About

By profession I’m a Principal Engineer working for a Financial Services company. As an Individual Contributor as well as a Leader, I get to explore various technologies and provide solutions to the business. The fundamental aspect of my job which I like the most is Problem Solving. Because I look at newer problems as opportunities to deliver something new or different. That motivates me to keep learning one or other technical aspect. In addition to the solutions, I also act as a Mentor to my fellow team members and colleagues. As part of mentorship, I get to interact with diverse set of people. Through these interactions, I also learn new things.


I get bored doing same things again and again and again. So, I keep exploring new technologies and also make my hands dirty. I strongly believe in mutual knowledge sharing to enhance one's knowledge. Through this blog, I intend to share my learning and experience with others. I hope these posts would be useful for someone looking for a solution to a specific problem or in general, knowledge about a technology or a concept.

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

Comments

Post a Comment

Popular posts from this blog

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...
Read more

Data Visualization using Pandas - Univariate Plots

Image
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 ...
Read more

Car Evaluation - Another Classifier Program

Image
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...
Read more