How Banks Prevent Loan Defaults Using Data Science?

About the Webinar

Data science finds its usage and application in various techniques and methods. The machine learning and data science algorithms can be used in predictive analytics. For example, the algorithms can be used to build a predictive model that helps in finding out if any misbehavior exists in loan repayments either by the server or by the customer. This model has helped many banking and financial firms to have a cross-checked status of their security and loan defaults. This expert-led webinar focuses on teaching you how to build such models and what forms the basis of such data science models.

Agenda

• Performing Data Import from Various Sources
• Performing EDA Operations such as Missing Value, Locating Outliers, and Noise Removal
• Performing Data Manipulation (Optional)
• Implementing Data Visualization
• Integrating ML Concepts for Prediction of Loan Defaults

What You Will Learn

• Basics of a loan default model using data science
• Data importing 
• Knowledge about EDA operations in data science
• Introduction to ML concepts 
• Predictive analytics model for loan defaults

About the Speaker

Sumit carries around 7+ experience in the field of data science and machine learning. He holds a BE (H) degree from BITS Pilani and has worked for reputed organizations such as Tata, Jio, etc. Sumit has an excellent track record in training 1,000+ learners and mentoring over 50 data science aspirants.