Aalap Sangviker

Data Analyst fresher pursuing a Master’s degree in Data Science with strong technical proficiency in Python (Pandas, NumPy), SQL, and data analysis workflows. Hands-on experience in data cleaning, ETL processes, exploratory data analysis, and data visualization using BI tools such as Power BI and Tableau. Solid foundation in statistics, machine learning, and data-driven problem solving.

Breast Cancer Prediction using XGBoost & XAI 

• Built hybrid ML system for mammographic breast cancer detection, achieving 97.64% accuracy with XGBoost (outperforming ViT, NAS, and Hierarchical CNN).
• Compared multiple models (ViT, CNN, NAS, XGBoost) to capture local/global image patterns.
• Integrated Integrated Gradients and LIME for transparent predictions and key region highlighting.
• Developed Swagger UI RESTful API for real-time image upload, prediction, and XAI visualization.

Brain Tumor Detection using Generative AI 

• Created deep learning system for brain tumor detection in MRI scans using CNNs, AlexNet, GoogleNet-LSTM, ResNet-LSTM, Vanilla GAN, and DCGAN.
• Used GANs (Vanilla & DCGAN) for synthetic data generation on BraTS dataset to improve model training and generalization.
• Achieved high accuracy, precision, recall, F1-score, and AUC through model ensemble and evaluation.
• Deployed web interface for real-time tumor classification and segmentation.