Project: Multiple Linear Regression with scikit-learn
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 R^2 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 …
Instructor Details
Courses : 7
Specification: Project: Multiple Linear Regression with scikit-learn
|
1 review for Project: Multiple Linear Regression with scikit-learn
Add a review Cancel reply
This site uses Akismet to reduce spam. Learn how your comment data is processed.
Provider | |
---|---|
Duration | 5 hours |
Year | 2019 |
Level | Beginner |
Language | English |
Certificate | Yes |
Quizzes | Yes |
Roland N L –
It helps a lot that the programming assignment ( the functions and methods of the various Python libraries for data analysis) is demonstrated in real time. Thus, one can learn or try to memorize the correct syntax without the need to spend a lot of time to figure out where one forgot a dot, parentheses, square brackets, or an underscore; and focus more on the theoretical model (in this case multiple linear regression) and its related concepts themselves.