diff --git a/.ipynb_checkpoints/Stock_Price_Prediction-checkpoint.ipynb b/.ipynb_checkpoints/Stock_Price_Prediction-checkpoint.ipynb index e8b17a3..6018533 100644 --- a/.ipynb_checkpoints/Stock_Price_Prediction-checkpoint.ipynb +++ b/.ipynb_checkpoints/Stock_Price_Prediction-checkpoint.ipynb @@ -1,2717 +1,9 @@ { -<<<<<<< HEAD - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "qCDSjVhXLr_Z" - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.linear_model import LinearRegression\n", - "from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error, accuracy_score, precision_score, confusion_matrix, recall_score, f1_score" - ] - }, - { - "cell_type": "code", - "source": [ - "from google.colab import drive\n", - "drive.mount('/content/drive')\n", - "df = pd.read_csv('drive/My Drive/Colab Notebooks/Stock Price Prediction RNN/SBIN.csv')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "SOQbXSiB-g5G", - "outputId": "6ae02a27-02b0-4bd9-a1ae-a7029056f32e" - }, - "execution_count": 22, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" - ] - } - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 206 - }, - "id": "Sc4id6VxL8BS", - "outputId": "568d039c-faf4-4636-bfc1-70b9ef83367b" - }, - "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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\n" - ] - }, - "metadata": {}, - "execution_count": 23 - } - ], - "source": [ - "# Load the dataset\n", - "#df = pd.read_csv('/content/SBIN.NS.csv')\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "id": "7LaYGXsfN-8y" - }, - "outputs": [], - "source": [ - "# Drop the 'Date' and 'Adj Close' columns\n", - "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 206 - }, - "id": "pqbTBdnBOKJc", - "outputId": "21da8a7f-4f3e-4f4f-e32b-3b90c230ce55" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - " Open High Low Close Volume\n", - "0 18.691147 18.978922 18.540184 18.823240 43733533.0\n", - "1 18.894005 18.964767 17.738192 18.224106 56167280.0\n", - "2 18.327892 18.568489 17.643839 17.738192 68296318.0\n", - "3 17.502312 17.832542 17.223972 17.676863 86073880.0\n", - "4 17.738192 17.785366 17.459852 17.577793 76613039.0" - ], - "text/html": [ - "\n", - "
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\n" - ] - }, - "metadata": {}, - "execution_count": 25 - } - ], - "source": [ - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "id": "dydEPoNeM6eN" - }, - "outputs": [], - "source": [ - "# Handle missing values\n", - "from sklearn.impute import SimpleImputer\n", - "imputer = SimpleImputer(strategy='mean')\n", - "df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "id": "OQ3cGqgTMBwt" - }, - "outputs": [], - "source": [ - "# Select features and target variable\n", - "X = df[['Open', 'High', 'Low', 'Volume']]\n", - "y = df['Close']" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "id": "9Oz-bwJOMEWD" - }, - "outputs": [], - "source": [ - "# Split the data into training and testing sets\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "ugapDyXODtn3" - }, - "outputs": [], - "source": [ - "# Scale the features using Min-Max scaling\n", - "from sklearn.preprocessing import MinMaxScaler\n", - "scaler = MinMaxScaler()\n", - "X_train_scaled = scaler.fit_transform(X_train)\n", - "X_test_scaled = scaler.transform(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "997ZEgibCZIO", - "outputId": "2a45a8e3-71b0-47f3-bd66-91bcdc028c76" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(5659, 4)" - ] - }, - "metadata": {}, - "execution_count": 29 - } - ], - "source": [ - "X_train.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "bmtt76RuCeyG", - "outputId": "658075af-e75d-45b1-f6cf-756e349a32d1" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(1415, 4)" - ] - }, - "metadata": {}, - "execution_count": 30 - } - ], - "source": [ - "X_test.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "CeJkUJ92Ciqd", - "outputId": "93dec527-ea2e-42e6-c70b-a9491c71d917" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(5659,)" - ] - }, - "metadata": {}, - "execution_count": 31 - } - ], - "source": [ - "y_train.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "7HGC7VuTCjWc", - "outputId": "64dc2569-b4b4-4c2e-d416-1cf77c41ac75" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(1415,)" - ] - }, - "metadata": {}, - "execution_count": 32 - } - ], - "source": [ - "y_test.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "c6Ek8jRlO2_I" - }, - "source": [ - "## 1. LINEAR REGRESSION" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "id": "RdZ1SpzdMHAJ" - }, - "outputs": [], - "source": [ - "# Create a linear regression model\n", - "model1 = LinearRegression()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "mPM035IzMY04", - "outputId": "07379dba-cfe8-4814-b972-d08b12f224ac" - }, - "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" - ] - }, - "metadata": {}, - "execution_count": 34 - } - ], - "source": [ - "y_train.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 74 - }, - "id": "qBhQ9HbYMI3d", - "outputId": "52e0655f-1d23-47b7-decc-7a7ca35c0470" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "LinearRegression()" - ], - "text/html": [ - "
LinearRegression()
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" - ] - }, - "metadata": {}, - "execution_count": 35 - } - ], - "source": [ - "# Train the model\n", - "model1.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "id": "X269co2kMS4z" - }, - "outputs": [], - "source": [ - "# Make predictions on the test set\n", - "pred1 = model1.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "id": "QK8GvDYPOd0Y" - }, - "outputs": [], - "source": [ - "# Calculate evaluation metrics\n", - "rmse1 = np.sqrt(mean_squared_error(y_test, pred1))\n", - "mae1 = mean_absolute_error(y_test, pred1)\n", - "mape1 = mean_absolute_percentage_error(y_test, pred1)\n", - "accuracy1 = accuracy_score(y_test > pred1, y_test > pred1.round())\n", - "precision1 = precision_score(y_test > pred1, y_test > pred1.round())\n", - "confusion1 = confusion_matrix(y_test > pred1, y_test > pred1.round())\n", - "recall1 = recall_score(y_test > pred1, y_test > pred1.round())\n", - "f11 = f1_score(y_test > pred1, y_test > pred1.round())" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "dEi49xtEOtne", - "outputId": "0000b074-3187-41de-fbac-4ae75cbda6bd" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "RMSE: 1.6881364643681482\n", - "MAE: 0.9433353485344729\n", - "MAPE: 0.006085435990853812\n", - "Accuracy: 0.8296819787985866\n", - "Precision: 0.8623595505617978\n", - "Confusion Matrix:\n", - " [[560 98]\n", - " [143 614]]\n", - "Recall: 0.8110964332892999\n", - "F1 Score: 0.8359428182437032\n" - ] - } - ], - "source": [ - "# Print the evaluation metrics\n", - "print(\"RMSE:\", rmse1)\n", - "print(\"MAE:\", mae1)\n", - "print(\"MAPE:\", mape1)\n", - "print(\"Accuracy:\", accuracy1)\n", - "print(\"Precision:\", precision1)\n", - "print(\"Confusion Matrix:\\n\", confusion1)\n", - "print(\"Recall:\", recall1)\n", - "print(\"F1 Score:\", f11)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GxtMzlg-gR2P" - }, - "source": [ - "## 2. SVR" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "id": "o7K9r7EXWRjQ" - }, - "outputs": [], - "source": [ - "from sklearn.svm import SVR" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "id": "0xQewd7QWTtq" - }, - "outputs": [], - "source": [ - "# Create an SVR model\n", - "model2 = SVR()" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "id": "DuNes3s6U2IV" - }, - "outputs": [], - "source": [ - "# Split the data into training and testing sets\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "# Scale the features using Min-Max scaling\n", - "scaler = MinMaxScaler()\n", - "X_train_scaled = scaler.fit_transform(X_train)\n", - "X_test_scaled = scaler.transform(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 74 - }, - "id": "22SaCsQmfhgP", - "outputId": "2121e992-399d-4b78-e42c-fc20b9d52189" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "SVR()" - ], - "text/html": [ - "
SVR()
