Analysis of customer churn prediction using machine learning and deep learning algorithms

Breast cancer is one of the most prevalent and chronic disease that affect women. To overcome this disease, effective medical treatment is required. Early detection of the disease plays an important role for suitable medication and survival of patient. To identify the breast cancer in the patients, standard imaging technique mammography is used. Due to the subtle and varied nature of cancer tissues interpreting mammogram images can be a challenge to doctors. Machine learning (ML) and Deep Learning (DL) techniques offer promising solutions that provide efficient breast cancer detection from mammograms. In this review paper a comprehensive review of ML and DL algorithms and their applications in mammogram image analysis are presented. Various supervised and unsupervised learning techniques, such as convolutional neural networks (CNNs), support vector machines (SVMs), random forests, and other popular ML and DL models are discussed in paper. The integration of these DL methods that are efficiently used in image preprocessing techniques, feature extraction, and classification strategies. The overall survey focusses on various performance metrics, datasets, and benchmarks used in existing studies. Further the strengths and limitations of different approaches used by various researchers are identified. By understating current research trends this paper aims to contribute to the ongoing development of more accurate and reliable breast cancer detection systems using advanced ML techniques.

Introduction: The Churn means Attrition. In the world of competition and customer needs, the superior quality service, to predict customer churn, has become a pressing issue for the HRMS Vendors or service providers. Machine Learning is capable to solve this challenging problem. The customer churn can be predicted, and proactively, HRMS vendors can apply a strategy to retain customers and manage the customer relationship. Objectives: The objective of this paper is to meticulously analyse and summarize the previous studies conducted for customer churn prediction. Furthermore, this paper will serve as a valuable resource for future research in the context of HRMS vendors or service providers. Methods: This paper focuses on machine learning algorithms like Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting, Neural Network, etc. The paper highlights the accuracy of the outperformed models. This paper includes the previous studies between 2021 and 2025. In total, 30 papers were reviewed meticulously. To provide more clarity, all the previous studies are summarized in a table format. The paper was searched on Google with the keywords ‘Customer Churn’ AND ‘Machine Learning’. The Duplicates were removed, and the previous studies were analyzed. Results: The findings showcase that various machine learning models like RF, DT, Logistic Regression, Gradient Boosting are applied to predict churn. Furthermore, by this analysis it is clearly stated that hybrid or ensemble models work best for customer churn model prediction. This study guides on selecting the best model as per the relevant industry. Conclusions: This comprehensive survey has meticulously analysed the implementation of machine learning algorithms to predict customer churn and highlighted its importance to fully transform the business to build client retention strategies. In this paper, different machine models are examined, and for each previous study, the machine learning models used & their results are discussed.

This study presents a comparative analysis of the classification performance of facial and emotion recognition systems using Machine Learning (ML) and Deep Learning (DL) algorithms. The primary objective of this work is to evaluate the applicability of emotion recognition in fields such as psychotherapy and crime analysis, using the FER-2013 dataset.The study was conducted with ML algorithms such as Support Vector Machines (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), Decision Trees, and Gradient Boosting, as well as the DL algorithm, Convolutional Neural Network (CNN). Supported by the application of feature selection, preprocessing, and feature extraction techniques, the model’s performance was measured using standard metrics such as accuracy, precision, F1-score, and AUC-ROC.The experimental results showed that the highest classification accuracy (46.61%) was achieved with the CNN model. While the ML models generally offered lower accuracy, they provided advantages in terms of computational efficiency in specific scenarios.This study aims to contribute to the literature by providing a comparative analysis of ML and DL algorithms and by highlighting the effect of data preprocessing on performance. The findings set targets for future work, such as real-time system integration and hybrid model development.

The aim of this study is to develop a predictive model utilizing deep learning and machine learning techniques that will inform clinical decision-making by predicting the 1-year postoperative recovery of patients with lumbar disk herniation. The clinical data of 470 inpatients who underwent tubular microdiscectomy (TMD) between January 2018 and January 2021 were retrospectively analyzed as variables. The dataset was randomly divided into a training set (n = 329) and a test set (n = 141) using a 10-fold cross-validation technique. Various deep learning and machine learning algorithms including Random Forests, Extreme Gradient Boosting, Support Vector Machines, Extra Trees, K-Nearest Neighbors, Logistic Regression, Light Gradient Boosting Machine, and MLP (Artificial Neural Networks) were employed to develop predictive models for the recovery of patients with lumbar disk herniation 1 year after surgery. The cure rate score of lumbar JOA score 1 year after TMD was used as an outcome indicator. The primary evaluation metric was the area under the receiver operating characteristic curve (AUC), with additional measures including decision curve analysis (DCA), accuracy, sensitivity, specificity, and others. The heat map of the correlation matrix revealed low inter-feature correlation. The predictive model employing both machine learning and deep learning algorithms was constructed using 15 variables after feature engineering. Among the eight algorithms utilized, the MLP algorithm demonstrated the best performance. Our study findings demonstrate that the MLP algorithm provides superior predictive performance for the recovery of patients with lumbar disk herniation 1 year after surgery.

