Data
Smart City — energy consumption prediction
By Davy AGONMA — Freelance web developer & AI integrator
Problem
Steering a city's residential energy use means being able to anticipate it, while the available data mostly describes lifestyle habits: remote work, mobility, digital usage, vehicle charging.
Solution
A supervised regression project: dataset exploration and cleaning, encoding of urban habit variables, training a Random Forest Regressor to predict a household's consumption in kWh, then performance evaluation.
Outcome
A working prediction model and a complete analysis notebook, built as part of my data science transition.
Similar projects
Data science & machine learning: what this project covers
The data projects in this portfolio come from my transition into data science: supervised regression, clustering, recommendation systems and constraint programming. They're built in Python, covering the full cycle — exploration, cleaning, modelling, evaluation.
They show what I can do with data today, alongside web development and automation: prepare a dataset, pick a model that fits the problem, and make the result usable inside an interface.
Written by Davy AGONMA, freelance web developer & ai integrator.