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" - ] - }, - "metadata": {}, - "execution_count": 42 - } - ], - "source": [ - "# Train the model\n", - "model2.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": { - "id": "OQ1nL4oYfkAC" - }, - "outputs": [], - "source": [ - "# Make predictions on the test set\n", - "pred2 = model2.predict(X_test)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": { - "id": "nRYTwydsfpjb" - }, - "outputs": [], - "source": [ - "# Calculate evaluation metrics\n", - "rmse2 = np.sqrt(mean_squared_error(y_test, pred2))\n", - "mae2 = mean_absolute_error(y_test, pred2)\n", - "mape2 = mean_absolute_percentage_error(y_test, pred2)\n", - "accuracy2 = accuracy_score(y_test > pred2, y_test > pred2.round())\n", - "precision2 = precision_score(y_test > pred2, y_test > pred2.round())\n", - "confusion2 = confusion_matrix(y_test > pred2, y_test > pred2.round())\n", - "recall2 = recall_score(y_test > pred2, y_test > pred2.round())\n", - "f12 = f1_score(y_test > pred2, y_test > pred2.round())" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "656J5oz5fzq6", - "outputId": "ce67d2d8-0bc8-4e6d-d6b5-6b78e7e1c59b" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "RMSE: 147.71103599153602\n", - "MAE: 110.99419106508152\n", - "MAPE: 1.9715076513294716\n", - "Accuracy: 0.9992932862190813\n", - "Precision: 1.0\n", - "Confusion Matrix:\n", - " [[727 0]\n", - " [ 1 687]]\n", - "Recall: 0.998546511627907\n", - "F1 Score: 0.9992727272727273\n" - ] - } - ], - "source": [ - "# Print the evaluation metrics\n", - "print(\"RMSE:\", rmse2)\n", - "print(\"MAE:\", mae2)\n", - "print(\"MAPE:\", mape2)\n", - "print(\"Accuracy:\", accuracy2)\n", - "print(\"Precision:\", precision2)\n", - "print(\"Confusion Matrix:\\n\", confusion2)\n", - "print(\"Recall:\", recall2)\n", - "print(\"F1 Score:\", f12)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hcIfVMWdgcKt" - }, - "source": [ - "## 3. Random Forest" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": { - "id": "f7raXT_hf2ij" - }, - "outputs": [], - "source": [ - "from sklearn.ensemble import RandomForestRegressor\n", - "# Create a Random Forest model\n", - "model3 = RandomForestRegressor()" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": { - "id": "TadNM7MEU7fh" - }, - "outputs": [], - "source": [ - "# Split the data into training and testing sets\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "# Scale the features using Min-Max scaling\n", - "scaler = MinMaxScaler()\n", - "X_train_scaled = scaler.fit_transform(X_train)\n", - "X_test_scaled = scaler.transform(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 74 - }, - "id": "fF002Yepgk55", - "outputId": "d148c589-4879-4e2d-8b0f-5b5ca01a2a53" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "RandomForestRegressor()" - ], - "text/html": [ - "
RandomForestRegressor()
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" - ] - }, - "metadata": {}, - "execution_count": 48 - } - ], - "source": [ - "# Train the model\n", - "model3.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": { - "id": "8nRU_pzEgnCt" - }, - "outputs": [], - "source": [ - "# Make predictions on the test set\n", - "pred3 = model3.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": { - "id": "4aKEXGVUgsry" - }, - "outputs": [], - "source": [ - "# Calculate evaluation metrics\n", - "rmse3 = np.sqrt(mean_squared_error(y_test, pred3))\n", - "mae3 = mean_absolute_error(y_test, pred3)\n", - "mape3 = mean_absolute_percentage_error(y_test, pred3)\n", - "accuracy3 = accuracy_score(y_test > pred3, y_test > pred3.round())\n", - "precision3 = precision_score(y_test > pred3, y_test > pred3.round())\n", - "confusion3 = confusion_matrix(y_test > pred3, y_test > pred3.round())\n", - "recall3 = recall_score(y_test > pred3, y_test > pred3.round())\n", - "f13 = f1_score(y_test > pred3, y_test > pred3.round())" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "8pPzsCY1g305", - "outputId": "72c4ea56-2610-41c6-f286-4c8289d3f0ac" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "RMSE: 2.189635498596314\n", - "MAE: 1.250413817712252\n", - "MAPE: 0.007984509559881612\n", - "Accuracy: 0.8551236749116607\n", - "Precision: 0.8558823529411764\n", - "Confusion Matrix:\n", - " [[628 98]\n", - " [107 582]]\n", - "Recall: 0.8447024673439768\n", - "F1 Score: 0.8502556610664718\n" - ] - } - ], - "source": [ - "# Print the evaluation metrics\n", - "print(\"RMSE:\", rmse3)\n", - "print(\"MAE:\", mae3)\n", - "print(\"MAPE:\", mape3)\n", - "print(\"Accuracy:\", accuracy3)\n", - "print(\"Precision:\", precision3)\n", - "print(\"Confusion Matrix:\\n\", confusion3)\n", - "print(\"Recall:\", recall3)\n", - "print(\"F1 Score:\", f13)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "mZsLwLivhLGH" - }, - "source": [ - "## 4. Gradient Boosting Models (GBM)" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": { - "id": "TI8idoxOg6jF" - }, - "outputs": [], - "source": [ - "import xgboost as xgb\n", - "# Create an XGBoost model\n", - "model4 = xgb.XGBRegressor()" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": { - "id": "7r9xJDtOVBEA" - }, - "outputs": [], - "source": [ - "# Split the data into training and testing sets\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "# Scale the features using Min-Max scaling\n", - "scaler = MinMaxScaler()\n", - "X_train_scaled = scaler.fit_transform(X_train)\n", - "X_test_scaled = scaler.transform(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 248 - }, - "id": "2gpbDxshhexj", - "outputId": "b2b1a681-7ede-4d66-be5d-1a8606d0f470" - }, - "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": [ - "
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" - ] - }, - "metadata": {}, - "execution_count": 54 - } - ], - "source": [ - "# Train the model\n", - "model4.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": { - "id": "Jj9DXdUPhh9V" - }, - "outputs": [], - "source": [ - "# Make predictions on the test set\n", - "pred4 = model4.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": { - "id": "TdH60Sllhn5O" - }, - "outputs": [], - "source": [ - "# Calculate evaluation metrics\n", - "rmse4 = np.sqrt(mean_squared_error(y_test, pred4))\n", - "mae4 = mean_absolute_error(y_test, pred4)\n", - "mape4 = mean_absolute_percentage_error(y_test, pred4)\n", - "accuracy4 = accuracy_score(y_test > pred4, y_test > pred4.round())\n", - "precision4 = precision_score(y_test > pred4, y_test > pred4.round())\n", - "confusion4 = confusion_matrix(y_test > pred4, y_test > pred4.round())\n", - "recall4 = recall_score(y_test > pred4, y_test > pred4.round())\n", - "f14 = f1_score(y_test > pred4, y_test > pred4.round())" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "qpnLeFyZhwB3", - "outputId": "4dcac062-ec60-4b2c-ab4b-dcda1b0f2341" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "RMSE: 2.733930065274145\n", - "MAE: 1.502457380471909\n", - "MAPE: 0.010026410639661481\n", - "Accuracy: 0.8840989399293286\n", - "Precision: 0.8948106591865358\n", - "Confusion Matrix:\n", - " [[613 75]\n", - " [ 89 638]]\n", - "Recall: 0.8775790921595599\n", - "F1 Score: 0.8861111111111112\n" - ] - } - ], - "source": [ - "# Print the evaluation metrics\n", - "print(\"RMSE:\", rmse4)\n", - "print(\"MAE:\", mae4)\n", - "print(\"MAPE:\", mape4)\n", - "print(\"Accuracy:\", accuracy4)\n", - "print(\"Precision:\", precision4)\n", - "print(\"Confusion Matrix:\\n\", confusion4)\n", - "print(\"Recall:\", recall4)\n", - "print(\"F1 Score:\", f14)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "d8nSGoyuh9dx" - }, - "source": [ - "## 5. Extreme Gradient Boosting (XGBoost)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": { - "id": "DyhhdlZAhx94" - }, - "outputs": [], - "source": [ - "import xgboost as xgb\n", - "# Create an XGBoost model\n", - "model5 = xgb.XGBRegressor()" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": { - "id": "Z_AD0lVOVHwB" - }, - "outputs": [], - "source": [ - "# Split the data into training and testing sets\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "# Scale the features using Min-Max scaling\n", - "scaler = MinMaxScaler()\n", - "X_train_scaled = scaler.fit_transform(X_train)\n", - "X_test_scaled = scaler.transform(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 248 - }, - "id": "RAIwxIp5iH9Z", - "outputId": "d2b4aa97-7e07-4015-c308-76a292b0929f" - }, - "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",
