Shiveshwar kr-shiveshwar

## app.R
library(ggmap)
library(readr)
library(ggplot2)
library(ggraph)

load("eway_wo_zero_nodes")
load("transx_wo_zero")
load("pincodes_repository")

pincodes_repository <- pincodes_repository1

## Viz_Seaborn.ipynb

      
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                kr-shiveshwar
                / Viz_Seaborn.ipynb
            
            
              Created
              February 8, 2017 10:19
            
              
                Visualization using Seaborn
              
          
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## Bi-variate.R
#####################################
#       BiVariate Analysis          #
#####################################
qplot(income,age,data = Telecom_train,geom=c("point", "smooth"),color=churn)
qplot(income,data = Telecom_train,geom="density")

#as the data is skewed we will perform log transformation
qplot(log(income),data = Telecom_train,geom="density")
qplot(log(income),age,data = Telecom_train,geom=c("point", "smooth"),color=churn)

## Region.R
# Histogram
library(ggplot2)
qplot(region,data = Telecom_train,geom = "bar",fill=factor(region))
qplot(region,data = Telecom_train,geom = "bar",fill=churn,position = "stack")

# flip plot by 90-degree
'adding coord_flip() will flip by 90 - degree'
qplot(region,data = Telecom_train,geom = "bar",fill=churn,position = "stack") + coord_flip()

## Churn_Prediction.ipynb

      
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                kr-shiveshwar
                / Churn_Prediction.ipynb
            
            
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              January 30, 2018 00:20
            
              
                Churn_Prediction_Telecom
              
          
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## ClusterAnalysisTelecom.ipynb

      
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                kr-shiveshwar
                / ClusterAnalysisTelecom.ipynb
            
            
              Created
              December 7, 2016 23:12
            
          
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	library(ggmap)
	library(readr)
	library(ggplot2)
	library(ggraph)

	load("eway_wo_zero_nodes")
	load("transx_wo_zero")
	load("pincodes_repository")

	pincodes_repository <- pincodes_repository1
	#####################################
	# BiVariate Analysis #
	#####################################
	qplot(income,age,data = Telecom_train,geom=c("point", "smooth"),color=churn)
	qplot(income,data = Telecom_train,geom="density")

	#as the data is skewed we will perform log transformation
	qplot(log(income),data = Telecom_train,geom="density")
	qplot(log(income),age,data = Telecom_train,geom=c("point", "smooth"),color=churn)
	# Histogram
	library(ggplot2)
	qplot(region,data = Telecom_train,geom = "bar",fill=factor(region))
	qplot(region,data = Telecom_train,geom = "bar",fill=churn,position = "stack")

	# flip plot by 90-degree
	'adding coord_flip() will flip by 90 - degree'
	qplot(region,data = Telecom_train,geom = "bar",fill=churn,position = "stack") + coord_flip()