Problem $1$: Personalized Health Diagnosis Using Ensemble Learning

Develop a machine learning model that analyzes patient medical histories and sensor

data to provide personalized diagnoses for rare diseases. Use advanced ensemble

techniques $($e$.$g$.,$ boosting, bagging$)$ and explore the integration of medical image

recognition with decision trees or random forests to enhance accuracy.

Problem $2$: Real$-$Time Fraud Detection System for Financial Transactions

Build a machine learning system for detecting fraudulent transactions in real$-$time for

an e$-$commerce platform. The system should incorporate anomaly detection

techniques, ensemble learning methods, and adapt to evolving fraud patterns using

online learning algorithms.

Problem $3$: Predictive Maintenance for Industrial Equipment

Create a machine learning model that predicts equipment failure in a large

manufacturing plant by analysing sensor data and machine logs$.$ Use time$-$series

analysis and clustering techniques to identify patterns and implement deep learning

methods for long$-$term predictive accuracy.