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-              "             num_parallel_tree=None, random_state=None, ...)
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" - ] - }, - "metadata": {}, - "execution_count": 60 - } - ], - "source": [ - "# Train the model\n", - "model5.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": { - "id": "XmJds5fYiKT3" - }, - "outputs": [], - "source": [ - "# Make predictions on the test set\n", - "pred5 = model5.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": { - "id": "lZ1A0-L8iNCM" - }, - "outputs": [], - "source": [ - "# Calculate evaluation metrics\n", - "rmse5 = np.sqrt(mean_squared_error(y_test, pred5))\n", - "mae5 = mean_absolute_error(y_test, pred5)\n", - "mape5 = mean_absolute_percentage_error(y_test, pred5)\n", - "accuracy5 = accuracy_score(y_test > pred5, y_test > pred5.round())\n", - "precision5 = precision_score(y_test > pred5, y_test > pred5.round())\n", - "confusion5 = confusion_matrix(y_test > pred5, y_test > pred5.round())\n", - "recall5 = recall_score(y_test > pred5, y_test > pred5.round())\n", - "f15 = f1_score(y_test > pred5, y_test > pred5.round())" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "7IkE-RAmiWNo", - "outputId": "cf4c1d84-412b-4a18-f70c-65ce637772ea" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "RMSE: 2.733930065274145\n", - "MAE: 1.502457380471909\n", - "MAPE: 0.010026410639661481\n", - "Accuracy: 0.8840989399293286\n", - "Precision: 0.8948106591865358\n", - "Confusion Matrix:\n", - " [[613 75]\n", - " [ 89 638]]\n", - "Recall: 0.8775790921595599\n", - "F1 Score: 0.8861111111111112\n" - ] - } - ], - "source": [ - "# Print the evaluation metrics\n", - "print(\"RMSE:\", rmse5)\n", - "print(\"MAE:\", mae5)\n", - "print(\"MAPE:\", mape5)\n", - "print(\"Accuracy:\", accuracy5)\n", - "print(\"Precision:\", precision5)\n", - "print(\"Confusion Matrix:\\n\", confusion5)\n", - "print(\"Recall:\", recall5)\n", - "print(\"F1 Score:\", f15)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "A_J776rtiovq" - }, - "source": [ - "## 6. AdaBoostRegressor" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": { - "id": "HNq66cXRiYPJ" - }, - "outputs": [], - "source": [ - "from sklearn.ensemble import AdaBoostRegressor\n", - "# Create an AdaBoost model\n", - "model6 = AdaBoostRegressor()" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": { - "id": "qPHH6rG0VW4V" - }, - "outputs": [], - "source": [ - "# Split the data into training and testing sets\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "# Scale the features using Min-Max scaling\n", - "scaler = MinMaxScaler()\n", - "X_train_scaled = scaler.fit_transform(X_train)\n", - "X_test_scaled = scaler.transform(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 74 - }, - "id": "P0oB5wjQivBr", - "outputId": "8726c583-6782-4504-b0ac-d2ef4ccbca4c" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "AdaBoostRegressor()" - ], - "text/html": [ - "
AdaBoostRegressor()
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" - ] - }, - "metadata": {}, - "execution_count": 66 - } - ], - "source": [ - "# Train the model\n", - "model6.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": { - "id": "Bf1m5ukOi2VM" - }, - "outputs": [], - "source": [ - "# Make predictions on the test set\n", - "pred6 = model6.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": { - "id": "oFWSqC4ai6gd" - }, - "outputs": [], - "source": [ - "# Calculate evaluation metrics\n", - "rmse6 = np.sqrt(mean_squared_error(y_test, pred6))\n", - "mae6 = mean_absolute_error(y_test, pred6)\n", - "mape6 = mean_absolute_percentage_error(y_test, pred6)\n", - "accuracy6 = accuracy_score(y_test > pred6, y_test > pred6.round())\n", - "precision6 = precision_score(y_test > pred6, y_test > pred6.round())\n", - "confusion6 = confusion_matrix(y_test > pred6, y_test > pred6.round())\n", - "recall6 = recall_score(y_test > pred6, y_test > pred6.round())\n", - "f16 = f1_score(y_test > pred6, y_test > pred6.round())" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "BsajWJGBjC80", - "outputId": "1af1194f-9a33-40af-8578-c99832509c1b" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "RMSE: 9.283285018137352\n", - "MAE: 7.574989783595977\n", - "MAPE: 0.16829256716397573\n", - "Accuracy: 0.9901060070671378\n", - "Precision: 0.9900990099009901\n", - "Confusion Matrix:\n", - " [[901 5]\n", - " [ 9 500]]\n", - "Recall: 0.9823182711198428\n", - "F1 Score: 0.9861932938856016\n" - ] - } - ], - "source": [ - "# Print the evaluation metrics\n", - "print(\"RMSE:\", rmse6)\n", - "print(\"MAE:\", mae6)\n", - "print(\"MAPE:\", mape6)\n", - "print(\"Accuracy:\", accuracy6)\n", - "print(\"Precision:\", precision6)\n", - "print(\"Confusion Matrix:\\n\", confusion6)\n", - "print(\"Recall:\", recall6)\n", - "print(\"F1 Score:\", f16)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Q9DzOt3CkWFX" - }, - "source": [ - "## 7. Decision Tree" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": { - "id": "23DZ2biSjF9a" - }, - "outputs": [], - "source": [ - "from sklearn.tree import DecisionTreeRegressor\n", - "# Create a Decision Tree model\n", - "model7 = DecisionTreeRegressor()" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": { - "id": "Ajo2RAVAVb7H" - }, - "outputs": [], - "source": [ - "# Split the data into training and testing sets\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "# Scale the features using Min-Max scaling\n", - "scaler = MinMaxScaler()\n", - "X_train_scaled = scaler.fit_transform(X_train)\n", - "X_test_scaled = scaler.transform(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 74 - }, - "id": "6mQEQf-ykc9F", - "outputId": "f1a62020-4125-4aea-e7e4-11acffdc5169" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "DecisionTreeRegressor()" - ], - "text/html": [ - "
DecisionTreeRegressor()
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" - ] - }, - "metadata": {}, - "execution_count": 72 - } - ], - "source": [ - "# Train the model\n", - "model7.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": { - "id": "BFJ9q_tvkgRC" - }, - "outputs": [], - "source": [ - "# Make predictions on the test set\n", - "pred7 = model7.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": { - "id": "9IxfYZbYkjv1" - }, - "outputs": [], - "source": [ - "# Calculate evaluation metrics\n", - "rmse7 = np.sqrt(mean_squared_error(y_test, pred7))\n", - "mae7 = mean_absolute_error(y_test, pred7)\n", - "mape7 = mean_absolute_percentage_error(y_test, pred7)\n", - "accuracy7 = accuracy_score(y_test > pred7, y_test > pred7.round())\n", - "precision7 = precision_score(y_test > pred7, y_test > pred7.round())\n", - "confusion7 = confusion_matrix(y_test > pred7, y_test > pred7.round())\n", - "recall7 = recall_score(y_test > pred7, y_test > pred7.round())\n", - "f17 = f1_score(y_test > pred7, y_test > pred7.round())\n" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "AnZXMYb8kooV", - "outputId": "273fa9ed-d6f2-4c4d-fb0e-a643f5ef5732" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "RMSE: 3.193539964582351\n", - "MAE: 1.6240937361593886\n", - "MAPE: 0.010136361140005275\n", - "Accuracy: 0.8579505300353357\n", - "Precision: 0.8700410396716827\n", - "Confusion Matrix:\n", - " [[578 95]\n", - " [106 636]]\n", - "Recall: 0.8571428571428571\n", - "F1 Score: 0.8635437881873728\n" - ] - } - ], - "source": [ - "# Print the evaluation metrics\n", - "print(\"RMSE:\", rmse7)\n", - "print(\"MAE:\", mae7)\n", - "print(\"MAPE:\", mape7)\n", - "print(\"Accuracy:\", accuracy7)\n", - "print(\"Precision:\", precision7)\n", - "print(\"Confusion Matrix:\\n\", confusion7)\n", - "print(\"Recall:\", recall7)\n", - "print(\"F1 Score:\", f17)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LH-B-Xd6k5UD" - }, - "source": [ - "## 8. KNeighborsRegressor(KNN)" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": { - "id": "JVDSed7yktFY" - }, - "outputs": [], - "source": [ - "from sklearn.neighbors import KNeighborsRegressor\n", - "# Create a KNN model\n", - "model8 = KNeighborsRegressor()" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": { - "id": "XJHb5SxrVgVp" - }, - "outputs": [], - "source": [ - "# Split the data into training and testing sets\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "# Scale the features using Min-Max scaling\n", - "scaler = MinMaxScaler()\n", - "X_train_scaled = scaler.fit_transform(X_train)\n", - "X_test_scaled = scaler.transform(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 74 - }, - "id": "9fn64o-ZlBka", - "outputId": "dc5e6af2-de37-46ee-cde7-e0a3baa31a1f" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "KNeighborsRegressor()" - ], - "text/html": [ - "
