https://github.com/sayamalt/e-commerce-text-classification

Successfully established a machine learning model that can accurately classify an e-commerce product into one of four categories, namely "Books", "Clothing & Accessories", "Household" and "Electronics", based on the product's description.
https://github.com/sayamalt/e-commerce-text-classification

categorical-encoding cross-validation exploratory-data-analysis hyperparameter-optimization machine-learning model-deployment model-training-and-evaluation text-classification text-preprocessing text-vectorization

Last synced: 6 days ago
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Successfully established a machine learning model that can accurately classify an e-commerce product into one of four categories, namely "Books", "Clothing & Accessories", "Household" and "Electronics", based on the product's description.

• Host: GitHub
• URL: https://github.com/sayamalt/e-commerce-text-classification
• Owner: SayamAlt
• Created: 2023-09-19T10:34:28.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-09-22T17:12:35.000Z (over 1 year ago)
• Last Synced: 2024-11-07T12:47:55.744Z (about 2 months ago)
• Topics: categorical-encoding, cross-validation, exploratory-data-analysis, hyperparameter-optimization, machine-learning, model-deployment, model-training-and-evaluation, text-classification, text-preprocessing, text-vectorization
• Language: Jupyter Notebook
• Homepage: https://e-commerce-category-prediction.streamlit.app/
• Size: 10.6 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# About Dataset

This is the classification based E-commerce text dataset for 4 categories - "Electronics", "Household", "Books" and "Clothing & Accessories", which almost cover 80% of any E-commerce website.

The dataset is in ".csv" format with two columns: the first is the class name, and the second is the datapoint of that class. The data point is the product and description from the e-commerce website.

The dataset has the following features :

Data Set Characteristics: Multivariate

Number of Instances: 50425

Number of classes: 4

Area: Computer science

Attribute Characteristics: Real

Number of Attributes: 1

Associated Tasks: Classification

Missing Values? No

Gautam. (2019). E commerce text dataset (version - 2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3355823