From fa9bcdeb562ba3c024189a0172f63d567b930384 Mon Sep 17 00:00:00 2001 From: Piyush Kumar Mondal <146834305+piyush182004@users.noreply.github.com> Date: Sun, 20 Oct 2024 19:13:21 +0530 Subject: [PATCH] itz_piyush --- 18ALGOStock_Price_Prediction.ipynb | 5722 ++++++++++++++++++++++++++++ 1 file changed, 5722 insertions(+) create mode 100644 18ALGOStock_Price_Prediction.ipynb diff --git a/18ALGOStock_Price_Prediction.ipynb b/18ALGOStock_Price_Prediction.ipynb new file mode 100644 index 0000000..ab521b1 --- /dev/null +++ b/18ALGOStock_Price_Prediction.ipynb @@ -0,0 +1,5722 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "8tzEK_mSvRoh" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.impute import SimpleImputer\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.svm import SVR\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error\n", + "from sklearn.neighbors import KNeighborsRegressor\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense,LSTM" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "NbBSc2jLvZWx", + "outputId": "3d158d54-f370-4e7b-fdb8-eb7f7fac2928" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Date Open High Low Close Adj Close \\\n", + "0 01-01-1996 18.691147 18.978922 18.540184 18.823240 12.409931 \n", + "1 02-01-1996 18.894005 18.964767 17.738192 18.224106 12.014931 \n", + "2 03-01-1996 18.327892 18.568489 17.643839 17.738192 11.694577 \n", + "3 04-01-1996 17.502312 17.832542 17.223972 17.676863 11.654142 \n", + "4 05-01-1996 17.738192 17.785366 17.459852 17.577793 11.588827 \n", + "\n", + " Volume \n", + "0 43733533.0 \n", + "1 56167280.0 \n", + "2 68296318.0 \n", + "3 86073880.0 \n", + "4 76613039.0 " + ], + "text/html": [ + "\n", + "
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Linear Regression**" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "uTbRFCB4xXHU" + }, + "outputs": [], + "source": [ + "# Create a linear regression model\n", + "model1 = LinearRegression()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 241 + }, + "id": "UKaUaJ6sxaYG", + "outputId": "f0d54db6-c8ae-41a1-a75b-f74b253ac50d" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "5286 257.350006\n", + "3408 129.464996\n", + "5477 279.350006\n", + "6906 588.500000\n", + "530 21.644367\n", + "Name: Close, dtype: float64" + ], + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "execution_count": 22 + } + ], + "source": [ + "y_train.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 + }, + "id": "6iJA5FrBxdEs", + "outputId": "78cfad0c-d5f9-4d8f-e0e5-7da423825bd6" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LinearRegression()" + ], + "text/html": [ + "
LinearRegression()
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" + ] + }, + "metadata": {}, + "execution_count": 23 + } + ], + "source": [ + "# Train the model\n", + "model1.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "k-T73PFExiZD", + "outputId": "6f0182e2-7527-4b2c-bd30-957b14b1db53" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "RMSE: 1.6881364642878485\n", + "MAE: 0.9433353484789417\n", + "MAPE: 0.006085435991202993\n", + "\n" + ] + } + ], + "source": [ + "rmse, mae, mape = evaluate_model(model1, X_test, y_test)\n", + "metrics[\"Model\"].append(\"Linear Regressor\")\n", + "metrics[\"RMSE\"].append(rmse)\n", + "metrics[\"MAE\"].append(mae)\n", + "metrics[\"MAPE\"].append(mape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qEVWWYIS592D" + }, + "source": [ + "# 2. Support Vector Regression" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "LeUTf8Vhxj_k" + }, + "outputs": [], + "source": [ + "# Create an SVR model\n", + "model2 = SVR()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 + }, + "id": "ud3Yhe5Vzvyh", + "outputId": "6f3cac1e-1fb4-4299-ee0b-ce02047eab4e" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "SVR()" + ], + "text/html": [ + "
SVR()
