Multiple Linear Regression with scikit-learn
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
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.
Habilidades que você desenvolverá
Aprender passo a passo
Em um vídeo reproduzido em uma tela dividida com a área de trabalho, seu instrutor o orientará sobre esses passos:
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
Como funcionam os projetos guiados
Sua área de trabalho é um espaço em nuvem, acessado diretamente do navegador, sem necessidade de nenhum download
Em um vídeo de tela dividida, seu instrutor te orientará passo a passo
Avaliações
Principais avaliações do MULTIPLE LINEAR REGRESSION WITH SCIKIT-LEARN
Great course. Thanks to the instructor, The rhyme platform is sometimes very slow, content: (7/10),Audio clarity: (5/10), video clarity: (8/10), Rhyme platform performance: (4/10).
Very good for freshers. Discussed the basic concepts and implemented them. They have a virtual computer so you need not install or download anything.
Nice project for beginners. In the last video, there was a very useful concept of synergy which could be helpful for intermediate learners.
This project is great. Clearly explained and well delivered. I will highly recommend to take this project. The instructor is great!
Perguntas Frequentes – FAQ
Perguntas Frequentes – FAQ
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