gopisumanth Goto Github PK

2015

Public material for CS109

2015lab1

adityakamath16

Beautiful readme for GitHub profile, enhance the profile and create a portfolio.

coursera

data-science-projects-2019

Contains all Data Science problem I worked to improve my coding skills in R

data-sciene-2020

code snippets for simple yet elegant data science problems that help in understanding various ML components.

facebook-link-prediction

Taken data from facebook's recruting challenge on kaggle.

ga_ann

Optimising ANN architecture using genetic algorithm.

gopisumanth

Welcome!

logistic-reg

Logistic regression from scratch with NumPy

lushlyrics-insecure

microsoft-malware-detection

In the past few years, the malware industry has grown very rapidly that, the syndicates invest heavily in technologies to evade traditional protection, forcing the anti-malware groups/communities to build more robust softwares to detect and terminate these attacks. The major part of protecting a computer system from a malware attack is to identify whether a given piece of file/software is a malware.

mips-32-bit

designing 32 bit mips processor using logisim

numpyann

Implementation of Artificial Neural Networks using NumPy

oreillyhealthcaredata

pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

personalized-cancer-diagnosis

Classify the given genetic variations/mutations based on evidence from text-based clinical literature.

pyspark-tutorial

PySpark-Tutorial provides basic algorithms using PySpark

quora-question-pairs

Identify which questions asked on Quora are duplicates of questions that have already been asked.

reducing_commercial_aviation_fatalities

sql

Assignment on IMDB database using sqlite3 and pandas

uci-human-activity-recognition

This project is to build a model that predicts the human activities such as Walking, Walking_Upstairs, Walking_Downstairs, Sitting, Standing or Laying. This dataset is collected from 30 persons(referred as subjects in this dataset), performing different activities with a smartphone to their waists. The data is recorded with the help of sensors (accelerometer and Gyroscope) in that smartphone. This experiment was video recorded to label the data manually.