[Project Addition] Credit Risk Prediction using Neural Networks

[Srijansarkar17]

Deep Learning Simplified Repository (Proposing new issue)

:red_circle: Credit Risk Prediction using Neural Networks :
:red_circle: To be able to correctly predict the credit risk of an individual with the help of various featrues :
:red_circle: credit_risk_data.csv :
:red_circle: I will be training this model based on various methods. First I will preprocess the data and then apply some data visualization. Then I will use neural networks for training. For training, I will use various activation functions and various loss metrics for getting the best accuracy score. I will also use numerous hidden layers for the best accuracy :

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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:red_circle::yellow_circle: Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

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:white_check_mark: To be Mentioned while taking the issue :

• Full name : Srijan Sarkar
• GitHub Profile Link : https://github.com/Srijansarkar17
• Email ID : srijanb463@gmail.com
• Participant ID (if applicable): NA
• Approach for this Project : I will be training this model based on various methods. First I will preprocess the data and then apply some data visualization. Then I will use neural networks for training. For training, I will use various activation functions and various loss metrics for getting the best accuracy score. I will also use numerous hidden layers for the best accuracy
• What is your participant role? (Mention the Open Source program) GSSOC

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[github-actions[bot]]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[abhisheks008]

What are the deep learning models you are planning to implement? Can you please specify.

[Srijansarkar17]

I will be using basic neural networks, but I will try to use many activation functions such as sigmoid, tanh etc and various loss functions.

[abhisheks008]

Can you specify the models/architectures you are planning to implement?

[Srijansarkar17]

I will be using 4 layers hidden neural network architechture with the activation function of hidden layers as 'relu' and the activation function of the output neuron as sigmoid. The loss function used in this case will be 'binary_crossentropy' because the output label is a binary classifier.

[abhisheks008]

I will be using 4 layers hidden neural network architechture with the activation function of hidden layers as 'relu' and the activation function of the output neuron as sigmoid. The loss function used in this case will be 'binary_crossentropy' because the output label is a binary classifier.

That's one algorithm. What are the other 3 models you are planning for this project.