📌 Goal:
Enhance brain tumor segmentation accuracy by combining multimodal MRI inputs with diffusion reconstructed error maps, enabling precise tumor region correction and improved clinical reliability.

🧠 Domain: Medical Imaging & Generative AI
🎯 Task: Brain Tumor Segmentation
📂 Dataset: BraTS 2020 – Multimodal MRI Dataset (T1, T1ce, T2, FLAIR + Ground Truth Masks)

Develop an automated segmentation model for liver tumors in CT scans and compare the effectiveness of U-Net and ResNet50 architectures in handling irregular boundaries and class imbalance.

🧠 Domain: Medical Imaging & Deep Learning
🎯 Task: Segmentation (Tumor vs. Background)
📂 Dataset: LiTS – Liver Tumor Segmentation Dataset (130 CT scans)

📌 Goal:
Improve brain tumor segmentation accuracy by synthesizing missing modalities using diffusion models and leveraging residual errors as attention cues for efficient, interpretable refinement.

🧠 Domain: Medical Imaging & Semi-Supervised Deep Learning
🎯 Task: Brain Tumor Segmentation (Whole Tumor)
📂 Dataset: BraTS 2020 — Multimodal MRI (355 subjects; FLAIR, T1, T2, T1ce)

Predict chemical presence and concentration patterns in cosmetic formulations using machine learning regression models on the California Safe Cosmetics Program dataset.

🧠 Domain: Data Science & Predictive Analytics
🎯 Task: Regression Modeling
📂 Dataset: California Safe Cosmetics Program (≈114K entries, 20 features)

📌 Goal:
Develop an automated model for early lung cancer detection using CT scans and patient attributes to assist clinicians in rapid, accurate diagnosis.

🧠 Domain: Medical Imaging & Predictive Analytics
🎯 Task: Classification (Cancer vs. Non-Cancer)
📂 Dataset: Kaggle – Lung Cancer Prediction Dataset (284 samples, 16 attributes)

📌 Goal:
Develop a robust fraud detection system using machine learning techniques on highly imbalanced financial transaction data to accurately identify fraudulent activity.

🧠 Domain: Financial Analytics & Machine Learning
🎯 Task: Classification (Fraud vs. Genuine)
📂 Dataset: Kaggle Credit Card Fraud Dataset (284,807 transactions, 492 fraud cases, 0.172%)

📌 Goal:
Predict the likelihood of diabetes based on demographic, lifestyle, and clinical factors using advanced ensemble learning models.

🧠 Domain: Healthcare Analytics & Machine Learning
🎯 Task: Binary Classification (Diabetic vs Non-Diabetic)
📂 Dataset: Kaggle Diabetes Prediction Dataset (100,000 records → 96,146 after cleaning)

📌 Goal:
Predict life expectancy across 193 countries (2000–2015) using regression-based machine learning models to identify the key health, economic, and social factors influencing global longevity.

🧠 Domain: Global Health Analytics & Predictive Modeling
🎯 Task: Regression (Continuous Prediction)
📂 Dataset: WHO Global Health Observatory & UN Data (193 countries × 22 features, 2000–2015)