We aim to create a comprehensive system for monitoring water and electricity consumption without specifying sensor or microcontroller details. This system will offer daily notifications, warnings, and usage statistics to encourage efficient resource use. Furthermore, we plan to incorporate an application that functions seamlessly across multiple platforms, providing a user-friendly experience. Additionally, we intend to utilize AI-driven data analysis and daily surveys to offer tailored recommendations for optimizing resource utilization."
Data Collection: Acquiring a diverse and representative dataset that captures the range of user preferences and behaviors relevant to the personalization task is crucial. Depending on the application, this may involve collecting user interaction data, demographic information, or other contextual data sources.
Data Preprocessing: Cleaning and preprocessing the raw data to remove noise, handle missing values, and transform it into a suitable format for training is essential. This step may also involve feature engineering to extract relevant features from the data.
Model Selection: Choosing an appropriate machine learning or deep learning model architecture for the personalization task is critical. Factors to consider include the nature of the data, the complexity of the task, and the trade-offs between model performance and computational resources.