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https://github.com/sugatagh/E-commerce-Text-Classification

Proper categorization of e-commerce products enhances the user experience and achieves better results with external search engines. The objective of the project is to classify a product into four given categories, based on its description available on an e-commerce platform.
https://github.com/sugatagh/E-commerce-Text-Classification

e-commerce natural-language-processing product-categorization text-classification text-normalization tf-idf word2vec

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Proper categorization of e-commerce products enhances the user experience and achieves better results with external search engines. The objective of the project is to classify a product into four given categories, based on its description available on an e-commerce platform.

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README 

        
# E-commerce Text Classification



## Project Report



**https://sugatagh.github.io/dsml/projects/e-commerce-text-classification/**



## Kaggle Notebook



**https://www.kaggle.com/code/sugataghosh/e-commerce-text-classification-tf-idf-word2vec**



## E-commerce Product Categorization



**E-commerce** (electronic commerce) is the activity of electronically buying or selling of products on online services or over the [**Internet**](https://en.wikipedia.org/wiki/Internet). It frequently uses the [**web**](https://en.wikipedia.org/wiki/World_Wide_Web), if not entirely, for at least some of the life cycle of a transaction, while it may also use other technologies like [**e-mail**](https://en.wikipedia.org/wiki/Email). Typical e-commerce transactions include the purchase of products (like books from Amazon) or services (such as music downloads in the form of digital distribution such as the iTunes Store). [**Online retail**](https://en.wikipedia.org/wiki/Online_shopping), [**electronic markets**](https://en.wikipedia.org/wiki/Electronic_markets), and [**online auctions**](https://en.wikipedia.org/wiki/Online_auction) are the three areas of e-commerce. E-commerce is supported by [**electronic business**](https://en.wikipedia.org/wiki/Electronic_business). The purpose of e-commerce is to enable customers to shop and pay online over the Internet, saving both time and space for consumers and businesses while also significantly improving transaction efficiency.



**Product categorization** or [**product classification**](https://en.wikipedia.org/wiki/Product_classification) is a type of [**economic taxonomy**](https://en.wikipedia.org/wiki/Economic_taxonomy) that refers to a system of categories into which a collection of products would fall under. Product categorization involves two separate tasks:



- Create, support, and expand the catalogue structure for the offered products

- Tagging products with the correct categories and attributes



While machine learning does not have much potential for use in the first task, it is possible to automate the second task, which is relatively laborious and time-consuming. **The problem considered in this notebook involves the categorization of products offered on e-commerce platforms based on the descriptions of the products mentioned therein.** The purpose of such categorization is to enhance the user experience and achieve better results with external search engines. Visitors can quickly find the products they need by navigating the catalogue or using the website's search engine.



## Text Classification



**Text classification** is a widely used [**natural language processing**](https://en.wikipedia.org/wiki/Natural_language_processing) task in different business problems. Given a statement or document, the task involves assigning to it an appropriate category from a pre-defined set of categories. The dataset of choice determines the set of categories. Text classification has applications in emotion classification, news classification, spam email detection, auto-tagging of customer queries etc.



In the present problem, the statements are the product descriptions and the categories are `Electronics`, `Household`, `Books` and `Clothing & Accessories`.



## Data



**Original source:** **https://doi.org/10.5281/zenodo.3355823**



**Kaggle dataset:** **https://www.kaggle.com/datasets/saurabhshahane/ecommerce-text-classification**



The dataset has been scraped from Indian e-commerce platform(s). It contains e-commerce text data for four categories: `Electronics`, `Household`, `Books` and `Clothing & Accessories`. Roughly speaking, these four categories cover $80$% of any e-commerce website, by and large. The dataset is in *.csv* format and consists of two columns. The first column gives the target class name and the second column gives the datapoint, which is the description of the product from the e-commerce website.



## Project Objective



The objective of the project is to classify [**e-commerce**](https://en.wikipedia.org/wiki/E-commerce) products into four categories, based on its description available in the e-commerce platforms. The categories are:

- `Electronics`

- `Household`

- `Books`

- `Clothing & Accessories`



## Overview



- We perform [**exploratory data analysis**](https://en.wikipedia.org/wiki/Exploratory_data_analysis) on the dataset.



- We consider a number of [**text normalization**](https://en.wikipedia.org/wiki/Text_normalization) techniques for product descriptions.



- We use [**TF-IDF**](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) vectorizer on the normalized product descriptions for _text vectorization_, compared the baseline performance of several classifiers, and perform [**hyperparameter tuning**](https://en.wikipedia.org/wiki/Hyperparameter_optimization) on the [**support vector machine**](https://en.wikipedia.org/wiki/Support-vector_machine) classifier with _linear kernel_. The tuned model obtains a validation accuracy of $0.952158$.



- In another direction, we employ a few selected _text normalization_ processes on the raw data on product descriptions. Then, we use [**Google**](https://en.wikipedia.org/wiki/Google)'s pre-trained [**Word2Vec**](https://en.wikipedia.org/wiki/Word2vec) model on the tokens, obtained from the partially normalized descriptions, to get the [**embeddings**](https://en.wikipedia.org/wiki/Word_embedding), which are converted to [**compressed sparse row**](https://en.wikipedia.org/wiki/Sparse_matrix) (CSR) format. The baseline performance of several classifiers are compared. Finally, we perform hyperparameter tuning on the [**XGBoost**](https://en.wikipedia.org/wiki/XGBoost) classifier. The tuned model obtains a validation accuracy of $0.948561$.



- We employ the tuned model with the highest validation accuracy to predict the labels of the test observations and obtained a test accuracy of $0.948939$. We present the confusion matrix depicting the test set performance of the model.