Repository for ML Project ( competition on Kaggle ) Competition Website: https://www.kaggle.com/competitions/ait-511-course-project-1-obesity-risk/overview
Maintaining a healthy lifestyle is becoming increasingly difficult. This dataset investigates how a person's weight category relates to their daily routines, eating habits, physical activity, and demographic information. Building models that can correctly categorize people into groups like inadequate weight, normal weight, overweight, or obesity levels is the task assigned to participants.
Features like age, gender, family history, food consumption patterns, physical activity, technology use, and modes of transportation are all included in the dataset. It is a realistic and difficult problem that combines machine learning, behavioral science, and healthcare because of these various factors.
Your objective is to create predictive models that can reveal hidden trends in lifestyle choices and advance knowledge of the risk factors for obesity and overweight.
Start Date: 29th Sept 2025 End Date: 26th Oct 2025
Submissions are evaluated using the accuracy score.
Submission File For each id row in the test set, you must predict the class value of the target, WeightCategory. The file should contain a header and have the following format:
id,WeightCategory
20758,Normal_Weight
20759,Normal_Weight
20760,Normal_Weight
etc.
The dataset for this competition (both train and test) was generated from a deep learning model trained on the Obesity or CVD risk dataset. Feature distributions are close to, but not exactly the same, as the original. Feel free to use the original dataset as part of this competition, both to explore differences as well as to see whether incorporating the original in training improves model performance.
Note: This dataset is particularly well suited for visualizations, clustering, and general EDA. Show off your skills!
- train.csv - the training set
- test.csv - the test set
- sample_submission.csv - a sample submission file in the correct format
- Atharva Pingale (MT2025026):
- Prashant Sharma (MT2025091):