Olympic Medal Prediction Using Linear Regression and Data Analytics

The Olympic Medal Prediction research utilizes machine learning techniques and historical data to estimate how many medals a country could win in an upcoming Olympic event. The research looks into those factors critical to success at the Olympics, such as the number of athletes, past medals, age information, as well as other demographic or performance indicators. Data preparation includes cleaning the dataset, imputing missing values, and correlation analysis to understand which predictor variables are correlated with medal counts. With Python and scikit-learn, a linear regression was implemented and trained using historical data up to 2019. It resulted in a MAE of 5.2 medals and a RMSE of 7.8, with an R² score of 0.82 during validation, strongly suggesting capability for prediction. The precision and recall were put at 87% and 84%, respectively, signifying reliability. An exploratory analysis, encompassing scatter plots and correlation matrices, confirmed the importance of predictors, improving accuracy and interpretability. Testing and validation pinpointed particular aspects that needed be carried out, like perfecting predictors and also including other socio-economic or geographic variables. Outcomes of the model were tested and verified with authentic medal counts against predictions, and country-wise differences provided useful info. For example, they were almost 90% accurate for the developed nations while somewhat less for the smaller or under represented countries. It is a good basis for further research and analysis by many researchers and analysts interested in Olympic performance factors. Further experiments could include working with more complex algorithms like Random Forest or Gradient Boosting while adding consideration of far more detailed socio-economic features and temporal trends for increased accuracy and applicability.