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xgboost-diabetes-pima-project
Gaballa-ML/xgboost-diabetes-pima-project
xgboost-diabetes-pima-project is a tabular classification model from Gaballa-ML. Use it for the tabular classification task on the model card, and read the license before you ship it in a product. It is set up for xgboost. The card lists the license as mit.
Trained on the Pima Indians Diabetes Dataset using GridSearchCV.
Access
Public
Updated Jan 5, 2026
Repo size
121 KB
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0
Public
Repo makeup
Click a slice to open those files.
.pkl121 KB · 98%
At a glance
- Task
- Tabular Classification
- Library
- xgboost
- License
- mit
- Access
- Public
- Created
- Jan 5, 2026
- Updated
- Jan 5, 2026
- SHA
- f9633efb
Datasets
Model card
From the Hugging Face model README
XGBoost Diabetes Prediction Model
Trained on the Pima Indians Diabetes Dataset using GridSearchCV.
Model Info
- Algorithm: XGBoostClassifier
- Best Params:
- n_estimators: 100
- max_depth: 3
- learning_rate: 0.01
- reg_lambda: 1.2
- Test Accuracy: ~79%
How to Use
from huggingface_hub import hf_hub_download
import pickle
model_path = hf_hub_download(repo_id="Gaballa-ML/xgboost-diabetes-pima-project", filename="model.pkl")
with open(model_path, 'rb') as f:
model = pickle.load(f)
# Predict: [Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age]
prediction = model.predict([[6, 148, 72, 35, 0, 33.6, 0.627, 50]])
print("Diabetes Prediction (0=No, 1=Yes):", prediction[0])
- Task
- Tabular Classification
- Library
- xgboost
- License
- mit
- Languages
- en
- Created
- Jan 5, 2026
- Updated
- Jan 5, 2026