Instructions to use climateindexai/total_return_prediction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use climateindexai/total_return_prediction with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://climateindexai/total_return_prediction") - Notebooks
- Google Colab
- Kaggle
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model = load_model('total_return_model.keras')
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## Training Details
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The Climate Index AI LSTM model leverages a comprehensive dataset that integrates historical climate, economic, and real estate data. This structure allows the model to capture the complex relationships between climate variables and financial conditions influencing property returns.
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model = load_model('total_return_model.keras')
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## Try it live
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[Live Demo](https://huggingface.co/spaces/climateindexai/total_return_prediction)
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## Training Details
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The Climate Index AI LSTM model leverages a comprehensive dataset that integrates historical climate, economic, and real estate data. This structure allows the model to capture the complex relationships between climate variables and financial conditions influencing property returns.
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