StockPrice_Prediction_LinearRegression.py

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error
from FinMind.data import DataLoader

dl = DataLoader()
stock_data = dl.taiwan_stock_daily(
    stock_id='2330', start_date='2010-01-01', end_date='2022-12-20'
)

tsmc = stock_data
tsmc.dropna(inplace=True)

# Remove outliers
tsmc = tsmc[tsmc["close"] > 0]

tsmc = tsmc.apply(pd.to_numeric, errors="coerce")
tsmc = tsmc.dropna(axis='columns')

# Split the data into a training set and a test set
train_data, test_data, train_target, test_target = train_test_split(tsmc.drop("close", axis=1), tsmc["close"], test_size=0.1)

# Create a linear regression model
model = LinearRegression()

# Train the model on the training data
model.fit(train_data, train_target)

# Make predictions on the test data
predictions = model.predict(test_data)

# Calculate the MAE and RMSE
mae = mean_absolute_error(test_target, predictions)
rmse = np.sqrt(mean_squared_error(test_target, predictions))

print(f"MAE: {mae:.2f}")
print(f"RMSE: {rmse:.2f}")

# Make a prediction for the next day
next_day_prediction = model.predict(np.asarray(tsmc.iloc[-1].drop('close', axis=0)).reshape(1, -1))
print(f"Prediction for next day: {next_day_prediction[0]:.2f}")