{"id":517,"date":"2024-08-27T21:04:23","date_gmt":"2024-08-28T01:04:23","guid":{"rendered":"https:\/\/eportfolios.macaulay.cuny.edu\/rrahman\/?p=517"},"modified":"2024-08-27T21:04:23","modified_gmt":"2024-08-28T01:04:23","slug":"machine-learning-prediction-for-cricket-performance","status":"publish","type":"post","link":"https:\/\/openlab.macaulay.cuny.edu\/reazwrahman\/2024\/08\/27\/machine-learning-prediction-for-cricket-performance\/","title":{"rendered":"Machine Learning Prediction for Cricket Performance"},"content":{"rendered":"<h1>Project Scope<\/h1>\n<p>This repository analyzes a number of machine learning models designed to predict whether a cricket player (batter and bowler) will exceed a specific performance threshold in upcoming matches. The project leverages open source historical data on players&#8217; performances in each individual game and extracts the most relevant features including runs scored, strike rates, recent performance in the last &#8216;N&#8217; games and match conditions, to train various classification models such as logistic regression, Random Forest, GBM, KNN and SVM.<\/p>\n<p>The analysis is focused around a) finding the right feature combinations for each classifier and b) coming up with a ranking system to compare the performance of the classifiers based on Accuracy, TPR and TNR.<\/p>\n<p>Github Repository: <a href=\"https:\/\/github.com\/reazwrahman\/ML-prediction-for-cricket\/tree\/main\">https:\/\/github.com\/reazwrahman\/ML-prediction-for-cricket\/tree\/main\u00a0<\/a><\/p>\n<p>Youtube Presentation Link: <a href=\"https:\/\/www.youtube.com\/watch?v=IfC2IZdt6nU\">https:\/\/www.youtube.com\/watch?v=IfC2IZdt6nU<\/a><\/p>\n<p>source data: <a href=\"https:\/\/data.world\/cclayford\/cricinfo-statsguru-data\">https:\/\/data.world\/cclayford\/cricinfo-statsguru-data<\/a><\/p>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Project Scope This repository analyzes a number of machine learning models designed to predict whether a cricket player (batter and bowler) will exceed a specific performance threshold in upcoming matches. The project leverages open source historical data on players&#8217; performances in each individual game and extracts the most relevant features including runs scored, strike rates, [&hellip;]<\/p>\n","protected":false},"author":605,"featured_media":521,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"portfolio_post_id":0,"portfolio_citation":"","portfolio_annotation":"","openlab_post_visibility":"","footnotes":""},"categories":[7],"tags":[],"class_list":["post-517","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-software-projects"],"_links":{"self":[{"href":"https:\/\/openlab.macaulay.cuny.edu\/reazwrahman\/wp-json\/wp\/v2\/posts\/517","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/openlab.macaulay.cuny.edu\/reazwrahman\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/openlab.macaulay.cuny.edu\/reazwrahman\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/openlab.macaulay.cuny.edu\/reazwrahman\/wp-json\/wp\/v2\/users\/605"}],"replies":[{"embeddable":true,"href":"https:\/\/openlab.macaulay.cuny.edu\/reazwrahman\/wp-json\/wp\/v2\/comments?post=517"}],"version-history":[{"count":0,"href":"https:\/\/openlab.macaulay.cuny.edu\/reazwrahman\/wp-json\/wp\/v2\/posts\/517\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/openlab.macaulay.cuny.edu\/reazwrahman\/wp-json\/wp\/v2\/media\/521"}],"wp:attachment":[{"href":"https:\/\/openlab.macaulay.cuny.edu\/reazwrahman\/wp-json\/wp\/v2\/media?parent=517"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/openlab.macaulay.cuny.edu\/reazwrahman\/wp-json\/wp\/v2\/categories?post=517"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/openlab.macaulay.cuny.edu\/reazwrahman\/wp-json\/wp\/v2\/tags?post=517"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}