Predicting Mobile Phone Prices with Machine Learning

The mobile phone industry is experiencing continuous growth, offering a wide range of mobile devices with varying features and prices. Prediction of mobile phones into different price ranges based on their features poses a significant challenge, which can be addressed through the utilization of machine learning algorithms. This research paper aims to implement three machine learning algorithms to effectively categorize mobile phones into specific price ranges, leveraging their distinctive attributes. The three machine learning algorithms: Decision Tree, Random Forest, and K-Nearest Neighbors are used for the prediction task. To accomplish this, a dataset from Kaggle containing mobile phone specifications, including RAM, internal storage, camera, battery, and many more, is utilized. The performance of each algorithm is evaluated in terms of accuracy. The proposed work holds the potential in assisting customers in selecting an appropriate mobile phone based on their budget and preferences. Moreover, it can provide valuable insights to mobile phone manufacturers in determining optimal pricing strategies for their products. By utilizing machine learning algorithms for price range prediction, this research contributes to the evolving landscape of the mobile phone industry, fostering better decision-making for both consumers and manufacturers.