Top 10 2023.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8aecfe2f-89ce-4ec0-8a18-a6835714c0e0",
   "metadata": {},
   "source": [
    "Age > 0 => Crimes on Humans  \n",
    "Years Considered: 2023 only  \n",
    "Crimes Considered: Only top 10 types of Crimes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc6c698f",
   "metadata": {},
   "source": [
    "To test the models with/without Latitude and Longitude, the variable `colsToRemove` in code chunk 3 has to be manually altered. By default, they have been included. They can be excluded simply be adding `, 'LAT', 'LON'` to the end of the list.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1a730309-a3b5-4b9c-865a-9679006e182d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from sklearn.svm import SVC\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
    "import keras_tuner as kt\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16d71786-4fcd-46f0-b0f9-8fc7e0c5801d",
   "metadata": {},
   "source": [
    "Dataset Cleaning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9e8459fb-2883-4008-9a68-c6a5007ec274",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data\n",
    "crimeData = pd.read_csv('LA Crime Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "be95672e-da52-4048-ab3a-01b65924f242",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of columns to drop\n",
    "colsToRemove = [\n",
    "    'DR_NO', 'Date Rptd', 'Rpt Dist No', 'Part 1-2', 'Mocodes', \n",
    "    'Status', 'Status Desc', 'Crm Cd 1', 'Crm Cd 2', 'Crm Cd 3', \n",
    "    'Crm Cd 4', 'LOCATION', 'Cross Street', 'Weapon Used Cd', 'Weapon Desc',\n",
    "    'Premis Cd', 'Premis Desc'\n",
    "]\n",
    "\n",
    "crimeDataCleaned = crimeData.drop(columns=colsToRemove)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c0d3711a-48c4-47e0-90e1-e5835d346514",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert DATE OCC to datetime and extract Month and Year\n",
    "crimeDataCleaned['DATE OCC'] = pd.to_datetime(crimeDataCleaned['DATE OCC'], format='%m/%d/%Y %I:%M:%S %p')\n",
    "crimeDataCleaned['Year'] = crimeDataCleaned['DATE OCC'].dt.year\n",
    "crimeDataCleaned['Month'] = crimeDataCleaned['DATE OCC'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f2ee6bbf-ec3a-466d-b59f-98e63cfc900e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE OCC</th>\n",
       "      <th>TIME OCC</th>\n",
       "      <th>AREA</th>\n",
       "      <th>Crm Cd</th>\n",
       "      <th>Vict Age</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LON</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>944235</td>\n",
       "      <td>944235.000000</td>\n",
       "      <td>944235.00000</td>\n",
       "      <td>944235.000000</td>\n",
       "      <td>944235.000000</td>\n",
       "      <td>944235.000000</td>\n",
       "      <td>944235.000000</td>\n",
       "      <td>944235.000000</td>\n",
       "      <td>944235.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2022-03-22 01:21:52.136491776</td>\n",
       "      <td>1338.047956</td>\n",
       "      <td>10.72113</td>\n",
       "      <td>500.810848</td>\n",
       "      <td>29.499314</td>\n",
       "      <td>33.992364</td>\n",
       "      <td>-118.070963</td>\n",
       "      <td>2021.744734</td>\n",
       "      <td>6.250125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>-4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-118.667600</td>\n",
       "      <td>2020.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2021-03-08 00:00:00</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>331.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.014500</td>\n",
       "      <td>-118.430700</td>\n",
       "      <td>2021.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2022-04-09 00:00:00</td>\n",
       "      <td>1419.000000</td>\n",
       "      <td>11.00000</td>\n",
       "      <td>442.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>34.058900</td>\n",
       "      <td>-118.322500</td>\n",
       "      <td>2022.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2023-04-10 00:00:00</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>16.00000</td>\n",
       "      <td>626.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>34.164900</td>\n",
       "      <td>-118.274000</td>\n",
       "      <td>2023.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2024-05-13 00:00:00</td>\n",
       "      <td>2359.000000</td>\n",
       "      <td>21.00000</td>\n",
       "      <td>956.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>34.334300</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2024.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>652.446103</td>\n",
       "      <td>6.09928</td>\n",
       "      <td>207.283501</td>\n",
       "      <td>21.882246</td>\n",
       "      <td>1.670190</td>\n",
       "      <td>5.789397</td>\n",
       "      <td>1.239839</td>\n",
       "      <td>3.490658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            DATE OCC       TIME OCC          AREA  \\\n",
       "count                         944235  944235.000000  944235.00000   \n",
       "mean   2022-03-22 01:21:52.136491776    1338.047956      10.72113   \n",
       "min              2020-01-01 00:00:00       1.000000       1.00000   \n",
       "25%              2021-03-08 00:00:00     900.000000       6.00000   \n",
       "50%              2022-04-09 00:00:00    1419.000000      11.00000   \n",
       "75%              2023-04-10 00:00:00    1900.000000      16.00000   \n",
       "max              2024-05-13 00:00:00    2359.000000      21.00000   \n",
       "std                              NaN     652.446103       6.09928   \n",
       "\n",
       "              Crm Cd       Vict Age            LAT            LON  \\\n",
       "count  944235.000000  944235.000000  944235.000000  944235.000000   \n",
