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.
- Install the modules required based on the type of implementation.
- Download the dataset you want to train and predict your system with (weather data)
- 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
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