Data Science Applications in E-commerce

This repository contains sample code and documentation for data analysis and machine learning applications suitable for an e-commerce context, as well as broadly for any business needing sophisticated text analysis and natural language processing.

Models

1. Text Pair Verification Model (verify_pair.ipynb)

The Jupyter notebook included shows a model intended to assess the correspondence between a keyword and a sentence. This model employs dual encoder Recurrent Neural Networks (RNNs) for encoding, and introduces a connection resembling residual networks to preserve the keyword context during sentence processing. One can also modify the encoders to pre-trained BERT models for exploiting the pre-trained features.

Document: Text Pair Verification Model Overview

2. Keyword Extraction Model (keyword_extract.ipynb)

This notebook outlines a sequence-to-sequence (Seq2Seq) model enhanced with an Attention mechanism, aimed at keyword extraction from texts. The application is typical in various data analysis initiatives.

Document: Keyword Extraction Model Overview

Technology Stack

• Programming Language: Python
• Key Libraries and Frameworks: TensorFlow, PyTorch, Pandas, NumPy
• Development Environment: Jupyter Notebooks
• Source Control Management: Git

Execution Instructions

1. Retrieve the repository:

   git clone https://github.com/your-username/your-repo.git

2. Execute the Jupyter notebooks within the environment:

   • verify_pair.ipynb
   • keyword_extract.ipynb

General Disclaimer

The contents of this repository are designed as a representation of common data analysis and machine learning techniques. The materials are intentionally generic to serve educational purposes and as portfolio samples.

Key Notes:

• The creation of content fully respects intellectual property and any binding legal obligations.
• The provided examples are generic and do not replicate any actual industrial or proprietary work.
• The focus is on demonstrating technical methods and concepts in data science and machine learning.
• There is no implication of endorsement or connection to any company or entity, and these methods are purely theoretical applications.

The repository is intended for showcasing skills and for educational usage within the field of data science.