University Admission Prediction Using Multiple Linear Regression

4.6
stars
122 ratings
Offered By
Coursera Project Network
4,286 already enrolled
In this Guided Project, you will:

Train Artificial Neural Network models to perform regression tasks

Perform exploratory data analysis

Understand the theory and intuition behind regression models and train them in Scikit Learn

Understand the difference between various regression models KPIs such as MSE, RMSE, MAE, R2, adjusted R2

Clock2 hours
IntermediateIntermediate
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this hands-on guided project, we will train regression models to find the probability of a student getting accepted into a particular university based on their profile. This project could be practically used to get the university acceptance rate for individual students using web application. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

regression modelsDeep LearningArtificial Intelligence (AI)Machine LearningPython Programming

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. Understand the problem statement

  2. Import libraries and datasets

  3. Perform Exploratory Data Analysis

  4. Perform Data Visualization

  5. Create Training and Testing Datasets

  6. Train and evaluate a linear regression model

  7. Train and evaluate an artificial neural networks model

  8. Train and Evaluate a Random Forest Regressor and Decision Tree Model

  9. Understand the various regression KPIs

  10. Calculate and Print Regression model KPIs

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

Reviews

TOP REVIEWS FROM UNIVERSITY ADMISSION PREDICTION USING MULTIPLE LINEAR REGRESSION

View all reviews

Frequently asked questions

Frequently Asked Questions

More questions? Visit the Learner Help Center.