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" + ] + }, + "metadata": {}, + "execution_count": 26 + } + ], + "source": [ + "# Train the model\n", + "model2.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "eiqL4fTuzxWH", + "outputId": "929b8a3f-a1b3-4947-cf05-32f945f79ec7" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "RMSE: 147.71103599153602\n", + "MAE: 110.99419106508152\n", + "MAPE: 1.9715076513294716\n", + "\n" + ] + } + ], + "source": [ + "rmse, mae, mape = evaluate_model(model2, X_test, y_test)\n", + "metrics[\"Model\"].append(\"SVR\")\n", + "metrics[\"RMSE\"].append(rmse)\n", + "metrics[\"MAE\"].append(mae)\n", + "metrics[\"MAPE\"].append(mape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PlDcozy-6OGR" + }, + "source": [ + "# 3. Random Forest Regressor" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "id": "iaN8nOOO6cBg" + }, + "outputs": [], + "source": [ + "model3 = RandomForestRegressor()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 + }, + "id": "wZ7x_Yp06fI_", + "outputId": "79b6b212-5b7e-4c58-c615-69aa210be892" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RandomForestRegressor()" + ], + "text/html": [ + "
RandomForestRegressor()
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" + ] + }, + "metadata": {}, + "execution_count": 29 + } + ], + "source": [ + "# Train the model\n", + "model3.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IwK7IZ3E6g_n", + "outputId": "250858e9-5a8b-4b79-81da-1f134e409a9b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "RMSE: 224.93130980701244\n", + "MAE: 162.98909493804314\n", + "MAPE: 0.7508266646157591\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "rmse, mae, mape = evaluate_model(model3, X_test_scaled, y_test)\n", + "metrics[\"Model\"].append(\"Random Forest\")\n", + "metrics[\"RMSE\"].append(rmse)\n", + "metrics[\"MAE\"].append(mae)\n", + "metrics[\"MAPE\"].append(mape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ijTIDEEa6izO" + }, + "source": [ + "# 4. Gradient Boosting Models" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "id": "EO6OFflr6nJo" + }, + "outputs": [], + "source": [ + "model4 = GradientBoostingRegressor()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 + }, + "id": "vrwnbrEi6o1X", + "outputId": "f4160be8-b2ea-45de-a589-99eef9b71724" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "GradientBoostingRegressor()" + ], + "text/html": [ + "
GradientBoostingRegressor()
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" + ] + }, + "metadata": {}, + "execution_count": 32 + } + ], + "source": [ + "# Train the model\n", + "model4.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-pTBa0fD6qqx", + "outputId": "e382e867-10dc-4529-bc2f-ca6f71625096" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "RMSE: 224.41069433522418\n", + "MAE: 162.2712281619757\n", + "MAPE: 0.7378541693598376\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but GradientBoostingRegressor was fitted with feature names\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "rmse, mae, mape = evaluate_model(model4, X_test_scaled, y_test)\n", + "metrics[\"Model\"].append(\"GBM\")\n", + "metrics[\"RMSE\"].append(rmse)\n", + "metrics[\"MAE\"].append(mae)\n", + "metrics[\"MAPE\"].append(mape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eGcU-e6C6sJI" + }, + "source": [ + "# 5. Extreme Graident Boosting" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "id": "0GQmPNFd6uxx", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "fdb35ef5-26f8-44a5-8abb-ed202d3d1137" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting xgboost\n", + " Downloading xgboost-2.1.1-py3-none-manylinux_2_28_x86_64.whl.metadata (2.1 kB)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.26.4)\n", + "Collecting nvidia-nccl-cu12 (from xgboost)\n", + " Downloading