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You will need to select two analytics environment such as SAS, R, Python, Watson Analytics JMP, Matlab, Tableau or any other environment which you want to use and has suitable advanced analytics capabilities. You MUST discuss the tools and the dataset you would like to use for the assessment with your tutor. You will need to teach yourself how to use the functionality of the technique you want to compare and contrast. (You cannot use MS Excel).

You will choose one Advanced Analytics capability from Artificial Intelligence, Machine Learning, Natural Language Processing, Predictive Analytics, Image Recognition and Analysis etc.

You will then develop your chosen environments to carry out the Advanced Analytics technique using your Big Data Source.You will produce a 6 page report (+/- 10 lines) in the Springer LNCS format (the page count starts at the beginning of the Introduction and finishes at the end of the Conclusions and Recommendations section, appendices are not included in the page count). The report will critically evaluate the capabilities of your two chosen Analytics Environments to perform the Advanced Analytics function you have chosen.

This section will introduce your project, the dataset you are analysing and its business context, and introduce and justify your choice of Analytics Technique, and your two Analytics Environments.This section will critically evaluate current knowledge relating to your chosen Analytics Technique, its purpose, an explanation of how it works, why it is used, its capabilities and limitations and the contribution that the technique could make in your chosen organisational context. You will also need to develop and justify the framework that you will use in the next section to critically evaluate your two environments.

This section will be based on practical applications of the chosen Analytics Technique in your two chosen environments, using your chosen source(s) of big data. It will compare and contrast the ease of use, the outputs from the two environments, the effectiveness of the analytics function and the two environments. This section will justify all the conclusions and recommendations that you present in the final section.

Features of Python

In recent era of development, we face different challenges related to automotive structure of systems. As our world undergoes in very fast pace, each and every objective and work needs perfection but human it barely performs such. So they go for programming and algorithm for doing such. In our everyday life, we see several intelligent applications in out computers and mobiles which include automated assistance, speech recognizer, face recognizer, map assistance and route director, product assistance, product comparison analyzer and many more (Dincer et al. 2017). We live in our world to make it beautiful and those are the inevitable requirements. Those applications are done using different programming languages as per the ability to write code and all.

Intelligent application or more specifically, Artificial Intelligence applications require something more than the conservative programming architecture. Thus, we have to choose such languages which meetthese criteria. Lots of Languages are available for this purpose like C, C++, Java, Python, Matlab, Ruby, PHP, R etc. Those have their separate library structure and different procedures. For AI applications, we should choose such languages which have enough rich library and wide variety of coding structure and should be human friendly. Thus much of the majority chose Python and R for this purpose (Volk et al. 2017).

Now first we see the features of those in a comparative manner.

Python was developed and introduced by Guido Van Rossum in 1991. It has rich backbone of C++. Python is a exclusive Object Oriented Language specially used for Scientific and Analytic purpose. Data visualization is very easy in Python and so that if data analysis and visualization comes in the scenario, we can perform such task using Python.

Features 

The important features and advantages of Python are (Antony et al. 2017)

  1. Python is Dynamically Typed Language means it does not require any variable to be assigned with its type rather those variable types are dynamically allocated.
  2. Python is Interpreted Language.
  3. The coding execution is very easy in Python
  4. Easy to read the codes and syntaxes.
  5. It is very expressive language as the syntax and codes are used mostly based on English meaningful phrases.
  6. Python is an Open-Source Language and all the packages need to be incorporated also they are open sources.
  7. This is a High Level OOP Language.
  8. It supports portability that is once you have written any code in any OS, this code can be run in any other platform or OS.
  9. It has an extensible facility of merging and transforming code to different languages like C, MATLAB etc.
  10. Python code support embedding into device for example Micro Python, Raspberry Pi.
  11. It has large standard library.
  12. GUI programming is much easier.
  • Applications:

Python is applicable in many areas like:

  • Numerical Computation
  • Scientific Computation
  • Machine Learning
  • Data Science
  • Data Mining
  • Web Development
  • Artificial Intelligence.

Another programming language of interest is R. R is basically and widely used for scientific and Statistical computations. It has a facility to have one of the richest libraries among all the languages (Nelson, 2016). As for the richness and wide availability, many of the data scientists and scientist of machine learning prefer R. R was introduced by Ross Ihaka and Robert Gentleman in 1993.

