22f1000024-notebook-t22024.ipynb

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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/crime-cast-forecasting-crime-categories/sample.csv\n",
      "/kaggle/input/crime-cast-forecasting-crime-categories/train.csv\n",
      "/kaggle/input/crime-cast-forecasting-crime-categories/test.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
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   "id": "ad684a97",
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    "tags": []
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   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, ExtraTreesClassifier\n",
    "from sklearn.preprocessing import MultiLabelBinarizer\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3f2ac82",
   "metadata": {
    "papermill": {
     "duration": 0.027755,
     "end_time": "2024-08-19T13:21:19.225828",
     "exception": false,
     "start_time": "2024-08-19T13:21:19.198073",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Dataset Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "903652cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:19.285958Z",
     "iopub.status.busy": "2024-08-19T13:21:19.285466Z",
     "iopub.status.idle": "2024-08-19T13:21:19.584797Z",
     "shell.execute_reply": "2024-08-19T13:21:19.583282Z"
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     "duration": 0.334543,
     "end_time": "2024-08-19T13:21:19.588279",
     "exception": false,
     "start_time": "2024-08-19T13:21:19.253736",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df = pd.read_csv(\"/kaggle/input/crime-cast-forecasting-crime-categories/train.csv\")\n",
    "test_df = pd.read_csv(\"/kaggle/input/crime-cast-forecasting-crime-categories/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ad0ea27",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:19.650017Z",
     "iopub.status.busy": "2024-08-19T13:21:19.649517Z",
     "iopub.status.idle": "2024-08-19T13:21:19.659488Z",
     "shell.execute_reply": "2024-08-19T13:21:19.658101Z"
    },
    "papermill": {
     "duration": 0.04393,
     "end_time": "2024-08-19T13:21:19.662947",
     "exception": false,
     "start_time": "2024-08-19T13:21:19.619017",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((20000, 22), (5000, 21))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9d541c5",
   "metadata": {
    "papermill": {
     "duration": 0.027676,
     "end_time": "2024-08-19T13:21:19.719449",
     "exception": false,
     "start_time": "2024-08-19T13:21:19.691773",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfd5bf7b",
   "metadata": {
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     "duration": 0.028361,
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     "start_time": "2024-08-19T13:21:19.747744",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Dataset Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5b78ba9b",
   "metadata": {
    "execution": {
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     "duration": 0.043672,
     "end_time": "2024-08-19T13:21:19.848295",
     "exception": false,
     "start_time": "2024-08-19T13:21:19.804623",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows in the training data:  20000\n",
      "Number of columns in the training data:  22\n",
      "Number of rows in the testing data:  5000\n",
      "Number of columns in the testing data:  21\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of rows in the training data: \", train_df.shape[0])\n",
    "print(\"Number of columns in the training data: \", train_df.shape[1])\n",
    "\n",
    "print(\"Number of rows in the testing data: \", test_df.shape[0])\n",
    "print(\"Number of columns in the testing data: \", test_df.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "52af35bc",
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    "execution": {
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     "start_time": "2024-08-19T13:21:19.879017",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "\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>Location</th>\n",
       "      <th>Cross_Street</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Date_Reported</th>\n",
       "      <th>Date_Occurred</th>\n",
       "      <th>Time_Occurred</th>\n",
       "      <th>Area_ID</th>\n",
       "      <th>Area_Name</th>\n",
       "      <th>Reporting_District_no</th>\n",
       "      <th>...</th>\n",
       "      <th>Victim_Age</th>\n",
       "      <th>Victim_Sex</th>\n",
       "      <th>Victim_Descent</th>\n",
       "      <th>Premise_Code</th>\n",
       "      <th>Premise_Description</th>\n",
       "      <th>Weapon_Used_Code</th>\n",
       "      <th>Weapon_Description</th>\n",
       "      <th>Status</th>\n",
       "      <th>Status_Description</th>\n",
       "      <th>Crime_Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4500    CARPENTER                    AV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.1522</td>\n",
       "      <td>-118.3910</td>\n",
       "      <td>03/09/2020 12:00:00 AM</td>\n",
       "      <td>03/06/2020 12:00:00 AM</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>N Hollywood</td>\n",
       "      <td>1563.0</td>\n",
       "      <td>...</td>\n",
       "      <td>75.0</td>\n",
       "      <td>M</td>\n",
       "      <td>W</td>\n",
       "      <td>101.0</td>\n",
       "      <td>STREET</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "      <td>Property Crimes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>45TH                         ST</td>\n",
       "      <td>ALAMEDA                      ST</td>\n",
       "      <td>34.0028</td>\n",
       "      <td>-118.2391</td>\n",
       "      <td>02/27/2020 12:00:00 AM</td>\n",
       "      <td>02/27/2020 12:00:00 AM</td>\n",
       "      <td>1345.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Newton</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>...</td>\n",
       "      <td>41.0</td>\n",
       "      <td>M</td>\n",
       "      <td>H</td>\n",
       "      <td>216.0</td>\n",
       "      <td>SWAP MEET</td>\n",
       "      <td>400.0</td>\n",
       "      <td>STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "      <td>Property Crimes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600 E  MARTIN LUTHER KING JR        BL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0111</td>\n",
       "      <td>-118.2653</td>\n",
       "      <td>08/21/2020 12:00:00 AM</td>\n",
       "      <td>08/21/2020 12:00:00 AM</td>\n",
       "      <td>605.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>Newton</td>\n",
       "      <td>1343.0</td>\n",
       "      <td>...</td>\n",
       "      <td>67.0</td>\n",
       "      <td>M</td>\n",
       "      <td>B</td>\n",
       "      <td>501.0</td>\n",
       "      <td>SINGLE FAMILY DWELLING</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "      <td>Property Crimes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14900    ORO GRANDE                   ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.2953</td>\n",
       "      <td>-118.4590</td>\n",
       "      <td>11/08/2020 12:00:00 AM</td>\n",
       "      <td>11/06/2020 12:00:00 AM</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Mission</td>\n",
       "      <td>1924.0</td>\n",
       "      <td>...</td>\n",
       "      <td>61.0</td>\n",
       "      <td>M</td>\n",
       "      <td>H</td>\n",
       "      <td>101.0</td>\n",
       "      <td>STREET</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "      <td>Property Crimes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7100 S  VERMONT                      AV</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.9787</td>\n",
       "      <td>-118.2918</td>\n",
       "      <td>02/25/2020 12:00:00 AM</td>\n",
       "      <td>02/25/2020 12:00:00 AM</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>77th Street</td>\n",
       "      <td>1245.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>X</td>\n",
       "      <td>X</td>\n",
       "      <td>401.0</td>\n",
       "      <td>MINI-MART</td>\n",
       "      <td>400.0</td>\n",
       "      <td>STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "      <td>Property Crimes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Location                     Cross_Street  \\\n",
       "0   4500    CARPENTER                    AV                              NaN   \n",
       "1           45TH                         ST  ALAMEDA                      ST   \n",
       "2    600 E  MARTIN LUTHER KING JR        BL                              NaN   \n",
       "3  14900    ORO GRANDE                   ST                              NaN   \n",
       "4   7100 S  VERMONT                      AV                              NaN   \n",
       "\n",
       "   Latitude  Longitude           Date_Reported           Date_Occurred  \\\n",
       "0   34.1522  -118.3910  03/09/2020 12:00:00 AM  03/06/2020 12:00:00 AM   \n",
       "1   34.0028  -118.2391  02/27/2020 12:00:00 AM  02/27/2020 12:00:00 AM   \n",
       "2   34.0111  -118.2653  08/21/2020 12:00:00 AM  08/21/2020 12:00:00 AM   \n",
       "3   34.2953  -118.4590  11/08/2020 12:00:00 AM  11/06/2020 12:00:00 AM   \n",
       "4   33.9787  -118.2918  02/25/2020 12:00:00 AM  02/25/2020 12:00:00 AM   \n",
       "\n",
       "   Time_Occurred  Area_ID    Area_Name  Reporting_District_no  ...  \\\n",
       "0         1800.0     15.0  N Hollywood                 1563.0  ...   \n",
       "1         1345.0     13.0       Newton                 1367.0  ...   \n",
       "2          605.0     13.0       Newton                 1343.0  ...   \n",
       "3         1800.0     19.0      Mission                 1924.0  ...   \n",
       "4         1130.0     12.0  77th Street                 1245.0  ...   \n",
       "\n",
       "   Victim_Age Victim_Sex  Victim_Descent Premise_Code     Premise_Description  \\\n",
       "0        75.0          M               W        101.0                  STREET   \n",
       "1        41.0          M               H        216.0               SWAP MEET   \n",
       "2        67.0          M               B        501.0  SINGLE FAMILY DWELLING   \n",
       "3        61.0          M               H        101.0                  STREET   \n",
       "4         0.0          X               X        401.0               MINI-MART   \n",
       "\n",
       "   Weapon_Used_Code                              Weapon_Description  Status  \\\n",
       "0               NaN                                             NaN      IC   \n",
       "1             400.0  STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)      IC   \n",
       "2               NaN                                             NaN      IC   \n",
       "3               NaN                                             NaN      IC   \n",
       "4             400.0  STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)      IC   \n",
       "\n",
       "  Status_Description   Crime_Category  \n",
       "0        Invest Cont  Property Crimes  \n",
       "1        Invest Cont  Property Crimes  \n",
       "2        Invest Cont  Property Crimes  \n",
       "3        Invest Cont  Property Crimes  \n",
       "4        Invest Cont  Property Crimes  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Head of the training data\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f813a75c",
   "metadata": {
    "papermill": {
     "duration": 0.029853,
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    "tags": []
   },
   "source": [
    "#### We can observe that 'Latitude' and 'Longitude' represent the same thing as 'Location'.\n",
    "#### 'Area_ID', 'Premise_Code', 'Weapon_Used_Code', 'Status' also represent the same thing as 'Area_Name', 'Premise_Description', 'Weapon_Description', 'Status_Description'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "58067adc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:20.186390Z",
     "iopub.status.busy": "2024-08-19T13:21:20.185860Z",
     "iopub.status.idle": "2024-08-19T13:21:20.194697Z",
     "shell.execute_reply": "2024-08-19T13:21:20.193347Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Location', 'Cross_Street', 'Latitude', 'Longitude', 'Date_Reported',\n",
       "       'Date_Occurred', 'Time_Occurred', 'Area_ID', 'Area_Name',\n",
       "       'Reporting_District_no', 'Part 1-2', 'Modus_Operandi', 'Victim_Age',\n",
       "       'Victim_Sex', 'Victim_Descent', 'Premise_Code', 'Premise_Description',\n",
       "       'Weapon_Used_Code', 'Weapon_Description', 'Status',\n",
       "       'Status_Description', 'Crime_Category'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All columns in the training dataset\n",
    "train_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "964af690",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:20.257943Z",
     "iopub.status.busy": "2024-08-19T13:21:20.257534Z",
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     "shell.execute_reply": "2024-08-19T13:21:20.266844Z"
    },
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     "duration": 0.044273,
