Binary classification: best ways to pre-procees the data About the dataset
I have a training dataset of


129 columns(last column being the classes, i.e., y values)
6068 rows


I have to train some algo to do binary classification. The data set has 


701 examples of type A
5367 examples of type B


The test set consists of 1398 examples. Here is the accuracy I got for various algorithms.


voting ensemble -> 0.73963
stochastic gradient boosting -> 0.77682
Adaboost -> 0.75107
bagging classifier(Decision tree) -> 0.76538
Random Forests -> 0.75250
Extra trees -> 0.75393


All the above results are from kaggle so they probably are just from half the test set. 
The above methods were implemented using scikit-learn library in python

Could someone please suggest ways to improve accuracy, may be by things like dimensionality reduction or better algorithms. Also, please provide sample code in possible.
A better clarity about the kind of problem that you are trying to solve will be really useful. 


I any case the first thing I will suggest, is please go through your features and try to understand the relationship with the response. All tree based methods are highly prone to overfitting and hence they don't generalise well.
If you come to the conclusion that some variables show a linear relationship and some show a non-linear then you might be better off using Linear methods such as LogisticRegression or LinearDiscriminantClassifier(if your features show an indication that they are sampled from a normal distribution). Obviously you can use PCA or regularization methods if you are certain that some features are highly correlated and offer no prediction value. 
Secondly, if you have done all the above then you are probably done with the feature selection step. The next approach is feature creation. See if certain variables are non-linear in nature. Then try adding higher degree polynomial terms. Try diagnosing interaction effects between features, try to include features for those interactions. If there are any features you think are redundant to the problem. Think again. See if you can extract any information out of them.


Kindly tag the link of the problem here.