In this project we make use of machine learning models to predict IPL score.
Use this link to access the dataset.
- mid: Unique match id.
- date: Date on which the match was played.
- venue: Stadium where match was played.
- battingteam: Batting team name.
- bowlingteam: Bowling team name.
- batsman: Batsman who faced that particular ball.
- bowler: Bowler who bowled that particular ball.
- runs: Runs scored by team till that point of instance.
- wickets: Number of Wickets fallen of the team till that point of instance.
- overs: Number of Overs bowled till that point of instance.
- runslast5: Runs scored in previous 5 overs.
- wicketslast5: Number of Wickets that fell in previous 5 overs.
- striker: max(runs scored by striker, runs scored by non-striker).
- non-striker: min(runs scored by striker, runs scored by non-striker).
- total: Total runs scored by batting team at the end of their innings.
- Linear Regression
- Lasso Regression
- Decision Tree
- Random Forest
- Boosting using decision trees
- SVM Regression
- MLP Regressor(Neural Network)
- This barplot will help us compare the performance of every models.
- The model with the best score was Random Forest.
- If your aim is to have a faster fitting of the model then use Decision trees as it is much faster with a pretty decent score.