IPL_SCORE_PREDICTION

OVERVIEW

In this project we make use of machine learning models to predict IPL score.

DATASET:

Use this link to access the dataset.

DATASET FEATURES:

• 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.

MODELS:

• Linear Regression
• Lasso Regression
• Decision Tree
• Random Forest
• Boosting using decision trees
• SVM Regression
• MLP Regressor(Neural Network)

MODEL PERFORMANCE:

• This barplot will help us compare the performance of every models.
• 

THE BEST MODEL:

• 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.