time-series-project (3).ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":9792981,"sourceType":"datasetVersion","datasetId":6000984}],"dockerImageVersionId":30787,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-11-03T08:12:36.420329Z","iopub.execute_input":"2024-11-03T08:12:36.421983Z","iopub.status.idle":"2024-11-03T08:12:36.435480Z","shell.execute_reply.started":"2024-11-03T08:12:36.421911Z","shell.execute_reply":"2024-11-03T08:12:36.433731Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"/kaggle/input/time-series-dataset/Exogenous_Data_1987_2024.xlsx\n/kaggle/input/time-series-dataset/Training_Data_From_2000_To_2023 (1).xlsx\n","output_type":"stream"}]},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.callbacks import ReduceLROnPlateau\nimport pandas as pd\nimport numpy as np\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\nimport matplotlib.pyplot as plt\nfrom tensorflow.keras.layers import LSTM, Dense, Dropout, Input, BatchNormalization\nfrom tensorflow.keras.models import Model\n\n# Load datasets\nexogenous_data_path = '/kaggle/input/time-series-dataset/Exogenous_Data_1987_2024.xlsx'\ntraining_data_path = '/kaggle/input/time-series-dataset/Training_Data_From_2000_To_2023 (1).xlsx'\n\nexogenous_data = pd.read_excel(exogenous_data_path)\ntraining_data = pd.read_excel(training_data_path)\n\n# Drop 'Date' column and shift the target ('idx') by 1 day for correct prediction\ndata = training_data.drop('Date', axis=1)\ndata['idx'] = data['idx'].shift(-1)  # Shift 'idx' column by one day to predict the next day\n\n# Drop the last row with NaN due to the shift\ndata = data.dropna()\n\n# Normalize the data for training\nscaler = MinMaxScaler(feature_range=(0, 1))\ndata_scaled = scaler.fit_transform(data)\n\n# Split the dataset (95% training, 5% testing)\ntrain_size = int(len(data_scaled) * 0.95)\ntrain, test = data_scaled[:train_size], data_scaled[train_size:]\n\n# Create datasets for training and testing\ndef create_dataset(dataset, time_step=1):\n    X, y = [], []\n    for i in range(len(dataset)-time_step-1):\n        X.append(dataset[i:(i+time_step), 1:])  # Input features (all except idx)\n        y.append(dataset[i + time_step, 0])  # Target (idx column)\n    return np.array(X), np.array(y)\n\n# Set the time step (lookback window)\ntime_step = 30\n\n# Create training and test datasets\nX_train, y_train = create_dataset(train, time_step)\nX_test, y_test = create_dataset(test, time_step)\n\n# Define the LSTM-based model structure\ndef build_lstm_model(lstm_units, dropout_rate):\n    num_features = X_train.shape[2]\n    \n    inputs = Input(shape=(time_step, num_features))\n\n    # Increased LSTM layers and units for better learning\n    x = LSTM(units=lstm_units, return_sequences=True)(inputs)\n    x = BatchNormalization()(x)\n    x = LSTM(units=lstm_units, return_sequences=True)(x)\n    x = BatchNormalization()(x)\n    x = LSTM(units=lstm_units)(x)\n    x = BatchNormalization()(x)\n    \n    # Dropout layer\n    x = Dropout(dropout_rate)(x)\n\n    # Added Dense layers for improved learning\n    x = Dense(64, activation='relu')(x)\n    x = Dense(32, activation='relu')(x)\n\n    # Output layer\n    outputs = Dense(1)(x)\n\n    # Build and compile the model\n    model = Model(inputs, outputs)\n    return model\n\n# Hyperparameters\nlstm_units = 256  # Increased number of LSTM units\ndropout_rate = 0.2  # Dropout rate remains same\nlearning_rate = 1e-3  # Increased learning rate for faster learning\nepochs = 50  # Increased the number of epochs to allow more learning\nbatch_size = 64  # Keep batch size the same\n\n# Build the LSTM model\nmodel = build_lstm_model(lstm_units, dropout_rate)\n\n# Compile the model with Adam optimizer\noptimizer = Adam(learning_rate=learning_rate)\nmodel.compile(optimizer=optimizer, loss='mean_squared_error')\n\n# Reduce learning rate when validation loss plateaus\nreduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5, min_lr=1e-6)\n\n# Fit the model with more epochs\nhistory = model.fit(\n    X_train, y_train,\n    epochs=epochs,\n    batch_size=batch_size,\n    validation_data=(X_test, y_test),\n    callbacks=[reduce_lr],\n    verbose=1\n)\n\n# After training, make predictions\ntrain_predict = model.predict(X_train)\ntest_predict = model.predict(X_test)\n\n# Inverse transform the predictions\ntrain_predict = scaler.inverse_transform(np.concatenate([train_predict, np.zeros((train_predict.shape[0], data.shape[1] - 1))], axis=1))[:, 0]\ntest_predict = scaler.inverse_transform(np.concatenate([test_predict, np.zeros((test_predict.shape[0], data.shape[1] - 1))], axis=1))[:, 0]\ny_train = scaler.inverse_transform(np.concatenate([y_train.reshape(-1, 1), np.zeros((y_train.shape[0], data.shape[1] - 1))], axis=1))[:, 0]\ny_test = scaler.inverse_transform(np.concatenate([y_test.reshape(-1, 1), np.zeros((y_test.shape[0], data.shape[1] - 1))], axis=1))[:, 0]\n\n# Calculate performance metrics\ntrain_rmse = np.sqrt(mean_squared_error(y_train, train_predict))\ntest_rmse = np.sqrt(mean_squared_error(y_test, test_predict))\ntrain_mae = mean_absolute_error(y_train, train_predict)\ntest_mae = mean_absolute_error(y_test, test_predict)\ntrain_r2 = r2_score(y_train, train_predict)\ntest_r2 = r2_score(y_test, test_predict)\n\n# Print performance metrics\nprint(f'Train RMSE: {train_rmse}, Test RMSE: {test_rmse}')\nprint(f'Train MAE: {train_mae}, Test MAE: {test_mae}')\nprint(f'Train R2: {train_r2}, Test R2: {test_r2}')\n\n# Plot predictions vs actual data\nplt.figure(figsize=(12, 6))\nplt.plot(y_test, label='Actual Data')\nplt.plot(test_predict, label='Predicted Data')\nplt.title('Predictions vs Actual Data')\nplt.xlabel('Time Step')\nplt.ylabel('idx')\nplt.legend()\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-11-04T06:33:45.602179Z","iopub.execute_input":"2024-11-04T06:33:45.602830Z","iopub.status.idle":"2024-11-04T06:35:18.024359Z","shell.execute_reply.started":"2024-11-04T06:33:45.602790Z","shell.execute_reply":"2024-11-04T06:35:18.023408Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Epoch 1/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 22ms/step - loss: 0.1435 - val_loss: 0.3174 - learning_rate: 0.0010\nEpoch 2/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0218 - val_loss: 0.4120 - learning_rate: 0.0010\nEpoch 3/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0102 - val_loss: 0.1185 - learning_rate: 0.0010\nEpoch 4/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 16ms/step - loss: 0.0055 - val_loss: 0.0636 - learning_rate: 0.0010\nEpoch 5/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0043 - val_loss: 0.1052 - learning_rate: 0.0010\nEpoch 6/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0045 - val_loss: 0.0021 - learning_rate: 0.0010\nEpoch 7/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0031 - val_loss: 8.9478e-04 - learning_rate: 0.0010\nEpoch 8/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0027 - val_loss: 0.0016 - learning_rate: 0.0010\nEpoch 9/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0016 - val_loss: 0.0140 - learning_rate: 0.0010\nEpoch 10/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 17ms/step - loss: 0.0015 - val_loss: 9.3306e-04 - learning_rate: 0.0010\nEpoch 11/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0013 - val_loss: 0.0060 - learning_rate: 0.0010\nEpoch 12/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0012 - val_loss: 0.0046 - learning_rate: 0.0010\nEpoch 13/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 9.7524e-04 - val_loss: 0.0078 - learning_rate: 5.0000e-04\nEpoch 14/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 9.9001e-04 - val_loss: 7.5060e-04 - learning_rate: 5.0000e-04\nEpoch 15/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 8.5892e-04 - val_loss: 6.5774e-04 - learning_rate: 5.0000e-04\nEpoch 16/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0013 - val_loss: 0.0016 - learning_rate: 5.0000e-04\nEpoch 17/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 17ms/step - loss: 9.8384e-04 - val_loss: 0.0022 - learning_rate: 5.0000e-04\nEpoch 18/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 8.4065e-04 - val_loss: 6.6578e-04 - learning_rate: 5.0000e-04\nEpoch 19/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 9.0788e-04 - val_loss: 3.0238e-04 - learning_rate: 5.0000e-04\nEpoch 20/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 9.6211e-04 - val_loss: 0.0035 - learning_rate: 