The aim of the project is to getthe insights of a marketing campaing. which campaign is performing better and how we can improve the target marketing

The objective of the project is to segment the customers and identifying the prospect to sell wealth managment/investment products

Used Clustering to segment the Customers

Collaborative Filtering is used to recommend the products

The objective of project is calculatethe Expected Credit Loss.

Expect Credit Loss = PD x LGD x EAD

Develop PD, LGD and EAD models using Machine Learning to calculate the current expected credit loss

The objective of project is identifying the default risk associated with the customer And calculating the Expected Credit Loss. Automated the lending and decision-making processes by using machine learning models i.e. Data driven based decisions. Enabled the analysis of credit risk by historical information about losses on loans and then building models that forecast future losses for similar groups of loans. Develop PD, LGD and EAD models using Machine Learning and conduct thorough independent tests to assess the model assumptions, accuracy and performances. Building Time Series Models for economic variables forecasting Interact with model owners/users, and regulators to address model issues and remediation actions. Interact with business units to better understand their portfolio, risk exposures and provide subject matter expert opinion on their modelling needs and Stay connected with model owners for ongoing monitoring activities Work on various ad hoc quantitative and other modelling assignments Ability to perform other model validation work assigned by the head of model risk management

The objective of the project is to identifying prospects for investing in banking products Segmenting the customers using there demographic, psychographic, lifestyles and lifestages Building RFM Models for Advisor Manager to identify the top performing Advisors Building Propensity Model for Identifying top Prospects Visualizing the segments and building models for previous campaign to get deep insights for Marketing Manager Product Recommendation to cross sell or up sell using Apriori Algorithm

Objective: The objective of project is tailoring new marketing campaigns for different cities And improving user booking rate Segmenting the customers using k-means clusters to identify marketing campaign results Used Decision Tree model to identify what lead a higher chance of booking for an individual