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---
license: mit
task_categories:
- tabular-classification
language:
- en
tags:
- education
- data-centric-ai
- label-noise
- cleanlab
pretty_name: Student Grades Dataset
size_categories:
- n<1K
---
# Student Grades Dataset
## Dataset Description
This dataset contains student grade data used in the cleanlab tutorial: [Improving ML Performance via Data Curation with Train vs Test Splits](https://docs.cleanlab.ai/stable/tutorials/improving_ml_performance.html).
The task is to predict each student's final letter grade (A, B, C, D, F) based on their exam scores and notes.
### Dataset Summary
- **Total Examples**: ~750 (train + test)
- **Task**: Multi-class classification
- **Features**:
- `exam_1`: Score on first exam (0-100)
- `exam_2`: Score on second exam (0-100)
- `exam_3`: Score on third exam (0-100)
- `notes`: Categorical notes about student (e.g., "great participation +10", "cheated on exam, gets 0pts")
- `stud_ID`: Unique student identifier
- **Label**: `noisy_letter_grade` - Letter grade (A, B, C, D, F)
### Dataset Structure
```python
from datasets import load_dataset
dataset = load_dataset("cleanlab/student-grades")
# Access splits
train_data = dataset["train"]
test_data = dataset["test"]
# Convert to pandas
import pandas as pd
df_train = train_data.to_pandas()
df_test = test_data.to_pandas()
```
### Data Splits
| Split | Examples |
|-------|----------|
| train | ~600 |
| test | ~130 |
### Dataset Fields
- **stud_ID** (string): Unique student identifier
- **exam_1** (float): First exam score (0-100)
- **exam_2** (float): Second exam score (0-100)
- **exam_3** (float): Third exam score (0-100)
- **notes** (string): Categorical notes about the student
- **noisy_letter_grade** (string): Final letter grade (A, B, C, D, F) - may contain label errors
## Dataset Creation
This dataset was created for educational purposes to demonstrate data-centric AI techniques using cleanlab. The data intentionally contains:
- **Label noise**: Some grades may be incorrectly labeled
- **Near duplicates**: Some examples are very similar or exact duplicates
- **Outliers**: Unusual data points that don't fit the distribution
These issues are introduced to help users learn how to detect and handle common data quality problems using cleanlab.
## Uses
### Primary Use Case
This dataset is designed for:
1. Learning data-centric AI techniques
2. Demonstrating cleanlab's capabilities for detecting label errors, outliers, and near duplicates
3. Teaching proper train/test data curation workflows
### Example Usage
```python
from datasets import load_dataset
from cleanlab import Datalab
# Load dataset
dataset = load_dataset("cleanlab/student-grades")
df_train = dataset["train"].to_pandas()
# Use cleanlab to detect issues
lab = Datalab(data=df_train, label_name="noisy_letter_grade", task="classification")
lab.find_issues()
lab.report()
```
## Tutorial
For a complete tutorial using this dataset, see:
[Improving ML Performance via Data Curation with Train vs Test Splits](https://docs.cleanlab.ai/stable/tutorials/improving_ml_performance.html)
## Licensing Information
MIT License
## Citation
If you use this dataset in your research, please cite the cleanlab library:
```bibtex
@software{cleanlab,
author = {Northcutt, Curtis G. and Athalye, Anish and Mueller, Jonas},
title = {cleanlab},
year = {2021},
url = {https://github.com/cleanlab/cleanlab},
}
```
## Contact
- **Maintainers**: Cleanlab Team
- **Repository**: https://github.com/cleanlab/cleanlab
- **Documentation**: https://docs.cleanlab.ai
- **Issues**: https://github.com/cleanlab/cleanlab/issues
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