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Machine Learning App using the StreamLit web framework that aims to eliminate the barrier in understanding machine learning model building by streamlining the process thereby allowing non-technical users to harness the power of machine learning through data visualization and input customization

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ishani-chakraborty/machine-learning-hyperparameter-optimization-app

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Machine Learning Hyperparameter Optimization App

This Automated Machine Learning Application gives a the solution Automated ML deployment through a local machine rather than a third-party source like Heroku and also allows and help Machine Learning be accessible and easy to navigate for non-technical users.

Tech stack:

- `streamlit`: web framework
- `pandas`: reading the dataframe
- `numpy`: handling with data matrices
- `base64`: encode results from the tuned hyperparameters as a csv file
- `plotly`: graphing 
- `sci-kit learn`: model building, Random Forest algorithm, in-built dataset, GridSearch

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Machine Learning App using the StreamLit web framework that aims to eliminate the barrier in understanding machine learning model building by streamlining the process thereby allowing non-technical users to harness the power of machine learning through data visualization and input customization

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