KNeighborsRegressor()
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" - ] - }, - "metadata": {}, - "execution_count": 78 - } - ], - "source": [ - "# Train the model\n", - "model8.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": { - "id": "hbfbbjcSlDn7" - }, - "outputs": [], - "source": [ - "# Make predictions on the test set\n", - "pred8 = model8.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": { - "id": "hnWyNv3blHdL" - }, - "outputs": [], - "source": [ - "# Calculate evaluation metrics\n", - "rmse8 = np.sqrt(mean_squared_error(y_test, pred8))\n", - "mae8 = mean_absolute_error(y_test, pred8)\n", - "mape8 = mean_absolute_percentage_error(y_test, pred8)\n", - "accuracy8 = accuracy_score(y_test > pred8, y_test > pred8.round())\n", - "precision8 = precision_score(y_test > pred8, y_test > pred8.round())\n", - "confusion8 = confusion_matrix(y_test > pred8, y_test > pred8.round())\n", - "recall8 = recall_score(y_test > pred8, y_test > pred8.round())\n", - "f18 = f1_score(y_test > pred8, y_test > pred8.round())" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "IPoDRkcMlMAr", - "outputId": "9892f42f-e65f-46c0-eeed-77ce32f6a7eb" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "RMSE: 148.73183825029315\n", - "MAE: 109.35229571264969\n", - "MAPE: 1.75024316976612\n", - "Accuracy: 0.9908127208480565\n", - "Precision: 0.9887820512820513\n", - "Confusion Matrix:\n", - " [[785 7]\n", - " [ 6 617]]\n", - "Recall: 0.9903691813804173\n", - "F1 Score: 0.9895749799518845\n" - ] - } - ], - "source": [ - "# Print the evaluation metrics\n", - "print(\"RMSE:\", rmse8)\n", - "print(\"MAE:\", mae8)\n", - "print(\"MAPE:\", mape8)\n", - "print(\"Accuracy:\", accuracy8)\n", - "print(\"Precision:\", precision8)\n", - "print(\"Confusion Matrix:\\n\", confusion8)\n", - "print(\"Recall:\", recall8)\n", - "print(\"F1 Score:\", f18)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "X5XtlzMXljps" - }, - "source": [ - "## 9. Artificial Neural Networks (ANN)" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": { - "id": "bJk1-9VhlRL6" - }, - "outputs": [], - "source": [ - "from sklearn.preprocessing import MinMaxScaler\n", - "from tensorflow.keras.models import Sequential\n", - "from tensorflow.keras.layers import Dense" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": { - "id": "sZVPMR9Wlo7-" - }, - "outputs": [], - "source": [ - "# Split the data into training and testing sets\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "# Scale the features using Min-Max scaling\n", - "scaler = MinMaxScaler()\n", - "X_train_scaled = scaler.fit_transform(X_train)\n", - "X_test_scaled = scaler.transform(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 84, - "metadata": { - "id": "vd1fDjQiltP4" - }, - "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": 85, - "metadata": { - "id": "ZIf94WLMlv04" - }, - "outputs": [], - "source": [ - "# Compile the model\n", - "model9.compile(loss='mean_squared_error', optimizer='adam')" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "FX5DTKqslxWf", - "outputId": "9253b26c-1a79-4390-975e-d14c28a5e2a8" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 86 - } - ], - "source": [ - "# Train the model\n", - "model9.fit(X_train_scaled, y_train, epochs=100, batch_size=32, verbose=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 87, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "OVW2qpNsmGVq", - "outputId": "34343782-f560-4dee-c307-ff0d0c52ab5a" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "45/45 [==============================] - 0s 1ms/step\n" - ] - } - ], - "source": [ - "# Make predictions on the test set\n", - "pred9 = model9.predict(X_test_scaled).flatten()" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": { - "id": "CqRmjMj2maJY" - }, - "outputs": [], - "source": [ - "# Calculate evaluation metrics\n", - "rmse9 = np.sqrt(mean_squared_error(y_test, pred9))\n", - "mae9 = mean_absolute_error(y_test, pred9)\n", - "mape9 = mean_absolute_percentage_error(y_test, pred9)\n", - "accuracy9 = accuracy_score(y_test > pred9, y_test > pred9.round())\n", - "precision9 = precision_score(y_test > pred9, y_test > pred9.round())\n", - "confusion9 = confusion_matrix(y_test > pred9, y_test > pred9.round())\n", - "recall9 = recall_score(y_test > pred9, y_test > pred9.round())\n", - "f19 = f1_score(y_test > pred9, y_test > pred9.round())\n" - ] - }, - { - "cell_type": "code", - "execution_count": 89, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "5zuwkC1emmh3", - "outputId": "5d6a0e05-3112-4d27-f5fb-ed665867b22d" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "RMSE: 2.7570259701356035\n", - "MAE: 1.7412277270507284\n", - "MAPE: 0.012205298865408084\n", - "Accuracy: 0.8904593639575972\n", - "Precision: 0.8242753623188406\n", - "Confusion Matrix:\n", - " [[805 97]\n", - " [ 58 455]]\n", - "Recall: 0.8869395711500975\n", - "F1 Score: 0.8544600938967135\n" - ] - } - ], - "source": [ - "# Print the evaluation metrics\n", - "print(\"RMSE:\", rmse9)\n", - "print(\"MAE:\", mae9)\n", - "print(\"MAPE:\", mape9)\n", - "print(\"Accuracy:\", accuracy9)\n", - "print(\"Precision:\", precision9)\n", - "print(\"Confusion Matrix:\\n\", confusion9)\n", - "print(\"Recall:\", recall9)\n", - "print(\"F1 Score:\", f19)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vjSMQNcOnFPJ" - }, - "source": [ - "## 10. LSTM(Long Short term Memory)" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": { - "id": "nCoyUanhnDKw" - }, - "outputs": [], - "source": [ - "from sklearn.preprocessing import MinMaxScaler\n", - "from tensorflow.keras.models import Sequential\n", - "from tensorflow.keras.layers import LSTM, Dense" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": { - "id": "ThcXESVEVv0U" - }, - "outputs": [], - "source": [ - "# Split the data into training and testing sets\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", - "# Scale the features using Min-Max scaling\n", - "scaler = MinMaxScaler()\n", - "X_train_scaled = scaler.fit_transform(X_train)\n", - "X_test_scaled = scaler.transform(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": { - "id": "uACvajfImrbB" - }, - "outputs": [], - "source": [ - "# Reshape the input data for LSTM\n", - "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)])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": { - "id": "r066pVYpnXH5" - }, - "outputs": [], - "source": [ - "# Create an LSTM model\n", - "model = Sequential()\n", - "model.add(LSTM(64, activation='relu', input_shape=(n_steps, n_features)))\n", - "model.add(Dense(1))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 94, - "metadata": { - "id": "YpSfHu6gov35" - }, - "outputs": [], - "source": [ - "# Compile the model\n", - "model.compile(loss='mean_squared_error', optimizer='adam')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 95, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0vHjcluaoxzP", - "outputId": "1eaafd31-9f91-4655-f437-e9199c0f7933" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 95 - } - ], - "source": [ - "# Train the model\n", - "model.