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Topic modelling is a method for finding a group of words (i.e. topics) from a collection of documents that best represents the information in the collection of text documents. It can also be thought of as a form of text mining - a way to obtain recurring patterns of words in textual data. The topics identified are crucial data points in helping the business figure out where to put their efforts in improving their product or services.
In this project we will use unsupervised technique - Kmeans, to cluster/ group reviews to identify main topics/ ideas in the sea of text. This will be applicable to any textual reviews. In this series, we will focus on twitter data which is more real world and more complex data compared to reviews obtained from review or survey forms.
Topic modelling provides us with methods to organize, understand and summarize large collections of textual information. It helps in:
There are different time series forecasting methods to forecast stock price, demand etc. In this machine learning project, you will learn to determine which forecasting method to be used when and how to apply with time series forecasting example.
Build a predictive model to correctly classify products between 9 product categories (fashion, electronics, etc.) using the Otto Group dataset.
In this supervised learning machine learning project, you will predict the availability of a driver in a specific area by using multi step time series analysis.