Disaster Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2023-11-04T19:52:07.926637300Z",
     "start_time": "2023-11-04T19:52:07.891290100Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Year  Month   Day  ISO           Region Continent  \\\n",
      "0  1900    NaN   NaN  CPV   Western Africa    Africa   \n",
      "1  1900    NaN   NaN  IND    Southern Asia      Asia   \n",
      "2  1902    4.0  18.0  GTM  Central America  Americas   \n",
      "3  1902    4.0   8.0  GTM  Central America  Americas   \n",
      "4  1902   10.0  24.0  GTM  Central America  Americas   \n",
      "\n",
      "                    Location Origin  Dis Mag Value Dis Mag Scale  Affected  \\\n",
      "0                Countrywide    NaN            NaN           Km2     11000   \n",
      "1                     Bengal    NaN            NaN           Km2   1250000   \n",
      "2  Quezaltenango, San Marcos    NaN            8.0       Richter      2000   \n",
      "3                        NaN    NaN            NaN           NaN      1000   \n",
      "4                        NaN    NaN            NaN           NaN      6000   \n",
      "\n",
      "  Longitude Geo Locations  \n",
      "0       NaN           NaN  \n",
      "1       NaN           NaN  \n",
      "2       -91           NaN  \n",
      "3       NaN           NaN  \n",
      "4       NaN           NaN  \n"
     ]
    }
   ],
   "source": [
    "# Importing necessary library\n",
    "import pandas as pd\n",
    "\n",
    "# Loading the dataset\n",
    "data_path = \"disaster_prediction.csv\"  # Replace with the path to your dataset\n",
    "data = pd.read_csv(data_path)\n",
    "\n",
    "# Displaying the first few rows of the dataset\n",
    "print(data.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "#Data PreProcessing\n",
    "# Fill missing values for 'Month' and 'Day' with 0\n",
    "data['Month'].fillna(0, inplace=True)\n",
    "data['Day'].fillna(0, inplace=True)\n",
    "\n",
    "# Fill missing values for 'Location' and 'Origin' with 'Unknown'\n",
    "data['Location'].fillna('Unknown', inplace=True)\n",
    "data['Origin'].fillna('Unknown', inplace=True)\n",
    "\n",
    "# One-hot encode the 'Region', 'Continent', and 'Location' columns\n",
    "data = pd.get_dummies(data, columns=['Region', 'Continent', 'Location'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-04T19:52:13.304344800Z",
     "start_time": "2023-11-04T19:52:12.774667900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1400x700 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<Figure size 1000x600 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Data Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Set the style\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(14, 7))\n",
    "sns.countplot(data=data, x='Year', order=data['Year'].value_counts().index)\n",
    "plt.title('Distribution of Disasters Over the Years')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n",
    "\n",
    "# Sum the one-hot encoded columns for each continent\n",
    "continent_columns = [col for col in data.columns if 'Continent_' in col]\n",
    "continent_counts = data[continent_columns].sum()\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=continent_counts.index, y=continent_counts.values)\n",
    "plt.title('Distribution of Disasters by Continent')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel('Number of Disasters')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-04T19:52:17.485197200Z",
     "start_time": "2023-11-04T19:52:16.686629700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "#Data Splitting:\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Features (Year in this case)\n",
    "X = data[['Year']]\n",
    "\n",
    "# Targets (Event occurrence, type, and continent)\n",
    "y_event = data['Origin']  # Assuming 'Origin' represents the type of event\n",
    "y_continent = data[[col for col in data.columns if 'Continent_' in col]]\n",
    "\n",
    "# Splitting data\n",
    "X_train, X_test, y_event_train, y_event_test, y_continent_train, y_continent_test = train_test_split(X, y_event, y_continent, test_size=0.2, random_state=42)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-04T19:57:35.599966300Z",
     "start_time": "2023-11-04T19:57:35.448438500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "RandomForestClassifier()",
      "text/html": "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Model Building\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Model for event prediction\n",
    "event_model = RandomForestClassifier()\n",
    "event_model.fit(X_train, y_event_train)\n",
    "\n",
    "# Model for continent prediction\n",
