{"repo":"vinodbavage31/ITSM-dataset-ML","free":true,"listed":false,"github":"https://github.com/vinodbavage31/ITSM-dataset-ML","clone":"git clone https://github.com/vinodbavage31/ITSM-dataset-ML.git","description":"End-to-end ML pipeline built on a 45K-record ITSM dataset to automate incident triage, predict high-priority tickets (96% Acc.), and forecast incident volume for proactive resource planning.","language":"Jupyter Notebook","stars":10,"topics":["data-science","data-visualization","feature-engineering","itsm","machine-learning","modelevaluation","scikit-learn","sql"],"license":null,"category":"machine-learning","readme_excerpt":"ABC Tech ITSM Incident Analysis & Predictive Automation Project Overview project UI : https://itsm-project-ui.vercel.app/ This project delivers a robust, end-to-end data science pipeline built to analyze historical IT Service Management (ITSM) incident data from a major tech firm, ABC Tech , and deploy predictive models for operational optimization. The primary objective was to move ITSM operations from reactive to proactive by automating triage and forecasting future resource needs. --- ✨ Key Achievements & Deliverables The project successfully addressed all four key client objectives, resulting in quantifiable improvements to efficiency and risk management. The project utilized a 45,000+ record, SQL-sourced ITSM dataset . Goal Description Key Achievement :--- :--- :--- 1. High Priority Prediction Forecast whether an incoming ticket will be high priority (P1/P2). 96% Accuracy and 0.89 AUC achieved by resolving critical data leakage. 2. Incident Forecasting Forecast incident volume across quarterly and annual horizons. Stabilized forecasting models (Exponential Smoothing, Regression) to provide reliable, non-negative volume projections for staffing and capacity planning. 3. Auto-Tagging Automatically assign the correct Priority (P1-P5) and Department (CI\\ Cat) . 75% Accuracy in multi-class Department Tagging and high-confidence Priority routing. 4. RFC Failure Prediction Predict the likelihood of a Request-for-Change (RFC) leading to a failure or misconfiguration. 98% Test F1","default_branch":null,"files":null,"tree":[],"storefront":"/r/vinodbavage31","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/vinodbavage31/ITSM-dataset-ML/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}