Multiple Linear Regression - Admissions Dataset

Machine-Learning, Kaggle-Admissions-Data

Kaggle Admissions Data to create a basic Machine Learning Multiple Linear regression model:

I have taken a Kaggle dataset to create a Predictive model to ascertain the likelihood of a Student getting an Admission. This dataset is inspired by the UCLA Graduate Dataset. The test scores and GPA are in the older format.

The dataset is owned by Mohan S Acharya.

Kaggle DataSet by Mohan S Acharya

"This dataset was built with the purpose of helping students in shortlisting universities with their profiles. The predicted output gives them a fair idea about their chances for a particular university."

-Mohan S Acharya

Mohan S Acharya, Asfia Armaan, Aneeta S Antony : A Comparison of Regression Models for Prediction of Graduate Admissions, IEEE International Conference on Computational Intelligence in Data Science 2019

Dataset:

• GRE Scores ( out of 340 )
• TOEFL Scores ( out of 120 )
• University Rating ( out of 5 )
• Statement of Purpose and Letter of Recommendation Strength ( out of 5 )
• Undergraduate GPA ( out of 10 )
• Research Experience ( either 0 or 1 )
• Chance of Admit ( ranging from 0 to 1 )

Importing the Data:

Performing Correlations between Variables to find the best fitting Variables/Parameters:

Nextly we create X and y variables and the Train and Test set:

Score (Model Accuracy):

The accuracy of the model is just 84%. This is a base model and we will try to improve the Model in Later Models.

Finally after fitting and creating the predictive model I fit in my own data to predict the likelihood of me getting an Admission:

Enjoy Thank you

Your's Truely,

Anuraag Rath