A machine learning project to determine cricket player prices in the Indian Premier League annual auctions. The dataset has been built by scraping data from the howstat website. A Support Vector Regression (SVR) model has been developed to predict prices for batsman and wicketkeepers, and a Linear Regression model has been built to predict prices for bowlers and all-rounders. The entire project has been built using Python and its libraries.
The requirements.txt file should list all Python libraries that your notebooks depend on, and they will be installed using:
pip install -r requirements.txt
To build the dataset "AuctionId.csv", run the following (in the specified order) on terminal:
python scrapers/id_scraper.py
python scrapers/stats_scraper.py
python scrapers/stats_analyzer.py
To process the dataset, run the following on terminal:
python ml/data_processing.py
The two files, ml/train.py and ml/train_bowl.py builds the batting/wicketkeeper model and bowler/allrounder models respectively.
The "IPLPlayerAuctionData.xlsx" dataset was taken from Kaggle. This dataset was upgraded by scraping from howstat.com, and the upgraded dataset is "AuctionId.csv". The following features were scraped:
- Career Statistics (Matches, Innings batted, Aggregate runs, Average, Highest Score, 30s, 50s, 100s, 4s, 6s, Scoring Rate, Overs Bowled, Wickets, 3 wickets in an innings, Economy Rate, Bowling Strike Rate)
- Career statistics until the auction year(Matches, Aggregate runs, Batting Average, Scoring Rate, Wickets taken, Economy Rate, Bowling Strike Rate, Bowling average)
- Last Season Statistics (Matches, Runs scored, Highest Score, Batting Average, Scoring Rate, Overs bowled, Wickets taken, Bowling average, Bowling Strike Rate)