       "mean      500.810848      29.499314      33.992364    -118.070963   \n",
       "min       110.000000      -4.000000       0.000000    -118.667600   \n",
       "25%       331.000000       0.000000      34.014500    -118.430700   \n",
       "50%       442.000000      30.000000      34.058900    -118.322500   \n",
       "75%       626.000000      45.000000      34.164900    -118.274000   \n",
       "max       956.000000     120.000000      34.334300       0.000000   \n",
       "std       207.283501      21.882246       1.670190       5.789397   \n",
       "\n",
       "                Year          Month  \n",
       "count  944235.000000  944235.000000  \n",
       "mean     2021.744734       6.250125  \n",
       "min      2020.000000       1.000000  \n",
       "25%      2021.000000       3.000000  \n",
       "50%      2022.000000       6.000000  \n",
       "75%      2023.000000       9.000000  \n",
       "max      2024.000000      12.000000  \n",
       "std         1.239839       3.490658  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crimeDataCleaned.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b4b6096-c2e5-4f96-b131-2788a8ff4496",
   "metadata": {},
   "source": [
    "Fix TIME OCC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5d443eab-20b0-4f13-8b51-b3c4954319da",
   "metadata": {},
   "outputs": [],
   "source": [
    "def correct_time_occ(time_occ):\n",
    "    time_occ_str = str(time_occ)  # Convert to string for consistency\n",
    "    length = len(time_occ_str)\n",
    "\n",
    "    if length == 1:\n",
    "        return f\"0{time_occ_str}00\"  # Single digit: prepend \"0\" and append \"00\"\n",
    "    elif length == 2:\n",
    "        return f\"{time_occ_str}00\"  # Two digits: append \"00\"\n",
    "    elif length == 3:\n",
    "        return f\"0{time_occ_str}\"  # Three digits: prepend \"0\"\n",
    "    elif length == 4:\n",
    "        return f\"{time_occ_str}\"  # Already in \"HHMM\" format, so return as is\n",
    "    else:\n",
    "        return time_occ_str  # Return as is for any other case\n",
    "\n",
    "# Apply the function to correct TIME.OCC values\n",
    "crimeDataCleaned['TIME OCC'] = crimeDataCleaned['TIME OCC'].apply(correct_time_occ)\n",
    "\n",
    "# Extract the hour from the corrected TIME OCC values\n",
    "crimeDataCleaned['hour'] = crimeDataCleaned['TIME OCC'].str[:2].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a4da8f78-1230-42cc-bc67-cd6ba09aef0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter out rows where Year is 2024\n",
    "crimeDataCleaned = crimeDataCleaned[crimeDataCleaned['Year'] != 2024]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "05b745ac-055d-44f1-969e-399e14e1543e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter out rows where hour is greater than 23\n",
    "crimeDataCleaned = crimeDataCleaned[crimeDataCleaned['hour'] <= 23]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4a0069df-2921-402a-95de-2b98102bbc88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DATE OCC        datetime64[ns]\n",
      "TIME OCC                object\n",
      "AREA                  category\n",
      "AREA NAME               object\n",
      "Crm Cd                category\n",
      "Crm Cd Desc             object\n",
      "Vict Age                 int64\n",
      "Vict Sex              category\n",
      "Vict Descent            object\n",
      "LAT                    float64\n",
      "LON                    float64\n",
      "Year                     int64\n",
      "Month                    int64\n",
      "hour                     int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# To ensure all columns have the right type assigned to it.\n",
    "\n",
    "# Convert categorical columns to 'category' dtype\n",
    "crimeDataCleaned['AREA'] = crimeDataCleaned['AREA'].astype('category')\n",
    "crimeDataCleaned['Crm Cd'] = crimeDataCleaned['Crm Cd'].astype('category')\n",
    "crimeDataCleaned['Vict Sex'] = crimeDataCleaned['Vict Sex'].astype('category')\n",
    "\n",
    "# Convert numerical columns to 'float' or 'int' dtype if needed\n",
    "crimeDataCleaned['Vict Age'] = crimeDataCleaned['Vict Age'].astype('int')  # if age can have decimal values; use int if only whole numbers\n",
    "crimeDataCleaned['LAT'] = crimeDataCleaned['LAT'].astype('float')\n",
    "crimeDataCleaned['LON'] = crimeDataCleaned['LON'].astype('float')\n",
    "crimeDataCleaned['Year'] = crimeDataCleaned['Year'].astype('int')\n",
    "crimeDataCleaned['Month'] = crimeDataCleaned['Month'].astype('int')\n",
    "crimeDataCleaned['hour'] = crimeDataCleaned['hour'].astype('int')\n",
    "\n",
    "# Display the data types to confirm the changes\n",
    "print(crimeDataCleaned.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "72e87369-dd31-4a63-a43b-e0dcc56fc0ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is where we filter out specific data to feed the model\n",
    "# Filter data to only include records where Vict Age > 0\n",
    "filtered_data = crimeDataCleaned[crimeDataCleaned['Vict Age'] > 0].copy()\n",
    "#Considering data only for the year 2023\n",
    "filtered_data = filtered_data[filtered_data['Year'] == 2023]\n",
    "# Step 2: Identify Top 10 Crime Types\n",
    "top_10_crimes = filtered_data['Crm Cd'].value_counts().nlargest(10).index\n",
    "filtered_data = filtered_data[filtered_data['Crm Cd'].isin(top_10_crimes)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5e810c2d-6d1d-478b-a9d1-33e067e4afa0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encode the target variable as category codes\n",
    "filtered_data['Crm Cd'] = filtered_data['Crm Cd'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a0b51daa-2acf-4348-a0e5-4a7f0507d864",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop unnecessary columns\n",
