Regression analysis is a statistical method used to examine the relationship between one dependent variable and one or more independent variables. In this project, we performed regression analysis to understand how various factors related to technology usage impact mental health. The steps involved in the regression analysis include:
- Data Collection: Gathering data from various sources related to mental health and technology usage.
- Data Preprocessing: Cleaning and preparing the data for analysis.
- Model Training: Training regression models to predict mental health outcomes based on technology usage.
- Model Evaluation: Evaluating the performance of the regression models using metrics such as R-squared and Mean Squared Error (MSE).
Classification is a machine learning technique used to categorize data into predefined classes. In this project, we used classification to predict whether an individual is likely to experience mental health issues based on their technology usage patterns. The steps involved in the classification process include:
- Data Collection: Collecting data related to mental health and technology usage.
- Data Preprocessing: Preparing the data for classification by handling missing values and normalizing features.
- Feature Engineering: Creating new features to improve the performance of the classification models.
- Model Training: Training various classification models such as Decision Trees, Random Forest, and Support Vector Machines (SVM).
- Model Evaluation: Evaluating the performance of the classification models using metrics such as accuracy, precision, recall, and F1-score.
