Utilizing Data Mining Techniques to Predict Loan Repayment
Can we predict who will repay the loans and who won’t?
Final Answer:
The task is to use data mining techniques to predict loan repayment by analyzing a dataset of 2000 loan customers. The goal is to build a system that can predict the likelihood of a new customer repaying a loan. The project involves summarizing the project requirements, listing the variables in the dataset, performing data cleaning, building machine learning models, and evaluating their performance. The final report should be submitted along with the models.
Business Understanding/ Problem Statements
Problem Statement: The task is to use data mining techniques to predict loan repayment by analyzing a dataset of 2000 loan customers. The goal is to build a system that can predict the likelihood of a new customer repaying a loan. The project methodology involves summarizing the project requirements, defining relevant terminology, listing the variables in the dataset, and deciding which variables should be considered for building the machine learning model.
Data Summary
Data Variables: The dataset contains variables that are either nominal or numeric, continuous or discrete. Each variable needs to be evaluated for its relevance in building the machine learning model. The decisions should be explained.
Data Preparation
Data Cleaning: Prior to the modeling process, data cleaning is performed to preprocess the data. This includes correcting mistyped entries and carrying out descriptive analysis. Histograms can be used to visualize the distribution of a variable before and after preprocessing.
Machine Learning Modelling
Techniques: Various machine learning techniques can be used, such as decision trees, logistic regression, or support vector machines. The models are represented using diagrams to show their structure. Hyperparameters of the models can be adjusted to improve performance. The data is split into training, validation, and testing sets. The models are evaluated using metrics such as accuracy, precision, recall, and the ROC curve.
Report Compilation and Submission
Submission Process: The report should cover all the required sections and be submitted along with the models built. The word limit for the report is 6000 words, with deductions for exceeding the limit. The report should be submitted as online text, and the models should be submitted as attachments.