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
File size: 302 Bytes
b0f581d | 1 2 3 4 5 6 7 8 9 10 11 | {
"timestamp": "2026-02-25T16:20:36.166556",
"test_samples": 548,
"test_batteries": 30,
"total_models_tested": 13,
"models_passed_95pct": 1,
"overall_pass_rate_pct": 7.6923076923076925,
"best_model": "svr",
"best_within_5pct": 95.07299270072993,
"mean_within_5pct": 34.9522740033689
} |