Data Science : Admission Prediction using Machine Learning

A practical Data Science Hands-on Guided Project on Graduate Admission Prediction Using Machine Learning
New
Rating: 4.0 out of 5 (24 ratings)
2,796 students
Data Science : Admission Prediction using Machine Learning
New
Rating: 4.0 out of 5 (24 ratings)
2,799 students
Using AI and Machine Learning to Predict Chance of Admit into Universities
Building, Training, Testing and Evaluating Machine learning Models
Learn to create heatmaps, correlation tables, scatter plots and distplot using Seaborn library
A-Z step by step guide into importing libraries, importing and exploring datasets, building a Machine learning model, training, testing and evaluating it.
Learn to work with Linear Regression Machine Learning Algorithm to create Machine Learning Models with approx 96 percent accuracy.
Importing, Exploring and Analyzing datasets and finding correlation between its variables

Requirements

  • Very basic knowledge of python and its libraries

Description

Would you like to learn how to predict Chance of Admission into Graduate School using Machine Learning?


Have you ever desired to build a Machine Learning Model?



If the answer to any of the question is “YES”, then you will love this project.



This is a Practical Hands-on Machine Learning Guided Project. You learn by Practice. No unnecessary lectures. No unnecessary details. Direct to the point.


Enrol Now and let’s build a Machine Learning Model together in under 1 hour. We will build a Machine Learning Model and we will feed the data of thousands of students and their GRE Score, TOEFL Score, CGPA, SOP. LOR, University rating and Research to the Model and train it in order to predict the Chance of Admit to Graduate School. In the end, we will test the model and evaluate its performance.


When you complete the project, you will be proud of yourself on what you have learned and achieved.


You will learn more in this one hour of Practice than hundreds of hours of unnecessary theoretical lectures. Learn the most important aspect of Data Science :

  • Importing all the necessary Libraries

  • Importing and Exploring Datasets

  • Building a Linear Regression Machine Learning Model

  • Training, Testing and Evaluating the model


We will build a Machine Learning model to predict Graduate Admissions. In this hands-on project, we will complete the following tasks:


  • Task 1: Brief theoretical information about Libraries, Dataset, Linear Regression Algorithm and Google Colab Environment

  • Task 2: Importing all the necessary Libraries

  • Task 3: Importing the Graduate Admission dataset to the Colab Environment

  • Task 4: Data Cleaning: Removing unnecessary columns

  • Task 5: Exploratory Data Analysis using graphs: Correlation & feature selection

  • Task 6: Splitting the Dataset into Training and Testing sets

  • Task 7: Building and Training Linear Regression Model

  • Task 8: Performance evaluation & Testing the model


Make a leap into Data science with this Hands-on guided project and showcase Machine Learning skills on your resume.

So, grab a coffee, turn on your laptop, click on the “Enrol Now” button and start learning right now.



Who this course is for:

  • Anyone who wants to build a Machine learning Model and evaluate its prediction
  • Anyone interested in Data science

Course content

5 sections • 7 lectures • 40m total length
  • Project Overview and Introduction to the Dataset
    05:58
  • Introduction to Libraries, Linear Regression Algorithm and Colab Platform
    04:29

Instructor

Creative Learning Solutions for the Digital Age
School of Disruptive Innovation
  • 4.3 Instructor Rating
  • 447 Reviews
  • 30,804 Students
  • 7 Courses

Welcome to the School of the Disruptive Innovation . We are here to teach you what they don't teach you in school. We are unconventional in our ways but we promise and we over deliver.

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