With the ongoing economic growth and the rise of new companies, customer churn has become a renewed focus for many businesses, particularly in the telecommunications and SaaS sectors. Accurately identifying lost customers using artificial intelligence technology is crucial for helping companies develop effective strategies to retain these valuable clients. This paper provides a comprehensive overview of recent research on the application of various machine learning models, including logistic regression, random forests, decision trees, XGBoost, and advanced deep learning models like Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN), to different types of datasets in the telecommunications and SaaS sectors. The study examines the workflow of each model, offering an in-depth analysis of their effectiveness and performance in predicting customer churn. In addition to evaluating the strengths and weaknesses of these models, the paper also addresses key challenges in machine learning, such as interpretability, applicability, and privacy concerns. It highlights current solutions employed by researchers, including SHapley Additive exPlanations (SHAP) for model interpretability, transfer learning, domain adaptation, and federated learning, to overcome these challenges. Furthermore, this paper emphasizes the ongoing need for further research to enhance the robustness and practical deployment of churn prediction models in these industries. In conclusion, this paper offers a comprehensive overview of current research on customer churn prediction models, highlighting the application of various machine learning techniques and addressing key challenges and solutions within the telecommunications and SaaS sectors.

Landslide identification using machine learning

Agriculture has been a vital sector for the majority of people, especially in countries like India. However, the increasing need for food production has led to intensive farming practices that have resulted in the deterioration of soil quality. This deterioration in soil quality poses significant challenges to both agricultural productivity and environmental sustainability. To address these challenges, advanced soil nutrient prediction systems that utilize machine learning and deep learning techniques are being developed. These advanced soil nutrient prediction systems utilize various sources of data, such as soil parameters, plant diseases, pests, fertilizer usage, and changes in weather patterns. By mapping and analyzing these data sources, machine learning algorithms can accurately predict the distribution of soil nutrients and other properties essential for precise agricultural practices. A previous study compared machine learning algorithms like SVM and Random Forest with deep learning algorithms CNN and LSTM for predicting crop yields. The most appropriate model is a significant challenge, but several studies have evaluated recommendation system models using deep machine learning techniques. Deep learning models attain accuracy above 90%, while many ML models achieve rates between 90% and 93%. Furthermore, the research seeks to propose specific crop suggestions grounded in soil nutrients for precision agriculture to enhance crop productivity.

Customer churn is still a big problem for the retail industry because it affects profits and long-term survival. Machine learning (ML) has become a powerful tool in the last few years for predicting and preventing churn by finding customers who are likely to leave and allowing data-driven retention strategies. This paper examines the utilisation of machine learning techniques for predicting customer churn in the retail sector, integrating results from previous research and suggesting a comprehensive methodological framework for effective implementation. The study analyses 30 peer-reviewed journal articles published until 2023, focusing on significant machine learning models like logistic regression, decision trees, random forests, gradient boosting machines, and deep learning techniques, emphasising their relative advantages and disadvantages in churn prediction scenarios. The methodology section outlines a systematic pipeline that encompasses data preprocessing, feature engineering, model training, evaluation metrics, and deployment considerations. Results from empirical implementations across diverse retail datasets demonstrate that ensemble methods (e.g., XGBoost, LightGBM) and neural networks consistently outperform traditional statistical models in predictive accuracy, though model interpretability remains a critical concern for practitioners. The paper also talks about how explainable AI can help people trust and use ML-driven churn prediction systems. The results highlight the necessity of incorporating ML models with customer relationship management (CRM) systems to facilitate prompt interventions, tailored offers, and the enhancement of loyalty programs. This study enhances academic scholarship and industry practice by addressing methodological, practical, and ethical considerations. The paper ends with suggestions for retailers who want to use ML tools and ideas for future research.