    "filtered_data = filtered_data.drop(columns=['DATE OCC', 'TIME OCC', 'AREA NAME', 'Crm Cd Desc', 'Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3417b996-89b5-49d9-a6a8-821c2022ecc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-hot encode categorical columns\n",
    "filtered_data = pd.get_dummies(filtered_data, columns=['AREA', 'Vict Sex', 'Vict Descent'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "71e310d9-8a79-424d-a4d8-cfcda2336bf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into features (X) and target (y)\n",
    "X = filtered_data.drop(columns=['Crm Cd'])\n",
    "y = filtered_data['Crm Cd']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5dff1013-4c94-4dc1-840d-f7f8b3f0fa1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert features to float32\n",
    "X = X.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1d73837f-07f5-453f-893b-fc57514fdbc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-hot encoding the target for neural networks\n",
    "y_one_hot = to_categorical(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b54b0840-073c-49ba-ac1e-50358a8be515",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "y_train_nn, y_test_nn = train_test_split(y_one_hot, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2dc4e408-fd22-4026-a709-ff1f6e108bf8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy: 27.32%\n"
     ]
    }
   ],
   "source": [
    "# Random Forest Classifier\n",
    "rf_model = RandomForestClassifier(random_state=42)\n",
    "rf_model.fit(X_train, y_train)\n",
    "y_pred_rf = rf_model.predict(X_test)\n",
    "rf_accuracy = accuracy_score(y_test, y_pred_rf)\n",
    "print(f\"Random Forest Accuracy: {rf_accuracy:.2%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "430dcb5f-3894-47ea-8fb3-2835dd2fac6b",
   "metadata": {},
   "source": [
    "Random Forest Accuracy with LAT and LON: 27.32%  \n",
    "Random Forest Accuracy without LAT and LON: 21.61%\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "331e455d-4457-485b-9cf5-8ec7f156a7ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get feature importances from the model\n",
    "importances = rf_model.feature_importances_\n",
    "features = X_train.columns  # Assuming X_train is your training dataset\n",
    "\n",
    "# Step 2: Sort the feature importances in descending order and get the top 5\n",
    "indices = np.argsort(importances)[::-1][:5]\n",
    "\n",
    "# Step 3: Create a list of the top 5 features and their importances\n",
    "top_features = [features[i] for i in indices]\n",
    "top_importances = importances[indices]\n",
    "\n",
    "# Step 4: Plot the top 5 features\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"Top 5 Feature Importances\")\n",
    "plt.bar(top_features, top_importances, color=\"b\", align=\"center\")\n",
    "plt.xlabel(\"Features\")\n",
    "plt.ylabel(\"Relative Importance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "572e1fe3-4978-4072-bd98-ffb7ddf196b8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "694/694 [==============================] - 5s 6ms/step - loss: 2.2788 - accuracy: 0.1857 - val_loss: 2.1655 - val_accuracy: 0.2288\n",
      "Epoch 2/50\n",
      "694/694 [==============================] - 3s 5ms/step - loss: 2.1481 - accuracy: 0.2243 - val_loss: 2.1420 - val_accuracy: 0.2237\n",
      "Epoch 3/50\n",
      "694/694 [==============================] - 7s 10ms/step - loss: 2.1190 - accuracy: 0.2366 - val_loss: 2.1235 - val_accuracy: 0.2367\n",
      "Epoch 4/50\n",
      "694/694 [==============================] - 5s 7ms/step - loss: 2.1007 - accuracy: 0.2432 - val_loss: 2.1109 - val_accuracy: 0.2362\n",
      "Epoch 5/50\n",
      "694/694 [==============================] - 3s 4ms/step - loss: 2.0910 - accuracy: 0.2468 - val_loss: 2.1070 - val_accuracy: 0.2360\n",
      "Epoch 6/50\n",
      "694/694 [==============================] - 4s 5ms/step - loss: 2.0816 - accuracy: 0.2521 - val_loss: 2.0950 - val_accuracy: 0.2481\n",
      "Epoch 7/50\n",
      "694/694 [==============================] - 6s 8ms/step - loss: 2.0750 - accuracy: 0.2543 - val_loss: 2.0875 - val_accuracy: 0.2489\n",
      "Epoch 8/50\n",
      "694/694 [==============================] - 5s 7ms/step - loss: 2.0699 - accuracy: 0.2562 - val_loss: 2.0821 - val_accuracy: 0.2485\n",
      "Epoch 9/50\n",
      "694/694 [==============================] - 6s 9ms/step - loss: 2.0662 - accuracy: 0.2573 - val_loss: 2.0822 - val_accuracy: 0.2494\n",
      "Epoch 10/50\n",
      "694/694 [==============================] - 5s 7ms/step - loss: 2.0630 - accuracy: 0.2617 - val_loss: 2.0890 - val_accuracy: 0.2458\n",
      "Epoch 11/50\n",
      "694/694 [==============================] - 3s 5ms/step - loss: 2.0587 - accuracy: 0.2606 - val_loss: 2.0687 - val_accuracy: 0.2565\n",
      "Epoch 12/50\n",
      "694/694 [==============================] - 4s 6ms/step - loss: 2.0583 - accuracy: 0.2625 - val_loss: 2.0648 - val_accuracy: 0.2593\n",
      "Epoch 13/50\n",
      "694/694 [==============================] - 3s 5ms/step - loss: 2.0542 - accuracy: 0.2617 - val_loss: 2.0659 - val_accuracy: 0.2572\n",
      "Epoch 14/50\n",
      "694/694 [==============================] - 6s 8ms/step - loss: 2.0522 - accuracy: 0.2649 - val_loss: 2.0753 - val_accuracy: 0.2549\n",
      "Epoch 15/50\n",
      "694/694 [==============================] - 5s 7ms/step - loss: 2.0489 - accuracy: 0.2653 - val_loss: 2.0633 - val_accuracy: 0.2602\n",
      "Epoch 16/50\n",
      "694/694 [==============================] - 2s 3ms/step - loss: 2.0489 - accuracy: 0.2654 - val_loss: 2.0599 - val_accuracy: 0.2600\n",
      "Epoch 17/50\n",
      "694/694 [==============================] - 6s 9ms/step - loss: 2.0475 - accuracy: 0.2662 - val_loss: 2.0605 - val_accuracy: 0.2600\n",
      "Epoch 18/50\n",
      "694/694 [==============================] - 5s 7ms/step - loss: 2.0444 - accuracy: 0.2678 - val_loss: 2.0610 - val_accuracy: 0.2615\n",
