This project analyzes historical stock prices and revenue data for Tesla and GameStop using Python.
The main objective is to explore time-series trends, visualize relationships between stock prices and company fundamentals, and extract data-driven insights relevant to financial analysis.
The analysis is performed in a Jupyter Notebook, combining data collection, cleaning, visualization, and interpretation in an end-to-end workflow.
- 🐍 Python
- 📊 Pandas
- 🔢 NumPy
- 🌐 BeautifulSoup (Web Scraping)
- 📉 Matplotlib
- 📓 Jupyter Notebook
- 🚀 Tesla shows strong alignment between revenue growth and a significant increase in stock price, especially after 2018.
- 🎮 GameStop experienced a dramatic stock price surge around 2021, while revenue did not exhibit a similar upward trend — indicating market-driven behavior rather than fundamental growth.
- 📈 Yahoo Finance — historical stock price data
- 💰 Macrotrends — company revenue data
stock-revenue-analysis/
├── notebooks/
│ └── Revenue_Data_and_Building_a_Dashboard.ipynb
├── images/
│ ├── gamestop_dashboard.png
│ └── tesla_dashboard.png
├── README.md
└── LICENSE
- This project was completed as part of the IBM Data Science curriculum.
- The notebook and documentation have been refined and structured for portfolio and professional presentation.
- Focus is placed on clarity, reproducibility, and insight, not just code execution.
Muhammad Rafi Akbar
Aspiring Data Analyst / Data Scientist / Data Engineer
This project is licensed under the MIT License.
You are free to use, modify, and distribute this project with proper attribution.