Olympic Medals Prediction

This project aims to predict the number of medals each country will earn in the Olympic Games using historical data and statistical analysis.

Table of Contents

• Introduction
• Data
• Methods
• Analysis
• Usage
• License

Introduction

Predicting Olympic medal counts is a complex challenge that involves various factors, including past performance, athletes' demographics, and historical context. This project leverages statistical methods in Python to analyze these factors and make predictions for upcoming Olympic Games.

Data

The dataset comprises several features, including:

• team: The team code for the country.
• country: The full name of the country.
• year: The year of the Olympic Games.
• athletes: Number of athletes representing the country.
• age: Average age of athletes.
• prev_medals: Number of medals won in previous Olympics.
• medals: Actual medals won in the Olympics.
• predictions: Predicted medals based on the model.

Sample Data

team	country	year	athletes	age	prev_medals	medals	predictions
AFG	Afghanistan	2012	6	24.8	1	1	0
ALB	Albania	2012	10	25.7	0	0	0

Methods

The analysis utilizes the following methods:

1. Calculation of absolute errors between predicted and actual medals.
2. Grouping errors by team to assess average prediction accuracy.
3. Analysis of error ratios based on historical medal counts.

The code snippets demonstrating some calculations include:

errors = (test["medals"] - test["predictions"]).abs()  
error_by_team = errors.groupby(test["team"]).mean()  
medals_by_team = test["medals"].groupby(test["team"]).mean()  
error_ratio = error_by_team / medals_by_team  

Analysis

The resulting error ratios indicate how well the model predicts for each team. A lower error ratio signifies better prediction accuracy. Various statistics and visualizations are analyzed to enhance understanding.