Stock Price Prediction with Machine Learning

A comprehensive end-to-end project that predicts next-day stock prices using historical data from Yahoo Finance. This project demonstrates a practical approach to time-series regression with tree-based models including Decision Tree Regressor, Random Forest Regressor, Gradient Boosting Regressor, and XGBoost.

Disclaimer: This project is for educational purposes only. Stock price prediction is inherently uncertain, and real trading or financial decisions require much more rigorous analysis, risk management, and domain expertise.

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Table of Contents

1. Overview
2. Project Features
3. Data Source
4. Installation & Requirements
5. Project Structure
6. Usage
7. Methodology
8. Results
9. Next Steps & Improvements
10. Contributing
11. License
12. Contact

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Overview

The Stock Price Prediction project shows how to:

• Collect stock data from Yahoo Finance using the yfinance library.
• Clean and explore time-series data.
• Create features (e.g., rolling averages, shift columns) to predict the next day’s closing price.
• Train and compare multiple tree-based regression models:
  • Decision Tree Regressor
  • Random Forest Regressor
  • Gradient Boosting Regressor
  • XGBoost Regressor
• Evaluate using standard regression metrics: MAE, MSE, RMSE, R².
• Interpret results via feature importance analysis.

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Project Features

• Time-Series Specific: Demonstrates a straightforward time-based train-test split.
• Feature Engineering: Includes rolling averages and a target shifted by one day (Close_Target).
• Multiple Ensemble Methods: Illustrates how bagging (Random Forest) and boosting (Gradient Boosting, XGBoost) can improve predictive performance.
• Hyperparameter Tuning: Shows how to tune models with GridSearchCV.
• Evaluation & Visualization: Checks errors (MAE, RMSE, etc.) and plots feature importances to understand model behavior.

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Data Source

This project uses historical stock data from Yahoo Finance, accessed via the yfinance Python library.

• Default Ticker: Tesla (TSLA)
• Example Date Range: 2016-01-01 to 2021-12-31

Feel free to change the ticker or date range in the code to explore different stocks or time spans.

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Installation & Requirements

Python Version

• Python 3.7+ recommended

Dependencies

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn
• xgboost
• yfinance

If you have a requirements.txt file included, simply run:

pip install -r requirements.txt

Otherwise, install manually via pip:

pip install pandas numpy matplotlib seaborn scikit-learn xgboost yfinance

Project Structure

├── README.md                      <- This README
├── stock_price_prediction.ipynb   <- Jupyter Notebook with full walkthrough
├── requirements.txt               <- (Optional) Python dependencies
└── ...

Key File: stock_price_prediction.ipynb
	•	Contains step-by-step code for data fetching, feature engineering, model training, and evaluation.

Usage
	1.	Clone or Download this repository:

git clone https://github.com/YourUsername/Stock-Price-Prediction.git
cd Stock-Price-Prediction


	2.	Install Dependencies (if using requirements.txt):

pip install -r requirements.txt


	3.	Run Jupyter Notebook:

jupyter notebook stock_price_prediction.ipynb

	•	Or run it as a .py script if you prefer.

	4.	Customize:
	•	In the notebook, change the ticker and date range (in the yfinance download cell) to experiment with different stocks or time periods.
	•	Adjust the feature engineering (rolling windows, shift steps, etc.) as you see fit.
	•	Update hyperparameters in the GridSearchCV sections to explore further tuning.

Methodology
	1.	Data Collection: Fetch daily stock data (Open, High, Low, Close, Adj Close, Volume) from Yahoo Finance using yfinance.download().
	2.	Data Cleaning & Exploration: Check for missing values, visual anomalies, and trends.
	3.	Feature Engineering:
	•	Target: Shift the Close price by one day to create Close_Target.
	•	Moving Averages: Rolling features like MA5 or MA10.
	4.	Time-Based Train/Test Split:
	•	Use historical data (e.g., 2016–2020) for training, and a more recent period (e.g., 2021) for testing.
	5.	Model Training:
	•	Decision Tree Regressor (baseline)
	•	Random Forest Regressor
	•	Gradient Boosting Regressor
	•	XGBoost Regressor
	6.	Hyperparameter Tuning:
	•	Use GridSearchCV to optimize n_estimators, max_depth, learning_rate, etc.
	7.	Evaluation:
	•	Calculate MAE, MSE, RMSE, R² on the test set.
	•	Compare models to see which performs best.
	8.	Interpretation:
	•	Examine feature importances to see which features influence predictions the most.
	9.	Results & Analysis:
	•	Summarize errors, plot predictions vs. actual values, discuss limitations.

Results

Here is a sample table of metrics (illustrative, not actual):

Model	MAE	MSE	RMSE	R²
Decision Tree (Tuned)	25.41	975.0	31.24	0.74
Random Forest (Tuned)	18.62	582.1	24.13	0.82
Gradient Boosting (Tuned)	17.93	537.5	23.19	0.84
XGBoost (Tuned)	16.10	490.0	22.13	0.86

	•	Typically, boosting methods (Gradient Boosting, XGBoost) can outperform a single Decision Tree or even a Random Forest, but exact performance depends on the data and hyperparameter tuning.
	•	Note: Absolute error values (MAE, RMSE) depend on the scale of the stock price.

A higher R² and a lower MAE/RMSE indicate better predictive performance.

Next Steps & Improvements
	•	Time-Series Cross-Validation: Instead of a single train/test split, implement a rolling or expanding window approach (e.g., TimeSeriesSplit in scikit-learn).
	•	Additional Features: Try adding more technical indicators (RSI, MACD, Bollinger Bands) or external data (market indices, sentiment, macroeconomic indicators).
	•	Hyperparameter Search: Use advanced search techniques (e.g., RandomizedSearchCV, Bayesian Optimization) for better and faster tuning.
	•	Model Stacking: Combine multiple ensemble models (e.g., stack XGBoost & Random Forest) for potentially improved results.
	•	Production Deployment: Wrap your final model in a Flask or FastAPI service for real-time predictions, along with monitoring and logging.

Contributing

Contributions, suggestions, and improvements are welcome! Please open an issue or submit a pull request if you have ideas for enhancements, bug fixes, or new features.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

Author: [Your Name or GitHub Handle]
Email: [your.email@domain.com]
GitHub: Your GitHub Profile

Feel free to reach out or open an issue if you have any questions. Enjoy exploring stock price predictions with these ensemble methods!