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "gEE06_TjozYv", - "outputId": "30306af7-2ec8-4733-db96-d3416a7fc6d4" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "44/44 [==============================] - 0s 4ms/step\n" - ] - } - ], - "source": [ - "# Make predictions on the test set\n", - "y_pred = model.predict(X_test_reshaped).flatten()" - ] - }, - { - "cell_type": "code", - "execution_count": 113, - "metadata": { - "id": "7k6C8DrxpB_Q" - }, - "outputs": [], - "source": [ - "# Calculate evaluation metrics\n", - "rmse10 = np.sqrt(mean_squared_error(y_test[n_steps-1:], y_pred))\n", - "mae10 = mean_absolute_error(y_test[n_steps-1:], y_pred)\n", - "mape10 = mean_absolute_percentage_error(y_test[n_steps-1:], y_pred)\n", - "accuracy10 = accuracy_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n", - "precision10 = precision_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n", - "recall10 = recall_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n", - "f110 = f1_score(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n", - "confusion10 = confusion_matrix(y_test[n_steps-1:] > y_pred, y_test[n_steps-1:] > y_pred.round())\n" - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "i_6-UUDhpi0c", - "outputId": "3dcc5761-03b6-4b52-dfe6-08dece835c8d" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "RMSE: 10.083053125286519\n", - "MAE: 7.973378150691296\n", - "MAPE: 0.12730792351246625\n", - "Accuracy: 0.9886201991465149\n", - "Precision: 0.9904912836767037\n", - "Recall: 0.984251968503937\n", - "F1 Score: 0.9873617693522907\n", - "Confusion Matrix:\n", - " [[765 6]\n", - " [ 10 625]]\n" - ] - } - ], - "source": [ - "# Print evaluation metrics\n", - "print(\"RMSE:\", rmse10)\n", - "print(\"MAE:\", mae10)\n", - "print(\"MAPE:\", mape10)\n", - "print(\"Accuracy:\", accuracy10)\n", - "print(\"Precision:\", precision10)\n", - "print(\"Recall:\", recall10)\n", - "print(\"F1 Score:\", f110)\n", - "print(\"Confusion Matrix:\\n\", confusion10)" - ] - }, - { - "cell_type": "code", - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# Assuming you have a list of accuracies from accuracy1 to accuracy10\n", - "accuracies = [accuracy1*100, accuracy2*100, accuracy3*100, accuracy4*100, accuracy5*100, accuracy6*100, accuracy7*100, accuracy8*100, accuracy9*100, accuracy10*100]\n", - "\n", - "# List of corresponding labels for each accuracy\n", - "labels = ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10']\n", - "\n", - "# Plotting the bar graph\n", - "plt.bar(labels, accuracies, color='blue')\n", - "plt.xlabel('Accuracy Variables')\n", - "plt.ylabel('Accuracy Values')\n", - "plt.title('Bar Graph of Accuracies')\n", - "plt.show()\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 472 - }, - "id": "qpWPtph9CGip", - "outputId": "c099cb8d-96af-4223-f499-743040aecdf1" - }, - "execution_count": 117, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# Assuming you have a list of RMSE values from rmse1 to rmse10\n", - "rmse_values = [rmse1, rmse2, rmse3, rmse4, rmse5, rmse6, rmse7, rmse8, rmse9, rmse10]\n", - "\n", - "# List of corresponding labels for each RMSE value\n", - "labels = ['RMSE1', 'RMSE2', 'RMSE3', 'RMSE4', 'RMSE5', 'RMSE6', 'RMSE7', 'RMSE8', 'RMSE9', 'RMSE10']\n", - "\n", - "# Plotting the bar graph\n", - "plt.bar(labels, rmse_values, color='green')\n", - "plt.xlabel('RMSE Variables')\n", - "plt.ylabel('RMSE Values')\n", - "plt.title('Bar Graph of RMSE')\n", - "plt.show()\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 472 - }, - "id": "RFaaCNH6Cfoa", - "outputId": "67a8f358-e3ce-4ad2-9c78-ebc75902beb4" - }, - "execution_count": 118, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# Assuming you have a list of MAE values from mae1 to mae10\n", - "mae_values = [mae1, mae2, mae3, mae4, mae5, mae6, mae7, mae8, mae9, mae10]\n", - "\n", - "# List of corresponding labels for each MAE value\n", - "labels = ['MAE1', 'MAE2', 'MAE3', 'MAE4', 'MAE5', 'MAE6', 'MAE7', 'MAE8', 'MAE9', 'MAE10']\n", - "\n", - "# Plotting the bar graph\n", - "plt.bar(labels, mae_values, color='orange')\n", - "plt.xlabel('MAE Variables')\n", - "plt.ylabel('MAE Values')\n", - "plt.title('Bar Graph of MAE')\n", - "plt.show()\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 472 - }, - "id": "nrZu-K-KDCJ2", - "outputId": "69165581-da05-4554-a464-a606eb87a734" - }, - "execution_count": 119, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# Assuming you have a list of MAPE values from mape1 to mape10\n", - "mape_values = [mape1, mape2, mape3, mape4, mape5, mape6, mape7, mape8, mape9, mape10]\n", - "\n", - "# List of corresponding labels for each MAPE value\n", - "labels = ['MAPE1', 'MAPE2', 'MAPE3', 'MAPE4', 'MAPE5', 'MAPE6', 'MAPE7', 'MAPE8', 'MAPE9', 'MAPE10']\n", - "\n", - "# Plotting the bar graph\n", - "plt.bar(labels, mape_values, color='purple')\n", - "plt.xlabel('MAPE Variables')\n", - "plt.ylabel('MAPE Values')\n", - "plt.title('Bar Graph of MAPE')\n", - "plt.show()\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 472 - }, - "id": "_c4Pe76fDNM-", - "outputId": "0e3d2f74-9042-4e2d-92c6-5ce61e967bd4" - }, - "execution_count": 120, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# Assuming you have a list of precision values from precision1 to precision10\n", - "precision_values = [precision1, precision2, precision3, precision4, precision5, precision6, precision7, precision8, precision9, precision10]\n", - "\n", - "# List of corresponding labels for each precision value\n", - "labels = ['Precision1', 'Precision2', 'Precision3', 'Precision4', 'Precision5', 'Precision6', 'Precision7', 'Precision8', 'Precision9', 'Precision10']\n", - "\n", - "# Plotting the bar graph\n", - "plt.bar(labels, precision_values, color='red')\n", - "plt.xlabel('Precision Variables')\n", - "plt.ylabel('Precision Values')\n", - "plt.title('Bar Graph of Precision')\n", - "plt.show()\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 472 - }, - "id": "ZDPV0M5rDTi6", - "outputId": "9db63164-3f42-47be-d302-d80d381d9b91" - }, - "execution_count": 121, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# Assuming you have a list of recall values from recall1 to recall10\n", - "recall_values = [recall1, recall2, recall3, recall4, recall5, recall6, recall7, recall8, recall9, recall10]\n", - "\n", - "# List of corresponding labels for each recall value\n", - "labels = ['Recall1', 'Recall2', 'Recall3', 'Recall4', 'Recall5', 'Recall6', 'Recall7', 'Recall8', 'Recall9', 'Recall10']\n", - "\n", - "# Plotting the bar graph\n", - "plt.bar(labels, recall_values, color='cyan')\n", - "plt.xlabel('Recall Variables')\n", - "plt.ylabel('Recall Values')\n", - "plt.title('Bar Graph of Recall')\n", - "plt.show()\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 472 - }, - "id": "39LBleNeDeuw", - "outputId": "3c6c40bc-f1da-44fb-da14-25ec6d6cf278" - }, - "execution_count": 122, - "outputs": [ - { - "output_type": "display_data", - "data": { 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RpREfAFCA0j6kzWFulGq52bZtm1q1aqVWrVpJkqKiotSqVSuNHz9eknT8+HFb0ZGkwMBArV69Whs2bFBQUJAmT56sjz/+mMvAAQCAjcUwDKO0Q5Sk9PR0eXt769y5c/Ly8irtOE7DGf9lc0v9wUSx4s93yWJ7l6xbZXs78v1dps65AQAA+CuUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCqUGwAAYCrlSjsAAADOwFLaAQpglHaAMoo9NwAAwFQoNwAAwFQ4LAWUgrK6+7us5gZwa2HPDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXucwMATor7CgE3hj03AADAVCg3AADAVCg3AADAVDjnBmUa5yQAAP4Xe24AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICplHq5mTFjhurUqSMPDw+1a9dOW7duve7006ZNU6NGjeTp6amAgACNGjVKFy9eLKG0AADA2ZVquVm6dKmioqIUHR2t+Ph4BQUFKTw8XKmpqQVOv3jxYo0ZM0bR0dHat2+f5syZo6VLl+rll18u4eTXZnHCFwAAt5JSLTdTpkzRk08+qSFDhqhp06aaPXu2ypcvr7lz5xY4/ZYtW9ShQwcNGDBAderUUbdu3dS/f/+/3NsDAABuHaVWbnJycrR9+3aFhYX9N4yLi8LCwhQXF1fgPHfeeae2b99uKzO//fab1qxZox49epRIZgAA4PzKldaK09LSlJubK19fX7txX19f7d+/v8B5BgwYoLS0NN11110yDEOXLl3S8OHDr3tYKjs7W9nZ2baf09PTi+YDAAAAp1TqJxQ7YtOmTXrjjTc0c+ZMxcfHa8WKFVq9erVee+21a84TExMjb29v2ysgIKAEEwMAgJJmMQzDKI0V5+TkqHz58lq2bJn69OljG4+IiNDZs2f173//O988oaGhat++vd5++23b2CeffKJhw4YpIyNDLi75u1pBe24CAgJ07tw5eXl5Fe2HknOewFuYXzC5iw65Sxa5Sxa5S5aZczsqPT1d3t7ehfr+LrU9N+7u7goODlZsbKxtLC8vT7GxsQoJCSlwngsXLuQrMK6urpKka3U0q9UqLy8vuxcAADCvUjvnRpKioqIUERGh1q1bq23btpo2bZoyMzM1ZMgQSdKgQYPk7++vmJgYSVLPnj01ZcoUtWrVSu3atdOhQ4f0yiuvqGfPnraSAwAAbm2lWm769