Predicting Medal Counts in Olympics Using Machine Learning Algorithms: A Comparative Analysis

The Olympics are one of the major international sports that occur once in every four years. So, athletes come from all over the world to compete with each other. Every country should analyze to evaluate previous performance that will identify past mistakes and help them grow in the future. The analyzing of the data on different parameters will give us a statistical overview of the various factors that lead to the development of the Olympic sports. To analyze the dataset and draw conclusions, we employed a variety of Python modules like ‘NumPy’, ‘seaborn’, ‘matplotlib’, ‘pandas. The main purpose of this paper was to analyze the large Olympic database-using Machine learning concepts to assess the occurrence of the Olympic games over the years also, the country- wise participation, number of female athletes, participants' ages, performance evaluations, etc., during the Olympics over the past century.

Introduction

Opening of the Olympics: The Olympic Games are regarded as the top sports tournaments in the world. This event, which happens once every four years, has more than 200 participant nations. The International Olympic Committee governs all aspects of the Olympic games. [1] It is a fascinating international competition that allows every athlete the chance to win gold for his or her country. These competitions feature athletes from all across the world. The five interlocking rings that make up the Olympic emblem stand for the solidarity of the five continents-America, Africa, Australia, Europe, and Asia. The colors are red, green, black and yellow. [2]