Skip to content

AdityaMogare/EcomForecast

Repository files navigation

📈 EcomForecast – Advanced Analytics for E-Commerce Sales Forecasting

EcomForecast is an intelligent forecasting platform designed to empower e-commerce stakeholders with advanced time series predictions. It integrates traditional statistical models and deep learning approaches (LSTM, GRU) to generate accurate sales forecasts from historical data.

Qualified as a finalist for ICCSI-2023 (CREST, IIT Madras) for innovation in business analytics and AI-driven decision support systems.


🎯 Project Objective

Enable e-commerce businesses to:

  • Predict future sales trends
  • Identify seasonal patterns and growth anomalies
  • Make inventory, marketing, and financial decisions proactively

💡 Key Features

  • 🧠 Multi-Model Support: Includes SMA, EMA, WMA, LSTM, GRU
  • 🧾 CSV Upload Interface: Clean preprocessing pipeline to handle raw e-commerce sales data
  • 📊 Visual Forecasting: Interactive plots showing actual vs predicted sales
  • 🛠️ Modular Design: Easily plug in new models or switch strategies
  • 🏆 Pitch Finalist: Selected for final presentation at IIT Madras (ICCSI-2023)

🧰 Tech Stack

Component Technology
Forecasting Models Pandas, NumPy, statsmodels, PyTorch
Deep Learning Models LSTM, GRU (using PyTorch/Keras)
UI Interface (Optional) Streamlit / Gradio
Visualization Matplotlib, Plotly
Data Format CSV time series

🗂 Folder Structure

About

A time-series forecasting platform using LSTM, GRU, and weighted moving averages to predict e-commerce sales trends and inventory demands.

Topics

Resources

Stars

0 stars

Watchers

0 watching

Forks

Releases

No releases published

Packages

 
 
 

Contributors