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Supervised ML project to predict patient obesity level based on lifestyle habits, with full pipeline: data cleaning, feature engineering, exploratory data analysis, data preprocessing, model training, comparison, evaluation, and deployment. Developed in Konecta's AI/ML internship program.

AI/ML Task 2 – Feature Engineering, Model Optimization and Model Performance Comparison using California Housing Dataset

AI/ML internship projects covering data preprocessing, exploratory data analysis, feature engineering, and model building using Python and Scikit-learn. Includes regression and classification tasks with performance evaluation and comparison.

Credit Scoring Model built using machine learning classification techniques to predict customer creditworthiness. Developed as part of the CodeAlpha AI/ML Internship, featuring feature engineering, model evaluation with Precision, Recall, F1-Score, and ROC-AUC, and comparison of multiple classifiers.

Predicting loan eligibility using ML classifiers like Logistic Regression and Decision Trees on the German Bank dataset. I led the feature engineering and model comparison to identify key risk drivers. This project demonstrates how AI streamlines credit assessment and improves accuracy in high-stakes financial decision-making.