Welcome to “Targeted Loan Offers with Machine Learning & Collaborative Filtering” – where you’ll gain hands-on expertise in creating highly personalized, data-driven loan recommendations that can transform customer engagement in banking! This course is designed for data scientists, financial analysts, and tech professionals eager to advance their skills in predictive modeling and recommendation systems tailored specifically for financial services.

In this comprehensive course, you’ll learn to combine the power of predictive machine learning models with collaborative filtering techniques to predict which customers are most likely to accept loan offers. Starting from data integration and preprocessing with Apache NiFi, you’ll build a simulated banking data warehouse on MySQL and use it to train and test various machine learning models, including Logistic Regression, Decision Trees, Random Forests, XGBoost, LightGBM, and Neural Networks. By mastering these models, you’ll be able to identify the best predictors of loan acceptance, enabling more targeted marketing.

Additionally, you’ll dive into item-based collaborative filtering methods using Cosine Similarity and Pearson Correlation to recommend the right loan products to the right customers. These techniques will equip you with tools to increase customer engagement and loan conversion rates effectively.

Join us to gain an edge in the rapidly evolving world of banking analytics and elevate your impact by providing personalized loan recommendations that deliver real value to your customers and organization!

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