University Recommender

• Scraped graduate profiles from Edulix using BeautifulSoup and predprocessed data.
• Implemented Wilcoxon Rank-Sum p-Test, Generative Additive Models, Kernel Density Estimation to study correlations between “desicion” variable and other variables(continuous)
• Trained and evaluated SVM, logistic Regression, XGBoost, RandomForest and AdaBoost.
• Attained the highest AUC-ROC(0.83) and AUC-PR(0.87) with XGBoost and chose it as our final model for admission prediction.
• Implemented KNN to recommend top ‘k’ universities that best suits with the profile.
• Created an interacive web page to deploy the models through flask.

Github