A-Infor/Python-OOP-LSTM-Drought-Predictor

AI Summary: The task involves standardizing variable naming conventions in a Python project that predicts the SPEI Drought Index using an LSTM neural network. The project currently mixes snake_case and camelCase naming, requiring a consistent application of snake_case across the entire codebase.

AI Summary: The task involves standardizing the variable naming conventions in a Python project that predicts the SPEI Drought Index using an LSTM neural network. The code currently mixes snake_case and camelCase, and needs to be consistently updated to use snake_case.

AI Summary: Rename variables and loop conditions within an object-oriented Python project (LSTM neural network for drought prediction) that currently use 'train' and 'test' to more neutral terms reflecting data splits like '80%' and '20%'. This involves refactoring code to improve readability and maintain consistency.

AI Summary: Refactor the LSTM neural network code to dynamically determine the train/test split based on percentages specified in a config.json file, rather than using hardcoded '80%' and '20%' values. Replace hardcoded labels like 'train' and 'test' with more flexible identifiers like 'first' and 'last' to improve code maintainability and adaptability.

AI Summary: Resolve a FutureWarning in a Python project using Pandas by modifying DataFrame concatenation to handle empty or all-NA entries. The project involves an LSTM neural network for drought prediction, and the warning arises during the concatenation of metrics dataframes.

AI Summary: Remove a duplicated line of code (`self.has_trained = True`) from the `neural_network.py` file within the LSTM neural network project for drought prediction. The duplicated line is harmless but should be removed for code cleanliness.