Weather Data Time Series

• The dataset I use has > 5000 samples.
• Using LSTM in a model architecture.
• Using a sequential model.
• The validation set is 20% of the total dataset.
• Using a sequential model.
• Using Embedding.
• Using the tokenizer function.
• Using the Learning Rate in the Optimizer.
• Has the accuracy of the model 98%.
• MAE <10% of data scale.

1. Install the modules required based on the type of implementation.
2. Download the dataset you want to train and predict your system with (weather data)
3. Train your data using Google Colab (https://colab.research.google.com/)

I highly encourage the community to step forward and improve this code further. You can fix any reported bug, propose or implement new features, write tests, etc.

Here is a quick list of things to remember -

• Check the open issues before creating a new one,
• Help me in reducing the number of open issues by fixing any existing bugs,
• Check the roadmap to see if you can help in implementing any new feature,
• You can contribute by writing unit and integration tests for this library,
• If you have any new idea that aligns with the goal of this library, feel free to raise a feature request and discuss it.

Skilled Android, DevOps and IoT Engineer with 3+ years of hands-on experience supporting, automating, and optimizing mission critical deployments in AWS, leveraging configuration management, CI/CD, and DevOps processes.

Copyright 2021 ksatriow

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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   http://www.apache.org/licenses/LICENSE-2.0

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