I have a complete python ML model (end to end) with an accuracy of 100% on the test set. But when i make real life predictions, i get barely 40% accuracy.
1.) Identify and fix possible causes of over-fitting.
2.) Show proof that if we test the improved model on a live dataset, we'll see an accuracy of say 70% (on live data)
3.) Provide solution on Jupiter notebook or [login to view URL]
Hi, I am artificial intelligence engineer with more 8 years of experience in Machine/Deep Learning. I have accomplished projects like yours. Feel free to contact me for further details. Thank you
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ok i understood your requirement but I have few doubts, text me so I can clear all my doubts right away, I can help you and i am ready for work. Thank you.
hay,i am a data scientist and i have long time experience with building ML models using python ,i can done your task with clean code and high performance just massage me for more details.
Hi i have no detailed clarity about data but if u give a better clarity on this data we can solve it. And im currently doing masters in data science im very keen and enthusiastic to solve this type of problem.