Digital Detox
Keywords:
Machine Learning, Predictive models, Online Learning, Mobile Addiction, Classification, Cat boost, Decision Tree, Random Forest, Accuracy, Precision, Recall, F score, Confusion matrix, Data PreprocessingAbstract
The project titled "Digital Detox" aims to utilize machine learning algorithms to predict the levels of mobile
addiction among individuals. Mobile addiction is a growing concern in modern society, with adverse effects on
mental health and productivity. This project focuses on using various predictive models to classify the addiction
level into categories such as low, moderate, and high. The proposed method includes a comprehensive data
analysis pipeline that begins with data preprocessing, including encoding categorical variables and scaling
features. The datasets is then split into training and testing subsets. Multiple machine learning algorithms,
including Decision Tree Classifier, Random Forest and Cat boost, are employed to train models on the dataset.
The performance of these models is evaluated using key metrics such as accuracy, precision, recall, and F-score.
The results are visualized through confusion matrices and classification reports. By leveraging these predictive
models, the project aims to provide valuable insights into the factors contributing to mobile addiction, enabling
targeted interventions and awareness campaigns.
