      Number of victims( For age group of 18-30 years)  \\\n","0                                                    4   \n","1                                                  269   \n","2                                                   10   \n","3                                                   45   \n","4                                                  191   \n","...                                                ...   \n","1013                                               650   \n","1014                                                60   \n","1015                                              1033   \n","1016                                                91   \n","1017                                              1033   \n","\n","      Number of victims( For age group of 30-50 years)  \\\n","0                                                    2   \n","1                                                  175   \n","2                                                    4   \n","3                                                   37   \n","4                                                  123   \n","...                                                ...   \n","1013                                               839   \n","1014                                                73   \n","1015                                              1021   \n","1016                                                63   \n","1017                                              1021   \n","\n","      Number of victims( For age group above 50 years)  \\\n","0                                                   67   \n","1                                                   67   \n","2                                                    2   \n","3                                                   11   \n","4                                                   12   \n","...                                                ...   \n","1013                                               210   \n","1014                                                24   \n","1015                                               210   \n","1016                                                19   \n","1017                                               120   \n","\n","      Total Number Of Victims  \n","0                          73  \n","1                         511  \n","2                          16  \n","3                          93  \n","4                         326  \n","...                       ...  \n","1013                     1699  \n","1014                      157  \n","1015                     2265  \n","1016                      173  \n","1017                     2174  \n","\n","[1018 rows x 7 columns]"]},"metadata":{"tags":[]},"execution_count":257}]},{"cell_type":"code","metadata":{"id":"EfP6imTFwk7j"},"source":["Y=df.drop(columns=[\"State\",\"Year\",\"Gender Group\",\"Total Number Of Victims\"])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Fte-3X4Bwk7j","outputId":"d5041850-b0c2-4bf6-c4f3-388f65bd9871"},"source":["Y"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>Number of victims( For age group of 18-30 years)</th>\n","      <th>Number of victims( For age group of 30-50 years)</th>\n","      <th>Number of victims( For age group above 50 years)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>67</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>269</td>\n","      <td>175</td>\n","      <td>67</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>10</td>\n","      <td>4</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>45</td>\n","      <td>37</td>\n","      <td>11</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>191</td>\n","      <td>123</td>\n","      <td>12</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>1013</th>\n","      <td>650</td>\n","      <td>839</td>\n","      <td>210</td>\n","    </tr>\n","    <tr>\n","      <th>1014</th>\n","      <td>60</td>\n","      <td>73</td>\n","      <td>24</td>\n","    </tr>\n","    <tr>\n","      <th>1015</th>\n","      <td>1033</td>\n","      <td>1021</td>\n","      <td>210</td>\n","    </tr>\n","    <tr>\n","      <th>1016</th>\n","      <td>91</td>\n","      <td>63</td>\n","      <td>19</td>\n","    </tr>\n","    <tr>\n","      <th>1017</th>\n","      <td>1033</td>\n","      <td>1021</td>\n","      <td>120</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>1018 rows × 3 columns</p>\n","</div>"],"text/plain":["      Number of victims( For age group of 18-30 years)  \\\n","0                                                    4   \n","1                                                  269   \n","2                                                   10   \n","3                                                   45   \n","4                                                  191   \n","...                                                ...   \n","1013                                               650   \n","1014                                                60   \n","1015                                              1033   \n","1016                                                91   \n","1017                                              1033   \n","\n","      Number of victims( For age group of 30-50 years)  \\\n","0                                                    2   \n","1                                                  175   \n","2                                                    4   \n","3                                                   37   \n","4                                                  123   \n","...                                                ...   \n","1013                                               839   \n","1014                                                73   \n","1015                                              1021   \n","1016                                                63   \n","1017                                              1021   \n","\n","      Number of victims( For age group above 50 years)  \n","0                                                   67  \n","1                                                   67  \n","2                                                    2  \n","3                                                   11  \n","4                                                   12  \n","...                                                ...  \n","1013                                               210  \n","1014                                                24  \n","1015                                               210  \n","1016                                                19  \n","1017                                               120  \n","\n","[1018 rows x 3 columns]"]},"metadata":{"tags":[]},"execution_count":259}]},{"cell_type":"code","metadata":{"id":"8Xn84Duvwk7j"},"source":["from sklearn.model_selection import train_test_split"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"mvtBfXNnwk7j"},"source":["X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.35,random_state=101)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":417},"id":"VmGI_zgJwk7j","executionInfo":{"status":"ok","timestamp":1626265491625,"user_tz":-330,"elapsed":441,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"dcced3c3-2c3c-4889-cd3b-7941e7498e53"},"source":["X_train"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>State</th>\n","      <th>Year</th>\n","      <th>Gender Group</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>789</th>\n","      <td>11</td>\n","      <td>2004</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>195</th>\n","      <td>29</td>\n","      <td>2006</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>449</th>\n","      <td>11</td>\n","      <td>2004</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>257</th>\n","      <td>22</td>\n","      <td>2008</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>39</th>\n","      <td>6</td>\n","      <td>2002</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>575</th>\n","      <td>1</td>\n","      <td>2008</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>838</th>\n","      <td>26</td>\n","      <td>2005</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>337</th>\n","      <td>0</td>\n","      <td>2001</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>523</th>\n","      <td>17</td>\n","      <td>2006</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>863</th>\n","      <td>16</td>\n","      <td>2006</td>\n","      <td>2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>661 rows × 3 columns</p>\n","</div>"],"text/plain":["     State  Year  Gender Group\n","789     11  2004             2\n","195     29  2006             0\n","449     11  2004             1\n","257     22  2008             0\n","39       6  2002             0\n","..     ...   ...           ...\n","575      1  2008             1\n","838     26  2005             2\n","337      0  2001             1\n","523     17  2006             1\n","863     16  2006             2\n","\n","[661 rows x 3 columns]"]},"metadata":{"tags":[]},"execution_count":131}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":450},"id":"FBqtdvg7wk7j","executionInfo":{"status":"ok","timestamp":1626264245009,"user_tz":-330,"elapsed":4,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"0194dcc4-7484-4409-c324-895777e87c48"},"source":["y_train"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>Number of victims( For age group of 18-30 years)</th>\n","      <th>Number of victims( For age group of 30-50 years)</th>\n","      <th>Number of victims( For age group above 50 years)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>231</th>\n","      <td>19</td>\n","      <td>18</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>806</th>\n","      <td>4</td>\n","      <td>8</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>700</th>\n","      <td>490</td>\n","      <td>385</td>\n","      <td>72</td>\n","    </tr>\n","    <tr>\n","      <th>434</th>\n","      <td>117</td>\n","      <td>84</td>\n","      <td>30</td>\n","    </tr>\n","    <tr>\n","      <th>950</th>\n","      <td>6</td>\n","      <td>9</td>\n","      <td>165</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>575</th>\n","      <td>855</td>\n","      <td>776</td>\n","      <td>210</td>\n","    </tr>\n","    <tr>\n","      <th>838</th>\n","      <td>8</td>\n","      <td>8</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>337</th>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>36</td>\n","    </tr>\n","    <tr>\n","      <th>523</th>\n","      <td>72</td>\n","      <td>133</td>\n","      <td>51</td>\n","    </tr>\n","    <tr>\n","      <th>863</th>\n","      <td>757</td>\n","      <td>677</td>\n","      <td>164</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>814 rows × 3 columns</p>\n","</div>"],"text/plain":["     Number of victims( For age group of 18-30 years)  ...  Number of victims( For age group above 50 years)\n","231                                                19  ...                                                 8\n","806                                                 4  ...                                                 3\n","700                                               490  ...                                                72\n","434                                               117  ...                                                30\n","950                                                 6  ...                                               165\n","..                                                ...  ...                                               ...\n","575                                               855  ...                                               210\n","838                                                 8  ...                                                 3\n","337                                                 3  ...                                                36\n","523                                                72  ...                                                51\n","863                                               757  ...                                               