Retail Transaction System

Project Summary

This is a Retail Transaction System. I built a simple Node.js server that reads retail sales data from a CSV file and turns it into API output for filtering and reporting.

What it does:

• loads sales data from data/retailTransaction.csv
• converts CSV rows into JSON transaction objects
• supports filters by payment method and product category
• gives quick API routes for the frontend to use

Main files and what they do

server.js

The main app file.

• uses express, fs, csv-parse, and cors
• reads data/retailTransaction.csv when the server starts
• converts each row into a JSON object
• groups transactions by payment method and product category
• exposes REST endpoints for the frontend or browser

package.json

Project info and dependencies.

• includes packages like express, csv-parse, and cors
• lets you install everything with npm install

data/retailTransaction.csv

The data file with retail sales records.

• is in CSV format
• includes fields like customer ID, product ID, payment method, category, price, and total amount

How it works

1. Server starts and reads the CSV file using fs.createReadStream().
2. csv-parse parses each row into an object.
3. All transaction objects are stored in memory.
4. The app builds lookup groups for payment methods and product categories.
5. Server runs on localhost:8081 and responds to API calls.

API endpoints

Use these routes to get transaction data:

• GET / — basic health check
• GET /retailData5 — first 5 transactions
• GET /byPaymentMethod/:paymentMethod — transactions by payment method
• GET /byProductCategory/:productCategory — transactions by category
• GET /productCategory — list of available categories
• GET /paymentMethod — list of available payment methods

How to run the project

From the project root run:

npm install
node server.js

Then open http://localhost:8081 in a browser or use the API routes.

What I learned

This project helped me practise reading CSV data in Node.js, building simple REST APIs, and connecting backend data with a frontend app. It was a good exercise for understanding basic server logic and filtering data in a real project.