Enroll Course: https://www.udemy.com/course/formacao-data-science-criacao-de-um-credit-score-com-python/
In the world of finance, understanding and mitigating risk is paramount. For anyone interested in how banks and financial institutions make lending decisions, the “Credit Score – Módulo 1: Regressão Logística em python” course on Udemy offers a fantastic introduction. This course delves into the widely adopted credit scoring methodology, a system designed to assess the risk of default in various financing scenarios. It’s the very technique that guides the decisions of major banks, finance companies, and credit card operators globally.
What makes this course particularly valuable is its hands-on approach. You’re not just passively learning; you’re actively building a credit score model alongside the instructor. All the materials developed during the course are made available to students, allowing for easy replication and further practice. The course is entirely in Portuguese and is heavily focused on practical application, making it ideal for those who want to understand the ‘how’ behind bank lending models.
The curriculum walks you through the step-by-step construction of a credit score model using Logistic Regression in Python, a technique favored for its effectiveness in this domain. You’ll learn the entire pipeline, from understanding and preparing your data, to selecting the right variables, and finally, fitting a logistic regression model. The explanations are didactic and rich in detail, with all the Python notebooks provided for you to follow along and experiment with.
At its core, credit scoring involves gathering information about a borrower and using it to predict their likelihood of repaying a loan. This includes personal data like age, gender, profession, marital status, dependents, address, and income. It also incorporates public information, such as regional default rates, census data, and labor market research. Most crucially, it leverages a consumer’s credit behavior, including outstanding debts, legal actions, credit inquiries, and defaults with creditors.
The credit score system employs mathematical formulas and statistical tools to process this wealth of information, assigning a score (or ‘note’) to individuals applying for loans or credit. A higher score signifies a lower risk for the lender, directly impacting lending decisions and terms.
If you’re looking to gain practical, real-world skills in financial risk assessment and machine learning, this course is a highly recommended starting point. It demystifies a critical aspect of the financial industry and equips you with the tools to build your own predictive models.
Enroll Course: https://www.udemy.com/course/formacao-data-science-criacao-de-um-credit-score-com-python/