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I'm a Data Science/ML intern in my final year B.Tech. CSE. Currently working on DashBoards & Predictive Analysis.
I like analysing thing,people,situations and trying to predict future scenario. I also like Mathematics and statistics, Cooking,BasketBall and Painting.
Apart from that I also like to write kernels and share my ML/Data Science knowledge with everyone.And currently also reading more about explainable AI.
ML/Data Science Intern with primary focus on creating Visulasiation+DashBoards for analysis purpose:Download Resume
Recently I started with NLP and after going through several articles and algorithms I thought to implement it. So in this kernel I am try to implement them. Data is of Stack OverFlow and I'll try to understand what the key words are in the coupus.
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Presenting users with the most relevant information is an important task for any product to fulfill. To do this properly, you need to be able to extract their preferences from your raw data. Here’s a framework for you to start doing that.
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Firstly why do we need Regularisation ??
The biggest reasons for regularization are
1) to avoid overfitting by not generating high coefficients for predictors that are sparse.
2) to stabilize the estimates especially when there's collinearity in the data