nvidia_nccl_cu12-2.23.4-py3-none-manylinux2014_x86_64.whl.metadata (1.8 kB)\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.13.1)\n", + "Downloading xgboost-2.1.1-py3-none-manylinux_2_28_x86_64.whl (153.9 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m153.9/153.9 MB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hDownloading nvidia_nccl_cu12-2.23.4-py3-none-manylinux2014_x86_64.whl (199.0 MB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.0/199.0 MB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: nvidia-nccl-cu12, xgboost\n", + "Successfully installed nvidia-nccl-cu12-2.23.4 xgboost-2.1.1\n" + ] + } + ], + "source": [ + "!pip install xgboost\n", + "import xgboost as xgb\n", + "# Create an XGBoost model\n", + "model5 = xgb.XGBRegressor()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 253 + }, + "id": "kfo1ZNft6xTp", + "outputId": "7f85c2f3-864c-47cc-b943-91265be33b98" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "XGBRegressor(base_score=None, booster=None, callbacks=None,\n", + " colsample_bylevel=None, colsample_bynode=None,\n", + " colsample_bytree=None, device=None, early_stopping_rounds=None,\n", + " enable_categorical=False, eval_metric=None, feature_types=None,\n", + " gamma=None, grow_policy=None, importance_type=None,\n", + " interaction_constraints=None, learning_rate=None, max_bin=None,\n", + " max_cat_threshold=None, max_cat_to_onehot=None,\n", + " max_delta_step=None, max_depth=None, max_leaves=None,\n", + " min_child_weight=None, missing=nan, monotone_constraints=None,\n", + " multi_strategy=None, n_estimators=None, n_jobs=None,\n", + " num_parallel_tree=None, random_state=None, ...)" + ], + "text/html": [ + "
XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+              "             colsample_bylevel=None, colsample_bynode=None,\n",
+              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+              "             gamma=None, grow_policy=None, importance_type=None,\n",
+              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
+              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
+              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+              "             num_parallel_tree=None, random_state=None, ...)
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" + ] + }, + "metadata": {}, + "execution_count": 36 + } + ], + "source": [ + "# Train the model\n", + "model5.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7QwLt9iS6zSj", + "outputId": "f4e99e28-380e-4bf9-839a-a068ad52ed93" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "RMSE: 224.66436370022384\n", + "MAE: 162.62070643817412\n", + "MAPE: 0.7441437311249671\n", + "\n" + ] + } + ], + "source": [ + "rmse, mae, mape = evaluate_model(model5, X_test_scaled, y_test)\n", + "metrics[\"Model\"].append(\"XGBoost\")\n", + "metrics[\"RMSE\"].append(rmse)\n", + "metrics[\"MAE\"].append(mae)\n", + "metrics[\"MAPE\"].append(mape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sUD1VQBF605K" + }, + "source": [ + "# 6. AdaBoost Regressor" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "id": "0foTLiQp63Y9" + }, + "outputs": [], + "source": [ + "model6 = AdaBoostRegressor()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 + }, + "id": "bkzSWYA365MO", + "outputId": "119f4e85-55ac-4a8b-c18d-768a7ecf85de" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "AdaBoostRegressor()" + ], + "text/html": [ + "
AdaBoostRegressor()
