AutoOptimizer provides tools to automatically optimize machine learning model for every dataset.

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:
1-sklearn
2-numpy	

#install package in jupyter notebook:
1-open anaconda prompt (recommended open as administrator)
2-pip install autooptimzer


#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.php/autooptimizer/


#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
>>www.GenesisCube.ir