Random Forest Model for Predicting Claims for Outages in Telecommunications Operating Companies in Peru (2016–2024)

Telecommunications operators face numerous challenges in maintaining service continuity due to the increasing complexity of their networks, and demand must be operationally monitored. Within this context, this research studies the development of a predictive model using Random Forest to forecast service outage claims in Peruvian telecommunications companies from 2016 to 2024. A quantitative, applied, non-experimental, and longitudinal design was used, utilizing 87,003 records from the National Open Data Platform. The model was validated using cross- validation and its accuracy was evaluated using the RMSE, MAE, and R² metrics. The results show good performance of the predictive model, achieving an RMSE of 42.80, an MAE of 33.38, and an R² of 0.72, reflecting its high accuracy. The general and specific hypotheses of the research were confirmed, demonstrating that Random Forest allows for reliable predictions of service outage claims. Furthermore, it aligns with SDG 9, which clarifies the value of its research in relation to service quality and infrastructure planning in Peru.