Metadata-Version: 2.1
Name: AlgoMaster
Version: 0.1.0
Summary: The Regression class simplifies regression analysis by providing a convenient and flexible approach for model training, evaluation, and hyperparameter tuning.
Author: sajo sam
Author-email: <sajosamambalakara@gmail.com>
Keywords: machine learning,classifiers,logistic regression,k-nearest neighbors,naive Bayes,random forests,support vector machines,ensemble methods,hyperparameter tuning,performance evaluation,comparison,multiple classifiersadvantages,Regression class,regression analysis, Python,model training,evaluation,hyperparameter tuning,encapsulating,regression algorithms,metrics,simplifies,building,comparing,regression models,accurate predictions,insights
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Description-Content-Type: text/markdown


# Project Title



Simplifying Regression and Classification Modeling



## Guide



### Installation setup



`pip install AlgoMaster`



### Classfication model



1.  Initialize the model



        `Classifier=AlgoMaster.Classifier(X,Y,test_size=0.2,random_state=20)`



2.  Train the model and predict the results in table format



        `Classifier.model_training()`



3.  Ensemble technique



        `Classifier.ensemble_prediction()`



4.  Single Training

    To predict unseen data



        `data=[1,2,3,4,5,6,7,8,9]

        Classifier.logistic_test(data)

        Classifier.KNeighbors_test(data)

        Classifier.GaussianNB_test(data)

        Classifier.Bagging_test(data)

        Classifier.ExtraTrees_test(data)

        Classifier.RandomForest_test(data)

        Classifier.DecisionTree_test(data)

        Classifier.AdaBoost_test(data)

        Classifier.GradientBoosting_test(data)

        Classifier.XGBoost_test(data)

        Classifier.SGD_test(data)

        Classifier.SVC_test(data)

        Classifier.Ridge_test(data)

        Classifier.BernoulliNB_test(data)`



5.  Hyperparameter Turning

    To find the best parameters for the model



        `Classifier.hyperparameter_tuning()`



6.  Single Hyperparameter Turning

    To find the best parameters for the model



        `Classifier.logistic_hyperparameter()

        Classifier.KNeighbors_hyperparameter()

        Classifier.GaussianNB_hyperparameter()

        Classifier.Bagging_hyperparameter()

        Classifier.ExtraTrees_hyperparameter()

        Classifier.RandomForest_hyperparameter()

        Classifier.DecisionTree_hyperparameter()

        Classifier.AdaBoost_hyperparameter()

        Classifier.GradientBoosting_hyperparameter()

        Classifier.XGBoost_hyperparameter()

        Classifier.SGD_hyperparameter()

        Classifier.SVC_hyperparameter()

        Classifier.Ridge_hyperparameter()

        Classifier.BernoulliNB_hyperparameter()`



### Regression model



1.  Initialize the model



        `Regressor=AlgoMaster.Regressor(X,Y,test_size=0.2,random_state=20)`



2.  Train the model and predict the results in table format



        `Regressor.model_training()`



3.  Ensemble technique



        `Regressor.ensemble_prediction()`



4.  Single Training



        `data=[1,2,3,4,5,6,7,8,9]

        Regressor.LinearRegression_test(data)

        Regressor.KNeighbors_test(data)

        Regressor.Bagging_test(data)

        Regressor.ExtraTrees_test(data)

        Regressor.RandomForest_test(data)

        Regressor.DecisionTree_test(data)

        Regressor.AdaBoost_test(data)

        Regressor.GradientBoosting_test(data)

        Regressor.XGBoost_test(data)

        Regressor.TheilSen_test(data)

        Regressor.SVR_test(data)

        Regressor.Ridge_test(data)

        Regressor.RANSAC_test(data)

        Regressor.ARD_test(data)

        Regressor.BayesianRidge_test(data)

        Regressor.HuberRegressor_test(data)

        Regressor.Lasso_test(data)

        Regressor.ElasticNet_test(data)`



5.  Hyperparameter Turning

    To find the best parameters for the model



        `Regressor.hyperparameter_tuning()`



6.  Single Hyperparameter Turning

    To find the best parameters for the model



        `Regressor.KNeighbors_hyperparameter()

        Regressor.Bagging_hyperparameter()

        Regressor.ExtraTrees_hyperparameter()

        Regressor.RandomForest_hyperparameter()

        Regressor.DecisionTree_hyperparameter()

        Regressor.AdaBoost_hyperparameter()

        Regressor.GradientBoosting_hyperparameter()

        Regressor.XGBoost_hyperparameter()

        Regressor.TheilSen_hyperparameter()

        <!-- Regressor.SVR_hyperparameter() -->

        Regressor.Ridge_hyperparameter()

        Regressor.RANSAC_hyperparameter()

        Regressor.ARD_hyperparameter()

        Regressor.BayesianRidge_hyperparameter()

        Regressor.Lasso_hyperparameter()

        Regressor.ElasticNet_hyperparameter()`

