Regression using Scikit-Learn

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Offered By
Coursera Community Project Network
In this Guided Project, you will:

Learn how to create Regression Models using Scikit-Learn

Learn about Linear Regression, Regression using Random Forest Algorithm, Regression using Support Vector Machine Algorithm

Clock1.5 hours
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

This project is aimed at students and practitioners of Data Sciences for building Predictive Analytics models for research and commercial purposes. Machine Learning can be used to solve prediction problems for classification and regression. In this project, we discuss about using Machine Learning for building Regression Models. We will use Python Language. In Python, we have many options for building Machine Learning solutions like Tensor Flow, Keras, etc. In this project, we use Scikit-Learn. Scikit-Learn provides a comprehensive array of tools for building regression models. The concepts learnt in this project can be extended to build Neural Networks using Tensor Flow or Keras or any other tool. This Guided Project was created by a Coursera community member.

Skills you will develop

Python ProgrammingMachine LearningScikit-Learnregression

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Loading Data

  2. Find the correlation between the dependent variable and the independent variables

  3. Building the Linear Regression Model

  4. Building the Random Forest and Support Vector Machine Regression Models

  5. Exploratory Data Analysis - Part 2

  6. Exploratory Data Analysis

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.