The Olympic Games are the world’s biggest multi-sport, athletic competitions with participation among 200 countries. The main objective of our project is to explore at how several aspects of the participant determine the effective impact on medal count in the Olympic games. This model will also help in predicting a country's success at the Olympic games while illustrating how the Olympic games have evolved over the years with how women's participation , representation and performance have improved over the years, as well as the involvement of different nations, sports, and events.
We have gathered the Dataset from Kaggle https://www.kaggle.com/datasets/heesoo37/120-years-of-olympic-history-athletes-and-results We have two datasets that were used in the project, the first "athlete_events.csv" with 271116 rows and 15 columns and the NOC_regions dataset that Each row corresponds to an individual athlete competing in an individual Olympic event (athlete-events).
The columns are: ID - Unique number for each athlete Name - Athlete's name Sex - M or F Age - Integer Height - In centimeters Weight - In kilograms Team - Team name NOC - National Olympic Committee 3-letter code Games - Year and season Year - Integer Season - Summer or Winter City - Host city Sport - Sport Event - Event Medal - Gold, Silver, Bronze, or NA
##Approach
Steps that were followed:
- Collection of Data
- Cleaning and Preprocessing the data
- Exploratory Data Analysis
- Feature Selection
- Training and testing on multiple models to find the best accurate prediction.
Prerana Uppalapati (015906644)
Hema Aishwarya Talluri (015952586)
Ramisetty Teja (015933138)
Varshith Talluri (015948816)
Models used:
- Logistic Regression
- K nearest neighbor
- Decision Tree
- Random Forgest Regression
- XG Boost