IPL prediction using machine learning (Record no. 18250)
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| 000 -LEADER | |
|---|---|
| fixed length control field | a |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20221117111037.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 221117b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 19099 |
| Author | Menon, Abhineet |
| 245 ## - TITLE STATEMENT | |
| Title | IPL prediction using machine learning |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.7(3), May-Jun |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | New Delhi |
| Name of publisher, distributor, etc. | Associated Management Consultants |
| Year | 2022 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 23-29p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | 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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4622 |
| Topical term or geographic name entry element | Computer Engineering |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 19100 |
| Co-Author | Khator, Dhruv |
| 773 0# - HOST ITEM ENTRY | |
| Place, publisher, and date of publication | New Delhi Associated Management Consultants |
| Title | Indian Journal of Computer Science |
| International Standard Serial Number | 2456-4133 |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | http://www.indianjournalofcomputerscience.com/index.php/tcsj/article/view/171267 |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 17/11/2022 | 2022-2138 | 17/11/2022 | 17/11/2022 | Articles Abstract Database |