E-Commerce Shipping Prediction

Project Overview

The e-commerce industry thrives on the promise of timely deliveries. However, the unpredictability of shipment arrival times can undermine customer satisfaction and trust. This project seeks to develop a robust predictive model to forecast whether an e-commerce shipment will be delivered on time. By leveraging historical shipment data and advanced machine learning techniques, we aim to provide reliable delivery estimates and optimize logistics operations.

Demo and Documentation

           

Index

• Project Structure
• Getting Started
  • Prerequisites
  • Installation
  • Running the Project
  • Deployment
  • Output Screenshots
• Contributing
• License
• Acknowledgements

Project Structure

Project Initialization and Planning Phase

• Define Problem Statements
• Project Proposal (Proposed Solution)
• Initial Project Planning Report

Data Collection and Preprocessing Phase

• Data Collection Plan & Raw Data Sources Identification Report
• Data Quality Report
• Data Exploration and Preprocessing Report

Model Development Phase

• Feature Selection Report
• Model Selection Report
• Initial Model Training Code, Model Validation, and Evaluation Report

Model Optimization and Tuning Phase

• Model Optimization and Tuning Report

Project Executable Files

• Model Training file
• Model Testing file
• Flask files (local deployment)

Documentation & Demonstration

• Project Documentation
• Project Demonstration

Getting Started

Prerequisites

• Python 3.x
• Jupyter Notebook
• Libraries: scikit-learn, pandas, numpy, seaborn, pickle, matplotlib, Flask

Installation

1. Clone the repository:

   git clone https://github.com/your_username/ecommerce-shipping-prediction.git
   cd ecommerce-shipping-prediction

2. Install the required libraries:

   pip install -r requirements.txt

Running the Project

1. Navigate to the Jupyter Notebook file in Sub-Folder 5:

   cd '5. Project Executable Files'
   jupyter notebook Ecommerce_Shipping_Prediction.ipynb

2. Run all cells in the notebook to train and evaluate the model.

Deployment

1. Navigate to the Flask application directory in Sub-Folder 5:

   cd '5. Project Executable Files/Flask-Ecommerce'

2. Run the Flask application:

   python app.py

Output Screenshots

Contributing

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature_branch

3. Make your changes and commit them:

   git commit -m "Feature description"

4. Push to the branch:

   git push origin feature_branch

5. Create a new Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

• Kaggle for the dataset
• Team members for their contributions