Welcome to my GitHub!
This space reflects my work and interests in Data Science, Business Analytics, and Applied Machine Learning across domains such as BFSI, E-commerce, and Retail.
I hold a Masterβs in Business Analytics and specialize in designing data-driven solutions that enable organizations to make informed, scalable, and interpretable decisions. My work focuses on turning complex data into actionable insights while ensuring models are aligned with real-world strategies, policies, and business objectives.
- Predictive Modeling & Forecasting: Customer behavior, demand forecasting, credit/loan performance, churn analysis
- Segmentation & Personalization: Customer profiling, clustering, and recommendation systems
- Risk & Fraud Analytics: Anomaly detection, rule-based and ML-driven fraud prevention
- Model Monitoring & Governance: Performance tracking, fairness, explainability (LIME, SHAP), regulatory alignment
- NLP Applications: Text classification, sentiment analysis, document intelligence for financial, retail, and e-commerce data
- Business Intelligence: Dashboards, KPI tracking, operational efficiency analytics
Languages and Environments
Python
, SQL
, Pyspark
β Jupyter Notebook, Google Colab, VSCode
Core Concepts
- Statistics & Analytics: Hypothesis Testing, ANOVA, Z-Test, T-Test, Chi-Square, A/B Testing, Experimental Design
- Machine Learning: Regression (Linear, Logistic), Decision Trees, Random Forest, XGBoost, Clustering, Time Series Forecasting (ARIMA, Prophet, LSTM)
- Advanced Techniques: Feature Engineering, Model Monitoring & Validation, Explainability (SHAP, LIME), Anomaly Detection, Recommendation Systems
- NLP: Text Classification, Sentiment Analysis, Topic Modeling, NER β NLTK, spaCy, Gensim, Hugging Face Transformers
- Data Engineering: Pandas, NumPy, PySpark, Featuretools, ETL Pipelines, Data Cleaning & Transformation
- Visualization & BI: Matplotlib, Seaborn, Plotly, Power BI, Tableau β Storytelling with Data & KPI Dashboards
- Deployment & MLOps: Docker, Kubernetes, MLflow, CI/CD Pipelines
- Cloud & Warehousing: AWS (S3, Redshift, SageMaker), Google BigQuery, Snowflake, GCP (Vertex AI)
- Version Control: Git, GitHub, GitLab
- Project & Collaboration Tools: GitHub Projects, Jira, Agile/Scrum
Project | Domain | Description |
---|---|---|
Credit Scoring Model | Credit Risk | End-to-end logistic regression model for risk-based lending with WOE/IV and scorecard implementation |
Fraud Detection Engine | Fraud Analytics | Feature engineering and anomaly detection pipeline for first-party fraud identification |
Nifty Sector Analysis | Market Risk | Return-based clustering framework for sectoral analysis and stock screening |
Loan Default Predictor | Credit Risk | Classification model using borrower attributes and financial behavior |
Coming Soon! | Test Case | Test. |
Currently working on articles related to model interpretability, credit scoring frameworks, and risk strategy.
π Follow me on Medium
- Interested in data-driven solutions for credit risk or fraud? Letβs discuss
- Found something valuable? Feel free to star or fork the project
- Have feedback or ideas? I welcome collaboration and open-source contributions
- π LinkedIn
- π§ ping.sunilk@gmail.com
- π Website: sunkumx.github.io
- π° Medium
βIn numbers we find patterns, in patterns we find truth.β
Thanks for visiting! π