Integration of Regression Model into the Streamlit Deployment📊🚀

[Odeyiany2]

What is Needed

We’ve identified the best regression model for our project, created by @Soumyadeep-Basak. Now, we need contributors to:

1. Run the workflow in the notebook using this best model
2. Save all preprocessing steps, feature engineering techniques, and the trained model to the model directory
3. Combine the preprocessing, feature engineering, and model into a pipeline format and integrate this pipeline into the streamlit deployment file so users can get real-time predictions.

How to Contribute

1. Execute the workflow in the notebook and save the trained model to the model directory
2. Access the current script for the streamlit interface and integrate the model into the code.
3. Test your code: It is pertinent that you check your code runs smoothly before making a pull request.
4. Create a Pull Request: Submit a pull request to the repository with your code in the appropriate script and include any necessary documentation.

Getting Started
Before you begin, ensure you have read the Contribution Guidelines in the repository.

We are excited to see your contributions! Happy Hacking! 🎉

[yusufokunlola]

I hope to contribute to this issue before Hacktober Fest ends.

[yusufokunlola]

I hope to contribute to this issue before Hacktober Fest ends.

Due to limited computational resources, I'm unable to work on this issue. I attempted to run the notebook workflow using WSL with Ubuntu 24.04 on my laptop, but VS Code became unresponsive, and I received disconnection timeouts from WSL. Could you suggest any solution to resolve this?

[Sammybams]

Try using Kaggle Notebook or Colab.

…

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