Deep Learning for College Graduates Employment Prediction A Computational Approach

This project presents a machine learning framework for predicting financial risk and optimizing debt management in the telecommunications sector. The framework integrates Random Forest, Decision Tree, Adaptive Stacked Feature-Weighted Ensemble (ASFWE), and an Autoencoder-Tree Hybrid model to capture complex patterns in customer demographics, usage behavior, payment history, and socioeconomic factors. The dataset undergoes preprocessing steps including data cleaning, feature engineering, outlier detection, and normalization to ensure robust model training. Random Forest and Decision Tree models identify nonlinear relationships in customer data, while ASFWE leverages stacked ensembles with feature weighting to enhance predictive performance. The Autoencoder-Tree Hybrid captures latent representations of customer behavior, providing a nuanced understanding of risk factors. The system classifies customers according to their likelihood of default or delayed payment and evaluates multiple scenarios to support strategic financial decisions. Experimental results demonstrate that ensemble models outperform individual models in accuracy, F1-score, and robustness. This framework offers telecommunications providers a scalable, explainable, and proactive decision-support tool for debt monitoring, enabling early risk detection, improving collection strategies, and enhancing overall financial resilience.

Keywords: Financial Risk, Debt Management, Telecommunications, Random Forest, Decision Tree, ASFWE, Autoencoder-Tree Hybrid, Ensemble Learning, Predictive Analytics, Scenario Modeling.