This project demonstrates a complete ETL (Extract, Transform, Load) pipeline for stock market data using Python. It was developed as part of the NPower Canada Junior Data Analyst program and showcases practical skills in data extraction, cleaning, transformation, and visualization.
The goal of this project is to automate the process of collecting historical stock data, transforming it into a clean and structured format, and preparing it for analysis or visualization. The project focuses on two popular stocks: Tesla (TSLA) and GameStop (GME).
- Python 3.x
- Pandas for data manipulation
- NumPy for numerical operations
- Matplotlib / Seaborn for visualization
- Google Colab for interactive development
- Git & GitHub for version control and publishing
- Pulls historical stock data from Coursera.
- Handles file reading and basic validation.
- Cleans missing or inconsistent data.
- Normalizes column formats and renames headers.
- Data for visualization.
- Line charts
- Chart size optimization