    "continent_model = RandomForestClassifier()\n",
    "continent_model.fit(X_train, y_continent_train)\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-04T19:58:19.888129400Z",
     "start_time": "2023-11-04T19:58:19.029620800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Event Prediction Accuracy: 76.97%\n",
      "Continent Prediction Accuracy: 2.73%\n"
     ]
    }
   ],
   "source": [
    "#Evaluation\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Predictions\n",
    "y_event_pred = event_model.predict(X_test)\n",
    "y_continent_pred = continent_model.predict(X_test)\n",
    "\n",
    "# Evaluation\n",
    "event_accuracy = accuracy_score(y_event_test, y_event_pred)\n",
    "continent_accuracy = accuracy_score(y_continent_test, y_continent_pred)\n",
    "\n",
    "print(f\"Event Prediction Accuracy: {event_accuracy * 100:.2f}%\")\n",
    "print(f\"Continent Prediction Accuracy: {continent_accuracy * 100:.2f}%\")\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-04T19:58:38.865375200Z",
     "start_time": "2023-11-04T19:58:38.005233100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "161/162 [============================>.] - ETA: 0s - loss: 5.5338 - event_output_loss: 3.9110 - continent_output_loss: 1.6228 - event_output_accuracy: 0.6401 - continent_output_accuracy: 0.2065WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "162/162 [==============================] - 8s 24ms/step - loss: 5.5493 - event_output_loss: 3.9257 - continent_output_loss: 1.6235 - event_output_accuracy: 0.6402 - continent_output_accuracy: 0.2066 - val_loss: 18.4171 - val_event_output_loss: 16.8552 - val_continent_output_loss: 1.5619 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.0337\n",
      "Epoch 2/100\n",
      "159/162 [============================>.] - ETA: 0s - loss: 5.3948 - event_output_loss: 3.7682 - continent_output_loss: 1.6266 - event_output_accuracy: 0.5498 - continent_output_accuracy: 0.1732WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "162/162 [==============================] - 3s 19ms/step - loss: 5.3845 - event_output_loss: 3.7580 - continent_output_loss: 1.6265 - event_output_accuracy: 0.5425 - continent_output_accuracy: 0.1724 - val_loss: 21.0968 - val_event_output_loss: 19.5373 - val_continent_output_loss: 1.5595 - val_event_output_accuracy: 0.0000e+00 - val_continent_output_accuracy: 0.1249\n",
      "Epoch 3/100\n",
      "161/162 [============================>.] - ETA: 0s - loss: 5.0870 - event_output_loss: 3.4614 - continent_output_loss: 1.6255 - event_output_accuracy: 9.7050e-05 - continent_output_accuracy: 0.1382WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "162/162 [==============================] - 3s 20ms/step - loss: 5.0888 - event_output_loss: 3.4634 - continent_output_loss: 1.6253 - event_output_accuracy: 9.6965e-05 - continent_output_accuracy: 0.1382 - val_loss: 23.7491 - val_event_output_loss: 22.1899 - val_continent_output_loss: 1.5591 - val_event_output_accuracy: 0.0000e+00 - val_continent_output_accuracy: 0.0337\n",
      "Epoch 4/100\n",
      "160/162 [============================>.] - ETA: 0s - loss: 4.9689 - event_output_loss: 3.3455 - continent_output_loss: 1.6234 - event_output_accuracy: 0.0000e+00 - continent_output_accuracy: 0.0936WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "162/162 [==============================] - 3s 19ms/step - loss: 5.0084 - event_output_loss: 3.3856 - continent_output_loss: 1.6228 - event_output_accuracy: 0.0000e+00 - continent_output_accuracy: 0.0940 - val_loss: 25.3799 - val_event_output_loss: 23.8199 - val_continent_output_loss: 1.5600 - val_event_output_accuracy: 0.0000e+00 - val_continent_output_accuracy: 0.0337\n",
      "Epoch 5/100\n",
      "160/162 [============================>.] - ETA: 0s - loss: 4.9930 - event_output_loss: 3.3696 - continent_output_loss: 1.6235 - event_output_accuracy: 0.0000e+00 - continent_output_accuracy: 0.1726WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "162/162 [==============================] - 3s 19ms/step - loss: 4.9859 - event_output_loss: 3.3631 - continent_output_loss: 1.6228 - event_output_accuracy: 0.0000e+00 - continent_output_accuracy: 0.1716 - val_loss: 26.2978 - val_event_output_loss: 24.7279 - val_continent_output_loss: 1.5698 - val_event_output_accuracy: 0.0000e+00 - val_continent_output_accuracy: 0.0337\n",
      "Epoch 6/100\n",
      "161/162 [============================>.] - ETA: 0s - loss: 4.9608 - event_output_loss: 3.3374 - continent_output_loss: 1.6234 - event_output_accuracy: 0.0000e+00 - continent_output_accuracy: 0.1098WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "162/162 [==============================] - 3s 19ms/step - loss: 4.9729 - event_output_loss: 3.3496 - continent_output_loss: 1.6233 - event_output_accuracy: 0.0000e+00 - continent_output_accuracy: 0.1097 - val_loss: 26.9902 - val_event_output_loss: 25.4270 - val_continent_output_loss: 1.5632 - val_event_output_accuracy: 0.0000e+00 - val_continent_output_accuracy: 0.0337\n",
