Prediction Analysis of AI Adoption in Various Domain Using Random Forest Algorithm

Client churn is still a problem in this industry even though the telecom sector was among the first to use information mining along with machine learning techniques to extract meaningful insights from massive data sets. This proposal offers an innovative approach to reducing customer attrition in the telecom industry, along with the application of a technique known as “strategically pitching” or “market container investigation,” which is often used in retail contexts. For a considerable amount of time, administration administrators in the media communications industry have been dealing with client churn, a common yet incredibly harmful phenomenon. Successfully reducing client churn can be achieved by separating clients who are prone to churning in the early stages and taking precautions. As a result, the focus of this research is developing the churn prediction model; also, this analysis is divided into two distinct sections. Since the beginning, we have introduced a model known as MRF (Modified Random Forest), which helps to both prevent backslide issues and increase accuracy. We tested our framework using the orange datasets that were provided. Two scenarios—stir and upselling—are compared between the existing and predicted approaches in order to assess our calculation. The results demonstrate that our model beats the current method for recollecting typical unexpected woods in regards to AUC and gathering precision after comparing it to a number of currently available stir figure models.