Pratik-model 0.1.1

This package directly gives you output performance on 13 different algorithms

- The best thing about this package is that you do not have to train and predict every classification or regression algorithm to check performance.

- This package directly gives you output performance on 13 different algorithms.



How to use it  - 

For Classification

x= Independent variables

y= Dependent variables



* From Pratik_model import smart_classifier

* model = smart_classifier(x,y)

* model.accuracy_score()

* model.classification_report()

* model.confusion_matrix()

* model.cross_validation()

* model.mean_absolute_error()

* model.precision_score()

* model.recall_score()

* model.mean_absolute_error()

* model.mean_absolute_error()

* model.mean_squared_error()

* model.cross_validation()



For Regression -



* From Pratik_model import smart_regressor

* model=smart_regressor(x,y)

* model.r2_score()

* model.mean_absolute_error()

* model.mean_absolute_error()

* model.mean_squared_error()

* model.cross_validation()

* model.overfitting()



Check Pratik_Model_Package.ipynb file on Github for practical code.



Pratik_model for Classification: 

It will check the performance on this Classification models:

- Passive Aggressive Classifier

- Decision Tree Classifier

- Random Forest Classifier

- Extra Trees Classifier

- Logistic Regression

- Ridge Classifier

- K Neighbors Classifier

- Support Vector Classification

- Naive Bayes Classifier

- LGBM Classifier

- CatBoost Classifier

- XGB Classifier



And for classification problems Pratik_model can give the output of:

- Accuracy Score.

- Classification Report

- Confusion Matrix

- Cross validation (Cross validation score)

- Mean Absolute Error

- Mean Squared Error

- Overfitting (will give accuracy of training and testing data.)

- Precision Score

- Recall Score



Pratik_model for Regression: 

Similarly, It will check performance on this Regression model:

- Passive Aggressive Regressor

- Gradient Boosting Regressor

- Decision Tree Regressor

- Random Forest Regressor

- Extra Trees Regressor

- Lasso Regression

- K Neighbors Regressor

- Linear Regression

- Support Vector Regression

- LGBM Regressor

- CatBoost Regressor

- XGB Regressor



And for Regression problem Pratik_model

can give an output of:

- R2 Score.

- Cross validation (Cross validation score)

- Mean Absolute Error

- Mean Squared Error

- Overfitting (will give accuracy of training and testing data.)





First Release

0.0.7 (29/3/2022)



Thank You!!.



License-File: LICENSE.txt