Multiple Linear Regression with scikit-learn

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Neste projeto guiado, você irá:

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
BeginnerBásico
CloudSem necessidade de download
VideoVídeo em tela dividida
Comment DotsInglês
LaptopApenas em desktop

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

Machine LearningPython ProgrammingData Visualization (DataViz)Linear RegressionScikit-Learn

Aprender passo a passo

Em um vídeo reproduzido em uma tela dividida com a área de trabalho, seu instrutor o orientará sobre esses passos:

  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

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

Instrutores

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