      "Epoch 19/50\n",
      "694/694 [==============================] - 5s 7ms/step - loss: 2.0425 - accuracy: 0.2692 - val_loss: 2.0648 - val_accuracy: 0.2599\n",
      "Epoch 20/50\n",
      "694/694 [==============================] - 5s 7ms/step - loss: 2.0439 - accuracy: 0.2690 - val_loss: 2.0602 - val_accuracy: 0.2626\n",
      "Epoch 21/50\n",
      "694/694 [==============================] - 4s 6ms/step - loss: 2.0408 - accuracy: 0.2700 - val_loss: 2.0725 - val_accuracy: 0.2553\n",
      "Epoch 22/50\n",
      "694/694 [==============================] - 4s 6ms/step - loss: 2.0383 - accuracy: 0.2706 - val_loss: 2.0621 - val_accuracy: 0.2612\n",
      "Epoch 23/50\n",
      "694/694 [==============================] - 4s 5ms/step - loss: 2.0375 - accuracy: 0.2709 - val_loss: 2.0657 - val_accuracy: 0.2572\n",
      "Epoch 24/50\n",
      "694/694 [==============================] - 4s 6ms/step - loss: 2.0360 - accuracy: 0.2729 - val_loss: 2.0609 - val_accuracy: 0.2598\n",
      "Epoch 25/50\n",
      "694/694 [==============================] - 3s 5ms/step - loss: 2.0357 - accuracy: 0.2728 - val_loss: 2.0568 - val_accuracy: 0.2628\n",
      "Epoch 26/50\n",
      "694/694 [==============================] - 4s 5ms/step - loss: 2.0326 - accuracy: 0.2728 - val_loss: 2.0661 - val_accuracy: 0.2594\n",
      "Epoch 27/50\n",
      "694/694 [==============================] - 4s 5ms/step - loss: 2.0321 - accuracy: 0.2731 - val_loss: 2.0581 - val_accuracy: 0.2609\n",
      "Epoch 28/50\n",
      "694/694 [==============================] - 3s 5ms/step - loss: 2.0324 - accuracy: 0.2731 - val_loss: 2.0522 - val_accuracy: 0.2637\n",
      "Epoch 29/50\n",
      "694/694 [==============================] - 4s 6ms/step - loss: 2.0312 - accuracy: 0.2734 - val_loss: 2.0528 - val_accuracy: 0.2640\n",
      "Epoch 30/50\n",
      "694/694 [==============================] - 4s 5ms/step - loss: 2.0299 - accuracy: 0.2745 - val_loss: 2.0575 - val_accuracy: 0.2627\n",
      "Epoch 31/50\n",
      "694/694 [==============================] - 4s 6ms/step - loss: 2.0283 - accuracy: 0.2748 - val_loss: 2.0609 - val_accuracy: 0.2593\n",
      "Epoch 32/50\n",
      "694/694 [==============================] - 3s 5ms/step - loss: 2.0270 - accuracy: 0.2760 - val_loss: 2.0514 - val_accuracy: 0.2646\n",
      "Epoch 33/50\n",
      "694/694 [==============================] - 3s 5ms/step - loss: 2.0264 - accuracy: 0.2771 - val_loss: 2.0505 - val_accuracy: 0.2642\n",
      "Epoch 34/50\n",
      "694/694 [==============================] - 4s 5ms/step - loss: 2.0255 - accuracy: 0.2764 - val_loss: 2.0534 - val_accuracy: 0.2611\n",
      "Epoch 35/50\n",
      "694/694 [==============================] - 4s 5ms/step - loss: 2.0241 - accuracy: 0.2779 - val_loss: 2.0487 - val_accuracy: 0.2651\n",
      "Epoch 36/50\n",
      "694/694 [==============================] - 4s 5ms/step - loss: 2.0234 - accuracy: 0.2775 - val_loss: 2.0478 - val_accuracy: 0.2651\n",
      "Epoch 37/50\n",
      "694/694 [==============================] - 4s 6ms/step - loss: 2.0228 - accuracy: 0.2785 - val_loss: 2.0532 - val_accuracy: 0.2642\n",
      "Epoch 38/50\n",
      "694/694 [==============================] - 4s 6ms/step - loss: 2.0212 - accuracy: 0.2778 - val_loss: 2.0538 - val_accuracy: 0.2604\n",
      "Epoch 39/50\n",
      "694/694 [==============================] - 4s 6ms/step - loss: 2.0213 - accuracy: 0.2781 - val_loss: 2.0534 - val_accuracy: 0.2639\n",
      "Epoch 40/50\n",
      "694/694 [==============================] - 4s 6ms/step - loss: 2.0220 - accuracy: 0.2763 - val_loss: 2.0519 - val_accuracy: 0.2652\n",
      "Epoch 41/50\n",
      "694/694 [==============================] - 4s 6ms/step - loss: 2.0192 - accuracy: 0.2794 - val_loss: 2.0548 - val_accuracy: 0.2656\n",
      "Epoch 42/50\n",
      "694/694 [==============================] - 3s 5ms/step - loss: 2.0189 - accuracy: 0.2789 - val_loss: 2.0509 - val_accuracy: 0.2635\n",
      "Epoch 43/50\n",
      "694/694 [==============================] - 4s 6ms/step - loss: 2.0176 - accuracy: 0.2780 - val_loss: 2.0516 - val_accuracy: 0.2618\n",
      "Epoch 44/50\n",
      "694/694 [==============================] - 3s 5ms/step - loss: 2.0175 - accuracy: 0.2783 - val_loss: 2.0482 - val_accuracy: 0.2672\n",
      "Epoch 45/50\n",
      "694/694 [==============================] - 3s 4ms/step - loss: 2.0163 - accuracy: 0.2799 - val_loss: 2.0466 - val_accuracy: 0.2651\n",
      "Epoch 46/50\n",
      "694/694 [==============================] - 4s 5ms/step - loss: 2.0163 - accuracy: 0.2793 - val_loss: 2.0559 - val_accuracy: 0.2594\n",
      "Epoch 47/50\n",
      "694/694 [==============================] - 4s 6ms/step - loss: 2.0150 - accuracy: 0.2799 - val_loss: 2.0461 - val_accuracy: 0.2651\n",
      "Epoch 48/50\n",
      "694/694 [==============================] - 4s 6ms/step - loss: 2.0144 - accuracy: 0.2794 - val_loss: 2.0549 - val_accuracy: 0.2619\n",
      "Epoch 49/50\n",
      "694/694 [==============================] - 4s 6ms/step - loss: 2.0139 - accuracy: 0.2804 - val_loss: 2.0541 - val_accuracy: 0.2630\n",
      "Epoch 50/50\n",
      "694/694 [==============================] - 4s 6ms/step - loss: 2.0133 - accuracy: 0.2812 - val_loss: 2.0456 - val_accuracy: 0.2677\n",
      "694/694 [==============================] - 2s 3ms/step - loss: 2.0456 - accuracy: 0.2677\n",
      "Neural Network Accuracy: 26.77%\n"
     ]
    }
   ],
   "source": [
    "# Neural Network\n",
    "model = Sequential()\n",
    "model.add(Dense(128, activation='relu', input_shape=(X_train.shape[1],)))\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(Dense(y_train_nn.shape[1], activation='softmax'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "\n",
    "# Train the neural network\n",
    "history = model.fit(X_train, y_train_nn, epochs=50, validation_data=(X_test, y_test_nn), batch_size=128)\n",