      "Epoch 7/100\n",
      "160/162 [============================>.] - ETA: 0s - loss: 4.9709 - event_output_loss: 3.3504 - continent_output_loss: 1.6205 - event_output_accuracy: 0.0000e+00 - continent_output_accuracy: 0.0646WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "162/162 [==============================] - 3s 19ms/step - loss: 4.9644 - event_output_loss: 3.3423 - continent_output_loss: 1.6221 - event_output_accuracy: 0.0000e+00 - continent_output_accuracy: 0.0649 - val_loss: 27.6462 - val_event_output_loss: 26.0850 - val_continent_output_loss: 1.5613 - val_event_output_accuracy: 0.0000e+00 - val_continent_output_accuracy: 0.0337\n",
      "Epoch 8/100\n",
      "159/162 [============================>.] - ETA: 0s - loss: 4.9876 - event_output_loss: 3.3645 - continent_output_loss: 1.6231 - event_output_accuracy: 0.0000e+00 - continent_output_accuracy: 0.0616WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "162/162 [==============================] - 3s 19ms/step - loss: 4.9584 - event_output_loss: 3.3356 - continent_output_loss: 1.6228 - event_output_accuracy: 0.0000e+00 - continent_output_accuracy: 0.0615 - val_loss: 28.0797 - val_event_output_loss: 26.5186 - val_continent_output_loss: 1.5611 - val_event_output_accuracy: 0.0000e+00 - val_continent_output_accuracy: 0.0337\n",
      "Epoch 9/100\n",
      "160/162 [============================>.] - ETA: 0s - loss: 4.9622 - event_output_loss: 3.3403 - continent_output_loss: 1.6219 - event_output_accuracy: 9.7656e-05 - continent_output_accuracy: 0.0524WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "162/162 [==============================] - 3s 19ms/step - loss: 4.9545 - event_output_loss: 3.3321 - continent_output_loss: 1.6225 - event_output_accuracy: 9.6965e-05 - continent_output_accuracy: 0.0525 - val_loss: 28.4270 - val_event_output_loss: 26.8649 - val_continent_output_loss: 1.5621 - val_event_output_accuracy: 0.0000e+00 - val_continent_output_accuracy: 0.0337\n",
      "Epoch 10/100\n",
      "160/162 [============================>.] - ETA: 0s - loss: 4.9608 - event_output_loss: 3.3410 - continent_output_loss: 1.6198 - event_output_accuracy: 9.7656e-05 - continent_output_accuracy: 0.0964WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "162/162 [==============================] - 3s 19ms/step - loss: 4.9505 - event_output_loss: 3.3280 - continent_output_loss: 1.6225 - event_output_accuracy: 9.6965e-05 - continent_output_accuracy: 0.0965 - val_loss: 28.7290 - val_event_output_loss: 27.1656 - val_continent_output_loss: 1.5634 - val_event_output_accuracy: 0.0000e+00 - val_continent_output_accuracy: 0.0337\n",
      "Epoch 11/100\n",
      "160/162 [============================>.] - ETA: 0s - loss: 4.9713 - event_output_loss: 3.3484 - continent_output_loss: 1.6229 - event_output_accuracy: 9.7656e-05 - continent_output_accuracy: 0.0735WARNING:tensorflow:`evaluate()` received a value for `sample_weight`, but `weighted_metrics` were not provided.  Did you mean to pass metrics to `weighted_metrics` in `compile()`?  If this is intentional you can pass `weighted_metrics=[]` to `compile()` in order to silence this warning.\n",
      "162/162 [==============================] - 3s 20ms/step - loss: 4.9493 - event_output_loss: 3.3268 - continent_output_loss: 1.6225 - event_output_accuracy: 9.6965e-05 - continent_output_accuracy: 0.0733 - val_loss: 28.8143 - val_event_output_loss: 27.2519 - val_continent_output_loss: 1.5624 - val_event_output_accuracy: 0.0000e+00 - val_continent_output_accuracy: 0.0337\n",
      "101/101 [==============================] - 1s 4ms/step\n",
      "F1 Score for Event Prediction: 0.49\n",
      "F1 Score for Continent Prediction: 0.00\n"
     ]
    }
   ],
   "source": [
    "#Lstm\n",
    "#Model Building\n",
    "# Necessary Imports\n",
    "from keras.models import Model\n",
    "from keras.layers import Input, LSTM, Dense, Dropout\n",
    "from keras.utils import to_categorical\n",
    "from sklearn.preprocessing import MinMaxScaler, LabelEncoder\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.utils import class_weight\n",
    "import numpy as np\n",
    "\n",
    "# Normalize the 'Year' and 'Dis Mag Value' columns\n",
    "data['Year'] = MinMaxScaler().fit_transform(data[['Year']])\n",
    "# Handle missing values and normalize 'Dis Mag Value'\n",
    "data['Dis Mag Value'].fillna(data['Dis Mag Value'].mean(), inplace=True)\n",
