Data Quality Checker

A minimal, professional web tool for instant CSV data quality analysis and reporting. Upload your dataset and get a comprehensive health score, column-level insights, outlier detection, and actionable recommendations — all in the browser, with zero backend dependencies.

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✨ Features

• Drag & Drop Upload — Upload any .csv file directly in the browser
• Data Health Score — A 0–100 composite score based on missing values, duplicates, and inconsistencies
• Column-Level Insights — Per-column type detection, missing value counts, outlier flagging, and fix recommendations
• Duplicate Row Detection — Identifies exact duplicate records across the full dataset
• Outlier Detection — Uses the IQR (Interquartile Range) method to flag statistical anomalies in numeric columns
• Mixed Type Detection — Detects columns with inconsistent data types
• Full Schema Breakdown — A detailed table view of every column with type, missing count, unique count, and status
• Sample Dataset — Try it instantly with a built-in sample CSV, no file needed
• Animated Pipeline UI — Real-time pipeline status feedback with smooth animations via Framer Motion

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🛠️ Tech Stack

Layer	Technology
Framework	React 19 + TypeScript
Build Tool	Vite 6
Styling	Tailwind CSS v4
CSV Parsing	PapaParse
Animations	Framer Motion
Icons	Lucide React

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🚀 Getting Started

Prerequisites

• Node.js v18 or higher
• npm (comes with Node.js)

Installation & Running Locally

# 1. Clone the repository
git clone https://github.com/bhaveshpatil093/data-quality-checker.git
cd data-quality-checker

# 2. Install dependencies
npm install

# 3. Start the development server
npm run dev

The app will be live at http://localhost:3000.

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📁 Project Structure

├── index.html              # HTML entry point
├── vite.config.ts          # Vite + Tailwind configuration
├── tsconfig.json           # TypeScript configuration
├── package.json            # Dependencies & scripts
└── src/
    ├── main.tsx            # React root mount
    ├── index.css           # Global styles & Tailwind theme tokens
    └── App.tsx             # Main application component (all logic & UI)

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📊 How the Health Score Works

The Data Health Score (0–100) is calculated as:

healthScore = 100 - missingPenalty - duplicatePenalty - inconsistencyPenalty

Penalty	Weight	Description
missingPenalty	60%	Proportion of missing cells across the full dataset
duplicatePenalty	20%	Proportion of duplicate rows
inconsistencyPenalty	10% each	Outlier ratio + mixed-type column ratio

Score Range	Label
80–100	✅ Good
50–79	⚠️ Moderate
0–49	❌ Poor

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📋 Available Scripts

Command	Description
npm run dev	Start dev server on port 3000
npm run build	Build production bundle to dist/
npm run preview	Preview the production build
npm run lint	TypeScript type check
npm run clean	Remove the dist/ directory

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👥 Built By

Team Elite — Bhavesh Patil & Shreya Shelar Built for the Databricks–Accenture Hackathon.

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📄 License

Licensed under the Apache 2.0 License.