     "end_time": "2024-08-19T13:21:20.271226",
     "exception": false,
     "start_time": "2024-08-19T13:21:20.226953",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location                  object\n",
       "Cross_Street              object\n",
       "Latitude                 float64\n",
       "Longitude                float64\n",
       "Date_Reported             object\n",
       "Date_Occurred             object\n",
       "Time_Occurred            float64\n",
       "Area_ID                  float64\n",
       "Area_Name                 object\n",
       "Reporting_District_no    float64\n",
       "Part 1-2                 float64\n",
       "Modus_Operandi            object\n",
       "Victim_Age               float64\n",
       "Victim_Sex                object\n",
       "Victim_Descent            object\n",
       "Premise_Code             float64\n",
       "Premise_Description       object\n",
       "Weapon_Used_Code         float64\n",
       "Weapon_Description        object\n",
       "Status                    object\n",
       "Status_Description        object\n",
       "Crime_Category            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Types of each column in the training dataset:\n",
    "\n",
    "train_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7bf03b7e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:20.332202Z",
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     "iopub.status.idle": "2024-08-19T13:21:20.371050Z",
     "shell.execute_reply": "2024-08-19T13:21:20.369515Z"
    },
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     "exception": false,
     "start_time": "2024-08-19T13:21:20.300710",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location                     0\n",
       "Cross_Street             16552\n",
       "Latitude                     0\n",
       "Longitude                    0\n",
       "Date_Reported                0\n",
       "Date_Occurred                0\n",
       "Time_Occurred                0\n",
       "Area_ID                      0\n",
       "Area_Name                    0\n",
       "Reporting_District_no        0\n",
       "Part 1-2                     0\n",
       "Modus_Operandi            2741\n",
       "Victim_Age                   0\n",
       "Victim_Sex                2624\n",
       "Victim_Descent            2624\n",
       "Premise_Code                 0\n",
       "Premise_Description          5\n",
       "Weapon_Used_Code         12665\n",
       "Weapon_Description       12665\n",
       "Status                       0\n",
       "Status_Description           0\n",
       "Crime_Category               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total number of null values present in each feature\n",
    "\n",
    "train_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "77d2b1b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:20.437279Z",
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     "iopub.status.idle": "2024-08-19T13:21:20.493388Z",
     "shell.execute_reply": "2024-08-19T13:21:20.491836Z"
    },
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     "duration": 0.091953,
     "end_time": "2024-08-19T13:21:20.496491",
     "exception": false,
     "start_time": "2024-08-19T13:21:20.404538",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Time_Occurred</th>\n",
       "      <th>Area_ID</th>\n",
       "      <th>Reporting_District_no</th>\n",
       "      <th>Part 1-2</th>\n",
       "      <th>Victim_Age</th>\n",
       "      <th>Premise_Code</th>\n",
       "      <th>Weapon_Used_Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20000.000000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>7335.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>33.940704</td>\n",
       "      <td>-117.893072</td>\n",
       "      <td>1352.380350</td>\n",
       "      <td>10.834250</td>\n",
       "      <td>1129.599200</td>\n",
       "      <td>1.418150</td>\n",
       "      <td>30.135000</td>\n",
       "      <td>297.176950</td>\n",
       "      <td>366.405044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.126810</td>\n",
       "      <td>7.377726</td>\n",
       "      <td>646.100291</td>\n",
       "      <td>6.033166</td>\n",
       "      <td>603.330519</td>\n",
       "      <td>0.493267</td>\n",
       "      <td>21.863645</td>\n",
       "      <td>212.007298</td>\n",
       "      <td>120.485687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-118.663400</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-2.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>101.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>34.009200</td>\n",
       "      <td>-118.429700</td>\n",
       "      <td>930.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>632.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>312.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>34.058700</td>\n",
       "      <td>-118.323000</td>\n",
       "      <td>1430.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1162.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>203.000000</td>\n",
       "      <td>400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>34.165025</td>\n",
       "      <td>-118.274400</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1622.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>501.000000</td>\n",
       "      <td>400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>34.328100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2359.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>2197.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>969.000000</td>\n",
       "      <td>515.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Latitude     Longitude  Time_Occurred       Area_ID  \\\n",
       "count  20000.000000  20000.000000   20000.000000  20000.000000   \n",
       "mean      33.940704   -117.893072    1352.380350     10.834250   \n",
       "std        2.126810      7.377726     646.100291      6.033166   \n",
       "min        0.000000   -118.663400       1.000000      1.000000   \n",
       "25%       34.009200   -118.429700     930.000000      6.000000   \n",
       "50%       34.058700   -118.323000    1430.000000     11.000000   \n",
       "75%       34.165025   -118.274400    1900.000000     16.000000   \n",
       "max       34.328100      0.000000    2359.000000     21.000000   \n",
       "\n",
       "       Reporting_District_no      Part 1-2    Victim_Age  Premise_Code  \\\n",
       "count           20000.000000  20000.000000  20000.000000  20000.000000   \n",
       "mean             1129.599200      1.418150     30.135000    297.176950   \n",
       "std               603.330519      0.493267     21.863645    212.007298   \n",
       "min               101.000000      1.000000     -2.000000    101.000000   \n",
       "25%               632.000000      1.000000     12.000000    101.000000   \n",
       "50%              1162.000000      1.000000     31.000000    203.000000   \n",
       "75%              1622.000000      2.000000     46.000000    501.000000   \n",
       "max              2197.000000      2.000000     99.000000    969.000000   \n",
       "\n",
       "       Weapon_Used_Code  \n",
       "count       7335.000000  \n",
       "mean         366.405044  \n",
       "std          120.485687  \n",
       "min          101.000000  \n",
       "25%          312.000000  \n",
       "50%          400.000000  \n",
       "75%          400.000000  \n",
       "max          515.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualising key statistics:\n",
    "\n",
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeec7ccd",
   "metadata": {
    "papermill": {
     "duration": 0.029468,
     "end_time": "2024-08-19T13:21:20.555439",
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     "start_time": "2024-08-19T13:21:20.525971",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Correlation-Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2c1003be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:20.617873Z",
     "iopub.status.busy": "2024-08-19T13:21:20.617222Z",
     "iopub.status.idle": "2024-08-19T13:21:21.465923Z",
     "shell.execute_reply": "2024-08-19T13:21:21.464594Z"
    },
    "papermill": {
     "duration": 0.88443,
     "end_time": "2024-08-19T13:21:21.469831",
     "exception": false,
     "start_time": "2024-08-19T13:21:20.585401",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# List of numerical columns\n",
    "numerical_cols = ['Latitude', 'Longitude', 'Time_Occurred', 'Area_ID', 'Part 1-2', 'Victim_Age', 'Premise_Code', 'Weapon_Used_Code']\n",
    "\n",
    "# Select only numerical columns from the DataFrame\n",
    "numerical_df = train_df[numerical_cols]\n",
    "\n",
    "# Calculate the correlation matrix for numerical columns\n",
    "corr = numerical_df.corr()\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "# Draw the heatmap\n",
    "sns.heatmap(\n",
    "    corr,\n",
    "    annot=True,\n",
    "    cmap='viridis',\n",
    "    fmt='.2f',\n",
    "    linewidths=0.5,\n",
    "    vmin=-1,\n",
    "    vmax=1,\n",
    "    cbar_kws={'shrink': .8},\n",
    "    square=True,\n",
    "    annot_kws={\"size\": 10, \"color\": 'black'}\n",
    ")\n",
    "\n",
    "# Set the title fontsize and fontweight\n",
    "plt.title('Correlation Heatmap for Numerical Columns', fontsize=16, fontweight='bold')\n",
    "\n",
    "# Remove axes labels and ticks\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.yticks(rotation=0)\n",
    "\n",
    "# plot the graph\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39ec41c9",
   "metadata": {
    "papermill": {
     "duration": 0.032435,
     "end_time": "2024-08-19T13:21:21.535214",
     "exception": false,
     "start_time": "2024-08-19T13:21:21.502779",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### As we can observe here there isn't much correlation between features here."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1afc9099",
   "metadata": {
    "papermill": {
     "duration": 0.031871,
     "end_time": "2024-08-19T13:21:21.598816",
     "exception": false,
     "start_time": "2024-08-19T13:21:21.566945",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Distribution of 'Crime_Category'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cd05beae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:21.667990Z",
     "iopub.status.busy": "2024-08-19T13:21:21.667491Z",
     "iopub.status.idle": "2024-08-19T13:21:22.027973Z",
     "shell.execute_reply": "2024-08-19T13:21:22.026532Z"
    },
    "papermill": {
     "duration": 0.397678,
     "end_time": "2024-08-19T13:21:22.031060",
     "exception": false,
     "start_time": "2024-08-19T13:21:21.633382",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6)) \n",
    "sns.countplot(data=train_df, x='Crime_Category')\n",
    "plt.title('Distribution of Crime Category')\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels\n",
    "plt.xlabel('Crime Category')  # Set x-axis label\n",
    "plt.ylabel('Count')  # Set y-axis label\n",
    "plt.tight_layout()  # Adjust layout to fit labels\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "490ff77f",
   "metadata": {
    "papermill": {
     "duration": 0.03267,
     "end_time": "2024-08-19T13:21:22.096306",
     "exception": false,
     "start_time": "2024-08-19T13:21:22.063636",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### The target vector 'Crime_Category' has lot of Property Crimes followed by Violent crimes majorly compared to the other crimes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8d52479f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:22.162746Z",
     "iopub.status.busy": "2024-08-19T13:21:22.162257Z",
     "iopub.status.idle": "2024-08-19T13:21:22.651822Z",
     "shell.execute_reply": "2024-08-19T13:21:22.650566Z"
    },
    "papermill": {
     "duration": 0.526643,
     "end_time": "2024-08-19T13:21:22.655084",
     "exception": false,
     "start_time": "2024-08-19T13:21:22.128441",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "sns.boxplot(data=train_df, x='Crime_Category', y='Victim_Age', palette='Set2')\n",
    "plt.title('Victim Age by Crime Category')\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('Crime Category')\n",
    "plt.ylabel('Victim Age')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7595f49e",
   "metadata": {
    "papermill": {
     "duration": 0.033258,
     "end_time": "2024-08-19T13:21:22.723155",
     "exception": false,
     "start_time": "2024-08-19T13:21:22.689897",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### We can observe key statistics like median, range, etc.. of each 'Crime_Category' from the above box plots."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "751054a9",
   "metadata": {
    "papermill": {
     "duration": 0.033772,
     "end_time": "2024-08-19T13:21:22.791495",
     "exception": false,
     "start_time": "2024-08-19T13:21:22.757723",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Visualising Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fa0ff556",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:22.861264Z",
