I'm a statistics and data science professional passionate about building intelligent, explainable, and automation-driven systems. I work at the intersection of cybersecurity, healthcare, and finance — leveraging LLMs, low-code tools, and machine learning to solve real-world problems.
Currently pursuing my M.Sc. in Statistics and Data Science at SVKM's NMIMS, Mumbai.
- Built SOC automation pipelines using n8n and local LLMs
- Developed a RAG-based chatbot using LangChain + FAISS
- Automated newsletters from structured sources (Jira, SAST)
- Designed a rule mitigation engine using LangGraph
- Conducted SEO and social audits using UberSuggest and Google Search Console
- Benchmarked client metrics and supported business proposal building
- Analyzed Blackberrys' loyalty program and compared it to competitor strategies
- Fraud detection on 24M+ transactions using XGBoost + GNN
- ROC-AUC: 0.9619, Precision: 88%, Recall: 72%
- User clustering via KMeans after PCA + VIF
- Neural Collaborative Filtering with Hit@10: 96.4%, Precision@K: 91%
- 70K+ EHR records analyzed using Logistic Regression and Random Forest
- ROC-AUC: 0.65 with class imbalance challenges handled
- Causal ML using IPTW + PSM to evaluate asthma drug performance
- Drug_S reduced risk by 47%; Drug_D favored for cost
- XGBoost model: R² = 0.682, RMSE = 0.45
- ODE-based model of glucose-insulin-BMI dynamics
- Achieved 90.5% prediction accuracy using linear regression
- Visualized results with box plots and radar charts
- The Data Analyst Course: Complete Data Analyst Bootcamp (2024)
- Applied AI in Cybersecurity, Finance, and Healthcare
- Graph ML, Causal Inference, SHAP-based Explainability
- Real-world ML deployment using low-code and LLM pipelines