Dataset

This dataset was created by R. Joseph Manoj, PhD

Contents

The Customer log dataset is a 12.5 GB JSON file and it contains 18 columns and 26,259,199 records. There are 12 string columns and 6 numeric columns, which may also contain null or NaN values. The columns include userId, artist, auth, firstName, gender, itemInSession, lastName, length, level, location, method, page, registration, sessionId, song,status, ts and userAgent. As evident from the column names, the dataset contains various user-related information, such as user identifiers, demographic details (firstName, lastName, gender), interaction details (artist, song, length, itemInSession, sessionId, registration, lastinteraction) and technical details (userAgent, method, page, location, status, level, auth).

This dataset is for ABC Multistate bank with following columns:

1. customer_id, unused variable.
2. credit_score, used as input.
3. country, used as input.
4. gender, used as input.
5. age, used as input.
6. tenure, used as input.
7. balance, used as input.
8. products_number, used as input.
9. credit_card, used as input.
10. active_member, used as input.
11. estimated_salary, used as input.
12. churn, used as the target. 1 if the client has left the bank during some period or 0 if he/she has not.

Aim is to Predict the Customer Churn for ABC Bank.

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Churn prediction aims to detect customers intended to leave a service provider. Retaining one customer costs an organization from 5 to 10 times than gaining a new one. Predictive models can provide correct identification of possible churners in the near future in order to provide a retention solution. This paper presents a new prediction model based on Data Mining (DM) techniques. The proposed model is composed of six steps which are; identify problem domain, data selection, investigate data set, classification, clustering and knowledge usage. A data set with 23 attributes and 5000 instances is used. 4000 instances used for training the model and 1000 instances used as a testing set. The predicted churners are clustered into 3 categories in case of using in a retention strategy. The data mining techniques used in this paper are Decision Tree, Support Vector Machine and Neural Network throughout an open source software name WEKA.

In recent years, with the continuous improvement of the financial system and the rapid development of the banking industry, the competition of the banking industry itself has intensified. At the same time, with the rapid development of information technology and Internet technology, customers’ choice of financial products is becoming more and more diversified, and customers’ dependence and loyalty to banking institutions is becoming less and less, and the problem of customer churn in commercial banks is becoming more and more prominent. How to predict customer behavior and retain existing customers has become a major challenge for banks to solve. Therefore, this study takes a bank’s business data on Kaggle platform as the research object, uses multiple sampling methods to compare the data for balancing, constructs a bank customer churn prediction model for churn identification by GA-XGBoost, and conducts interpretability analysis on the GA-XGBoost model to provide decision support and suggestions for the banking industry to prevent customer churn. The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. The GA-XGBoost classifier was identified as the best solution for the customer churn problem. (3) Using Shapley values, we explain how each feature affects the model results, and analyze the features that have a high impact on the model prediction, such as the total number of transactions in the past year, the amount of transactions in the past year, the number of products owned by customers, and the total sales balance. The contribution of this paper is mainly in two aspects: (1) this study can provide useful information from the black box model based on the accurate identification of churned customers, which can provide reference for commercial banks to improve their service quality and retain customers; (2) it can provide reference for customer churn early warning models of other related industries, which can help the banking industry to maintain customer stability, maintain market position and reduce corporate losses.

Dataset

This dataset was created by Aarushi Kamboj

Contents

Dataset

This dataset was created by Faiqa Mehboob

Contents

The market size of the Customer Churn Software Market is categorized based on Type (Cloud Based, Web Based) and Application (Telecommunications, Banking and Finance, Retail and E-commerce, Healthcare, Insurance, Others) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

This report provides insights into the market size and forecasts the value of the market, expressed in USD million, across these defined segments.

Analyzing customers’ characteristics and giving the early warning of customer churn based on machine learning algorithms, can help enterprises provide targeted marketing strategies and personalized services, and save a lot of operating costs. Data cleaning, oversampling, data standardization and other preprocessing operations are done on 900,000 telecom customer personal characteristics and historical behavior data set based on Python language. Appropriate model parameters were selected to build BPNN (Back Propagation Neural Network). Random Forest (RF) and Adaboost, the two classic ensemble learning models were introduced, and the Adaboost dual-ensemble learning model with RF as the base learner was put forward. The four models and the other four classical machine learning models-decision tree, naive Bayes, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) were utilized respectively to analyze the customer churn data. The results show that the four models have better performance in terms of recall rate, precision rate, F1 score and other indicators, and the RF-Adaboost dual-ensemble model has the best performance. Among them, the recall rates of BPNN, RF, Adaboost and RF-Adaboost dual-ensemble model on positive samples are respectively 79%, 90%, 89%,93%, the precision rates are 97%, 99%, 98%, 99%, and the F1 scores are 87%, 95%, 94%, 96%. The RF-Adaboost dual-ensemble model has the best performance, and the three indicators are 10%, 1%, and 6% higher than the reference. The prediction results of customer churn provide strong data support for telecom companies to adopt appropriate retention strategies for pre-churn customers and reduce customer churn.

