Linear regression analysis is one of the most popular predictive modeling techniques in predictive analytics and data science. It serves the purpose of predicting the outcome of a given data set by assessing the relationship between different variables. Linear regression analysis is used to predict the outcome of a given set of data by fitting a linear relationship between the dependent variable and the independent variables present in the data. The process of using linear regression to predict outcomes is referred to as linear modeling.
The purpose of this post is to explain the use of the statsmodels package in linear regression prediction. The statsmodels is a python library that contains a variety of regression modules, such as OLS (Ordinary Least Squares Regression) and Logit (Logit Regression). The OLS module is used for linear modeling, while the Logit module is used for logistic regression.
In this blog post, we will be using the OLS module from the statsmodels package to create a linear regression model and perform predictions. The data set used will be the classic Gapminder dataset, which contains worldwide data for population, health, and economic characteristics over time. The goal is to predict the population of a given country in the year 2016, given the country’s population, health, and economic characteristics from 2007 to 2015.
The first step is to import the necessary packages and modules.
import pandas as pd import statsmodels.formula.api as sm
Next, we will load the Gapminder dataset.
gapminder_data = pd.read_csv('gapminder.csv')
Now that the data is loaded, we can create a linear regression model that takes into account the population, health, and economic characteristics of the countries in the Gapminder dataset.
model = sm.ols(“population ~ health + economy”, data=gapminder_data).fit()
With the model fit, we can then use it to predict the population for a given country in 2016, given its population, health, and economic characteristics from 2007 to 2015.
predictions = model.predict( X = {‘population': unspecified_population, ‘health’: unspecified_health, ‘economy’: unspecified_economy})
Finally, we can use the predictions to determine the population of the country in 2016.
The statsmodels package provides a powerful set of tools for linear modeling and prediction. This post provides an overview of how to use the `statsmodels’ package for linear regression prediction. The use of linear models in predictive analytics is essential for determining the outcome of a given dataset.