To predict an individual's loan eligibility based on basic user inputs, a Support Vector Classifier is employed due to its effectiveness in handling non-linear relationships in binary classification problems. The process begins with loading and exploring the dataset, visualizing loan status distributions, and analyzing relationships among categorical features. Data cleaning involves removing outliers from income and loan amount variables, and categorical variables are numerically encoded. Correlation analysis is performed to identify highly correlated features, with steps taken to address potential multicollinearity. The dataset is split into training and validation sets, and class imbalance is managed using RandomOverSampler. Missing values are imputed, and feature values are normalized using StandardScaler. The SVC model is trained and evaluated, with performance assessed using ROC AUC scores. A confusion matrix and classification report are generated to provide further insights into the model's effectiveness. This study aims to enhance the loan eligibility prediction process, offering a reliable tool for determining loan eligibility based on key financial indicators.