An Introduction To Regression In Python With Statsmodels And Scikit Le

Associate Data Science Course in Python by DataCamp Inc Data: The datasets utilized in this course include the Taiwan Real Estate dataset, the S&P 500 Yearly Returns dataset, the Facebook Advertising Workflow dataset, and the Churn dataset. See Table 1, Table 2, Table 3, and Table 4 for the column names and descriptions for each dataset. You’ll learn the basics of this popular statistical model, what regression is, and how linear and logistic regressions differ. You’ll then learn how to fit simple linear regression models with numeric and categorical explanatory variables, and how to describe the relationship between the response and explanatory variables using model coefficients. Hi, my name is Maarten and welcome to the course.

You will be learning about regression, a statistical tool to analyze the relationships between variables. Let’s start with an example. This dataset on Swedish motor insurance claims is as simple as it gets. Each row represents a region in Sweden, and the two variables are the number of claims made in that region, and the total payment made by the insurance company for those claims, in Swedish... Recommended Video CourseStarting With Linear Regression in Python Watch Now This tutorial has a related video course created by the Real Python team.

Watch it together with the written tutorial to deepen your understanding: Starting With Linear Regression in Python Linear regression is a foundational statistical tool for modeling the relationship between a dependent variable and one or more independent variables. It’s widely used in data science and machine learning to predict outcomes and understand relationships between variables. In Python, implementing linear regression can be straightforward with the help of third-party libraries such as scikit-learn and statsmodels. By the end of this tutorial, you’ll understand that: To implement linear regression in Python, you typically follow a five-step process: import necessary packages, provide and transform data, create and fit a regression model, evaluate the results, and make predictions.

This approach allows you to perform both simple and multiple linear regressions, as well as polynomial regression, using Python’s robust ecosystem of scientific libraries. In this article, we will discuss how to use statsmodels using Linear Regression in Python. Linear regression analysis is a statistical technique for predicting the value of one variable(dependent variable) based on the value of another(independent variable). The dependent variable is the variable that we want to predict or forecast. In simple linear regression, there's one independent variable used to predict a single dependent variable. In the case of multilinear regression, there's more than one independent variable.

The independent variable is the one you're using to forecast the value of the other variable. The statsmodels.regression.linear_model.OLS method is used to perform linear regression. Linear equations are of the form: Syntax: statsmodels.regression.linear_model.OLS(endog, exog=None, missing='none', hasconst=None, **kwargs) Return: Ordinary least squares are returned. Importing the required packages is the first step of modeling.

The pandas, NumPy, and stats model packages are imported. Discover content by tools and technology Python, with its rich ecosystem of libraries like NumPy, statsmodels, and scikit-learn, has become the go-to language for data scientists. Its ease of use and versatility make it perfect for both understanding the theoretical underpinnings of linear regression and implementing it in real-world scenarios. In this guide, I'll walk you through everything you need to know about linear regression in Python. We'll start by defining what linear regression is and why it's so important.

Then, we'll look into the mechanics, exploring the underlying equations and assumptions. You'll learn how to perform linear regression using various Python libraries, from manual calculations with NumPy to streamlined implementations with scikit-learn. We'll cover both simple and multiple linear regression, and I'll show you how to evaluate your models and enhance their performance. Linear regression is a statistical method used to model the relationship between a dependent variable (target) and one or more independent variables (predictors). The objective is to find a linear equation that best describes this relationship. Linear regression is widely used for predictive modeling, inferential statistics, and understanding relationships in data.

Its applications include forecasting sales, assessing risk, and analyzing the impact of different variables on a target outcome. I’ve built dozens of regression models over the years, and here’s what I’ve learned: the math behind linear regression is straightforward, but getting it right requires understanding what’s happening under the hood. That’s where statsmodels shines. Unlike scikit-learn, which optimizes for prediction, statsmodels gives you the statistical framework to understand relationships in your data. Let’s work through linear regression in Python using statsmodels, from basic implementation to diagnostics that actually matter. Statsmodels is a Python library that provides tools for estimating statistical models, including ordinary least squares (OLS), weighted least squares (WLS), and generalized least squares (GLS).

Think of it as the statistical counterpart to scikit-learn. Where scikit-learn focuses on prediction accuracy, statsmodels focuses on inference: understanding which variables matter, quantifying uncertainty, and validating assumptions. The library gives you detailed statistical output including p-values, confidence intervals, and diagnostic tests. This matters when you’re not just predicting house prices but explaining to stakeholders why square footage matters more than the number of bathrooms. Start with the simplest case: one predictor variable. Here’s a complete example using car data to predict fuel efficiency:

The goal is to gain the skills you need to fit simple linear and logistic regressions. Exploring the relationships between variables in real-world datasets, including motor insurance claims, Taiwan house prices, fish sizes, and more. Linear regression and logistic regression are two of the most widely used statistical models. There are many packages that implement linear regression in Python. As detailed in our last reading, however, depending on whether they are intended for use in prediction or inference, the way these packages operate can vary substantially. In this reading, we will look at how linear regression has been implemented in two major packages — statsmodels and scikit-learn.

Both of these packages can fit a wide range of linear models, but the way they are organized and the results they report reflect the different audiences for whom they were designed. Broadly speaking, statsmodels is a library written by statisticians for statisticians, biostatisticians, social scientists, and natural scientists. It can do prediction, but its focus is inference, and as we will see that is reflected throughout the package. scikit-learn, by contrast, was written by and for computer scientists interested in machine learning. Its focus is on prediction, and while it includes a far more diverse collection of machine learning models than statsmodels, it does not include all the features someone doing inference might expect for evaluating... Because it is the more feature-rich library when it comes to regression, we will start our exploration of linear regression in Python with statsmodels.

If you have any interest in inference, are coming from a programming language like R or Stata, and/or have a background in statistics, social science, or the natural sciences, then statsmodels is the package... You’ll learn the basics of this popular statistical model, what regression is, and how linear and logistic regressions differ. You’ll then learn how to fit simple linear regression models with numeric and categorical explanatory variables, and how to describe the relationship between the response and explanatory variables using model coefficients. Before you can run any statistical models, it’s usually a good idea to visualize your dataset. Here, you’ll look at the relationship between house price per area and the number of nearby convenience stores using the Taiwan real estate dataset. One challenge in this dataset is that the number of convenience stores contains integer data, causing points to overlap.

To solve this, you will make the points transparent. taiwan_real_estate is available as a pandas DataFrame. ### Instructions - Import the seaborn package, aliased as sns. - Using taiwan_real_estate, draw a scatter plot of “price_twd_msq” (y-axis) versus “n_convenience” (x-axis). - Draw a trend line calculated using linear regression. Omit the confidence interval ribbon.

Note: The scatter_kws argument, pre-filled in the exercise, makes the data points 50% transparent. While sns.regplot() can display a linear regression trend line, it doesn’t give you access to the intercept and slope as variables, or allow you to work with the model results as variables. That means that sometimes you’ll need to run a linear regression yourself.

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