Predicting Country Medal Counts: Unveiling Patterns in Historical and Current Sports Data
This project aims to predict a country's future medal count in sports events using machine learning models. By analyzing a combination of historical and current data, including factors such as past medal counts, athlete participation, demographics, and other relevant features, the goal is to identify patterns contributing to a country's success. The hypothesis is that a well-trained machine learning model, leveraging a comprehensive dataset, can provide accurate predictions for a country's medal count in upcoming sports events. This analysis enhances our understanding of the diverse factors influencing a country's performance in sports competitions.
Most machine learning projects typically followed a similar outline, and I also adopted this structure. This framework proved effective in addressing various machine learning challenges.
Project Steps
- Form a hypothesis.
- Find and explore the data.
- (If necessary) Reshape the data to predict your target.
- Clean the data for ML.
- Pick an error metric.
- Split your data.
- Train a model.
I employed Linear Regression to analyze and predict the relationship between variables in the dataset. This model is well-suited for scenarios where a linear relationship exists between the input features and the target variable.
Additionally, the K-Nearest Neighbors algorithm was utilized to make predictions based on the similarity of data points. KNN is a versatile algorithm that doesn't assume a specific structure in the data, making it suitable for various types of datasets.
