The objective of this project is to develop a machine learning model that can predict the inmersion level of an individua based on relevant features such as age, duration of use, and other factors using the ”vr dataset” dataset.
This project is focused on building machine learning classifiers to predict the level of immersion based on various features.
The project is divided into two parts:
ML_part_1.ipynb: The goal is to generate a classifier to predict the immersion level on a scale from 1 to 5. The classifier used is a Random Forest Classifier. However, the model performance on this dataset is poor, there are several reasons that may affect this issue some of these will be analyzed and discussed during the code development.
ML_part_2.ipynb: Given the poor performance in part one, the problem is simplified. Instead of predicting a specific immersion level, the classifier will predict whether the immersion level is "good" or "bad". Additionally, feature selection techniques will be applied to select different types of features to make the data easier to learn.