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This project covers the steps carried out in the analysis and cleaning of an unstructured Car review dataset. The dataset is made up of reviews of 10,678 customers for four different car models (Toyota, Hyundai, Ford, and Kia), with their respective car recommendation choices to other people, over a period. The aim is to develop a suitable model that will utilize the information in the dataset and classify new/future customer reviews appropriately. This information would help the car manufacturers/industry’s to make informed decisions based on predicted positive and negative car recommendation reviews to others.

naive-bayes-classification supervised-learning support-vector-classifier text-mining tf-idf umap unsupervised-machine-learning

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