Mobile Price Classification
Classifying phone price tiers from hardware specifications
- Role
- Data science
- Period
- 2025
- Status
- Open source
- Links
- GitHub ↗
Overview
A classification study predicting a phone's price tier from hardware specs such as RAM, battery, screen and camera; includes a data-collection script, Jupyter analysis and a containerized prediction service.
- A self-collected dataset (phone specs CSV) and a separate data-collection module.
- Training in the notebook, inference in a Python service packaged with a Dockerfile.
01
Problem
How predictable is a phone's price tier from its specs? Instead of a ready-made dataset, I wanted to collect the features myself and build the whole classification pipeline (collection → training → serving).
02
Approach
A separate data-collection module writes phone specs to CSV. Feature analysis and classifier training happen in a Jupyter notebook; the trained model is served from a Python service packaged with a Dockerfile.
Outcome
An end-to-end classification pipeline: collection, analysis, training and containerized inference.
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