The objectives of this project involve understanding and analyzing the dataset explained earlier through machine learning techniques while applying robust feature selection algorithms and dimensionality reduction techniques such as Principal Component Analysis (PCA) to enhance the performance and interpretability of machine learning models. We also aim to apply Explainable AI (XAI) techniques to the dataset above, aiming to provide insights into the model decisions.
This dataset contains 10,129 instances and 19 attributes with 18 features and one of the attributes 'Room_Occupancy_Count' recognized as a target variable.