? The rapid growth of digital financial services has increased the demand for intelligent systems capable of automating loan approval processes. Traditional loan evaluation methods rely heavily on manual decision-making and require significant time and effort, which may lead to inconsistent and inefficient results. To address these challenges, this research proposes a Loan Eligibility Prediction System using Apache Spark and Machine Learning techniques. The proposed system analyzes various financial attributes of loan applicants such as income, employment length, loan amount, interest rate, loan intent, and credit history to determine the eligibility of a loan applicant. ? Apache Spark is used as the primary framework for distributed data processing, enabling efficient handling of large datasets and scalable machine learning model training. A machine learning pipeline is implemented using PySpark to preprocess data, perform feature engineering, and train predictive models capable of identifying loan approval risk. Furthermore, a web-based interface developed using the Flask framework allows users to input loan application details and receive real-time eligibility predictions. ? The experimental results demonstrate that the proposed system effectively predicts loan eligibility and supports financial institutions in improving decision-making processes. The integration of big data technologies with machine learning techniques significantly enhances prediction accuracy, processing efficiency, and scalability. This research contributes to the development of intelligent financial decision support systems capable of reducing risk and improving loan approval processes in modern banking environments.
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