Big_Data_Analytics_For_Obesity

Conducted research and developed a system under Dr Booma Poolan Marikannan on provisional analysis for obesity issues using numerous data mining techniques by using a past medical dataset from the Kaggle.  Executed the project using tools such as PyCaret, Pandas, NumPy, Matplotlib, Seaborn, Scikit-Learn, and Pickle, and evaluated the classification models to classify obesity based on the value of BMI by using Classification Report and Confusion Matrix. Achievement - Implemented supervised machine learning techniques, such as Quadratic Discriminant Analysis, K-Nearest Neighbour, and Random Forest, with an accuracy of 91% to forecast customers' profitability based on consumer products.