Student Performance Exploratory Analysis

I have the Student Performance Factors dataset ready and I want a clear, insightful exploratory data analysis carried out in a well-commented Jupyter notebook. The focus is on understanding the structure of the data, spotting patterns, and surfacing any relationships worth investigating later with predictive models.

A core requirement is a full set of histograms that show the distribution of every relevant numeric variable—properly labelled and easy to read. If you discover that additional visuals such as correlation heatmaps or box plots would add real value to the narrative, feel free to include them, but the histograms are non-negotiable.

Please clean the data, document each step, and discuss any assumptions you make along the way. Use Python with pandas, NumPy, seaborn and/or matplotlib so the work is reproducible.

Deliverables
• A Jupyter notebook that runs end-to-end from raw data import to final visuals and commentary
• All histogram images saved separately in a /figures folder
• A brief written summary (Markdown cell or standalone PDF) highlighting key observations

Acceptance criteria
The notebook must execute without errors, every histogram must have clear titles and axis labels, and the summary should link each observation back to specific visuals.

If something about the dataset surprises you, point it out—I’m interested in insights, not just charts.