Assignment — Part 4: Student Performance Analysis & Prediction

Overview

This project demonstrates an end-to-end Data Engineering and Machine Learning workflow using Python. It covers:

• Data Exploration: Using pandas to inspect, clean, and aggregate student performance data.
• Data Visualization: Creating insightful charts using both matplotlib and seaborn to understand feature distributions and relationships.
• Machine Learning: Building, evaluating, and interpreting a Logistic Regression model using scikit-learn to predict student success (Pass/Fail).

Files

• part4_visualization_ml.ipynb: The main Jupyter Notebook containing all analysis, visualizations, and ML code.
• students.csv: The dataset containing student grades, attendance, and study hours.
• *.png: Various plot images generated during the visualization tasks.

How to Run

1. Clone the repository.
2. Ensure you have the required libraries installed (pip install pandas matplotlib seaborn scikit-learn).
3. Open part4_visualization_ml.ipynb in Jupyter Notebook or VS Code and run the cells sequentially.