Background:
With the advent of data analysis and machine learning, there is a growing
impetus of analyzing and generating models on historic data. The data comes in numerous forms
and shapes with an abundance of challenges. The most sorted form of data for analysis is the numerical
data. With the plethora of algorithms and tools it is quite manageable to deal with such data.
Another form of data is of categorical nature, which is subdivided into, ordinal (order wise) and
nominal (number wise). This data can be broadly classified as Sequential and Non-Sequential. Sequential
data analysis is easier to preprocess using algorithms.
Objective:
The challenge of applying machine learning algorithms on categorical data of nonsequential
nature is dealt in this paper.
Methods:
Upon implementing several data analysis algorithms on such data, we end up getting a
biased result, which makes it impossible to generate a reliable predictive model. In this paper, we
will address this problem by walking through a handful of techniques which during our research
helped us in dealing with a large categorical data of non-sequential nature. In subsequent sections,
we will discuss the possible implementable solutions and shortfalls of these techniques.
Results:
The methods are applied to sample datasets available in public domain and the results
with respect to accuracy of classification are satisfactory.
Conclusion:
The best pre-processing technique we observed in our research is one hot encoding,
which facilitates breaking down the categorical features into binary and feeding it into an Algorithm
to predict the outcome. The example that we took is not abstract but it is a real – time production services
dataset, which had many complex variations of categorical features. Our Future work includes
creating a robust model on such data and deploying it into industry standard applications.
Efficient binary embedding of categorical data using BinSketch
