Data Science : Admission Prediction using Machine Learning

A practical Data Science Hands-on Guided Project on Graduate Admission Prediction Using Machine Learning

What you'll learn
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

More Info