Forecasting the Prices using Machine Learning Techniques: Special Reference to used Mobile Phones

Customers now find it difficult to choose the best gadget due to the quick spread of smartphones and the variety of models that are accessible. Machine learning algorithms have been used to estimate mobile phone pricing based on their characteristics in order to solve this problem. In this study, we investigate the use of different machine learning techniques, such as logistic regression, decision trees, random forests, and XGBoosting, to forecast the cost of mobile phones. Our main goal is to identify the most suitable machine learning model for this work and gain an understanding of the variables that affect the price range of mobile phones. The research's conclusions can help both makers and consumers make wise decisions about the features and costs of mobile phones. The study emphasized how crucial it is to choose datasets carefully in order to achieve a complete representation of mobile phone models with a range of features and costs. The study also sought to pinpoint the essential elements that significantly affect mobile phone prices through feature importance analysis. Essential metrics like accuracy score, F1-score, and classification reports were used to evaluate the machine learning models' effectiveness in forecasting the price range. Hyperparameter tuning methods like GridSearchCV were used to improve model performance. The research's overall goal was to offer useful information that would help producers and users alike make decisions about the costs and features of mobile phones.