It's changed!

It's changed now! The domain as well as the scope of this site. All my technical learning will be shared in this blog. It's not specific to any one or two technoligies. Rather, any technology which I come across and learn which I feel like sharing with others, I'll publish in this site.

I hope to keep my writing as regular and consistent as possible. Let's see, how it goes!

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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 ...
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Car Evaluation - Another Classifier Program

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After my successful first classifier program in Python, I took up another data set “ Car Evaluation ” for my second classifier program. I took this data set from  UCI Machine Learning Repository . I was expecting this program to be a repeat of what I did with Iris data set. However, surprisingly it was not a repeat but there were a couple of learning from this exercise. This data set has these 6 attributes with these values.  buying: vhigh, high, med, low. maint: vhigh, high, med, low. doors: 2, 3, 4, 5more. persons: 2, 4, more. lug_boot: small, med, big. safety: low, med, high The output class values are unacc (Unacceptable) acc (Acceptable) good vgood If we observe the attributes carefully, all attributes have string values. This was my first obstacle in the program. Some of the methods of scikit did not work because of string values. After a little bit of Googling I understood that I need to convert these string to numbers. This is where  Encoding Categorical feat...
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