The document discusses a predictive model developed to assess the chances of graduate admission using two regression models (linear regression and KNN) and three classification models (decision tree, Bayesian, KNN). The model utilizes a dataset containing various academic metrics such as GRE scores, TOEFL scores, GPA, and recommendation strength, aiming to provide prospective students with insights on their admission probabilities. Results indicate linear regression outperforms KNN, with CGPA being the most influential factor in admission chances.

1. Graduate Admission Prediction:
Comparing Regression and Classification
models
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2. Data Analytics (ECMP5005D)
Team 4
Sebastian Duque Salazar
Faiza Ullah
Muskaan Sultana Shaik
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3. Introduction
● As international graduate students, the foremost
concern is determining the chances of admission
to reputable universities.
● To address this, a predictive model employing two
regression and three classification techniques
have been developed.
● The dataset, sourced from Kaggle, is credited to
Mohan S Acharya and draws inspiration from the
UCLA dataset.
● It is useful for the graduate students as a
prediction tool to check their chances of getting
into the university of their choice.
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4. Overview
● We have developed two regression models: Linear
regression and KNN. This will be a tool for students.
● The models that are developed in classification are:
Decision Tree Model, Bayesian model, KNN.
● The models will predict student’s chances of being
accepted based on their characteristics. This will help
students to save time.
● There is an increase in graduate students in recent years
and they do not know their chances to get admitted to a
certain university.
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5. Dataset
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1. GRE Scores ( out of 340 )
2. TOEFL Scores ( out of 120 )
3. University Rating ( out of 5 )
4. Statement of Purpose Strength ( out of 5 )
5. Letter of Recommendation Strength ( out of 5 )
6. Undergraduate GPA ( out of 10 )
7. Research Experience ( either 0 or 1 )
Target Variable - Chance of Admit ( ranging from 0 to 1 for
regression models and 3 categories i.e. high, medium, low
for classification models)

6. Methodology
Data extraction
Data Exploratory
Analysis (EDA)
Data cleaning Model creation
and predictions
Regression and
Classification
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7. Methodology - Data cleaning and data splitting
Data cleaning
● Null observations were removed
● Remove unnecessary features and rename variables
● Transform categorical variables into factors
● Standardization of data
● Outliers and leverages were removed
Data splitting
Train Dataset -
80%
Test Dataset
20% 7
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8. Methodology - Data Exploratory Analysis
Helped us to understand the relations and patterns of our data.
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9. Methodology - Regression Models creation
Linear Regression
● Step backward model was used as feature selection
● We got an adjusted R-squared of 0.823
● Outliers were removed
The model accomplished all the assumptions: Linearity, Normality,
Heteroscedasticity and Multicollinearity
Chance= −1.359 + 0.00216×GRE + 0.00266×TOEFL + 0.00782×Rating +
0.01271×LOR + 0.12003×CGPA + 0.02375×Research
Final equation
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10. Methodology - Regression Models creation
KNN
● 10-fold cross validation was used for tuning hyperparameter K.
● A 10-40 search grid was defined
● The best K was 21. Which is very close to √500 = 22.3
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11. Transformation of target variable
● Target variable chance was transformed into 3 categories: low,
medium and high.
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Chance of being admitted
low
Medium
High

12. Methodology - Classification Models creation
KNN
● 10-fold cross validation was used for tuning hyperparameter K.
● A 10-40 search grid was defined
● The best K was 15
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13. Methodology - Classification Models creation
Decision Tree
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14. Methodology - Classification Models creation
Bayesian Model
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● Naive Bayes function was used
● Confusion matrix was used to assess the model’s performance
Predicted Low Medium High
Low 19 6 0
Medium 5 16 14
High 1 3 35
Accuracy was 0.71

15. Methodology - Regression Models creation
Linear Regression
● Step backward model was used as feature selection
● We got an adjusted R-squared of 0.823
● Outliers were removed
The model accomplished all the assumptions: Linearity, Normality,
Heteroscedasticity and Multicollinearity
Chance= −1.359 + 0.00216×GRE + 0.00266×TOEFL + 0.00782×Rating +
0.01271×LOR + 0.12003×CGPA + 0.02375×Research
Final equation
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16. What were the results of the
regression models?
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17. Regression Models
Linear KNN
Root Mean
Squared Error
(RMSE)
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18. Classification Models
Decision Tree KNN
Accuracy
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Bayesian

19. Results: Linear Regression Vs KNN Regression
0.00
0.025
0.050
0.075
0.0925
0.06197
KNN Regression Linear Regression
Model
RMSE
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20. Results: Classification models
0.00
0.025
0.050
0.075
0.0727 0.7071
Decision Tree
Accuracy
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0.06197
Naive Bayes KNN

21. Feature Vs Importance of All Predictor Values
0.5
0.0
1.5
1.0
2.5
2.0
3.0
CGPA GRE SOP TOEFL LOR Rating Research
Importance
Features
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22. ● Linear Regression Model outperforms the KNN regression model
● CGPA has the biggest impact on admission chances
● Higher rated universities are more likely to accept applicants
● Applicants with higher tend to apply to high rated universities
● Research plays an important role for low rating universities
● CGPA, GRE, TOEFL and research experience are the most
important factors.
● Finally, students with good academic records have more
probability to get into graduate programs
Conclusions
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23. CREDITS: This presentation template was created
by Slidesgo, including icons by Flaticon,
infographics & images by Freepik
Thank You
Q&A
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