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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A false sense of security

So, a couple of days ago, I got into a little squabble with my dad about what to do with some cash that I had lying around in my bank. I wanted to invest the money to generate a better interest rate than the pathetic ~0.5% offered by most banks. My dad leaned more toward just parking my money with a random advisor and trusting them to invest it while I was more inclined toward managing the money on my own. Unable to come to a conclusion, we decided that an experiment was in an order and decided to split the money in a 2:1 ratio, parking 2/3 of the money with this new, popular investment firm and leaving 1/3 of the money for me to invest with.

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