The number of possible values is often limited to a fixed set. We know that the categorical variables contain the label values rather than numerical values. So, here we handling categorical features by One Hot Encoding, thus first of all we will discuss One Hot Encoding. There are many techniques for handling the categorical variables, some are : The value of data point in any categorical feature is not in numerical form, rather it was in object form. Such as:-īut here we will only discuss Categorical Features, The Categorical Features are those features in which datatype is an Object type. The concept of transparency for the machine learning models is a complicated thing as different models often require different approaches for the different kinds of data. This is the third step in any data science project life cycle. Feature engineering is the most important art in machine learning which creates a huge difference between a good model and a bad model. We can also say that feature engineering is the same as applied machine learning. These features can be used to improve the performance of machine learning algorithms and if the performance increase then it will give the best accuracy. So, Feature Engineering is the process of extracting features from raw data using the domain knowledge of the problem. In this article, we will learn about how can we able to handle multi categorical variables using the Feature Engineering technique One Hot Encoding.īut before going ahead, let us have a brief discussion on Feature engineering and One Hot Encoding.
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