sleep-hours-predictor-automl-ag

Model Card for keerthikoganti/sleep-hours-predictor-automl-ag

This dataset contains self-reported technology-use patterns and routines.

Model Details

Model Description

This dataset contains self-reported technology-use patterns and routines, plus sleep timing/quality attributes. It was created as a class exercise for supervised learning on tabular data. The primary task is regression — predicting nightly sleep_hours. The same features can also support simple classification tasks if desired.

• Developed by: Keerthi Koganti
• Model type: AutoML (AutoGluon Tabular ensemble; model family chosen via search)
• Language(s) (NLP): English
• Task: Tabular Regression
• Target column: sleep_hours (continuous, hours slept per night)
• License: Carnegie Mellon educational use
• Framework: autogluon.tabular
• Repo artifacts: autogluon_sleep_model.zip (zipped native AutoGluon predictor directory) metrics.json (test-set metrics for reproducibility)

Model Sources

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• Repository: Iris314/Students_sleep_tabular

Uses

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Direct Use

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Classroom demos of AutoML on tabular regression tasks

Baseline experiments for feature engineering and evaluation

Comparing different AutoML presets and model search spaces

Out-of-Scope Use

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Production deployment or any sleep/health recommendation system

Generalization beyond course context and small cohort data

Clinical or safety-critical applications

Bias, Risks, and Limitations

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Small sample size and potential sampling bias.

Self-report bias in device use and sleep estimates.

Domain shift likely for other age groups, locations, or lifestyles.

Recommendations

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Use primarily for teaching and demonstration of tabular ML workflows. If you publish results, disclose the split strategy, preprocessing, and any imputations/encodings performed by AutoGluon.

How to Get Started with the Model

Use the code below to get started with the model.

import pathlib, shutil, zipfile import huggingface_hub as hf from autogluon.tabular import TabularPredictor

REPO = "keerthikoganti/sleep-hours-predictor-automl-ag" ZIPNAME = "autogluon_sleep_model.zip"

dest = pathlib.Path("hf_download") dest.mkdir(exist_ok=True)

Download zipped predictor directory

zip_path = hf.hf_hub_download( repo_id=REPO, filename=ZIPNAME, repo_type="model", local_dir=str(dest), local_dir_use_symlinks=False, )

Extract to folder

extract_dir = dest / "predictor_dir" if extract_dir.exists(): shutil.rmtree(extract_dir) extract_dir.mkdir(parents=True, exist_ok=True)

with zipfile.ZipFile(zip_path, "r") as zf: zf.extractall(str(extract_dir))

Load predictor from native directory

predictor = TabularPredictor.load(str(extract_dir))

Example: predictions on a new DataFrame X

preds = predictor.predict(X)

Training Details

Training Data

Dataset: Iris314/Students_sleep_tabular

Splits: 80/20 random split (random_state=42) on the augmented data (≈300 rows)

Target column: sleep_hours

Training Procedure

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Library: AutoGluon Tabular

Presets: "best_quality" (ensembles across boosted trees, RF, kNN, neural nets, etc.)

Training time limit: 300 seconds

Evaluation metric (internal): AutoGluon default for regression (root_mean_squared_error)

Training Hyperparameters

• Training regime: time_limit: 300s

presets: "best_quality"

random_state: 42

problem_type: inferred automatically by AutoGluon

eval_metric: AutoGluon default for regression

Evaluation

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Testing Data

Held-out 20% of the augmented split (≈60 rows)

Metrics (replace with actuals from metrics.json)

R² = 0.78

MAE = 0.62 hours

RMSE = 0.95 hours

Environmental Impact

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Training was lightweight and classroom-focused:

Hardware: CPU runtime in Google Colab (no GPU)

Training wall-time: ≤ 5 minutes

Estimated emissions: negligible

Cloud provider: Colab (Google)

Model Card Contact

Keerthi Koganti — [email protected]