     "iopub.status.busy": "2024-08-19T13:21:22.859956Z",
     "iopub.status.idle": "2024-08-19T13:21:24.178801Z",
     "shell.execute_reply": "2024-08-19T13:21:24.177576Z"
    },
    "papermill": {
     "duration": 1.356833,
     "end_time": "2024-08-19T13:21:24.181701",
     "exception": false,
     "start_time": "2024-08-19T13:21:22.824868",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "sns.heatmap(train_df.isnull(), cbar=False, cmap='Oranges', vmin=0, vmax=1)\n",
    "plt.title('Missing Values in Training Data')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Data Points')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3f3afff",
   "metadata": {
    "papermill": {
     "duration": 0.034281,
     "end_time": "2024-08-19T13:21:24.250561",
     "exception": false,
     "start_time": "2024-08-19T13:21:24.216280",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### We can observe from missing values graphical representation that a lot of Cross_Street values are missing.\n",
    "#### We can also observe from a quick glance that wherever Modus_Operandi is missing, Victim_Sex and Victim_Descent are also misisng.\n",
    "\n",
    "#### Lot of 'Weapon_Used_Code' values are also missing. It might indicate crimes in which no weapon is involved or the weapon involved is not recorded."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "169ed2bb",
   "metadata": {
    "papermill": {
     "duration": 0.035136,
     "end_time": "2024-08-19T13:21:24.321380",
     "exception": false,
     "start_time": "2024-08-19T13:21:24.286244",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Distribution of 'Victim_Age'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9c1e1c47",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:24.392241Z",
     "iopub.status.busy": "2024-08-19T13:21:24.391738Z",
     "iopub.status.idle": "2024-08-19T13:21:25.119348Z",
     "shell.execute_reply": "2024-08-19T13:21:25.117770Z"
    },
    "papermill": {
     "duration": 0.766533,
     "end_time": "2024-08-19T13:21:25.122344",
     "exception": false,
     "start_time": "2024-08-19T13:21:24.355811",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "sns.histplot(train_df['Victim_Age'].dropna(), bins=30, kde=True)\n",
    "plt.title('Distribution of Victim Age')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ce424ae",
   "metadata": {
    "papermill": {
     "duration": 0.035928,
     "end_time": "2024-08-19T13:21:25.194341",
     "exception": false,
     "start_time": "2024-08-19T13:21:25.158413",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### A lot of crimes tend to have 0 Age. So, either the crimes happen to infants or the Age is missing. As the proportion is lot higher we can assume the Age is missing for these rows.\n",
    "#### Right Skewed graph."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e12a0b9",
   "metadata": {
    "papermill": {
     "duration": 0.035972,
     "end_time": "2024-08-19T13:21:25.266307",
     "exception": false,
     "start_time": "2024-08-19T13:21:25.230335",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Visualising crimes by hour of the day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fcec02a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:25.342778Z",
     "iopub.status.busy": "2024-08-19T13:21:25.342292Z",
     "iopub.status.idle": "2024-08-19T13:21:26.048768Z",
     "shell.execute_reply": "2024-08-19T13:21:26.047177Z"
    },
    "papermill": {
     "duration": 0.749611,
     "end_time": "2024-08-19T13:21:26.051819",
     "exception": false,
     "start_time": "2024-08-19T13:21:25.302208",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a function to extract hours from the time format\n",
    "def extract_hour(time):\n",
    "    if pd.notna(time):\n",
    "        time_str = str(int(time)).zfill(4)  # Ensure 4-digit string format\n",
    "        return int(time_str[:2])  # Extract hour\n",
    "    return None\n",
    "\n",
    "# Use a temporary Series to avoid modifying the DataFrame\n",
    "hours = train_df['Time_Occurred'].apply(extract_hour)\n",
    "\n",
    "# Plot the histogram of hours\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.histplot(hours.dropna(), bins=24, kde=False, color='blue')\n",
    "plt.title('Crimes by Hour of Day')\n",
    "plt.xlabel('Hour of Day')\n",
    "plt.ylabel('Count')\n",
    "plt.xticks(range(0, 24))  # Ensure all hours are shown on x-axis\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41c69511",
   "metadata": {
    "papermill": {
     "duration": 0.037516,
     "end_time": "2024-08-19T13:21:26.126346",
     "exception": false,
     "start_time": "2024-08-19T13:21:26.088830",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### We can observe that most of the crimes happen in the middle of the day.\n",
    "#### The graph is somewhat Left Skewed with most of the crimes happeing in the second half of the day."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4a8bbd1",
   "metadata": {
    "papermill": {
     "duration": 0.037978,
     "end_time": "2024-08-19T13:21:26.202859",
     "exception": false,
     "start_time": "2024-08-19T13:21:26.164881",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b75c94d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:26.280614Z",
     "iopub.status.busy": "2024-08-19T13:21:26.280127Z",
     "iopub.status.idle": "2024-08-19T13:21:26.290822Z",
     "shell.execute_reply": "2024-08-19T13:21:26.289635Z"
    },
    "papermill": {
     "duration": 0.054176,
     "end_time": "2024-08-19T13:21:26.293963",
     "exception": false,
     "start_time": "2024-08-19T13:21:26.239787",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Seperate the target vector from train_df for ease of preprocessing\n",
    "y = train_df['Crime_Category']\n",
    "train_df.drop('Crime_Category', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4d5c5b31",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:26.371806Z",
     "iopub.status.busy": "2024-08-19T13:21:26.370697Z",
     "iopub.status.idle": "2024-08-19T13:21:26.378665Z",
     "shell.execute_reply": "2024-08-19T13:21:26.377440Z"
    },
    "papermill": {
     "duration": 0.049736,
     "end_time": "2024-08-19T13:21:26.381440",
     "exception": false,
     "start_time": "2024-08-19T13:21:26.331704",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((20000, 21), (5000, 21))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a7d126c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:26.459564Z",
     "iopub.status.busy": "2024-08-19T13:21:26.459104Z",
     "iopub.status.idle": "2024-08-19T13:21:26.493739Z",
     "shell.execute_reply": "2024-08-19T13:21:26.492477Z"
    },
    "papermill": {
     "duration": 0.077328,
     "end_time": "2024-08-19T13:21:26.496647",
     "exception": false,
     "start_time": "2024-08-19T13:21:26.419319",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location                     0\n",
       "Cross_Street             16552\n",
       "Latitude                     0\n",
       "Longitude                    0\n",
       "Date_Reported                0\n",
       "Date_Occurred                0\n",
       "Time_Occurred                0\n",
       "Area_ID                      0\n",
       "Area_Name                    0\n",
       "Reporting_District_no        0\n",
       "Part 1-2                     0\n",
       "Modus_Operandi            2741\n",
       "Victim_Age                   0\n",
       "Victim_Sex                2624\n",
       "Victim_Descent            2624\n",
       "Premise_Code                 0\n",
       "Premise_Description          5\n",
       "Weapon_Used_Code         12665\n",
       "Weapon_Description       12665\n",
       "Status                       0\n",
       "Status_Description           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Null values of each feature to preprocess them\n",
    "train_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4ee1f631",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:26.574874Z",
     "iopub.status.busy": "2024-08-19T13:21:26.574363Z",
     "iopub.status.idle": "2024-08-19T13:21:26.581439Z",
     "shell.execute_reply": "2024-08-19T13:21:26.580171Z"
    },
    "papermill": {
     "duration": 0.049209,
     "end_time": "2024-08-19T13:21:26.584528",
     "exception": false,
     "start_time": "2024-08-19T13:21:26.535319",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns of Train Data:  ['Location', 'Cross_Street', 'Latitude', 'Longitude', 'Date_Reported', 'Date_Occurred', 'Time_Occurred', 'Area_ID', 'Area_Name', 'Reporting_District_no', 'Part 1-2', 'Modus_Operandi', 'Victim_Age', 'Victim_Sex', 'Victim_Descent', 'Premise_Code', 'Premise_Description', 'Weapon_Used_Code', 'Weapon_Description', 'Status', 'Status_Description']\n",
      "Columns of Test Data:  ['Location', 'Cross_Street', 'Latitude', 'Longitude', 'Date_Reported', 'Date_Occurred', 'Time_Occurred', 'Area_ID', 'Area_Name', 'Reporting_District_no', 'Part 1-2', 'Modus_Operandi', 'Victim_Age', 'Victim_Sex', 'Victim_Descent', 'Premise_Code', 'Premise_Description', 'Weapon_Used_Code', 'Weapon_Description', 'Status', 'Status_Description']\n"
     ]
    }
   ],
   "source": [
    "# Columns of Train and Test Data:\n",
    "\n",
    "print(\"Columns of Train Data: \", list(train_df.columns) )\n",
    "print(\"Columns of Test Data: \", list(test_df.columns))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "361d5ef2",
   "metadata": {
    "papermill": {
     "duration": 0.040278,
     "end_time": "2024-08-19T13:21:26.662166",
     "exception": false,
     "start_time": "2024-08-19T13:21:26.621888",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Impute values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8684228f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:26.740147Z",
     "iopub.status.busy": "2024-08-19T13:21:26.739280Z",
     "iopub.status.idle": "2024-08-19T13:21:26.813019Z",
     "shell.execute_reply": "2024-08-19T13:21:26.811748Z"
    },
    "papermill": {
     "duration": 0.11607,
     "end_time": "2024-08-19T13:21:26.815942",
     "exception": false,
     "start_time": "2024-08-19T13:21:26.699872",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Location</th>\n",
       "      <th>Cross_Street</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Date_Reported</th>\n",
       "      <th>Date_Occurred</th>\n",
       "      <th>Time_Occurred</th>\n",
       "      <th>Area_ID</th>\n",
       "      <th>Area_Name</th>\n",
       "      <th>Reporting_District_no</th>\n",
       "      <th>...</th>\n",
       "      <th>Modus_Operandi</th>\n",
       "      <th>Victim_Age</th>\n",
       "      <th>Victim_Sex</th>\n",
       "      <th>Victim_Descent</th>\n",
       "      <th>Premise_Code</th>\n",
       "      <th>Premise_Description</th>\n",
       "      <th>Weapon_Used_Code</th>\n",
       "      <th>Weapon_Description</th>\n",
       "      <th>Status</th>\n",
       "      <th>Status_Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1500    LEIGHTON                     AV</td>\n",
       "      <td>False</td>\n",
       "      <td>34.0128</td>\n",
       "      <td>-118.3045</td>\n",
       "      <td>03/03/2020 12:00:00 AM</td>\n",
       "      <td>03/03/2020 12:00:00 AM</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>376.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0416 1241 1243 1813 1821 2000</td>\n",
       "      <td>28.0</td>\n",
       "      <td>F</td>\n",
       "      <td>H</td>\n",
       "      <td>501.0</td>\n",
       "      <td>SINGLE FAMILY DWELLING</td>\n",
       "      <td>400.0</td>\n",
       "      <td>STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100 S  NORMANDIE                    AV</td>\n",
       "      <td>False</td>\n",
       "      <td>34.0726</td>\n",
       "      <td>-118.3029</td>\n",
       "      <td>06/01/2020 12:00:00 AM</td>\n",
       "      <td>04/25/2020 12:00:00 AM</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Olympic</td>\n",
       "      <td>2014.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0344 0394</td>\n",
       "      <td>26.0</td>\n",
       "      <td>M</td>\n",
       "      <td>B</td>\n",
       "      <td>502.0</td>\n",
       "      <td>MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>300 E  111TH                        ST</td>\n",
       "      <td>False</td>\n",
       "      <td>33.9348</td>\n",
       "      <td>-118.2695</td>\n",
       "      <td>08/28/2020 12:00:00 AM</td>\n",
       "      <td>08/27/2020 12:00:00 AM</td>\n",
       "      <td>900.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>1844.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1822 0701 1914 0355 1202 0100</td>\n",
       "      <td>62.0</td>\n",
       "      <td>F</td>\n",
       "      <td>B</td>\n",
       "      <td>721.0</td>\n",
       "      <td>HIGH SCHOOL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1300 S  LA BREA                      AV</td>\n",
       "      <td>False</td>\n",
       "      <td>34.0497</td>\n",
       "      <td>-118.3442</td>\n",
       "      <td>12/23/2020 12:00:00 AM</td>\n",
