Tabular Regression
Keras
Joblib
English
battery
state-of-health
remaining-useful-life
time-series
regression
lstm
transformer
xgboost
lightgbm
random-forest
ensemble
Instructions to use NeerajCodz/aiBatteryLifeCycle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use NeerajCodz/aiBatteryLifeCycle with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://NeerajCodz/aiBatteryLifeCycle") - Notebooks
- Google Colab
- Kaggle
| { | |
| "timestamp": "2026-02-25T16:31:44.904601", | |
| "test_samples": 548, | |
| "test_batteries": 30, | |
| "total_models_tested": 16, | |
| "models_passed_95pct": 1, | |
| "overall_pass_rate_pct": 6.25, | |
| "best_model": "random_forest", | |
| "best_within_5pct": 95.07299270072993, | |
| "mean_within_5pct": 53.809306569343065 | |
| } |