AdritPal08/EDA-and-ML-Model-Training-of-Student-Performance-Data
The Exploratory Data Analysis and Machine Learning Model Training for the Student Performance Data
Implements a complete ML pipeline with automated data validation, feature engineering, and comparative model evaluation using metrics like RMSE, MAE, and R² score to identify optimal predictors of student test performance. The workflow spans raw Kaggle data through statistical analysis, multivariate correlation exploration, and visualization-driven insights before applying scaling/normalization techniques and training multiple models for cross-validation. Targets regression and classification tasks on educational datasets with emphasis on handling categorical variables and identifying feature importance across demographic and socioeconomic factors.
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Mar 22, 2023
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