       "      <td>12/03/2020 12:00:00 AM</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Wilshire</td>\n",
       "      <td>765.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>PARKING LOT</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11000    MORRISON                     ST</td>\n",
       "      <td>False</td>\n",
       "      <td>34.1611</td>\n",
       "      <td>-118.3704</td>\n",
       "      <td>08/30/2020 12:00:00 AM</td>\n",
       "      <td>08/29/2020 12:00:00 AM</td>\n",
       "      <td>130.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>N Hollywood</td>\n",
       "      <td>1555.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1501</td>\n",
       "      <td>37.0</td>\n",
       "      <td>F</td>\n",
       "      <td>W</td>\n",
       "      <td>501.0</td>\n",
       "      <td>SINGLE FAMILY DWELLING</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AO</td>\n",
       "      <td>Adult Other</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",
       "      <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>4995</th>\n",
       "      <td>4600    MASCOT                       ST</td>\n",
       "      <td>False</td>\n",
       "      <td>34.0409</td>\n",
       "      <td>-118.3408</td>\n",
       "      <td>06/05/2020 12:00:00 AM</td>\n",
       "      <td>06/04/2020 12:00:00 AM</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Wilshire</td>\n",
       "      <td>775.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>STREET</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>2200 E  7TH                          ST</td>\n",
       "      <td>False</td>\n",
       "      <td>34.0347</td>\n",
       "      <td>-118.2253</td>\n",
       "      <td>12/02/2020 12:00:00 AM</td>\n",
       "      <td>11/25/2020 12:00:00 AM</td>\n",
       "      <td>1530.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Hollenbeck</td>\n",
       "      <td>471.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1300 0325</td>\n",
       "      <td>0.0</td>\n",
       "      <td>X</td>\n",
       "      <td>X</td>\n",
       "      <td>116.0</td>\n",
       "      <td>OTHER/OUTSIDE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>LANGDON                      AV</td>\n",
       "      <td>True</td>\n",
       "      <td>34.2392</td>\n",
       "      <td>-118.4698</td>\n",
       "      <td>11/21/2020 12:00:00 AM</td>\n",
       "      <td>11/21/2020 12:00:00 AM</td>\n",
       "      <td>2100.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Mission</td>\n",
       "      <td>1961.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0913 1817 0416</td>\n",
       "      <td>38.0</td>\n",
       "      <td>M</td>\n",
       "      <td>H</td>\n",
       "      <td>101.0</td>\n",
       "      <td>STREET</td>\n",
       "      <td>400.0</td>\n",
       "      <td>STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)</td>\n",
       "      <td>AA</td>\n",
       "      <td>Adult Arrest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>400 E  5TH                          ST</td>\n",
       "      <td>False</td>\n",
       "      <td>34.0453</td>\n",
       "      <td>-118.2443</td>\n",
       "      <td>03/01/2020 12:00:00 AM</td>\n",
       "      <td>02/29/2020 12:00:00 AM</td>\n",
       "      <td>2335.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Central</td>\n",
       "      <td>147.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0416</td>\n",
       "      <td>41.0</td>\n",
       "      <td>M</td>\n",
       "      <td>B</td>\n",
       "      <td>502.0</td>\n",
       "      <td>MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)</td>\n",
       "      <td>500.0</td>\n",
       "      <td>UNKNOWN WEAPON/OTHER WEAPON</td>\n",
       "      <td>AO</td>\n",
       "      <td>Adult Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>10100 S  SAN PEDRO                    ST</td>\n",
       "      <td>False</td>\n",
       "      <td>33.9438</td>\n",
       "      <td>-118.2695</td>\n",
       "      <td>10/09/2020 12:00:00 AM</td>\n",
       "      <td>10/09/2020 12:00:00 AM</td>\n",
       "      <td>320.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>Southeast</td>\n",
       "      <td>1823.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1822 0329</td>\n",
       "      <td>49.0</td>\n",
       "      <td>M</td>\n",
       "      <td>B</td>\n",
       "      <td>122.0</td>\n",
       "      <td>VEHICLE, PASSENGER/TRUCK</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IC</td>\n",
       "      <td>Invest Cont</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Location  Cross_Street  Latitude  \\\n",
       "0      1500    LEIGHTON                     AV         False   34.0128   \n",
       "1       100 S  NORMANDIE                    AV         False   34.0726   \n",
       "2       300 E  111TH                        ST         False   33.9348   \n",
       "3      1300 S  LA BREA                      AV         False   34.0497   \n",
       "4     11000    MORRISON                     ST         False   34.1611   \n",
       "...                                        ...           ...       ...   \n",
       "4995   4600    MASCOT                       ST         False   34.0409   \n",
       "4996   2200 E  7TH                          ST         False   34.0347   \n",
       "4997           LANGDON                      AV          True   34.2392   \n",
       "4998    400 E  5TH                          ST         False   34.0453   \n",
       "4999  10100 S  SAN PEDRO                    ST         False   33.9438   \n",
       "\n",
       "      Longitude           Date_Reported           Date_Occurred  \\\n",
       "0     -118.3045  03/03/2020 12:00:00 AM  03/03/2020 12:00:00 AM   \n",
       "1     -118.3029  06/01/2020 12:00:00 AM  04/25/2020 12:00:00 AM   \n",
       "2     -118.2695  08/28/2020 12:00:00 AM  08/27/2020 12:00:00 AM   \n",
       "3     -118.3442  12/23/2020 12:00:00 AM  12/03/2020 12:00:00 AM   \n",
       "4     -118.3704  08/30/2020 12:00:00 AM  08/29/2020 12:00:00 AM   \n",
       "...         ...                     ...                     ...   \n",
       "4995  -118.3408  06/05/2020 12:00:00 AM  06/04/2020 12:00:00 AM   \n",
       "4996  -118.2253  12/02/2020 12:00:00 AM  11/25/2020 12:00:00 AM   \n",
       "4997  -118.4698  11/21/2020 12:00:00 AM  11/21/2020 12:00:00 AM   \n",
       "4998  -118.2443  03/01/2020 12:00:00 AM  02/29/2020 12:00:00 AM   \n",
       "4999  -118.2695  10/09/2020 12:00:00 AM  10/09/2020 12:00:00 AM   \n",
       "\n",
       "      Time_Occurred  Area_ID    Area_Name  Reporting_District_no  ...  \\\n",
       "0            2000.0      3.0    Southwest                  376.0  ...   \n",
       "1            1700.0     20.0      Olympic                 2014.0  ...   \n",
       "2             900.0     18.0    Southeast                 1844.0  ...   \n",
       "3            2200.0      7.0     Wilshire                  765.0  ...   \n",
       "4             130.0     15.0  N Hollywood                 1555.0  ...   \n",
       "...             ...      ...          ...                    ...  ...   \n",
       "4995         2100.0      7.0     Wilshire                  775.0  ...   \n",
       "4996         1530.0      4.0   Hollenbeck                  471.0  ...   \n",
       "4997         2100.0     19.0      Mission                 1961.0  ...   \n",
       "4998         2335.0      1.0      Central                  147.0  ...   \n",
       "4999          320.0     18.0    Southeast                 1823.0  ...   \n",
       "\n",
       "                     Modus_Operandi Victim_Age  Victim_Sex Victim_Descent  \\\n",
       "0     0416 1241 1243 1813 1821 2000       28.0           F              H   \n",
       "1                         0344 0394       26.0           M              B   \n",
       "2     1822 0701 1914 0355 1202 0100       62.0           F              B   \n",
       "3                               NaN        0.0           0              0   \n",
       "4                              1501       37.0           F              W   \n",
       "...                             ...        ...         ...            ...   \n",
       "4995                            NaN        0.0           0              0   \n",
       "4996                      1300 0325        0.0           X              X   \n",
       "4997                 0913 1817 0416       38.0           M              H   \n",
       "4998                           0416       41.0           M              B   \n",
       "4999                      1822 0329       49.0           M              B   \n",
       "\n",
       "     Premise_Code                           Premise_Description  \\\n",
       "0           501.0                        SINGLE FAMILY DWELLING   \n",
       "1           502.0  MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)   \n",
       "2           721.0                                   HIGH SCHOOL   \n",
       "3           108.0                                   PARKING LOT   \n",
       "4           501.0                        SINGLE FAMILY DWELLING   \n",
       "...           ...                                           ...   \n",
       "4995        101.0                                        STREET   \n",
       "4996        116.0                                 OTHER/OUTSIDE   \n",
       "4997        101.0                                        STREET   \n",
       "4998        502.0  MULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)   \n",
       "4999        122.0                      VEHICLE, PASSENGER/TRUCK   \n",
       "\n",
       "     Weapon_Used_Code                              Weapon_Description Status  \\\n",
       "0               400.0  STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)     IC   \n",
       "1                 0.0                                             NaN     IC   \n",
       "2                 0.0                                             NaN     IC   \n",
       "3                 0.0                                             NaN     IC   \n",
       "4                 0.0                                             NaN     AO   \n",
       "...               ...                                             ...    ...   \n",
       "4995              0.0                                             NaN     IC   \n",
       "4996              0.0                                             NaN     IC   \n",
       "4997            400.0  STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)     AA   \n",
       "4998            500.0                     UNKNOWN WEAPON/OTHER WEAPON     AO   \n",
       "4999              0.0                                             NaN     IC   \n",
       "\n",
       "     Status_Description  \n",
       "0           Invest Cont  \n",
       "1           Invest Cont  \n",
       "2           Invest Cont  \n",
       "3           Invest Cont  \n",
       "4           Adult Other  \n",
       "...                 ...  \n",
       "4995        Invest Cont  \n",
       "4996        Invest Cont  \n",
       "4997       Adult Arrest  \n",
       "4998        Adult Other  \n",
       "4999        Invest Cont  \n",
       "\n",
       "[5000 rows x 21 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function definitions for imputing each column:\n",
    "\n",
    "def transform_cross_street(df):\n",
    "    df['Cross_Street'] = df['Cross_Street'].fillna(0).astype(bool)\n",
    "    return df\n",
    "\n",
    "def transform_victim_sex(df):\n",
    "    df['Victim_Sex'] = df['Victim_Sex'].fillna(0)\n",
    "    return df\n",
    "\n",
    "def transform_victim_descent(df):\n",
    "    df['Victim_Descent'] = df['Victim_Descent'].fillna(0)\n",
    "    return df\n",
    "  \n",
    "def transform_weapon_used_code(df):\n",
    "    df['Weapon_Used_Code'] = df['Weapon_Used_Code'].fillna(0)\n",
    "    return df\n",
    "    \n",
    "# Pipeline for imputing desired columns:\n",
    "\n",
    "imputer_pipeline = Pipeline(steps=[\n",
    "    ('cross_street_imputer', FunctionTransformer(transform_cross_street)),\n",
    "    ('victim_sex_imputer', FunctionTransformer(transform_victim_sex)),\n",
    "    ('victim_descent_imputer', FunctionTransformer(transform_victim_descent)),\n",
    "    ('weapon_used_code_imputer', FunctionTransformer(transform_weapon_used_code))\n",
    "])\n",
    "\n",
    "# Fit the datasets on the pipeline and transform them:\n",
    "\n",
    "imputer_pipeline.fit_transform(train_df)\n",
    "imputer_pipeline.transform(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "049a7bcb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:26.897461Z",
     "iopub.status.busy": "2024-08-19T13:21:26.896913Z",
     "iopub.status.idle": "2024-08-19T13:21:26.904932Z",
     "shell.execute_reply": "2024-08-19T13:21:26.903858Z"
    },
    "papermill": {
     "duration": 0.053203,
     "end_time": "2024-08-19T13:21:26.908004",
     "exception": false,
     "start_time": "2024-08-19T13:21:26.854801",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((20000, 21), (5000, 21))"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5a3e5833",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:26.988951Z",
