pywedge 0.4.1

Cleans raw data, runs baseline models

pywedge

Cleans raw data, runs baseline models.

Cleans the raw dataframe to fed into ML models. Following data pre_processing will be carried out,

1. segregating numeric & categorical columns
2. missing values imputation for numeric & categorical columns
3. standardization
4. feature importance
5. SMOTE
6. baseline model

Pre_process_data() Inputs:

1. train = train dataframe
2. test = stand out test dataframe (without target column)
3. c = any redundant column to be removed (like ID column etc., at present supports a single column removal, subsequent version will provision multiple column removal requirements)
4. y = target column name as a string
5. type = Classification / Regression

Returns:

1. new_X (cleaned feature columns in dataframe)
2. new_y (cleaned target column in dataframe)
3. new_test (cleaned stand out test dataset)

baseline_model()

• For classification - classification_summary()
• For Regression - Regression_summary()

Inputs:

1. new_x
2. new_y

Returns: Various baseline model metrics

THIS IS IN BETA VERSION