Introduction to Regression with statsmodels in Python
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.
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Course Description
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.Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Statistics Fundamentals in Python
Go To Track- 1
Simple Linear Regression Modeling
FreeYou’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.
A tale of two variables50 xpWhich one is the response variable?50 xpVisualizing two numeric variables100 xpFitting a linear regression50 xpEstimate the intercept50 xpEstimate the slope50 xpLinear regression with ols()100 xpCategorical explanatory variables50 xpVisualizing numeric vs. categorical100 xpCalculating means by category100 xpLinear regression with a categorical explanatory variable100 xp - 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.
Making predictions50 xpPredicting house prices100 xpVisualizing predictions100 xpThe limits of prediction100 xpWorking with model objects50 xpExtracting model elements100 xpManually predicting house prices100 xpRegression to the mean50 xpHome run!50 xpPlotting consecutive portfolio returns100 xpModeling consecutive returns100 xpTransforming variables50 xpTransforming the explanatory variable100 xpTransforming the response variable too100 xpBack transformation100 xp - 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.
Quantifying model fit50 xpCoefficient of determination100 xpResidual standard error100 xpVisualizing model fit50 xpResiduals vs. fitted values50 xpQ-Q plot of residuals50 xpScale-location50 xpDrawing diagnostic plots100 xpOutliers, leverage, and influence50 xpLeverage50 xpInfluence50 xpExtracting leverage and influence100 xp - 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 ratios, and quantify model performance using confusion matrices.
Why you need logistic regression50 xpExploring the explanatory variables100 xpVisualizing linear and logistic models100 xpLogistic regression with logit()100 xpPredictions and odds ratios50 xpProbabilities100 xpMost likely outcome100 xpOdds ratio100 xpLog odds ratio100 xpQuantifying logistic regression fit50 xpCalculating the confusion matrix100 xpDrawing a mosaic plot of the confusion matrix100 xpAccuracy, sensitivity, specificity100 xpMeasuring logistic model performance100 xpCongratulations50 xp
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
Statistics Fundamentals in Python
Go To Trackdatasets
Customer churn dataTaiwan real estate dataAd conversion dataS&P 500 dataFish measurement datacollaborators
Maarten Van den BroeckSenior Content Developer at DataCamp
Maarten is an aquatic ecologist and teacher by training and a data scientist by profession. He is also a certified Power BI and Tableau data analyst. After his career as a PhD researcher at KU Leuven, he wished that he had discovered DataCamp sooner. He loves to combine education and data science to develop DataCamp courses. In his spare time, he runs a symphonic orchestra.
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