Stock Price Prediction using Principal Component Analysis (PCA)

This project aims to predict stock prices using Principal Component Analysis (PCA) to reduce the dimensionality of stock market data. By leveraging PCA, we simplify the prediction process, making it more efficient and effective.

Problem Definition

Predicting stock prices is a challenging task due to the volatile nature of financial markets. This project utilizes PCA to reduce the complexity of the data and enhance the prediction accuracy.

Motivation

Accurate stock price prediction can lead to significant financial gains. PCA helps mitigate the curse of dimensionality, making it a suitable technique for analyzing high-dimensional stock market data.

Methodology

1. Data Preprocessing: Fetching and standardizing historical stock data.
2. PCA Application: Reducing dimensionality by selecting key principal components.
3. Model Training: Training a linear regression model on the transformed data.
4. Prediction & Evaluation: Predicting stock prices and evaluating model performance.

Results

The project successfully demonstrates the effectiveness of PCA in predicting stock prices, reducing data complexity, and improving model performance.

Conclusion

PCA proves to be a powerful tool in stock price prediction, offering a streamlined approach to handle high-dimensional data. This project lays the groundwork for further exploration in financial forecasting using PCA.