• Dhinesh.J
• S.Kavitha
• Duvvuri abhishek
• Alwin sanjai.Z
• Dominic.P

*Large amounts of data have been generated as a result of the advancement of technology in the modern era. The 2.5 quintillions of data produced daily by people with Internet connections in 2023 are proof of this. By 2024, there will likely be 5.3 billion Internet users worldwide. This means that sophisticated and effective models, tools, or techniques are required to investigate, evaluate, and extract valuable hidden information from massive amounts of data. Machine learning methods including clustering, decision trees, and logistic regression have become more important inrecent years, particularly in churn prediction. The technique of estimating the percentage of customers who avoid using or might cease subscribing to a product or service provided by an organization or company is known as customer churn prediction. Though various prediction models have been proposed, most research attention has been given to measuring the efficiency of prediction models, rather than identifying its application for sustainable economic development. In this paper, we investigate the determining factor for customer attrition in the banking sector using Power BI, Tableau. Dataset from world bank spain was preprocessed with four key client factors were utilized. The LRRFSGB calculation accessible within the Control Bi,Tableau program was utilized for preparing and testing. The comes about appear that customer account adjust could be a key deciding variable for churning. Moreover, the comes about appear that churning happens less in male than female clients. This work will providebanks with valuable information on building successful client maintenance procedures. Building an viable and precise client churn forecast demonstrate is an critical inquire about issue for both scholastics and professionals.

Customer churn, KNN, LRRFSGB algorithms, Power BI , Tableau

"CUSTOMER RETENTION AND CHURN PREDICTION QUANTITAIVE AND QUALITITATIVE APPROACH IN BANKING INDUSTRY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.d11-d17, May-2024, Available :http://www.jetir.org/papers/JETIR2405302.pdf

"CUSTOMER RETENTION AND CHURN PREDICTION QUANTITAIVE AND QUALITITATIVE APPROACH IN BANKING INDUSTRY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppd11-d17, May-2024, Available at : http://www.jetir.org/papers/JETIR2405302.pdf