Tabular Regression
Scikit-learn
Joblib
regression
tabular
advertising
marketing
roi-prediction
xgboost
Instructions to use speedupp/ad-roi-regression-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use speedupp/ad-roi-regression-model with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("speedupp/ad-roi-regression-model", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
| { | |
| "best_model": "RandomForest", | |
| "features": [ | |
| "ad_spend", | |
| "impressions", | |
| "conversion_rate", | |
| "customer_acquisition_cost", | |
| "platform", | |
| "product_category", | |
| "cost_per_impression", | |
| "spend_cac_ratio" | |
| ], | |
| "feature_importances": { | |
| "ad_spend": 0.0438, | |
| "impressions": 0.0442, | |
| "conversion_rate": 0.1226, | |
| "customer_acquisition_cost": 0.1501, | |
| "platform": 0.0824, | |
| "product_category": 0.0615, | |
| "cost_per_impression": 0.4377, | |
| "spend_cac_ratio": 0.0578 | |
| }, | |
| "metrics": { | |
| "Ridge": { | |
| "MAE": 0.8472, | |
| "RMSE": 1.0287, | |
| "R2": 0.4118, | |
| "CV_R2_mean": 0.4088, | |
| "CV_R2_std": 0.0152 | |
| }, | |
| "RandomForest": { | |
| "MAE": 0.7742, | |
| "RMSE": 0.9334, | |
| "R2": 0.5157, | |
| "CV_R2_mean": 0.4923, | |
| "CV_R2_std": 0.0164 | |
| }, | |
| "GradientBoosting": { | |
| "MAE": 0.7933, | |
| "RMSE": 0.9567, | |
| "R2": 0.4912, | |
| "CV_R2_mean": 0.4574, | |
| "CV_R2_std": 0.0145 | |
| }, | |
| "XGBoost": { | |
| "MAE": 0.795, | |
| "RMSE": 0.9671, | |
| "R2": 0.4801, | |
| "CV_R2_mean": 0.4449, | |
| "CV_R2_std": 0.0192 | |
| }, | |
| "ExtraTrees": { | |
| "MAE": 0.7697, | |
| "RMSE": 0.9266, | |
| "R2": 0.5227, | |
| "CV_R2_mean": 0.4877, | |
| "CV_R2_std": 0.0189 | |
| } | |
| }, | |
| "platforms": [ | |
| "Facebook", | |
| "Instagram", | |
| "Google_Ads", | |
| "YouTube", | |
| "Car_Commuting_Network" | |
| ], | |
| "product_categories": [ | |
| "Electronics", | |
| "Fashion", | |
| "Food_Beverage", | |
| "Health_Wellness", | |
| "Automotive", | |
| "Travel", | |
| "Finance", | |
| "Education", | |
| "Real_Estate", | |
| "SaaS" | |
| ], | |
| "recommendations": { | |
| "Electronics": { | |
| "best_platform": "Google_Ads", | |
| "avg_roi": 5.942 | |
| }, | |
| "Fashion": { | |
| "best_platform": "Google_Ads", | |
| "avg_roi": 5.07 | |
| }, | |
| "Food_Beverage": { | |
| "best_platform": "Google_Ads", | |
| "avg_roi": 5.198 | |
| }, | |
| "Health_Wellness": { | |
| "best_platform": "Google_Ads", | |
| "avg_roi": 5.569 | |
| }, | |
| "Automotive": { | |
| "best_platform": "Google_Ads", | |
| "avg_roi": 5.578 | |
| }, | |
| "Travel": { | |
| "best_platform": "Google_Ads", | |
| "avg_roi": 5.468 | |
| }, | |
| "Finance": { | |
| "best_platform": "Google_Ads", | |
| "avg_roi": 5.873 | |
| }, | |
| "Education": { | |
| "best_platform": "Google_Ads", | |
| "avg_roi": 5.674 | |
| }, | |
| "Real_Estate": { | |
| "best_platform": "Google_Ads", | |
| "avg_roi": 5.371 | |
| }, | |
| "SaaS": { | |
| "best_platform": "Google_Ads", | |
| "avg_roi": 6.124 | |
| } | |
| }, | |
| "dataset_size": 5000 | |
| } |