<h1Supervised Machine Learning Algorithms for Predicting Student Dropout and Academic Success: A Comparative Study

In this project, I applied a resampling technique (oversampling) to the imbalanced dataset and compare the performance of different Machine Learning models. I have found the best accuracy score with Random forest (85%)

The purpose of this project is to create and compare different ML models to predict students admission to graduate school. As a starting point, I used a tutorial from UCLA. This tutorial applies Logistic Regression with R, but I did it with Python. After predicting students admission with Logistic Regression, I decided to fix the unequal class distribution of the original dataset.

This imbalancement was not handled in the tutorial, so it was such a great opportunity for me to fix the imbalanced dataset. After the resampling process, I used the Logistic Regression again with the new dataset. To take the project further, I not only used Logistic Regression, but implemented other three algorithms (Decision Tree, SVM-SVC and Random Forest), along with the performance measurement and K-fold for all of the models. In conclusion, I created a graphic to compare the accuracies score for the different ML algorithms (Accuracy Comparison Graph).

Check out a static version of the notebook with Jupyter NBViewer from the comfort of your web browser.

A researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don’t admit, is a binary variable.This data set has a binary response (outcome, dependent) variable called admit, which is equal to 1 if the individual was admitted to graduate school, and 0 otherwise. There are three predictor variables: gre, gpa, and rank. We will treat the variables gre and gpa as continuous. The variable rank takes on the values 1 through 4. Institutions with a rank of 1 have the highest prestige, while those with a rank of 4 have the lowest. Source : UCLA

Check out the dataset

This project is organized in two Chapters. In Chapter 1 I worked with the original dataset (which was imbalanced). In Chapter 2 I did a resampling (oversampling), repeated the logistic regression and included other algarithms.

• PART 1: Data Handling -> Importing Data with Pandas, Cleaning Data, Data description.
• PART 2: Data Analysis -> Supervised ML Technique:Logistic Regression.
• PART 3: Valuation of the Analysis -> Performance measurement + K-folds cross validation to evaluate results locally.

• PART 1: Data Handling -> Importing Data with Pandas, Cleaning Data, Data description.
• PART 2: Data Analysis -> Supervised ML Technique: Logistic Regression, Decision Tree, SVM (SVC) and Random Forest
• PART 3: Valuation of the Analysis -> Performance measurement + K-folds cross validation to evaluate results locally + Accuracy comparison graph

• Numpy
• Pandas
• SciKit-Learn
• Matplotlib
• Seaborn

In this project I have applied a widely adopted technique for dealing with unbalanced datasets called resampling. Random Sampling involves creating a new transformed version of the data with a new class distribution. The goal is to reduce the influence of the data on our ML algorithm. Generally, oversampling is preferable as under sampling can result in the loss of important data. I chose to apply oversampling and then use four ML algorithms: Logistic Regression, Decision Tree, SVM (SVC) and Random Forest. To all these modeles I did a Valuation Analisis (Performance Measurement and K-Fold). Finally, I created a graph to compare the accuracy of the different models. I have found the best accuracy score with Random forest (0.8545454545454545).