Pinned Repositories
Data-Science-in-Two-Minutes
Quick descriptions and answers of common data science tasks and questions
DP-100-Designing-and-Implementing-a-Data-Science-Solutio
HiveMinD
As a part of the Insight Data Science Fellowship I consulted for a healthcare company to improve accuracy of online diagnosis of clinical cases.
interpretable_machine_learning_with_python
Practical techniques for interpreting machine learning models.
Journal-Finder
Find suitable journals for your scientific article from the 'title' and 'abstract'.
Kickstarter-data-exploration
Explore kickstarter projects and to determine what parameters influence whether the project is a success or failure
Kickstarter-Success-Prediction
Predicting the success of kickstarter campaigns with machine learning
Predicting-Conversion
The data revolution has a lot to do with the fact that now we are able to collect all sorts of data about people who buy something on our site as well as people who don't. This gives us a tremendous opportunity to understand what's working well (and potentially scale it even further) and what's not working well (and fix it). The goal of this challenge is to build a model that predicts conversion rate and, based on the model, come up with ideas to improve revenue.
Predicting-Employee-Churn
Employee turn-over is a very costly problem for companies. The cost of replacing an employee if often larger than 100K USD, taking into account the time spent to interview and find a replacement, placement fees, sign-on bonuses and the loss of productivity for several months. It is only natural then that data science has started being applied to this area. Understanding why and when employees are most likely to leave can lead to actions to improve employee retention as well as planning new hiring in advance. This application of DS is sometimes called people analytics or people data science. Goal is to predict when employees are going to quit by understanding the main drivers of employee churn.
Pricing-Test
Pricing optimization is, non surprisingly, another area where data science can provide huge value. The goal here is to evaluate whether a pricing test running on the site has been successful.
vivekrajasekharan's Repositories
vivekrajasekharan/Kickstarter-Success-Prediction
Predicting the success of kickstarter campaigns with machine learning
vivekrajasekharan/Predicting-Employee-Churn
Employee turn-over is a very costly problem for companies. The cost of replacing an employee if often larger than 100K USD, taking into account the time spent to interview and find a replacement, placement fees, sign-on bonuses and the loss of productivity for several months. It is only natural then that data science has started being applied to this area. Understanding why and when employees are most likely to leave can lead to actions to improve employee retention as well as planning new hiring in advance. This application of DS is sometimes called people analytics or people data science. Goal is to predict when employees are going to quit by understanding the main drivers of employee churn.
vivekrajasekharan/DP-100-Designing-and-Implementing-a-Data-Science-Solutio
vivekrajasekharan/interpretable_machine_learning_with_python
Practical techniques for interpreting machine learning models.
vivekrajasekharan/HiveMinD
As a part of the Insight Data Science Fellowship I consulted for a healthcare company to improve accuracy of online diagnosis of clinical cases.
vivekrajasekharan/Journal-Finder
Find suitable journals for your scientific article from the 'title' and 'abstract'.
vivekrajasekharan/Predicting-Conversion
The data revolution has a lot to do with the fact that now we are able to collect all sorts of data about people who buy something on our site as well as people who don't. This gives us a tremendous opportunity to understand what's working well (and potentially scale it even further) and what's not working well (and fix it). The goal of this challenge is to build a model that predicts conversion rate and, based on the model, come up with ideas to improve revenue.
vivekrajasekharan/Pricing-Test
Pricing optimization is, non surprisingly, another area where data science can provide huge value. The goal here is to evaluate whether a pricing test running on the site has been successful.
vivekrajasekharan/AI-100-Design-Implement-Azure-AISol
Lab files for AI100T01A ILT Course
vivekrajasekharan/aind2-cnn
AIND Term 2 -- Lesson on Convolutional Neural Networks
vivekrajasekharan/Coding-Challenges
My python solutions to problems from leetcode etc.
vivekrajasekharan/cv-tricks.com
Repository for all the tutorials and codes shared at cv-tricks.com
vivekrajasekharan/deep-learning
Repo for the Deep Learning Nanodegree Foundations program.
vivekrajasekharan/dermatologist-ai
vivekrajasekharan/DP-200-Implementing-an-Azure-Data-Solution
vivekrajasekharan/DP-201-Designing-an-Azure-Data-Solution
vivekrajasekharan/DP100
Labs for Course DP-100: Designing and Implementing Data Science Solutions on Microsoft Azure
vivekrajasekharan/fast-style-transfer
TensorFlow CNN for fast style transfer ⚡🖥🎨🖼
vivekrajasekharan/gym
A toolkit for developing and comparing reinforcement learning algorithms.
vivekrajasekharan/Insight
Insight
vivekrajasekharan/Insight_Bos_17c
vivekrajasekharan/machine-learning
Content for Udacity's Machine Learning curriculum
vivekrajasekharan/MachineLearningNotebooks
Python notebooks with ML and deep learning examples with Azure Machine Learning | Microsoft
vivekrajasekharan/magenta
Magenta: Music and Art Generation with Machine Intelligence
vivekrajasekharan/minimal-flask-example
The simplest complex example that I can think of to show main Flask app concepts.
vivekrajasekharan/My-Solutions-to-DAT210x
My Solutions to the 'Microsoft Data Science Course - Python' Used: PCA and Isomap; RF to classify human activity from wearable data; DT to determine if mushroom is edible; SVC to recognize hand-written numbers & detect Parkinson’s; KNeighbors to determine if a tumor is malignant from a cancer dataset; KMeans to find clusters from call records & a crime dataset.
vivekrajasekharan/opencv
Open Source Computer Vision Library
vivekrajasekharan/reinforcement-learning
Reinforcement learning material, code and exercises for Udacity Nanodegree programs.
vivekrajasekharan/rl-cheatsheet
RL Notation and Pseudocode for Udacity's MLND program
vivekrajasekharan/TensorFlow-Examples
TensorFlow Tutorial and Examples for Beginners with Latest APIs