164\n","\n","[814 rows x 3 columns]"]},"metadata":{"tags":[]},"execution_count":105}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":417},"id":"HdP_C0bkwk7k","executionInfo":{"status":"ok","timestamp":1626262790975,"user_tz":-330,"elapsed":5,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"3c71b65e-4604-44d7-a747-a266a011adf3"},"source":["X_test"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>State</th>\n","      <th>Year</th>\n","      <th>Gender Group</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>902</th>\n","      <td>22</td>\n","      <td>2007</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>240</th>\n","      <td>5</td>\n","      <td>2008</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>429</th>\n","      <td>26</td>\n","      <td>2003</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>945</th>\n","      <td>30</td>\n","      <td>2008</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>147</th>\n","      <td>15</td>\n","      <td>2005</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>899</th>\n","      <td>19</td>\n","      <td>2007</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>399</th>\n","      <td>30</td>\n","      <td>2002</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>837</th>\n","      <td>25</td>\n","      <td>2005</td>\n","      <td>2</td>\n","    </tr>\n","    <tr>\n","      <th>331</th>\n","      <td>29</td>\n","      <td>2010</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>495</th>\n","      <td>24</td>\n","      <td>2005</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>306 rows × 3 columns</p>\n","</div>"],"text/plain":["     State  Year  Gender Group\n","902     22  2007             2\n","240      5  2008             0\n","429     26  2003             1\n","945     30  2008             2\n","147     15  2005             0\n","..     ...   ...           ...\n","899     19  2007             2\n","399     30  2002             1\n","837     25  2005             2\n","331     29  2010             0\n","495     24  2005             1\n","\n","[306 rows x 3 columns]"]},"metadata":{"tags":[]},"execution_count":83}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":450},"id":"sr1gCHmwwk7k","executionInfo":{"status":"ok","timestamp":1626262800058,"user_tz":-330,"elapsed":4,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"9537079b-6b03-4e04-c9c9-8435be0dd7ef"},"source":["Y_test"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>Number of victims( For age group of 18-30 years)</th>\n","      <th>Number of victims( For age group of 30-50 years)</th>\n","      <th>Number of victims( For age group above 50 years)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>902</th>\n","      <td>39</td>\n","      <td>54</td>\n","      <td>9</td>\n","    </tr>\n","    <tr>\n","      <th>240</th>\n","      <td>3</td>\n","      <td>2</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>429</th>\n","      <td>8</td>\n","      <td>10</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>945</th>\n","      <td>597</td>\n","      <td>800</td>\n","      <td>210</td>\n","    </tr>\n","    <tr>\n","      <th>147</th>\n","      <td>62</td>\n","      <td>45</td>\n","      <td>16</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>899</th>\n","      <td>950</td>\n","      <td>924</td>\n","      <td>210</td>\n","    </tr>\n","    <tr>\n","      <th>399</th>\n","      <td>512</td>\n","      <td>543</td>\n","      <td>197</td>\n","    </tr>\n","    <tr>\n","      <th>837</th>\n","      <td>535</td>\n","      <td>501</td>\n","      <td>87</td>\n","    </tr>\n","    <tr>\n","      <th>331</th>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>32</td>\n","    </tr>\n","    <tr>\n","      <th>495</th>\n","      <td>35</td>\n","      <td>36</td>\n","      <td>3</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>306 rows × 3 columns</p>\n","</div>"],"text/plain":["     Number of victims( For age group of 18-30 years)  ...  Number of victims( For age group above 50 years)\n","902                                                39  ...                                                 9\n","240                                                 3  ...                                                 1\n","429                                                 8  ...                                                 3\n","945                                               597  ...                                               210\n","147                                                62  ...                                                16\n","..                                                ...  ...                                               ...\n","899                                               950  ...                                               210\n","399                                               512  ...                                               197\n","837                                               535  ...                                                87\n","331                                                 3  ...                                                32\n","495                                                35  ...                                                 3\n","\n","[306 rows x 3 columns]"]},"metadata":{"tags":[]},"execution_count":84}]},{"cell_type":"code","metadata":{"id":"9UWMt65qwk7k"},"source":["from sklearn.linear_model import LinearRegression"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"rYI5jgWcwk7k"},"source":["reg=LinearRegression()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lJP72jlWwk7k","executionInfo":{"status":"ok","timestamp":1626265672978,"user_tz":-330,"elapsed":443,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"e2c9c056-9020-409a-9756-27d30ea76b4f"},"source":["reg.fit(X_train,y_train)   # Training our model"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"]},"metadata":{"tags":[]},"execution_count":135}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ji5h1d5L5JdH","executionInfo":{"status":"ok","timestamp":1626264738289,"user_tz":-330,"elapsed":3,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"6a96212c-e416-4a7b-9b94-1dd9fae11903"},"source":["print(reg.intercept_)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[  430.98034696 -1782.29328311  -372.82752537]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kdzj2Vre5QAo","executionInfo":{"status":"ok","timestamp":1626264120197,"user_tz":-330,"elapsed":3,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"23768bae-7511-4890-a8ed-a995601aad4d"},"source":["reg.coef_"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 1.31603451e+00, -5.40285578e-01,  1.38657161e+02],\n","       [ 2.13946274e+00, -7.67162669e-02,  1.47137519e+02],\n","       [ 