Multiple Linear Regression with scikit-learn

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在此指導項目中,您將:

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初級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

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.

您要培養的技能

  • Machine Learning
  • Python Programming
  • Data Visualization (DataViz)
  • Linear Regression
  • Scikit-Learn

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  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

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

授課教師

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