ML Project In the loan eligibility project, my role was pivotal in its development and deployment. I took charge of designing and implementing the Streamlit web application, creating an intuitive interface for users to input essential details such as gender, marital status, monthly income, and loan amount. Additionally, I integrated a pre-trained machine learning model, loaded from 'classifier.pkl,' to accurately predict loan approval status. My responsibilities included managing the entire development lifecycle, from coding the application in Python to ensuring a seamless user experience. This project showcases my expertise in web application development, machine learning integration, and a user-centric approach to solving real-world problems.
I developed a Streamlit web application for quick loan eligibility checks. Users can input their gender, marital status, monthly income, and desired loan amount, and the app utilizes a pre-trained machine learning model to predict loan approval. The application boasts a clean interface for seamless user interaction. Python, coupled with pandas for data manipulation, Streamlit for web app creation, and a pre-trained model loaded from 'classifier.pkl,' forms the robust tech stack. This project not only demonstrates my proficiency in deploying machine learning models but also emphasizes the user-centric approach in making financial decisions through an intuitive web interface.