Project overview
In this project, you’ll assume the role of a data analyst working on a machine learning project to predict the NBA’s Most Valuable Player (MVP) for a given season. You’ll focus on web scraping to gather the necessary data on NBA player and team statistics from 1991 onwards.
Using Python libraries like requests, Beautiful Soup, and Selenium, you’ll scrape data from multiple web pages, parse the relevant tables, and combine them into unified datasets. This project allows you to apply and enhance your web scraping skills in a practical, sports analytics context. You’ll gain experience extracting data from both static and dynamically-loaded web pages.
**Objective:** Scrape, parse, and combine multiple seasons of NBA player and team statistics to enable future machine learning analysis for predicting season MVPs.
Key skill required
To complete this project, it's recommended to build these foundational skills in Python
- Installing and importing Python packages
- Manipulating data using Python lists and dictionaries
- Writing parameterized Python functions
- Using Python loops and conditionals to control program flow
Projects steps
Step 1: Project Overview
Step 2: Exploring the Pages to Scrape
Step 3: Downloading MVP Voting Tables
Step 4: Parsing and Combining MVP Voting Tables
Step 5: Downloading Player Stats: A Pitfall of JavaScript Pages
Step 6: Downloading Player Stats: Scraping JavaScript Pages
Step 7: Parsing and Combining Player Stats
Step 8: Downloading Team Data
Step 9: Parsing and Combining Team Data
Step 10: Wrapping Up
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