Customer churn prediction is a critical challenge for modern businesses seeking to maintain long-term customer relationships and reduce revenue loss. This study explores the application of deep learning and machine learning models for predicting customer churn using real-world business datasets. The dataset includes demographic, account, and behavioural attributes of customers that influence churn likelihood. Models such as Logistic Regression, Random Forest, and Deep Neural Networks (DNN) were trained and evaluated using metrics including accuracy, precision, recall, F1-score, and ROC-AUC. The experimental results reveal that the Deep Neural Network model achieves the highest prediction accuracy of 94.1%, outperforming traditional machine learning algorithms. Random Forest offers an effective trade-off between accuracy and interpretability, while Logistic Regression serves as a reliable baseline. This research provides a comparative framework for organizations aiming to leverage AI-based methods to predict and prevent customer churn, leading to improved retention strategies and business profitability.

The problems of plant leaf disease are rather serious in the world agricultural industry, leading to a significant decrease in crop quantity and quality, consequently, resulting in a huge loss in the economy and food insecurity. Detection and successful classification of plant diseases at the initial stage is essential to further agricultural output and the quality production of food. The recent improvements in the field of artificial intelligence (AI), specifically, machine learning (ML) and deep learning (DL), have shown significant prospects in automating and enhancing methods of diagnosing plant leaf diseases by using a wide variety of ML and DL algorithms. This review article presents an in-depth analysis of thirty novel methods created by researchers to diagnose and classify plant leaf diseases. They are such conventional classifiers as Support Vector Machines (SVM), Random Forests (RF), and K-Nearest Neighbors (KNN), along with some advanced DL architectures, such as Convolutional Neural Networks (CNN), VGG16, ResNet50, InceptionResNetV2, EfficientNet, and various hybrids. The review analysis takes into consideration the methodologies applied, performance metrics, and insights in practice, as well as the strengths and weaknesses of both. The most crucial findings of the review show that deep learning models, and CNNs in particular, tend to be more accurate, robust, and feature extractors than traditional models of MLs. The performance of classification is also enhanced by numerous hybrid models that will use ML together with DL, and transfer learning has been an effective method to enhance the generalization using small datasets. Nevertheless, with all this progress, the issues of diversity of datasets, computational resource requirements and model interpretability are still to be explored in the future.

Coastal aquifers are vital for sustaining ecosystems and providing freshwater for agriculture and domestic use. However, rising groundwater salinity in these regions demands innovative approaches for accurate modeling and prediction. In recent decades, machine learning (ML) and deep learning (DL) techniques have been extensively used across various water resource management fields, often yielding promising outcomes. This review examines the growing usage of ML and DL techniques in modeling groundwater salinity in coastal aquifers. The analysis is based on 104 peer-reviewed journal articles including the review articles indexed in PubMed, ScienceDirect, Google Scholar, Scopus, and SpringerLink using keywords like "saltwater intrusion", "machine learning", “deep learning”, and "groundwater salinity". The review discusses recent advancements, challenges, and future directions for improving ML model accuracy and applicability. Studies reveal an increasing reliance on ML techniques to predict groundwater salinity and manage seawater intrusion across various coastal geological settings. Researchers have employed a range of ML models, from traditional methods like Artificial Neural Networks and Adaptive Neuro Fuzzy Inference Systems to advanced DL-based models like Convolutional Neural Networks and Long Short-Term Memory Networks, often using ensemble methods to enhance accuracy. Key findings include the top-tier output of ensemble and optimized models, including Random Forest and Grasshopper Optimization Algorithm-XGBoost. These studies highlight the importance of model selection, input variable ranking, and computational efficiency, demonstrating ML's global relevance in environmental management. The review highlights the prospects of ML, DL, and ensemble models to redefine groundwater management in coastal aquifers, offering more effective and sustainable solutions to the challenges posed by groundwater salinity and saltwater intrusion. Future advancements will focus on further integrating DL, explainable artificial intelligence, and ensemble approaches, along with other innovative techniques, to explore sparsely studied variables, model new or unique study areas, and apply ML methods for managing groundwater salinity.

ObjectiveEnhanced technology in computer and internet has driven scale and quality of data to be improved in various areas including healthcare sectors. Machine Learning (ML) has played a pivotal role in efficiently analyzing those big data, but a general misunderstanding of ML algorithms still exists in applying them (e.g., ML techniques can settle a problem of small sample size, or deep learning is the ML algorithm). This paper reviewed the research of diagnosing mental illness using ML algorithm and suggests how ML techniques can be employed and worked in practice.MethodsResearches about mental illness diagnostic using ML techniques were carefully reviewed. Five traditional ML algorithms-Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN)-frequently used for mental health area researches were systematically organized and summarized.ResultsBased on literature review, it turned out that Support Vector Machines (SVM), Gradient Boosting Machine (GBM), Random Forest, Naïve Bayes, and K-Nearest Neighborhood (KNN) were frequently employed in mental health area, but many researchers did not clarify the reason for using their ML algorithm though every ML algorithm has its own advantages. In addition, there were several studies to apply ML algorithms without fully understanding the data characteristics.ConclusionResearchers using ML algorithms should be aware of the properties of their ML algorithms and the limitation of the results they obtained under restricted data conditions. This paper provides useful information of the properties and limitation of each ML algorithm in the practice of mental health.

