Metadata-Version: 2.1
Name: rapidpredict
Version: 0.0.0.8
Summary: rapid predict is a python package to simplifies the process of fitting and evaluating multiple machine learning models on a dataset.
Author: Synthetic Dataset AI Team
Author-email: <nematiusa@gmail.com>
Keywords: python,pandas,numpy,scikit-learn,scipy,matplotlib,seaborn
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
Classifier: License :: Free To Use But Restricted
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Requires-Dist: click
Requires-Dist: scikit-learn
Requires-Dist: pandas
Requires-Dist: tqdm
Requires-Dist: joblib
Requires-Dist: lightgbm
Requires-Dist: xgboost
Requires-Dist: itables
Requires-Dist: catboost
Requires-Dist: colorlover
Requires-Dist: seaborn
Requires-Dist: plotly
Requires-Dist: IPython


# RapidPredict

RapidPredict is a Python library that simplifies the process of fitting and evaluating multiple machine learning models from scikit-learn. It's designed to provide a quick way to test various algorithms on a given dataset and compare their performance. 





# Installation



To install Rapid Predict from PyPI:



    pip install rapidpredict



# Usage



To use Rapid Predict in a project:



    import rapidpredict







## Classification



Example :



    from rapidpredict.supervised import *

    from sklearn.model_selection import train_test_split

    from sklearn.datasets import load_breast_cancer

    data = load_breast_cancer()

    X = data.data

    y= data.target



    X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.25,random_state =123)





    clf = rapidclassifier(verbose= 0,ignore_warnings=True, custom_metric=None)

    models , predictions = clf.fit(X_train, X_test, y_train, y_test)







    |Model |Accuracy	 |Balanced Accuracy |	ROC | AUC |	Recall |	Precision | F1 Score |	5 Fold F1 |	Time  Taken |

    |-------------------------------|------|------|------|------|------|------|------|------|

    | QuadraticDiscriminantAnalysis | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.96 | 0.09 |

    | RandomForestClassifier        | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.96 | 1.21 |

    | LogisticRegression            | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.98 | 0.17 |

    | ExtraTreesClassifier          | 0.99 | 0.98 | 0.98 | 0.99 | 0.99 | 0.99 | 0.97 | 0.80 |

    | RidgeClassifier               | 0.99 | 0.98 | 0.98 | 0.99 | 0.99 | 0.99 | 0.96 | 0.13 |

    | LinearSVC                     | 0.99 | 0.98 | 0.98 | 0.99 | 0.99 | 0.99 | 0.96 | 0.10 |

    | SVC                           | 0.99 | 0.98 | 0.98 | 0.99 | 0.99 | 0.99 | 0.97 | 0.10 |

    | RidgeClassifierCV             | 0.99 | 0.98 | 0.98 | 0.99 | 0.99 | 0.99 | 0.96 | 0.17 |

    | LabelPropagation              | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.94 | 0.17 |

    | LabelSpreading                | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.96 | 0.19 |

    | SGDClassifier                 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.97 | 0.09 |

    | Perceptron                    | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.97 | 0.08 |

    | KNeighborsClassifier          | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.97 | 0.11 |

    | DecisionTreeClassifier        | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.93 | 0.09 |

    | BernoulliNB                   | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.98 | 0.93 | 0.09 |

    | LinearDiscriminantAnalysis    | 0.98 | 0.97 | 0.97 | 0.98 | 0.98 | 0.98 | 0.96 | 0.14 |

    | CalibratedClassifierCV        | 0.98 | 0.97 | 0.97 | 0.98 | 0.98 | 0.98 | 0.97 | 0.24 |

    | AdaBoostClassifier            | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.95 | 0.89 |

    | PassiveAggressiveClassifier   | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.09 |

    | XGBClassifier                 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.97 | 0.45 |

    | BaggingClassifier             | 0.97 | 0.96 | 0.96 | 0.97 | 0.97 | 0.97 | 0.95 | 0.32 |

    | NuSVC                         | 0.97 | 0.95 | 0.95 | 0.97 | 0.97 | 0.96 | 0.95 | 0.12 |

    | NearestCentroid               | 0.97 | 0.95 | 0.95 | 0.97 | 0.97 | 0.96 | 0.94 | 0.08 |

    | GaussianNB                    | 0.97 | 0.95 | 0.95 | 0.97 | 0.97 | 0.96 | 0.94 | 0.08 |

    | ExtraTreeClassifier           | 0.94 | 0.94 | 0.94 | 0.94 | 0.94 | 0.94 | 0.93 | 0.08 |

    | DummyClassifier               | 0.62 | 0.50 | 0.50 | 0.62 | 0.39 | 0.48 | 0.77 | 0.08 |







## Plot Target values



    plot_target(y)



![plot target](./image/plot_target.png)







## Comparing models suing bar graph

 

  compareModels_bargraph(predictions["F1 Score"] ,models.index)

 



![](./image/compareModels_bargraph.png)  





## Comparing models suing box plot

 

    compareModels_boxplot(predictions["F1 Score"] ,models.index)





![](./image/compareModels_boxplot.png)





    







This code updated from github  ["Lazypredic-Shankar Rao Pandala"](https://github.com/shankarpandala/lazypredict) 

