Explore stock price trends using Python, web scraping, and financial APIs. Originally created as a capstone assignment in the IBM Data Science Professional Certificate on Coursera.
- Collect and analyze real-world stock data using Python
- Visualize market movement trends for select companies
- Apply EDA techniques using public datasets
- Build readable, accessible charts for interpretation
Stock-Data-Analysis/
├── README.md
├── Final_Assignment.ipynb
├── visuals/
│ ├── stock_price_plot.png
│ └── trend_analysis_chart.png
└── data/
└── collected_stocks.csv
- Python
- Pandas
- Matplotlib & Seaborn
- yfinance
- BeautifulSoup
- Jupyter Notebook
- Data sourced from open/public APIs (Yahoo Finance)
- Visuals are made for human-readability, avoiding black-box charts
- Interpretations are kept neutral — no investment advice
- Encourages ethical modeling and human-centered presentation
- Yahoo Finance stock data via
yfinance - Data includes: historical prices, volume, and trend indicators
- Additional scraping was performed using BeautifulSoup for metadata
You may adapt this notebook for:
- Financial time series forecasting
- Teaching basic market analysis
- Feature engineering demos
MIT License. Feel free to fork and build on it.