In this project, we are going to explore and analyze a dataset which contains specifications of two thousand mobile phones and try to predict optimum price ranges for a list of mobile phones in the market by applying various machine learning algorithms such as logistic regression, decision tree, random forest and k-nearest neighbors(knn).
We split our data into training and validation datasets and apply ML pipeline on these two. Than, run the most accurate model(knn) to predict target values of test dataset. You can reach the dataset from documentation on ReadTheDocs
Please contact zealptekin@gmail.com