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Text cleaning and processing is an important task in every machine learning project where the task is to make sense of textual data. How to construct features from Text Data and further to it, create synthetic features are again critical tasks. On top of it how to apply machine learning models to create classifiers are also difficult.
Build a predictive model to correctly classify products between 9 product categories (fashion, electronics, etc.) using the Otto Group dataset.
In this machine learning pricing project, we implement a retail price optimization algorithm using regression trees. This is one of the first steps to building a dynamic pricing model.
In this project, we are going to predict different qualities of wine using different ML models.