LendFriend

Deskripsi

This project aims to develop a web-based loan application system incorporating a machine learning model to predict loan approval. The project's methodologies include data collection from historical loan records, preprocessing this data for accuracy and completeness, and implementing machine learning models such as logistic regression and decision trees to evaluate credit risk. The core concept was inspired by the need to streamline the loan approval process and reduce errors in risk assessment. The innovative aspect of this project lies in its automated risk evaluation feature, which enhances decision-making efficiency for lenders. The primary problem addressed is the susceptibility of loan approval processes to human error in risk assessment. Our proposed solution is an intelligent system that automates the initial screening of loan applicants. Borrowers submit their information, which the system then validates and processes through a predictive model. The system provides an initial approval or rejection prediction, which the admin reviews to make a final decision. This solution significantly reduces the chances of approving high-risk loans, ensuring a more reliable lending process. During the development, several challenges were encountered, including data quality issues and model accuracy. We approached these challenges by implementing robust data cleaning procedures and iteratively refining our models to improve performance. This iterative development process allowed us to enhance the system's accuracy and reliability continuously. The target users for this project are primarily borrowers seeking loans and the lending institution's admin staff. For borrowers, the system simplifies the application process and provides quick feedback. For admins, it offers a reliable tool to assess loan applications, ensuring that decisions are based on data-driven insights. Through this project, we gained valuable technical skills in machine learning, data preprocessing, and web application development. Additionally, we improved our problem-solving and project management abilities, working effectively as a team to overcome challenges and deliver a functional product. Special instructions for the project included ensuring the system's compliance with data security standards, particularly for authentication and authorization components. We adhered to these requirements by implementing secure login and access controls, ensuring user data is protected. In summary, this project successfully developed an automated loan approval system that leverages machine learning to enhance risk assessment. It streamlines the application process for borrowers and provides reliable decision-making support for lenders, demonstrating significant potential to improve the efficiency and reliability of loan approval processes.