End-to-end analysis of large-scale e-commerce transaction data to identify revenue drivers, profit leakages, and future demand trends using time-series forecasting.
This project is based on an Amazon-style e-commerce dataset containing order-level transactional data including sales, profit, categories, and dates.
The business objective is to:
The analysis focuses on transforming raw data into actionable insights through structured EDA, visualization, and forecasting.
This project analyzes e-commerce data to uncover sales trends, improve profit margins, and enhance customer insights through data-driven decisions.
Python, Pandas, NumPy, Matplotlib, Seaborn, Prophet â used for advanced data preprocessing, visualization, and forecasting.
Achieved reliable sales forecasting accuracy using Prophet by modeling seasonality and trend components. Identified category-level profit inefficiencies and high-impact revenue drivers through exploratory analysis and KPI dashboards.
Overcoming noisy datasets, handling missing values, and fine-tuning forecasting parameters for seasonal e-commerce demand cycles were key challenges.
Adding real-time dashboards, integrating ML models for anomaly detection, and expanding to multi-region forecasting for global optimization.
