Перейти к основному содержимому

Главная Python

Курс

Intermediate Regression with statsmodels in Python

Средний уровеньУровень навыков

Обновлено 05.2022

Learn to perform linear and logistic regression with multiple explanatory variables.

Начать курс бесплатно

PythonProbability & Statistics

4 ч

14 видео

52 Упражнения

4,300 XP

15,760

Справка об успешном завершении

Создать бесплатный аккаунт

Продолжить через Google Показать больше вариантов

или

Продолжая, вы принимаете наши Условия использования, нашу Политику конфиденциальности и соглашаетесь с тем, что ваши данные хранятся в США.

Любимая обучающимися из тысяч компаний

Обучаете команду?

Попробуйте для бизнеса

Описание курса

Linear regression and logistic regression are the two most widely used statistical models and act like master keys, unlocking the secrets hidden in datasets. In this course, you’ll build on the skills you gained in "Introduction to Regression in Python with statsmodels", as you learn about linear and logistic regression with multiple explanatory variables. Through hands-on exercises, you’ll explore the relationships between variables in real-world datasets, Taiwan house prices and customer churn modeling, and more. By the end of this course, you’ll know how to include multiple explanatory variables in a model, discover how interactions between variables affect predictions, and understand how linear and logistic regression work.

Необходимые условия

Introduction to Regression with statsmodels in Python

1

Parallel Slopes

Extend your linear regression skills to parallel slopes regression, with one numeric and one categorical explanatory variable. This is the first step towards conquering multiple linear regression.

Parallel slopes linear regression

Fitting a parallel slopes linear regression

Interpreting parallel slopes coefficients

Visualizing each explanatory variable

Visualizing parallel slopes

Predicting parallel slopes

Predicting with a parallel slopes model

Visualizing parallel slopes model predictions

Manually calculating predictions

Assessing model performance

Comparing coefficients of determination

Comparing residual standard error

Начать главу

2

Interactions

Explore the effect of interactions between explanatory variables. Considering interactions allows for more realistic models that can have better predictive power. You'll also deal with Simpson's Paradox: a non-intuitive result that arises when you have multiple explanatory variables.

Models for each category

One model per category

Predicting multiple models

Visualizing multiple models

Assessing model performance

One model with an interaction

Specifying an interaction

Interactions with understandable coeffs

Making predictions with interactions

Predicting with interactions

Manually calculating predictions with interactions

Simpson's Paradox

Modeling eBay auctions

Modeling each auction type

Начать главу

3

Multiple Linear Regression

See how modeling and linear regression make it easy to work with more than two explanatory variables. Once you've mastered fitting linear regression models, you'll get to implement your own linear regression algorithm.

Two numeric explanatory variables

Interactive 3D scatter plot

Visualizing three numeric variables

Modeling two numeric explanatory variables

Visualizing two numeric explanatory variables

Including an interaction

More than two explanatory variables

Visualizing many variables

Different levels of interaction

Predicting again

How linear regression works

The sum of squares

Linear regression algorithm

Начать главу

4

Multiple Logistic Regression

Extend your logistic regression skills to multiple explanatory variables. You’ll also learn about logistic distribution, which underpins this form of regression, before implementing your own logistic regression algorithm.

Multiple logistic regression

Logistic regression with two explanatory variables

Logistic regression prediction

Visualizing multiple explanatory variables

Confusion matrix

The logistic distribution

Cumulative distribution function

Inverse cumulative distribution function

Logistic distribution parameters

How logistic regression works

Likelihood & log-likelihood

Logistic regression algorithm

Congratulations!

Начать главу

Intermediate Regression with statsmodels in Python

Курс
завершён

Получить сертификат об окончании

Добавьте эту квалификацию в профиль LinkedIn, резюме или CV
Поделитесь в социальных сетях и в обзоре эффективностиЗаписаться сейчас

Присоединяйтесь к более чем 19 миллионам обучающихся и начните Intermediate Regression with statsmodels in Python уже сегодня!

Создать бесплатный аккаунт

Продолжить через Google Показать больше вариантов

или

Продолжая, вы принимаете наши Условия использования, нашу Политику конфиденциальности и соглашаетесь с тем, что ваши данные хранятся в США.

Развивайте свои навыки работы с данными с помощью DataCamp для мобильных устройств.

Успевайте в обучении на ходу с помощью наших мобильных курсов и ежедневных 5-минутных заданий по программированию.