Metadata-Version: 2.1
Name: nextstep
Version: 0.0.11
Summary: USEP price prediction
Home-page: https://github.com/YangYuesong0323/nextstep
Author: Yang Yuesong
Author-email: yangyuesongyys@gmail.com
License: UNKNOWN
Description: # nextstep
        
        # Introduction
        Nextstep integrates major popular machine learning algorithms, taking all the hassle data analysts or data scientists could possibly face when deal with multiple matching learning packages. At the same time, it lifts the programming constraints by extracting key parameters into a configuration dictionary, empowering less experienced python users the ability to explore machine learning. 
        
        Nextstep was originally developed for a data science challenge which involved price prediction. So it has a dedicated module to obtain data (oil and weather). It evolves into a machine learning prediction toolkit.
        
        # Installation
        First time installation
        ```bash
        pip install nextstep
        ```
        Upgrade to the latest version
        ```bash
        pip install nexrstep --ungrade
        ```
        # Quick Tutorial
        ## getData module
        **1. generate oil prices**
        ```python
        from nextstep.getData.oil import *
        oil_prices.process()
        ```
        *brent_daily.csv* and *wti_daily.csv* will be generated at the current directory. They contain historical oil price until the most recent day.
        
        **2. generate weather data**
        This function relies on an API key from [worldweatheronline](https://www.worldweatheronline.com/developer/). It is free for 60 days as of 27/3/2020.
        ```python
        from nextstep.getData.weather import weather
        config = {
        		'frequency' : 1,
        		'start_date' : '01-Jan-2020',
        		'end_date' : '31-Jan-2020',
        		'api_key' : 'your api key here',
        		'location_list' : ['singapore'],
        		'location_label' : False
        		}
        data = weather(config).get_weather_data()
        ```
        ## model module
        Every ML model has a unique config. Please fill in accrodingly.
        ### random forest
        ```python
        # examples, please fill in according to your project scope
        from nextstep.model.random_forest import random_forest
        config = {
                    'label_column' : 'USEP',
                    'train_size' : 0.9,
                    'seed' : 66,
                    'n_estimators' : 10,
                    'bootstrap' : True,
                    'criterion' : 'mse',
                    'max_features' : 'sqrt'
        }
        random_forest_shell = random_forest(config)
        random_forest_shell.build_model(data)
        ```
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
