Metadata-Version: 2.1
Name: ml-helper
Version: 0.0.21
Summary: Helpers to speed up and structure machine learning projects
Home-page: https://github.com/akoury/ml-helper
Author: akoury
License: MIT
Description: # ML Helper
        ---
        Helpers to speed up and structure machine learning projects.
        
        The library is available in [Pypi](https://pypi.org/project/ml-helper/)
        
        ### Installing
        ---
        
        
        The easiest way to install ml-helper is through ```pip```
        
        ```python
        pip install ml-helper
        ```
        
        To use it in your project, you must first import the library
        
        ```python
        from ml_helper.helper import Helper
        ```
        
        And then create a Helper object with a dictionary of keys related to your project
        
        ```python
        KEYS = {
            'SEED': 1,
            'TARGET': 'y',
            'METRIC': 'r2',
            'TIMESERIES': True,
            'SPLITS': 5
        }
        
        hp = Helper(KEYS)
        ```
        
        After this, you may use the helper object's many functions
        
        #### Dependencies
        
        ML-Helper requires:
        * Python (>3.5)
        * Numpy (>=1.16)
        * Pandas (>=0.23.4)
        * Seaborn (>=0.9)
        * Scikit-learn (>=0.20)
        * Natplotlib (>=3)
        * Scipy (>=1)
        * Imblearn
        * Vecstack
        
        ### Functionality
        ---
        
        The functionality is separated into 4 groups:
        * Data Exploration
            * Missing Data
            * Boxplot of numerical variables
            * Coefficient of variation
            * Correlation (numerical and categorical)
            * Under Represented Features
            * Target Variable Distribution
            * Feature Importance
            * PCA Component Variance
        * Data Preparation
            * Convert features to categories
            * Drop multiple columns
        * Modeling
            * Cross Validation (with stratified kfolds, or time series split depending on use case)
                * Randomized Grid Search
            * Pipeline: Collection of models and pipeline steps that get performed and scored
            * Predict: Predict on unseen data
            * Stack Predict: Build a stacked model and perform a prediction
        * Regression
            * Plots for predictions
        * Classification
            * ROC Curve
            * Classification Report
        * Others
            * Select features based on types
            * Split X and y
            * Plot models/pipelines
        
        ### Working Examples
        ---
        If you wish to see the library in use, you may view the notebooks in the [examples](examples) section.
        
        Also, you can see the implementation in their corresponding Kaggle Kernels:
        
        * [Bike Sharing in Washington D.C.: Time Series Regression](https://www.kaggle.com/akoury/bike-sharing-in-washington-d-c-using-ml-helper)
        
        * [Employee Attrition: Classification](https://www.kaggle.com/akoury/employee-attrition-basis-to-create-ml-helper-lib)
        
        ### ML-Helper Coding Style
        ---
        Ml-Helper complies to PEP8 and uses ```black``` for coding standards
        
        ### Versioning
        ---
        [SemVer](http://semver.org/) is used for versioning. 
        
        ### License
        ---
        This project is licensed under the MIT License - see the [License](license.txt) file for details
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