AlgoMaster 0.1.0

The Regression class simplifies regression analysis by providing a convenient and flexible approach for model training, evaluation, and hyperparameter tuning.

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()`