Dataset

This dataset was created by kavinp0301

Released under Apache 2.0

Contents

Dataset

This dataset was created by Kunal Gaike

Contents

Dataset

This dataset was created by Al Amin

Contents

Customer churn prediction is vital for organizations to mitigate costs and foster growth. Ensemble learning models are commonly used for churn prediction. Diversity and prediction performance are two essential principles for constructing ensemble classifiers. Therefore, developing accurate ensemble learning models consisting of diverse base classifiers is a considerable challenge in this area. In this study, we propose two multi-objective evolutionary ensemble learning models based on clustering (MOEECs), which are include a novel diversity measure. Also, to overcome the data imbalance problem, another objective function is presented in the second model to evaluate ensemble performance. The proposed models in this paper are evaluated with a dataset collected from a mobile operator database. Our first model, MOEEC-1, achieves an accuracy of 97.30% and an AUC of 93.76%, outperforming classical classifiers and other ensemble models. Similarly, MOEEC-2 attains an accuracy of 96.35% and an AUC of 94.89%, showcasing its effectiveness in churn prediction. Furthermore, comparison with previous churn models reveals that MOEEC-1 and MOEEC-2 exhibit superior performance in accuracy, precision, and F-score. Overall, our proposed MOEECs demonstrate significant advancements in churn prediction accuracy and outperform existing models in terms of key performance metrics. These findings underscore the efficacy of our approach in addressing the challenges of customer churn prediction and its potential for practical application in organizational decision-making.

Dataset to train and test a churn classifier model for a ecommerce company.

In recent years, with the continuous improvement of the financial system and the rapid development of the banking industry, the competition of the banking industry itself has intensified. At the same time, with the rapid development of information technology and Internet technology, customers’ choice of financial products is becoming more and more diversified, and customers’ dependence and loyalty to banking institutions is becoming less and less, and the problem of customer churn in commercial banks is becoming more and more prominent. How to predict customer behavior and retain existing customers has become a major challenge for banks to solve. Therefore, this study takes a bank’s business data on Kaggle platform as the research object, uses multiple sampling methods to compare the data for balancing, constructs a bank customer churn prediction model for churn identification by GA-XGBoost, and conducts interpretability analysis on the GA-XGBoost model to provide decision support and suggestions for the banking industry to prevent customer churn. The results show that: (1) The applied SMOTEENN is more effective than SMOTE and ADASYN in dealing with the imbalance of banking data. (2) The F1 and AUC values of the model improved and optimized by XGBoost using genetic algorithm can reach 90% and 99%, respectively, which are optimal compared to other six machine learning models. The GA-XGBoost classifier was identified as the best solution for the customer churn problem. (3) Using Shapley values, we explain how each feature affects the model results, and analyze the features that have a high impact on the model prediction, such as the total number of transactions in the past year, the amount of transactions in the past year, the number of products owned by customers, and the total sales balance. The contribution of this paper is mainly in two aspects: (1) this study can provide useful information from the black box model based on the accurate identification of churned customers, which can provide reference for commercial banks to improve their service quality and retain customers; (2) it can provide reference for customer churn early warning models of other related industries, which can help the banking industry to maintain customer stability, maintain market position and reduce corporate losses.

The Big Data and Machine Learning (BDML) in Telecom market is experiencing robust growth, driven by the explosive increase in mobile data traffic, the rise of 5G networks, and the increasing need for personalized customer experiences. The market, valued at approximately $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033, reaching an estimated $60 billion by 2033. This expansion is fueled by several key factors. Telecom operators are leveraging BDML for network optimization, predictive maintenance, fraud detection, customer churn prediction, and personalized service offerings. The adoption of descriptive, predictive, and prescriptive analytics across various applications, including processing, storage, and analysis of vast datasets, is a significant driver. Furthermore, advancements in machine learning algorithms and feature engineering techniques are empowering telecom companies to extract deeper insights from their data, leading to significant efficiency gains and improved revenue streams. The increasing availability of cloud-based BDML solutions is also fostering wider adoption, particularly among smaller operators. However, challenges remain. Data security and privacy concerns, the need for skilled data scientists and engineers, and the high initial investment costs associated with implementing BDML solutions can hinder market growth. Despite these restraints, the strategic advantages offered by BDML are undeniable, making its adoption crucial for telecom companies aiming to stay competitive in a rapidly evolving landscape. Segments like predictive analytics and machine learning for network optimization are expected to experience the most significant growth during the forecast period, driven by the increasing complexity of telecom networks and the demand for proactive network management. Geographic regions such as North America and Asia Pacific, with their advanced technological infrastructure and substantial investments in 5G, are anticipated to lead the market, followed by Europe and other regions.

Dataset

This dataset was created by Shiyamaladevi R S

Contents

Literature review of papers on churn prediction in telecommunication.