AutoClassifierRegressor 0.0.6

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, preprocessing=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)
    6. preprocessing = if set as True then data will be preprocessed, which includes fillna and label encoding for categorical variables

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, preprocessing=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)
    6. preprocessing = if set as True then data will be preprocessed, which includes fillna and label encoding for categorical variables

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)

Output:

    1. Output will be in the form of html file with tabular analyis of all important classifiers or regressors along with poular evaluation metrics.
    2. Html file will be saved in current or in given path.
    3. All ML models will be saved in /Models folder in current or in given path.

Prerequisites:

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