课程
Introduction to Regression with statsmodels in Python
中级技能水平
更新时间 2026年3月PythonProbability & Statistics4 小时14 视频53 练习4,150 经验值59,106成就声明
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Use Python statsmodels For Linear and Logistic Regression
Linear regression and logistic regression are two of the most widely used statistical models. They act like master keys, unlocking the secrets hidden in your data. In this course, you’ll gain the skills to fit simple linear and logistic regressions.Through hands-on exercises, you’ll explore the relationships between variables in real-world datasets, including motor insurance claims, Taiwan house prices, fish sizes, and more.
Discover How to Make Predictions and Assess Model Fit
You’ll start this 4-hour course by learning what regression is and how linear and logistic regression differ, learning how to apply both. Next, you’ll learn how to use linear regression models to make predictions on data while also understanding model objects.As you progress, you’ll learn how to assess the fit of your model, and how to know how well your linear regression model fits. Finally, you’ll dig deeper into logistic regression models to make predictions on real data.
Learn the Basics of Python Regression Analysis
By the end of this course, you’ll know how to make predictions from your data, quantify model performance, and diagnose problems with model fit. You’ll understand how to use Python statsmodels for regression analysis and be able to apply the skills to real-life data sets.先决条件
Introduction to Data Visualization with SeabornIntroduction to Statistics in Python1
Simple Linear Regression Modeling
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.
2
Predictions and model objects
In this chapter, you’ll discover how to use linear regression models to make predictions on Taiwanese house prices and Facebook advert clicks. You’ll also grow your regression skills as you get hands-on with model objects, understand the concept of "regression to the mean", and learn how to transform variables in a dataset.
3
Assessing model fit
In this chapter, you’ll learn how to ask questions of your model to assess fit. You’ll learn how to quantify how well a linear regression model fits, diagnose model problems using visualizations, and understand each observation's leverage and influence to create the model.
4
Simple Logistic Regression Modeling
Learn to fit logistic regression models. Using real-world data, you’ll predict the likelihood of a customer closing their bank account as probabilities of success and odds, and quantify model performance using confusion matrices.
Introduction to Regression with statsmodels in Python
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