I am from Odisha, currently living in bangalore, India. During the daytime, you may spot me chitchatting in real-world resources, sipping on tea, reading AI blogs, or pretending to get things done. At night, you can find me jamming to some EDM song on repeat, pushing data to Google Colab, or doing some interesting applied ML assignments. I am studying and practicing Exploratory Data sis, Machine Learning, and Deep Learning now. Yet, I don't consider myself an expert in anything because there is just so much more to learn and explore. Currently, I am interested in Data Science and Python Programming, working with big and small data and examining the social and cultural impacts of social media and digital communication.

I honestly still don't know what I'm doing with my life, but I am excited to see where Machine Learning will take me next.

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I am always looking for something fun and fabulous to do.

RPA developing

This is one of the latest techs. that I learned last year. I love to develop some interesting RPA with UiPath.

Applied ML

I am an avid reader and a keen observer, having a deep interest in data analysis, AI, machine learning and business.

Web developing/design

Along with programming and Data Science, I keep practicing my skill in web designing. I give my time to every new release on HTML, CSS, and JS.

Operating Systems

I have a good hand on some of the brilliant OS that are available in the market. I have a separate feeling for Ubuntu.

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Learn Python the Hard Way Exercises

These are the exercises from the most popular Python Book "The Hard Way to Learn Python" by Zed A Shaw and some of the practiced pieces of stuff on Python for Beginners.

Amazon Fine Food Reviews Analysis

Given a Amazon fine food review, determine whether the review is positive (Rating of 4 or 5) or negative (rating of 1 or 2) with different Machine Learning algorithms.


The website serves as a platform for users to ask and answer questions, and, through membership and active participation, to vote questions and answers up or down and edit questions and answers in a fashion similar to a wiki or Digg. As of April 2014 Stack Overflow has over 4,000,000 registered users, and it exceeded 10,000,000 questions in late August 2015. Based on the type of tags assigned to questions, the top eight most discussed topics on the site are: Java, JavaScript, C#, PHP, Android, jQuery, Python and HTML.


Quora has given an (almost) real-world dataset of question pairs, with the label of is_duplicate along with every question pair. The objective was to minimize the logloss of predictions on duplicacy in the testing dataset. Given a pair of questions q1 and q2, train a model that learns the function: f(q1, q2) → 0 or 1 where 1 represents that q1 and q2 have the same intent and 0 otherwise.


Product recommendations are the alternative way of navigating through the online shop. Showing similar products to the user which user is searching for.This case study is based on Recommending similar products (apparel) to the given product (apparel) in Amazon e-commerce websites. The recommendation engine, uses information about 1,80,000 products and each product will have multiple features named.


This competition was hosted by WWW 2015 / BIG 2015 and the following Microsoft groups: Microsoft Malware Protection Center, Microsoft Azure Machine Learning and Microsoft Talent Management. For this problem I got a log loss of 0.006.


Netflix provided a lot of anonymous rating data, and a prediction accuracy bar that is 10% better than what Cinematch can do on the same training data set. (Accuracy is a measurement of how closely predicted ratings of movies match subsequent actual ratings.)


A lot has been said during the past several years about how precision medicine and, more concretely, how genetic testing is going to disrupt the way diseases like cancer are treated. But this is only partially happening due to the huge amount of manual work still required. Memorial Sloan Kettering Cancer Center (MSKCC) launched this competition, accepted by the NIPS 2017 Competition Track, because we need your help to take personalized medicine to its full potential.


In this case study, I solve a Time Series and Regression Problem to predict the demand of Yellow Taxis in the New York City in a interval of 10 minute Requirements.


A Deep Learning Case Study to predict the steering angle of a car. Where input is image of road(using Constitutional Neural Networks).


This project is to build a model that predicts the human activities such as Walking, Walking_Upstairs, Walking_Downstairs, Sitting, Standing or Laying. This dataset is collected from 30 persons(referred as subjects in this dataset), performing different activities with a smartphone to their waists. The data is recorded with the help of sensors (accelerometer and Gyroscope) in that smartphone.


This project is based on social media link prediction whether two users are going to be friend in future or not.

MLP architectures on MNIST dataset

A step towords different MLP architectures on MNIST dataset. This experiments are being implimented in TensorFlow and Keras by using Google colab GPU.

Open CV Medicine Sensor

Here I have tried to make a python application for medical industry. It simply clicks the photo of a medicine cover and shows it's uses. This project is yet under construction. I have used python3.6, OpenCV and Tesserect intensively.

What motivates me to learn?

just watch this video.

Video Credit: http://growthtribe.io/