---
license: mit
language:
  - en
tags:
  - education
  - tabular
  - synthetic
  - student-performance
  - computer-science
pretty_name: Student Internal Assessment Marks (CSE, Sem V)
size_categories:
  - n<1K
task_categories:
  - tabular-classification
  - tabular-regression
---

# Student Internal Assessment Marks (Synthetic, Wide Format)

## Dataset description

This dataset contains **70 synthetic student records** for **B.Tech Computer Science and Engineering**, **Semester V** (academic year **2025–26**). Each row is one student with internal assessment marks across **five core subjects**, plus semester totals and letter grades.

The data is **structurally realistic** (roll numbers, subject codes, mid-term pattern, grading bands) but **not sourced from real university systems**. Names and roll numbers are **fabricated for teaching, demos, and ML practice**. Do not use this dataset to identify or evaluate real individuals.

### Intended uses

- Learning **pandas**, **CSV analytics**, and **EDA** on tabular academic data  
- **Regression** (e.g., predict `percentage` or subject finals from mids)  
- **Classification** (e.g., predict `grade` from marks)  
- **Dashboard / visualization** prototypes  
- **Pipeline demos** (ingest → validate → model → report)

### Out of scope

- Official accreditation, placement, or admissions decisions  
- Any claim of real institutional data from VVIT or other colleges  

## Files

| File | Description |
|------|-------------|
| `student_data.csv` | **Primary file** — one row per student (wide / reporting layout), 70 rows + header |

## Dataset structure

- **Rows:** 70 students  
- **Subjects:** 5 (each assessed out of **30** per subject; **150** total per student)  
- **Format:** CSV, UTF-8, comma-separated  

### Subjects

| Code | Short | Full name |
|------|-------|-----------|
| CS501 | DSA | Data Structures and Algorithms |
| CS502 | DBMS | Database Management Systems |
| CS503 | OS | Operating Systems |
| CS504 | CN | Computer Networks |
| CS505 | SE | Software Engineering |

### Assessment rule (per subject)

For each subject, two internal tests **Mid1** and **Mid2** are scored out of **30**. The recorded final (out of 30) is:

\[
\text{Final\_30} = 0.80 \times \max(\text{Mid1}, \text{Mid2}) + 0.20 \times \min(\text{Mid1}, \text{Mid2})
\]

Semester **total** is the sum of the five `*_Final_30` columns (`total_out_of_150`).  
**percentage** = `100 × total_out_of_150 / 150`.  
**grade** follows the scale below.

### Grade scale

| Grade | Percentage |
|-------|------------|
| O | ≥ 90% |
| A+ | 80–89% |
| A | 70–79% |
| B+ | 60–69% |
| B | 50–59% |
| C | 40–49% |
| F | &lt; 40% |

## Data fields (`student_data.csv`)

### Student & cohort metadata

| Column | Type | Description |
|--------|------|-------------|
| `academic_year` | string | e.g. `2025-26` |
| `semester` | string | Roman numeral semester, e.g. `V` |
| `branch` | string | Degree program |
| `section` | string | Section label (`A` or `B`) |
| `roll_number` | string | Synthetic roll ID, e.g. `23BQ1A4201` |
| `student_name` | string | Synthetic full name |

### Per-subject marks (repeat for DSA, DBMS, OS, CN, SE)

| Column pattern | Type | Description |
|----------------|------|-------------|
| `{SUBJ}_Mid1` | integer | First internal test (0–30) |
| `{SUBJ}_Mid2` | integer | Second internal test (0–30) |
| `{SUBJ}_Final_30` | float | Weighted final out of 30 |

`{SUBJ}` ∈ `DSA`, `DBMS`, `OS`, `CN`, `SE`.

### Aggregates

| Column | Type | Description |
|--------|------|-------------|
| `total_out_of_150` | float | Sum of five finals |
| `percentage` | float | Overall percentage |
| `grade` | string | Letter grade (O, A+, A, …) |

## Example records

```csv
academic_year,semester,branch,section,roll_number,student_name,DSA_Mid1,DSA_Mid2,DSA_Final_30,...,percentage,grade
2025-26,V,B.Tech Computer Science and Engineering,A,23BQ1A4201,Aarav Kumar,18,18,18.0,...,73.6,A