    "\n",
    "# Evaluate the neural network\n",
    "nn_loss, nn_accuracy = model.evaluate(X_test, y_test_nn)\n",
    "print(f\"Neural Network Accuracy: {nn_accuracy:.2%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9083ecaa-904e-4561-a52f-ebc676396cd0",
   "metadata": {},
   "source": [
    "Neural Network Accuracy with LAT and LON: 26.69%.  \n",
    "Neural Network Accuracy without LAT and LON: 27.06\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "647cea40-432d-432d-b16a-1ebf88d34f77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the accuracy and loss graphs\n",
    "plt.figure(figsize=(14, 5))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
    "plt.title('Model Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.title('Model Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f1e07439-1b75-4be3-8bb6-bf4d992d5f19",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 108 candidates, totalling 324 fits\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  13.2s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   6.5s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=  10.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  19.4s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  31.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  27.5s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  40.3s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  34.4s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  43.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  13.3s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  12.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=  12.1s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  17.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  24.2s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  24.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  27.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  38.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  21.1s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  10.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  12.1s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=  13.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  28.2s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  24.6s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  24.1s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  36.6s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  25.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  22.6s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   7.2s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   6.2s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   5.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  10.3s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  13.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  14.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  13.4s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=   8.3s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=   8.5s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   2.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   2.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   2.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   5.6s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   5.6s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=   5.6s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=   8.2s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  12.2s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=   8.4s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   2.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   2.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   2.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   5.4s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   5.5s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   5.5s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   8.2s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   8.2s\n",
      "[CV] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=   8.2s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   2.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   2.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   2.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   5.4s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   5.4s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   5.5s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=   8.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=   8.1s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=   8.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   3.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   2.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   3.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   5.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   5.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   5.9s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   8.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   8.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=   8.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   3.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   3.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   3.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   5.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   6.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   6.0s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=   8.7s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=   8.8s\n",
