https://github.com/teja-1403/coursera-python-project-for-data-science-honors
This project focuses on comparing stock prices and revenue data for Tesla and GameStop. It utilizes yfinance for stock data extraction and web scraping for revenue data. The project includes building interactive dashboards to visualize and compare stock prices with revenue for both companies, offering insights into their financial performance.
https://github.com/teja-1403/coursera-python-project-for-data-science-honors
data-science python webscraping
Last synced: 2 months ago
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This project focuses on comparing stock prices and revenue data for Tesla and GameStop. It utilizes yfinance for stock data extraction and web scraping for revenue data. The project includes building interactive dashboards to visualize and compare stock prices with revenue for both companies, offering insights into their financial performance.
- Host: GitHub
- URL: https://github.com/teja-1403/coursera-python-project-for-data-science-honors
- Owner: teja-1403
- License: mit
- Created: 2025-01-20T07:16:27.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-20T08:20:08.000Z (over 1 year ago)
- Last Synced: 2025-01-20T08:24:48.490Z (over 1 year ago)
- Topics: data-science, python, webscraping
- Language: Jupyter Notebook
- Homepage: https://www.coursera.org/learn/python-project-for-data-science/peer/WU7xb/analyzing-historical-stock-revenue-data-and-building-a-dashboard
- Size: 382 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Coursera-Python-Project-for-Data-Science-Honors
This project focuses on comparing the stock price and revenue data of Tesla and GameStop. The stock data is extracted using the `yfinance` library, and the revenue data is gathered via web scraping techniques. The goal is to build dashboards that visualize and compare stock prices versus revenue for both companies.
## Project Overview
Table of Contents:
- [Introduction](#introduction)
- [Objectives](#objectives)
- [Detailed Overview](#detailed-overview)
- [Step 1: Extracting Tesla Stock Data Using yfinance](#step-1-extracting-tesla-stock-data-using-yfinance)
- [Step 2: Extracting Tesla Revenue Data Using Web Scraping](#step-2-extracting-tesla-revenue-data-using-web-scraping)
- [Step 3: Extracting GameStop Stock Data Using yfinance](#step-3-extracting-gamestop-stock-data-using-yfinance)
- [Step 4: Extracting GameStop Revenue Data Using Web Scraping](#step-4-extracting-gamestop-revenue-data-using-web-scraping)
- [Step 5: Tesla Stock and Revenue Dashboard](#step-5-tesla-stock-and-revenue-dashboard)
- [Step 6: GameStop Stock and Revenue Dashboard](#step-6-gamestop-stock-and-revenue-dashboard)
- [Step 7: Sharing the Assignment Notebook](#step-7-sharing-the-assignment-notebook)
- [Grading Criteria](#grading-criteria)
## Introduction
In this project, we focus on gathering and visualizing stock and revenue data for Tesla and GameStop. Stock prices are fetched using the `yfinance` library, while revenue data is scraped from websites like Macrotrends. The goal is to create a dashboard that shows the relationship between the stock prices and revenues for both companies.
## Objectives
- **Extract Stock Data**: Use the `yfinance` library to fetch the stock price data for Tesla and GameStop.
- **Web Scraping**: Collect revenue data for both companies using web scraping techniques.
- **Data Visualization**: Create interactive dashboards to compare stock prices with revenue data.
- **Share the Notebook**: Share the Jupyter Notebook containing the code and results.
## Detailed Overview
### Step 1: Extracting Tesla Stock Data Using yfinance
- Use the `yfinance` library to download Tesla's stock data for the past year.
- Visualize the stock price over time.
### Step 2: Extracting Tesla Revenue Data Using Web Scraping
- Use the `requests` library and `BeautifulSoup` to scrape revenue data from sources like Macrotrends.
- Clean and organize the revenue data for analysis.
### Step 3: Extracting GameStop Stock Data Using yfinance
- Similarly, extract GameStop's stock data for the past year using `yfinance`.
- Visualize the stock price over time.
### Step 4: Extracting GameStop Revenue Data Using Web Scraping
- Scrape GameStop's revenue data from websites using `BeautifulSoup`.
- Clean and format the revenue data for easy comparison with Tesla.
### Step 5: Tesla Stock and Revenue Dashboard
- Create a dashboard using `matplotlib` or `plotly` to visualize Tesla's stock price vs its revenue.
- Provide insights into the trends and comparisons.
### Step 6: GameStop Stock and Revenue Dashboard
- Build a similar dashboard for GameStop, comparing stock prices to its revenue.
- Provide visualizations and insights based on the data.
### Step 7: Sharing the Assignment Notebook
- Share the Jupyter Notebook with your peer’s assignment for review and grading.
## Grading Criteria
The project will be graded based on the following tasks:
- **Question 1 - Extracting Tesla Stock Data Using yfinance** (2 Points)
- **Question 2 - Extracting Tesla Revenue Data Using Web Scraping** (1 Point)
- **Question 3 - Extracting GameStop Stock Data Using yfinance** (2 Points)
- **Question 4 - Extracting GameStop Revenue Data Using Web Scraping** (1 Point)
- **Question 5 - Tesla Stock and Revenue Dashboard** (2 Points)
- **Question 6 - GameStop Stock and Revenue Dashboard** (2 Points)
- **Question 7 - Sharing Your Assignment Notebook** (2 Points)
### Full Points: Working code that yields correct results
### Partial Points: Partially correct code or results
### No Points: Did not attempt the problem or did not upload any solution
For each task, screenshots of the code and results should be taken as instructed in the final project Jupyter Notebook. Each task must be completed and shared with working code.
## Conclusion
This project provides a hands-on application of stock data extraction, web scraping, and data visualization techniques. It helps to develop a better understanding of how stock prices and financial performance (revenue) can be compared visually using interactive dashboards.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.