🧠 9 real-world ML projects from AI/ML internship at Stemeta.ai | Classification · NLP · Fraud Detection · Flask API | Python · Scikit-Learn

This repository contains a machine learning pipeline for predicting bank marketing campaign success using the Bank Marketing Dataset. It includes data preprocessing, model training (Logistic Regression and Random Forest), and threshold optimization to improve recall for the minority class. The final model is evaluated using precision, recall.

End-to-end binary classifier on 150K+ records. Used SMOTE to resolve severe class imbalance (8K → 111K samples), benchmarked 5 models, achieved 89.1% accuracy and AUC 0.818 with Random Forest. Production-ready with pickle deployment.

ICU patient mortality prediction using machine learning. The project explores Logistic Regression, KNN, and Random Forest models with focus on handling class imbalance, optimizing recall for mortality detection, and applying threshold tuning for clinical decision support.

Fraud detection data preprocessing pipeline with SMOTE balancing, PCA dimensionality reduction, and feature engineering.