Here are a couple:

1) https://www.kdnuggets.com/datasets/index.html

2) https://archive.ics.uci.edu/ml/datasets.html

Good luck!

Machine learning works on a simple rule – if you put garbage in, you will only get garbage to come out. By garbage here, I mean noise in data.

This becomes even more important when the number of features are very large. You need not use every feature at your disposal for creating an algorithm. You can assist your algorithm by feeding in only those features that are really important. I have myself witnessed feature subsets giving better results than complete set of feature for the same algorithm. Or as Rohan Rao puts it – “Sometimes, less is better!”

Not only in the competitions but this can be very useful in industrial applications as well. You not only reduce the training time and the evaluation time, you also have less things to worry about!

Top reasons to use feature selection are:

• It enables the machine learning algorithm to train faster.
• It reduces the complexity of a model and makes it easier to interpret.
• It improves the accuracy of a model if the right subset is chosen.
• It reduces overfitting.
• The following methods can be used for feature selection:
• a. Filter methods (like LDA, ANOVA, Chi-square, pearson correlation)
• b. Wrapper methods (forward selection, backward selection, recursive feature elimination methods)

Thank you Mr.Faris....but it looks already prepared......in my case I want to prepare the variables for specific set of features from my results. Specifically for genes...

Thank you Mr. Zahid Mehmood for your information on feature selection. For me, I want to prepare the variables for specific set of features from my results. Specifically for genes...