Student Performance Prediction Using Logistic Regression

1. 1Collect data on student study hours, attendance, and marks
2. 2Preprocess the data by handling missing values and scaling features
3. 3Train a logistic regression model using the collected data
4. 4Evaluate the model's performance using metrics like accuracy and precision
5. 5Deploy the model to predict student performance and identify areas for improvement

Data scientists and educators can benefit from this model to identify at-risk students and provide targeted support

💡 Logistic regression can be used to predict binary outcomes like student performance based on relevant features

Use logistic regression to predict student success based on study hours, attendance, and marks #MachineLearning #Education