reduced_redundancy_stock_price_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d6a21042",
   "metadata": {},
   "source": [
    "DEPENDENCIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "15cc2438",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\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.neighbors import KNeighborsRegressor\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\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import xgboost as xgb\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1a47080a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the dataset\n",
    "df = pd.read_csv('SBIN.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8b945263",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data preprocessing\n",
    "df.drop(['Date', 'Adj Close'], axis=1, inplace=True)\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": 20,
   "id": "af430abe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select features and target variable\n",
    "X = df[['Open', 'High', 'Low', 'Volume']]\n",
    "y = df['Close']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "763efa5e",
   "metadata": {},
   "source": [
    "TRAINING AND TESTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2dddc0b9",
   "metadata": {},
   "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": 22,
   "id": "1c523190",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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": 23,
   "id": "73b110e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to train and evaluate model\n",
    "def train_and_evaluate_model(model, X_train, y_train, X_test, y_test, params=None):\n",
    "    if params:\n",
    "        model = GridSearchCV(model, params, scoring='neg_mean_squared_error', cv=5, n_jobs=-1)\n",
    "    model.fit(X_train, y_train)\n",
    "    predictions = model.predict(X_test)\n",
    "    \n",
    "    # Calculate evaluation metrics\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",
    "    accuracy = accuracy_score(y_test > predictions, y_test > predictions.round())\n",
    "    precision = precision_score(y_test > predictions, y_test > predictions.round())\n",
    "    confusion = confusion_matrix(y_test > predictions, y_test > predictions.round())\n",
    "    recall = recall_score(y_test > predictions, y_test > predictions.round())\n",
    "    f1 = f1_score(y_test > predictions, y_test > predictions.round())\n",
    "\n",
    "    # Print the evaluation metrics\n",
    "    print(f\"RMSE: {rmse}, MAE: {mae}, MAPE: {mape}, Accuracy: {accuracy}, Precision: {precision}, Confusion Matrix:\\n{confusion}, Recall: {recall}, F1 Score: {f1}\")\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "68f21263",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define models and their hyperparameters\n",
    "models_params = {\n",
    "    'Linear Regression': (LinearRegression(), None),\n",
    "    'SVR': (SVR(), {'C': [0.1, 1], 'epsilon': [0.01, 0.1, 0.5], 'kernel': ['sigmoid']}),\n",
    "    'Random Forest': (RandomForestRegressor(), None),\n",
    "    'Gradient Boosting': (GradientBoostingRegressor(), None),\n",
    "    'XGBoost': (xgb.XGBRegressor(), None),\n",
    "    'AdaBoost': (AdaBoostRegressor(), None),\n",
    "    'Decision Tree': (DecisionTreeRegressor(), None),\n",
    "    'KNeighbors': (KNeighborsRegressor(), {'n_neighbors': [3, 5, 7, 9, 11, 15, 20, 23, 25, 30, 60, 70, 150]})\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "af1992c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Linear Regression Model:\n",
      "RMSE: 1.6881364651923592, MAE: 0.9433353486266863, MAPE: 0.006085435968276418, Accuracy: 0.8296819787985866, Precision: 0.8623595505617978, Confusion Matrix:\n",
      "[[560  98]\n",
      " [143 614]], Recall: 0.8110964332892999, F1 Score: 0.8359428182437032\n",
      "\n",
      "SVR Model:\n",
      "RMSE: 134.5606234817444, MAE: 76.05976804010935, MAPE: 0.39705639844596075, Accuracy: 0.9978798586572438, Precision: 0.9970544918998527, Confusion Matrix:\n",
      "[[735   2]\n",
      " [  1 677]], Recall: 0.9985250737463127, F1 Score: 0.997789240972734\n",
      "\n",
      "Random Forest Model:\n",
      "RMSE: 2.191031983578823, MAE: 1.254384854932792, MAPE: 0.007957611370512454, Accuracy: 0.8551236749116607, Precision: 0.8556851311953353, Confusion Matrix:\n",
      "[[623  99]\n",
      " [106 587]], Recall: 0.847041847041847, F1 Score: 0.851341551849166\n",
      "\n",
      "Gradient Boosting Model:\n",
      "RMSE: 2.697993647984777, MAE: 1.6922021460047614, MAPE: 0.011881429758725463, Accuracy: 0.9017667844522969, Precision: 0.8982300884955752, Confusion Matrix:\n",
      "[[667  69]\n",
      " [ 70 609]], Recall: 0.8969072164948454, F1 Score: 0.8975681650700075\n",
      "\n",
      "XGBoost Model:\n",
      "RMSE: 2.733930065274145, MAE: 1.502457380471909, MAPE: 0.010026410639661481, Accuracy: 0.8840989399293286, Precision: 0.8948106591865358, Confusion Matrix:\n",
      "[[613  75]\n",
      " [ 89 638]], Recall: 0.8775790921595599, F1 Score: 0.8861111111111112\n",
      "\n",
      "AdaBoost Model:\n",
      "RMSE: 9.915909571695305, MAE: 8.180488940261938, MAPE: 0.1986601163272193, Accuracy: 0.992226148409894, Precision: 0.986, Confusion Matrix:\n",
      "[[911   7]\n",
      " [  4 493]], Recall: 0.9919517102615694, F1 Score: 0.9889669007021062\n",
      "\n",
      "Decision Tree Model:\n",
      "RMSE: 3.0678929179050054, MAE: 1.6473595220251132, MAPE: 0.010260672105066157, Accuracy: 0.8572438162544169, Precision: 0.8650137741046832, Confusion Matrix:\n",
      "[[585  98]\n",
      " [104 628]], Recall: 0.8579234972677595, F1 Score: 0.8614540466392319\n",
      "\n",
      "KNeighbors Model:\n",
      "RMSE: 3.261285106039988, MAE: 1.8071640369846882, MAPE: 0.014187783844224503, Accuracy: 0.8968197879858657, Precision: 0.9097744360902256, Confusion Matrix:\n",
      "[[664  60]\n",
      " [ 86 605]], Recall: 0.8755426917510853, F1 Score: 0.8923303834808259\n"
     ]
    }
   ],
   "source": [
    "# Train and evaluate all models\n",
    "for name, (model, params) in models_params.items():\n",
    "    print(f\"\\n{name} Model:\")\n",
    "    train_and_evaluate_model(model, X_train_scaled, y_train, X_test_scaled, y_test, params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fb66a3c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# KNN Hyperparameter Tuning\n",
    "def knn_hyperparameter_tuning(X_train, y_train):\n",
    "    k_values = range(1, 31)  # Example range for k\n",
    "    mse_values = []\n",
    "    \n",
    "    for k in k_values:\n",
    "        knn_model = KNeighborsRegressor(n_neighbors=k)\n",
    "        mse = -cross_val_score(knn_model, X_train, y_train, cv=5, scoring='neg_mean_squared_error').mean()\n",
    "        mse_values.append(mse)\n",
    "    \n",
    "    return k_values, mse_values\n",
    "\n",
    "# Perform KNN Hyperparameter Tuning\n",
    "k_values, mse_values = knn_hyperparameter_tuning(X_train_scaled, y_train)\n",
    "\n",
    "# Plotting the results\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(k_values, mse_values, marker='o', linestyle='-')\n",
    "plt.title('KNN Hyperparameter Tuning: MSE vs. Number of Neighbors')\n",
    "plt.xlabel('Number of Neighbors (k)')\n",
    "plt.ylabel('Mean Squared Error (MSE)')\n",
    "plt.xticks(k_values)  # Show all k values on x-axis\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b410ecfc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}