Customer attrition is especially an issue in industries such as retail, banking, and telecommunications where customer acquisition costs are significantly higher than the costs of retaining repeat customers. The customer lack of interest is now predictable through machine learning models, and deep learning has become instrumental in early intervention for retention. In order to assess the quality of churn prediction, the study tests six basic machine learning techniques: random forest, logistic regression, and the k-nearest neighbors method, as well as four deep learning techniques: long short term memory (LSTM), bidirectional LSTM, convolutional neural networks (CNN), and artificial neural networks (ANN). The performance of the model is then assessed via the evaluation matrices, including the accuracy, precision, recall, and F1-score from the customer's behavioral data after feature extraction from large datasets. The study reveals that DL models offer improved handling of the churn and non-churn customer classification and Random Forest as well as other ML models comparable accuracy. This research can conclude that LSTM and ANN models outshine in actual-world churn prediction circumstances, especially when long-term consumer behavior evaluation is required. To enhance the current outcomes of a given prediction model, this research focuses on data preprocessing and the utilization of bootstrapping, feature extraction, and the combination of multiple models. The implications of the study provide specific practical recommendations for firms to effectively manage customer churn and increase customer retention by employing data-dealing techniques.

Financial fraud poses threats to the transparency and integrity of financial systems and therefore requires more advanced detection methods in auditing. This study proposes the application of artificial intelligence, i.e., machine learning (ML) and deep learning (DL) algorithms, to identify fraudulent financial transactions from auditing information. Using a simulated 100 financial transactions dataset with labeled fraud indicators, four classification models were used: Logistic Regression, Random Forest, XGBoost, and Convolutional Neural Network (CNN). Preprocessing was done on the data using normalization and categorical encoding, followed by an 80:20 train-test split. The performance of the models was validated using key metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Of the models, XGBoost achieved the highest accuracy with 95% and F1-score of 0.93 for the fraud class. The results point to the effectiveness of ensemble and deep learning approaches in detecting fraud with high precision as useful aid to auditors and real-time financial monitoring systems.

In today's digital world, app stores have become an essential part of software distribution, providing customers with a wide range of applications and opportunities for software developers to showcase their work. This study elaborates on the importance of end-user feedback for software evolution. However, in the literature, more emphasis has been given to high-rating & popular software apps while ignoring comparatively low-rating apps. Therefore, the proposed approach focuses on end-user reviews collected from 64 low-rated apps representing 14 categories in the Amazon App Store. We critically analyze feedback from low-rating apps and developed a grounded theory to identify various concepts important for software evolution and improving its quality including user interface (UI) and user experience (UX), functionality and features, compatibility and device-specific, performance and stability, customer support and responsiveness and security and privacy issues. Then, using a grounded theory and content analysis approach, a novel research dataset is curated to evaluate the performance of baseline machine learning (ML), and state-of-the-art deep learning (DL) algorithms in automatically classifying end-user feedback into frequently occurring issues. Various natural language processing and feature engineering techniques are utilized for improving and optimizing the performance of ML and DL classifiers. Also, an experimental study comparing various ML and DL algorithms, including multinomial naive Bayes (MNB), logistic regression (LR), random forest (RF), multi-layer perception (MLP), k-nearest neighbors (KNN), AdaBoost, Voting, convolutional neural network (CNN), long short-term memory (LSTM), bidirectional long short term memory (BiLSTM), gated recurrent unit (GRU), bidirectional gated recurrent unit (BiGRU), and recurrent neural network (RNN) classifiers, achieved satisfactory results in classifying end-user feedback to commonly occurring issues. Whereas, MLP, RF, BiGRU, GRU, CNN, LSTM, and Classifiers achieved average accuracies of 94%, 94%, 92%, 91%, 90%, 89%, and 89%, respectively. We employed the SHAP approach to identify the critical features associated with each issue type to enhance the explainability of the classifiers. This research sheds light on areas needing improvement in low-rated apps and opens up new avenues for developers to improve software quality based on user feedback.