     "iopub.status.busy": "2024-08-19T13:21:26.987584Z",
     "iopub.status.idle": "2024-08-19T13:21:27.023719Z",
     "shell.execute_reply": "2024-08-19T13:21:27.022445Z"
    },
    "papermill": {
     "duration": 0.080131,
     "end_time": "2024-08-19T13:21:27.026683",
     "exception": false,
     "start_time": "2024-08-19T13:21:26.946552",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location                     0\n",
       "Cross_Street                 0\n",
       "Latitude                     0\n",
       "Longitude                    0\n",
       "Date_Reported                0\n",
       "Date_Occurred                0\n",
       "Time_Occurred                0\n",
       "Area_ID                      0\n",
       "Area_Name                    0\n",
       "Reporting_District_no        0\n",
       "Part 1-2                     0\n",
       "Modus_Operandi            2741\n",
       "Victim_Age                   0\n",
       "Victim_Sex                   0\n",
       "Victim_Descent               0\n",
       "Premise_Code                 0\n",
       "Premise_Description          5\n",
       "Weapon_Used_Code             0\n",
       "Weapon_Description       12665\n",
       "Status                       0\n",
       "Status_Description           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the null values again:\n",
    "train_df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49b930c6",
   "metadata": {
    "papermill": {
     "duration": 0.039156,
     "end_time": "2024-08-19T13:21:27.105590",
     "exception": false,
     "start_time": "2024-08-19T13:21:27.066434",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Feature Extraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9719c6a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:27.187008Z",
     "iopub.status.busy": "2024-08-19T13:21:27.186495Z",
     "iopub.status.idle": "2024-08-19T13:21:27.259600Z",
     "shell.execute_reply": "2024-08-19T13:21:27.258200Z"
    },
    "papermill": {
     "duration": 0.118065,
     "end_time": "2024-08-19T13:21:27.262911",
     "exception": false,
     "start_time": "2024-08-19T13:21:27.144846",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Function to extract Day, Month and Year from 'Date_Reported' & 'Date_Occurred' columns: \n",
    "\n",
    "def date_transformer(df):\n",
    "  df['Date_Reported'] = pd.to_datetime(df['Date_Reported'], format='%m/%d/%Y %I:%M:%S %p')\n",
    "  df['Date_Occurred'] = pd.to_datetime(df['Date_Occurred'], format='%m/%d/%Y %I:%M:%S %p')\n",
    "  df['Day_Reported'] = df['Date_Reported'].dt.day\n",
    "  df['Month_Reported'] = df['Date_Reported'].dt.month\n",
    "  df['Year_Reported'] = df['Date_Reported'].dt.year\n",
    "  df['Day_Occurred'] = df['Date_Occurred'].dt.day\n",
    "  df['Month_Occurred'] = df['Date_Occurred'].dt.month\n",
    "  df['Year_Occurred'] = df['Date_Occurred'].dt.year\n",
    "  df = df.drop(['Date_Reported', 'Date_Occurred'], axis=1)\n",
    "  return df\n",
    "\n",
    "# Pipeline for extracting desired features:\n",
    "\n",
    "date_transformer_pipeline = Pipeline(steps=[\n",
    "    ('transform_date', FunctionTransformer(date_transformer))\n",
    "])\n",
    "\n",
    "# Fit and Transform the datasets on the pipeline:\n",
    "\n",
    "transformed_train_df = date_transformer_pipeline.fit_transform(train_df)\n",
    "transformed_test_df = date_transformer_pipeline.transform(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8e67742a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:27.344743Z",
     "iopub.status.busy": "2024-08-19T13:21:27.344236Z",
     "iopub.status.idle": "2024-08-19T13:21:27.352324Z",
     "shell.execute_reply": "2024-08-19T13:21:27.351145Z"
    },
    "papermill": {
     "duration": 0.051932,
     "end_time": "2024-08-19T13:21:27.355117",
     "exception": false,
     "start_time": "2024-08-19T13:21:27.303185",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((20000, 27), (5000, 27))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "824faa88",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:27.436596Z",
     "iopub.status.busy": "2024-08-19T13:21:27.435578Z",
     "iopub.status.idle": "2024-08-19T13:21:27.451151Z",
     "shell.execute_reply": "2024-08-19T13:21:27.449933Z"
    },
    "papermill": {
     "duration": 0.060008,
     "end_time": "2024-08-19T13:21:27.454523",
     "exception": false,
     "start_time": "2024-08-19T13:21:27.394515",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Drop all the redundant columns\n",
    "\n",
    "columns_to_drop = ['Date_Reported', 'Date_Occurred', 'Location','Weapon_Description', 'Status_Description', 'Premise_Description', 'Area_Name']\n",
    "train_df.drop(columns_to_drop, axis=1, inplace=True)\n",
    "test_df.drop(columns_to_drop, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4d12030e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:27.536097Z",
     "iopub.status.busy": "2024-08-19T13:21:27.534871Z",
     "iopub.status.idle": "2024-08-19T13:21:27.543621Z",
     "shell.execute_reply": "2024-08-19T13:21:27.542378Z"
    },
    "papermill": {
     "duration": 0.052722,
     "end_time": "2024-08-19T13:21:27.546316",
     "exception": false,
     "start_time": "2024-08-19T13:21:27.493594",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((20000, 20), (5000, 20))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f561e723",
   "metadata": {
    "papermill": {
     "duration": 0.039897,
     "end_time": "2024-08-19T13:21:27.625320",
     "exception": false,
     "start_time": "2024-08-19T13:21:27.585423",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Feature Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "00242573",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:27.707962Z",
     "iopub.status.busy": "2024-08-19T13:21:27.707495Z",
     "iopub.status.idle": "2024-08-19T13:21:27.741388Z",
     "shell.execute_reply": "2024-08-19T13:21:27.740012Z"
    },
    "papermill": {
     "duration": 0.077872,
     "end_time": "2024-08-19T13:21:27.744678",
     "exception": false,
     "start_time": "2024-08-19T13:21:27.666806",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Select all the numerical features to scale them:\n",
    "numerical_features = train_df.select_dtypes(include=['float64', 'int32']).columns\n",
    "\n",
    "# Initializing the MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# Scaling the numerical features\n",
    "train_df[numerical_features] = scaler.fit_transform(train_df[numerical_features])\n",
    "test_df[numerical_features] = scaler.transform(test_df[numerical_features])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe204b90",
   "metadata": {
    "papermill": {
     "duration": 0.038523,
     "end_time": "2024-08-19T13:21:27.823371",
     "exception": false,
     "start_time": "2024-08-19T13:21:27.784848",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Feature Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "790b07b2",
   "metadata": {
    "papermill": {
     "duration": 0.039752,
     "end_time": "2024-08-19T13:21:27.901982",
     "exception": false,
     "start_time": "2024-08-19T13:21:27.862230",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### OHE 'Victim_Sex', 'Victim_Descent', 'Status'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7f45404e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:27.982723Z",
     "iopub.status.busy": "2024-08-19T13:21:27.982256Z",
     "iopub.status.idle": "2024-08-19T13:21:28.267746Z",
     "shell.execute_reply": "2024-08-19T13:21:28.266329Z"
    },
    "papermill": {
     "duration": 0.329958,
     "end_time": "2024-08-19T13:21:28.271074",
     "exception": false,
     "start_time": "2024-08-19T13:21:27.941116",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/sklearn/preprocessing/_encoders.py:202: UserWarning: Found unknown categories in columns [1] during transform. These unknown categories will be encoded as all zeros\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# Ensure all the columns are of type 'String'\n",
    "categorical_features = ['Victim_Sex', 'Victim_Descent', 'Status']\n",
    "for col in categorical_features:\n",
    "    train_df[col] = train_df[col].astype(str)\n",
    "    test_df[col] = test_df[col].astype(str)\n",
    "\n",
    "# Column transformer for one-hot encoding\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('cat', OneHotEncoder(sparse_output=False, drop='first', handle_unknown='ignore'), categorical_features)\n",
    "    ],\n",
    "    remainder='passthrough'\n",
    ")\n",
    "\n",
    "# Fit and transform the training data\n",
    "train_transformed = preprocessor.fit_transform(train_df)\n",
    "\n",
    "# Transform the test data\n",
    "test_transformed = preprocessor.transform(test_df)\n",
    "\n",
    "# Get the feature names from the ColumnTransformer\n",
    "feature_names = preprocessor.get_feature_names_out()\n",
    "\n",
    "# Remove 'cat___' and 'remainder___' prefixes\n",
    "feature_names_cleaned = [name.replace('cat__', '').replace('remainder__', '') for name in feature_names]\n",
    "\n",
    "# Convert the transformed arrays back to DataFrames\n",
    "train_df_transformed = pd.DataFrame(train_transformed, columns=feature_names_cleaned)\n",
    "test_df_transformed = pd.DataFrame(test_transformed, columns=feature_names_cleaned)\n",
    "\n",
    "# To not let Modus_Operandi get converted from String to Float\n",
    "float_columns = train_df_transformed.columns.drop('Modus_Operandi')\n",
    "\n",
    "# Convert all other columns to float\n",
    "train_df_transformed[float_columns] = train_df_transformed[float_columns].astype(float)\n",
    "test_df_transformed[float_columns] = test_df_transformed[float_columns].astype(float)\n",
    "\n",
    "# Update train_df and test_df with the transformed data\n",
    "train_df = train_df_transformed\n",
    "test_df = test_df_transformed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b820bf72",
   "metadata": {
    "papermill": {
     "duration": 0.037882,
     "end_time": "2024-08-19T13:21:28.346642",
     "exception": false,
     "start_time": "2024-08-19T13:21:28.308760",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Multi One Hot Encode 'Modus_Operandi'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5c032638",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:28.424675Z",
     "iopub.status.busy": "2024-08-19T13:21:28.424009Z",
     "iopub.status.idle": "2024-08-19T13:21:28.886716Z",
     "shell.execute_reply": "2024-08-19T13:21:28.885084Z"
    },
    "papermill": {
     "duration": 0.506214,
     "end_time": "2024-08-19T13:21:28.890644",
     "exception": false,
     "start_time": "2024-08-19T13:21:28.384430",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Convert non-string entries to empty strings\n",
    "train_df['Modus_Operandi'] = train_df['Modus_Operandi'].fillna('').astype(str)\n",
    "\n",
    "# Now apply the split function\n",
    "train_df['Modus_Operandi'] = train_df['Modus_Operandi'].apply(lambda x: x.split())\n",
    "\n",
    "# Apply MultiLabelBinarizer\n",
    "mlb = MultiLabelBinarizer()\n",
    "modus_operandi_encoded = mlb.fit_transform(train_df['Modus_Operandi'])\n",
    "\n",
    "# Convert the encoded data into a DataFrame\n",
    "modus_operandi_df = pd.DataFrame(modus_operandi_encoded, columns=mlb.classes_)\n",
    "\n",
    "# Concatenate with the original DataFrame\n",
    "train_df = pd.concat([train_df, modus_operandi_df], axis=1)\n",
    "\n",
    "# Drop the original 'Modus_Operandi' column\n",
    "train_df.drop('Modus_Operandi', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4297dd60",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:28.973380Z",
     "iopub.status.busy": "2024-08-19T13:21:28.972962Z",
     "iopub.status.idle": "2024-08-19T13:21:29.061225Z",
     "shell.execute_reply": "2024-08-19T13:21:29.059502Z"
    },
    "papermill": {
     "duration": 0.132481,
     "end_time": "2024-08-19T13:21:29.064667",
     "exception": false,
     "start_time": "2024-08-19T13:21:28.932186",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Convert non-string entries to empty strings\n",
    "test_df['Modus_Operandi'] = test_df['Modus_Operandi'].fillna('').astype(str)\n",
    "\n",
    "# Now apply the split function\n",
    "test_df['Modus_Operandi'] = test_df['Modus_Operandi'].apply(lambda x: x.split())\n",
    "\n",
    "# Apply MultiLabelBinarizer\n",
    "mlb = MultiLabelBinarizer()\n",
    "modus_operandi_encoded = mlb.fit_transform(test_df['Modus_Operandi'])\n",
    "\n",
    "# Convert the encoded data into a DataFrame\n",
    "modus_operandi_df = pd.DataFrame(modus_operandi_encoded, columns=mlb.classes_)\n",
    "\n",
    "# Concatenate with the original DataFrame\n",
    "test_df = pd.concat([test_df, modus_operandi_df], axis=1)\n",
    "\n",
    "# Drop the original 'Modus_Operandi' column\n",
    "test_df.drop('Modus_Operandi', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7ae356a7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:29.146708Z",
     "iopub.status.busy": "2024-08-19T13:21:29.145661Z",
