This report details a machine learning application designed to predict the price category of mobile phones using PySpark. The study begins with an introduction to the factors influencing mobile phone pricing, emphasizing the role of specifications and the value proposition for both customers and manufacturers. The core objective is to evaluate various machine learning methods for optimal price determination. The report then explores key system concepts including machine learning pipelines (data ingestion, feature transformation, and selection), collaborative filtering (using the ALS algorithm for matrix decomposition and price category prediction), logistic regression (for nominal response variables and model performance evaluation), and K-Means clustering (for unsupervised learning and grouping data based on features). The methodology involves data exploration, cleaning, model training, and evaluation using metrics such as Mean Squared Error (MSE) and area under the ROC curve. The report concludes by emphasizing the importance of selecting appropriate machine learning models and the significance of preliminary steps for leveraging the benefits of machine learning, with references and code appendices included for further detail.