nextstep 0.0.10

USEP price prediction

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

pip install nextstep

Upgrate to the latest version

pip install nexrstep --ungrade

Quick Tutorial

getData module

1. generate oil prices

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. It is free for 60 days as of 27/3/2020.

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

# 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)