Pinned Repositories
ACM-ICPC-North-America-Qualifier-2016
Bitcoin-Price-Prediction-using-Bayesian-Regression
Competitive-Programming
Dynamic-Programming-Classics
Solution to classical problems of Dynamic Programming
hackerEarth
Problems solved on hackerEarth
l-calc
Automatically exported from code.google.com/p/l-calc
Market-Segmentation
Music-Recommender-System-Using-Collaborative-Filtering-Technique
Project is Music Recommender System using Apache Spark and Python. Project suggests different songs or musical artists to a user. This is important to many music streaming services, such as Pandora and Spotify. In addition, this type of recommender system could also be used as a means of suggesting TV shows or movies to a user (e.g., Netflix).
ONVIFSENSE---Final-Year-Project
Onvifsense is a central node application that facilitates the video streaming and feature discovery among devices. To ensure interoperability among devices, irrespective of their manufacturers I used profile S of ONVIF standards. I used disjoint sets to determine connected devices in the graph and dfs bfs for path finding.
PlanIt---goCode-2015--Goibibo-Hackathon
PlanIt was made for Goibibo hackathon, goCode 2015 held at Bangalore.
rahulgutal4's Repositories
rahulgutal4/Music-Recommender-System-Using-Collaborative-Filtering-Technique
Project is Music Recommender System using Apache Spark and Python. Project suggests different songs or musical artists to a user. This is important to many music streaming services, such as Pandora and Spotify. In addition, this type of recommender system could also be used as a means of suggesting TV shows or movies to a user (e.g., Netflix).
rahulgutal4/Bitcoin-Price-Prediction-using-Bayesian-Regression
rahulgutal4/ONVIFSENSE---Final-Year-Project
Onvifsense is a central node application that facilitates the video streaming and feature discovery among devices. To ensure interoperability among devices, irrespective of their manufacturers I used profile S of ONVIF standards. I used disjoint sets to determine connected devices in the graph and dfs bfs for path finding.
rahulgutal4/ACM-ICPC-North-America-Qualifier-2016
rahulgutal4/Competitive-Programming
rahulgutal4/Dynamic-Programming-Classics
Solution to classical problems of Dynamic Programming
rahulgutal4/hackerEarth
Problems solved on hackerEarth
rahulgutal4/l-calc
Automatically exported from code.google.com/p/l-calc
rahulgutal4/Market-Segmentation
rahulgutal4/PlanIt---goCode-2015--Goibibo-Hackathon
PlanIt was made for Goibibo hackathon, goCode 2015 held at Bangalore.
rahulgutal4/Quora-Duplicate-Question-Pair-Detection
rahulgutal4/Quora-Duplicate-Question-Pairs
rahulgutal4/SPOJ
Problem Solved on SPOJ
rahulgutal4/Supervised-Learning-Techniques-for-Sentiment-Analytics
In this project, supervised learning techniques were implemented for Sentiment Analysis. Classification techniques used were logistic regression as well as naive bayes classifier. The task of generating feature vector is performed using two methods: 1. A traditional NLP technique where the features are simply “important” words and the feature vectors are simple binary vectors 2. the Doc2Vec technique where document vectors are learned via artificial neural networks
rahulgutal4/Twitter-Sentiment-Analytics-Using-Apache-Spark-Streaming
In this project, basic twitter sentiment analytics is performed using Apache Spark Streaming API's. Real time tweets data stream is processed. Apace Kafka is used as queuing service for data streams.