- PREDICT STUDENT'S DROPOUT AND ACADEMIC SUCCESS
- Python, Pandas, Keras, Scikit-learn
- Used exploratory data analysis to find patterns and trends in student performance data in a dataset of 4k rows and 35
- Developed an innovative predictive model leveraging machine learning algorithms to accurately identify student risk
- Engineered advanced data preprocessing techniques including one-hot encoding, normalization, and feature scaling
- Determined the most effective hyperparameter configuration through the utilization of techniques such as grid
- Predicted student outcomes with 91% accuracy by using various types of machine learning algorithms and ensemble