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VIKAS SINGH KAVIYA's Projects

awesome-nlp icon awesome-nlp

:book: A curated list of resources dedicated to Natural Language Processing (NLP)

bus-management-system icon bus-management-system

This application developed to provide a tool for the different colleges to easily maintain the college bus information. It was designed using Java and backend was made using MySql.

churn-prediction icon churn-prediction

Telecom company was losing its customers provided its users' dataset. I applied various ML algorithms, ROC curve and confusion matrix to identify users who will churn and what minimum offer to give them so they don't churn and company stays in profit.

coa-project icon coa-project

DEVELOPED AN I.S.A WHICH PERFORMS BASIC ARITHMETIC AND LOGICAL FUNCTIONS.

courseraml icon courseraml

I took Andrew Ng's Machine Learning course on Coursera and did the homework assigments... but, on my own in python because I love jupyter notebooks!

fasttext icon fasttext

Library for fast text representation and classification.

handwritten-digit-recognition icon handwritten-digit-recognition

Enhanced the real-time accuracy of Handwritten digits recognition using various ML modules embedded with Image Processing Techniques; obtained 97.7% accuracy on MNIST Dataset using 3 layered Neural Networks.

mlconvgec2018 icon mlconvgec2018

Code and model files for the paper: "A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction" (AAAI-18).

nlc icon nlc

Neural Language Correction implemented on Tensorflow

smtgec2017 icon smtgec2017

A statistical machine translation (SMT)-based grammatical error correction system that makes use of neural network joint models (NNJM) and and character-level SMT for spelling correction.

tensorflow icon tensorflow

Computation using data flow graphs for scalable machine learning

titanic-survivor icon titanic-survivor

The job was to predict if a passenger survived the sinking Titanic or not with the help of machine learning. Majority of the task was to analyze what sorts of passengers were likely to survive the tragedy through graphs, analytics tools and ML modules.

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