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" + ] + }, + "metadata": {}, + "execution_count": 39 + } + ], + "source": [ + "# Train the model\n", + "model6.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZKxqdmp166pF", + "outputId": "df8e8b41-a077-4aa7-a71f-df3010428289" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "RMSE: 211.84016190199145\n", + "MAE: 150.27932429061372\n", + "MAPE: 0.7057669522844586\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but AdaBoostRegressor was fitted with feature names\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "rmse, mae, mape = evaluate_model(model6, X_test_scaled, y_test)\n", + "metrics[\"Model\"].append(\"AdaBoost Regressor\")\n", + "metrics[\"RMSE\"].append(rmse)\n", + "metrics[\"MAE\"].append(mae)\n", + "metrics[\"MAPE\"].append(mape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mtfkPIRi67xo" + }, + "source": [ + "# 7. Decision Tree" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "id": "E6EyzrH36_Fq" + }, + "outputs": [], + "source": [ + "model7 = DecisionTreeRegressor()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 + }, + "id": "DTp5VIYx7AWt", + "outputId": "74192862-7b4e-409d-e614-5ff84e130ce4" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "DecisionTreeRegressor()" + ], + "text/html": [ + "
DecisionTreeRegressor()
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" + ] + }, + "metadata": {}, + "execution_count": 42 + } + ], + "source": [ + "# Train the model\n", + "model7.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3YC-pSgv7Dh4", + "outputId": "3e0ecf0d-77bb-4e52-cffb-14a0c78cb3fa" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "RMSE: 224.85857555038172\n", + "MAE: 162.88870413804315\n", + "MAPE: 0.7490024715971244\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "rmse, mae, mape = evaluate_model(model7, X_test_scaled, y_test)\n", + "metrics[\"Model\"].append(\"Decision Tree\")\n", + "metrics[\"RMSE\"].append(rmse)\n", + "metrics[\"MAE\"].append(mae)\n", + "metrics[\"MAPE\"].append(mape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WfJAZHnP7E_2" + }, + "source": [ + "# 8. KNeighbors Regressor" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "id": "smujnWTRzzDL" + }, + "outputs": [], + "source": [ + "# Create a KNN model\n", + "model8 = KNeighborsRegressor()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 + }, + "id": "zeokqhKd0Aj8", + "outputId": "b42fe301-c5ad-42c8-d147-314e13995429" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "KNeighborsRegressor()" + ], + "text/html": [ + "
KNeighborsRegressor()
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" + ] + }, + "metadata": {}, + "execution_count": 45 + } + ], + "source": [ + "# Train the model\n", + "model8.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "X2uNfESC0CA8", + "outputId": "f56c942d-5f58-4c8e-c86f-ce5a98a57cdf" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "RMSE: 224.35603706259303\n", + "MAE: 162.1962430618594\n", + "MAPE: 0.7365233640314862\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but KNeighborsRegressor was fitted with feature names\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "rmse, mae, mape = evaluate_model(model8, X_test_scaled, y_test)\n", + "metrics[\"Model\"].append(\"KNN\")\n", + "metrics[\"RMSE\"].append(rmse)\n", + "metrics[\"MAE\"].append(mae)\n", + "metrics[\"MAPE\"].append(mape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X3yNCskZ7KMV" + }, + "source": [ + "# 9. Artificial Neural Networks" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "id": "syd9MRhf0Df1" + }, + "outputs": [], + "source": [ + "# Create an ANN model\n", + "model9 = Sequential()\n", + "model9.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n", + "model9.add(Dense(16, activation='relu'))\n", + "model9.add(Dense(1, activation='linear'))" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "id": "pdlxN-Dp0IZr" + }, + "outputs": [], + "source": [ + "# Compile the model\n", + "model9.compile(loss='mean_squared_error', optimizer='adam')" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qcryLURL0KIH", + "outputId": "1a31f08a-6f56-4f8b-dd4b-21c42080a75a" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 49 + } + ], + "source": [ + "# Train the model\n", + "model9.