anikethsukhtankar/data-mining-classification

Stars
1
Language
R
License
MIT License
Created almost 7 years ago
Updated almost 7 years ago

anikethsukhtankar/data-mining-classification

anikethsukhtankar

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

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.

bitcoin-mining-elixir

The goal of this project was to use Elixir and the actor model to build a good solution to the bitcoin mining problem that runs well on multi-core machines. Our code was simultaneously run on 8 machines with one functioning as server and the other 7 as workers, and we were able to mine a coin with 7 leading zeroes. Technologies used: Erlang, Elixir

gossip-push-sum-protocol

The goal of this project was to determine the convergence of Gossip type algorithms through a simulator based on actors written in Elixir. Full, Line, 2D and Imperfect 2D topologies were implemented for both Push Sum and Gossip algorithms. We could demonstrate convergence of these algorithms with nodes upto 80000 without hitting system limits. Technologies used: Erlang, Elixir, Gossip Protocol

ChatterBot

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.

Jupyter Notebook

Cracking-The-Coding-Interview

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.

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.

huffman-encoding-using-d-ary-heaps

Developed encoder & decoder implementations for Huffman Encoding using Java framework. Instrumented code & performed experiments to determine bound on running times for Huffman tree generation using Binary Heap, Four Way Heap and Pairing Heap and used the fastest among them for Encoder implementation.

twitter-clone-websockets-phoenix

The goal of this project was to implement a Twitter Clone and a client tester/simulator in Elixir. Through this project, we could also demonstrate performance improvement in using Erlang message passing over WebSocket interface implementation in Phoenix. The Twitter Clone has the following components: -A Twitter like engine with the following functionality- • Register account • Send tweet. Tweets can have hashtags (e.g. #COP5615isgreat) and mentions (@bestuser) • Subscribe to user's tweets • Re-tweets (so that your subscribers get an interesting tweet you got by other means) • Allow querying tweets subscribed to, tweets with specific hashtags, tweets in which the user is mentioned (my mentions) • If the user is connected, deliver the above types of tweets live (without querying) -A tester/simulator to test the engine by- • Simulating as many users as possible • Simulating periods of live connection and disconnection for users • Simulating a Zipf distribution on the number of subscribers. For accounts with a lot of subscribers, increase the number of tweets. Make some of these messages re-tweets Technologies used: Erlang, Elixir, Phoenix web framework, WebSocket