    "data['Dis Mag Value'] = MinMaxScaler().fit_transform(data[['Dis Mag Value']])\n",
    "\n",
    "# Label Encoding for the 'Origin' column\n",
    "data['Origin'] = LabelEncoder().fit_transform(data['Origin'])\n",
    "\n",
    "# Convert the dataset into sequences with multiple features\n",
    "def create_sequences_multifeature(data, sequence_length):\n",
    "    X, y_event, y_continent = [], [], []\n",
    "    for i in range(len(data) - sequence_length):\n",
    "        seq_data = data[['Year', 'Dis Mag Value']].iloc[i:i+sequence_length].values\n",
    "        X.append(seq_data)\n",
    "        y_event.append(data['Origin'].iloc[i+sequence_length])\n",
    "        y_continent.append(data[[col for col in data.columns if 'Continent_' in col]].iloc[i+sequence_length].values)\n",
    "    return np.array(X), np.array(y_event), np.array(y_continent)\n",
    "\n",
    "# Define the sequence length and create the sequences\n",
    "sequence_length = 10\n",
    "X_seq, y_event_seq, y_continent_seq = create_sequences_multifeature(data, sequence_length)\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "train_size = int(0.8 * len(X_seq))\n",
    "X_train_seq, X_test_seq = X_seq[:train_size], X_seq[train_size:]\n",
    "y_event_train_seq, y_event_test_seq = y_event_seq[:train_size], y_event_seq[train_size:]\n",
    "y_continent_train_seq, y_continent_test_seq = y_continent_seq[:train_size], y_continent_seq[train_size:]\n",
    "\n",
    "# Convert event labels to one-hot encoding\n",
    "y_event_train_seq = to_categorical(y_event_train_seq, num_classes=num_event_types)\n",
    "y_event_test_seq = to_categorical(y_event_test_seq, num_classes=num_event_types)\n",
    "\n",
    "from keras.callbacks import EarlyStopping\n",
    "\n",
    "# ... [previous preprocessing code]\n",
    "\n",
    "# Adjusted Model Building\n",
    "inputs = Input(shape=(sequence_length, 2))\n",
    "lstm_out = LSTM(128, return_sequences=True)(inputs)\n",
    "lstm_out = Dropout(0.2)(lstm_out)\n",
    "lstm_out = LSTM(64, return_sequences=True)(lstm_out)\n",
    "lstm_out = Dropout(0.2)(lstm_out)\n",
    "lstm_out = LSTM(32, return_sequences=True)(lstm_out)\n",
    "lstm_out = Dropout(0.2)(lstm_out)\n",
    "lstm_out = LSTM(16)(lstm_out)\n",
    "\n",
    "# Event prediction branch\n",
    "event_output = Dense(num_event_types, activation='softmax', name='event_output')(lstm_out)\n",
    "\n",
    "# Continent prediction branch with added complexity\n",
    "continent_dense = Dense(64, activation='relu')(lstm_out)\n",
    "continent_dense = Dropout(0.2)(continent_dense)\n",
    "continent_dense = Dense(32, activation='relu')(continent_dense)\n",
    "continent_output = Dense(num_continents, activation='softmax', name='continent_output')(continent_dense)\n",
    "\n",
    "model = Model(inputs=inputs, outputs=[event_output, continent_output])\n",
    "model.compile(optimizer='adam',\n",
    "              loss={'event_output': 'categorical_crossentropy', 'continent_output': 'categorical_crossentropy'},\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model with sample weights and early stopping\n",
    "model.fit(X_train_seq,\n",
    "          {'event_output': y_event_train_seq, 'continent_output': y_continent_train_seq},\n",
    "          epochs=100,\n",
    "          batch_size=64,\n",
    "          validation_split=0.2,\n",
    "          sample_weight={'event_output': sample_weights_event, 'continent_output': sample_weights_continent},\n",
    "          callbacks=[early_stopping],\n",
    "          verbose=1)\n",
    "\n",
    "# Make predictions for events and continents\n",
    "event_predictions, continent_predictions = model.predict(X_test_seq)\n",
    "\n",
    "# Convert softmax outputs to class labels and compute F1 scores\n",
    "f1_event = f1_score(np.argmax(y_event_test_seq, axis=1), np.argmax(event_predictions, axis=1), average='weighted')\n",
    "f1_continent = f1_score(np.argmax(y_continent_test_seq, axis=1), np.argmax(continent_predictions, axis=1), average='weighted')\n",
    "print(f\"F1 Score for Event Prediction: {f1_event:.2f}\")\n",
    "print(f\"F1 Score for Continent Prediction: {f1_continent:.2f}\")\n",
    "\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-11-04T20:58:29.119016500Z",