2.44251579e-01, -6.07897973e-02,  2.83918832e+01]])"]},"metadata":{"tags":[]},"execution_count":99}]},{"cell_type":"code","metadata":{"id":"09fdKDWTwk7k","outputId":"ced7e7ae-44e3-4eba-a797-af45cd14ba3d"},"source":["Y_test"],"execution_count":null,"outputs":[{"output_type":"execute_result","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>Number of victims( For age group of 18-30 years)</th>\n","      <th>Number of victims( For age group of 30-50 years)</th>\n","      <th>Number of victims( For age group above 50 years)</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>838</th>\n","      <td>8</td>\n","      <td>8</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>761</th>\n","      <td>120</td>\n","      <td>206</td>\n","      <td>78</td>\n","    </tr>\n","    <tr>\n","      <th>334</th>\n","      <td>421</td>\n","      <td>243</td>\n","      <td>56</td>\n","    </tr>\n","    <tr>\n","      <th>43</th>\n","      <td>1</td>\n","      <td>2</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>323</th>\n","      <td>5</td>\n","      <td>13</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>475</th>\n","      <td>672</td>\n","      <td>372</td>\n","      <td>72</td>\n","    </tr>\n","    <tr>\n","      <th>560</th>\n","      <td>140</td>\n","      <td>121</td>\n","      <td>8</td>\n","    </tr>\n","    <tr>\n","      <th>649</th>\n","      <td>231</td>\n","      <td>5</td>\n","      <td>105</td>\n","    </tr>\n","    <tr>\n","      <th>538</th>\n","      <td>79</td>\n","      <td>134</td>\n","      <td>10</td>\n","    </tr>\n","    <tr>\n","      <th>840</th>\n","      <td>523</td>\n","      <td>542</td>\n","      <td>124</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>306 rows × 3 columns</p>\n","</div>"],"text/plain":["     Number of victims( For age group of 18-30 years)  \\\n","838                                                 8   \n","761                                               120   \n","334                                               421   \n","43                                                  1   \n","323                                                 5   \n","..                                                ...   \n","475                                               672   \n","560                                               140   \n","649                                               231   \n","538                                                79   \n","840                                               523   \n","\n","     Number of victims( For age group of 30-50 years)  \\\n","838                                                 8   \n","761                                               206   \n","334                                               243   \n","43                                                  2   \n","323                                                13   \n","..                                                ...   \n","475                                               372   \n","560                                               121   \n","649                                                 5   \n","538                                               134   \n","840                                               542   \n","\n","     Number of victims( For age group above 50 years)  \n","838                                                 3  \n","761                                                78  \n","334                                                56  \n","43                                                  4  \n","323                                                 1  \n","..                                                ...  \n","475                                                72  \n","560                                                 8  \n","649                                               105  \n","538                                                10  \n","840                                               124  \n","\n","[306 rows x 3 columns]"]},"metadata":{"tags":[]},"execution_count":269}]},{"cell_type":"markdown","metadata":{"id":"bAtIKCdk4jA_"},"source":["## predict here"]},{"cell_type":"code","metadata":{"id":"2A5NoRYX0coB"},"source":["predictions = reg.predict(X_test) # predicting in never seen values"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":286},"id":"FkOx9Blt7tgI","executionInfo":{"status":"ok","timestamp":1626264760786,"user_tz":-330,"elapsed":1043,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"b132b998-ba10-420c-e032-1029706bf633"},"source":["plt.scatter(y_test,predictions) # with test size 0.35"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.collections.PathCollection at 0x7f9f91ee2a50>"]},"metadata":{"tags":[]},"execution_count":123},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XEDX7tuz7yk-","executionInfo":{"status":"ok","timestamp":1626264778158,"user_tz":-330,"elapsed":459,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"d4fb418b-c42b-4d98-d536-281a603d5512"},"source":["print('R square', metrics.r2_score(y_test, predictions))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["R square 0.10145819537407397\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":282},"id":"UPwKU8ii59KE","executionInfo":{"status":"ok","timestamp":1626264321130,"user_tz":-330,"elapsed":522,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"46bf63ab-0a4b-46f8-9708-99afd5307b0b"},"source":["plt.scatter(y_test,predictions) # with test size 0.2"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.collections.PathCollection at 