Store Sales - Deep Learning Solution¶
Welcome to the documentation for the Store Sales - Deep Learning Solution project. This project provides a reproducible pipeline for forecasting store sales using deep learning, developed for the Kaggle Store Sales - Time Series Forecasting competition.
Overview¶
The goal of this project is to predict daily sales for a large Ecuadorian grocery retailer using historical sales data, promotions, oil prices, holidays, and store information. The solution leverages modern data science best practices, modular code, and advanced deep learning techniques.
Key Features¶
- Automated data processing and feature engineering pipelines
- Deep learning model built with Keras/TensorFlow
- Support for reproducible experiments and easy extensibility
- Modular project structure following Cookiecutter Data Science principles
Documentation Structure¶
- Getting Started: How to set up and run the project
- Data Preparation: Steps for processing raw data
- Feature Engineering: Creating features for modeling
- Model Training: Training the deep learning model
- Model Inference: Generating predictions
- Deep Learning Approach: Details of the modeling methodology
- Project Structure: Explanation of the codebase layout
- Future Improvements: Ideas for further development
- References: Useful links and resources
For more details on each step, use the navigation menu or follow the links above.