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Xu6Cwjey0MaP", + "outputId": "77fd4697-9175-4f35-f814-f3b463dc180b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "45/45 [==============================] - 0s 1ms/step\n", + "RMSE: 2.9928054434827525\n", + "MAE: 1.963869507604453\n", + "MAPE: 0.014526009797749508\n", + "\n" + ] + } + ], + "source": [ + "rmse, mae, mape = evaluate_model(model9, X_test_scaled, y_test)\n", + "metrics[\"Model\"].append(\"ANN\")\n", + "metrics[\"RMSE\"].append(rmse)\n", + "metrics[\"MAE\"].append(mae)\n", + "metrics[\"MAPE\"].append(mape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Yet4TgKq7OZl" + }, + "source": [ + "# 10. Long Short Term Memory" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "id": "keiZDN4w7UH0" + }, + "outputs": [], + "source": [ + "n_features = X_train_scaled.shape[1]\n", + "n_steps = 10\n", + "n_samples_train = X_train_scaled.shape[0] - n_steps + 1\n", + "n_samples_test = X_test_scaled.shape[0] - n_steps + 1\n", + "\n", + "# Reshape the input data\n", + "X_train_reshaped = np.array([X_train_scaled[i:i+n_steps, :] for i in range(n_samples_train)])\n", + "X_test_reshaped = np.array([X_test_scaled[i:i+n_steps, :] for i in range(n_samples_test)])" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "id": "nRRTkQTD7Vjd" + }, + "outputs": [], + "source": [ + "model10 = Sequential()\n", + "model10.add(LSTM(64, activation='relu', input_shape=(n_steps, n_features)))\n", + "model10.add(Dense(1))" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "id": "3UJtO3wC7WWe" + }, + "outputs": [], + "source": [ + "# Compile the model\n", + "model10.compile(loss='mean_squared_error', optimizer='adam')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ld9dofMD7YNO", + "outputId": "f3ebf621-6bd2-4998-e147-7c13bc6aac88" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 55 + } + ], + "source": [ + "model10.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lOTdB8Bj7aXM", + "outputId": "c82c1ead-25f5-44b2-c382-dd5318cb9d7f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "44/44 [==============================] - 0s 3ms/step\n", + "RMSE: 15.359295807149854\n", + "MAE: 12.278894525379425\n", + "MAPE: 0.27883509462425415\n", + "\n" + ] + } + ], + "source": [ + "rmse, mae, mape = evaluate_model(model10, X_test_reshaped, y_test[n_steps-1:])\n", + "\n", + "# Store metrics\n", + "metrics[\"Model\"].append(\"LSTM\")\n", + "metrics[\"RMSE\"].append(rmse)\n", + "metrics[\"MAE\"].append(mae)\n", + "metrics[\"MAPE\"].append(mape)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **11. Lasso Regression**" + ], + "metadata": { + "id": "Rdjh94-UEoWG" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.linear_model import Lasso\n", + "\n", + "model11 = Lasso(alpha=0.001) # Decreased alpha for less regularization\n", + "model11.fit(X_train_scaled, y_train)\n", + "rmse, mae, mape = evaluate_model(model11, X_test_scaled, y_test)\n", + "\n", + "metrics[\"Model\"].append(\"Lasso Regression (Updated)\")\n", + "metrics[\"RMSE\"].append(rmse)\n", + "metrics[\"MAE\"].append(mae)\n", + "metrics[\"MAPE\"].append(mape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "OimOLwiuEwea", + "outputId": "000d4ef9-6843-4517-da09-a4a35d83016f" + }, + "execution_count": 69, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "RMSE: 3.5005772524019725\n", + "MAE: 2.1759919360445914\n", + "MAPE: 0.014216792315179878\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.470e+04, tolerance: 1.349e+04\n", + " model = cd_fast.enet_coordinate_descent(\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **12.Ridge Regression**" + ], + "metadata": { + "id": "XGqWkXGUFezu" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.linear_model import Ridge\n", + "\n", + "model12 = Ridge(alpha=0.5)\n", + "model12.fit(X_train_scaled, y_train)\n", + "rmse, mae, mape = evaluate_model(model12, X_test_scaled, y_test)\n", + "metrics[\"Model\"].append(\"Ridge Regression\")\n", + "metrics[\"RMSE\"].append(rmse)\n", + "metrics[\"MAE\"].append(mae)\n", + "metrics[\"MAPE\"].append(mape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yOeNMBAfFkih", + "outputId": "1893a251-e3f2-461f-dc6a-da90fe96129e" + }, + "execution_count": 60, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "RMSE: 2.7385522000826734\n", + "MAE: 1.6950420985832253\n", + "MAPE: 0.010816737779364599\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **13.ElasticNet Regression**" + ], + "metadata": { + "id": "ymQ6VQEXFuBs" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.linear_model import ElasticNet\n", + "\n", + "model13 = ElasticNet(alpha=0.05, l1_ratio=0.7)\n", + "model13.fit(X_train_scaled, y_train)\n", + "rmse, mae, mape = evaluate_model(model13, X_test_scaled, y_test)\n", + "metrics[\"Model\"].append(\"ElasticNet Regression\")\n", + "metrics[\"RMSE\"].append(rmse)\n", + "metrics[\"MAE\"].append(mae)\n", + "metrics[\"MAPE\"].append(mape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NwwymnjiFzM5", + "outputId": "5d2fd12d-e3af-4b92-e24f-085f7b08e512" + }, + "execution_count": 61, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "RMSE: 14.804090936708079\n", + "MAE: 11.800446698217334\n", + "MAPE: 0.24325036314896364\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **14.SVR with RBF Kernel**" + ], + "metadata": { + "id": "gPPw3I6fF2yd" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.svm import SVR\n", + "\n", + "model14 = SVR(kernel='rbf', C=10, epsilon=0.05)\n", + "model14.fit(X_train_scaled, y_train)\n", + "rmse, mae, mape = evaluate_model(model14, X_test_scaled, y_test)\n", + "metrics[\"Model\"].append(\"SVR (RBF)\")\n", + "metrics[\"RMSE\"].append(rmse)\n", + "metrics[\"MAE\"].append(mae)\n", + "metrics[\"MAPE\"].append(mape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "j_NV17B4F6ZM", + "outputId": "91c7c74b-173d-47a2-af42-8ef37b4cd0bb" + }, + "execution_count": 62, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "RMSE: 4.1606646051672405\n", + "MAE: 1.7711785377611675\n", + "MAPE: 0.015270499337866636\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **15.Bayesian Ridge Regression**" + ], + "metadata": { + "id": "OKM9CqwhF9-3" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.linear_model import BayesianRidge\n", + "\n", + "# Replace 'n_iter' with 'max_iter'\n", + "model15 = BayesianRidge(max_iter=300, tol=1e-4)\n", + "model15.fit(X_train_scaled, y_train)\n", + "rmse, mae, mape = evaluate_model(model15, X_test_scaled, y_test)\n", + "metrics[\"Model\"].append(\"Bayesian Ridge\")\n", + "metrics[\"RMSE\"].append(rmse)\n", + "metrics[\"MAE\"].append(mae)\n", + "metrics[\"MAPE\"].append(mape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wYNH0UAmGDlb", + "outputId": "61bcaad0-011c-494c-ac3b-5461d5ca2b89" + }, + "execution_count": 65, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "RMSE: 1.6881351101001205\n", + "MAE: 0.9434406049061286\n", + "MAPE: 0.006085921149239934\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **16.Huber Regressor**" + ], + "metadata": { + "id": "qvZL1xbwGjGu" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.linear_model import HuberRegressor\n", + "\n", + "model16 = HuberRegressor(epsilon=1.5)\n", + "model16.fit(X_train_scaled, y_train)\n", + "rmse, mae, mape = evaluate_model(model16, X_test_scaled, y_test)\n", + "metrics[\"Model\"].append(\"Huber Regressor\")\n", + "metrics[\"RMSE\"].append(rmse)\n", + "metrics[\"MAE\"].append(mae)\n", + "metrics[\"MAPE\"].append(mape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yNHH3AdxGiAk", + "outputId": "ac73afbf-8cfa-4e24-9c4e-72dc3d5299db" + }, + "execution_count": 66, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "RMSE: 