Authors :
C. Subalakshmi; G. Bhanu Praveen; C. V. Saketh; N. Reddy Samba Siva Reddy
Volume/Issue :
Volume 9 - 2024, Issue 1 - January
Google Scholar :
http://tinyurl.com/36frnns3
Scribd :
http://tinyurl.com/yk4bkh67
DOI :
https://doi.org/10.5281/zenodo.10613177
Abstract :
In the highly competitive telecom sector,
maintaining client loyalty is a critical obstacle to longterm profitability and expansion. This research uses the
Random Forest and Logistic Regression algorithms to
give a detailed investigation of customer attrition
prediction specifically for the telecom industry. Building
a strong predictive model to identify possible churners
will enable telecom businesses to implement focused
customer loyalty campaigns.
Our methodology incorporates a wide range of
telecom-specific characteristics, such as call trends,
usage information, and customer support exchanges. By
utilizing the Random Forest and Logistic Regression
methods, we may increase the forecasting accuracy by
exploring the complex patterns that indicate customer
churn. Carefully considered feature engineering
techniques are used to improve the model's capacity to
capture subtleties specific to the telecom . Our approach
is validated using a real-world telecom dataset that
includes a range of customer categories. Performance
metrics such as F1 score, recall, accuracy, and precision
show how well our model forecasts customer attrition in
the dynamic telecom market.
Keywords :
Customer Churn, Machine Learning, Telecom Sector, Performance Metrics
In the highly competitive telecom sector,
maintaining client loyalty is a critical obstacle to longterm profitability and expansion. This research uses the
Random Forest and Logistic Regression algorithms to
give a detailed investigation of customer attrition
prediction specifically for the telecom industry. Building
a strong predictive model to identify possible churners
will enable telecom businesses to implement focused
customer loyalty campaigns.
Our methodology incorporates a wide range of
telecom-specific characteristics, such as call trends,
usage information, and customer support exchanges. By
utilizing the Random Forest and Logistic Regression
methods, we may increase the forecasting accuracy by
exploring the complex patterns that indicate customer
churn. Carefully considered feature engineering
techniques are used to improve the model's capacity to
capture subtleties specific to the telecom . Our approach
is validated using a real-world telecom dataset that
includes a range of customer categories. Performance
metrics such as F1 score, recall, accuracy, and precision
show how well our model forecasts customer attrition in
the dynamic telecom market.
Keywords :
Customer Churn, Machine Learning, Telecom Sector, Performance Metrics
