Predictive Stock Price Analysis

Advanced Machine Learning for Financial Forecasting

Python LSTM scikit-learn pandas yfinance API matplotlib

Project Overview

This project implements advanced machine learning models to predict stock price movements using historical market data and technical indicators. The system compares multiple ML approaches including LSTM neural networks, Linear Regression, and Random Forest algorithms, providing an interactive dashboard for visualization and analysis.

Key Features

  • LSTM neural network implementation for time series forecasting
  • Multiple ML algorithm comparison (LSTM vs Linear Regression vs Random Forest)
  • Real-time stock data fetching using yfinance API
  • Technical indicators calculation and feature engineering
  • Interactive visualization dashboard with matplotlib
  • Model performance metrics and accuracy evaluation

Technical Implementation

Architecture

The project uses a multi-layered approach combining data preprocessing, feature engineering, model training, and evaluation:

  • Data Collection: Automated fetching of historical stock data via yfinance API
  • Preprocessing: Data cleaning, normalization, and sequence generation for LSTM
  • Feature Engineering: Technical indicators including moving averages, RSI, MACD
  • Model Training: LSTM with dropout layers, compared against baseline models
  • Evaluation: MSE, RMSE, and directional accuracy metrics
Technologies Used
  • Python: Core programming language
  • TensorFlow/Keras: LSTM neural network implementation
  • scikit-learn: Traditional ML algorithms and preprocessing
  • pandas: Data manipulation and analysis
  • NumPy: Numerical computations
  • matplotlib/seaborn: Data visualization
  • yfinance: Stock market data API

Challenges & Solutions

Challenge: Overfitting in LSTM Model

Solution: Implemented dropout layers, early stopping, and regularization techniques to improve model generalization.

Challenge: Feature Selection

Solution: Used correlation analysis and feature importance metrics to identify the most predictive technical indicators.

Challenge: Real-time Data Integration

Solution: Built a robust data pipeline with error handling for API rate limits and missing data.

What I Learned

  • Deep understanding of LSTM architecture and sequence modeling
  • Practical experience with financial time series analysis
  • Model comparison and performance evaluation techniques
  • API integration and data pipeline development
  • The importance of feature engineering in ML performance

Project Links

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