Multiple Linear Regressing using Python

This course focuses on writing a Python code for building and evaluating a robust Multiple linear regression model on a real-world dataset, through a guided approach for building appropriate solutions.

  • icon-videos 2 Videos
  • icon-data 1 Coding Case
  • icon-reading 2 Readings
  • icon-quiz 1 Quiz
Multiple Linear Regression using Python (2)
  • 2 bar graph
    Difficulty: Intermediate

    Foundational knowledge or experience in statistics or analytics is recommended.

  • Asset 1
    Duration: Approximately 4 hours

Course Overview

  • Multiple Linear Regression is an extension of Simple Linear regression as it takes more than one predictor variable to predict the response variable.
  • The goal of multiple linear regression (MLR) is to determine a mathematical relationship between the independent variables that may be both quantitative and qualitative and quantitative dependent variable.
  • Learn regression analysis and how to build a multiple linear regression model in Python using statsmodels and sklearn library.
  • Gain practical experience by applying the understanding and coding skills on real-world business scenarios.

What’s included

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Lifetime Access

Access this project for life once completed

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Flexible Scheduling

Start learning online immediately, at your own pace

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Desktop Only

We recommend completing this project on a desktop

Skills You Will Learn

Devarshee

Regression Analysis

Collinearity Analysis

Multiple Linear Regression Model

Model Building and Validation

Residual Analysis

Associated Learning Tracks

Syllabus

Fundamentals of Multiple Linear Regression in Python – Exploratory Data Analysis, Model Building, Residual Analysis and Model Evaluation.

How it Works

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