Student Performance Analytics System

Objective

Analyze student marks and attendance using Python, Pandas, and NumPy.

Features

• Load a CSV dataset using Pandas
• Calculate total and average marks
• Assign grades based on average
• Identify pass/fail students
• Calculate subject-wise average, highest, and lowest marks
• Summarize class performance and list top five students
• Analyze department averages and attendance
• Plot subject averages and top performers
• Export calculated results to CSV

Technologies

• Python
• Pandas
• NumPy
• Matplotlib
• Google Colab

Dataset

students.csv contains 20 sample records with Student_ID, Name, Department, Python, Maths, AI, and Attendance.

Example rules

• Pass: at least 35 marks in each subject.
• Grade: A >= 90, B >= 80, C >= 70, D >= 60, E >= 50, F < 50 based on average.
• These are example rules; update them if the trainer provides different rules.

How to run

1. Open the notebook in Google Colab.
2. Upload students.csv, or run the cell that creates the sample dataset.
3. Run notebook cells in order.
4. Download student_performance_results.csv from the final cell.

Important

Replace sample data if required and make sure you understand and can explain the code before submitting.