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.
Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.
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.
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
Learn step-by-step:
- Loading Data
- Find the correlation between the dependent variable and the independent variables
- Building the Linear Regression Model
- Building the Random Forest and Support Vector Machine Regression Models
- Exploratory Data Analysis - Part 2
- Exploratory Data Analysis
