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Datasets:
bhargavsai2005
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student_data

Tasks:
Tabular Classification
Tabular Regression
Modalities:
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Text
Formats:
csv
Languages:
English
Size:
< 1K
Tags:
education
tabular
Synthetic
student-performance
computer-science
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metadata 
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 < 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