     "start_time": "2023-11-04T20:57:20.572298100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "162/162 [==============================] - 1s 2ms/step - loss: 3.6338 - event_output_loss: 2.1569 - continent_output_loss: 1.4769 - event_output_accuracy: 0.8067 - continent_output_accuracy: 0.3832 - val_loss: 4.1307 - val_event_output_loss: 2.7037 - val_continent_output_loss: 1.4270 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 2/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.4141 - event_output_loss: 0.9701 - continent_output_loss: 1.4440 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3899 - val_loss: 4.0765 - val_event_output_loss: 2.6538 - val_continent_output_loss: 1.4227 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 3/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.2809 - event_output_loss: 0.8481 - continent_output_loss: 1.4329 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3924 - val_loss: 3.6994 - val_event_output_loss: 2.2632 - val_continent_output_loss: 1.4363 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 4/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.2431 - event_output_loss: 0.8116 - continent_output_loss: 1.4314 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3958 - val_loss: 3.7044 - val_event_output_loss: 2.2816 - val_continent_output_loss: 1.4227 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 5/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.2219 - event_output_loss: 0.7933 - continent_output_loss: 1.4286 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3971 - val_loss: 3.7423 - val_event_output_loss: 2.3163 - val_continent_output_loss: 1.4260 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 6/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.2151 - event_output_loss: 0.7874 - continent_output_loss: 1.4276 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3968 - val_loss: 3.7576 - val_event_output_loss: 2.3436 - val_continent_output_loss: 1.4140 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 7/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.2171 - event_output_loss: 0.7908 - continent_output_loss: 1.4263 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3953 - val_loss: 3.7697 - val_event_output_loss: 2.3547 - val_continent_output_loss: 1.4151 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 8/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.2054 - event_output_loss: 0.7824 - continent_output_loss: 1.4230 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3965 - val_loss: 3.7836 - val_event_output_loss: 2.3677 - val_continent_output_loss: 1.4159 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 9/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.2067 - event_output_loss: 0.7811 - continent_output_loss: 1.4256 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3972 - val_loss: 3.7883 - val_event_output_loss: 2.3723 - val_continent_output_loss: 1.4160 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 10/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.2099 - event_output_loss: 0.7878 - continent_output_loss: 1.4221 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3974 - val_loss: 3.8511 - val_event_output_loss: 2.4322 - val_continent_output_loss: 1.4189 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 11/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.2002 - event_output_loss: 0.7761 - continent_output_loss: 1.4241 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3975 - val_loss: 3.8049 - val_event_output_loss: 2.3898 - val_continent_output_loss: 1.4151 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 12/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1981 - event_output_loss: 0.7758 - continent_output_loss: 1.4223 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3971 - val_loss: 3.8254 - val_event_output_loss: 2.4059 - val_continent_output_loss: 1.4195 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 13/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1981 - event_output_loss: 0.7755 - continent_output_loss: 1.4226 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3971 - val_loss: 3.8607 - val_event_output_loss: 2.4445 - val_continent_output_loss: 1.4162 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 14/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1986 - event_output_loss: 0.7763 - continent_output_loss: 1.4223 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3975 - val_loss: 3.8299 - val_event_output_loss: 2.4146 - val_continent_output_loss: 1.4153 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 15/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1934 - event_output_loss: 0.7734 - continent_output_loss: 1.4200 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3981 - val_loss: 3.8542 - val_event_output_loss: 2.4334 - val_continent_output_loss: 1.4208 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 16/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1965 - event_output_loss: 0.7738 - continent_output_loss: 1.4227 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3971 - val_loss: 3.8501 - val_event_output_loss: 2.4294 - val_continent_output_loss: 1.4207 