Smartphone Repairability Index Web Scraper

Task List — Add the Repairability Index to a webscraping existing program

1. Scrape smartphone repairability index • Scrape the repairability index for all available smartphone models from the official/public source • Extract:

ADEME official dataset (best option)

structured + easier to scrape

https://data.ademe.fr/datasets/indice-de-reparabilite

1. Normalize repairability dataset • Apply the same normalization rules already used for the scraped repair price database • Standardize:

2. Match repairability data with existing device database • Match repairability records to the existing master device list • Use:

3. Link repairability data to existing pricing database • Connect the repairability index to the existing database containing: • scraped equipment / spare parts prices from Utopya

4. Keep repair price granularity by repair type • Keep prices separated by repair type, such as: • other repair categories already available in the scraped dataset

5. Build weighted repair price aggregation • Example: • WeFix = premium market reference • Save = standard market reference • Utopya = spare parts / cost reference • Compute:

6. Add timestamp and freshness tracking • Store the last update date for each scraped record • Make sure each repair price and repairability record has a timestamp

7. Add confidence score • Create a confidence score for each device and repair type based on: • Example: • higher confidence if several sources match correctly

8. Define fallback logic • Define what happens when: • Possible fallback:

9. Create final unified dataset

For each device, consolidate:

1. Export final output • Export the unified result as: • CSV and/or JSON • Ensure the output is ready to be used later for:

2. Deliverables

Final goal

Build a clean and unified database that connects: Budget: EUR 30–250 Skills: Python, Data Processing, Excel, Web Scraping, Data Mining, Data Extraction, Data Analysis, Database Management