Pengembangan Model Machine Learning Regresi sebagai Web Service untuk Prediksi Harga Pembelian Mobil dengan Metode CRISP-DM - Fattah, Ahmad Maulana Malik; Voutama, Apriade; Heryana, Nono; Sulistiyowati, Nina (JURIKOM (Jurnal Riset Komputer), 2022)
Along with the increasing public demand for car transportation modes, car sales businesses are also increasing. Efforts to exist and be competitive are carried out such as by applying machine learning models to determine the carâ€™s selling price based on its specification. Businesses can also stimulate sales by actively offering customers. The effectiveness of the active and massive offerings can be increased by personalizing the offers provided. This research uses a machine learning-based approach to learn customer profile data to predict the carâ€™s price they would buy. The research was conducted by adopting the CRISP-DM framework and developed using the Google Colaboratory and Azure Machine Learning platforms. The modeling stage developed six regression models, those are linear regression, Lasso, Ridge, Random Forest Regressor, Elastic-net, and Support Vector Regressor (SVR). After the evaluation stage, the Lasso regression model with the performance of R-squared (R2) of 0,99958 and Mean Absolute Error (MAE) of 2.284.865,29 deployed as a web service endpoint so it could be accessed in real-time. The web service required the customerâ€™s â€œGender, Age, Annual Salary, Credit Card Debt, and Net Worthâ€ to return a response of the recommended car price range prediction for the customer to buy. In further development, predictions obtained through web services can be implemented in public applications to display personalized car sales offers or pages based on customer profiles