bhargavsai2005/student_data

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	< 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

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