Senior Data Analyst with 2+ years of commercial experience in data science, analytics, strategy and sales and an upcoming master's graduate student in Data Analytics engineering from Northeastern University, Boston. Previously at Ganit Inc.
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The client initially relied on a heuristic forecasting approach characterized by high uncertainty, inconsistency, and limited accuracy, as evident in their forecasts, resulting in inventory imbalances. To address this issue, I developed and implemented a robust machine learning-based demand forecasting model at a granular level, specifically at the distribution level. This implementation significantly improved accuracy, raising it from 37% to an impressive 76%
This project focus on performing data engineering and analysis on Taxi data using various tools like GCP Storage, Python, Compute Instance, Mage Data Pipeline Tool, BigQuery, and Looker Studio
This project harnesses the power of cutting-edge machine learning algorithms and data analysis techniques to forecast match outcomes with a high degree of accuracy