  • Features

The features of R are:

  1. This is a well-developed, simple and effective programming language.
  2. It has an effective data handling and storage facility,
  3. It provides a range of operators for calculations on arrays, lists, vectors and matrices.
  4. It provides a large, coherent and integrated collection of tools for data analysis.
  5. It provides GUI facility.
  • Applications

R is applicable in many different areas like:

  • Data Analysis
  • Case matching
  • Statistical Analysis
  • Online data mining
  • Face and Tag detection
  • Data Science
  • Machine Learning
  • Artificial Intelligence

Let we discuss about one of applications which can be done using both Python and R. One of the most demanding and hot topic for now a days is Machine Learning.

Features of R

Machine Learning

Machine Learning is a set of algorithms with which the programmer will assign some objective and which tends to learn the algorithm as per the requirement. Here program is not done explicitly rather the algorithm is made such that it capable of learning from the environment with being explicit definition of each cases (Prakash, 2015).Machine Learning uses different integration of issues like data, algorithm analysis tool and a platform to execute these. When the part of analysis comes into the scenario, it is inevitable to use the statistics. Statistics helps the algorithm to analyze data is depth with application of statistical calculations.

Here we will make the comparative discussion on the following topics with analysis and simulation based on both Python & R. The topics are

  • Analytic tool
  • Classification
  • Association
  • Correlation
  • Regression
  • Clustering
  • Anomaly Detection

Analytical tool stands for the analysis on a given data. The dataset is provided and we have to analysis these with Python & R and have again to compare.

Data analysis with Python:

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

file=pd.read_csv(‘//fruit.csv)

file.head()

name=file[‘Name’]

col=file[‘Color Score’]

puri=file[‘Purification’]

plt.title(‘Plotting Color Score and

Purification of Fruit’)

plt.plot(col)

plt.plot(puri)

Data analysis with R:

library(tidyverse)

ggplot(data = fruits) +

geom_bar(mapping =aes(x = cut))

ggplot(data = fruits) +

geom_histogram(mapping =aes(x = Purification), binwidth =0.5)

smaller <-fruits %>%

filter(Color Score’<0.5)

ggplot(data = smaller, mapping =aes(x = Purification)) +

geom_histogram(binwidth =0.1)

Output

In Python, while analyzing data, we have to use libraries like pandas and matplotlib. Pandas library is exclusive for data mining and data extraction whereas matplotlib is applicable for data visualization. With these two libraries we can easily analyze and visualize the data. In R, ggplotis the library basically employed for analysis and visualization purpose. For this execution, tidyverse library is required. The histogram plot defines the amount of data is available for which extent.

  • Classification

Classification is a technique or better to say a set of algorithms to classify data from an entire dataset. Several algorithms are available like Logistic Regression, Neive Bayas, Gradient Descent, K-Nearest Neighbor, Decision Tree, Random Forest etc. They can be implemented using python as well as R. Classification is the technique to classify the data with respect to some parameter (Moshfeq et al. 2017)It is actually a Supervised Learning where data is known to us. Here choose some parameter to separate and segmentize the data as per required and based on the dataset. Let we see the code structure.

Using Python:

import numpy as np

import pandas as pd

Machine Learning

import matplotlib.pyplot as plt

link="<path>.csv"

file=pd.read_csv(link)

file.head()

collist=file.columns.tolist()

hd1=np.array(file[collist[0]])

hd1u=np.unique(hd1)

print(hd1u)hd2=np.array(file[collist[1]])

hd2u=np.unique(hd2)

print(hd2u)

hd3=np.array(file[collist[2]])

hd3u=np.unique(hd3)

print(hd3u)

Burglary=[]

CriminalDamage=[]

Drugs=[]

FraudorForgery=[]

OtherNotifiableOffences=[]

Robbery=[]

SexualOffences=[]

TheftandHandling=[]

ViolenceAgainstthePerson=[]

for i in range(len(hd1)):

    if hd2u[0] == hd2[i]:

        Burglary.append(i)

    elif hd2u[1] == hd2[i]:

        CriminalDamage.append(i)

    elif hd2u[2] == hd2[i]:

        Drugs.append(i)

    elif hd2u[3] == hd2[i]:

        FraudorForgery.append(i)

    elif hd2u[4] == hd2[i]:

        OtherNotifiableOffences.append(i)

    elif hd2u[5] == hd2[i]:

        Robbery.append(i)

    elif hd2u[6] == hd2[i]:

        SexualOffences.append(i)

    elif hd2u[7] == hd2[i]:

