Student Performance Predictor is an end-to-end machine learning project that implements a complete predictive modeling pipeline. It analyzes the impact of demographic, socioeconomic, and academic factors on student mathematics performance, performing data preprocessing, feature engineering, regression modeling (Linear, Ridge, Lasso, Random Forest,
flask-web gradient-boosting lasso-regression random-forest-regressor decision-tree-regressor standardscaler xgboost-regressor linear-regression-model catboost-regressor adaboost-regressor one-hot-encoder render-deployment ridge-regresson matplotlib-seaborn-and-plotly data-ingestion-and-preprocessing k-neighbours-regressor pandas-and-numpy
-
Updated
Oct 18, 2025 - Jupyter Notebook