Pinned Repositories
airflow
AirFlow is a system to programmatically author, schedule and monitor data pipelines.
bitcoin-mining-elixir
The goal of this project is to use Elixir and the actor model to build a good solution to the bitcoin mining problem that runs well on multi-core machines.
comparative_activity_recognition
curriculum
This repo contains the curriculum of Techtonica, a free tech training program for women and non-binary adults with low incomes.
data-mining-clustering
The goal of the project is to increase familiarity with the clustering packages, available in R to do data mining analysis on real-world problems. Several different clustering methods were used on the given datasets. The dataset was as provided. The original cluster column was used as initial label for comparison. kMeans, Hierarchical, DBScan and SNNClust were the clustering methods used on the smaller data set and kMeans was chosen for large data set.
face-recognition-using-sparse-representation
Implementation of a Weighted Sparse Representation based Classifier for identifying faces in face recognition systems
huffman-encoding-using-d-ary-heaps
The goal of this project is to implement a system that uses Huffman coding so that when enormous amount of data needs to be transferred, the overall data size is reduced.
J.O.B-Training-Repo-2
twitter-clone-websockets-phoenix
Implemented a Twitter clone and client tester/simulator using Elixir and Actor Facility provided in Erlang. Used Phoenix web framework to implement a Web Socket interface. Implemented public key based Authentication method for same.
yelp-dataset-challenge
The problem of predicting a user's star rating for a product, given the user's text review for that product, is called Review Rating Prediction and has lately become a popular problem in machine learning. In this project, we implement an approach which involves a combination of topic modeling and sentiment analysis to achieve this objective by treating Review Rating Prediction as a multi-class classification problem, and building different prediction models by using Latent Dirichlet Allocation as the underlying feature extraction method with three machine learning algorithms, (i) K Nearest Neighbors, (ii) Multinomial Naive Bayes and (iii) Random Forest. We analyze the performance of each of these models to come up with the best model for predicting the ratings from reviews. We use the dataset provided by Yelp for training and testing the models.
Shikha2410's Repositories
Shikha2410/face-recognition-using-sparse-representation
Implementation of a Weighted Sparse Representation based Classifier for identifying faces in face recognition systems
Shikha2410/data-mining-clustering
The goal of the project is to increase familiarity with the clustering packages, available in R to do data mining analysis on real-world problems. Several different clustering methods were used on the given datasets. The dataset was as provided. The original cluster column was used as initial label for comparison. kMeans, Hierarchical, DBScan and SNNClust were the clustering methods used on the smaller data set and kMeans was chosen for large data set.
Shikha2410/huffman-encoding-using-d-ary-heaps
The goal of this project is to implement a system that uses Huffman coding so that when enormous amount of data needs to be transferred, the overall data size is reduced.
Shikha2410/twitter-clone-websockets-phoenix
Implemented a Twitter clone and client tester/simulator using Elixir and Actor Facility provided in Erlang. Used Phoenix web framework to implement a Web Socket interface. Implemented public key based Authentication method for same.
Shikha2410/airflow
AirFlow is a system to programmatically author, schedule and monitor data pipelines.
Shikha2410/bitcoin-mining-elixir
The goal of this project is to use Elixir and the actor model to build a good solution to the bitcoin mining problem that runs well on multi-core machines.
Shikha2410/comparative_activity_recognition
Shikha2410/curriculum
This repo contains the curriculum of Techtonica, a free tech training program for women and non-binary adults with low incomes.
Shikha2410/J.O.B-Training-Repo-2
Shikha2410/yelp-dataset-challenge
The problem of predicting a user's star rating for a product, given the user's text review for that product, is called Review Rating Prediction and has lately become a popular problem in machine learning. In this project, we implement an approach which involves a combination of topic modeling and sentiment analysis to achieve this objective by treating Review Rating Prediction as a multi-class classification problem, and building different prediction models by using Latent Dirichlet Allocation as the underlying feature extraction method with three machine learning algorithms, (i) K Nearest Neighbors, (ii) Multinomial Naive Bayes and (iii) Random Forest. We analyze the performance of each of these models to come up with the best model for predicting the ratings from reviews. We use the dataset provided by Yelp for training and testing the models.
Shikha2410/data-mining-classification
The goal of the project is to increase familiarity with the classification packages, available in R to do data mining analysis on real-world problems. Several different classification methods were used on the given Life Expectancy dataset. The dataset was obtained from the Wikipedia website. The continent column was added as per the requirements to be used as class label. kNN, Support Vector Machine, C4.5 and RIPPER were the classification methods used on the data set.
Shikha2410/gossip-push-sum-protocol
The goal of this project is to determine the convergence of Gossip type algorithms through a simulator based on actors written in Elixir. Since actors in Elixir are fully asynchronous, the particular type of Gossip implemented is the so called Asynchronous Gossip.
Shikha2410/J.O.B-Training-Linux-1
Shikha2410/J.O.B-Training-Repo-1
Shikha2410/node
Node.js JavaScript runtime :sparkles::turtle::rocket::sparkles:
Shikha2410/pastry-protocol
The goal of this project is to implement in Elixir using the actor model the pastry protocol and a simple object access service to prove its usefulness.
Shikha2410/redi-dropper-client
redi-dropper-client
Shikha2410/twitter-clone-elixir
The goal of this project is to implement a Twitter Clone and a client tester/simulator. The problem statement is to implement an engine that can be paired up with WebSockets to provide full functionality. The client part (send/receive tweets) and the engine (distribute tweets) were simulated in separate OS processes.