     "iopub.status.idle": "2024-08-19T13:21:29.177762Z",
     "shell.execute_reply": "2024-08-19T13:21:29.176325Z"
    },
    "papermill": {
     "duration": 0.076339,
     "end_time": "2024-08-19T13:21:29.180557",
     "exception": false,
     "start_time": "2024-08-19T13:21:29.104218",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 522 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Victim_Sex_F  Victim_Sex_H  Victim_Sex_M  Victim_Sex_X  Victim_Descent_A  \\\n",
       "0           0.0           0.0           1.0           0.0               0.0   \n",
       "1           0.0           0.0           1.0           0.0               0.0   \n",
       "2           0.0           0.0           1.0           0.0               0.0   \n",
       "3           0.0           0.0           1.0           0.0               0.0   \n",
       "4           0.0           0.0           0.0           1.0               0.0   \n",
       "\n",
       "   Victim_Descent_B  Victim_Descent_C  Victim_Descent_D  Victim_Descent_F  \\\n",
       "0               0.0               0.0               0.0               0.0   \n",
       "1               0.0               0.0               0.0               0.0   \n",
       "2               1.0               0.0               0.0               0.0   \n",
       "3               0.0               0.0               0.0               0.0   \n",
       "4               0.0               0.0               0.0               0.0   \n",
       "\n",
       "   Victim_Descent_G  ...  2126  2157  2201  2204  2303  2304  3003  3004  \\\n",
       "0               0.0  ...     0     0     0     0     0     0     0     0   \n",
       "1               0.0  ...     0     0     0     0     0     0     0     0   \n",
       "2               0.0  ...     0     0     0     0     0     0     0     0   \n",
       "3               0.0  ...     0     0     0     0     0     0     0     0   \n",
       "4               0.0  ...     0     0     0     0     0     0     0     0   \n",
       "\n",
       "   4025  9999  \n",
       "0     0     0  \n",
       "1     0     0  \n",
       "2     0     0  \n",
       "3     0     0  \n",
       "4     0     0  \n",
       "\n",
       "[5 rows x 522 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f17841d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:29.264348Z",
     "iopub.status.busy": "2024-08-19T13:21:29.263250Z",
     "iopub.status.idle": "2024-08-19T13:21:29.271437Z",
     "shell.execute_reply": "2024-08-19T13:21:29.270277Z"
    },
    "papermill": {
     "duration": 0.05207,
     "end_time": "2024-08-19T13:21:29.273851",
     "exception": false,
     "start_time": "2024-08-19T13:21:29.221781",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((20000, 522), (5000, 409))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fe1a2f8",
   "metadata": {
    "papermill": {
     "duration": 0.040159,
     "end_time": "2024-08-19T13:21:29.353682",
     "exception": false,
     "start_time": "2024-08-19T13:21:29.313523",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3ab08fb",
   "metadata": {
    "papermill": {
     "duration": 0.039987,
     "end_time": "2024-08-19T13:21:29.433437",
     "exception": false,
     "start_time": "2024-08-19T13:21:29.393450",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2661b612",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:29.515804Z",
     "iopub.status.busy": "2024-08-19T13:21:29.515002Z",
     "iopub.status.idle": "2024-08-19T13:21:29.520717Z",
     "shell.execute_reply": "2024-08-19T13:21:29.519431Z"
    },
    "papermill": {
     "duration": 0.049749,
     "end_time": "2024-08-19T13:21:29.523202",
     "exception": false,
     "start_time": "2024-08-19T13:21:29.473453",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b47463f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:29.606218Z",
     "iopub.status.busy": "2024-08-19T13:21:29.605731Z",
     "iopub.status.idle": "2024-08-19T13:21:29.611401Z",
     "shell.execute_reply": "2024-08-19T13:21:29.609968Z"
    },
    "papermill": {
     "duration": 0.050742,
     "end_time": "2024-08-19T13:21:29.614387",
     "exception": false,
     "start_time": "2024-08-19T13:21:29.563645",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ce95b8bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:29.698972Z",
     "iopub.status.busy": "2024-08-19T13:21:29.698473Z",
     "iopub.status.idle": "2024-08-19T13:21:29.812987Z",
     "shell.execute_reply": "2024-08-19T13:21:29.811684Z"
    },
    "papermill": {
     "duration": 0.161545,
     "end_time": "2024-08-19T13:21:29.816310",
     "exception": false,
     "start_time": "2024-08-19T13:21:29.654765",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "907acf1d",
   "metadata": {
    "papermill": {
     "duration": 0.039657,
     "end_time": "2024-08-19T13:21:29.896143",
     "exception": false,
     "start_time": "2024-08-19T13:21:29.856486",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b2e59a4b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:29.980186Z",
     "iopub.status.busy": "2024-08-19T13:21:29.979645Z",
     "iopub.status.idle": "2024-08-19T13:21:29.987431Z",
     "shell.execute_reply": "2024-08-19T13:21:29.985998Z"
    },
    "papermill": {
     "duration": 0.053409,
     "end_time": "2024-08-19T13:21:29.990571",
     "exception": false,
     "start_time": "2024-08-19T13:21:29.937162",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import xgboost as xgb\n",
    "\n",
    "# from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# # Initialize LabelEncoder\n",
    "# label_encoder = LabelEncoder()\n",
    "\n",
    "# # Fit and transform y_train and y_test\n",
    "# y_train_encoded = label_encoder.fit_transform(y_train)\n",
    "# y_test_encoded = label_encoder.transform(y_test)\n",
    "\n",
    "# # Initialize the XGBoost classifier\n",
    "# xgb_model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='mlogloss')\n",
    "\n",
    "# # Fit the model on the encoded training data\n",
    "# xgb_model.fit(X_train, y_train_encoded)\n",
    "\n",
    "# # Predict on the encoded test data\n",
    "# y_pred_encoded = xgb_model.predict(X_test)\n",
    "\n",
    "# # Decode predictions back to original labels\n",
    "# y_pred = label_encoder.inverse_transform(y_pred_encoded)\n",
    "\n",
    "# # Calculate accuracy\n",
    "# accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "# # Print accuracy\n",
    "# print(\"Accuracy:\", accuracy)\n",
    "\n",
    "\n",
    "# report = classification_report(y_test, y_pred)\n",
    "# print(report)\n",
    "# # 0.9558"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deaf227d",
   "metadata": {
    "papermill": {
     "duration": 0.041682,
     "end_time": "2024-08-19T13:21:30.072703",
     "exception": false,
     "start_time": "2024-08-19T13:21:30.031021",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "| Category                        | Precision | Recall | F1-Score | Support |\n",
    "|---------------------------------|-----------|--------|----------|---------|\n",
    "| Crimes against Persons          | 0.75      | 0.77   | 0.76     | 57      |\n",
    "| Crimes against Public Order      | 0.83      | 0.87   | 0.85     | 543     |\n",
    "| Fraud and White-Collar Crimes   | 0.96      | 0.92   | 0.94     | 422     |\n",
    "| Other Crimes                    | 0.48      | 0.24   | 0.32     | 55      |\n",
    "| Property Crimes                 | 0.99      | 0.99   | 0.99     | 3461    |\n",
    "| Violent Crimes                  | 0.94      | 0.96   | 0.95     | 1462    |\n",
    "| **Accuracy**                    |           |        | 0.96     | 6000    |\n",
    "| **Macro Avg**                   | 0.82      | 0.79   | 0.80     | 6000    |\n",
    "| **Weighted Avg**                | 0.95      | 0.96   | 0.96     | 6000    |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e534288",
   "metadata": {
    "papermill": {
     "duration": 0.038864,
     "end_time": "2024-08-19T13:21:30.151883",
     "exception": false,
     "start_time": "2024-08-19T13:21:30.113019",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## XGBoost Hyper Parameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "99803acf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:30.233857Z",
     "iopub.status.busy": "2024-08-19T13:21:30.233397Z",
     "iopub.status.idle": "2024-08-19T13:21:30.240637Z",
     "shell.execute_reply": "2024-08-19T13:21:30.239442Z"
    },
    "papermill": {
     "duration": 0.051387,
     "end_time": "2024-08-19T13:21:30.243356",
     "exception": false,
     "start_time": "2024-08-19T13:21:30.191969",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # Initialize LabelEncoder\n",
    "# label_encoder = LabelEncoder()\n",
    "\n",
    "# # Fit and transform y_train and y_test\n",
    "# y_train_encoded = label_encoder.fit_transform(y_train)\n",
    "# y_test_encoded = label_encoder.transform(y_test)\n",
    "\n",
    "# # Initialize the XGBoost classifier\n",
    "# xgb_model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='mlogloss')\n",
    "\n",
    "# # Define the hyperparameter grid to search\n",
    "# param_dist = {\n",
    "#     'n_estimators': np.arange(50, 300, 50),\n",
    "#     'max_depth': np.arange(3, 10),\n",
    "#     'learning_rate': np.linspace(0.01, 0.3, 10),\n",
    "#     'subsample': np.linspace(0.6, 1.0, 5),\n",
    "#     'colsample_bytree': np.linspace(0.6, 1.0, 5),\n",
    "# }\n",
    "\n",
    "# # Initialize RandomizedSearchCV\n",
    "# random_search = RandomizedSearchCV(\n",
    "#     estimator=xgb_model,\n",
    "#     param_distributions=param_dist,\n",
    "#     n_iter=5,  # Number of parameter settings sampled\n",
    "#     scoring='accuracy',\n",
    "#     cv=3,  # 3-fold cross-validation\n",
    "#     verbose=1,\n",
    "#     random_state=42,\n",
    "#     n_jobs=-1  # Use all available cores\n",
    "# )\n",
    "\n",
    "# # Fit RandomizedSearchCV\n",
    "# random_search.fit(X_train, y_train_encoded)\n",
    "\n",
    "# # Get the best model\n",
    "# best_model = random_search.best_estimator_\n",
    "\n",
    "# # Predict on the encoded test data using the best model\n",
    "# y_pred_encoded = best_model.predict(X_test)\n",
    "\n",
    "# # Decode predictions back to original labels\n",
    "# y_pred = label_encoder.inverse_transform(y_pred_encoded)\n",
    "\n",
    "# # Calculate accuracy\n",
    "# accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "# # Print accuracy and best parameters\n",
    "# print(\"Best Parameters:\", random_search.best_params_)\n",
    "# print(\"Accuracy:\", accuracy)\n",
    "\n",
    "# # 0.95510"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23b31949",
   "metadata": {
    "papermill": {
     "duration": 0.039053,
     "end_time": "2024-08-19T13:21:30.322782",
     "exception": false,
     "start_time": "2024-08-19T13:21:30.283729",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "| Model Accuracy | Before Tuning | After Tuning |\n",
    "|----------------|---------------|--------------|\n",
    "| Accuracy       | 0.9580        | 0.95510       |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d6878ff",
   "metadata": {
    "papermill": {
     "duration": 0.039602,
     "end_time": "2024-08-19T13:21:30.403146",
     "exception": false,
     "start_time": "2024-08-19T13:21:30.363544",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8df839f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:30.486510Z",
     "iopub.status.busy": "2024-08-19T13:21:30.485979Z",
     "iopub.status.idle": "2024-08-19T13:21:30.492204Z",
     "shell.execute_reply": "2024-08-19T13:21:30.490865Z"
    },
    "papermill": {
     "duration": 0.051212,
     "end_time": "2024-08-19T13:21:30.494912",
     "exception": false,
     "start_time": "2024-08-19T13:21:30.443700",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# log_reg_model = LogisticRegression(C=1, solver='newton-cg')\n",
    "# log_reg_model.fit(X_train, y_train)\n",
    "# y_pred = log_reg_model.predict(X_test)\n",
    "# accuracy = accuracy_score(y_test, y_pred)\n",
    "# print(\"Accuracy:\", accuracy)\n",
    "# # 94.383"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66232e4d",
   "metadata": {
    "papermill": {
     "duration": 0.038937,
     "end_time": "2024-08-19T13:21:30.574110",
     "exception": false,
     "start_time": "2024-08-19T13:21:30.535173",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "| Category                         | Precision | Recall | F1-Score | Support |\n",
    "|----------------------------------|-----------|--------|----------|---------|\n",
    "| Crimes against Persons           | 0.61      | 0.44   | 0.51     | 57      |\n",
    "| Crimes against Public Order      | 0.81      | 0.83   | 0.82     | 543     |\n",
    "| Fraud and White-Collar Crimes    | 0.94      | 0.95   | 0.94     | 422     |\n",
    "| Other Crimes                     | 0.52      | 0.20   | 0.29     | 55      |\n",
    "| Property Crimes                  | 0.98      | 0.98   | 0.98     | 3461    |\n",
    "| Violent Crimes                   | 0.92      | 0.94   | 0.93     | 1462    |\n",
