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Household Energy Consumption Estimator Using Regression Models

Added Jun 2025 3 design docs

Most households have no idea which appliances drive their electricity bill, and utilities field endless calls from customers surprised by monthly charges. A simple estimator that translates appliance usage into predicted consumption helps people see where the kilowatt-hours actually go and which behavior changes would genuinely save money.

The intern builds a Flask web application in Python where users enter their appliances, wattages, and daily usage hours. Behind the form, a regression model built with standard data science tooling estimates monthly energy consumption and cost, and the app breaks the prediction down by appliance so users can see the biggest contributors at a glance. The intern assembles a training dataset, engineers features, fits and validates the machine learning model, and wires it into the web flow with clear, friendly result pages that make the numbers meaningful to a non-technical audience.

As an approachable first machine learning project, it teaches the full loop of collecting data, training and evaluating a regression model, and serving predictions through a web interface, while grounding the work in sustainability and practical energy awareness that users can act on immediately.

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