Smartphone Price Prediction Using Machine Learning Techniques

This research aimed to predict smart phone prices using two supervised machine learning algorithms: Decision Tree and Random Forest Regression. Data was collected from the Indian e-Commerce website Flip kart using Python libraries such as Beautiful Soup and Selenium, and was cleaned and pre-processed for analysis. The results showed that the Decision Tree algorithm had an R^2of 89.3%. The Random Forest classifier showed the R^2 value with an accuracy score of 82.8%. The study offers a method for accurately predicting smart phone prices that could be useful to determine the cost of their products and ultimately benefit the entire smart phone market. Key Word: Smartphone, Price Prediction, Machine Learning, Decision Tree, Random Forest Regression.