Building a machine learning model to accurately predict whether or not an applicant will be approved or declined is becoming increasingly important in the financial and banking industries. This model can help banks save time and money by reducing the amount of manual labor needed to review applications. It can also provide insights into applicants' creditworthiness and help banks make more informed decisions when approving or denying loans. With this model, banks can make better-informed decisions, improve their customer service, and ensure they are making sound investments.

Credit score cards are a key element of risk management in the financial sector. They use data provided by applicants to estimate their likelihood of defaulting on credit card payments and borrowing money in the future. This system provides a great way to screen potential customers and manage risks. Banks can use credit scores to objectively assess the level of risk involved when considering whether to approve a credit card for an applicant. This makes it easier for them to make an informed decision.

Build a machine learning model to predict whether an applicant is "Approved" or "Declined".

Input parameters include information such as the user's gender, whether they own a car or real estate, their income, education level, and family status.

After collecting the user's input, the code preprocesses the data by converting binary categorical features to numeric values and using a saved encoder to transform categorical features to numerical values.