Stock_Predictor.py

# Import necessary libraries
import yfinance as yf
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
import matplotlib.pyplot as plt
import numpy as np
import datetime

# Step 1: Fetch Stock Data
def fetch_stock_data(ticker, start_date, end_date):
    stock_data = yf.download(ticker, start=start_date, end=end_date)
    return stock_data

# Step 2: Feature Engineering
def preprocess_data(stock_data):
    stock_data['MA_10'] = stock_data['Close'].rolling(window=10).mean()
    stock_data['MA_50'] = stock_data['Close'].rolling(window=50).mean()
    stock_data['Lag_1'] = stock_data['Close'].shift(1)
    stock_data.dropna(inplace=True)
    return stock_data

# Step 3: Train-Test Split
def split_data(stock_data):
    X = stock_data[['Open', 'High', 'Low', 'Volume', 'MA_10', 'MA_50', 'Lag_1']]
    y = stock_data['Close']
    return train_test_split(X, y, test_size=0.2, random_state=42)

# Step 4: Train Linear Regression Model
def train_model(X_train, y_train):
    model = LinearRegression()
    model.fit(X_train, y_train)
    return model

# Step 5: Evaluate Model
def evaluate_model(model, X_test, y_test):
    y_pred = model.predict(X_test)
    mae = mean_absolute_error(y_test, y_pred)
    rmse = np.sqrt(mean_squared_error(y_test, y_pred))
    r2 = r2_score(y_test, y_pred)
    return y_pred, mae, rmse, r2

# Main Function
if __name__ == "__main__":
    # Parameters
    STOCK = str(input('Select a stock symbol: '))  # Choose a stock symbol
    START_DATE = '2020-01-01'
    today = datetime.date.today()
    END_DATE = today - datetime.timedelta(days=1)

    # Step 1: Fetch Data
    stock_data = fetch_stock_data(STOCK, START_DATE, END_DATE)
    print("Sample Data:")
    print(stock_data.head())

    # Step 2: Preprocess Data
    stock_data = preprocess_data(stock_data)

    # Step 3: Split Data
    X_train, X_test, y_train, y_test = split_data(stock_data)

    # Step 4: Train Model
    model = train_model(X_train, y_train)
    print("Model Trained!")
    print("Coefficients:", model.coef_)
    print("Intercept:", model.intercept_)

    # Step 5: Evaluate Model
    y_pred, mae, rmse, r2 = evaluate_model(model, X_test, y_test)
    print(f"Mean Absolute Error (MAE): {mae:.2f}")
    print(f"Root Mean Squared Error (RMSE): {rmse:.2f}")
    print(f"R² Score: {r2:.2f}")