5.0000e-04\nEpoch 21/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 8.6380e-04 - val_loss: 0.0129 - learning_rate: 5.0000e-04\nEpoch 22/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 17ms/step - loss: 0.0010 - val_loss: 0.0044 - learning_rate: 5.0000e-04\nEpoch 23/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 7.3392e-04 - val_loss: 6.4685e-04 - learning_rate: 5.0000e-04\nEpoch 24/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 7.1689e-04 - val_loss: 0.0061 - learning_rate: 5.0000e-04\nEpoch 25/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 6.0001e-04 - val_loss: 6.8745e-04 - learning_rate: 2.5000e-04\nEpoch 26/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0018 - val_loss: 0.0033 - learning_rate: 2.5000e-04\nEpoch 27/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0012 - val_loss: 0.0019 - learning_rate: 2.5000e-04\nEpoch 28/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 0.0015 - val_loss: 6.8640e-04 - learning_rate: 2.5000e-04\nEpoch 29/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 8.1388e-04 - val_loss: 9.7030e-04 - learning_rate: 2.5000e-04\nEpoch 30/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 6.9716e-04 - val_loss: 0.0024 - learning_rate: 1.2500e-04\nEpoch 31/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 6.2172e-04 - val_loss: 9.8480e-04 - learning_rate: 1.2500e-04\nEpoch 32/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 6.1262e-04 - val_loss: 0.0029 - learning_rate: 1.2500e-04\nEpoch 33/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 7.4250e-04 - val_loss: 0.0010 - learning_rate: 1.2500e-04\nEpoch 34/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 5.9185e-04 - val_loss: 0.0027 - learning_rate: 1.2500e-04\nEpoch 35/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 5.8073e-04 - val_loss: 9.7603e-04 - learning_rate: 6.2500e-05\nEpoch 36/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 5.3236e-04 - val_loss: 0.0012 - learning_rate: 6.2500e-05\nEpoch 37/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 5.1443e-04 - val_loss: 3.9742e-04 - learning_rate: 6.2500e-05\nEpoch 38/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 5.1695e-04 - val_loss: 0.0013 - learning_rate: 6.2500e-05\nEpoch 39/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 5.0436e-04 - val_loss: 0.0013 - learning_rate: 6.2500e-05\nEpoch 40/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 4.9369e-04 - val_loss: 0.0013 - learning_rate: 3.1250e-05\nEpoch 41/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 4.7536e-04 - val_loss: 0.0012 - learning_rate: 3.1250e-05\nEpoch 42/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 5.0887e-04 - val_loss: 6.8046e-04 - learning_rate: 3.1250e-05\nEpoch 43/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 17ms/step - loss: 5.0438e-04 - val_loss: 0.0012 - learning_rate: 3.1250e-05\nEpoch 44/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 4.9304e-04 - val_loss: 7.3889e-04 - learning_rate: 3.1250e-05\nEpoch 45/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 5.0664e-04 - val_loss: 6.4596e-04 - learning_rate: 1.5625e-05\nEpoch 46/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 4.7398e-04 - val_loss: 8.2674e-04 - learning_rate: 1.5625e-05\nEpoch 47/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 4.3436e-04 - val_loss: 0.0017 - learning_rate: 1.5625e-05\nEpoch 48/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 4.4144e-04 - val_loss: 5.7292e-04 - learning_rate: 1.5625e-05\nEpoch 49/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 4.5316e-04 - val_loss: 9.2130e-04 - learning_rate: 1.5625e-05\nEpoch 50/50\n\u001b[1m90/90\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 16ms/step - loss: 4.5491e-04 - val_loss: 6.4973e-04 - learning_rate: 7.8125e-06\n\u001b[1m179/179\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step\n\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step \nTrain RMSE: 56.231967710559445, Test RMSE: 105.01861417779872\nTrain MAE: 43.629637340036595, Test MAE: 91.4318771358794\nTrain R2: 0.9965125513266216, Test R2: 0.8107673310083781\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x600 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}