"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(x_train, y_train, batch_size=1, epochs = 2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Making predictions on the training and testing data"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m124/124\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 36ms/step\n",
"\u001b[1m31/31\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 30ms/step\n"
]
}
],
"source": [
"y_train_pred = model.predict(x_train)\n",
"y_test_pred = model.predict(x_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculating the performance metrics (MSE, MAE, R^2) for the training data"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"mse_train = mean_squared_error(y_train, y_train_pred)\n",
"mae_train = mean_absolute_error(y_train, y_train_pred)\n",
"r2_train = r2_score(y_train, y_train_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculating the performance metrics (MSE, MAE, R^2) for the testing data"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"mse_test = mean_squared_error(y_test, y_test_pred)\n",
"mae_test = mean_absolute_error(y_test, y_test_pred)\n",
"r2_test = r2_score(y_test, y_test_pred)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Printing the performance metrics for both training and testing data"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train MSE: 4.497497619348312e-05, Train MAE: 0.004725631741521278, Train R^2: 0.9940975452471996\n",
"Test MSE: 0.00133245446907416, Test MAE: 0.033651480057092904, Test R^2: 0.8951556125916854\n"
]
}
],
"source": [
"print(f\"Train MSE: {mse_train}, Train MAE: {mae_train}, Train R^2: {r2_train}\")\n",
"print(f\"Test MSE: {mse_test}, Test MAE: {mae_test}, Test R^2: {r2_test}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Displaying the summary of the LSTM model"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Model: \"sequential\"\n",
"\n"
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"text/plain": [
"\u001b[1mModel: \"sequential\"\u001b[0m\n"
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"metadata": {},
"output_type": "display_data"
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
"┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
"│ lstm (LSTM) │ (None, 100, 128) │ 66,560 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ lstm_1 (LSTM) │ (None, 64) │ 49,408 │\n",
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"│ dense (Dense) │ (None, 25) │ 1,625 │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ dense_1 (Dense) │ (None, 1) │ 26 │\n",
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
"\n"
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
"┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
"│ lstm (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m128\u001b[0m) │ \u001b[38;5;34m66,560\u001b[0m │\n",
"├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
"│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m) │ \u001b[38;5;34m49,408\u001b[0m │\n",
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"│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m25\u001b[0m) │ \u001b[38;5;34m1,625\u001b[0m │\n",
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"│ dense_1 (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m) │ \u001b[38;5;34m26\u001b[0m │\n",
"└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
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"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/html": [
" Total params: 352,859 (1.35 MB)\n",
"\n"
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"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m352,859\u001b[0m (1.35 MB)\n"
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"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/html": [
" Trainable params: 117,619 (459.45 KB)\n",
"\n"
],
"text/plain": [
"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m117,619\u001b[0m (459.45 KB)\n"
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"metadata": {},
"output_type": "display_data"
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" Non-trainable params: 0 (0.00 B)\n",
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"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/html": [
" Optimizer params: 235,240 (918.91 KB)\n",
"\n"
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"\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m235,240\u001b[0m (918.91 KB)\n"
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},
"metadata": {},
"output_type": "display_data"
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"source": [
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Making predictions on the testing data"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m31/31\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step\n"
]
}
],
"source": [
"predictions = model.predict(x_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Displaying the predictions"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
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},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predictions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Inversing the scaling transformation to get the original predictions"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
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},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"inv_predictions = scaler.inverse_transform(predictions)\n",
"inv_predictions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Inversing the scaling transformation for the actual test data"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
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" [209.02806091],\n",
" [206.99040222],\n",
" [209.5774231 ],\n",
" [213.06338501],\n",
" [215.99000549],\n",
" [217.52999878],\n",
" [221.27000427]])"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"inv_y_test = scaler.inverse_transform(y_test)\n",
"inv_y_test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculating the Root Mean Square Error (RMSE) for the test data"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"rmse = np.sqrt(np.mean( (inv_predictions - inv_y_test)**2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Displaying the RMSE value"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8.544777061846702"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rmse"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creating a DataFrame to compare original test data and predictions"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
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