CodePeak 2025 : Pnemonia Disease Prediction

[prathimacode-hub]

Define You:

• CodePeak 2025 Participant
• Contributor

Hey OS participants, good to see you.

This project will help us in predicting the pneumonia disease using the dataset given

Dataset : https://www.kaggle.com/broach/weekly-cdc-pneumonia-cases

If you like to work on this issue, follow the given guidelines for code as well as for the README. You can compare it using different algorithms for better modelling practices.

[Annie-1-code]

I would like to work on this issue.
Let us use linear support vector machines to perform this classification.Let x(p dimensional vector implying p umptoms),y(1 or -1 depending on pneumonia affected or not repectively) be a et of n points. Our job is to draw a hyperplane through them andthen figure out if the n+1 th point is pneumonia affeted or not

[prathimacode-hub]

You have to use alteast 3-4 algorithms and compare the results for any issue taken for. Never go with just 1 algorithm. It isn't sufficient and not a right way to get model creation done. @Annie-1-code

[Annie-1-code]

I have explored the dataset. I now want to clean the dataseti.e. remove all the non-null values. I tried looking at a simillar project
Black Friday Sales- Analysis and Prediction and looked into the data cleaning part of the file ML-ProjectKart/Black Friday Sales- Analysis and Prediction/Model
/black_friday_sales_analysis_and_prediction.ipynb

I could not understand why we were fill na value with 9, 12 and 9300 numbers. Could you help me undertand those or guide me toward a path to remove null values

[prathimacode-hub]

It depends on what we are trying to achieve. Either you have remove categorical columns if it's not useful for modelling, else have to replace with values. Values vary according to data in dataset. @Annie-1-code

[prathimacode-hub]

Follow this links for better reference : https://www.analyticsvidhya.com/blog/2021/05/dealing-with-missing-values-in-python-a-complete-guide/
https://towardsdatascience.com/6-different-ways-to-compensate-for-missing-values-data-imputation-with-examples-6022d9ca0779?gi=bb21e3e05253
https://www.freecodecamp.org/news/how-to-handle-missing-data-in-a-dataset/ @Annie-1-code

[prathimacode-hub]

@Annie-1-code, let me know if you need a meet call for discussion?

[Annie-1-code]

Sure, what about 11am tomorrow? If that i ok with you, I can set up a meet call.

[prathimacode-hub]

3pm works for me. Once you give you a confirmation on mentioned time, I shall share out to the meet link. @Annie-1-code

[Annie-1-code]

Sure. 3 pm works for me too