Automation & Applied Data
Price Automation & Budget Optimiser
A Python project combining web scraping, a genetic algorithm and a Streamlit interface to choose computer components within a budget. The approach can also be adapted to price monitoring and purchase planning.
- My role
- Applied data science project development
- From data collection to optimisation
- Python
- Last updated
- 01Price collection
- 02Preparation and constraints
- 03Genetic algorithm
The problem
Choosing computer components means balancing different needs within a fixed budget. Originally called A better build within budget, this project approaches that choice through price collection and an optimisation algorithm.
The solution
The project combines automated web price collection, data preparation and a genetic algorithm to search for component combinations that meet the constraints. Python supports the data workflow, while Streamlit provides the interface.
Business applications
The same approach could be adapted to track competitors’ prices or plan purchases within a fixed budget.
The project demonstrates the complete workflow; it does not publish an algorithm performance benchmark or live, up-to-date prices.