WHO'S
THIS GUY?

My name is Shritam. I am from Odisha, currently living in Bangalore, India and I am an aspiring data scientist.

I choose data science because, after my Bachelor of Technology in Computer Science Engineering, I was trying to think of ways that I could apply my technical skills and my data skills. I see my role as a Data Scientist or a Machine Learning Engineer is to help other people in the company, make decisions and prioritize their work by using the data that we collect and build problem-solving models. So, that could mean helping someone on the marketing team to figure out if one of their campaigns is working or not. Or That could mean helping a product manager to decide whether or not they should ship a product change and many more.

I remember when I first started the journey of data science, it was overwhelming. And what I spent a lot of time doing was looking at existing code that was out there on the internet or examples of analyses and using those as a basis for me try to expand on. So, there are lots of great examples online. I am kind of constantly searching on google for how to do varies things. And stack overflow has a lot of really awesome examples of algorithms in particular that we use in data science. So, for me, it's a lot about using worked examples to help me work on whatever project I'm working on and building on those.

What excites me about being a data science enthusiastic is getting to be able to solve different real-world problems every day.
It's really fun to make a better world with the data.

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7+ Real World Projects

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Skills

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

Applied ML

I am an avid reader and a keen observer, having a deep interest in data science and its applications towards society.

Web developing/design

Along with programming and Data Science, I keep updating my skills in web technologies. I love to spend some time with every new release on HTML, CSS, and JS.

RPA developing

Having an interest in software engineer automation in Robotic Processing Automation. Always interested in developing end to end processes by using the UiPath tool.

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.

STACKOVERFLOW TAG PREDICTOR

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 QUESTION PAIR SIMILARITY PROBLEM

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.

AMAZON FASHION DISCOVERY ENGINE

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.

MICROSOFT MALWARE DETECTION

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 MOVIE RECOMMENDATION SYSTEM

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.)

PERSONALIZED CANCER DIAGNOSIS

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.

TAXI DEMAND PREDICTION IN NEW YORK CITY

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.

SELF DRIVING CAR

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

HUMAN ACTIVITY RECOGNITION

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.

FACEBOOK FRIEND RECOMMENDATION USING GRAPH MINING

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/