0x7f9f9205a4d0>"]},"metadata":{"tags":[]},"execution_count":111},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO2df5AU55nfv88OgzSLzwzYnEoagcC2Cs4bDGtvGVzkDwvHwpYM3pMlYQXnFEd1+sepWEjZ82LrDChyWNfGRr66lHK68+XsE5EXITJGwmesCLlSIQZnye4KY4kYSdaikWxhw2CbHcHs7ps/pnvo6em3++2Z/vX2PJ8qip3unum3++3+vs/7vM/7vCSEAMMwDJMuuuIuAMMwDBM8LO4MwzAphMWdYRgmhbC4MwzDpBAWd4ZhmBQyJ+4CAMC73/1usXTp0riLwTAMoxXHjx//jRBikdO+RIj70qVLMTo6GncxGIZhtIKIXpPtY7cMwzBMCmFxZxiGSSEs7gzDMCmExZ1hGCaFsLgzDMOkkEREyzCMThTHShg+dApvlCu4Lp/DwIbl6O8txF0shmmAxZ1hfFAcK2Hb/hOoVGcAAKVyBdv2nwAA3wLPjYQafJ9ag8VdEX7AGAAYPnSqLuwmleoMhg+d8vU8BNlIJJl235tOuU9hwOKugNMDdt/IOO7fO45ZARRsDy03BOnljXLF13YZQTUSSSYIYe6E+xQWLO4K7Hz6ZNMDBgCzxjon1of2ydFJHHn5XP2YUrmCrSPjGH3tHB7uXxlJeRlvWm2Ar8vnUHIQ8uvyOV/nD6qRSDJBCLPqfWKDqhmtxT2KCi2OlXB+qup5XKU6g6/89xO4eLm5ERAA9hydRN8NCzv+gUsC7ViUAxuWN3wXAHLZDAY2LK//tsozGVQjkWSCaMBU7hO7bpzRNhTSrNBSuQKBK66Snq/+EMWxUmDnGT50SvlYJ2E3ET5/iwkPN4vSi/7eAnbdthKFfA6Emktu120r0d9bwIPFE9g6Mt7wTG7bf8LxeRzYsBy5bKZp+/mLl7Bs8CDWDR0O9DmOA1lD5acBc7pP1sYUaK8+04y2lrtThQI1gR3YNwEgmFY7yG5ymrrcOiOrBycL0Yn+3kLTs1UcK2HP0UnYVySWuSH6ewsYfe1c03emqrP1spjWJwAtXQ5evRwVzOt0u35dXFwPFk/giWNnMCMEMkRY+54F+OVvK6HVq7bi7vYiVmcEto6MA1ATePtNv2vN4rp/XNYttJPLZvB2dabp5baSpi63zsjqlFAT6VZesOFDp6R1LxOZ51866/q8VKozuH/vODJEqBoDPDq5HFSEWfV33L6jg4vrweIJPH50sv55RoimsbkgjVJAY7cMkft+AWDgyQnPrq1502dE7eWZEQKPH53En/zlP6E4VsLAhuXIdjmfLGMUwuyab1m7RHoevxaLLhTHSlg3dFgrV8LAhuVwqtF2XGduVmK+O9u0rThWUjIaZgXqwm4SlsshjLrs7y3gyOB6vDp0K44Mrg+lQVJx3cTNE8fOeB5TnRHY+fTJwM6preUu3Eweg+qswPChUxh97ZzUMt9zbNLxu5XqLO4zrH872Qxh+PZVjl1tAPVzmdhDJXXGOmCY787iD29Pt2VVxhHl0N9bkNZtq115tx6e/Vk1x4vaIWiXQ9iDkmHWc1A9hDCZUREsQCl4QxVtxV2VUrnS1B16/OgkHj86iXwuq9RI2KnOCGk418P9K1Mb8mgXAKcH0U+oW5xRDoWAu/IDG5ZLG4wLlcb7JBsv8kPQLocw48mjqGcv100noq1bZoFDV9cv5UrrrWSpXNHKFREEqqKkalXGGeUQdFe+v7cgfSbtQtyu1R2GyyHMQUmOZlHHw9vsC23FffvGHmQzQd4K/3iFu6UN1Rdd1aqMM8rBLaSxVbZv7FFqMPxa3dkMIZ/LBlZOJ4IIW5ShSzRLmBQU72MLjgQp2rplzIf7gb0Tyv6ssKhUZ/DA3glsHRnH/FwWREB5qppI3187qEYOqVqVcUc5BNWVt49DXDWnCxcq8vp3ChEk1F7sQj6Hm1YswvMvnY3UfxxE2KKMuOs5CTjdXydUGwEVtBV3oPZybpX4OaPGbGCsrh6dwtZUUHlA87ms8rWGKShR4TQOkctmsHvzaul9SOIAYJhlSkM9t4v9/nbPzThOerxpxaLAzkkiZqsXAPr6+sTo6Kjv7xXHSti6d7ylQdEoKeRzODK4Pu5iBIJppZbKlbq1aZLLZny7DHTOCVIcK0l7jmmq8yDQuZ7DYN3QYcfejN/nhoiOCyH6nPZpa7kXx0oY2DeReGEH0uVbtLoygnhhdY1yMC12mUswKXWeFFHVtZ7DIopxCG3FffjQKVRnwlP2DFFgvvy0+hY7+YX1ihxKQp13QkKtpDRefoliHELbaJmwLaNZIZTDLd1idvz4FnWc7Rk1SblHbs9fUvzJaQ9BdEoeqEvk2sCG5U3RftkMBfrcKIs7EWWIaIyInjE+LyOiY0R0mohGiGiusf0q4/NpY//SwEprYX7OW3jbCZS8Lp9Tni1mRjkQagOKC7r9h63p/KBGRZLukczCyhCFEqrYCmkPQdS+8bI7BgJ2RPhxy3wRwIsA3ml8/jqA3UKI7xHRfwFwD4BHjf/PCyHeR0SfNY7bHGCZURwr4eLlac/jWr1XpuUlm3FoJ4jBM9mDuuPAyUBmCOrYdbWTpFV5ZBEgSRF2QN717yLCssGDWj8LgN6N1/ChU005g8x0KZEmDiOi6wHcCuDvjM8EYD2AfcYh3wHQb/z9aeMzjP0fM44PDC9/e17BqpdhtbZVfieoLrgsfrxcqbZlmSbJ2m2XsF7mVlw9YUyCChpZzvgZIVyfhaS4vuzYyyXrvSdhvMMJa/ll73scA6qPAPgLAH9kfH4XgLIQwjSfXwdgPtUFAGcAQAgxTUQXjON/Y/1BIroXwL0AsGSJPJuiE24TaQjAvKvmgMh/Ep4F3dkGS2bHph4MPDnR0MJ2EfDOq7Ouk1T8UhwrNYUVWmmnNVe1dnWw7sMYhGpn0DGpA8qySVVdDkEC9mchqYOwTuXKZgjZLmp4P5My3mHHXn4ZkQ6oEtGnALwlhDge2FkBCCEeE0L0CSH6Fi3yF7ifcekImBbJH96e9p2e4PxUtcGS6e8tYPiOVQ3W2TfvXI3x7TcHmsLULRc40F5rrmLt6mLdh5HaVXu/rQ17XZ6fquLS9Cx2b16NWYWwzaTeD6dyVWcE3nH1nET3nkxU8jIFPaCqYrmvA7CJiG4BcDVqPvdvAcgT0RzDer8egKkEJQCLAbxORHMAzAfw28BKDLX0mdVZgXwui3lXzalboxcvTXsmC7NbMlFYZ17i3U5rLrN2891ZrBs6jDfKFSWLLmrcehJB9jB09ts64SbOKj2fpN4P2fnLU1WMffXmiEvjH5X7NxNwaLenuAshtgHYBgBE9FEA/14IsYWIngRwO4DvAbgbwPeNrxwwPv/E2H9YBDwNVpau1c6FShXj269UvGrXSHW5taBwy9nSrmXqNPCXzRD+8PZ03W2VtIk4Xq6BIBuctOU9cRPn3ZtXe6YBCPN+tOP6072eVPIyzQKBBFCYtBPn/iUA9xPRadR86t82tn8bwLuM7fcDGGyviM2oip19BRxzEMzNrQNcWW4tKmQDXwu6s213M50G/ubNndM0Uu9E2C+ObOAuSteADqv4+MEtu6PKIHCr98NrENbJ9bd1ZBwPFtUWLdG9nmTvuJ120pDb8TVDVQjxYwA/Nv5+BcCHHY55G8AdAZRNittKOo1lcf4uAFcL3lxuLSqXRNiJpOzW7rLBg57fCfvFcbPO213A2g9JTOLVDl5Jurx6Pq3cD5VBWKcGWwDYc3QSfTcsVBq89luuJGEvfxRZU7RNHNb70I88o2EIwKtDtzruc0v65PVdvyQtEkWWtChDhFkhIimjW+IkwFnICXDNthgUSasvv0RdfpUkWMsGD0oFrROTrMn0a0F31tcYglviMG3TD2zf2APJutV13Gax9vcWpNEDQHAuCZVIlKjjimVd3G/cuSrUhYytuPmGw1jAWhVdIofciGJRaisqg7Bu71Pcg7Vx4LTYUDZD2L6xJ7BzaCvuAOA1N+ri5WnXl1L2wBHU/fpeePmP4xCTJEzA8fINy5rdsIUgqaGASUZlFSdZg+32/TTT31vA8O2NYdbDt68K9B3UVtx3Pn0SMx6DgtUZgQf2TkgtYicLlgBsWbsksJvsZdXEJSZRW3d2vAbIZCvShC0ESQ0FTCJmj9PM7W/FPmbT31vAlrVLPI9jgkPblL+qs09Nn7rTIE8UgzReIVydKiZe9z6u1Xt0D7mLCvsgqkDjUoFO79HD/SvRd8NCrccz2sE6FjI/l8XvL03XDdRSuYKBfRMAgpsJrK24t4LTxJywJyl5iVQni4nbvQ+z4XUbcOQl4dSQRb94DY4mNWVD2NgbQ6eQx+qMwM6ng4tz7yhxB6K3iJNqoeqAqhD4iQ5RnSDVqdalKp3a42wVlfQDgP98WG50nLjHYRHHZaF2An4TXXklUtM9DDIqOrnH2QpxNHraivs8yerhbiTVIu7UrmoQ+M3x7mZxJjUjYhLhHqc/5ueySrNP20lXbkfbaJmv/elKZLwC3XFlNaYkZ4xjWseve8AtbI/DINVJQjitTqisaJHtIuzYFFycu7aWez3n+oGTri1iFDMamfjw6x5wszi3SlJasB/ZGe5xqlN28aUTEIoLUFvLHag9XOPbb5Z2ZQrGhBgmvfhNKOVmcapMxmGYVpA9Q4V8LrS5Jtpa7sCVKIlypdq0klEum8FNKxbVc5bz4Jg++BnUbGVAWmZxsh+ZCYuBDcsxsG+iYXnQoBfnsKOtuHtNorhpxSI8dbzEg2Oa0cqgZlDuAY5cYkLFPqE+5JyN2oq71ySKdUOHfUVRMMnAb/RL0LAfOTw6Ocx0+NCppjUUqrMi1OdaW3GX5fY2t/MkCz3heksnnR5mGsdzre2Aqmw1JXM7D47pCddbOun0MNM4nmttxV22yIa5XfdluToVrrd00uk9sjiea23dMrJFss1UsTw4pidcb+mk09MVxPFca7vMnt2HB9RaQp4lxzDJg9/XcHBbZk9by50tPIbRB35fo0dby51hGKbTSeUC2QzDMIwcFneGYZgUwuLOMAyTQljcGYZhUgiLO8MwTAphcWcYhkkhLO4MwzAphMWdYRgmhbC4MwzDpBAWd4ZhmBTC4s4wDJNCWNwZhmFSCIs7wzBMCmFxZxiGSSHa5nNnGOYKxbES50pnGmBxZxjNsa9yVCpXsG3/CQBgge9gPN0yRHQ1Ef2UiCaI6CQR7TS2LyOiY0R0mohGiGiusf0q4/NpY//ScC8hGopjJawbOoxlgwexbugwimOluIvEMABqqxtZl68DgEp1BsOHTsVUIiYJqPjcLwFYL4RYBWA1gE8Q0VoAXwewWwjxPgDnAdxjHH8PgPPG9t3GcVpjWkalcgUCVywjFvhk0ykN8hsOC0+7bWc6A0+3jKitw/cH42PW+CcArAfwL43t3wGwA8CjAD5t/A0A+wD8NRGRSMJ6fi3iZhlxt1eOkx8YiGYdzaBdFUn2aV+Xz6HkIOTX5XMxlIZJCkrRMkSUIaJxAG8BeBbAywDKQohp45DXAZhPegHAGQAw9l8A8C6H37yXiEaJaPTs2bPtXUXIsGXkH6fezsCTExjYNxFJDyhIV0XSe24DG5Yjl800bMtlM/XGlOlMlMRdCDEjhFgN4HoAHwawot0TCyEeE0L0CSH6Fi1a1O7PhYrMAmLLSI6TuFZnBaozjR24sHzDQTbISfdp9/cWsOu2lSjkcyAAhXwOu25bmZieBeNM2G5DX9EyQogyET0P4CMA8kQ0x7DOrwdglqwEYDGA14loDoD5AH4bYJnrmF3lUrmCDBFmhEChzS7zg8UTeOLYGcwIgQwR7lqzGAMbljd08QF9LKO43Al+RNTr2FauIUhXhQ49t/7eAou5RkQR4aQSLbOIiPLG3zkAHwfwIoDnAdxuHHY3gO8bfx8wPsPYfzgMf3txrFTv4gPAjHGKVrvMxbESer76Qzx+dLL+WzNC4PGjkxh97ZyWllGc7gQ/IprvzkotmFavIUhXBffcmKCJojdIXrpLRB9AbcA0g1pjsFcI8RARvQfA9wAsBDAG4HNCiEtEdDWAfwTQC+AcgM8KIV5xO0dfX58YHR31VfDeh36E81NV6f5CPocjg+sBeFt+9lbUThcBr+y61Vf5ksC6ocOO1ms+l8W8q+aEas073dNsFwGEBtdMNkOAqLlsTAjAlrVL8HD/Suk1WOvXPF9Yg7ey5yOfy2LHpp7EN/JM8lg2eBBOyksAXh1S1xoiOi6E6HPapxIt8wJqQm3f/gpq/nf79rcB3KFcuhZxE3bgSpfZrfsDADsOnES54v5bs6L2O7q9xDK3QblSrV9zWBNezN/yEtyLl6ab7r8AsOfoJPpuWKjkEpHV8a7bVjY0AO1ey86nTzY8d+VKNRWThZIcCZQmrPe5y3Aj2wmyN5jaGarmTZJ1f768/wVMVWeVf8/sLun0Esj8znbCCuuU+YGt25YNHnT8rkDtXqv4zqMIVe3vLWD40Kkmo0L3kNg0zm5NYmNlv89Owh70OJ62icPyuazr/qnL0yiOlaSWnx9hB6489EkNh3PCye8so1Su4L3bfoAHiye8Dw4QN0vljXJFyXce1YCnDgOrfkl6JJBfkhq26nSfASBDFNo4nrbivmNTj2vhz0/Vusz5bvdGQJUMkXYvgVOI3AKX+2EOIEcp8AMbloMk+67L55TC/KIa8IxrYDXMkLm0NVhJbaxk93NWCLw6dCuODK4PvHehrbj39xYw30O4K9UZCAFl69UNp24UoBbGF9cUeKfu6faNPZ7344ljZyIqYa0et6xd0iTwVuu8v7eAI4PrpS+B38iYVuskjslCYVuisoZJAFqmbEhqYxWHYaCtuANA2WNQFQAuVKrYddtKZEhmH7aHW+XE2UWUnRuA5/2QNWQq52xFNB/uX4ndm1e3HGrqZxKP033ZOjKOpQpljmOyUNiWqJvrLikuDT8kNWw1DsNA6wHVfHfWM2rG7NpvHRlv6RwEOIYsAd6VE2dOGrdzHxlc73o//DaExbFSU9SR34G5difhqH7f6b6Y9atS5qgnC4VtiVqjmpwGrnUbME7qhENZ9FiY91VrcfcyMK2Vqho50nQOl31eVlucXUSvc7vdj7vWLFY+j9scgbiFwckt5XXv4y6znXZm2qpGjZgNliz2Om6Xhh/iEFFVojYMtBb3Cx7x6Z/5UO1GmhNh3Kxwv+Rz2UinwPvF69xOFg4ArHvvQjzcv1L5PLIoABN7PHpUL50sxE+lt5ckMfNriVpTclifd5VeSVqyS3Iqhhpa+9y9HrpnJt6s+1eB4IQdAFQ8F3Fm6/M6t5P/+JHNq7Hnzz/i6zxevSGzjqIef5C5pVQG2JMkZq2OJwDNz7uXrz5t2SU7JZ+/DK0t95tWLMLjRyel+71mnraDymBunF1ElXMHYeFkJDPtgCvCUBwr4YG9E03HhekCkVnfFypV7N682tG6tZY5SbQznmDHrVfiNqt43dDhxLk53Ejj5Cy/aC3uz78UbB54AqTTgu10ESmlJIizixjFud3u1a7bau6dbftPtBxK2ipuLgbrfUnibMZWUbmXXr0S+zPjRySTdC95gR3Nxd3NJUBQi6YxMRNReSURM5kRouEhT9KDHTRu11aQiGjBENF1Q4dd72VYLhBVX3Wa/LNeQQOt9EpURdKvpRz2+5LUePco0VbcvfxnAsCtH7gWe45ONnS7nTIT2n3RQGPX9KYVi+o53q1YfZgqD7aODYDXS+slom4vU5gukCRHTYSFU12YbqdW1zlwE0mvRFgySzkKl4kOg8Nh64Fnyt8oCCPlbz6XxaXp2aYHfcvaJei7YaHvm+qWolP2INnTDjuJYNLzwstS7i7ozmLsqzcDcH9IZd/PEOEbd65K9LW3SxyNedDndKv/t6uznj1cpxS2qmmc2yHp71txrIQHnpzAjCXVdaaL8I07/L0Tbil/tRX3pZJsgoC7S6bVB8jtgXzDiACRlUWW2rad8kSFrFEDgEc2r/Z8EJP+kgWNLBQRSP51y3Liy3oDKjg930HlMvciyT3lnq/+EBcvNzeM8+ZmcPKhTyj/jpu4ax0KKWPL2iXSaJZWfW5uYWJuXT0z7E8WuZN0H6DbtalMgY9jyr4bYYbHtRuKGCde6SoKxnPgR9hlbreoUgR45SSKEydhd9veCtr63N3ou2Ehnn/pbKA+Ny8frsogrBNBPdBhWSkDG5bjPkmqAtWGKSmDlmH7etsNRYwTr3QV5uC417yGDBFmhXB9BpOaIiBtaCvuRPL0A8OHToXyAHktPmGKa7uWjV/M9WTNQeJSuYKBfRMNZWuV/t6CdLWqqAangmq4wg6PCyIUMS5Uoku8rk/V7dSJg91xoK24uw0VlMqVyB8gq/C7DUJ1zw1+7dKdT59siP4BatFAO58+Gcjv79jUE5ulFaS1HXZ43Pxc1nXiXJKtU5XoErdQS6doHLdGOSm9uTSjrbjL4quBml/QnGBkn6yydWQ8dKGX9Rq2bwxnMWVZ1JBqjL8XcVpaQVrbYYbHFcdKuHh5Wrq/1VDEqFDp6cqOcbLWeYaoO27zQ4JCW3Ef2LAcA09OoDrbbMILoMFqDfpB83ITpLHbGZelFaS1Haavd/jQqabeE9AYMppkVNNVeB1jwjNE3Yli3EFbcTcfENlg3/mpKpYOHkTBCEMM6kFTbSiiFMO8xB3gtc6sDgRpbYfZ6MoaG5UcRElB5ZlVfa55hqg7URiA2oo7cGVFercRfK99ywYP+rqxSbRIdmzqaerFZLsIOzb1xFIeK+0OhgZt4YTV6OowIzJK+H54E7YBqH2ce7vdGL/pZ5NokfT3FjB8x6qGWPJhnzPdwiCINL9Ji5OXkbZ0ue3C9yN+tLbcTaswCFSt76RaJEmMPpD1ch7Y6y9MM4nXZieN4yztwPcjfrQV9+JYCffvHYfDeKoj1jBE2VdUrG+egKGO7H7aM2qmBR0aoSjh+9FI1OkQtHXLfHn/C1Jhty+SZIYhmlORZeFGKta3Lm6CJOB2P5M8FZ9hgsacaGh1UQ7smwh1dShtLfep6qx0n7nSjqyFbNf6ZotEjZtWLGpKuWwlzHGKJCeNYjqPsCcaOqGtuLvhJb7sDwyf4lgJTx0vuaZiCGucgifQMEkj7ImGTqRS3JO+/F3aka2ZaiXMcYokhqsyTNSkUtz5JQ4HFVeHaTW7CXvYU/GTGK7KMFGjrbi75Zbhlzh4VF0dXmlvo1icRBauOj+Xxbqhw+yKYyJHlsWW7NEfAaJttMzAhuVNUTEmccecpxE3V4eVuNZMteI0gSbbRbh4ebqtCVUM0yqyjmyYC+FpK+79vQVsWbvEMezRFJAwV93pNFRdHbKGNUMUWcioU7jqO66e0xStwOGYTFTIwq+DzAJpR1txB4CH+1di9+bVjjHnQUx9Z66gujSabNp51Ith25dYC3rZRYbxQxzpGLT1uZvIol44YiJYVOcGJDXMNKlpI5jOII73Qntxl8ERE8Hi5+FMYpgpp41g4ibq9yK14s6WWvAkUbRVSWqPgmHCwlPciWgxgO8CuAa1DLmPCSG+RUQLAYwAWArglwDuFEKcJyIC8C0AtwCYAvCvhRD/N5ziy2FLjbGjc+PEMH5RGVCdBvCAEOL9ANYC+AIRvR/AIIDnhBA3AnjO+AwAnwRwo/HvXgCPBl5qBTjBF8MwnYyn5S6EeBPAm8bfvyeiFwEUAHwawEeNw74D4McAvmRs/64QQgA4SkR5IrrW+J1IYUuNYZhOxVcoJBEtBdAL4BiAayyC/SvU3DZATfjPWL72urHN/lv3EtEoEY2ePXvWZ7EZhmEYN5TFnYjeAeApAPcJIX5n3WdY6b7mWgkhHhNC9Akh+hYtWuTnqwzDMIwHSuJORFnUhH2PEGK/sfnXRHStsf9aAG8Z20sAFlu+fr2xjWEYhokIT3E3ol++DeBFIcQ3LbsOALjb+PtuAN+3bP8zqrEWwIU4/O0MwzCdjEqc+zoA/wrACSIaN7Z9GcAQgL1EdA+A1wDcaez7AWphkKdRC4X8fKAlZhiGYTxRiZb5X2heltTkYw7HCwBfaLNcDMMwTBtonTiMYRiGcYbFnWEYJoWwuDMMw6QQFneGYZgUktqskGlGZaFqhmE6GxZ3G0kXTtWFqhmG6WxY3C3oIJy8wlTnkHRDg0k2qRZ3vy9HnMKpWlZeYaoz0MHQYJJNKsW9OFbCjgMnUa5cWRRZ5eWISzj9vMi8wlRnwD00pl1SFy1jCqVV2E3Ml0OGTCDDFk63F9lOHKuoM9HDPTSmXVIn7k5CacXt5YhLOP28yLzCVGcQl6HBpAet3TJOfmovy8bt5QhjEWUVX7qXq8XpN44Mrm+5TEzy4TWAmXahWp6veOnr6xOjo6O+vmP3UwO1h//qbBfOTzW7ZMz9UVq5TmUkAFvWLsHD/Svr2x4snsDjRyebvv+5tUvQd8NCx+tkaz1eoohk4WgZxgsiOi6E6HPcp6u4rxs67Gjt5nNZXJqebXLNzJubwdf+NBpBNF9Kp/IBNYHfvXl1vSyya1nQnUX33DmO+wr5nG/rncUiGGSGBTe4TNS4ibu2bhmZcJYrVax770L875fPNaz7N3V5BqOvnQvdurppxSI8dbzk6vcXQEPUg8yVdH6qKu2F+B1Y49C64OBIFkYHtB1QzZAsxTxwxCbsQE1QHz86ieJYcCv+mYJZKlcgUBPMPUcnXYXdxCrObuMAsuvsIsKywYNYN3RY6Zr8ROQkjeJYCeuGDvu63jDhSBZGB7QV95kW3Uk7DpwMrAxOgqlaKquguw2SzQjRFMFjbjcblG37T3gKnq6C5NSAqlxvmHAkC6MD2oq7i+HuilP8e6u0Koz2qIf+3gLyuazjsRkiVKozdQveyZJXscB1FaQk9jh4rgGjA1qKe3GshHbHgYPo6suE0S6/2S7Cgu6sa1z6jk09Ugvd/D+XzUh7LF4Nja6ClMQeB881YHRAywHVdqy2Bd3ZwAYXZbHIn/lQAc+/dNZXVIo9xr6LqEnITfZ+wj8AABEjSURBVAveSeC9LPAwYvijIKnpFvp7C4m/d0xno6W4t2q1ZTOE7Rt7Aot2aFUwZSGJVsFYNnjQ8bszQiCbIVRnrgi8zAIPavJTnCGUPJmHYVpDS3GXWXNuFCyitHVk3PGYVhoNvxacaq/B9RpFrQdSnqpKxTao3onT79w3Mo4dB05ix6ae0EVe1x4Hw8SNluK+9F3q4v4522xQQC6c+W7nQU2TICxY1V6Dk8VqUp0V9et4o1ypu6ms3w+qdyLL1VOuVCOLk2cXCMP4R0txP/rKeeVj9xybrE/tX9CdxfaNPRjYsBwD+yYaXBtAbdLQ6p0/wo5NPQCAnU+frE8iymW7MD0r6t9p1RJWHSA0f/M+SS/DOsHJqSxBDUS6Hc8TdxgmuWgZLeMnxt166PmpKgb2TQAA5s11btfKlSruGxnHfSPjDbNDK9XZpsbAKSTPKwrHT0hif28BBcWBw0p1Bg/snaifL6jQR6/jkx4nzzCdipbi7jY71YvqjMDwoVO4EFC8u1XcVCbc+A1JdDpexowQ9fMFFfrodf64o1YYhnFGS3G/a83itr5fMkINg8AqbioTbvzGSDsdn8vKq83qKgkiFtv8nQUO4xEctcIwyUVLn3vfDQsdU+SqQmg9fYEVu7j58af7Dbk0jy+OlequJRnm+YIaiDR/h7NKMow+aCnu7U49DyLJcYYIn/lQo3hGMeFm+NCpJt9/mOezwlErDKMPWrpl/Ma4mzi5FlplRgiM/PRM3Z9eHCvh4qXppuOCdl14DWCyq4RhGEBTy50Iyrll7IsoyBbGkJHtAqZna3/bT1mdFfUsk04x6WboZZDWrtvkpgK7ShiGMdDSclcVdqdBRD/RJwDwx+/M4dWhW6WunHKlKp3o0z13TuBCK4uCeWTzahwZXB+asCctpzrDMO5oabl74TQr1cQ+nd2rnVCJ45YdUypXUBwrKQuuyoBlHIt48ypODKMfWq6hulSSVMtk3twMTj70CaXf8nLTmGuV9j70I8cl79zWOQXU19Z0WpfT/P2gXTte51V1ZWWIMCsER84wTEy4raGqpVvGi4uXZ1zdBlYXw9TlaWS7nGPerYOT2zf2IJtpPM7MMunm6lFdWELm2jk/VQ115SGV2HxZz8TvalAMw0SHluKuMv9IJqj2WaTnp6oAob4Skjn71e6v7+8tYPj2VQ2TgoZvX1UPD9x1m7MbCPB27RTHSq69hzBXHlKJzVcJrYxrdSQeC2AYZ7T0uat4kmSi5WSpmnHjvxy61fU33eK8+3sLGD50ynecu9nYeBFWDheV2Hy3DJVWos4zw2MBDCNHS8tdJbeMVZys1p3MQi5Xqm1bfa3kc5G5Y+yENTFJpczWVAZxlFFGEtdXZZik4CnuRPT3RPQWEf3Msm0hET1LRL8w/l9gbCci+isiOk1ELxDRB8MotFfqAKs42d0wbux8+mRb5Woln4uKtRvmxCTVMvf3FnBkcL1U4AmIfPJUEtdXZZikoOKW+QcAfw3gu5ZtgwCeE0IMEdGg8flLAD4J4Ebj3xoAjxr/B0rBYyUma1oAVcsYqPnf/YQuOuF3ir7MLRJlJIqfMju5aAjAlrVLAi+jV4hmUtdXZZgk4Gm5CyH+J4Bzts2fBvAd4+/vAOi3bP+uqHEUQJ6Irg2qsCYDG5bDzTGz5+gkHizWfK9+rbiou/Qyt8g37lyFV4duDXViUis4Wfq7N6+WzitolTDSJzNMJ9HqgOo1Qog3jb9/BeAa4+8CgDOW4143tr0JG0R0L4B7AWDJkiW+Tt7fW8Doa+ew5+iko6tFoCbwfTcs9L3eatRdeh3XCI0igZjKMoE63juGiYq2o2WEEIKIfM+EEkI8BuAxoDaJye/3TUtRlvpXQL5EnRcqrpkg099ytsVmwkqfzDCdQqvi/msiulYI8abhdnnL2F4CYF1J43pjW+Bs+duf4MjLdm9R+5iNwrb9L2DXbR9wFI7iWAkDT07UF6oulSsYeLKWY92adz0Mi3LN157Fr39/uf75mj+ai2Nf+Xjbv5s02J/OMO3RqrgfAHA3gCHj/+9btv9bIvoeagOpFyzum8B4sHgiFGG3UqnO4r6Rcew4cBKfWnUtnn/pbF2oz128VBd2k+qswLb9L9QXtXCLv3YTfrd9dmEHgF///jLWfO3Z1Am808At+9MZRh1PcSeiJwB8FMC7ieh1ANtRE/W9RHQPgNcA3Gkc/gMAtwA4DWAKwOdDKHNbqzD5pVypNpzPfSbpLLb87U9w9JXzTeGa1vhrmfC77evvLTQJu4lsu86wP51h2iOVicOSCkHubnBLQNad7cLP/8MnXa/ba3YtwzDpwy1xmJbpB3Rlfi4rtfzPT1Uds04CwFR1th7ayTAMo4KW6Qd0JNtFuHi5eRk+K25pFZ44dka6j2EYxg6Lu4GZFTIMMkSYO6fLc2Frt7QKXikXGIZhrLC4G4xvvxm/HLoVj2xeLc3v3ip3rVmMi5e9UyAU8jlpOmOVZGkMwzAmHSHuC7rdrXJrMqz+3gKG77iStz2fy6IdrV/33oV4/qWznseZYX5b1jjP1r1rzWLH7QzDME5oOaBK5J3TPZ/LYsemnnpc+daRccdUBU7ZDO2zHs3Ycz9pDIAra7ku84juKeRzuGnFonrYXy7bhUvTs5gVNYv9rjWL8XD/Sjwz8SbKleZB17BcSmFNxGIYJny0FHcvYbeGBZoTimTCrpLN0BR7r/VW7b9tpkiQhT/mc1mMb7+5adJTpTrruPbqjk09DTNjgdpA7Y5NPUpl8oPTRKytI+MYfe1c4EnCGIYJHi3dMl6LRlgzB8pS/maIfGczdMpCKPPY2FcycspeaIqy6qITdpdRIZ/D8B2rQrGmncpkJmTjpewYJvloabkPbFiO+/eOY1ZiwVszB7ot7jx86BS2jowruxycZk3etGIRnjpecp0m7zXb0s+iE1ElypKVSaDx/jIMk0y0nKEKAH/yl/+ESnVWur+Qz+GNcgVdRI5hhAQ0uGpMF00rLod2fdMyd0/GKLv5fyFCv7ebC4oAvMozYhkmdlI5Q9VN2IErOWBk8eH2rQJXctZYBd5JuIFmK/zI4HppWbzEX7YAtVl28/9SuYL7R8brvRbrYGvQDGxYLh2E5syMDJN8tBT3Vny+pvVrt9jtPH50Eq+e/QP2/PlHUBwrNbh/SuVKU454e3Ivp7K6JQOz/m82ALLeBgDM4soFzAjh2CAFgWxBFM7MyDB6oKVbpvehH0nzsMhwS9rlxOfWLsH+469jyqOHYJLPZTHvqjlN1rmbe0PmZlk2eNBzMW8rYbpJOBySYZJL6twyfoUdqAm7nyX0njh2xteU/3KlWo9Bt1rnbueU5Xn329yG2TzzSkcMoydahkL6xZyo5MdX3G4uFzOU0euc5nHWBaEZhmHaRUtx95sNQKBmgQ5sWK783QyRNM+LKm+UK44x7k7HyeLxGYZhWkFLcfdrU5uTnvp7C9iy1jl3i5271iyW5nlR5bp8Dv29Bey6baXrxCu/LiM7nFKMYRg7Woq71wxVK/bcMV5RJRmiek6Yh/tXojvb2i2ynre/t4Ajg+vxyObVjjNV3VxGC7qznpa/aoPFMEznoKW4q4biyXLHyLJELujO4uVdtzQ0AP/xtg94iqvqea1WvJk+wMwfI0tRsH1jT8N3ctmuepZKa0PEMAxjRctoGTMG275QdhcB77w6iwuVqmvY3vaNPRjYN9GweEY2Q9i+sTkBlz0GfX4ui99fmsaMJfdBF9WW0CtPuZ/X/D2nfV4pCjhihWEYP2gZ527STgx2XN9lGIYJCrc4d63FnWEYppNJ3SQmkweLJ/Dfjk02ZYe0zvwM2spmq51hGB3Q1nL/+Dd/jF+8ddH3ubJd1JQD3U2wrfvm57K4eHm6wVfvtKgGwzBMFKTOcn+weKIlYQeA6qzAwJPjdat+59MnG9IZWFMCAGhI+uW0xJ05w5TFnWGYJKGluNujZPxSnQVu/PJByHKCVaozuG9kvJ5J0ot2JiAxDMOEgZZx7kGgkuxRNb8M5zdnGCZpdKy4BwXnN2cYJomwuLdBhogHUxmGSSQs7i2Sy2bwjTtXsbAzDJNItBxQjRNzRSeOb2cYJsl0vLgX8jm89buK0gBrIZ9zXQibYRgmKWjplrnxj+e1/Ru5bAaPbF6NI4PrMXzHamS73LOi88ApwzA6oaW4P3v/R6UCv6A765iDPdtFWNCdbUq1C9QyLg7fsaohFe/n1i5xTM3LMAyjA9qmH/CCc8AwDJN2Upd+QAVZ3nSGYZhOQEu3DMMwDOMOizvDMEwKYXFnGIZJISzuDMMwKYTFnWEYJoUkIhSSiM4CeK3Fr78bwG8CLE6S4WtNJ3yt6SSKa71BCLHIaUcixL0diGhUFueZNvha0wlfazqJ+1rZLcMwDJNCWNwZhmFSSBrE/bG4CxAhfK3phK81ncR6rdr73BmGYZhm0mC5MwzDMDZY3BmGYVKI1uJORJ8golNEdJqIBuMuT7sQ0WIiep6Ifk5EJ4noi8b2hUT0LBH9wvh/gbGdiOivjOt/gYg+GO8V+IOIMkQ0RkTPGJ+XEdEx43pGiGiusf0q4/NpY//SOMvtFyLKE9E+InqJiF4koo+kuE63Gs/uz4joCSK6Oi31SkR/T0RvEdHPLNt81yMR3W0c/wsiujus8mor7kSUAfCfAXwSwPsB3EVE74+3VG0zDeABIcT7AawF8AXjmgYBPCeEuBHAc8ZnoHbtNxr/7gXwaPRFbosvAnjR8vnrAHYLId4H4DyAe4zt9wA4b2zfbRynE98C8EMhxAoAq1C75tTVKREVAPw7AH1CiH8GIAPgs0hPvf4DgE/YtvmqRyJaCGA7gDUAPgxgu9kgBI4QQst/AD4C4JDl8zYA2+IuV8DX+H0AHwdwCsC1xrZrAZwy/v4bAHdZjq8fl/R/AK43Xob1AJ5Bbe3x3wCYY69fAIcAfMT4e45xHMV9DYrXOR/Aq/byprROCwDOAFho1NMzADakqV4BLAXws1brEcBdAP7Gsr3huCD/aWu548qDZPK6sS0VGF3UXgDHAFwjhHjT2PUrANcYf+t8Dx4B8BcAzKXJ3wWgLISYNj5br6V+ncb+C8bxOrAMwFkA/9VwQf0dEc1DCutUCFEC8J8ATAJ4E7V6Oo501quJ33qMrH51FvfUQkTvAPAUgPuEEL+z7hO15l7r+FUi+hSAt4QQx+MuSwTMAfBBAI8KIXoBXMSVrjuAdNQpABjuhU+j1qBdB2Aemt0YqSVp9aizuJcALLZ8vt7YpjVElEVN2PcIIfYbm39NRNca+68F8JaxXdd7sA7AJiL6JYDvoeaa+RaAPBGZSz9ar6V+ncb++QB+G2WB2+B1AK8LIY4Zn/ehJvZpq1MA+BcAXhVCnBVCVAHsR62u01ivJn7rMbL61Vnc/w+AG42R+LmoDdwciLlMbUFEBODbAF4UQnzTsusAAHNU/W7UfPHm9j8zRubXArhg6SImFiHENiHE9UKIpajV22EhxBYAzwO43TjMfp3m9d9uHJ8YC8kNIcSvAJwhouXGpo8B+DlSVqcGkwDWElG38Syb15q6erXgtx4PAbiZiBYYPZ2bjW3BE/cARZuDG7cA+H8AXgbwlbjLE8D1/HPUunUvABg3/t2Cmh/yOQC/APA/ACw0jifUIoZeBnACtSiF2K/D5zV/FMAzxt/vAfBTAKcBPAngKmP71cbn08b+98Rdbp/XuBrAqFGvRQAL0lqnAHYCeAnAzwD8I4Cr0lKvAJ5AbSyhilqP7J5W6hHAvzGu+TSAz4dVXk4/wDAMk0J0dsswDMMwEljcGYZhUgiLO8MwTAphcWcYhkkhLO4MwzAphMWdYRgmhbC4MwzDpJD/D9Wyt7Vcng7sAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yXvhO0qT6IiI","executionInfo":{"status":"ok","timestamp":1626264347940,"user_tz":-330,"elapsed":466,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"e08411da-0ab5-47aa-b4b9-98daa5a4c871"},"source":["print('R square', metrics.r2_score(y_test, predictions))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["R square 0.07006525441635103\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":282},"id":"SINynV3o0qpX","executionInfo":{"status":"ok","timestamp":1626263217533,"user_tz":-330,"elapsed":1020,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"4d036826-330c-4de2-bc3d-6d7a71409aa2"},"source":["plt.scatter(Y_test,predictions) # with test size 0.3"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.collections.PathCollection at 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":386},"id":"5NtmpeE64quo","executionInfo":{"status":"ok","timestamp":1626263992155,"user_tz":-330,"elapsed":2021,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"cfcafc16-66e8-43c0-8e55-fd235962c410"},"source":["sns.displot((Y_test-predictions))#bins=50)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<seaborn.axisgrid.FacetGrid at 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\n","text/plain":["<Figure size 647.75x360 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"-aUHA_zM04yu"},"source":["from sklearn import metrics"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AFpPtPCg1ATj","executionInfo":{"status":"ok","timestamp":1626263222173,"user_tz":-330,"elapsed":439,"user":{"displayName":"David ray","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjVP3M8T9zzpZqGi6Nk8zshBHfnXDE3PTKA1DULmQ=s64","userId":"11929545398087895742"}},"outputId":"38ab4aac-38bb-4585-82aa-55defc35a5e5"},"source":["print('R square', metrics.r2_score(Y_test, predictions))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["R square 0.09153364624444497\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"EPiRJ68Qwk7k","outputId":"8f772a2a-3c7c-482b-d7d0-49b811d94870"},"source":["#reg.predict(X_train)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[407.91126431, 408.61751492,  89.39838903],\n","       [278.21166093, 264.3985914 ,  60.18139215],\n","       [266.44909547, 244.9025422 ,  56.15802736],\n","       ...,\n","       [153.88006525, 127.69909639,  32.60220783],\n","       [259.47216506, 237.68407857,  54.39700412],\n","       [413.91757365, 423.76624034,  92.20029393]])"]},"metadata":{"tags":[]},"execution_count":270}]},{"cell_type":"code","metadata":{"id":"JT36eIy4wk7k","outputId":"f8c66dc5-f288-4f0e-fdb7-48f800f3d6af"},"source":["reg.score(X_test,Y_test)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.12641176259644074"]},"metadata":{"tags":[]},"execution_count":271}]},{"cell_type":"code","metadata":{"id":"4mU5nN2Ywk7l","outputId":"05a6fb60-657e-4f98-ae20-a4dfee6d6ab7"},"source":["reg.predict([[1,2010,2]])"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[  