      "[CV] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=   8.8s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   6.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   6.1s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   6.1s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  12.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  12.1s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  12.3s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  18.1s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  17.9s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  18.0s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   5.4s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   5.4s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   5.5s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  10.9s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  10.8s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  10.7s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  16.5s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  16.6s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  16.5s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   5.1s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   5.1s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   5.1s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  10.1s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  10.0s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  10.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  15.0s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  13.7s\n",
      "[CV] END max_depth=20, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  15.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   5.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   5.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   5.3s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  10.3s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   9.3s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=   9.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  13.8s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  13.8s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  15.6s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   4.9s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   5.1s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   5.1s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  10.3s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  10.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  10.4s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  15.5s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  16.1s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  15.7s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   4.9s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   4.8s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   5.0s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   9.8s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   9.9s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=   9.9s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  14.6s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  14.7s\n",
      "[CV] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  13.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   4.3s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   4.3s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   4.3s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   8.5s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   8.6s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   8.6s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  13.1s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  12.5s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  12.5s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   4.1s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   4.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   4.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   8.4s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   8.4s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   8.4s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  12.5s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  12.6s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  12.5s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   4.1s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   4.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   4.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   8.6s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   8.2s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   8.3s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  12.6s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  13.5s\n",
      "[CV] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  13.5s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   8.3s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   8.1s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   8.0s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  16.4s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  16.7s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  16.4s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  25.3s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  26.1s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  26.1s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   7.0s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   7.1s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   7.1s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  13.9s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  14.1s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  13.5s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  19.9s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  19.9s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  19.4s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   6.1s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   6.1s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   6.0s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  11.9s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  12.4s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  12.2s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  18.1s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  18.3s\n",
