Demand Forecasting (Domain: Digital Supply Chain)- Implemented machine learning predictive model in Azure Databricks to automate demand planning process and supply chain analytics for United Kingdom and Ireland for a leading global FMCG.- Built forecasting models using an ensemble of algorithms like XG Boost, Random Forest, ARIMA to have a better view on the long-term forecast which can optimize the supply chain.- Improved demand forecasting accuracy by 8-10% compared to traditional methods, optimizing resource allocation.Pricing Analytics• Pricing Analytics is a tool used to measure how pricing metrics affect business, determine revenue andprofitability at a specific price point and optimize a business pricing strategy for optima revenue management.• Contributed on automating the python script for various components of Demand Forecasting, Price Optimization and Sales Forecasting. Data Profiling and Matching – Fuzzy • Leveraged Python and Fuzzy Matching to attain best matches for 58,800 rows against 30,000 values, categorizing into Full, Probable, and Lower tiers, showcasing data manipulation expertise for impactful decision-making. Data Standardization - Python• Performed data conversion to a standardized format for enhanced user processing, addressing diverse representations of the 'COUNTRY' column (e.g., INDIA, India, IND, ind). Utilized Python scripting to achieve seamless standardization based on user input. Excel File Comparison - Python• Automated Python-powered Excel file comparison, intricately scrutinizing column values between source and destination tables. My automated script deftly identified disparities, promptly highlighting altered column names, row numbers, and value fluctuations, bolstering data precision.

> Collected data for various professors and their email addresses.

Experienced the vast field of Statistics Gained knowledge about different topics