In this project, we'll predict who will win basketball games in the NBA. We'll start by web scraping NBA box scores. We'll read in and clean the html using BeautifulSoup. Then we'll parse the box scores into pandas DataFrames that we can use for machine learning. Then we'll do feature selection to identify good predictors, and train a machine learning model to make predictions. We'll end by computing rolling predictors and improving the model.
This is a complete end-to-end project, and we'll be using web scraping, data cleaning, and machine learning.
Project Steps
- Scrape standings and box score data
- Clean up html with BeautifulSoup
- Parse the box scores to get a DataFrame
- Run feature selection to identify predictors
- Train an ML model
- Compute rolling predictors to improve the model
You can find the code for this project here
File overview:
get_data.ipynb- download the box scores from basketball reference.parse_data.ipynb- clean the data to get a pandas DataFrame.predict.ipynb- make predictions using machine learning
To complete this project, you'll need to have a good understanding of:
- Python syntax, including functions, if statements, and data structures
- Data cleaning
- Pandas syntax
- Using Jupyter notebook
You'll also need to know the basics of machine learning.
Please make sure you've completed these Dataquest courses (or know the material) before trying this project:
- Python Introduction
- For Loops and If Statements
- Dictionaries In Python
- Functions and Jupyter Notebook
- Python Intermediate
- Pandas and NumPy Fundamentals
- Data Cleaning
- Machine Learning Fundamentals
To follow this project, please install the following locally:
- JupyerLab
- Python 3.8+
- Python packages
- pandas
- scikit-learn
- beautifulsoup4
- playwright
We'll download our dataset from Basketball Reference.