Surya Vamsi Rallabandi's starred repositories
bodyapps-android
Bodyapps Measurement App
rnn_lstm_from_scratch
How to build RNNs and LSTMs from scratch with NumPy.
Learning-Quantitative-Finance-with-R
Code repository of Learning Quantitative Finance with R by Packt
Quantitative_Finance
A Data Science project using Python in order to predict whether an equity in the IT sector has a positive growth or a negative growth with the use of technical indicators concluding with a comparative analysis of performance of various machine learning models
watson-voice-bot
Create a Watson Assistant chatbot that uses voice over a web browser.
Watson-Assistant-Testing-Tools
collection of Jupyter Notebooks in Python that can be used to test your Watson Assistant workspace
botium-bindings
The Selenium for Chatbots
watson-conversation-variables
Samples using context variables and (system) entities in IBM Watson Assistant (formerly Conversation) service on IBM Cloud
healthcare-insurance
The main objective of this project was to understand the health insurance uptake and find out the main factors that influence this uptake
Predicting-Insurance-Premium
Statistical analysis and prediction of medical insurance premium based on beneficiary's health & lifestyle (regression modelling in R)
health-insurance-marketplace-analytics
Analytics on data pulled from health insurance marketplaces. Some related to DDOD (https://github.com/demand-driven-open-data) use cases.
Predicting-Health-Insurance-Cost
Predicting health insurance cost from Morality data using Machine Learning techniques
Watson-Conversation-External-API
An example to get you up and running with calling external APIs from Watson Conversation. Blog post:
pystock-data
(UNMAINTAINED) US stock market data since 2009
concrete_NLP_tutorial
An NLP workshop about concrete solutions to real problems
wallstreet
Real time stock and option data.
fuzzywuzzy
Fuzzy String Matching in Python
chatbot-retrieval
Dual LSTM Encoder for Dialog Response Generation
seq2seq-chatbot
Chatbot in 200 lines of code using TensorLayer