-
Fidelity: Add credit knowledge to your LIFE !!!
We have developed a web application using Angular, HTML, and CSS, coupled with a Python backend. Our project aims to predict credit knowledge, a binary classification (1 or 0), for each state based on user-provided CSV files. The input file includes columns such as Unique ID, Age, Gender, City, State, City Type, Education, Has a Credit Card, Has Internet Access, Uses a Mobile App, Used a Credit Card, Credit Score, Credit History, Employment Status, Employment History, Income, Debt-to-Income (DTI) Ratio, Current Debts, and Credit Payment History. Users can interact with the application by selecting a state from a dropdown menu, uploading a CSV file, and submitting the form. Upon submission, the application checks if the state in the uploaded file matches the selected state. If not, it alerts the user about the mismatch. If the states align, the backend processes the data using a pre-trained machine learning model. The application then dynamically renders the percentage of instances with credit knowledge equal to 1, based on the defined criteria. This user-friendly interface streamlines the process of credit knowledge prediction and provides insightful results for informed decision-making.
