Vulnerable Cancer Patients Analysis and Prediction

[abhisheks008]

ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : Vulnerable Cancer Patients Analysis and Prediction :red_circle: Aim : The aim of this project to find out the cancer patients and predict them. :red_circle: Dataset : https://www.kaggle.com/datasets/irinhoque/mental-health-insights-vulnerable-cancer-patients :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 :
• GitHub Profile Link :
• Participant ID (If not, then put NA) :
• Approach for this Project :
• What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.)

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Happy Contributing 🚀

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

[Yuvika-14]

hi abhisekh Full name: Yuvika Singh git hub profile link:https://github.com/Yuvika-14/Yuvikademo.git approch: ensemble methods, gradient boosting, neural networks. Take care of the missing data if there are any categorical values then will use one hot encoder or label encoder depending upon the issue.

[abhisheks008]

One issue at a time @Yuvika-14

[CoderOMaster]

Hi @abhisheks008 Name: Keshav Arora GitHub: www.github.com/CoderOMaster Approach:EDA,use of NLP wrt rnn and it's versions,BERT,etc JWOC

[abhisheks008]

Issue assigned to you @CoderOMaster