      "[CV] END max_depth=30, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  18.1s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   6.5s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   6.5s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   6.8s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  12.7s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  13.0s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  12.9s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  18.5s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  18.8s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  18.9s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   6.0s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   6.2s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   6.0s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  11.9s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  11.8s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  12.4s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  18.2s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  18.6s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  18.1s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   5.5s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   5.6s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   5.5s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  10.7s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  11.0s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  10.8s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  16.3s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  16.3s\n",
      "[CV] END max_depth=30, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  16.3s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   5.1s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   5.1s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   5.1s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   9.9s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  10.2s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=  10.3s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  15.0s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  15.5s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  15.6s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   4.9s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   4.8s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   4.9s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   9.8s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   9.7s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   9.7s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  14.3s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  14.2s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  14.2s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   4.8s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   4.9s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   4.8s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   9.5s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   9.6s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   9.7s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  14.9s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  14.3s\n",
      "[CV] END max_depth=30, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  14.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   8.8s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   8.5s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   8.6s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  17.6s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  17.7s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=200; total time=  17.6s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  27.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  26.8s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=2, n_estimators=300; total time=  27.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   7.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   6.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=100; total time=   6.8s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  13.7s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  13.7s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=200; total time=  13.5s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  20.3s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  20.4s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=5, n_estimators=300; total time=  20.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   5.8s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   5.8s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=100; total time=   5.8s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  11.7s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  11.5s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=200; total time=  11.4s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  16.7s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  17.6s\n",
