Dream Housing Finance uses a Logistic Regression algorithm to predict loan eligibility based on customer details such as gender, marital status, education, income, loan amount, and credit history. The dataset includes features like applicant and coapplicant income, loan amount, loan term, and property area, with the target variable being loan approval status. Data preprocessing involved handling missing values, encoding categorical variables, and training the model, achieving an accuracy score of 80%. The goal is to automate loan eligibility prediction in real-time for loan applicants.