📘 Project Overview
This project demonstrates how to analyze students’ performance using NumPy, a powerful Python library for numerical computation. It covers a range of operations like averages, reshaping, data cleaning, and identifying top-performing students — ideal for beginners learning data analysis using Python.
| Tool | Purpose |
|---|---|
| Python 3.10+ | Programming language |
| NumPy | Array operations & analysis |
| Google Colab / Jupyter Notebook | Code execution & visualization |
📁 Dataset
The dataset represents marks of 10 students across 5 subjects (out of 100):
marks = np.array([ [78, 85, 90, 88, 76], [92, 80, 75, 85, 89], [65, 70, 72, 68, 60], [88, 90, 92, 86, 84], [55, 60, 65, 58, 62], [75, 78, 80, 82, 79], [95, 88, 90, 94, 92], [60, 65, 70, 68, 66], [82, 85, 88, 80, 83], [70, 72, 74, 68, 71] ])
⚙️ Output Summary
| Operation | Result (Example) |
|---|---|
| Average Marks per Student | [83.4, 84.2, 67.0, 88.0, ...] |
| Average Marks per Subject | [76.0, 77.3, 79.6, 77.7, 76.2] |
| Top Scorer | Student 7 with total 484 marks |
| Above 80 in All Subjects | Students 4 and 7 |
| After Grace Marks | All marks increased by +5 |
| Reshaped Array | Converted to 5×10 and flattened again |
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