IPL prediction using machine learning
By: Menon, Abhineet
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Contributor(s): Khator, Dhruv.
Publisher: New Delhi Associated Management Consultants 2022Edition: Vol.7(3), May-Jun.Description: 23-29p.Subject(s): Computer EngineeringOnline resources: Click here
In:
Indian Journal of Computer ScienceSummary: Cricket is amongst the most popular sports in the world. Indian Premier League, more commonly known as IPL is the biggest domestic cricket league in the world. It generates a lot of revenue along with excitement among fans. Many bookers, bettors, and fans like to predict the outcome of a particular match which changes with every ball. This project studies and compares different Machine Learning techniques that can be applied to predict the outcome of a match. Features like team strength and individual strength of a player are also included along with conventional features like toss, home ground, weather and pitch conditions that are taken into account for predicting the result. Machine Learning algorithms such as Naïve Bayes, Random Forest Classifier, Logistic Regression, XGBoost, AdaBoost, and Decision Tree are selected to determine the predictive model with highest accuracy.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2022-2138 |
Cricket is amongst the most popular sports in the world. Indian Premier League, more commonly known as IPL is the biggest domestic cricket league in the world. It generates a lot of revenue along with excitement among fans. Many bookers, bettors, and fans like to predict the outcome of a particular match which changes with every ball. This project studies and compares different Machine Learning techniques that can be applied to predict the outcome of a match. Features like team strength and individual strength of a player are also included along with conventional features like toss, home ground, weather and pitch conditions that are taken into account for predicting the result. Machine Learning algorithms such as Naïve Bayes, Random Forest Classifier, Logistic Regression, XGBoost, AdaBoost, and Decision Tree are selected to determine the predictive model with highest accuracy.
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