Metadata-Version: 2.1
Name: AutoClassifierRegressor
Version: 0.0.3
Summary: Tools for getting analysis of all classifiers and regressors
Home-page: https://github.com/anagha-bhople/auto_classifier_regressor
Author: Anagha Bhople
Author-email: Anagha Bhople <bhoplea34@gmail.com>, Swapnil Dewalkar <swapnildewalkar1995@gmail.com>
License: MIT License
        
        Copyright (c) [2022] [Anagha Bhople]
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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        SOFTWARE.
Project-URL: Homepage, https://github.com/anagha-bhople/auto_classifier_regressor
Project-URL: Bug Tracker, https://github.com/anagha-bhople/auto_classifier_regressor/issues
Keywords: ML classifier regressor neural network sklearn analysis
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown

## Package Installation

#### pip install AutoClassifierRegressor

## Package Import

#### from AutoClassifierRegressor import regression_report_generation

#### from AutoClassifierRegressor import classification_report_generation

## For Regression call this function with following parameters

#### regression_report_generation(dataframe, "target name", path="desired folder name", saveModel=True, normalisation=True)

##### Arguments

    1. Dataframe name (required)
    2. Target variable for regression (required)
    3. path = name of folder (optional)
    4. saveModel = if set as True then all ML models will be saved in "Models" folder (optional)
    5. normalisation = if set as True data will be normalised (optional)

##### Example:

    df=pd.read_csv("/content/sample_data/california_housing_train.csv")
    regression_report_generation(df, "median_house_value", path="Housing_data", saveModel=True, normalisation=True)

## For Classification call this function with following parameters

#### classification_report_generation(dataframe, "target label", n= no classes, path="desired folder name", saveModel=True)

##### Arguments

    1. Dataframe name (required)
    2. Target variable for classification (required)
    3. n=2 for binary classification (required) and n=no of classes for multiclass classification (required)
    4. path = name of folder (optional)
    5. saveModel = if set as True then all ML models will be saved in "Models" folder (optional)

###### Example:

    df=pd.read_csv("data.csv")
    classification_report_generation(df, "diagnosis", n=2, path="binary_classification_reports", saveModel=True)

    df = pd.read_csv('Iris.csv')
    classification_report_generation(df, "Species", n=3, path="classification_model_Multiclass", saveModel=True)

### Prerequisites:

    1. Do necessary data processing for better results
    2. Install all dependancies
