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.
I am always looking for something fun and fabulous to do.
I am an avid reader and a keen observer, having a deep interest in data science and its applications towards society.
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.
Having an interest in software engineer automation in Robotic Processing Automation. Always interested in developing end to end processes by using the UiPath tool.
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 Convolutional 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.
In this case study, I have made an Image Captioning refers to the process of generating textual description from an image – based on the objects and actions in the image. #TensorFlow2.0
My aim was to accurately forecast sales of Walmart as it is key for its ability to function. The data set for analysis was obtained from Kaggle and it contains weekly sales of various departments within different stores over different period of time.