1.7227752104860061\n", + "MAE: 0.9183987164012762\n", + "MAPE: 0.005933218462550846\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **17.Passive Aggressive Regressor**" + ], + "metadata": { + "id": "6dK0ArL6G2xM" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.linear_model import PassiveAggressiveRegressor\n", + "\n", + "model17 = PassiveAggressiveRegressor(max_iter=1500, tol=1e-4)\n", + "model17.fit(X_train_scaled, y_train)\n", + "rmse, mae, mape = evaluate_model(model17, X_test_scaled, y_test)\n", + "metrics[\"Model\"].append(\"Passive Aggressive Regressor\")\n", + "metrics[\"RMSE\"].append(rmse)\n", + "metrics[\"MAE\"].append(mae)\n", + "metrics[\"MAPE\"].append(mape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XaFuxTwlG8v0", + "outputId": "058987b1-9be4-4f4e-d1c2-b5125356699b" + }, + "execution_count": 67, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "RMSE: 2.064959947140465\n", + "MAE: 1.3041435991086505\n", + "MAPE: 0.011270470851679414\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **18.ARIMA**" + ], + "metadata": { + "id": "v8qPX8UlIzse" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install statsmodels\n", + "!pip install pmdarima\n", + "import pandas as pd\n", + "import numpy as np\n", + "from statsmodels.tsa.arima.model import ARIMA\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error\n", + "from pmdarima import auto_arima\n", + "\n", + "\n", + "# Assuming you have your data loaded and preprocessed as before (df, X_train, X_test, y_train, y_test)\n", + "\n", + "# Automatic ARIMA Order Selection using auto_arima\n", + "# Find the best (p, d, q) values\n", + "best_model = auto_arima(y_train, seasonal=False, trace=True, error_action='ignore', suppress_warnings=True)\n", + "p, d, q = best_model.order\n", + "\n", + "# Fit the ARIMA model\n", + "model18 = ARIMA(y_train, order=(p, d, q))\n", + "model18_fit = model18.fit()\n", + "\n", + "# Make predictions\n", + "# Use len(y_train) as the starting point for predictions\n", + "# Forecast for the length of y_test\n", + "predictions = model18_fit.predict(start=len(y_train), end=len(y_train) + len(y_test) - 1)\n", + "\n", + "# Evaluate the model\n", + "rmse = np.sqrt(mean_squared_error(y_test, predictions))\n", + "mae = mean_absolute_error(y_test, predictions)\n", + "mape = mean_absolute_percentage_error(y_test, predictions)\n", + "\n", + "print(f\"ARIMA RMSE: {rmse}\")\n", + "print(f\"ARIMA MAE: {mae}\")\n", + "print(f\"ARIMA MAPE: {mape}\\n\")\n", + "\n", + "# Store metrics\n", + "metrics[\"Model\"].append(\"ARIMA\")\n", + "metrics[\"RMSE\"].append(rmse)\n", + "metrics[\"MAE\"].append(mae)\n", + "metrics[\"MAPE\"].append(mape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "eWJ9s78bI4kj", + "outputId": "93cd67e4-f1e8-4c70-a174-efe9e039663b" + }, + "execution_count": 75, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: statsmodels in /usr/local/lib/python3.10/dist-packages (0.14.4)\n", + 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\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m87.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hInstalling collected packages: Cython, pmdarima\n", + "Successfully installed Cython-3.0.11 pmdarima-2.0.4\n", + "Performing stepwise search to minimize aic\n", + " ARIMA(2,0,2)(0,0,0)[0] : AIC=inf, Time=1.02 sec\n", + " ARIMA(0,0,0)(0,0,0)[0] : AIC=78005.789, Time=0.04 sec\n", + " ARIMA(1,0,0)(0,0,0)[0] : AIC=75551.578, Time=0.08 sec\n", + " ARIMA(0,0,1)(0,0,0)[0] : AIC=76619.836, Time=0.35 sec\n", + " ARIMA(2,0,0)(0,0,0)[0] : AIC=74813.034, Time=0.18 sec\n", + " ARIMA(3,0,0)(0,0,0)[0] : AIC=74364.052, Time=0.21 sec\n", + " ARIMA(4,0,0)(0,0,0)[0] : AIC=74074.892, Time=0.36 sec\n", + " ARIMA(5,0,0)(0,0,0)[0] : AIC=73959.511, Time=0.34 sec\n", + " ARIMA(5,0,1)(0,0,0)[0] : AIC=inf, Time=2.61 sec\n", + " ARIMA(4,0,1)(0,0,0)[0] : AIC=inf, Time=1.82 