University-Student-Performance-Analytics-System

Analyze and visualize student performance data across multiple sections to identify trends in grades, attendance, and overall academic success.

🧩 Dataset

Each record represents a student with the following columns:

• Student_ID
• Name
• Section
• Attendance_%
• Midterm
• Final
• Assignments
• (You’ll later calculate) → Final_Grade, Pass/Fail

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🧠 Tasks

1. Data Generation / Loading
   • Create or load student data (around 30–50 students).
   • Use NumPy to generate random but realistic marks and attendance values.
2. Data Cleaning
   • Handle missing or duplicate values.
   • Ensure numeric columns have correct data types.
3. Feature Engineering
   • Compute weighted Final Grade using Midterm, Final, and Assignments.
   • Add Pass/Fail column based on grade threshold (e.g., 60%).
4. Statistical Analysis
   • Find average, median, and standard deviation of grades.
   • Compare section-wise performance (A, B, C).
   • Find correlation between attendance and grades.
5. Visualizations (Matplotlib)
   • Histogram of final grades
   • Bar chart: average grade per section
   • Scatter plot: attendance vs final grade
6. Business / Academic Insights
   • Which section performed best?
   • Does higher attendance lead to better grades?
   • What percentage of students passed or failed?
7. Export
   • Save the cleaned and processed dataset to CSV.