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Projeto

Predicting Credit Card Approvals

Iniciante
Actualizado 12/2023
Build a machine learning model to predict if a credit card application will get approved.
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Incluído comPremium or Teams

PythonData ManipulationMachine LearningApplied Finance45 minutos12 Tasks1,500 XP35,610

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Descrição do Projeto

Predicting Credit Card Approvals

Commercial banks receive a lot of applications for credit cards. Many of them get rejected for many reasons, like high loan balances, low income levels, or too many inquiries on an individual's credit report, for example. Manually analyzing these applications is mundane, error-prone, and time-consuming (and time is money!). Luckily, this task can be automated with the power of machine learning and pretty much every commercial bank does so nowadays. In this project, you will build an automatic credit card approval predictor using machine learning techniques, just like the real banks do.The dataset used in this project is the Credit Card Approval dataset from the UCI Machine Learning Repository.

Predicting Credit Card Approvals

Build a machine learning model to predict if a credit card application will get approved.
Iniciar projeto gratuitamente
  • 1

    Credit card applications

  • 2

    Inspecting the applications

  • 3

    Splitting the dataset into train and test sets

  • 4

    Handling the missing values (part i)

  • 5

    Handling the missing values (part ii)

  • 6

    Handling the missing values (part iii)

  • 7

    Preprocessing the data (part i)

  • 8

    Preprocessing the data (part ii)

  • 9

    Fitting a logistic regression model to the train set

  • 10

    Making predictions and evaluating performance

  • 11

    Grid searching and making the model perform better

  • 12

    Finding the best performing model

    Junte-se a mais 16 milhões de alunos e comece Predicting Credit Card Approvals hoje!

    Crie sua conta gratuita

    ou

    Ao continuar, você aceita nossos Termos de Uso, nossa Política de Privacidade e que seus dados são armazenados nos EUA.