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 17/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1910 - event_output_loss: 0.7685 - continent_output_loss: 1.4226 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3976 - val_loss: 3.8861 - val_event_output_loss: 2.4567 - val_continent_output_loss: 1.4293 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 18/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1903 - event_output_loss: 0.7678 - continent_output_loss: 1.4225 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3970 - val_loss: 3.8435 - val_event_output_loss: 2.4275 - val_continent_output_loss: 1.4160 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 19/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1898 - event_output_loss: 0.7673 - continent_output_loss: 1.4224 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3972 - val_loss: 3.8552 - val_event_output_loss: 2.4345 - val_continent_output_loss: 1.4207 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 20/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1899 - event_output_loss: 0.7674 - continent_output_loss: 1.4226 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3969 - val_loss: 3.8695 - val_event_output_loss: 2.4466 - val_continent_output_loss: 1.4229 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 21/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1916 - event_output_loss: 0.7711 - continent_output_loss: 1.4205 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3977 - val_loss: 3.8884 - val_event_output_loss: 2.4551 - val_continent_output_loss: 1.4333 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 22/50\n",
      "162/162 [==============================] - 0s 1ms/step - loss: 2.1936 - event_output_loss: 0.7708 - continent_output_loss: 1.4227 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3977 - val_loss: 3.8977 - val_event_output_loss: 2.4772 - val_continent_output_loss: 1.4206 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 23/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1854 - event_output_loss: 0.7657 - continent_output_loss: 1.4197 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3979 - val_loss: 3.8891 - val_event_output_loss: 2.4570 - val_continent_output_loss: 1.4320 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 24/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1878 - event_output_loss: 0.7673 - continent_output_loss: 1.4205 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3972 - val_loss: 3.8879 - val_event_output_loss: 2.4620 - val_continent_output_loss: 1.4259 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 25/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1873 - event_output_loss: 0.7658 - continent_output_loss: 1.4214 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3964 - val_loss: 3.8862 - val_event_output_loss: 2.4600 - val_continent_output_loss: 1.4262 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 26/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1929 - event_output_loss: 0.7707 - continent_output_loss: 1.4222 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3978 - val_loss: 3.8874 - val_event_output_loss: 2.4661 - val_continent_output_loss: 1.4213 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 27/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1849 - event_output_loss: 0.7629 - continent_output_loss: 1.4219 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3988 - val_loss: 3.8979 - val_event_output_loss: 2.4738 - val_continent_output_loss: 1.4241 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 28/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1858 - event_output_loss: 0.7647 - continent_output_loss: 1.4211 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3986 - val_loss: 3.9099 - val_event_output_loss: 2.4891 - val_continent_output_loss: 1.4209 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 29/50\n",
      "162/162 [==============================] - 0s 1ms/step - loss: 2.1815 - event_output_loss: 0.7613 - continent_output_loss: 1.4202 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3982 - val_loss: 3.9335 - val_event_output_loss: 2.5078 - val_continent_output_loss: 1.4257 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 30/50\n",
      "162/162 [==============================] - 0s 1ms/step - loss: 2.1867 - event_output_loss: 0.7655 - continent_output_loss: 1.4212 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3997 - val_loss: 3.9068 - val_event_output_loss: 2.4867 - val_continent_output_loss: 1.4201 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 31/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1806 - event_output_loss: 0.7613 - continent_output_loss: 1.4193 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3970 - val_loss: 3.9526 - val_event_output_loss: 2.5169 - val_continent_output_loss: 1.4357 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 32/50\n",
