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library(xgboost)
library(Matrix)
library(data.table)
library(vcd)

data(Arthritis)
df <- data.table(Arthritis, keep.rownames = F)

head(df[,AgeDiscret := as.factor(round(Age/10,0))])

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library(Hmisc)    # useful for multi histogram
hist.data.frame(full)  # histogram of each variable
pairs(full)            # pairwise scatter plots


data = mx.symbol.Variable('data')
fc1 = mx.symbol.FullyConnected(data=data, num_hidden=7)
sigm1 = mx.symbol.Activation(data=fc1, act_type="relu")
fc2 = mx.symbol.FullyConnected(data=sigm1, num_hidden=2)
net = mx.symbol.SoftmaxOutput(data=fc2)

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library(kohonen)
library(Hmisc)    # useful for multi histogram

#loading client data
client <- read.csv(file = "C:/Users/eye1/Desktop/Customer Data.csv")

#Feature selection
data_train <- client[, c("NO_OF_PRODUCTS_6","Balance","LOGIN_DAYS_L3M", "ONLINE_TRANSACTION_L3M")]#data_train <- iris[c(1,2,3,4)]

#Explore the data

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# based on Manuel Bernal's code

######## SOM GENERATOR ###########
#Loading Packages needed
library(png) # For writePNG function
library(kohonen)
library(shiny)
library(ggplot2)
library(jsonlite)
library(Hmisc)

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# based on Manuel Bernal's code

######## SOM GENERATOR ###########
#Loading Packages needed
library(png) # For writePNG function
library(kohonen)
library(shiny)
library(ggplot2)
library(jsonlite)
library(Hmisc)

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library(shiny)

shinyUI(fluidPage(
  titlePanel("Self-Organized-Maps"),
  fluidRow(
    sidebarPanel(
      fileInput("file1", "Select CSV:", accept = c("text/csv", "text/comma-separated-values,text/plain", ".csv")),
      selectInput("sep", label = "Separator", 
                  choices = list("Coma" = ",", "Semicolon" = ";", "Tab" = "\t"),
                  selected = 1),

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require(mlbench)
require(caret)
require(MLmetrics)
require(plyr)
require(ade4)

data(Satellite)
summary(Satellite)
dim(Satellite)
my_data <- Satellite

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library(caret)
set.seed(1)

data<-read.csv(url('https://datahack-prod.s3.ap-south-1.amazonaws.com/train_file/train_u6lujuX_CVtuZ9i.csv'))


preProcValues <- preProcess(data, method = c("medianImpute","center","scale"))
library('RANN')
data_processed <- predict(preProcValues, data)

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library(RODBC)
library(tm)
library(wordcloud)
library(ggplot2)
library(ROracle)

 a <- read.csv(file = 'C:/Users/eye1/Desktop/text.csv')
 names(a) <- 'feedback'
narrative <- a$feedback
str(narrative)

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library(data.table)

#download data
flights <- fread("https://raw.githubusercontent.com/wiki/arunsrinivasan/flights/NYCflights14/flights14.csv")
flights

#Subset
flights[origin == "JFK" & month == 6L] # by column
flights[1:2] #by row