// Hello, I'm
Sudip Biswas

What I do
From understanding business requirements and exploring raw data to delivering clear insights, I manage the complete analytics process end-to-end.
Data Analysis & Insight Generation
I clean, process, and analyze data to uncover patterns, trends, and actionable insights that support smarter business decisions.
Dashboard & Data Visualization
I create interactive dashboards and visual reports that transform complex data into clear, easy-to-understand stories
Business Intelligence & Reporting
I develop structured reports and performance metrics to track performance, improve efficiency, and support strategic growth.
Skills
Education
2027
MBA
Business Analytics and Data Science
Vidyasagar University
Focused on business intelligence, data analytics, and data-driven decision-making.
2022
M.Sc.
Applied Mathematics
University of Kalyani
Built a strong foundation in statistical analysis, mathematical modeling, and analytical problem-solving.
Projects:
- Experienced inefficiencies in inventory management, demand forecasting, and supplier distribution across multiple warehouse locations.
- Identified regional demand gaps, supplier contribution imbalance, lead time variations, and mismatches between inventory and reorder levels.
- Developed an interactive Power BI dashboard to optimize inventory planning, improve demand forecasting, and support efficient supply chain decision-making.
- Faced difficulty in identifying pricing patterns, premium locations, and investment opportunities due to unstructured and inconsistent property data.
- Identified high-value localities, pricing impact of RERA approval and property status, builder-driven price differences, and weak correlation between area and price per sqft.
- Performed end-to-end EDA using Pandas, Seaborn, and Matplotlib to clean data, analyze trends, and generate actionable insights for real estate decision-making.
- Faced challenges in understanding demand patterns, forecasting accuracy, and seasonal variability across multiple product categories in e-commerce sales data.
- Identified high predictability in grocery demand (~80% accuracy), high volatility and forecasting error in electronics, and increased sales variability during festive periods.
- Applied a 30-day moving average forecasting model and built visualizations to evaluate MAPE, uncover seasonal trends, and support data-driven inventory and supply planning.
- Faced challenges in predicting match outcomes using complex ball-by-ball data, requiring effective feature engineering and handling of real-time match scenarios.
- Identified that chasing teams had higher win probability (55.1%), logistic regression achieved the best performance (AUC: 0.760), and feature engineering had greater impact than complex models.
- Built an end-to-end ML pipeline with EDA, feature engineering, multiple model comparison, and a real-time prediction system to estimate match outcomes based on first innings performance and contextual factors.
- Faced challenges in understanding user behavior, identifying high-intent customers, and reducing revenue loss due to cart abandonment and churn risk across the platform.
- Identified key segments like buyers, browsers, and researchers, along with critical insights such as 63.1% cart abandonment, high CLV churn exposure, and strong impact of session depth and timing on revenue.
- Developed an interactive AI Powered dashboard integrated with Excel-based analysis and AI-driven insights to enable user segmentation, revenue optimization, churn prevention, and targeted marketing strategies.
- Faced challenges in accurately classifying tumors as benign or malignant using medical diagnostic data while avoiding overfitting and ensuring reliable model generalization.
- Identified strong class separation with high predictive performance (ROC-AUC: 0.9934, Accuracy: 98.25%) and minimal false negatives, highlighting the effectiveness of proper regularization and validation techniques.
- Built a deep neural network with batch normalization, dropout, L2 regularization, and stratified cross-validation to deliver a robust and reliable cancer detection system.