Metadata-Version: 2.1
Name: PreProcessingNinja
Version: 0.0.1
Summary: A data preprocessing helper consists of your basic preprocessing needs
Home-page: https://github.com/Bijoy99roy/PreProcessingNinja
Author: Bijoy Kumar Roy
License: UNKNOWN
Keywords: preprocessing,preprocessing ninja
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: scikit-learn


# PreProcessing Ninja

A PreProcessing library for your basic PreProcessing needs

[![forthebadge](https://forthebadge.com/images/badges/made-with-python.svg)](https://www.python.org)

[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)  
## Features of the package

 - impute_missing_value: Helps to impute missing values
 - scale_data: Scales the data
 - encode_categorical_data: Helps in encoding categorical variables
 - remove_outliers: Helps in removing outliers
## Use
```cmd
pip install PreProcessingNinja
```



## Example

```python
from preprocessing_ninja import PreProcessingNinja
ninja = PreProcessingNinja()

#creating column dictionary
d = {
    'column1':'mean',
    'column2':'mean',
    'column3':'most_frequent'
}

#calling method
df = ninja.impute_missing_value(datafra,e, d)
```


## Note

There might be bugs.


## Authors

- [@BijoyKumarRoy](https://www.linkedin.com/in/bijoy-kumar-roy-4b0975189/)



