In this project, we aim to predict the risk of defaulting on a loan based on customer behavior using Machine Learning and Deep Learning techniques. We start by exploring the dataset and understanding its structure and contents. The dataset contains various features related to customer behavior, such as credit history, income, employment status, loan amount, and more. We analyze the distribution of these features to gain insights into their characteristics and potential impact on loan default. Next, we preprocess the data by handling missing values, encoding categorical variables, and normalizing numerical features. This ensures that the data is in a suitable format for training Machine Learning models. After evaluating the performance of these Machine Learning models, we turn our attention to Deep Learning techniques. We design and train an Artificial Neural Network (ANN) to predict the risk flag for loan default. The ANN consists of multiple layers of interconnected neurons that learn hierarchical representations of the input features.