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:

>-{ sklearn - numpy - pandas }


#Install package:

>-pip install autooptimzer


#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, DecisionTreeClassifier, DecisionTreeRegressor
RandomForestClassifier, RandomForestRegressor, GradientBoostingClassifier, GradientBoostingRegressor, AdaBoostClassifier, AdaBoostRegressor, SupportVectorClassifier, SupportVectorRegressor
BaggingClassifier, BaggingRegressor, ExtraTreesClassifier }


>Metrics for Your Regression Model

>Clear data by removing outliers

Download AutoOptimizer Document: https://genesiscube.ir/wp-content/uploads/2023/01/Auto-Optimizer-document-0.8.6.pdf

for more information visit: https://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