The primary goal of this project is to analyze loan data, addressing key questions through hypothesis testing, data visualization, and ultimately building a predictive model using the KNN Classifier.
Briefly introduce the dataset, highlighting key features and its relevance to the loan analysis and prediction objectives.
Explore the dataset through hypothesis testing and visualizations. Answer crucial questions about the data, such as demographic-based variations in loan status and other relevant insights.
Detail the steps taken to clean the dataset, ensuring data reliability for subsequent analysis. Address any anomalies or inconsistencies to enhance the quality of the data.
Describe the implementation of the KNN Classifier for predictive modeling. Explain how the model is built and its significance in predicting loan-related outcomes.