scikit-learn多元线性回归

Multiple Linear Regression with scikit-learn

1308 次查看
Rhyme
Coursera
  • 完成时间大约为 2 个小时
  • 初级
  • 英语
注:本课程由Coursera和Linkshare共同提供,因开课平台的各种因素变化,以上开课日期仅供参考

你将学到什么

Build univariate and multivariate linear regression models in Python using scikit-learn

Perform Exploratory Data Analysis (EDA) and data visualization with seaborn

Evaluate model fit and accuracy using numerical measures such as R² and RMSE

Model interaction effects in regression using basic feature engineering techniques

课程概况

In this 2-hour long project-based course, you will build and evaluate multiple linear regression models using Python. You will use scikit-learn to calculate the regression, while using pandas for data management and seaborn for data visualization. The data for this project consists of the very popular Advertising dataset to predict sales revenue based on advertising spending through media such as TV, radio, and newspaper.

By the end of this project, you will be able to:

– Build univariate and multivariate linear regression models using scikit-learn
– Perform Exploratory Data Analysis (EDA) and data visualization with seaborn
– Evaluate model fit and accuracy using numerical measures such as R² and RMSE
– Model interaction effects in regression using basic feature engineering techniques

This course runs on Coursera’s hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, this means instant access to a cloud desktop with Jupyter Notebooks and Python 3.7 with all the necessary libraries pre-installed.

Notes:
– You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.
– This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

课程大纲

Project: Multiple Linear Regression with scikit-learn

In this project-based course, you will build and evaluate multiple linear regression models using Python. You will use scikit-learn to calculate the regression, while using pandas for data management and seaborn for plotting. The data for this project consists of the very popular Advertising dataset to predict sales revenue based on advertising spending through media such as TV, radio, and newspaper. By the end of this project, you will be able to build univariate and multivariate linear regression models using scikit-learn, perform Exploratory Data Analysis (EDA) and data visualization with seaborn, evaluate model fit and accuracy using numerical measures such as R² and RMSE, and model interaction effects in regression using basic feature engineering techniques.

课程项目

Introduction and Overview

Load the Data

Relationships between Features and Target

Multiple Linear Regression Model

Feature Selection

Model Evaluation Using Train/Test Split and Model Metrics

Interaction Effect (Synergy) in Regression Analysis

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