      "162/162 [==============================] - 0s 1ms/step - loss: 2.1808 - event_output_loss: 0.7601 - continent_output_loss: 1.4207 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3978 - val_loss: 3.9494 - val_event_output_loss: 2.5257 - val_continent_output_loss: 1.4236 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 33/50\n",
      "162/162 [==============================] - 0s 1ms/step - loss: 2.1805 - event_output_loss: 0.7607 - continent_output_loss: 1.4198 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3978 - val_loss: 3.9360 - val_event_output_loss: 2.5122 - val_continent_output_loss: 1.4239 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 34/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1833 - event_output_loss: 0.7635 - continent_output_loss: 1.4198 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3982 - val_loss: 3.9260 - val_event_output_loss: 2.4949 - val_continent_output_loss: 1.4311 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 35/50\n",
      "162/162 [==============================] - 0s 1ms/step - loss: 2.1771 - event_output_loss: 0.7563 - continent_output_loss: 1.4208 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3988 - val_loss: 3.9457 - val_event_output_loss: 2.5219 - val_continent_output_loss: 1.4238 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 36/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1818 - event_output_loss: 0.7618 - continent_output_loss: 1.4200 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3991 - val_loss: 3.9140 - val_event_output_loss: 2.4937 - val_continent_output_loss: 1.4204 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 37/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1753 - event_output_loss: 0.7571 - continent_output_loss: 1.4182 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3987 - val_loss: 3.9516 - val_event_output_loss: 2.5295 - val_continent_output_loss: 1.4221 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 38/50\n",
      "162/162 [==============================] - 0s 1ms/step - loss: 2.1748 - event_output_loss: 0.7552 - continent_output_loss: 1.4196 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3989 - val_loss: 3.9810 - val_event_output_loss: 2.5456 - val_continent_output_loss: 1.4355 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 39/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1749 - event_output_loss: 0.7561 - continent_output_loss: 1.4188 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3989 - val_loss: 3.9570 - val_event_output_loss: 2.5406 - val_continent_output_loss: 1.4165 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 40/50\n",
      "162/162 [==============================] - 0s 1ms/step - loss: 2.1776 - event_output_loss: 0.7568 - continent_output_loss: 1.4208 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3989 - val_loss: 3.9702 - val_event_output_loss: 2.5498 - val_continent_output_loss: 1.4205 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 41/50\n",
      "162/162 [==============================] - 0s 1ms/step - loss: 2.1750 - event_output_loss: 0.7569 - continent_output_loss: 1.4181 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3982 - val_loss: 3.9782 - val_event_output_loss: 2.5578 - val_continent_output_loss: 1.4204 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 42/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1793 - event_output_loss: 0.7580 - continent_output_loss: 1.4213 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3984 - val_loss: 3.9642 - val_event_output_loss: 2.5451 - val_continent_output_loss: 1.4191 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 43/50\n",
      "162/162 [==============================] - 0s 1ms/step - loss: 2.1773 - event_output_loss: 0.7560 - continent_output_loss: 1.4213 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3980 - val_loss: 3.9732 - val_event_output_loss: 2.5343 - val_continent_output_loss: 1.4389 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 44/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1758 - event_output_loss: 0.7562 - continent_output_loss: 1.4196 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3982 - val_loss: 4.0501 - val_event_output_loss: 2.6210 - val_continent_output_loss: 1.4290 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 45/50\n",
