SIT384 Cyber security analytics Credit Task 5.2C: Linear Regression Task description: You are given one dataset "admission_predict.cav". Its description is as follows: Context This dataset is created for prediction of Graduate Admissions from an Indian perspective. Content The dataset contains several parameters which are considered important during the application for Masters Programs. The parameters included are : 1. GRE Scores ( out of 340 ) 2. TOEFL Scores ( out of 120 ) 3. University Rating ( out of 5 ) 4. Statement of Purpose and Letter of Recommendation Strength ( out of 5 ) 5. Undergraduate GPA ( out of 10 ) 6. Research Experience ( either 0 or 1 ) 7. Chance of Admit ( ranging from 0 to 1 ) Acknowledgements 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 $ Acharya. Inspiration 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. Citation Please cite the following if you are interested in using the dataset : Mohan 5 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 Sample data: Serial GRE TOEFL University Chance of No. Score Score Rating SOP LOR CGPA Research Admit 337 118 4 4.5 4.5 9.65 0.92 NP 324 107 4 4 4.5 8.87 0.76 3 316 104 3 3 3.5 8 1 0.72 322 110 3.5 2.5 8.67 0.8 5 314 103 2 3 8.21 0 0.65 (The above data is for demonstration purposes only. Please download the full version of admission_predict.csv.) You are asked to