Project Overview
This capstone project served as a deep dive into applying machine learning concepts to real-world financial data. The primary goal was to build a predictive model capable of forecasting stock price movements based on historical data. The project emphasizes the end-to-end data science pipeline, from raw data acquisition to final model evaluation and visualization.
Tech Stack & Tools
- Python: The core language for all scripting and model development.
- Pandas & NumPy: Essential libraries for efficient data manipulation and numerical operations.
- Scikit-learn: Used for building and evaluating various machine learning models, including time-series analysis.
- Matplotlib & Plotly: Utilized for creating professional-grade, interactive data visualizations and reports.
- Jupyter Notebooks: The primary environment for research, development, and presenting the analysis.
My Process
Phase 1: Data Acquisition & Preprocessing
I began by fetching historical stock data from a financial API. The raw data required extensive cleaning, including handling missing values, normalizing time-series data, and feature engineering to create meaningful inputs for the model.
Phase 2: Model Selection & Training
After preprocessing, I explored various machine learning models. I split the data into training and testing sets, performed cross-validation, and fine-tuned hyperparameters to find the optimal model for the predictive task.
Phase 3: Analysis & Reporting
The final step involved evaluating the model's performance using metrics like mean squared error and visualizing the results. I generated a final report summarizing the methodology, findings, and the model's predictive capabilities.
Results & Future Work
The project successfully demonstrated the application of machine learning to financial data, with the final model achieving a reliable level of predictive accuracy. The clear visualizations and report serve as a strong portfolio piece.
Future Enhancements:
- Integrate real-time data streaming for live predictions.
- Explore more advanced models, such as LSTMs or other deep learning approaches.
- Build a user-friendly dashboard to display predictions and visualizations.