RSNA Lumbar Spine Analysis

[adi271001]

ML-Crate Repository (Proposing new issue)

:red_circle: Project Title : RSNA Lumbar Spine Analysis :red_circle: Aim : The aim of this project is to develop a model for detecting and classifying degenerative spine conditions accurately. :red_circle: Dataset : https://www.kaggle.com/competitions/rsna-2024-lumbar-spine-degenerative-classification :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 do a exploratory data analysis before creating any model.

1. Data cleaning and preprocessing
2. EDA
3. Models Building: models used includes random forest , svm , xgb , gradient boosting , knn ,Logistic regression , adaboost , decision trees, extra trees
4. Evaluation and accuracy plots

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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 : Aditya D

• GitHub Profile Link : https://www.github.com/adi271001

• Participant ID (If not, then put NA) : NA

• Approach for this Project :

  1. Data cleaning and preprocessing
  2. EDA
  3. Models Building: models used includes random forest , svm , xgb , gradient boosting , knn ,Logistic regression , adaboost , decision trees, extra trees
  4. Evaluation and accuracy plots

• What is your participant role? (Mention the Open Source Program name. Eg. HRSoC, GSSoC, GSOC etc.) VSOC Contributor

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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]

Implement 6-7 models for this problem statement.

Assigned @adi271001