Multiple Linear Regression with scikit-learn
7 735 déjà inscrits
7 735 déjà inscrits
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.
Data Visualization (DataViz)
Votre enseignant(e) vous guidera étape par étape, grâce à une vidéo en écran partagé sur votre espace de travail :
Votre espace de travail est un bureau cloud situé dans votre navigateur, aucun téléchargement n'est requis.
Votre enseignant(e) vous guide étape par étape dans une vidéo en écran partagé
par PR15 mai 2020
Whatever explained is satisfactory ,but it is short.We looking for more big projects.
par VK23 août 2020
good for beginners, loved the way the instructor explained about synergy (interaction among features)
par ZY22 mai 2020
Best Course to linear regression basic to get advanced knowledge in neural network
par PA10 juin 2020
Overall a good project, just a few functions here and there whose use I needed to figure out myself.