Time Series Forecasting
Transformers
English
lstm
regression
transformer
inform_Severity_prediction
crisis_prediction
Instructions to use sayyedAhmed/INFORM_Severity_Prediction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sayyedAhmed/INFORM_Severity_Prediction with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sayyedAhmed/INFORM_Severity_Prediction", dtype="auto") - Notebooks
- Google Colab
- Kaggle
The imputation and model pipelines for the INFORM Severity data.
Ridge Regression with 88% accuracy(alpha=25 and 10-fold CV)
LightGBM with 99% accuracy (10 fold CV and reg_alpha: 1.96248962962756, reg_lambda: 4.688549449064171)
There is a 1 single pipeline for FeatureEngineering, Categorical encoding, Transformations and Imputations.
We just need to deploy the final_pipelines for both the models stored in file: final_pipe.pkl
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