Data Science Basic | Regression Analysis by Diljeet Singh | Learn Smarter
K12 Students

Academics

AI-Powered learning for Grades 8–12, aligned with major Indian and international curricula.

Academics

Grades

Grade 8 Grade 9 Grade 10 Grade 11 Grade 12

Curriculum

CBSE ICSE IB
Professionals

Professional Courses

Industry-relevant training in Business, Technology, and Design to help professionals and graduates upskill for real-world careers.

Professional Courses
Games

Interactive Games

Fun, engaging games to boost memory, math fluency, typing speed, and English skills—perfect for learners of all ages.

games
Typing
Memory
Math
English Adventures
Knowledge
Regression Analysis

Regression analysis is a statistical method employed to predict continuous outcomes by examining relationships between variables. It covers both simple and multiple linear regression techniques using Python, emphasizing model fitting and evaluation metrics for effective predictive performance.

Enroll to start learning

You’ve not yet enrolled in this course. Please enroll for free to listen to audio lessons, classroom podcasts and take mock test.

Sections

  • 1

    What Is Regression?

    Regression is a statistical method used to predict continuous outcomes based on input features.

    Learning Practice

  • 2

    Simple Linear Regression

    Simple Linear Regression models the relationship between one independent variable and one dependent variable using a linear equation.

    Learning Practice

  • 2.1

    Equation

    This section introduces the mathematical equation for simple linear regression, illustrating its components and significance.

    Learning Practice

  • 2.2

    Python Implementation

    This section covers the implementation of linear regression in Python using scikit-learn, focusing on both simple and multiple linear regression models.

    Learning Practice

  • 3

    Multiple Linear Regression

    Multiple linear regression predicts a dependent variable using two or more independent variables.

    Learning Practice

  • 3.1

    Example

    Learning

  • 3.2

    Interpretation

    This section covers the interpretation of regression analysis results, focusing on coefficients and performance metrics.

    Learning Practice

  • 4

    Evaluating Regression Models

    This section covers key metrics for evaluating regression models, including MAE, MSE, RMSE, and R².

    Learning Practice

  • 4.1

    Metric Description

    This section outlines various metrics used to evaluate regression models, including Mean Absolute Error, Mean Squared Error, RMSE, and R² score.

    Learning Practice

  • 4.2

    Example

    Learning

  • 5

    Visualizing Regression

    This section discusses visualizing regression models using scatter plots and regression lines.

    Learning Practice

  • 5.1

    Scatter Plot With Line

    The section discusses how to visualize regression models using scatter plots with regression lines to represent relationships between variables.

    Learning Practice

  • 6

    Assumptions In Linear Regression

    This section covers the key assumptions underlying linear regression, which are crucial for ensuring reliable predictions.

    Learning Practice

  • 6.1

    Assumption Details

    This section covers the critical assumptions underlying linear regression analysis that must be validated for accurate predictions.

    Learning Practice

References

Chapter 10_ Regression Analysis.pdf

Class Notes

Memorization

What we have learnt

  • Regression is used to predi...
  • Simple regression involves ...
  • Scikit-learn simplifies the...

Revision Tests