---
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