CSVision

This project is a full-stack web application for uploading, mapping, and merging two datasets (CSV files) to analyze relationships between two datasets. Built for the EY Data Integration Challenge at Hack-The-Valley-X.

Features

• Upload two CSV datasets (e.g., weather data and energy consumption data)
• Automatic column mapping using exact match and string similarity
• Merges and concatenates all rows from both datasets, combining mapped columns and including unmapped columns
• Displays merged data and column mapping in a clean, interactive web UI
• Built with React (frontend) and Node.js/Express (backend)

How It Works

1. Upload: Select and upload two CSV files via the dashboard.
2. Mapping: The backend automatically maps columns between the two datasets using name similarity.
3. Merging: All rows from both datasets are merged. Mapped columns are unified; unmapped columns are appended.
4. Visualization: The merged data and column mapping are displayed in the web UI for further analysis.

Tech Stack

• Frontend: React, Tailwind CSS
• Backend: Node.js, Express, Multer, csv-parser, string-similarity
• Other: CORS, file upload handling

Getting Started

Prerequisites

• Node.js (v14+ recommended)
• npm

Installation

1. Clone the repository

   git clone https://github.com/Yatriba-Rathod/Hack-The-Valley-X.git
   cd Hack-The-Valley-X

2. Install backend dependencies

   cd backend
   npm install

3. Install frontend dependencies

   cd ../frontend
   npm install

Running the App

1. Start the backend server

   cd backend
   node server.js

2. Start the frontend

   cd ../frontend
   npm start

3. Open http://localhost:3000 in your browser.

Project Structure

backend/
  server.js
  package.json
  uploads/
frontend/
  src/
    App.js
    components/
      DataUploader.jsx
  package.json

Example Use Case

• Dataset 1: Weather data (temperature, humidity, etc.)
• Dataset 2: Energy consumption data (energy usage, occupancy, etc.)
• Goal: Analyze how weather conditions affect energy consumption by merging and mapping relevant columns.

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Built for Hack-The-Valley-X EY Data Integration Challenge.