This is basic classification project on a loan dataset using various Machine Learning algorithms. The dataset contains information about applicants, including attributes such as gender, marital status, income, loan amount, credit history, etc. Various steps are involved where missing values are handled by imputing mean or mode depending on the attribute. Data visualization techniques such as histograms, countplots are used to explore and understand the data distribution and relationships. Feature engineering is performed to create new features such as the logarithm of loan amount and total income, followed by Label Encoding technique. The data is split into training and testing sets and several classification algorithms such as Random Forest Classifier, Naive Bayes, Decision Tree, and K Neighbors Classifier are used. The accuracy of each trained model is evaluated using metrics such as accuracy score and confusion matrix. Classification reports are generated to provide detailed evaluation metrics including precision, recall, F1-score, and support for each class. Confusion matrices are visualized using seaborn's heatmap to analyze the performance of the models.