Personalized Smart Home Automation Using Machine Learning: Predicting User Activities.

Modern homes are using more intelligent systems to cut energy use and keep occupants comfortable. This project outlines how to build a personalized smart‑home automation system that uses machine learning to predict user activities from past sensor data. By looking at environmental parameters, motion‑detection patterns, and appliance‑usage logs, the system continuously adjusts automation profiles to match each person’s habits. The plan calls for a network of microcontrollers and sensors that gather real‑time environmental data. That data is fed into classification algorithms such as Decision Trees or Support Vector Machines. The resulting predictive model lets the system control lighting, heating, ventilation, and air‑conditioning (HVAC) automatically, without manual input. The architecture focuses

on low‑latency decision‑making and energy efficiency, providing a solid base for next‑generation home‑energy management. Systematic data collection and model training show that machine learning can run at the edge for fast, localized automation. Implementation support includes sensor calibration, data‑pre‑processing pipelines, and deploying the trained model on embedded hardware.

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