TSLA Stock Price Prediction
Project Overview
A comprehensive machine learning project that predicts Tesla stock price movements using technical indicators, advanced modeling techniques, and sentiment analysis from financial news. The project progresses through multiple phases, from baseline models to deployment with interactive Streamlit dashboards.
Project Phases
Phase 1: Baseline Model with Technical Indicators
Implemented core technical analysis indicators including moving averages, RSI, MACD, and volume metrics.
- Feature engineering with technical indicators
- Data preprocessing and normalization
- Initial model evaluation and baseline performance
Phase 2: Sentiment Analysis Integration
Added sentiment analysis from financial news to capture market sentiment and investor confidence.
- Scraped and processed financial news articles
- Natural language processing for sentiment scoring
- Integrated sentiment features into prediction model
Phase 3: Advanced Models (XGBoost)
Implemented gradient boosting with XGBoost for improved prediction accuracy and feature importance analysis.
- Hyperparameter tuning and optimization
- Cross-validation and model selection
- Feature importance interpretation
Phase 4: Deployment with Streamlit
Deployed the model as an interactive web application with real-time predictions and visualization dashboards.
- Interactive prediction interface
- Real-time data visualization
- Model performance metrics display
Technical Implementation
This project leverages a comprehensive tech stack for financial data analysis:
- Data Processing: Pandas for data manipulation and cleaning
- Technical Analysis: TA-Lib or custom implementations for technical indicators
- Sentiment Analysis: NLTK, TextBlob, or VADER for extracting sentiment from news
- Machine Learning: XGBoost for gradient boosting and prediction modeling
- Visualization: Matplotlib, Plotly for interactive charts and dashboards
- Deployment: Streamlit for building and hosting the web application
Key Insights & Analysis
The project demonstrates the power of combining multiple data sources and advanced modeling techniques:
- Multi-factor Modeling: Combines technical indicators with sentiment analysis for comprehensive predictions
- Market Psychology: Sentiment analysis captures investor sentiment and market news impact
- Technical Patterns: Moving averages, momentum indicators, and volume analysis reveal market trends
- Adaptive Learning: XGBoost learns complex non-linear relationships in stock price data
- Real-time Deployment: Streamlit enables interactive exploration of predictions and model insights
Skills Demonstrated
This project showcases expertise across multiple domains of data science and machine learning:
- Financial Data Analysis: Working with time series data and technical indicators
- Natural Language Processing: Extracting sentiment and insights from text data
- Advanced ML Techniques: Gradient boosting, hyperparameter tuning, and model evaluation
- Data Visualization: Creating compelling dashboards and interactive visualizations
- Full-stack Deployment: Building and deploying machine learning applications end-to-end
- Project Lifecycle: From baseline models to production-ready applications
Repository Structure
Projects/
├── src/
├── phase1_baseline.py - Baseline model with technical indicators
├── phase2_sentiment.py - Sentiment analysis integration
└── phase3_xgboost.py - Advanced XGBoost implementation
├── data/ - Historical stock data and news articles
├── notebooks/ - Jupyter notebooks for analysis
├── results/ - Model outputs and visualizations
└── app.py - Streamlit deployment application