End-to-End Product Data Scraping, Processing, and Structuring for E-commerce Upload -- 2

Project Title: End-to-End Product Data Scraping, Processing, and Structuring for E-commerce Upload ⸻ Project Overview: I am looking to hire an experienced freelancer or team to perform large-scale data scraping, processing, and structuring of product data from multiple competitor websites. The objective is to create a complete, clean, and standardized dataset, along with fully processed images, that can be directly uploaded to my e-commerce platform with minimal manual effort. This project includes: • Deep scraping (beyond visible listings) • Data cleaning and structuring • SKU generation • Image downloading, renaming, watermarking, and format conversion • Organized cloud storage and delivery ⸻ Scope of Work 1. Data Scraping (Comprehensive Coverage) You must extract all models and all associated spare parts, not just what is visible on listing pages. ⸻ A. [login to view URL] Target URLs: • [login to view URL] • [login to view URL] Critical Requirement: Maxbhi brand pages show limited results (~60), but significantly more models exist via search. You must extract: • ALL models and their images • ALL spare parts for each model Brands to Cover: Nothing, Apple, Samsung, OnePlus, Oppo, Vivo, Realme, Pixel, Asus, Honor, Infinix, iQOO, Lenovo, LG, Xiaomi, Tecno ⸻ B. [login to view URL] Extract: • ALL models • ALL spare parts • Model-level images Brands to Cover: Nothing, Apple (including iPads and Apple Watches), Samsung, OnePlus, Oppo, Vivo, Realme, Pixel, Asus, Honor, Infinix, iQOO, Lenovo, LG, Xiaomi, Tecno, Poco ⸻ C. [login to view URL] Source: [login to view URL] Extract: • All Apple Watches • All iPads • All spare parts associated with these devices ⸻ 2. Data Extraction Requirements For each product, extract: • Product Name • Color (if available) • Model Name • Category (e.g., display, battery, etc.) • Description • Actual Price • Markup Price / MRP • Discount (if available) • Brand Name Ensure clean, complete, and consistent data. ⸻ 3. Data Structuring (CSV Output) Deliver a well-structured CSV file where: • Each row = one product • Each column = one attribute Mandatory Columns: • Product Name • Color • Model Name • Category • Description • Markup Price • Actual Price • Brand Name • img_1 • img_2 • img_3 • img_4 • img_5 • img_6 ⸻ 4. SKU Generation • Generate a unique SKU for each product • Maintain a consistent, scalable format ⸻ 5. Image Extraction and Processing A. Image Collection • Download all product images (not just URLs) B. Naming Convention (Strict) Format: [login to view URL] Example: [login to view URL] Requirements: • Include Product Name + Color + SAHII + SKU + Image Number • Remove competitor references from names C. Multiple Images Handling • Map images into CSV columns (img_1 to img_6) • Each column must contain the correct file name D. Image Processing • Convert all images to WebP • Apply watermark “[login to view URL]” (provided PNG) • Maintain quality with compression ⸻ 6. Storage & Organization • Upload all images to a Mega ([login to view URL]) folder • Maintain clean folder structure • Ensure exact mapping between CSV and images ⸻ 7. Final Deliverables A. CSV File • Clean, structured, complete dataset • One row per product • All required columns filled • Correct image mapping B. Image Dataset • All images downloaded, renamed, watermarked, converted to WebP • Uploaded to Mega C. Data Integrity • No duplicate entries • No missing mappings between products and images • As little competitor branding in output images as possible • Complete coverage of ALL spare parts for each model ⸻ Key Expectations • Exhaustive scraping (not surface-level) • Ability to bypass pagination and search limitations • High accuracy and attention to detail • Output must be ready for direct e-commerce upload

Mã dự án: 40327360