        TheftandHandling.append(i)

    elif hd2u[8] == hd2[i]:

        ViolenceAgainstthePerson.append(i)

    else:

        pass

print("Burglary:n",Burglary,"n")

print("CriminalDamage:n",CriminalDamage,"n")

print("Drugs:n",Drugs,"n")

print("FraudorForgery:n",FraudorForgery,"n")

print("OtherNotifiableOffences:n",OtherNotifiableOffences,"n")

print("Robbery:n",Robbery,"n")

print("SexualOffences:n",SexualOffences,"n")

print("TheftandHandling:n",TheftandHandling,"n")

print("ViolenceAgainstthePerson:n",ViolenceAgainstthePerson,"n")

list1=file.iloc[Drugs[0]].tolist()[3:]

plt.figure(figsize=(30,10))

plt.title("Drugs Damage Comparative Analysis",fontsize=28)

for i in range(len(list1)-1):

    list3=list1+file.iloc[Drugs[i]].tolist()[3:]

plt.plot(list3,"r",label='Criminal Damage(2016-2018)')

plt.xlabel("Damage quantity")

plt.ylabel("Type of Damage")

plt.legend(loc='upper right',prop={'size': 16})

plt.grid()

Using R:

library(caret)

set.seed(7267166)

trainIndex=createDataPartition(mydata$prog, p=0.7)$Resample1

train=mydata[trainIndex, ]

test=mydata[-trainIndex, ]

print(table(mydata$prog))

print(table(train$prog))

library(e1071)   ## Classifier

NB=naiveBayes(prog~science+socst, data=train)

print(NB)

library(naivebayes)

newNB=naive_bayes(prog~sesf+science+socst,usekernel=T, data=train)

printALL(newNB)

Comparative Discussion:

In this classification issue, it was done using Naïve Bayes. This is the algorithm purely dependent upon Statistics. Python deals with it good but as R is specifically used for Statistical methods, R leads in this domain in terms of Code length and for the techniques applied and the algorithm implied in it (Saeid et al. 2017).

It is basically use to analyze the market issue. It includes the concept how a customer buys a product along with other subordinate products. If that are traceable, the owner of the market place will able to identify the associative nature of the product which are seem to be kept together for better sell (Cramer et al. 2017). It is again a statistical issue. So, here the suitable language will be R.

Using Python

import itertools"""prompt user to enter support and confidence values in percent"""

support = int(input("Please enter support value in %: "))

confidence = int(input("Please enter confidence value in %: "))

"""Compute candidate 1-itemset"""

C1 = {}

"""total number of transactions contained in the file"""

transactions = 0

D = []

T = []

with open("DataSet1.txt", "r") as f:

    for line in f:

        T = []

        transactions += 1

        for word in line.split():

            T.append(word)

            if word not in C1.keys():

                C1[word] = 1

            else:

                count = C1[word]

                C1[word] = count + 1

        D.append(T)

print ("-------------------------TEST DATASET----------------------------")

print (D)

print ("-----------------------------------------------------------------")

#prin t "--------------------CANDIDATE 1-ITEMSET------------------------- "

#print C1

#print "-----------------------------------------------------------------"

"""Compute frequent 1-itemset"""

L1 = []

for key in C1:

    if (100 * C1[key]/transactions) >= support:

        list = []

        list.append(key)

        L1.append(list)

print ("----------------------FREQUENT 1-ITEMSET-------------------------")

print (L1)

print ("-----------------------------------------------------------------")

"""apriori_gen function to compute candidate k-itemset, (Ck) , using frequent (k-1)-itemset, (Lk_1)"""

Analysis of Data using Python and R

def apriori_gen(Lk_1, k):

    length = k

    Ck = []

    for list1 in Lk_1:

        for list2 in Lk_1:

            count = 0

            c = []

            if list1 != list2:

                while count < length-1:

                    if list1[count] != list2[count]:

                        break

                    else:

                        count += 1

                else:

                    if list1[length-1] < list2[length-1]:

                        for item in list1:

                            c.append(item)

                        c.append(list2[length-1])

                        if not has_infrequent_subset(c, Lk_1, k):

                            Ck.append(c)

                            c = []

    return Ck

"""function to compute 'm' element subsets of a set S"""

def findsubsets(S,m):

    return set(itertools.combinations(S, m))

