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In this tutorial we will discuss label encoding in Python. In data science, we often work with datasets that contain categorical variables, where the values are represented by strings. For example, when we work with datasets for salary estimation based on different sets of features, we often see job title being entered in words, for example: Manager, Director, Vice-President, President, and so on. The complication it creates is the fact that machine learning algorithms in fact can work with categorical features, yet they have to be in numeric form. There are multiple ways to solve this problem and a lot depends on the algorithm you will be working with.

A robot vacuum cleaner escape a hotel and went on the loose for an entire day, according to BBC News. The automated device went missing on Thursday after it failed to stop at the front door of the Travelodge hotel in Cambridge, England, per BBC News. Once it was through the door, the media outlet reported that it just kept going. Staff said it "could be anywhere" as the robot vacuum cleaner escaped its duties, according to the Express. The assistant manager for the hotel posted on social media asking for help to find the vacuum cleaner that had "run for its life," the Express said.

Bots are officially everywhere -- they're available to assist with almost every part of our lives, from shopping and gift-giving to news-tracking and political decision-making. They can even tell us what to make for dinner. The thing that seems odd to me, though, is that the entire bot conversation has been focused on A.I., machine learning (ML), and natural language processing (NLP). This makes me question whether we're looking at bots the wrong way. Having been in the business of building software for quite some time, and learned about workflows, business logic and other riveting concepts, I can't help but feel there's a misunderstanding about what constitutes a bot and what is just an access layer to the bot.