A Novel Smartphone Recommendation System Using Ensemble Machine Learning

Due to the proliferation of internet evaluations brought on by the rising demand for smartphones, consumers find it challenging to make accurate selections when purchasing. In this paper, we offer ensemble voting methods based on TF-IDF (Term Frequency-Inverse Document Frequency) features for clas-sifying mobile phone ratings. We use a recently assembled dataset comprising over 13,000 smartphone reviews from the Flipkart website. The suggested approach includes feature extraction using the TF-IDF, data cleaning, balancing, and voting-based model prediction. To identify the recently created Flipkart dataset, the suggested method created an ensemble voting mechanism based on machine learning techniques. According to the experimental findings, the suggested method performs more accurately and efficiently than conventional machine learning techniques. At 98.0 %, the model achieved the greatest accuracy. The suggested method can be expanded to additional e-commerce platforms with sizable datasets of online evaluations and assist customers in making updated purchase preferences.