Suresh Gorakala sureshgorakala

## Running_first_neural_network_model_on_google_colaboratory.ipynb

      
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                sureshgorakala
                / Running_first_neural_network_model_on_google_colaboratory.ipynb
            
            
              Created
              September 8, 2018 17:08
            
          
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## search_engine_svd.r
#load required pacakges
if(!require("readtext"))
  install.packages("readtext")
library(readtext)

if(!require("tm"))
  install.packages("tm")
library(tm)

if(!require("stringr"))

## getting started with RStudio.ipynb

      
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                sureshgorakala
                / getting started with RStudio.ipynb
            
            
              Created
              December 2, 2017 09:15
            
              
                getting started with RStudio
              
          
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## information_retreival_part1.r
#load required pacakges
if(!require("readtext"))
  install.packages("readtext")
library(readtext)

if(!require("tm"))
  install.packages("tm")
library(tm)

if(!require("stringr"))

## NLP_Session_1.py
import nltk

#reading text into python
path = "~/textCourpus.txt"
f = open(path,'r')
lines = [line.replace('\n','') for line in f.readlines()]
#lines2 = [line.replace('\n','') for line in f.readlines()]

type(lines)
len(lines)

## PCA using R
library(pca)
data("crimtab") #load data
head(crimtab) #show sample data
dim(crimtab) #check dimensions
str(crimtab) #show structure of the data
sum(crimtab)
colnames(crimtab)
apply(crimtab,2,var) #check the variance accross the variables
pca =prcomp(crimtab) #applying principal component analysis on crimtab data
par(mar = rep(2, 4)) #plot to show variable importance

## Codefile
import pandas
import numpy
import scipy.stats
import seaborn
import matplotlib.pyplot as plt


# any additional libraries would be imported here

data = pandas.read_csv('C:\\Suresh\\Blog Posts\\datasets\\nesarc_pds1134\\SPLITDATA\\CourseData.csv', low_memory=False)

## blogpost.r

#mean of the rows
apply(m, 1, mean)

# mean of the columns
apply(m, 2, mean)

# divide all values by 2
apply(m, 1:2, function(x) x/2)

## include.html
<!DOCTYPE html>
<html>

<head>

</head>

<body>
  <h1>HTML UI</h1>
  <table>

## server.R
library(twitteR)
library(tm)
library(stringr)
library(wordcloud)
#fetch data
load("D:/Suresh R&D/Emirates Airlines SNA/twitteR_credentials")
registerTwitterOAuth(twitCred)
tweets = searchTwitter("@emirates", lang="en",cainfo="D:/Suresh R&D/Emirates Airlines SNA/cacert.pem")

#remove retweets
	#load required pacakges
	if(!require("readtext"))
	install.packages("readtext")
	library(readtext)

	if(!require("tm"))
	install.packages("tm")
	library(tm)

	if(!require("stringr"))
	import nltk

	#reading text into python
	path = "~/textCourpus.txt"
	f = open(path,'r')
	lines = [line.replace('\n','') for line in f.readlines()]
	#lines2 = [line.replace('\n','') for line in f.readlines()]

	type(lines)
	len(lines)
	library(pca)
	data("crimtab") #load data
	head(crimtab) #show sample data
	dim(crimtab) #check dimensions
	str(crimtab) #show structure of the data
	sum(crimtab)
	colnames(crimtab)
	apply(crimtab,2,var) #check the variance accross the variables
	pca =prcomp(crimtab) #applying principal component analysis on crimtab data
	par(mar = rep(2, 4)) #plot to show variable importance
	import pandas
	import numpy
	import scipy.stats
	import seaborn
	import matplotlib.pyplot as plt


	# any additional libraries would be imported here

	data = pandas.read_csv('C:\\Suresh\\Blog Posts\\datasets\\nesarc_pds1134\\SPLITDATA\\CourseData.csv', low_memory=False)

	#mean of the rows
	apply(m, 1, mean)

	# mean of the columns
	apply(m, 2, mean)

	# divide all values by 2
	apply(m, 1:2, function(x) x/2)
	<!DOCTYPE html>
	<html>

	<head>

	</head>

	<body>
	<h1>HTML UI</h1>
	<table>
	library(twitteR)
	library(tm)
	library(stringr)
	library(wordcloud)
	#fetch data
	load("D:/Suresh R&D/Emirates Airlines SNA/twitteR_credentials")
	registerTwitterOAuth(twitCred)
	tweets = searchTwitter("@emirates", lang="en",cainfo="D:/Suresh R&D/Emirates Airlines SNA/cacert.pem")

	#remove retweets