ajay-sampath's Stars
LeCoupa/awesome-cheatsheets
👩💻👨💻 Awesome cheatsheets for popular programming languages, frameworks and development tools. They include everything you should know in one single file.
ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
500 AI Machine learning Deep learning Computer vision NLP Projects with code
mit-han-lab/streaming-llm
[ICLR 2024] Efficient Streaming Language Models with Attention Sinks
h2oai/h2o-llmstudio
H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs. Documentation: https://docs.h2o.ai/h2o-llmstudio/
https-deeplearning-ai/tensorflow-1-public
ptyadana/SQL-Data-Analysis-and-Visualization-Projects
SQL data analysis & visualization projects using MySQL, PostgreSQL, SQLite, Tableau, Apache Spark and pySpark.
youssefHosni/Data-Science-Interview-Preperation-Resources
Resoruce to help you to prepare for your comming data science interviews
youssefHosni/Practical-Machine-Learning
Practical machine learning notebook & articles covers the machine learning end to end life cycle.
justmarkham/DAT4
General Assembly's Data Science course in Washington, DC
jasp-stats/jasp-desktop
JASP aims to be a complete statistical package for both Bayesian and Frequentist statistical methods, that is easy to use and familiar to users of SPSS
DashBarkHuss/100-days-of-code
Fork this template for the 100 days journal - to keep yourself accountable (multiple languages available)
milaan9/93_Python_Data_Analytics_Projects
This repository contains all the data analytics projects that I've worked on in python.
bigdatabysumitm/NotesOfYouTubeSQLSeries
datapublishings/Course-python-data-science
opengeos/aws-open-data-geo
A list of open geospatial datasets on AWS
Babunashvili/Books-To-Read-Before-You-Die
rbind/simplystats
Simply Statistics
pik1989/MLProject-ChurnPrediction
LinkedInLearning/transformers-text-classification-for-nlp-using-bert-2478096
This repo is for the Linkedin Learning course: Transformers: Text Classification for NLP using BERT
ashishpatel26/Coursera-Guided-Projects-2021
Coursera Guided Projects 2021
kshashank03/data-science-projects
LinkedInLearning/tensorflow-working-with-nlp-2439112
TensorFlow: Working with NLP
sahil-pattnayak/Great-Learning-AIML-Projects
This repository contains all the projects completed as part of the PGP AIML.
karthikreddymathuru/Telecom-Customer-Churn-Prediction
LinkedInLearning/building-nlp-pipelines-with-spacy-3094275
This is a repository for the LinkedIn Learning course Building NLP Pipelines with SpaCy
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.
niti007/Power-BI-Projects--Hotel-Data
hemprakashp/Loan-Default-Prediction-Hackathon
Mayank-MP05/How-NOT-to-loose-a-customer-ML-Challenge
ML Competition on Hackerearth - Rank 172/12219
singhshalini1102/Text-Classification---US-Airlines-tweets