stdClass Object
(
    [id] => 16053
    [paper_index] => 202505-02-021611
    [title] => CUSTOMER CHURN PREDICTION IN TELECOM INDUSTRY USING MACHINE LEARNING
    [description] => 
    [author] => Ms. K Rajalakshmi, Mr. M. Selva Kumar
    [googlescholar] => 
    [doi] => 
    [year] => 2025
    [month] => May
    [volume] => 10
    [issue] => 5
    [file] => fm/jpanel/upload/2025/May/202505-02-021611.pdf
    [abstract] => Customer churn prediction is a critical task for businesses aiming to improve customer retention and minimize revenue loss. This process leverages machine learning techniques to analyse historical customer data and predict the likelihood of a customer leaving the service or product offering. By identifying at-risk customers early, companies can implement targeted retention strategies, improving customer satisfaction and long-term profitability. The article explores the key steps involved in building a churn prediction model, including data collection, pre-processing, and feature engineering. It highlights popular machine learning algorithms such as logistic regression, decision trees, random forests, and gradient boosting methods, discussing their strengths and applications in churn prediction. The article addresses challenges like class imbalance and model interpretability, offering solutions for accurate and actionable results. Ultimately, this approach empowers businesses to make data-driven decisions, optimizing customer engagement and reducing churn through timely interventions.
    [keywords] => Customer Churn, Machine Learning, Customer Retention, Predictive Analytics, Data Pre-processing
    [doj] => 2025-05-15
    [hit] => 
    [status] => 
    [award_status] => P
    [orderr] => 36
    [journal_id] => 2
    [googlesearch_link] => 
    [edit_on] => 
    [is_status] => 1
    [journalname] => EPRA International Journal of Research & Development (IJRD)
    [short_code] => IJSR
    [eissn] =>  2455-7838 (Online)
    [pissn] => - -
    [home_page_wrapper] => images/products_image/2-n.png
)
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