Targeted Loan Offers in Banking

What You’ll Learn

• Understand the fundamentals of machine learning in loan recommendations
• Build targeted, personalized loan recommendation models for banking
• Utilize algorithms like logistic regression, decision trees, random forests, and neural networks
• Implement collaborative filtering techniques (Cosine Similarity and Pearson Correlation) for loan personalization
• Set up a data integration pipeline with Apache NiFi for data processing and analysis
• Create a simulated banking data warehouse on MySQL for structured data management
• Fine-tune models and evaluate them with metrics like ROC-AUC, precision, recall, and F1-score
• Apply advanced feature selection to optimize prediction accuracy

Requirements

• Basic understanding of machine learning concepts and algorithms
• Familiarity with Python programming
• Knowledge of SQL and relational databases
• Understanding of data analysis and handling tools

Description

In the competitive field of banking, personalized loan recommendations are key to customer engagement and revenue growth. This course equips you with the skills to develop data-driven loan recommendation models using machine learning and collaborative filtering. You’ll explore a variety of algorithms, including logistic regression, decision trees, random forests, and neural networks, to build an effective predictive model. Furthermore, item-based collaborative filtering techniques like cosine similarity and Pearson correlation will enhance your ability to deliver personalized loan offers.

Starting with a clear business problem and leveraging Apache NiFi for data integration, you’ll work on a simulated banking data warehouse to develop and test your model. This course also focuses on key metrics for model evaluation, such as ROC-AUC and F1-score, ensuring your model’s accuracy and impact. By the end, you’ll have a powerful toolkit to create smarter, more relevant loan recommendations that boost customer acceptance rates.

Who This Course is For

• Data Scientists – Looking to specialize in financial services and banking recommendations
• Machine Learning Engineers – Eager to apply ML techniques in banking contexts
• Banking and Finance Professionals  – Interested in personalizing customer loan offers with data
• Data Analysts – Seeking to expand their knowledge in predictive modeling and collaborative filtering
• Product Managers in Financial Services – Wanting to implement data-driven personalization strategies
• SQL & Database Enthusiasts – Keen to understand data integration for banking