AutoClassifierRegressor 0.0.4

Tools for getting analysis of all classifiers and regressors

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