7.95387041,  10.82645257, 383.57888423, 365.06321779,\n","         80.57713874]])"]},"metadata":{"tags":[]},"execution_count":152}]},{"cell_type":"code","metadata":{"id":"j75npdDEwk7l"},"source":["def prediction(state, year, gender):\n","    state_code=df_states.get(state)\n","    gender_code=df_gender_group.get(gender)\n","    \n","    features=[state_code, year, gender_code]\n","    input_data=np.array(features)\n","    input_data_reshape=input_data.reshape(1,-1)\n","    prediction_data=reg.predict(input_data_reshape)\n","    print(\"Number of victims( For age group of 10-15 years): \", int(prediction_data[0][0]), \"\\n\")\n","    print(\"Number of victims( For age group of 15-18 years): \", int(prediction_data[0][1]), \"\\n\")\n","    print(\"Number of victims( For age group of 18-30 years): \", int(prediction_data[0][2]), \"\\n\")\n","    print(\"Number of victims( For age group of 30-50 years): \", int(prediction_data[0][3]), \"\\n\")\n","    print(\"Number of victims( For age group above 50 years): \", int(prediction_data[0][4]), \"\\n\")\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"0Q_N0Kkrwk7l","outputId":"36261b86-9fa9-47d2-981e-96874fd6ef24"},"source":["prediction(\"Sikkim\",2010, \"Murder - Female Victims\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Number of victims( For age group of 10-15 years):  4 \n","\n","Number of victims( For age group of 15-18 years):  5 \n","\n","Number of victims( For age group of 18-30 years):  140 \n","\n","Number of victims( For age group of 30-50 years):  124 \n","\n","Number of victims( For age group above 50 years):  32 \n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"rlc5CU-vwk7l","outputId":"1b9bf8ee-c4c2-4771-81b3-e3c528f70d7e"},"source":["prediction(\"Assam\",2024,\"Murder - Male Victims\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Number of victims( For age group of 10-15 years):  6 \n","\n","Number of victims( For age group of 15-18 years):  4 \n","\n","Number of victims( For age group of 18-30 years):  232 \n","\n","Number of victims( For age group of 30-50 years):  218 \n","\n","Number of victims( For age group above 50 years):  54 \n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"S3tafXmBwk7l","outputId":"f48528a4-73c0-4d53-df76-1ac18139ed5d"},"source":["prediction(\"Assam\",2025,\"Murder - Male Victims\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Number of victims( For age group of 10-15 years):  6 \n","\n","Number of victims( For age group of 15-18 years):  4 \n","\n","Number of victims( For age group of 18-30 years):  231 \n","\n","Number of victims( For age group of 30-50 years):  217 \n","\n","Number of victims( For age group above 50 years):  54 \n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"IP9oMBKPwk7l","outputId":"176183e1-e229-4b68-d1ac-131bedfc909d"},"source":["prediction(\"Assam\",2026,\"Murder - Male Victims\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Number of victims( For age group of 10-15 years):  6 \n","\n","Number of victims( For age group of 15-18 years):  4 \n","\n","Number of victims( For age group of 18-30 years):  229 \n","\n","Number of victims( For age group of 30-50 years):  216 \n","\n","Number of victims( For age group above 50 years):  54 \n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"IH6DTftvwk7m","outputId":"d4502d2d-b9b2-4fca-a46d-c83e877546b9"},"source":["prediction(\"Assam\",2020,\"Murder - Female Victims\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Number of victims( For age group of 10-15 years):  4 \n","\n","Number of victims( For age group of 15-18 years):  2 \n","\n","Number of victims( For age group of 18-30 years):  105 \n","\n","Number of victims( For age group of 30-50 years):  85 \n","\n","Number of victims( For age group above 50 years):  28 \n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Sq7sT7kDwk7m","outputId":"efd125bf-971a-43e3-f4cd-ac3313d4638d"},"source":["prediction(\"Assam\",2021,\"Murder - Female Victims\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Number of victims( For age group of 10-15 years):  4 \n","\n","Number of victims( For age group of 15-18 years):  2 \n","\n","Number of victims( For age group of 18-30 years):  104 \n","\n","Number of victims( For age group of 30-50 years):  84 \n","\n","Number of victims( For age group above 50 years):  28 \n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"fozu9Hu5wk7m","outputId":"d2b1e17a-99c4-4e51-9f1f-4abb67c53d19"},"source":["prediction(\"Assam\",2025,\"Murder - Female Victims\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Number of victims( For age group of 10-15 years):  4 \n","\n","Number of victims( For age group of 15-18 years):  1 \n","\n","Number of victims( For age group of 18-30 years):  98 \n","\n","Number of victims( For age group of 30-50 years):  81 \n","\n","Number of victims( For age group above 50 years):  28 \n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"u4nu4kXvwk7m","outputId":"87cb4ab0-5a48-4653-f31c-4ef04e8b88b9"},"source":["prediction(\"Assam\",2023,\"Murder - Female Victims\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Number of victims( For age group of 10-15 years):  4 \n","\n","Number of victims( For age group of 15-18 years):  2 \n","\n","Number of victims( For age group of 18-30 years):  101 \n","\n","Number of victims( For age group of 30-50 years):  83 \n","\n","Number of victims( For age group above 50 years):  28 \n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Niw_LQvKwk7m","outputId":"c1ab0214-9272-4c83-aa03-621ca65b547a"},"source":["prediction(\"Assam\",2024,\"Murder - Female Victims\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Number of victims( For age group of 10-15 years):  4 \n","\n","Number of victims( For age group of 15-18 years):  1 \n","\n","Number of victims( For age group of 18-30 years):  99 \n","\n","Number of victims( For age group of 30-50 years):  82 \n","\n","Number of victims( For age group above 50 years):  28 \n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"yJ2foMAOwk7n","outputId":"2f62c12a-1019-461d-fe70-ce9eb6855519"},"source":["reg.score(X_test,Y_test)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.06720321363199229"]},"metadata":{"tags":[]},"execution_count":168}]}]}