• Khushi Hayaran
• Dr. Spandan Ranpariya

Obesity is a health concern that is rapidly becoming a major issue in terms of individual health and work task performance. This study suggests a Support Vector Machine (SVM)-based obesity classification model using the dataset from various health parameters like BMI, age, weight, height, and lifestyle factors. Our model aims to distinguish individuals into several obesity classifications. Performance comparison work has been done by tuning hyper parameters through GridSearchCV and kernel selection optimization, which allows our model to make more predictions that are accurate. We utilized a confusion matrix to visualize the analysis of prediction outcomes and misclassification patterns. The research findings substantiate the efficacy of SVM in predicting obesity trends, and they provide helpful information towards health awareness initiatives, informing the audience about the possible means. The research highlights the significant role of machine learning in preventive care and provides a larger horizon for expanding the broader impacts into larger populations.

Obesity, Machine Learning, Support Vector Machine (SVM), Linear Kernel.

"Machine Learning enabled Obesity Classificaion using Health and Lifestyle Indicators ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 12, page no.199-206, December-2025, Available :http://www.jetir.org/papers/JETIRHE06025.pdf

"Machine Learning enabled Obesity Classificaion using Health and Lifestyle Indicators ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 12, page no. pp199-206, December-2025, Available at : http://www.jetir.org/papers/JETIRHE06025.pdf