The World Cup is one of the most widely followed sports events in the world and predicting the winner has always been a topic of interest for many people. In this study, I aimed to use advanced machine learning techniques to analyze historical data and make predictions about the outcome of the tournament. The goal of this project is to predict the winner of the 2022 World Cup using various classification techniques, feature extraction methods, and datasets.

Two datasets were used in this study.

• The first dataset contains match scores from 1982 to present, including information about the teams, scores, and tournament stage[1]

• The second dataset contains FIFA rank changes, which provides information about the overall performance of teams throughout the years[2]

By combining these datasets, I want to gain a better understanding of the performance of teams in the World Cup and their chances of winning the tournament.

I implemented various classification techniques such as logistic regression, decision trees, and neural networks, and feature extraction methods to analyze the data. In addition, I use various evaluation metrics such as accuracy, precision, recall, and F1 score to compare the performance of the models and select the best one.

The goal of this project is not only to predict the winner of the 2022 World Cup but also to provide an in-depth analysis of the performance of different teams and the factors that contribute to their success. By analyzing the historical data, I tried to gain insights that can help in understanding the dynamics of the tournament and the factors that influence the outcome.

[1] https://www.kaggle.com/datasets/martj42/international-football-results-from-1872-to-2017

[2] https://www.kaggle.com/datasets/cashncarry/fifaworldranking