Azad, Md Imran BinIslam, TameemHaque, RidwanulDhar, LinkonElahe, Md. Monjur E2025-09-042025-09-0420252025-06ID 24141133ID 20341016ID 20201190ID 23141076http://hdl.handle.net/10361/26664Cataloged from PDF version of thesis.Includes bibliographical references (pages 88-89).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2025.Football match prediction has gained significant attention in recent years, especially with the advent of machine learning models. This thesis presents the development and implementation of a predictive model for football match outcomes, leveraging historical team and player statistics. The model employs Scikit-learn logistic regression model„ combined with supporting statistical techniques, to estimate match results, probable goal scorers, and influential factors behind predictions.The vital use of LLM for subjective information incorporation, this research provides a comprehensive analysis of football match forecasting, considering multiple parameters such as team performance, player attributes, and league statistics, with an website at the end demonstrating the fruitful results.105 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Machine learningPoisson regressionLogistic regressionHome advantagePlayer statisticsMachine learning.Poisson algebras.Logistic regression analysis.Thesis