      "[CV] END max_depth=None, min_samples_leaf=1, min_samples_split=10, n_estimators=300; total time=  17.7s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   6.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   6.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   6.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  11.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  12.4s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=200; total time=  11.6s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  17.3s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  17.4s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=2, n_estimators=300; total time=  17.3s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   5.6s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   5.6s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   5.6s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  11.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  11.6s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=200; total time=  11.3s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  16.8s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  16.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=5, n_estimators=300; total time=  16.8s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   5.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   5.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=100; total time=   5.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  10.4s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  10.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=200; total time=  10.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  15.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  15.3s\n",
      "[CV] END max_depth=None, min_samples_leaf=2, min_samples_split=10, n_estimators=300; total time=  15.3s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   4.7s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   4.7s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   4.7s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   9.4s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   9.4s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time=   9.4s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  14.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  14.1s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=2, n_estimators=300; total time=  14.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   4.7s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   4.7s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=100; total time=   4.7s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   9.3s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   9.4s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=200; total time=   9.4s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  13.9s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  14.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=5, n_estimators=300; total time=  14.0s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   4.5s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   4.6s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   4.6s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   9.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   9.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=200; total time=   9.2s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  13.7s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  13.8s\n",
      "[CV] END max_depth=None, min_samples_leaf=4, min_samples_split=10, n_estimators=300; total time=  13.8s\n",
      "Best Parameters: {'max_depth': 20, 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 300}\n",
      "Best Accuracy: 27.06%\n",
      "Random Forest Test Accuracy: 26.38%\n"
     ]
    }
   ],
   "source": [
    "# Define the parameter grid for Random Forest\n",
    "param_grid = {\n",
    "    'n_estimators': [100, 200, 300],\n",
    "    'max_depth': [10, 20, 30, None],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4]\n",
    "}\n",
    "\n",
    "# Initialize the Random Forest model\n",
    "rf_model = RandomForestClassifier(random_state=42)\n",
    "\n",
    "# Perform Grid Search or Randomized Search\n",
    "grid_search = GridSearchCV(estimator=rf_model, param_grid=param_grid, cv=3, n_jobs=None, verbose=2)\n",
    "\n",
    "# Fit the model to the training data\n",
    "grid_search.fit(X_train, y_train)\n",
    "\n",
    "# Get the best parameters and accuracy\n",
    "best_rf = grid_search.best_estimator_\n",
    "print(f\"Best Parameters: {grid_search.best_params_}\")\n",
    "print(f\"Best Accuracy: {grid_search.best_score_:.2%}\")\n",
    "\n",
    "# Evaluate on the test set\n",
    "y_pred_rf = best_rf.predict(X_test)\n",
    "rf_accuracy = accuracy_score(y_test, y_pred_rf)\n",
    "print(f\"Random Forest Test Accuracy: {rf_accuracy:.2%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca9016b3-ce80-45d3-8970-fce0d72b195b",
   "metadata": {},
   "source": [
    "Random Forest Test Accuracy with LAT and LON: 28.68%\n",
    "Random Forest Test Accuracy without LAT and LON :-\n",
    "Best Parameters: {'max_depth': 20, 'min_samples_leaf': 4, 'min_samples_split': 10, 'n_estimators': 300}\n",
    "Best Accuracy: 27.06%\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "df455a53-e1d1-4255-b099-7c216950a4d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get feature importances from the model\n",
    "feature_importances = best_rf.feature_importances_\n",
    "\n",
    "# Sort the feature importances in descending order\n",
    "indices = np.argsort(feature_importances)[::-1]\n",
    "\n",
    "# Plot the feature importances\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.title(\"Feature Importance in Random Forest Model\")\n",
    "plt.bar(range(X_train.shape[1]), feature_importances[indices], align=\"center\")\n",
    "plt.xticks(range(X_train.shape[1]), X_train.columns[indices], rotation=90)\n",
    "plt.xlim([-1, X_train.shape[1]])\n",
    "plt.xlabel(\"Features\")\n",
    "plt.ylabel(\"Importance\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}