Context and problematic
Client : the teams in charge of supervising the activity of IT support.
Need : Have a global and precise vision of all areas to better allocate resources, reduce tickets and be pro-active on emerging/ growing themes.
Constraint : Data (tickets) not annotated and not standardized between the teams (templates, chat, emails,…)
Identification des tendances pour chaque sujet (émergent, constant, plus d’actualité)
1 Data Architect for V1
- Definition / framing of the project
- Data cleaning and standardization
- Ticket analysis based on unsupervised Machine Learning methods:
Clustering of tickets (LDA, DBSCAN, K-Means…)
Topic Modeling / characterization of clusters
Trend detection on identified topics
- Automation of the analysis and the final report that shall present the results
V1 carried out for 2 perimeters, discussions in progress for the putting into production and the launch of a V2.
Deliverable: automated report
Subjects identified & degree of precision: OK for the 2 perimeters
Subject trends: OK for both perimeters
Clustering : Scikit-Learn, Gensim
Topic Modeling : Gensim, Wordcloud,
Visualisations / Restitution: Plotly, Jupyter