Fix my Django Application using PCA/Sklearn Model to predict new inputs

All application is working.

The logic of the application is, to classify the user input, which they provide, and classify them

as 0 or 1.

I am using PCA (n_components = 25) which take train data and convert it to pca(n=25), this is model input, but what it does in real time,

it take user inputs and feed them to predict directly, and the features are totally different and it always classify any instance to 1 always and but what happens is the user input, is when it predicts in real time, it did not convert it to pca, and just send to model to predict and thereby, it always predicts recurrence , even in non-recurence instances.

Also, the index of the input is mismatched. nd that is why, it predicts all to one class.

Please add, understood in your bid, if you have read the task. Thanks.

Skills: Django, Flask, HTML, Machine Learning, Python

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About the Employer:
( 0 reviews ) New Delhi, India

Project ID: #19031021

2 freelancers are bidding on average $58 for this job


-- understood -- Hi Mridula, It sounds like an interesting challenge and very good fit. Please share your application and let me know when you are OK to discuss, thanks. Best, -Mike

$90 AUD in 3 days
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I have 1.5 years of experience including Full Stack Develipment, Data Engineering and Data Science. I have experience of deploying models on Django. Your problem is solvable. Feel free to trust.

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