SmellSweep

SmellSweep is a comprehensive tool designed to address data quality issues by detecting and refactoring data smells within datasets. It offers a multifaceted approach to data quality management, leveraging methodologies and techniques such as data parsing with Pandas, backend development with Flask, and frontend development with React.js. Key features include 26 implemented data smell detection algorithms, metrics calculation, interactive data visualization with Recharts, and seamless integration into a user-friendly interface.

Table of Contents

• Introduction
• Features
• Installation
• Usage

Introduction

In the realm of data science and analytics, the reliability and accuracy of insights derived from datasets are contingent upon the quality and integrity of the data itself. Data quality issues can manifest in various forms, leading to erroneous analyses, flawed models, and ultimately, unreliable decision-making. Recognizing the critical importance of data quality management, we introduce SmellSweep, a comprehensive tool meticulously crafted to detect and refactor data smells within datasets.

Features

• Detect and refactor 26 different data smells within datasets.
• Utilizes methodologies such as data parsing with Pandas, backend development with Flask, and frontend development with React.js.
• Interactive data visualization with Recharts.
• User-friendly interface for seamless integration into data analysis workflows.

Installation

To install SmellSweep, follow these steps:

1. Clone the repository: git clone https://github.com/your-username/smellsweep.git
2. Navigate to the project directory: cd smellsweep
3. Install dependencies: npm install for the frontend and pip install -r requirements.txt for the backend.

Usage

To use SmellSweep, follow these steps:

1. Run the backend server: python app.py
2. Start the frontend application: npm start
3. Access the application in your browser at http://localhost:3000