Project Experience in Artificial Intelligence and Data Science

Clustering Customer Complaint Data for a Manufacturing Company

Idea is to Analyze the unstructured data and identify the critical clauses / Phrases which point to the complaint issues, where companies can pinpoint and rectify the issues or use Preventive Mechanism. The approach involves multi-modal networks followed by consulting on the outcomes

Price Suggestion for Products - FMCG

Large consumer retail company wanted to use machine learning to arrive at an appropriate price for its products across its product line. Our team was given a task to build machine learning model for suggesting price of the product. The client shared us the past data which includes product prices and various other variables that affect the prices. The data also included feedback from the buyer which is in unstructured format

Using Machine Learning to Optimize Customer Remediation Process

Large Multinational Financial Company wants to optimize their customer remediation process by incorporating Machine learning. The company needs to identify the impacted customers on a regular basis for a given issue and to predict the appropriate remediation amount to be paid. As part of the project, We built a Machine learning model using ensemble techniques on random forest and gradient descent methods to identify the impacted population and to predict the remediation amount range. This model on subsequent training and tuning yielded 91 % accuracy in predicting the amount range and was fully incorporated into the client process. This project involves integrating Artificial Intelligence based response systems

A.I Priority matrix

Additional Information

It is important for organizations to identify the best possible A.I project to be implemented that can assure a minimum Return on investment.