🧮 NumPy Student Performance Analysis

This project analyzes student exam performance data using NumPy.
It demonstrates how numerical computations can be done efficiently without Pandas.

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📘 Project Overview

• Generate synthetic student data (500 students × 4 subjects)
• Perform ranking and percentile analysis
• Compare top 10% and bottom 10% students
• Normalize scores using Z-score
• Handle missing values and fill them with mean
• Predict total marks using Linear Algebra (@ matrix multiplication)

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🧠 Concepts Covered

• NumPy arrays and operations
• Vectorization and broadcasting
• Random number generation
• NaN handling and imputation
• Aggregations and slicing
• Linear algebra (np.dot, @ operator)

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📊 Sample Results

• Average, Total, Top/Bottom ranking
• Pass percentage (≥ 200 marks)
• Correlation among subjects
• Weighted total score prediction

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🚀 How to Run

1. Clone this repo or download the .ipynb file
2. Open it in Jupyter Notebook / JupyterLab
3. Run all cells in sequence
4. Requires:

   pip install numpy