    "|                                  |           |        |          |         |\n",
    "| **Accuracy**                     |           |        | 0.94     | 6000    |\n",
    "| **Macro avg**                    | 0.80      | 0.72   | 0.75     | 6000    |\n",
    "| **Weighted avg**                 | 0.94      | 0.94   | 0.94     | 6000    |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36deba5a",
   "metadata": {
    "papermill": {
     "duration": 0.039142,
     "end_time": "2024-08-19T13:21:30.652585",
     "exception": false,
     "start_time": "2024-08-19T13:21:30.613443",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3ac22dc9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:30.735410Z",
     "iopub.status.busy": "2024-08-19T13:21:30.734915Z",
     "iopub.status.idle": "2024-08-19T13:21:30.740946Z",
     "shell.execute_reply": "2024-08-19T13:21:30.739515Z"
    },
    "papermill": {
     "duration": 0.050224,
     "end_time": "2024-08-19T13:21:30.743953",
     "exception": false,
     "start_time": "2024-08-19T13:21:30.693729",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# # Initialize and train model\n",
    "# model = RandomForestClassifier()\n",
    "# model.fit(X_train, y_train)\n",
    "\n",
    "# # Predict and evaluate\n",
    "# y_pred = model.predict(X_test)\n",
    "# print(\"Random Forest Accuracy:\", accuracy_score(y_test, y_pred))\n",
    "# #  0.9453"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "317cda9e",
   "metadata": {
    "papermill": {
     "duration": 0.042204,
     "end_time": "2024-08-19T13:21:30.827151",
     "exception": false,
     "start_time": "2024-08-19T13:21:30.784947",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "| Category                         | Precision | Recall | F1-Score | Support |\n",
    "|----------------------------------|-----------|--------|----------|---------|\n",
    "| Crimes against Persons           | 0.71      | 0.26   | 0.38     | 57      |\n",
    "| Crimes against Public Order      | 0.86      | 0.79   | 0.83     | 543     |\n",
    "| Fraud and White-Collar Crimes    | 0.94      | 0.91   | 0.93     | 422     |\n",
    "| Other Crimes                     | 0.50      | 0.07   | 0.13     | 55      |\n",
    "| Property Crimes                  | 0.98      | 0.98   | 0.98     | 3461    |\n",
    "| Violent Crimes                   | 0.90      | 0.98   | 0.94     | 1462    |\n",
    "|                                  |           |        |          |         |\n",
    "| **Accuracy**                     |           |        | 0.94     | 6000    |\n",
    "| **Macro avg**                    | 0.82      | 0.67   | 0.70     | 6000    |\n",
    "| **Weighted avg**                 | 0.94      | 0.94   | 0.94     | 6000    |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c8d2362",
   "metadata": {
    "papermill": {
     "duration": 0.039678,
     "end_time": "2024-08-19T13:21:30.909638",
     "exception": false,
     "start_time": "2024-08-19T13:21:30.869960",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a7604a1c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:30.993712Z",
     "iopub.status.busy": "2024-08-19T13:21:30.993243Z",
     "iopub.status.idle": "2024-08-19T13:21:30.999053Z",
     "shell.execute_reply": "2024-08-19T13:21:30.997641Z"
    },
    "papermill": {
     "duration": 0.051543,
     "end_time": "2024-08-19T13:21:31.002403",
     "exception": false,
     "start_time": "2024-08-19T13:21:30.950860",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "# # Initialize and train model\n",
    "# model = GradientBoostingClassifier()\n",
    "# model.fit(X_train, y_train)\n",
    "\n",
    "# # Predict and evaluate\n",
    "# y_pred = model.predict(X_test)\n",
    "# print(\"Gradient Boosting Accuracy:\", accuracy_score(y_test, y_pred))\n",
    "# ## 0.951\n",
    "# report = classification_report(y_test, y_pred)\n",
    "# print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c46ca859",
   "metadata": {
    "papermill": {
     "duration": 0.039397,
     "end_time": "2024-08-19T13:21:31.082230",
     "exception": false,
     "start_time": "2024-08-19T13:21:31.042833",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "| Category                         | Precision | Recall | F1-Score | Support |\n",
    "|----------------------------------|-----------|--------|----------|---------|\n",
    "| Crimes against Persons           | 0.67      | 0.68   | 0.68     | 57      |\n",
    "| Crimes against Public Order      | 0.83      | 0.85   | 0.84     | 543     |\n",
    "| Fraud and White-Collar Crimes    | 0.96      | 0.93   | 0.94     | 422     |\n",
    "| Other Crimes                     | 0.48      | 0.24   | 0.32     | 55      |\n",
    "| Property Crimes                  | 0.99      | 0.99   | 0.99     | 3461    |\n",
    "| Violent Crimes                   | 0.94      | 0.96   | 0.95     | 1462    |\n",
    "|                                  |           |        |          |         |\n",
    "| **Accuracy**                     |           |        | 0.95     | 6000    |\n",
    "| **Macro avg**                    | 0.81      | 0.77   | 0.78     | 6000    |\n",
    "| **Weighted avg**                 | 0.95      | 0.95   | 0.95     | 6000    |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a0dbb63",
   "metadata": {
    "papermill": {
     "duration": 0.04339,
     "end_time": "2024-08-19T13:21:31.166620",
     "exception": false,
     "start_time": "2024-08-19T13:21:31.123230",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a292fb0b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:31.253986Z",
     "iopub.status.busy": "2024-08-19T13:21:31.253542Z",
     "iopub.status.idle": "2024-08-19T13:21:31.259814Z",
     "shell.execute_reply": "2024-08-19T13:21:31.258437Z"
    },
    "papermill": {
     "duration": 0.054855,
     "end_time": "2024-08-19T13:21:31.262788",
     "exception": false,
     "start_time": "2024-08-19T13:21:31.207933",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# from sklearn.ensemble import ExtraTreesClassifier\n",
    "\n",
    "# # Initialize and train model\n",
    "# model = ExtraTreesClassifier()\n",
    "# model.fit(X_train, y_train)\n",
    "\n",
    "# # Predict and evaluate\n",
    "# y_pred = model.predict(X_test)\n",
    "# print(\"Extra Trees Accuracy:\", accuracy_score(y_test, y_pred))\n",
    "# # # 0.95033\n",
    "# report = classification_report(y_test, y_pred)\n",
    "# print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5fabcef",
   "metadata": {
    "papermill": {
     "duration": 0.039163,
     "end_time": "2024-08-19T13:21:31.341798",
     "exception": false,
     "start_time": "2024-08-19T13:21:31.302635",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "|                           | Precision | Recall | F1-Score | Support |\n",
    "|---------------------------|-----------|--------|----------|---------|\n",
    "| Crimes against Persons    | 0.75      | 0.32   | 0.44     | 57      |\n",
    "| Crimes against Public Order | 0.86      | 0.82   | 0.84     | 543     |\n",
    "| Fraud and White-Collar Crimes | 0.95      | 0.92   | 0.93     | 422     |\n",
    "| Other Crimes              | 0.67      | 0.11   | 0.19     | 55      |\n",
    "| Property Crimes           | 0.98      | 0.99   | 0.99     | 3461    |\n",
    "| Violent Crimes            | 0.90      | 0.97   | 0.93     | 1462    |\n",
    "| **Accuracy**              |           |        | 0.95     | 6000    |\n",
    "| **Macro Avg**             | 0.85      | 0.69   | 0.72     | 6000    |\n",
    "| **Weighted Avg**          | 0.94      | 0.95   | 0.94     | 6000    |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d11be0",
   "metadata": {
    "papermill": {
     "duration": 0.039147,
     "end_time": "2024-08-19T13:21:31.421639",
     "exception": false,
     "start_time": "2024-08-19T13:21:31.382492",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Comparision of models:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75eca223",
   "metadata": {
    "papermill": {
     "duration": 0.039043,
     "end_time": "2024-08-19T13:21:31.500302",
     "exception": false,
     "start_time": "2024-08-19T13:21:31.461259",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Precision (TP/TP+FP):"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5bb38a6",
   "metadata": {
    "papermill": {
     "duration": 0.039044,
     "end_time": "2024-08-19T13:21:31.578577",
     "exception": false,
     "start_time": "2024-08-19T13:21:31.539533",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "| Category                      | XGBoost | Logistic Regression | RandomForestClassifier | GradientBoostingClassifier | ExtraTreesClassifier |\n",
    "|-------------------------------|---------|---------------------|------------------------|----------------------------|----------------------|\n",
    "| Crimes against Persons        | 0.75    | 0.61                | 0.71                   | 0.67                       | 0.75                 |\n",
    "| Crimes against Public Order   | 0.83    | 0.81                | 0.86                   | 0.83                       | 0.86                 |\n",
    "| Fraud and White-Collar Crimes | 0.96    | 0.94                | 0.94                   | 0.96                       | 0.95                 |\n",
    "| Other Crimes                  | 0.48    | 0.52                | 0.50                   | 0.48                       | 0.67                 |\n",
    "| Property Crimes               | 0.99    | 0.98                | 0.98                   | 0.99                       | 0.98                 |\n",
    "| Violent Crimes                | 0.94    | 0.92                | 0.90                   | 0.94                       | 0.90                 |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f85b9bc",
   "metadata": {
    "papermill": {
     "duration": 0.040681,
     "end_time": "2024-08-19T13:21:31.660530",
     "exception": false,
     "start_time": "2024-08-19T13:21:31.619849",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "\n",
    "#### XGBoost and GradientBoostingClassifer achieve highest Precision for Property Crimes (0.99).\n",
    "#### GradientBoostingClassifer and Logistic Regression perform worse on Crime against Persons.\n",
    "#### ExtraTreesClassifer seem more balanced than other models as far as Precision is concerned."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d96d134",
   "metadata": {
    "papermill": {
     "duration": 0.039494,
     "end_time": "2024-08-19T13:21:31.743433",
     "exception": false,
     "start_time": "2024-08-19T13:21:31.703939",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Recall (TP/TP+FN):"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13f2f0d3",
   "metadata": {
    "papermill": {
     "duration": 0.039442,
     "end_time": "2024-08-19T13:21:31.823720",
     "exception": false,
     "start_time": "2024-08-19T13:21:31.784278",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "| Category                      | XGBoost | Logistic Regression | RandomForestClassifier | GradientBoostingClassifier | ExtraTreesClassifier |\n",
    "|-------------------------------|---------|---------------------|------------------------|----------------------------|----------------------|\n",
    "| Crimes against Persons        | 0.77    | 0.44                | 0.26                   | 0.68                       | 0.32                 |\n",
    "| Crimes against Public Order   | 0.87    | 0.83                | 0.79                   | 0.85                       | 0.82                 |\n",
    "| Fraud and White-Collar Crimes | 0.92    | 0.95                | 0.91                   | 0.93                       | 0.92                 |\n",
    "| Other Crimes                  | 0.24    | 0.20                | 0.07                   | 0.24                       | 0.11                 |\n",
    "| Property Crimes               | 0.99    | 0.98                | 0.98                   | 0.99                       | 0.99                 |\n",
    "| Violent Crimes                | 0.96    | 0.94                | 0.98                   | 0.96                       | 0.97                 |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "587a542c",
   "metadata": {
    "papermill": {
     "duration": 0.039422,
     "end_time": "2024-08-19T13:21:31.903063",
     "exception": false,
     "start_time": "2024-08-19T13:21:31.863641",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### RandomForestClassifier performs the worst for Crimes against Persons (0.26) and Other Crimes (0.07), suggesting it might be missing many actual cases of these categories.\n",
    "\n",
    "#### XGBost seems very much balanced in terms of Recall."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feb36430",
   "metadata": {
    "papermill": {
     "duration": 0.040431,
     "end_time": "2024-08-19T13:21:31.983438",
     "exception": false,
     "start_time": "2024-08-19T13:21:31.943007",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### F-1 Score (2 x Precision x Recall)/(Precison+Recall) :"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc0ff5e2",
   "metadata": {
