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.Download CV
I am always looking for something fun and fabulous to do.
This is one of the latest techs. that I learned last year. I love to develop some interesting RPA with UiPath.
I am an avid reader and a keen observer, having a deep interest in data analysis, AI, machine learning and business.
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.
I have a good hand on some of the brilliant OS that are available in the market. I have a separate feeling for Ubuntu.
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.
This is a Domain SpacificTwitter Bot which follow the Followers, like the recent tweets and reply to each tweet as per it's domain. This project has been completely build using Python Kivy.
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.
Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements.
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.
A step towords different MLP architectures on MNIST dataset. This experiments are being implimented in TensorFlow and Keras by using Google colab GPU.
This is a demo Django project. I have hosted a personal blogging platform in www.pythonanywhere.com/. The feed on this demo blogging website is not updated.