About

By profession I’m a SharePoint Architect working for a Financial Services company. Sometime around Jan 2020, I started getting to know about Machine Learning. The huge opportunities which lies ahead for Machine Learning to solve business problems created a lot of interest in me. I have started this journey in the year 2020 and I’m using this platform to share my experience and learning with fellow non-ML developers. If you want to know how it all started, do check out my first post “The ML bug bit me!”.

Disclaimer: Opinions expressed in this site don’t necessarily reflect the opinion of my employer.

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Ordinal Encoder, OneHotEncoder and LabelBinarizer in Python

Usually in the data set we work on, we will have both numerical data as well as categorical data (non-numeric). There are algorithms which do not work on categorical data. These algorithms are designed that way to enhance efficiency. So in such cases how do we train the model with categorical data? This is where we need to apply a technique called Encoding. Encoding transforms categorical data into numeric. There are various Encoders available in sci-kit learn library. In this post, I'll share my learning on three Encoders. Ordinal Encoder This performs ordinal (integer) encoding of categorical data. Let's look at an example. I have an array of categorical data. This is a 2 dimensional array. multiArray = np.array([[ 'Karnataka' ,  'KA' ], [ 'Maharastra' ,  'MH' ], [ 'Gujarat' ,  'GJ' ]]) Then I use OrdinalEncoder to transform this data. ordinalEncoder = OrdinalEncoder() ordinalEncoderArray = ordinalEncoder.fit_transform(mult
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Data Visualization using Pandas - Univariate Plots

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Data visualization is an important step in ML. That helps us to understand the data in visual representation. Why does it matter? Because it helps us to understand the distribution of the data, any outliers, and also relationship between multiple attributes. Experts also mention that understanding the distribution helps us in choosing correct algorithm. I cannot comment on this statement as of now, as I do not have enough knowledge on that. When I reach that stage, I’ll definitely share. Pandas library provides methods to plot data in multiple ways. In fact, I did use one of those plots called  Box and Whisker Plots  in my previous examples. However, those plots were used to see the difference between multiple algorithms. In this post, I’ll share how to visualize the data which is an important step much before we apply any ML algorithm. I’ll use the data set which was used for  Binary Classification . The data set is   Pima Indians Diabetes Database  which has 8 features. If we plot at
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Natural Language Toolkit (NLTK)

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