This is the third project I completed as part of the Advanced Machine Learning module from my post-graduate certification in AI/ Machine Learning from University of Texas' McCombs School of Business.

This assignment dives deeper into machine learning by implementing and evaluating ensemble methods. The notebook covers techniques like bagging and boosting, using models such as Random Forests and AdaBoost. It includes performance comparisons, visualizations, and insights into how ensemble learning improves model accuracy and robustness.

Prédiction de la souscription d'un dépôt à terme

Analyze the data of Visa applicants, build a predictive model to facilitate the process of visa approvals, and based on important factors that significantly influence the Visa status recommend a suitable profile for the applicants for whom the visa should be certified or denied.

Analyze the data of ABC consulting company, build a predictive model based on the parameters like age, salary, work experience and predict the preferred mode of transport.

Project for the "Computational and Statistical Learning" course.