Previsão do sucesso de leads : uma abordagem em machine learning

Mestrado em Métodos Quantitativos para a Decisão Económica e EmpresarialEste Trabalho Final de Mestrado advém de um estágio realizado na empresa Quidgest S.A., em parceria com o Instituto Superior de Economia e Gestão (ISEG), no âmbito do mestrado de Métodos Quantitativos para a Decisão Económica e Empresarial (MQDEE). Este trabalho resulta da necessidade em aumentar o sucesso de vendas, através da previsão do sucesso de leads, recorrendo a técnicas e algoritmos de Machine Learning. Neste domínio, foi realizada uma prévia análise e compreensão do cenário empresarial, para que a utilização dos algoritmos pudesse oferecer uma adequada compatibilidade com os padrões de negócio da empresa. Previamente, foram aplicadas técnicas de tratamento, aumento e limpeza de dados, nomeadamente: SMOTE; ADASYN e TOMEK. Relativamente aos algoritmos de Machine Learning, foram utilizados algoritmos assentes no âmbito da aprendizagem supervisionada, nomeadamente: Support Vector Machines; Random Forest; Neural Networks; Adaboost e C5.0. As etapas deste trabalho foram alinhadas com a metodologia CRISP-DM, que é uma das mais conhecidas abordagens no que toca à logística de processos para a construção de sistemas computacionais. Os softwares utilizados foram: software R (com recurso ao R Studio); Microsoft SQL Server e o GENIO, que é o software próprio da Quidgest S.A.This Master's Final Work results from an internship performed at Quidgest S.A., in a partnership with Instituto Superior de Economia e Gestão (ISEG), within the scope of the Master of Quantitative Methods for Economic and Business Decision (MQDEE). The purpose of this work is to increase the company's sales success, by predicting the success of leads, using Machine Learning techniques and algorithms. In this domain, a previous analysis of the business scenario was studied and analyzed, to allow that the usage of the algorithms could be suitable with the company's standard business processes. Then, techniques for processing, increasing and cleaning the data were applied, such as: SMOTE; ADASYN and TOMEK, and then, algorithms within the scope of supervised learning were used, such as: Support Vector Machines; Random Forest; Neural Networks; Adaboost and C5.0. The sequential steps of this work were aligned with the CRISP-DM methodology, which is one of the most used tools in terms of logistics for building computer systems. The used softwares were: R software (using R Studio); Microsoft SQL Server and GENIO, which is the owned and created software by Quidgest S.A.info:eu-repo/semantics/publishedVersio

Description and performance of track and primary-vertex reconstruction with the CMS tracker

A description is provided of the software algorithms developed for the CMS tracker both for reconstructing charged-particle trajectories in proton-proton interactions and for using the resulting tracks to estimate the positions of the LHC luminous region and individual primary-interaction vertices. Despite the very hostile environment at the LHC, the performance obtained with these algorithms is found to be excellent. For tbar t events under typical 2011 pileup conditions, the average track-reconstruction efficiency for promptly-produced charged particles with transverse momenta of p(T) > 0.9GeV is 94% for pseudorapidities of |η| < 0.9 and 85% for 0.9 < |η| < 2.5. The inefficiency is caused mainly by hadrons that undergo nuclear interactions in the tracker material. For isolated muons, the corresponding efficiencies are essentially 100%. For isolated muons of p(T) = 100GeV emitted at |η| < 1.4, the resolutions are approximately 2.8% in p(T), and respectively, 10μm and 30μm in the transverse and longitudinal impact parameters. The position resolution achieved for reconstructed primary vertices that correspond to interesting pp collisions is 10–12μm in each of the three spatial dimensions. The tracking and vertexing software is fast and flexible, and easily adaptable to other functions, such as fast tracking for the trigger, or dedicated tracking for electrons that takes into account bremsstrahlung