Stock Price Trend Analysis Using Machine Learning

This project explores the application of machine learning techniques to analyze historical stock price data and study time-series prediction patterns.

The goal of the project is not financial forecasting accuracy, but to understand data behavior, model limitations, and trend analysis in volatile datasets.

Key contributions:
• Collected and cleaned historical stock price data
• Applied feature engineering and normalization techniques
• Implemented and compared multiple models including:
– Moving Average
– Linear Regression
– LSTM (Long Short-Term Memory)
• Evaluated model outputs and analyzed prediction limitations
• Visualized actual vs predicted trends using charts

Key takeaways:
• Financial time-series data is highly volatile and difficult to predict accurately
• Machine learning models are better suited for trend analysis than exact forecasting
• Emphasis on analytical thinking and model evaluation rather than prediction claims

Technologies used:
Python, Jupyter Notebook, Machine Learning, Time Series Analysis, Data Visualization