This is my first project in my machine learning learning journey. It focuses on predicting stock prices using historical data and a variety of machine learning algorithms. It focuses on predicting stock prices using historical data and a variety of machine learning algorithms. The project helped me explore data preprocessing, visualization, feature engineering, and model evaluation — all essential parts of a typical ML pipeline.
The notebook provides a complete workflow for stock price prediction. It uses historical stock data, processes and visualizes it, applies machine learning models, and evaluates their performance. The goal is to analyze and forecast future stock prices based on past trends. I have used 'sp500 stocks' dataset from KAGGLE as my data to the model
- Data loading and preprocessing using
pandasandnumpy - Visualization of stock trends with
matplotlib - Feature engineering (technical indicators)
- Model training with:
- Linear Regression
- Random Forest Regressor
- Decision Tree Regressor
- Model evaluation using R² Score and Mean Squared Error
Project1.ipynb # Jupyter Notebook with the full code README.md # This file