eunkydPavz48Tpx4oRatmyptWvX2k4yTkpKsttTM27cOFksFo0bN07Hjh1TjRo11LNnT/3zn/8srY8AAACcTKmdc1NaHDlmdyPK6rFPchcdcpcscpcscpcsM+d2VJk45wYAAKA4UG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpUG4AAICpOFxusrKydOHCBdvPR48e1bRp07R+/foiDQYAAHAjHC43vXv31sKFCyVJZ8+eVbt27TR58mT17t1bs2bNKvKAAAAAjnC43MTHxys0NFSStGzZMvn6+uro0aNauHCh3n333SIPCAAA4AiHy82FCxdUqVIlSdL69ev14IMPysXFRe3bt9fRo0eLPCAAAIAjHC439evX18qVK5WcnKx169apW7dukqTU1FR5eXkVeUAAAABHOFxuxo8fr9GjR6tOnTpq27atQkJCJF3ei9OqVasiDwgAAOAIi2EYhqMznThxQsePH1dQUJBcXC73o61bt8rLy0uNGzcu8pBFKT09Xd7e3jp37lyx7GmyFPkSb15hfsHkLjrkLlnkLlnkLllmzu0oR76/b+g+N35+fqpUqZI2bNigrKwsSVKbNm2cvtgAAADzc7jcnDp1Sl26dFHDhg3Vo0cPHT9+XJIUGRmpF154ocgDAgAAOMLhcjNq1Ci5ubkpKSlJ5cuXt43369dPa9euLdJwAAAAjirn6Azr16/XunXrVKtWLbvxBg0acCk4AAAodQ7vucnMzLTbY3PF6dOnZbVaiyQUAADAjXK43ISGhtoevyBJFotFeXl5euutt9S5c+ciDQcAAOAoh8vNW2+9pQ8//FD33nuvcnJy9OKLL6p58+b6/vvvNWnSJIcDzJgxQ3Xq1JGHh4fatWunrVu3Xnf6s2fPasSIEapZs6asVqsaNmyoNWvWOLxeAABgTg6Xm+bNm+vXX3/VXXfdpd69eyszM1MPPvigduzYoXr16jm0rKVLlyoqKkrR0dGKj49XUFCQwsPDlZqaWuD0OTk56tq1qxITE7Vs2TIdOHBAH330kfz9/R39GAAAwKRu6CZ+RaVdu3Zq06aN3n//fUlSXl6eAgIC9Mwzz2jMmDH5pp89e7befvtt7d+/X25ubje0Tm7iVzByFx1ylyxylyxylywz53aUI9/fDl8t9f3331/3/Y4dOxZqOTk5Odq+fbvGjh1rG3NxcVFYWJji4uIKnGfVqlUKCQnRiBEj9O9//1s1atTQgAED9NJLL8nV1bXAebKzs5WdnW37OT09vVD5AABA2eRwubn77rvzjVks/+2Nubm5hVpOWlqacnNz5evrazfu6+ur/fv3FzjPb7/9po0bN+qxxx7TmjVrdOjQIT399NP6888/FR0dXeA8MTExmjhxYqEyAQCAss/hc27OnDlj90pNTdXatWvVpk0brV+/vjgy2uTl5cnHx0cffvihgoOD1a9fP/3jH//Q7NmzrznP2LFjde7cOdsrOTm5WDMCAIDS5fCeG29v73xjXbt2lbu7u6KiorR9+/ZCLad69epydXVVSkqK3XhKSor8/PwKnKdmzZpyc3OzOwTVpEkTnThxQjk5OXJ3d883j9Vq5f47AADcQm7owZkF8fX11YEDBwo9vbu7u4KDgxUbG2sby8vLU2xsrEJCQgqcp0OHDjp06JDy8vJsY7/++qtq1qxZYLEBAAC3Hof33OzatcvuZ8MwdPz4cb355ptq2bKlQ8uKiopSRESEWrdurbZt22ratGnKzMzUkCFDJEmDBg2Sv7+/YmJiJElPPfWU3n//fT333HN65plndPDgQb3xxht69tlnHf0YAADApBwuNy1btpTFYtH/XkHevn17zZ0716Fl9evXTydPntT48eN14sQJtWzZUmvXrrWdZJyUlCQXl//uXAoICNC6des0atQotWjRQv7+/nruuef00ksvOfoxAACASTl8n5v/fTimi4uLatSoIQ8PjyINVly4z03ByF10yF2yyF2yyF2yzJzbUcV6n5vatWvfcDAAAIDiVqhy8+677xZ6gZz/AgAASlOhDksFBgYWbmEWi3777bebDlWcOCxVMHIXHXKXLHKXLHKXLDPndlSRH5Y6cuRIkQQDAAAobkV2nxsAAABn4PAJxZL0+++/a9WqVUpKSlJOTo7de1OmTCmSYAAAADfC4XITGxurXr16qW7dutq/f7+aN2+uxMREGYahO+64ozgyAgAAFJrDh6XGjh2r0aNHa/fu3fLw8NDy5cuVnJysTp06qW/fvsWREQAAoNAcLjf79u3ToEGDJEnlypVTVlaWKlasqFdffVWTJk0q8oAAAACOcLjcVKhQwXaeTc2aNXX48GHbe2lpaUWXDAAA4AY4fM5N+/bttXnzZjVp0kQ9evTQCy+8oN27d2vFihVq3759cWQEAAAotEKXm9OnT6tq1aqaMmWKMjIyJEkTJ05URkaGli5dqgYNGnClFAAAKHWFfnCmh4eH+vTpo8jISHXt2rW4cxUb7lBcMHIXHXKXLHKXLHKXLDPndpQj39+FPufmo48+0smTJ9W9e3fVqVNHEyZMUGJi4s1mBQAAKFKFLjePP/64YmNjdejQIUVERGjBggWqX7++unbtqqVLl+a7mR8AAEBpcPhqqcDAQE2cOFFHjhzR2rVr5ePjo6FDh6pmzZo8ERwAAJS6Qp9zcz3Lly/XsGHDdPbsWeXm5hZFrmLDOTcFI3fRIXfJInfJInfJMnNuRxX5U8ELcvToUc2bN08LFixQcnKyOnfurMjIyBtdHAAAQJFwqNxkZ2dr+fLlmjt3rjZt2iR/f38NHjxYQ4YMUZ06dYopIgAAQOEVutw8/fTTWrJkiS5cuKDevXtrzZo16tq1qywWZ9whBgAAblWFLjebN29WdHS0Bg4cqGrVqhVnJgAAgBtW6HKza9eu4swBAABQJBy+FBwAAMCZUW4AAICpUG4AAICpUG4AAICpFOqEYkdOJm7RosUNhwEAALhZhSo3LVu2lMVi0bWe1HDlPYvF4vSPXwAAAOZWqHJz5MiR4s4BAABQJApVbmrXrl3cOQAAAIpEocrNqlWrCr3AXr163XAYAACAm1WoctOnT59CLYxzbgAAQGkrVLnJy8sr7hwAAABFgvvcAAAAUyn0gzOvlpmZqe+++05JSUnKycmxe+/ZZ58tkmAAAAA3wuFys2PHDvXo0UMXLlxQZmamqlatqrS0NJUvX14+Pj6UGwAAUKocPiw1atQo9ezZU2fOnJGnp6d+/PFHHT16VMHBwXrnnXeKIyMAAEChOVxuEhIS9MILL8jFxUWurq7Kzs5WQECA3nrrLb388svFkREAAKDQHC43bm5ucnG5PJuPj4+SkpIkSd7e3kpOTi7adAAAAA5y+JybVq1a6eeff1aDBg3UqVMnjR8/XmlpaVq0aJGaN29eHBkBAAAKzeE9N2+88YZq1qwpSfrnP/+pKlWq6KmnntLJkyf1wQcfFHlAAAAAR1iMaz3q26TS09Pl7e2tc+fOycvLq8iXbynyJd68wvyCyV10yF2yyF2yyF2yzJzbUY58fzu85+bIkSM6ePBgvvGDBw8qMTHR0cUBAAAUKYfLzeDBg7Vly5Z84z/99JMGDx5cFJkAAABumMPlZseOHerQoUO+8fbt2yshIaEoMgEAANwwh8uNxWLR+fPn842fO3eOJ4IDAIBS53C56dixo2JiYuyKTG5urmJiYnTXXXcVaTgAAABHOXyfm0mTJqljx45q1KiRQkNDJUn/+c9/lJ6ero0bNxZ5QAAAAEc4vOemadOm2rVrlx555BGlpqbq/PnzGjRokPbv389N/AAAQKnjPjdFrKzeb4DcRYfcJYvcJYvcJcvMuR1VrPe5kS4fhho4cKDuvPNOHTt2TJK0aNEibd68+UYWBwAAUGQcLjfLly9XeHi4PD09FR8fr+zsbEmXr5Z64403ijwgAACAIxwuN6+//rpmz56tjz76SG5ubrbxDh06KD4+vkjDAQAAOMrhcnPgwAF17Ngx37i3t7fOnj1bFJkAAABumMPlxs/PT4cOHco3vnnzZtWtW7dIQgEAANwoh8vNk08+qeeee04//fSTLBaL/vjjD/3rX//S6NGj9dRTTxVHRgAAgEJz+CZ+Y8aMUV5enrp06aILFy6oY8eOslqtGj16tJ555pniyAgAAFBoN3yfm5ycHB06dEgZGRlq2rSpKlasqKysLHl6ehZ1xiLFfW4KRu6iQ+6SRe6SRe6SZebcjir2+9xIkru7u5o2baq2bdvKzc1NU6ZMUWBg4I0uDgAAoEgUutxkZ2dr7Nixat26te68806tXLlSkjRv3jwFBgZq6tSpGjVq1A2FmDFjhurUqSMPDw+1a9dOW7duLdR8S5YskcViUZ8+fW5ovQAAwHwKXW7Gjx+vWbNmqU6dOkpMTFTfvn01bNgwTZ06VVOmTFFiYqJeeuklhwMsXbpUUVFRio6OVnx8vIKCghQeHq7U1NTrzpeYmKjRo0fbHt4JAAAgOVBuPv/8cy1cuFDLli3T+vXrlZubq0uXLmnnzp169NFH5erqekMBpkyZoieffFJDhgxR06ZNNXv2bJUvX15z58695jy5ubl67LHHNHHiRC4/BwAAdgpdbn7//XcFBwdLkpo3by6r1apRo0bJYrnxU5lycnK0fft2hYWF/TeQi4vCwsIUFxd3zfleffVV+fj4KDIy8obXDQAAzKnQl4Ln5ubK3d39vzOWK6eKFSve1MrT0tKUm5srX19fu3FfX1/t37+/wHk2b96sOXPmKCEhoVDryM7Otj3/Srp8tjUAADCvQpcbwzA0ePBgWa1WSdLFixc1fPhwVahQwW66FStWFG3Cq5w/f16PP/64PvroI1WvXr1Q88TExGjixInFlgkAADiXQpebiIgIu58HDhx40yuvXr26XF1dlZKSYjeekpIiPz+/fNMfPnxYiYmJ6tmzp20sLy9P0uU9SQcOHFC9evXs5hk7dqyioqJsP6enpysgIOCmswMAAOdU6HIzb968Il+5u7u7goODFRsba7ucOy8vT7GxsRo5cmS+6Rs3bqzdu3fbjY0bN07nz5/X9OnTCywtVqvVtrcJAACYn8OPXyhqUVFRioiIUOvWrdW2bVtNmzZNmZmZGjJkiCRp0KBB8vf3V0xMjDw8PNS8eXO7+StXrixJ+cYBAMCtqdTLTb9+/XTy5EmNHz9eJ06cUMuWLbV27VrbScZJSUlycbnhGykDAIBbzA0/W6qs4tlSBSN30SF3ySJ3ySJ3yTJzbkeVyLOlAAAAnBHlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmIpTlJsZM2aoTp068vDwULt27bR169ZrTvvRRx8pNDRUVapUUZUqVRQWFnbd6QEAwK2l1MvN0qVLFRUVpejoaMXHxysoKEjh4eFKTU0tcPpNmzapf//++vbbbxUXF6eAgAB169ZNx44dK+HkAADAGVkMwzBKM0C7du3Upk0bvf/++5KkvLw8BQQE6JlnntGYMWP+cv7c3FxVqVJF77//vgYNGvSX06enp8vb21vnzp2Tl5fXTef/X5YiX+LNK8wvmNxFh9wli9wli9wly8y5HeXI93ep7rnJycnR9u3bFRYWZhtzcXFRWFiY4uLiCrWMCxcu6M8//1TVqlULfD87O1vp6el2LwAAYF6lWm7S0tKUm5srX19fu3FfX1+dOHGiUMt46aWXdNttt9kVpKvFxMTI29vb9goICLjp3AAAwHmV+jk3N+PNN9/UkiVL9MUXX8jDw6PAacaOHatz587ZXsnJySWcEgAAlKRypbny6tWry9XVVSkpKXbjKSkp8vPzu+6877zzjt5880198803atGixTWns1qtslqtRZIXAAA4v1Ldc+Pu7q7g4GDFxsbaxvLy8hQbG6uQkJBrzvfWW2/ptdde09q1a9W6deuSiAoAAMqIUt1zI0lRUVGKiIhQ69at1bZtW02bNk2ZmZkaMmSIJGnQoEHy9/dXTEyMJGnSpEkaP368Fi9erDp16tjOzalYsaIqVqxYap8DAAA4h1IvN/369dPJkyc1fvx4nThxQi1bttTatWttJxknJSXJxeW/O5hmzZqlnJwcPfzww3bLiY6O1oQJE0oyOgAAcEKlfp+bksZ9bgpG7qJD7pJF7pJF7pJl5tyOKjP3uQEAAChqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqlBsAAGAqTlFuZsyYoTp16sjDw0Pt2rXT1q1brzv9559/rsaNG8vDw0O333671qxZU0JJAQCAsyv1crN06VJFRUUpOjpa8fHxCgoKUnh4uFJTUwucfsuWLerfv78iIyO1Y8cO9enTR3369NGePXtKODkAAHBGFsMwjNIM0K5dO7Vp00bvv/++JCkvL08BAQF65plnNGbMmHzT9+vXT5mZmfrqq69sY+3bt1fLli01e/bsv1xfenq6vL29de7cOXl5eRXdB/k/liJf4s0rzC+Y3EWH3CWL3CWL3CXLzLkd5cj3d6nuucnJydH27dsVFhZmG3NxcVFYWJji4uIKnCcuLs5uekkKDw+/5vQAAODWUq40V56Wlqbc3Fz5+vrajfv6+mr//v0FznPixIkCpz9x4kSB02dnZys7O9v287lz5yRdboC3irL6Scldsshdsshdsshdsooj95Xv7cIccCrVclMSYmJiNHHixHzjAQEBpZCmdHiXdoAbRO6SRe6SRe6SRe6SVZy5z58/L2/v66+hVMtN9erV5erqqpSUFLvxlJQU+fn5FTiPn5+fQ9OPHTtWUVFRtp/z8vJ0+vRpVatWTRaLMx6pvNxOAwIClJycXCznBRUXcpcscpcscpcscpesspDbMAydP39et912219OW6rlxt3dXcHBwYqNjVWfPn0kXS4fsbGxGjlyZIHzhISEKDY2Vs8//7xtbMOGDQoJCSlweqvVKqvVajdWuXLloohf7Ly8vJz2D9n1kLtkkbtkkbtkkbtkOXvuv9pjc0WpH5aKiopSRESEWrdurbZt22ratGnKzMzUkCFDJEmDBg2Sv7+/YmJiJEnPPfecOnXqpMmTJ+u+++7TkiVLtG3bNn344Yel+TEAAICTKPVy069fP508eVLjx4/XiRMn1LJlS61du9Z20nBSUpJcXP57Udedd96pxYsXa9y4cXr55ZfVoEEDrVy5Us2bNy+tjwAAAJxIqZcbSRo5cuQ1D0Nt2rQp31jfvn3Vt2/fYk5VeqxWq6Kjo/MdTnN25C5Z5C5Z5C5Z5C5ZZTX3tZT6TfwAAACKUqk/fgEAAKAoUW4AAICpUG4AAICpUG7KMIvFopUrV0qSEhMTZbFYlJCQUKqZCoPcJa+sZi+Lucti5ivKYvaymPmKspC9LGQsCOXmBg0ePFgWi0UWi0Vubm4KDAzUiy++qIsXL5Z2NJtnn31WwcHBslqtatmypSTnz71z5071799fAQEB8vT0VJMmTTR9+nSnz33q1Cl1795dt912m6xWqwICAjRy5Eilp6c7ffarnTp1SrVq1ZLFYtGAAQOcPveVfAW9nDWzJM2fP18tWrSQh4eHfHx8NGLECKf/czJ//vwyub1//vlndenSRZUrV1aVKlUUHh6unTt3Ov32li4/FPrqbVy5cmWNHj3aqTIW9D3zv3bt2qXQ0FB5eHgoICBAb731VrHncopLwcuq7t27a968efrzzz+1fft2RUREyGKxaNKkSaUdzWbo0KH66aeftGvXLtuYM+fevn27fHx89MknnyggIEBbtmzRsGHDdPvttzt1bhcXF/Xu3Vuvv/66atSooUOHDmnEiBE6ffq03N3dnTr71SIjI9WiRQsdO3ZMknP/Wbli3rx56t69u6TLN/k8e/asFixY4LSZp0yZosmTJ+vtt99Wu3btlJmZqcTERK1YscKpt3e/fv1s2/mK4OBgZWVl6ZdffnHKzBkZGerevbt69eqlmTNn6tKlS4qOjlZ4eLi6devm1Nt7586d+uabb1S/fn0tXLhQv//+u0aNGqX33ntPrq6uTpHxioK+Z65IT09Xt27