      "162/162 [==============================] - 0s 1ms/step - loss: 2.1807 - event_output_loss: 0.7613 - continent_output_loss: 1.4194 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3973 - val_loss: 4.0849 - val_event_output_loss: 2.6563 - val_continent_output_loss: 1.4286 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 46/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1757 - event_output_loss: 0.7570 - continent_output_loss: 1.4187 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3972 - val_loss: 4.0067 - val_event_output_loss: 2.5864 - val_continent_output_loss: 1.4203 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 47/50\n",
      "162/162 [==============================] - 0s 1ms/step - loss: 2.1743 - event_output_loss: 0.7554 - continent_output_loss: 1.4189 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3988 - val_loss: 3.9834 - val_event_output_loss: 2.5664 - val_continent_output_loss: 1.4170 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 48/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1720 - event_output_loss: 0.7534 - continent_output_loss: 1.4186 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3981 - val_loss: 3.9810 - val_event_output_loss: 2.5546 - val_continent_output_loss: 1.4264 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 49/50\n",
      "162/162 [==============================] - 0s 2ms/step - loss: 2.1731 - event_output_loss: 0.7540 - continent_output_loss: 1.4192 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3994 - val_loss: 3.9762 - val_event_output_loss: 2.5568 - val_continent_output_loss: 1.4194 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "Epoch 50/50\n",
      "162/162 [==============================] - 0s 1ms/step - loss: 2.1727 - event_output_loss: 0.7534 - continent_output_loss: 1.4193 - event_output_accuracy: 0.8493 - continent_output_accuracy: 0.3980 - val_loss: 3.9681 - val_event_output_loss: 2.5492 - val_continent_output_loss: 1.4189 - val_event_output_accuracy: 0.5964 - val_continent_output_accuracy: 0.3920\n",
      "101/101 [==============================] - 0s 672us/step\n",
      "101/101 [==============================] - 0s 658us/step\n",
      "F1 Score for Event Prediction: 0.49\n",
      "F1 Score for Continent Prediction: 0.26\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Model\n",
    "from keras.layers import Input, Dense, Flatten, Dropout\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "# Model Building\n",
    "inputs = Input(shape=(X_train_dense.shape[1],))\n",
    "dense_out = Dense(128, activation='relu')(inputs)\n",
    "dense_out = Dropout(0.2)(dense_out)\n",
    "dense_out = Dense(64, activation='relu')(dense_out)\n",
    "event_output = Dense(y_event_train_seq.shape[1], activation='softmax', name='event_output')(dense_out)\n",
    "continent_output = Dense(y_continent_train_seq.shape[1], activation='softmax', name='continent_output')(dense_out)\n",
    "\n",
    "model = Model(inputs=inputs, outputs=[event_output, continent_output])\n",
    "model.compile(optimizer='adam',\n",
    "              loss={'event_output': 'categorical_crossentropy', 'continent_output': 'categorical_crossentropy'},\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "history = model.fit(X_train_dense,\n",
    "                    {'event_output': y_event_train_seq, 'continent_output': y_continent_train_seq},\n",
    "                    epochs=50,\n",
    "                    batch_size=64,\n",
    "                    validation_split=0.2,\n",
    "                    verbose=1)\n",
    "\n",
    "# Make predictions\n",
    "event_predictions = model.predict(X_test_dense)[0]\n",
    "continent_predictions = model.predict(X_test_dense)[1]\n",
    "\n",
    "# Convert softmax outputs to class labels\n",
    "event_pred_labels = np.argmax(event_predictions, axis=1)\n",
    "continent_pred_labels = np.argmax(continent_predictions, axis=1)\n",
    "\n",
    "# Convert true labels (which are one-hot encoded) to class labels\n",
    "true_event_labels = np.argmax(y_event_test_seq, axis=1)\n",
    "true_continent_labels = np.argmax(y_continent_test_seq, axis=1)\n",
    "\n",
    "# Compute F1 scores\n",
    "f1_event = f1_score(true_event_labels, event_pred_labels, average='weighted')\n",
    "f1_continent = f1_score(true_continent_labels, continent_pred_labels, average='weighted')\n",
    "\n",
    "print(f\"F1 Score for Event Prediction: {f1_event:.2f}\")\n",
    "print(f\"F1 Score for Continent Prediction: {f1_continent:.2f}\")\n",
    "\n"
   ],
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   "cell_type": "code",
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   "source": [
    "\n"
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    "ExecuteTime": {
     "end_time": "2023-11-04T21:07:17.600270900Z",
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