• Prof. Anand S Pashupathimath
• Ankita V Gulaganji
• Apeksha K Shirguppi
• K Bhoomika
• Samiya ShaikSandi

In modern IT industries, Incident Management plays a major role in ensuring efficient customer support. Traditionally in IT industries, assigning incoming tickets to relevant teams remains a manual process, leading to human errors, resource inefficiencies, and longer response and resolution times, ultimately lead to poor customer service. This project aims to address these challenges by developing a Al-based classifier model for text classification that automatically assigns incoming tickets to the appropriate Assignment groups based on their descriptions and also aiming to improve cost effectiveness and reduce resolution times. Our approach involves comprehensive data preprocessing techniques, including cleaning, translation, tokenization, and lemmatization. We assess the performance of different machine learning algorithms, such as Bi-gram models, Gensim LDA, Word2Vec, GloVe embeddings, Bidirectional LSTM, Random Forest, and SVM classifiers.In summary, this research offers a comprehensive approach to incident management, leveraging NLP and ML techniques to automate incident analysis and allocation.The proposed solution demonstrates our evaluation, based on training and test accuracies, shows that the LSTM model trained on resampled data achieves the highest accuracy of 87%. Our research contributes to enhancing incident management practices in the IT industry.

Al based classifier model, data preprocessing RF,SVM,LSTM.

"Automatic IT Ticket Assignment", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.g246-g250, April-2024, Available :http://www.jetir.org/papers/JETIR2404629.pdf

"Automatic IT Ticket Assignment", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppg246-g250, April-2024, Available at : http://www.jetir.org/papers/JETIR2404629.pdf