dFBYWptmzZ2v37t0aOnSoKleurGHDhhVbJvbc3ASr1So/Pz8FBASoT58+CgsL04YNGyRdfoxETEyMAgMD5enpqaCgIC1btsxu/r179+r++++Xl5eXKlWqpNDQUB0+fFjS5X9tdO3aVdWrV5e3t7c6deqk+Ph4h/K9++67GjFihOrWrVtmcg8dOlTTp09Xp06dVLduXQ0cOFBDhgxRUlKSU+euUqWKnnrqKbVu3Vq1a9dWly5d9PTTT+s///mP02/zK2bNmqWzZ89q9OjRtrGykLty5cry8/OTn5+fPD095enp6bSZz5w5o3HjxmnhwoUaMGCA6tWrpxYtWqhXr15Ov72vbNcrL1dXVx0/flyBgYFOm3n//v06ffq0Xn31VTVq1EjNmjVTdHS0UlJSlJGR4dTbe+nSpapSpYqaNWumkJAQ9e3bV7NmzdKlS5e0bt06p8goXft75op//etfysnJ0dy5c9WsWTM9+uijevbZZzVlyhSH1uMoyk0R2bNnj7Zs2SJ3d3dJl59GvnDhQs2ePVt79+7VqFGjNHDgQH333XeSpGPHjqljx46yWq3auHGjtm/frqFDh+rSpUuSLj/1NCIiQps3b9aPP/6oBg0aqEePHjp//vwtl/vcuXP5bizl7Ln/+OMPrVixQp06dcr3njNm/+WXX/Tqq69q4cKFdncEd/bckjRixAhVr15dbdu21cGDB50684YNG5SXl6djx46pSZMmqlWrlh555BElJyeXme19xcKFC1WuXDm7hxg6W+ZGjRqpWrVqmjNnjnJycpSVlaU5c+aoSZMmqlixolNv7+zsbLm6utqNnThxQnl5ecrJyXGKjIURFxenjh072rardPlw24EDB3TmzJkiW08+Bm5IRESE4erqalSoUMGwWq2GJMPFxcVYtmyZcfHiRaN8+fLGli1b7OaJjIw0+vfvbxiGYYwdO9YIDAw0cnJyCrW+3Nxco1KlSsaXX35pG5NkfPHFF4ZhGMaRI0cMScaOHTvyzRsdHW0EBQWVudyGYRg//PCDUa5cOaNr165lIvejjz5qeHp6GpKMnj17GllZWU6/zS9evGi0aNHCWLRokWEYhvHtt98akoz+/fs7dW7DMIxXX33V2Lx5sxEfH2+8+eabhouLi+Hi4uK0mWNiYgw3NzejUaNGxtq1a424uDijS5cuRqNGjYzHH3/c6bf31Zo0aWI0atTI6TPv3r3bqFevnu3PRqNGjYzExESn//9y3bp1tkxXZ5RkPP/8806R8WpXf89crWvXrsawYcPsxvbu3WtIMn755ZdC5boRnHNzEzp37qxZs2YpMzNTU6dOVbly5fTQQw9p7969unDhgrp27Wo3fU5Ojlq1aiVJSkhIUGhoqNzc3ApcdkpKisaNG6dNmzYpNTVVubm5unDhgpKSkm6Z3Hv27FHv3r0VHR2tQ4cOlYncU6dOVXR0tH799VeNHTtWUVFRkpx7m48dO1ZNmjTRwIED873nzLkl6ZVXXrH9d6tWrbR48WIdOHBACQkJTpk5Ly9Pf/75p959911169ZNkvTpp5/aDo04+/a+Ii4uTvv27dP999+vgIAAp82clZWlyMhIdejQQZ9++qlyc3P1zjvv6L777lOrVq2cent369ZNrVu31o4dO5SVlSV3d3fdcccdio+PV/v27XXo0KFSz+jMKDc3oUKFCqpfv74kae7cuQoKCtKcOXNsD/FcvXq1/P397ea5cnjF09PzusuOiIjQqVOnNH36dNWuXVtWq1UhISG23ZFmz/3LL7+oS5cuGjZsmMaNG6fBgweXidxXzkdo3LixqlatqtDQUD3yyCNOnX3jxo3avXu37Vi98X9PZFmyZIkaNmzotLkLUqNGDe3atUsBAQGyWq1Ol7lmzZqSpKZNm9plrl69ujIzM+Xj41MmtvfHH3+sli1bqlq1ajp79qzTZl68eLESExMVFxdnO9y6ePFiValSRdWqVVOVKlWcNrskNWvWTP7+/po5c6aqVKmi3377Tc2bN9fu3btVp04dp8j4V/z8/JSSkmI3duVnPz+/IlvP/6LcFBEXFxe9/PLLioqK0q+//iqr1aqkpKQCz7mQpBYtWtiu6CioVf/www+aOXOmevToIUlKTk5WWlraLZF77969uueeexQREaF//vOfZSb3/8rLy5Mk5ebmOnX25cuXKysry/bzzz//rKFDh+ree++1fQZnzF2Q06dPy83NzfaXu7Nl7tChgyTpwIEDqlWrli1zWlqagoKC7KZ1tuxXZGRk6LPPPlNMTIy2bdvm1JkvXLggFxcXWSwWu4wWi8VW4p01+9WunNe0dOlSVa1aVXPmzNFLL73kVBmvJSQkRP/4xz/sMmzYsEGNGjVSlSpVinRdV+OE4iLUt29fubq66oMPPtDo0aM1atQoLViwQIcPH1Z8fLzee+89LViwQJJs90B59NFHtW3bNh08eFCLFi3SgQMHJEkNGjTQokWLtG/fPv3000967LHH/rKF/69Dhw4pISFBJ06cUFZWlhISEnT69Gm7Lyxny71nzx517txZ3bp1U1RUlE6cOKETJ04UeF8HZ8q9Zs0azZs3T3v27FFiYqJWr16t4cOHq0OHDvlOXHS27PXq1VPz5s1tr8DAQEmSt7d3vhO5nSn3l19+qY8//lh79uzRoUOHNGvWLO3atSvfVRvOlLlhw4bq3bu3nnvuOW3ZskV79uxRRESEGjduXOC/Yp0p+xVLly7VpUuXCjyM6WyZu3btqjNnzmjEiBHat2+f9u7dqyFDhqhcuXK2vWjOml26/Pdhenq69u7dq9dee01vvvmmPvzwQ6fKWND3TEJCgm3vz4ABA+Tu7q7IyEjt3btXS5cu1fTp022H7ItNsZ3NY3IRERFG7969843HxMQYNWrUMDIyMoxp06YZjRo1Mtzc3IwaNWoY4eHhxnfffWebdufOnUa3bt2M8uXLG5UqVTJCQ0ONw4cPG4ZhGPHx8Ubr1q0NDw8Po0GDBsbnn39u1K5d25g6daptfv3FiV6dOnWynYB29atr165Omzs6OrrAzBUqVHDq7b1x40YjJCTE8Pb2ti3jpZdeMs6cOVMm/qxc7eoTip0599dff220bNnSqFixolGhQgUjKCjIaN++vdGrVy+nzWwYhnHu3Dlj6NChRuXKlY2qVasaDzzwgJGUlFRm/pyEhIQYAwYMMAyjbPw9uH79eqNDhw6Gt7e3UaVKFeOee+4x4uLiykR2Pz8/o1y5coaHh4fRrl07Y82aNU6X8VrfM0eOHLHLcNdddxlWq9Xw9/c33nzzzXzbvahZ/i88AACAKXBYCgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBgAAmArlBkCps1gsWrlypSQpMTFRFotFCQkJJZ7jRtY9ePBg9enT57rT3H333Xr++edvKhuAwqPcALewwYMHy2KxyGKxyM3NTYGBgXrxxRcLfNyFs7j99ts1fPjwAt9btGiRrFbrDT8fJyAgQMePH7c9OBFA2US5AW5x3bt31/Hjx/Xbb79p6tSp+uCDDxQdHV3asa4pMjJSS5YssXvY5xXz5s1Tr169VL16dYeXm5OTI1dXV/n5+alcOZ4pDJRllBvgFme1WuXn56eAgAD16dNHYWFh2rBhg+39vLw8xcTEKDAwUJ6engoKCtKyZcvslrF3717df//98vLyUqVKlRQaGqrDhw9LuvyU8a5du6p69ery9vZWp06dFB8ff8N5Bw4cqKysLC1fvtxu/MiRI9q0aZMiIyN1+PBh9e7dW76+vqpYsaLatGmjb775xm76OnXq6LXXXtOgQYPk5eWlYcOG5TsslZubq8jISNtnb9SokaZPn15grokTJ6pGjRry8vLS8OHDbQ8OLEh2drZGjx4tf39/VahQQe3atdOmTZts7x89elQ9e/ZUlSpVVKFCBTVr1kxr1qy5sQ0G3IIoNwBs9uzZoy1btsjd3d02FhMTo4ULF2r27Nnau3evRo0apYEDB+q7776TJB07dkwdO3aU1WrVxo0btX37dg0dOlSXLl2SJJ0/f14RERHavHmzfvzxRzVo0EA9evTQ+fPnbyhj9erV1bt3b82dO9dufP78+apVq5a6deumjIwM9ejRQ7GxsdqxY4e6d++unj17KikpyW6ed955R0FBQdqxY4deeeWVfOvKy8tTrVq19Pnnn+uXX37R+PHj9fLLL+uzzz6zmy42Nlb79u3Tpk2b9Omnn2rFihWaOHHiNT/DyJEjFRcXpyVLlmjXrl3q27evunfvroMHD0qSRowYoezsbH3//ffavXu3Jk2aVODT5QFcQ7E/mhOA04qIiDBcXV2NChUqGFar1ZBkuLi4GMuWLTMMwzAuXrxolC9f3tiyZYvdfJGRkUb//v0NwzCMsWPHGoGBgUZOTk6h1pmbm2tUqlTJ+PLLL21jcuCp5YZhGGvXrjUsFovx22+/GYZhGHl5eUbt2rWNcePGXXOeZs2aGe+9957t59q1axt9+vSxm6Yw6x4xYoTx0EMP2X6OiIgwqlatamRmZtrGZs2aZVSsWNHIzc01DOPyk5Ofe+45wzAM4+jRo4arq6tx7Ngxu+V26dLFGDt2rGEYhnH77bcbEyZMuGYGANfHgWXgFte5c2fNmjVLmZmZmjp1qsqVK6eHHnpIknTo0CFduHBBXbt2tZsnJydHrVq1kiQlJCQoNDRUbm5uBS4/JSVF48aN06ZNm5Samqrc3FxduHAh314UR3Tt2lW1atXSvHnz9Oqrryo2NlZJSUkaMmSIJCkjI0MTJkzQ6tWrdfz4cV26dElZWVn51tm6deu/XNeMGTM0d+5cJSUlKSsrSzk5OWrZsqXdNEFBQSpfvrzt55CQEGVkZCg5OVm1a9e2m3b37t3Kzc1Vw4YN7cazs7NVrVo1SdKzzz6rp556SuvXr1dYWJgeeughtWjRotDbB7jVUW6AW1yFChVUv359SdLcuXMVFBSkOXPmKDIyUhkZGZKk1atXy9/f324+q9UqSfL09Lzu8iMiInTq1ClNnz5dtWvXltVqVUhIyHXPSfkrLi4uGjx4sBYsWKAJEyZo3rx56ty5s+rWrStJGj16tDZs2KB33nlH9evXl6enpx5++OF866xQocJ117NkyRKNHj1akydPVkhIiCpVqqS3335bP/300w1nz8jIkKurq7Zv3y5XV1e7964cenriiScUHh6u1atXa/369YqJidHkyZP1zDPP3PB6gVsJ5QaAjYuLi15++WVFRUVpwIABatq0qaxWq5KSktSpU6cC52nRooUWLFigP//8s8C9Nz/88INmzpypHj16SJKSk5Nv+FLtqw0ZMkSvv/66VqxYoS+++EIff/yx3ToHDx6sBx54QNLlQpGYmOjwOn744Qfdeeedevrpp21jV06UvtrOnTuVlZVlK3o//vijKlasqICAgHzTtmrVSrm5uUpNTVVoaOg11x0QEKDhw4dr+PDhGjt2rD766CPKDVBInFAMwE7fvn3l6uqqGTNmqFKlSho9erRGjRqlBQsW6PDhw4qPj9d7772nBQsWSLp8cmx6eroeffRRbdu2TQcPHtSiRYt04MABSVKDBg20aNEi7du3Tz/99JMee+yxv9zbUxiBgYG65557NGzYMFmtVj344IO29xo0aKAVK1YoISFBO3fu1IABA5SXl+fwOho0aKBt27Zp3bp1+vXXX/XKK6/o559/zjddTk6OIiMj9csvv2jNmjWKjo7WyJEj5eKS/6/Yhg0b6rHHHtOgQYO0YsUKHTlyRFu3blVMTIxWr14tSXr++ee1bt06HTlyRPHx8fr222/VpEkTh/MDtyrKDQA75cqV08iRI/XWW28pMzNTr732ml555RXFxMSoSZMm6t69u1avXq3AwEBJUrVq1bRx40ZlZGSoU6dOCg4O1kcffWTbizNnzhydOXNGd9xxhx5//HE9++yz8vHxKZKskZGROnPmjAYMGCAPDw/b+JQpU1SlShXdeeed6tmzp8LDw3XHHXc4vPz/9//+nx588EH169dP7dq106lTp+z24lzRpUsXNWjQQB07dlS/fv3Uq1cvTZgw4ZrLnTdvngYNGqQXXnhBjRo1Up8+ffTzzz/rb3/7m6TLl6CPGDHCtr0bNmyomTNnOpwfuFVZDMMwSjsEAABAUWHPDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMBXKDQAAMJX/D3PqyuT3IxkKAAAAAElFTkSuQmCC\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "13cZXvb0DsvK" - }, - "execution_count": null, - "outputs": [] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} -======= "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, + "metadata": { "id": "qCDSjVhXLr_Z" }, @@ -2725,8 +17,9 @@ "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.svm import SVR\n", - "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor\n", - "from sklearn.tree import DecisionTreeRegressor\n", + + "from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor, DecisionTreeRegressor\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", @@ -2735,45 +28,22 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "SOQbXSiB-g5G", - "outputId": "6ae02a27-02b0-4bd9-a1ae-a7029056f32e" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running in local system\n" - ] - } - ], + + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ - "try:\n", - " import google.colab \n", - " In_colab=True\n", - "except:\n", - " In_colab=False\n", - "\n", - "if(In_colab):\n", - " print(\"running in google colab\")\n", - " from google.colab import drive \n", - " drive.mount('/content/drive')\n", - " df = pd.read_csv('drive/My Drive/Colab Notebooks/Stock Price Prediction RNN/SBIN.csv')\n", - "\n", - "else :\n", - " print(\"Running in local system\")\n", - " path=r\"C:\\Users\\SHRISTI\\OneDrive\\Desktop\\GitHub\\Stock-Price-Prediction\"\n", - " df=pd.read_csv(r\"C:\\Users\\SHRISTI\\OneDrive\\Desktop\\GitHub\\Stock-Price-Prediction\\Data\\SBIN.csv\")\n" + "from google.colab import drive\n", + "drive.mount('/content/drive')\n", + "df = pd.read_csv('drive/My Drive/Colab Notebooks/Stock Price Prediction RNN/SBIN.csv')" + ] }, { "cell_type": "code", - "execution_count": 11, + + "execution_count": 26, + "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -2884,7 +154,9 @@ "4 76613039.0 " ] }, - "execution_count": 11, + + "execution_count": 26, + "metadata": {}, "output_type": "execute_result" } @@ -2897,7 +169,9 @@ }, { "cell_type": "code", - "execution_count": 23, + + "execution_count": 28, + "metadata": { "id": "7LaYGXsfN-8y" }, @@ -2909,7 +183,9 @@ }, { "cell_type": "code", - "execution_count": 25, + + "execution_count": 30, + "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -3001,7 +277,9 @@ "4 17.738192 17.785366 17.459852 17.577793 76613039.0" ] }, - "execution_count": 25, + + "execution_count": 30, + "metadata": {}, "output_type": "execute_result" } @@ -3012,7 +290,9 @@ }, { "cell_type": "code", - "execution_count": 27, + + "execution_count": 32, + "metadata": { "id": "dydEPoNeM6eN" }, @@ -3025,7 +305,9 @@ }, { "cell_type": "code", - "execution_count": 29, + + "execution_count": 34, + "metadata": { "id": "OQ3cGqgTMBwt" }, @@ -3038,7 +320,9 @@ }, { "cell_type": "code", - "execution_count": 31, + + "execution_count": 36, + "metadata": { "id": "9Oz-bwJOMEWD" }, @@ -3050,7 +334,9 @@ }, { "cell_type": "code", - "execution_count": 33, + + "execution_count": 38, + "metadata": { "id": "ugapDyXODtn3" }, @@ -3064,7 +350,9 @@ }, { "cell_type": "code", - "execution_count": 35, + + "execution_count": 40, + "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -3079,7 +367,9 @@ "(5659, 4)" ] }, - "execution_count": 35, + + "execution_count": 40, + "metadata": {}, "output_type": "execute_result" } @@ -3090,7 +380,9 @@ }, { "cell_type": "code", - "execution_count": 37, + + "execution_count": 42, + "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -3105,7 +397,9 @@ "(1415, 4)" ] }, - "execution_count": 37, + + "execution_count": 42, + "metadata": {}, "output_type": "execute_result" } @@ -3116,7 +410,9 @@ }, { "cell_type": "code", - "execution_count": 39, + + "execution_count": 44, + "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -3131,7 +427,9 @@ "(5659,)" ] }, - "execution_count": 39, + + "execution_count": 44, + "metadata": {}, "output_type": "execute_result" } @@ -3142,7 +440,9 @@ }, { "cell_type": "code", - "execution_count": 41, + + "execution_count": 46, + "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -3157,7 +457,9 @@ "(1415,)" ] }, - "execution_count": 41, + + "execution_count": 46, + "metadata": {}, "output_type": "execute_result" } @@ -3168,7 +470,9 @@ }, { "cell_type": "code", - "execution_count": 43, + + "execution_count": 48, + "metadata": {}, "outputs": [], "source": [ @@ -3188,7 +492,9 @@ }, { "cell_type": "code", - "execution_count": 45, + + "execution_count": 50, + "metadata": {}, "outputs": [], "source": [ @@ -3211,7 +517,9 @@ }, { "cell_type": "code", - "execution_count": 48, + + "execution_count": 53, + "metadata": { "id": "RdZ1SpzdMHAJ" }, @@ -3223,7 +531,9 @@ }, { "cell_type": "code", - "execution_count": 50, + + "execution_count": 55, + "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -3243,7 +553,9 @@ "Name: Close, dtype: float64" ] }, - "execution_count": 50, + + "execution_count": 55, + "metadata": {}, "output_type": "execute_result" } @@ -3254,7 +566,9 @@ }, { "cell_type": "code", - "execution_count": 52, + + "execution_count": 142, + "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -3267,7 +581,9 @@ { "data": { "text/html": [ - "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], "text/plain": [ "LinearRegression()" ] }, - "execution_count": 52, + + "execution_count": 142, + "metadata": {}, "output_type": "execute_result" } @@ -3689,9 +1082,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + + "execution_count": 63, + "metadata": { + "id": "X269co2kMS4z" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 1.6881364651923558\n", + "MAE: 0.9433353486266928\n", + "MAPE: 0.006085435968276741\n", + "\n" + ] + } + ], + "source": [ "rmse, mae, mape = evaluate_model(model1, X_test_scaled, y_test)\n", "metrics[\"Model\"].append(\"Linear Regressor\")\n", @@ -3711,7 +1119,9 @@ }, { "cell_type": "code", - "execution_count": 56, + + "execution_count": 65, + "metadata": { "id": "0xQewd7QWTtq" }, @@ -3723,7 +1133,9 @@ }, { "cell_type": "code", - "execution_count": 58, + + "execution_count": 144, + "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -3736,7 +1148,9 @@ { "data": { "text/html": [ - "
SVR()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + "
SVR()
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" + ], "text/plain": [ "SVR()" ] }, - "execution_count": 58, + + "execution_count": 144, + "metadata": {}, "output_type": "execute_result" } @@ -4158,9 +1658,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + + "execution_count": 68, + "metadata": { + "id": "OQ1nL4oYfkAC" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 17.574809673127547\n", + "MAE: 6.278157692070486\n", + "MAPE: 0.09040265035344064\n", + "\n" + ] + } + ], + "source": [ "rmse, mae, mape = evaluate_model(model2, X_test_scaled, y_test)\n", "metrics[\"Model\"].append(\"SVR\")\n", @@ -4180,7 +1695,9 @@ }, { "cell_type": "code", - "execution_count": 62, + + "execution_count": 72, + "metadata": { "id": "f7raXT_hf2ij" }, @@ -4191,7 +1708,9 @@ }, { "cell_type": "code", - "execution_count": 64, + + "execution_count": 146, + "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -4204,7 +1723,9 @@ { "data": { "text/html": [ - "
RandomForestRegressor()
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" + "
RandomForestRegressor()
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" + ], "text/plain": [ "RandomForestRegressor()" ] }, - "execution_count": 64, + + "execution_count": 146, + "metadata": {}, "output_type": "execute_result" } @@ -4626,9 +2223,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + + "execution_count": 80, + "metadata": { + "id": "8nRU_pzEgnCt" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 2.2053909891328036\n", + "MAE: 1.2608162799481166\n", + "MAPE: 0.008015308194076972\n", + "\n" + ] + } + ], + "source": [ "rmse, mae, mape = evaluate_model(model3, X_test_scaled, y_test)\n", "metrics[\"Model\"].append(\"Random Forest\")\n", @@ -4648,7 +2260,9 @@ }, { "cell_type": "code", - "execution_count": 69, + + "execution_count": 83, + "metadata": { "id": "TI8idoxOg6jF" }, @@ -4659,7 +2273,9 @@ }, { "cell_type": "code", - "execution_count": 71, + + "execution_count": 148, + "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -4672,7 +2288,9 @@ { "data": { "text/html": [ - "
GradientBoostingRegressor()
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" + "
GradientBoostingRegressor()
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" + ], "text/plain": [ "GradientBoostingRegressor()" ] }, - "execution_count": 71, + + "execution_count": 148, + "metadata": {}, "output_type": "execute_result" } @@ -5094,9 +2790,24 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + + "execution_count": 87, + "metadata": { + "id": "Jj9DXdUPhh9V" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE: 2.6985863368468084\n", + "MAE: 1.692542658558929\n", + "MAPE: 0.011883244132236716\n", + "\n" + ] + } + ], + "source": [ "rmse, mae, mape = evaluate_model(model4, X_test_scaled, y_test)\n", "metrics[\"Model\"].append(\"GBM\")\n", @@ -5116,7 +2827,9 @@ }, { "cell_type": "code", - "execution_count": 76, + + "execution_count": 90, + "metadata": { "id": "DyhhdlZAhx94" }, @@ -5129,7 +2842,9 @@ }, { "cell_type": "code", - "execution_count": 78, + + "execution_count": 150, + "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -5142,7 +2857,9 @@ { "data": { "text/html": [ - "
XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "
XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
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        "             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",
@@ -5556,7 +3349,9 @@
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        "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
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-       "             num_parallel_tree=None, random_state=None, ...)
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