Project: Multiple Linear Regression with scikit-learn

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提供方
Rhyme
在此指导项目中,您将:

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

Clock2 hours
Beginner初级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
LaptopDesktop only

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.

您要培养的技能

Machine LearningPython ProgrammingData Visualization (DataViz)Linear RegressionScikit-Learn

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的讲师将指导您完成每个步骤:

  1. Introduction and Overview

  2. Load the Data

  3. Relationships between Features and Target

  4. Multiple Linear Regression Model

  5. Feature Selection

  6. Model Evaluation Using Train/Test Split and Model Metrics

  7. Interaction Effect (Synergy) in Regression Analysis

指导项目工作原理

您的工作空间就是浏览器中的虚拟桌面,无需下载。

在分屏视频中,您的讲师会为您提供分步指导

常见问题

常见问题

  • By purchasing a guided project, you'll get everything you need to complete the guided project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.

  • Because your workspace contains a cloud desktop that is sized for a laptop or desktop computer, guided projects are not available on your mobile device.

  • Guided project instructors are subject matter experts who have experience in the skill, tool or domain of their project and are passionate about sharing their knowledge to impact millions of learners around the world.

  • You can download and keep any of your created files from the guided project. To do so, you can use the “File Browser” feature while you are accessing your cloud desktop.

  • Financial aid is not available for guided projects.

  • Auditing is not available for guided projects.

  • At the top of the page, you can press on the experience level for this guided project to view any knowledge prerequisites. For every level of guided project, your instructor will walk you through step-by-step.

  • Yes, everything you need to complete your guided project will be available in a cloud desktop that is available in your browser.

  • You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.