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+ ]
+ },
+ "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"
+ ],
+ "text/plain": [
+ "\u001b[1mModel: \"sequential\"\u001b[0m\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
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+ "data": {
+ "text/html": [
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\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",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense (Dense) │ (None, 25) │ 1,625 │\n",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ dense_1 (Dense) │ (None, 1) │ 26 │\n",
+ "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
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+ "┃\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",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ 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",
+ "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
+ "│ 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"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Total params: 352,859 (1.35 MB)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m352,859\u001b[0m (1.35 MB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "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"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
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+ "data": {
+ "text/html": [
+ " Non-trainable params: 0 (0.00 B)\n",
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+ ],
+ "text/plain": [
+ "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/html": [
+ " Optimizer params: 235,240 (918.91 KB)\n",
+ "
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+ ],
+ "text/plain": [
+ "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m235,240\u001b[0m (918.91 KB)\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "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"
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+ }
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+ "source": [
+ "predictions = model.predict(x_test)"
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+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Displaying the predictions"
+ ]
+ },
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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": [
+ {
+ "data": {
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+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
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+ ],
+ "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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+ " [221.82324219],\n",
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+ " [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": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " original_test_data | \n",
+ " predictions | \n",
+ "
\n",
+ " \n",
+ " | Date | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 2020-09-10 | \n",
+ " 110.907524 | \n",
+ " 107.823250 | \n",
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\n",
+ " \n",
+ " | 2020-09-11 | \n",
+ " 109.451424 | \n",
+ " 106.904861 | \n",
+ "
\n",
+ " \n",
+ " | 2020-09-14 | \n",
+ " 112.734970 | \n",
+ " 105.830391 | \n",
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\n",
+ " \n",
+ " | 2020-09-15 | \n",
+ " 112.910873 | \n",
+ " 106.819199 | \n",
+ "
\n",
+ " \n",
+ " | 2020-09-16 | \n",
+ " 109.578453 | \n",
+ " 107.817070 | \n",
+ "
\n",
+ " \n",
+ "
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+ "