Ingeniería Estadística – Proyectos del Estudiante

Proyectos Capstone

Design of a Prediction System for Evaluating the Success of Telemarketing Campaign Deposits for Customers of BANKUNI Company Using Random Forest and Naïve Bayes Techniques  Descargar Descargar
Design of a Two-Stage Multilevel Regression Model with Truncation and Censoring of the Dependents Variables  Descargar Descargar
Design of a Fast Food Managing Application Using SQL Data Base  Descargar Descargar
Design of an Inventory Management System for a Small-Medium Size Enterprise  Descargar Descargar
Comparison Between Neural Networks and Decision Trees Methods for Predicting the Criminal Behavior of Young People Using Information from the National Census 2016  Descargar Descargar
Design of a Statistical System for Predicting the Quantity and Characteristics of Non-Performing Loans Using the Method of Bootstrap with Crossed Validation, and Support Vector Machines  Descargar Descargar
Design of an Statistical Systems for Classifying of Financial Fraud Using Neural Networks  Descargar Descargar
Identification of Main Factors and Variables Describing the Quantity and Distribution of Fatal Vehicular Accidents in Metropolitan City of Lima Using data Mining Techniques: Random Forest, Boosting, Decision Trees.  Descargar Descargar

Proyectos del Estudiante

Application of Asymmetric Logarithmic Regression Method, Logit Kernel Regression, and Support Vector Machine for Predicting the Satisfaction Level of Clients of Telecommunication Companies  Descargar Descargar
Comparison of Cross Selling Models with Symmetrical and Asymmetrical Links with Classical and Bayesian Estimation for Predicting Client Propensity for Acquiring Credit Cards of Financial Bank Corp.  Descargar Descargar
Comparison of Classic Bootstrap Regression and Bayesian Bootstrap Regression Methods  Descargar Descargar
Implementation of a VAR Bayesian Model for Predicting Commodities Price and GNP Growing Rate  Descargar Descargar
Prediction of Cellphone Losses in Sell-Points of Telecom Companies Using Logistic Regression Model and Random Forest Technique  Descargar Descargar

Proyectos Capstone  Descargar Descargar