BSc IT Project Guide: Stock Price Prediction with Random Forest
1. Introduction
The Stock Price Prediction with Random Forest project aims to utilize machine learning algorithms, specifically the Random Forest regression model, to predict the future prices of stocks based on historical stock data and other relevant market indicators.
2. Objectives
- Understand stock market trends using historical data
- Implement a Random Forest regression model for stock price prediction
- Evaluate and compare prediction performance
- Build a user-friendly interface for forecasting stock prices
3. System Requirements
Hardware Requirements:
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- Processor: Intel i5 or above
- RAM: 8GB minimum
- Storage: 256GB SSD or more
Software Requirements:
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- Python 3.x
- Libraries: pandas, numpy, sklearn, matplotlib, yfinance
- Jupyter Notebook or any Python IDE
- Optional: Flask for web interface
4. Methodology
1. Data Collection: Use APIs (like Yahoo Finance) to collect
stock price data.
2. Data Preprocessing: Handle missing data, normalize/scale features.
3. Feature Engineering: Create relevant features such as moving averages,
volume, etc.
4. Model Training: Train the Random Forest model on the dataset.
5. Model Evaluation: Use MAE, RMSE, R² to evaluate performance.
6. Deployment: Build a basic web interface for predictions (optional).
5. Expected Outcome
- A functional Random Forest model capable of predicting
stock prices
- Visualization of actual vs predicted stock prices
- Interface to input stock ticker and display predictions
6. Conclusion
This project demonstrates the practical use of machine learning in finance by accurately predicting stock prices using Random Forest models, enabling better decision-making for investors and analysts.