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USEP price prediction

Project description

nextstep

Introduction

Nextstep integrates major popular machine learning algorithms, offering data scientists an all-in-one package. At the same time, it lifts the programming constraints by extracting key hyper-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) via open API or web-scraping. It evolves into a machine learning prediction toolkit.

Installation

First time installation

pip install nextstep

Upgrade to the latest version

pip install nextstep --upgrade

Quick Tutorial

getData module

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.

generate weather data

This function relies on an API key from worldweatheronline. It is free for 60 days as of 27/3/2020. It will generate csv data files in the current directory.

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 accordingly.

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) # build model

arima

from nextstep.model.arima import arima
config = {
		'lag' : 7,
		'differencing' : 0,
		'window_size' : 2,
		'label_column' : 'USEP',
		'train_size' : 0.8,
		'seed' : 33
	}
arima_shell = arima(config)

arima_shell.autocorrelation(data) # plot autocorrelation to determine p, lag order
arima_shell.partial_autocorrelation(data) # plot partial autocorrelation to determine q, moving average widow size
arima_shell.build_model(data) # build model

# residual plot to check model performance
arima_shell.residual_plot()
arima_shell.residual_density_plot()

Contributing

Pull requests are welcome

Author

yuesong Yang

Project details


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