Fiaschetti, MaurizioAlalawi, Zainab2023-12-262023-12-262023-09-15https://hdl.handle.net/20.500.14154/70441Predicting Customer renewals and retention cancelation is a crucial guide for SaaS companies across the board. The continuous development of machine learning technique methods has substantially enhanced the prospect of achieving precise forecasts. As this machinery becomes more sophisticated and capable, SaaS businesses are better equipped to harness their potential in their business model. This is because the power of advanced analytics, data-driven, and predictive algorithms has made subscription-based businesses increasingly capable of identifying patterns and indicators of customer churn and customer loyalty. Therefore, this paper aims to utilize binary classification machine-learning algorithms in Python to forecast subscription termination and customers who will persist the subscriptions for a SaaS company within a specified timeframe.Customer retention and customer loss are crucial metrics in subscription-based industries like SaaS companies. Customer discharge is a significant concern for this type of business, as clients have the flexibility to terminate the service at any time. This can lead to adverse effects on the company’s revenue stream. If SaaS businesses can accurately predict the number of customers who will cancel their subscriptions and those who will continue using their services within a specific timeframe, they can more effectively forecast their revenue, cash flow, and any future growth plan accordingly. Predicting subscription renewals and cancelations remains a challenging problem for any SaaS company. However, with the ongoing advancement in machine learning and artificial intelligence, the potential for accurately forecasting this issue has significantly improved. The study examines customer attrition and customer retention prediction in a quantitative method by utilizing several different machine learning algorithms with Python, namely Logistics regression, Naïve Baye, and random forest algorithms. Data was collected from the case company’s database and manipulated to fit the algorithms. The dataset includes the customers' business data such as spend, customer platform usage data, customer service history data, and the date of the next payment. To identify the best hyperparameters for each machine- learning algorithm, A tuning technique, in particular Grid Search, was employed. Subsequently, the algorithm models were trained and assessed using optimized hyperparameters on the fitted data. Once the models were trained, they were applied to test data to obtain the analysis results. The model’s performance was measured on the quantitative model performance metrics. including F1-Score, Area under Curve (AUC), and Accuracy.49enMachine LearningSaasPredictingAttritionClassificationThesis