HelperFunctions for EDA and model building in python

Under active development!

Do you work on data analysis and model building projects on a daily basis?

Did you notice the number of lines of code that are duplicated acorss your analyses?

This is a collection of functions that be used for analysis and model building. It follows the convention of creating an object instance of the class HelperFunctionsML, upon which we can apply a set of functions/actions.

Every action on the dataset is applied inplace , i.e the changed/updated dataset is updated and this new dataset is used for the next function call that is applied. or simply put, the class treats the dataset as a shared variable across various functions.

To convert the dataset into a HelperFunctionsML object:

obj = HelperFunctionsML(pd.read_csv("dataset.csv"))

Once the dataset is converted into the HelperFunctionsML object, we have access to some metadata and some useful functions Some of the functions available are"

cat_num_extract - This function returns the names of the Categorical and Nmeric attributes.
list_of_na_cols
impute_categorical_cols
impute_numeric_cols
create_dummy_data_frame

Interested in using these functions? checkout my ipython notebook which outlines how these functions can be used.

I used the functions in this repository for analysis and model building, you can refer to the github code here[https://github.com/SCK22/CustomerEngagement] Suggestions are welcome, please click here to send an email: S.Chaithanya Kumar