Customer relationship management (CRM) is an important element in all forms of industry. This process involves ensuring that the customers of a business are satisfied with the product or services that they are paying for. Since most businesses collect and store large volumes of data about their customers; it is easy for the data analysts to use that data and perform predictive analysis. One aspect of this includes customer retention and customer churn. Customer churn is defined as the concept of understanding whether or not a customer of the company will stop using the product or service in future. In this paper a supervised machine learning algorithm has been implemented using Python to perform customer churn analysis on a given data-set of Telco, a mobile telecommunication company. This is achieved by building a decision tree model based on historical data provided by the company on the platform of Kaggle. This report also investigates the utility of extreme gradient boosting (XGBoost) library in the gradient boosting framework (XGB) of Python for its portable and flexible functionality which can be used to solve many data science related problems highly efficiently. The implementation result shows the accuracy is comparatively improved in XGBoost than other learning models.
The Internet of Things (IoT) role is instrumental in the technological advancement of the healthcare industry. Both the hardware and the core level of software platforms are the progress resulted from the accompaniment of Medicine 4.0. Healthcare IoT systems are the emergence of this foresight. The communication systems between the sensing nodes and the processors; and the processing algorithms to produce output obtained from the data collected by the sensors are the major empowering technologies. At present, many new technologies supplement these empowering technologies. So, in this research work, a practical feature extraction and classification technique is suggested for handling data acquisition besides data fusion to enhance treatment-related data. In the initial stage, IoT devices are gathered and pre-processed for fusion processing. Dynamic Bayesian Network is considered an improved balance for tractability, a tool for CDF operations. Improved Principal Component Analysis is deployed for feature extraction along with dimension reduction. Lastly, this data learning is attained through Hybrid Learning Classifier Model for data fusion performance examination. In this research, Deep Belief Neural Network and Support Vector Machine are hybridized for healthcare data prediction. Thus, the suggested system is probably a beneficial decision support tool for multiple data sources prediction and predictive ability enhancement.
The Internet of Things (IoT) in the healthcare market is propelled forward by the implementation of digital systems for monitoring and analysing health problems. IoT and smart devices can contribute to a highly smart environment. Smart medical devices interconnected with smartphone apps can collect medical and other required health data. "Data Fusion (DF)" refers to integrating data and knowledge from multiple sources. However, these techniques are also applied to other domains, including text processing. Using data from multiple distributed sources, the objective of DF in multisensory contexts is to reduce the chance of detection errors and increase their reliability. The objective is to increase scalability, performance efficiency, and identification. A medical device's ability to scale up or down demonstrates its capacity to respond to environmental factors. A more scalable system performs as expected, with no interruptions, and makes the best available use of the resource management it has. To ensure that these tracking devices all work the same way, it is essential to form a specialised group to develop uniformity in areas such as communication channels, aggregation of data, and smart interfaces. The main contribution of this research is pre-processing, DF using the Improved Context-aware Data Fusion (ICDF) algorithm, feature extraction via Improved Principal Component Analysis (IPCA), feature selection through the Enhanced Recursive Feature Elimination (ERFE) algorithm, and a classifier using an ensemble-based Machine Learning (ML) model. The Improved Dynamic Bayesian Network (IDBN) is a good trade-off for tractability, becoming a tool for ICDF operations. The simulation results show that the proposed ICDF model achieves higher performance in terms of 97% accuracy, 96% precision, 97% recall, and 97% F1 score in the healthcare system.
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