sec\n", + " ARIMA(5,0,0)(0,0,0)[0] intercept : AIC=73094.800, Time=0.63 sec\n", + " ARIMA(4,0,0)(0,0,0)[0] intercept : AIC=73094.769, Time=0.44 sec\n", + " ARIMA(3,0,0)(0,0,0)[0] intercept : AIC=73097.614, Time=0.31 sec\n", + " ARIMA(4,0,1)(0,0,0)[0] intercept : AIC=73096.761, Time=0.83 sec\n", + " ARIMA(3,0,1)(0,0,0)[0] intercept : AIC=73099.606, Time=0.55 sec\n", + " ARIMA(5,0,1)(0,0,0)[0] intercept : AIC=73096.799, Time=1.13 sec\n", + "\n", + "Best model: ARIMA(4,0,0)(0,0,0)[0] intercept\n", + "Total fit time: 10.937 seconds\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: An unsupported index was provided. As a result, forecasts cannot be generated. To use the model for forecasting, use one of the supported classes of index.\n", + " self._init_dates(dates, freq)\n", + "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: An unsupported index was provided. As a result, forecasts cannot be generated. To use the model for forecasting, use one of the supported classes of index.\n", + " self._init_dates(dates, freq)\n", + "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: An unsupported index was provided. As a result, forecasts cannot be generated. To use the model for forecasting, use one of the supported classes of index.\n", + " self._init_dates(dates, freq)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "ARIMA RMSE: 155.08416914567002\n", + "ARIMA MAE: 124.61616145375949\n", + "ARIMA MAPE: 2.6139414956995837\n", + "\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:837: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n", + " return get_prediction_index(\n", + "/usr/local/lib/python3.10/dist-packages/statsmodels/tsa/base/tsa_model.py:837: FutureWarning: No supported index is available. In the next version, calling this method in a model without a supported index will result in an exception.\n", + " return get_prediction_index(\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **RMSE PLOT**" + ], + "metadata": { + "id": "4eV6XvILHH2R" + } + }, + { + "cell_type": "code", + "source": [ + "metrics_df = pd.DataFrame(metrics)\n", + "\n", + "# RMSE Plot\n", + "plt.figure(figsize=(12, 6))\n", + "plt.bar(metrics_df['Model'], metrics_df['RMSE'], color='skyblue')\n", + "plt.title('RMSE for Different Models')\n", + "plt.xlabel('Model')\n", + "plt.ylabel('RMSE')\n", + "plt.xticks(rotation=45, ha='right')\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 349 + }, + "id": "VKYiQHEoHKTW", + "outputId": "422ee235-af66-4364-c369-989aa88e6177" + }, + "execution_count": 76, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **MAE Plot**" + ], + "metadata": { + "id": "a9OLdEnhIBpg" + } + }, + { + "cell_type": "code", + "source": [ + "# MAE Plot\n", + "plt.figure(figsize=(12, 6))\n", + "plt.bar(metrics_df['Model'], metrics_df['MAE'], color='lightgreen')\n", + "plt.title('MAE for Different Models')\n", + "plt.xlabel('Model')\n", + "plt.ylabel('MAE')\n", + "plt.xticks(rotation=45, ha='right')\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 349 + }, + "id": "mx69_xSJIMbV", + "outputId": "bb6d2f65-57ab-4790-db94-1e6f57cb844f" + }, + "execution_count": 77, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **MAPE Plot**" + ], + "metadata": { + "id": "vnoQkSzCIQ9C" + } + }, + { + "cell_type": "code", + "source": [ + "# MAPE Plot\n", + "plt.figure(figsize=(12, 6))\n", + "plt.bar(metrics_df['Model'], metrics_df['MAPE'], color='salmon')\n", + "plt.title('MAPE for Different Models')\n", + "plt.xlabel('Model')\n", + "plt.ylabel('MAPE')\n", + "plt.xticks(rotation=45, ha='right')\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 349 + }, + "id": "Xeh4ofl-IXHp", + "outputId": "ec96fd12-3a26-4371-ae6b-b6369b88ff33" + }, + "execution_count": 78, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" 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