Low-No code Platforms for Predictive Analytics

In the data-driven landscape of modern business, predictive analytics plays a pivotal role inanticipating and mitigating customer churn—a critical challenge for organizations. However, thetraditional complexities of machine learning hinder accessibility for decision-makers. EnterMachine Learning as a Service (MLaaS), offering a gateway to predictive modeling without theneed for extensive coding or infrastructure.This thesis presents a comprehensive evaluation of cloud-based and cloud-agonostic AutoML(Automated Machine Learning) platforms for customer churn prediction. The study focuses onfour prominent platforms: Azure ML, AWS SageMaker, GCP Vertex AI, and Databricks. Theevaluation encompasses various performance metrics including accuracy, AUC-ROC, precision,recall to assess the predictive capabilities of each platform. Furthermore, the ease of use andlearning curve for model development are compared, considering factors such as data preparation,training steps, and coding requirements. Additionally, model training times are analyzed toidentify platform efficiencies. Preliminary results indicate that AWS SageMaker exhibits thehighest accuracy, suggesting strong predictive capabilities. GCP Vertex AI excels in AUC,indicating robust discriminatory power. Azure ML demonstrates a balanced performance,achieving notable accuracy and AUC scores. Databricks being platform independent is a winnerand has also shown good metrics. Its capability to generate notebook is an added advantage whichcan be modified by experts to fine tune the results more. This research provides valuable insightsfor organizations seeking to implement different AutoML solutions for customer churnprediction