Understanding Categorical Data

Feature engineering is a crucial step in building a performant machine learning model. Understanding categorical variables and encoding those variables with the right encoding techniques is paramount during the data cleaning and preparation stage. A survey published on Forbes says that Data preparation accounts for about 80% of data scientists’ work. Data scientists spend 60% of their time cleaning and organizing data.

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TravoBOT – “Move freely in the pandemic” (AWS Serverless Chatbot)

Of all the things that this pandemic has taught us, one of the primary concern that many people are still facing is the uncertainty in planning their travel – either in case of emergency or for leisure. And this resulted in TravoBOT – A chatbot that helps the user by collating information from different data sources and provide user with a travel recommendation to a particular destination.

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