Student Performance Analysis and Prediction

Welcome to an exploration into the performance of students based on several interesting factors. Our curiosity is piqued by the relationship between study habits, attendance, parental education, and more on the final outcomes. If you find this notebook useful, please consider upvoting it.

we performed a comprehensive analysis of student performance. We started with data cleaning and preprocessing, followed by several visualization techniques including correlation heatmaps, pair plots, histograms and grouped bar plots which provided valuable insights into the factors driving student success.

A predictive model using logistic regression was built to forecast the Pass/Fail outcome. The accuracy, confusion matrix, and ROC curve enabled us to assess the model's performance, while the permutation importance highlighted which features were most influential.

The approach, combining both exploratory analysis and predictive modeling, ensures a robust understanding of the data. For future analysis, exploring more sophisticated models like Random Forests or Gradient Boosting Machines or incorporating feature engineering might further improve prediction accuracy. Additionally, cross-validation and parameter optimization would be beneficial to enhance model robustness.