https://github.com/labrijisaad/technical-test-nlp-category-correction

This repo has a Jupyter Notebook for an e-commerce NLP and data manipulation technical test.
https://github.com/labrijisaad/technical-test-nlp-category-correction

color-extraction data-processing dimension-extraction nlp

Last synced: 16 days ago
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This repo has a Jupyter Notebook for an e-commerce NLP and data manipulation technical test.

• Host: GitHub
• URL: https://github.com/labrijisaad/technical-test-nlp-category-correction
• Owner: labrijisaad
• Created: 2023-12-15T15:28:42.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-01-02T13:33:42.000Z (about 1 year ago)
• Last Synced: 2024-11-06T09:08:37.269Z (2 months ago)
• Topics: color-extraction, data-processing, dimension-extraction, nlp
• Language: Jupyter Notebook
• Homepage:
• Size: 12.1 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# `Technical Test`: NLP Category Correction 📊🔍

## Overview

This repository contains a Jupyter Notebook for a technical test focusing on NLP (Natural Language Processing) and data

manipulation, specifically tailored for e-commerce data analysis.

## Features

- **Data Preprocessing** 🔄: Importing libraries, reading data, renaming columns, and date conversion.

- **Dimension & Color Extraction** 📏🎨: Functions to extract dimensions and colors from product descriptions.

- **Categorization Correction** 🏷️: Algorithms to check and correct product categorization.

- **Data Analysis** 📈: Visualization and statistics of the processed data.

## Getting Started 🚀

Follow these steps to run the [notebook](./notebooks/Technical%20Test.ipynb) locally:

1. **Clone the Repository**

   ```bash

   git clone https://github.com/labrijisaad/Technical-Test-NLP-Category-Correction.git

   ```

2. **Set Up the Environment**

    - Run `make setup` to create a virtual environment and install dependencies.

3. **Launch Jupyter Lab**

    - Execute `make jupyter` to activate the virtual environment and start Jupyter Lab.

4. **Navigate to the Notebook**

    - Open the `/notebooks` directory and run the Jupyter Notebook to explore the data.

## Contributions 🤝

Your contributions are welcome! Check out

the [issues page](https://github.com/labrijisaad/Technical-Test-NLP-Category-Correction/issues).

## 🙌 Connect with Me: