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

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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

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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.

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A. Maxbhi.com

Target URLs:
• https://www.maxbhi.com/searchmodel.html?brand_id=6268&ptid=0&query_v3=ipad
• https://www.maxbhi.com/searchmodel.html?brand_id=6271&ptid=0&query_v3=tab

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

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B. Cellspare.com

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

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C. Injuredgadgets.com

Source: https://www.injuredgadgets.com/

Extract:
• All Apple Watches
• All iPads
• All spare parts associated with these devices

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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.

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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

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4. SKU Generation
• Generate a unique SKU for each product
• Maintain a consistent, scalable format

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5. Image Extraction and Processing

A. Image Collection
• Download all product images (not just URLs)

B. Naming Convention (Strict)

Format:
ProductName_Color_SAHII_SKU_Img1.webp

Example:
Ringer_iPhone14ProMax_Black_SAHII_12345_Img1.webp

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 “sahii.in” (provided PNG)
• Maintain quality with compression

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6. Storage & Organization
• Upload all images to a Mega (mega.nz) folder
• Maintain clean folder structure
• Ensure exact mapping between CSV and images

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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

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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