AutoOptimizer provides tools to automatically optimize machine learning model for a dataset with very little user intervention.

It refers to techniques that allow semi-sophisticated machine learning practitioners and non-experts 
to discover a good predictive model pipeline for their machine learning algorithm task quickly,
with very little intervention other than providing a dataset.


#Prerequisites:

jupyterlab or: {sklearn, matplotlib, numpy}	


#Usage:


>Optimize scikit learn supervised, unsupervised and ensemble learning models using python.


{DBSCAN, KMeans, MeanShift,  LogisticRegression, LinearRegression, KNeighborsClassifier, KNeighborsRegressor,
RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier, SupportVectorClassifier, DecisionTree}


>Metrics for Your Regression Model


>Clear data by removing outliers

>>for more information visit: http://genesiscube.ir/index-6.html



#Contact and Contributing:
Please share your good ideas with us.
Simply letting us know how we can improve the programm to serve you better.
Thanks for contributing with the program.

>>https://github.com/mrb987/autooptimizer
>>info@Genesiscube.ir