To develop a machine learning model for predicting asthma disease in students based on various factors such as age, gender, environmental factors, and educational background.
To analyze the performance of different machine learning algorithms in predicting asthma disease in students.
To provide insights and recommendations for early detection and prevention of asthma disease in students through machine learning techniques.
Collect relevant data related to asthma disease, including demographic information, environmental factors, and educational background of students.
Preprocess the data by handling missing values, encoding categorical variables, and scaling numerical features.
Implement various machine learning algorithms such as logistic regression, decision trees, random forests, and support vector machines for predicting asthma disease in students.
Evaluate the performance of the machine learning models using metrics such as accuracy, precision, recall, and F1 score.
Interpret the results and provide recommendations for educational institutions to effectively identify and support students at risk of asthma disease.
