Built a K-Means clustering algorithm for a given very large image and
parallelized it using Openmp, CUDA and MPI. For time complexity CUDA out performed better in Tesla model ,
where as in Nvidia GPU: CUDA out performed mush more better
because of number of cores present is more in Nvidia GPU.
The aim of this project is to predict heart and kidney disease using data mining techniques
and machine learning algorithms.This project implements 6 classificiation models using scikit-learn:
Logistic Regression, Naïve Bayes, Support Vector Classifier,KNN, Nerual Network and Decision
Tree Model to investigate their performance on heart and kidney disease datasets obtained from the
UCI data repository and from Kaggle.com.
Implemented K-disjoint paths from source to destination
with differentiated path costs by providing maximum number of conflicts while doing
iteration and Dijkstra's algorithm on 1K+ network nodes to find shortest path.
To formulate a new methodology for RR algorithm that enhances
the performance of the time-sharing systems by reducing the waiting time, turnaround time
and number of context switches.
Implemented packages to built circle, Cohen clipping, dashed/dotted lines,
ellipse, parabola, transformations using Bresenham's algorithm, mid point algorithm, Scanline filling and clipping algorithm.
Interests & Other Activities
Active in coding challenging websites like Hackerrank, leetcode, interviewBit, codechef etc.
Learning basics of Machine Learning from Coursera.
Learnt basics of Database management system.
Top performer in state level Basketball competition.