Financial Asset Price Prediction

Financial Asset Price Prediction

This project aims to build a machine learning model to predict the future behavior of financial assets based on historical market data. The dataset includes time-series information such as opening price, closing price, high, low, trading volume, and other relevant financial indicators.

The workflow involves:

Exploring and visualizing time-series data to identify trends, patterns, and volatility.

Cleaning and preprocessing the data, handling missing values and scaling numerical features.

Creating input features from historical prices and indicators.

Splitting the dataset into training and testing sets while preserving temporal order.

Training predictive models such as Linear Regression, Decision Trees, and other regression-based algorithms.

Evaluating model performance using metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), and R² score.

This project helps in understanding market behavior and provides a foundation for forecasting asset prices, supporting data-driven decision-making in finance and investment strategies.