    "papermill": {
     "duration": 0.040844,
     "end_time": "2024-08-19T13:21:32.064815",
     "exception": false,
     "start_time": "2024-08-19T13:21:32.023971",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "| Category                      | XGBoost | Logistic Regression | RandomForestClassifier | GradientBoostingClassifier | ExtraTreesClassifier |\n",
    "|-------------------------------|---------|---------------------|------------------------|----------------------------|----------------------|\n",
    "| Crimes against Persons        | 0.76    | 0.51                | 0.38                   | 0.68                       | 0.44                 |\n",
    "| Crimes against Public Order   | 0.85    | 0.82                | 0.83                   | 0.84                       | 0.84                 |\n",
    "| Fraud and White-Collar Crimes | 0.94    | 0.94                | 0.93                   | 0.94                       | 0.93                 |\n",
    "| Other Crimes                  | 0.32    | 0.29                | 0.13                   | 0.32                       | 0.19                 |\n",
    "| Property Crimes               | 0.99    | 0.98                | 0.98                   | 0.99                       | 0.99                 |\n",
    "| Violent Crimes                | 0.95    | 0.93                | 0.94                   | 0.95                       | 0.93                 |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46c6a450",
   "metadata": {
    "papermill": {
     "duration": 0.04014,
     "end_time": "2024-08-19T13:21:32.145052",
     "exception": false,
     "start_time": "2024-08-19T13:21:32.104912",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### RandomForestClassifier also seems to be performing worse in terms of F-1 Score.\n",
    "#### XGBoost performs the best in every category in F-1 Score.\n",
    "\n",
    "### Overall XGBoost seems like a well balanced algorithm for our model considering all of the above metrics."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2714c90",
   "metadata": {
    "papermill": {
     "duration": 0.039687,
     "end_time": "2024-08-19T13:21:32.224938",
     "exception": false,
     "start_time": "2024-08-19T13:21:32.185251",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Submission"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75e25382",
   "metadata": {
    "papermill": {
     "duration": 0.038612,
     "end_time": "2024-08-19T13:21:32.302475",
     "exception": false,
     "start_time": "2024-08-19T13:21:32.263863",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "543cf8c9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:32.382153Z",
     "iopub.status.busy": "2024-08-19T13:21:32.381727Z",
     "iopub.status.idle": "2024-08-19T13:21:32.387799Z",
     "shell.execute_reply": "2024-08-19T13:21:32.386471Z"
    },
    "papermill": {
     "duration": 0.049617,
     "end_time": "2024-08-19T13:21:32.390417",
     "exception": false,
     "start_time": "2024-08-19T13:21:32.340800",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = train_df\n",
    "X_test = test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "60d18a5c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:32.469422Z",
     "iopub.status.busy": "2024-08-19T13:21:32.469002Z",
     "iopub.status.idle": "2024-08-19T13:21:32.474542Z",
     "shell.execute_reply": "2024-08-19T13:21:32.473060Z"
    },
    "papermill": {
     "duration": 0.04787,
     "end_time": "2024-08-19T13:21:32.477202",
     "exception": false,
     "start_time": "2024-08-19T13:21:32.429332",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# X.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "aace175d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:32.560939Z",
     "iopub.status.busy": "2024-08-19T13:21:32.560560Z",
     "iopub.status.idle": "2024-08-19T13:21:32.644197Z",
     "shell.execute_reply": "2024-08-19T13:21:32.642716Z"
    },
    "papermill": {
     "duration": 0.128792,
     "end_time": "2024-08-19T13:21:32.648138",
     "exception": false,
     "start_time": "2024-08-19T13:21:32.519346",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n",
      "/tmp/ipykernel_19/2256086796.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  X_test[col] = 0\n"
     ]
    }
   ],
   "source": [
    "# Find columns that are in X but not in X_test\n",
    "cols_in_X_not_in_X_test = list(set(X.columns) - set(X_test.columns))\n",
    "\n",
    "# Find columns that are in X_test but not in X\n",
    "cols_in_X_test_not_in_X = list(set(X_test.columns) - set(X.columns))\n",
    "\n",
    "# Add missing columns in X_test and set their values to 0\n",
    "for col in cols_in_X_not_in_X_test:\n",
    "    X_test[col] = 0\n",
    "\n",
    "# Add missing columns in X and set their values to 0\n",
    "for col in cols_in_X_test_not_in_X:\n",
    "    X[col] = 0\n",
    "\n",
    "# Ensure that the columns are in the same order for both datasets\n",
    "X_test = X_test[X.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "72018eca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:32.730586Z",
     "iopub.status.busy": "2024-08-19T13:21:32.730192Z",
     "iopub.status.idle": "2024-08-19T13:21:32.735834Z",
     "shell.execute_reply": "2024-08-19T13:21:32.734487Z"
    },
    "papermill": {
     "duration": 0.049533,
     "end_time": "2024-08-19T13:21:32.738458",
     "exception": false,
     "start_time": "2024-08-19T13:21:32.688925",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# X.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1998f2a",
   "metadata": {
    "papermill": {
     "duration": 0.040207,
     "end_time": "2024-08-19T13:21:32.820359",
     "exception": false,
     "start_time": "2024-08-19T13:21:32.780152",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## XGBoost - Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6cc6436f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:32.903950Z",
     "iopub.status.busy": "2024-08-19T13:21:32.903306Z",
     "iopub.status.idle": "2024-08-19T13:21:32.910390Z",
     "shell.execute_reply": "2024-08-19T13:21:32.908934Z"
    },
    "papermill": {
     "duration": 0.051651,
     "end_time": "2024-08-19T13:21:32.913361",
     "exception": false,
     "start_time": "2024-08-19T13:21:32.861710",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import xgboost as xgb\n",
    "\n",
    "# # Initialize LabelEncoder\n",
    "# label_encoder = LabelEncoder()\n",
    "\n",
    "# y_encoded = label_encoder.fit_transform(y)\n",
    "\n",
    "# # Initialize the XGBoost classifier\n",
    "# xgb_model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='mlogloss')\n",
    "\n",
    "# # fit the model on training data\n",
    "# xgb_model.fit(X, y_encoded)\n",
    "\n",
    "# # Predict on test data\n",
    "# y_pred_encoded = xgb_model.predict(X_test)\n",
    "\n",
    "# # change predictions back to original labels\n",
    "# y_pred = label_encoder.inverse_transform(y_pred_encoded)\n",
    "# # 0.95580"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "20720629",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:32.998144Z",
     "iopub.status.busy": "2024-08-19T13:21:32.997266Z",
     "iopub.status.idle": "2024-08-19T13:21:49.663752Z",
     "shell.execute_reply": "2024-08-19T13:21:49.662257Z"
    },
    "papermill": {
     "duration": 16.713387,
     "end_time": "2024-08-19T13:21:49.666991",
     "exception": false,
     "start_time": "2024-08-19T13:21:32.953604",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "\n",
    "# Initialize LabelEncoder\n",
    "label_encoder = LabelEncoder()\n",
    "\n",
    "y_encoded = label_encoder.fit_transform(y)\n",
    "\n",
    "# Initialize the XGBoost classifier\n",
    "xgb_model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='mlogloss')\n",
    "\n",
    "# fit the model on training data\n",
    "xgb_model.fit(X, y_encoded)\n",
    "\n",
    "# Predict on test data\n",
    "y_pred_encoded = xgb_model.predict(X_test)\n",
    "\n",
    "# change predictions back to original labels\n",
    "y_pred = label_encoder.inverse_transform(y_pred_encoded)\n",
    "# 0.95580"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "766460ea",
   "metadata": {
    "papermill": {
     "duration": 0.039728,
     "end_time": "2024-08-19T13:21:49.747601",
     "exception": false,
     "start_time": "2024-08-19T13:21:49.707873",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## RandomForestCLassifier - Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fe4e5ca0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:49.832564Z",
     "iopub.status.busy": "2024-08-19T13:21:49.832120Z",
     "iopub.status.idle": "2024-08-19T13:21:49.837818Z",
     "shell.execute_reply": "2024-08-19T13:21:49.836618Z"
    },
    "papermill": {
     "duration": 0.051231,
     "end_time": "2024-08-19T13:21:49.840844",
     "exception": false,
     "start_time": "2024-08-19T13:21:49.789613",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# model = RandomForestClassifier()\n",
    "# model.fit(X, y)\n",
    "\n",
    "# y_pred = model.predict(X_test)\n",
    "# # 0.94140"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee1a956f",
   "metadata": {
    "papermill": {
     "duration": 0.040089,
     "end_time": "2024-08-19T13:21:49.921502",
     "exception": false,
     "start_time": "2024-08-19T13:21:49.881413",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## GradientBoostingCLassifier - Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "6fc1faa4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:50.005844Z",
     "iopub.status.busy": "2024-08-19T13:21:50.004911Z",
     "iopub.status.idle": "2024-08-19T13:21:50.011122Z",
     "shell.execute_reply": "2024-08-19T13:21:50.009763Z"
    },
    "papermill": {
     "duration": 0.054043,
     "end_time": "2024-08-19T13:21:50.016020",
     "exception": false,
     "start_time": "2024-08-19T13:21:49.961977",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "# model = GradientBoostingClassifier()\n",
    "# model.fit(X, y)\n",
    "\n",
    "# y_pred = model.predict(X_test)\n",
    "# # 0.94240"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c99fe6e",
   "metadata": {
    "papermill": {
     "duration": 0.039622,
     "end_time": "2024-08-19T13:21:50.099775",
     "exception": false,
     "start_time": "2024-08-19T13:21:50.060153",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## ExtraTreesClassifier - Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "adc1fe3c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:50.183160Z",
     "iopub.status.busy": "2024-08-19T13:21:50.182629Z",
     "iopub.status.idle": "2024-08-19T13:21:50.188530Z",
     "shell.execute_reply": "2024-08-19T13:21:50.187363Z"
    },
    "papermill": {
     "duration": 0.050487,
     "end_time": "2024-08-19T13:21:50.191170",
     "exception": false,
     "start_time": "2024-08-19T13:21:50.140683",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# from sklearn.ensemble import ExtraTreesClassifier\n",
    "\n",
    "# model = ExtraTreesClassifier()\n",
    "# model.fit(X, y)\n",
    "\n",
    "# y_pred = model.predict(X_test)\n",
    "\n",
    "# # # 0.95180"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d35e1e0f",
   "metadata": {
    "papermill": {
     "duration": 0.040283,
     "end_time": "2024-08-19T13:21:50.271338",
     "exception": false,
     "start_time": "2024-08-19T13:21:50.231055",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## LogisticRegression - Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "15f5c2c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:50.353425Z",
     "iopub.status.busy": "2024-08-19T13:21:50.352864Z",
     "iopub.status.idle": "2024-08-19T13:21:50.358546Z",
     "shell.execute_reply": "2024-08-19T13:21:50.357183Z"
    },
    "papermill": {
     "duration": 0.049825,
     "end_time": "2024-08-19T13:21:50.361238",
     "exception": false,
     "start_time": "2024-08-19T13:21:50.311413",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# log_reg_model = LogisticRegression(C=1, solver='newton-cg')\n",
    "# log_reg_model.fit(X, y)\n",
    "# y_pred = log_reg_model.predict(X_test)\n",
    "# # 0.94060"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c13e6cd8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-08-19T13:21:50.446716Z",
     "iopub.status.busy": "2024-08-19T13:21:50.446268Z",
     "iopub.status.idle": "2024-08-19T13:21:50.470596Z",
     "shell.execute_reply": "2024-08-19T13:21:50.469144Z"
    },
    "papermill": {
     "duration": 0.071024,
     "end_time": "2024-08-19T13:21:50.473976",
     "exception": false,
     "start_time": "2024-08-19T13:21:50.402952",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "                           \"ID\": np.arange(1,5001), \n",
    "                           \"Crime_Category\": y_pred,\n",
    "                          }) \n",
    "\n",
    "submission.to_csv('submission.csv',index=False)"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
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   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
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