greatlearning

adityayardi1/AIML

ML AI Case Studies and Projects

dzooli/gl-pythondatascience

Python for Machine Learning course materials and notes and a short introduction to data science in Python

GL-NLP-Project/nlp-capstone-project

AIML Online Capstone AUTOMATIC TICKET ASSIGNMENT The Real Problem One of the key activities of any IT function is to “Keep the lights on” to ensure there is noimpact to the Business operations. IT leverages Incident Management process to achieve theabove Objective. An incident is something that is unplanned interruption to an IT service orreduction in the quality of an IT service that affects the Users and theBusiness. The main goalof Incident Management process is to provide a quick fix / workarounds or solutions thatresolves the interruption and restores the service to its full capacity to ensure no businessimpact.In most of the organizations, incidents are created by various Business and IT Users, End Users/ Vendors if they have access to ticketing systems, and from the integrated monitoringsystems and tools. Assigning the incidents to the appropriate person or unit in the support team has critical importance to provide improved user satisfaction while ensuring better allocation of support resources. The assignment of incidents to appropriate IT groups is still a manual process in many of the IT organizations.Manual assignment of incidents is time consuming and requires human efforts. There may bemistakes due to human errors and resource consumption is carried out ineffectively because ofthe misaddressing. On the other hand, manual assignment increases the response and resolution times which result in user satisfaction deterioration / poor customer service. Business Domain Value In the support process, incoming incidents are analyzed and assessed by organization’s support teams to fulfill the request. In many organizations, better allocation and effective usage of the valuable support resources will directly result in substantial cost savings. Currently the incidents are created by various stakeholders (Business Users, IT Users and Monitoring Tools) within IT Service Management Tool and are assigned to Service Desk teams (L1 / L2 teams). This team will review the incidents for right ticket categorization, priorities and then carry out initial diagnosis to see if they can resolve. Around ~54% of the incidents are resolved by L1 / L2 teams. Incase L1 / L2 is unable to resolve, they will then escalate / assign the tickets to Functional teams from Applications and Infrastructure (L3 teams). Some portions of incidents are directly assigned to L3 teams by either Monitoring tools or Callers / Requestors. L3 teams will carry out detailed diagnosis and resolve the incidents. Around ~56% of incidents are resolved by Functional / L3 teams. Incase if vendor support is needed, they will reach out for their support towards incident closure. L1 / L2 needs to spend time reviewing Standard Operating Procedures (SOPs) before assigning to Functional teams (Minimum ~25-30% of incidents needs to be reviewed for SOPs before ticket assignment). 15 min is being spent for SOP review for each incident. Minimum of ~1 FTE effort needed only for incident assignment to L3 teams.During the process of incident assignments by L1 / L2 teams to functional groups, there were multiple instances of incidents getting assigned to wrong functional groups. Around ~25% of Incidents are wrongly assigned to functional teams. Additional effort needed for Functional teams to re-assign to right functional groups. During this process, some of the incidents are in queue and not addressed timely resulting in poor customer service.Guided by powerful AI techniques that can classify incidents to right functional groups can help organizations to reduce the resolving time of the issue and can focus on more productive tasks.

ramanks19/AIML-Projects

Projects which were completed as part of assignments of Great Learning's PGP in Artificial Intelligence and Machine Learning

ramanks19/Capstone-Project-AIML

This project was done as part of Capstone Project for PGP in Artificial Intelligence and Machine Learning by Great Learning

Arvindhh931/Healthcare_Analyitics-CAPSTONE-Project-

Importance of HBA1c in predictive Modeling of probability of Hospital Re-admission (CAPSTONE PROJECT)

KimothiA/PGP-AIML-GreatLearning

Projects undertaken as part of the PGP in Artificial Intelligence and Machine Learning 2019-20. These are elementary projects driving learning value

kpm-titanium/PGP-BABI

SelvaJenner/NLP_Statistical_Author_Features_Prediction

Author Features Prediction Multilabel classification problem using NLP

tsummey/Air-Pollution-Case-Study

Data analytics case study of air pollution

tsummey/Car-MPG-Case-Study

Data Analysis of fuel efficiency