Cardiovascular Disease Risk Prediction DL Model

[Avdhesh-Varshney]

Deep Learning Simplified Repository (Proposing new issue)

:red_circle: Project Title : Cardiovascular Disease Risk Prediction Model
:red_circle: Aim : The aim of the project is to analyze and predict whether the person having the chances of CVD.
:red_circle: Dataset : https://www.kaggle.com/datasets/alphiree/cardiovascular-diseases-risk-prediction-dataset
:red_circle: Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

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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 : Avdhesh Varshney
• GitHub Profile Link : https://github.com/Avdhesh-Varshney
• Email ID : avdheshvarshney786@gmail.com
• Participant ID (if applicable): SWOC'24 Contributor
• Approach for this Project :
  • Analyzing the dataset.
  • EDA processing.
  • Filtering the dataset or performing feature engineering so that which can easily fit on the model.
  • Feature selection using tree-based models, etc.
  • Creating deep neural networks model, rnn models, lstm models, etc.
  • Tune the hyper-parameters and improving the loss and accuracy of the model.
  • Summarizing all the algorithm's efficiency and scores at the end of the notebook.
  • Model comparison with their precision, f1 score and recall.
  • Identifying the best fit algorithm and plot the graph of that algorithm as how it's fit it on training set and test set.
• What is your participant role? SWOC'24

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Happy Contributing 🚀

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

[Avdhesh-Varshney]

@abhisheks008 please assign this issue to me to work under SWOC'24

[abhisheks008]

Assigned to @Avdhesh-Varshney under SWOC S4.