"""has_infrequent_subsets function to determine if pruning is required to remove unfruitful candidates (c) using the Apriori property, with prior knowledge of frequent (k-1)-itemset (Lk_1)"""

def has_infrequent_subset(c, Lk_1, k):

    list = []

    list = findsubsets(c,k)

    for item in list:

        s = []

        for l in item:

            s.append(l)

        s.sort()

        if s not in Lk_1:

            return True

    return False

"""frequent_itemsets function to compute all frequent itemsets"""

def frequent_itemsets():

    k = 2

    Lk_1 = []

    Lk = []

    L = []

    count = 0

    transactions = 0

    for item in L1:

        Lk_1.append(item)

    while Lk_1 != []:

        Ck = []

        Lk = []

        Ck = apriori_gen(Lk_1, k-1)

        #print "-------------------------CANDIDATE %d-ITEMSET---------------------" % k

        #print "Ck: %s" % Ck

        #print "------------------------------------------------------------------"

        for c in Ck:

            count = 0

            transactions = 0

            s = set(c)

            for T in D:

                transactions += 1

                t = set(T)

                if s.issubset(t) == True:

                    count += 1

            if (100 * count/transactions) >= support:

                c.sort()

                Lk.append(c)

        Lk_1 = []

        print ("-----------------------FREQUENT %d-ITEMSET------------------------" % k)

        print (Lk)

        print ("------------------------------------------------------------------")

        for l in Lk:

            Lk_1.append(l)

        k += 1

        if Lk != []:

            L.append(Lk)

"""generate_association_rules function to mine and print all the association rules with given support and confidence value"""

def generate_association_rules():

    s = []

    r = []

    length = 0

    count = 1

    inc1 = 0

    inc2 = 0

    num = 1

    m = []

    L= frequent_itemsets()

    print ("---------------------ASSOCIATION RULES------------------")

    print ("RULES t SUPPORT t CONFIDENCE")

    print ("--------------------------------------------------------")

    for list in L:

        for l in list:

            length = len(l)

            count = 1

            while count < length:

                s = []

                r = findsubsets(l,count)

                count += 1

                for item in r:

                    inc1 = 0

                    inc2 = 0

                    s = []

                    m = []

                    for i in item:

                        s.append(i)

                    for T in D:

                        if set(s).issubset(set(T)) == True:

                            inc1 += 1

                        if set(l).issubset(set(T)) == True:

                            inc2 += 1

                    if 100*inc2/inc1 >= confidence:

                        for index in l:

                            if index not in s:

                                m.append(index)

                        print ("Rule#  %d : %s ==> %s %d %d" %(num, s, m, 100*inc2/len(D), 100*inc2/inc1))

                        num += 1  

Using R

library(arules)

class(market)

inspect(head(market, 3))

size(head(market)) # number of items in each observation

LIST(head(market, 3))

freqtItem <- eclat (market, parameter = list(supp = 0.07, maxlen = 15))

inspect(freqtItem)

itemFreq(market, topN=10, type="absolute", main="Item Frequency")

Output

Starting order_item:               32434489

Items with support >= 0.01:           10906

Remaining order_item:              29843570

Remaining orders with 2+ items:     3013325

Remaining order_item:              29662716

Item pairs:                        30622410

Item pairs with support >= 0.01:      48751

CPU times: user 11min 19s, sys: 1min 46s, total: 13min 6s

Wall time: 13min 5s

Correlation

Correlation is one of the most widely used statistical concepts it providesa definitions and intuition behind several types of correlation and illustrate how to calculate correlation using the Python pandas library (Han et al. 2017).It is a mutually connected with association. Correlation means one data is connected or related with another data with how much degree of similarity. For a business purpose, It is mandatory to maintain the clarity between the pictures of customer type and the bought items. Correlation deals with such types of topics where statistics are essential.

Using Python

import pandas as pd

path =

mpg_data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data', delim_whitespace=True, header=None,names = ['mpg', 'cylinder', 'displacemnt','horsepwr','weight', 'acceleration', 'model_year', 'origin', 'name'],

            na_values='?')

mpg_data['mpg'].corr(mpg_data['weight'])

# pairwise correlation

mpg_data.drop(['model_year', 'origin'], axis=1).corr(method='sperman')

# plot correlated values

plt.rcParams['figure.figsize'] = [16, 6]

fig, ax = plt.subplots(nrws=1, ncls=3)

ax=ax.flatten()

cols = ['weight', 'horsepower', 'acceleration']

colors=['#415052', '#f33234', '#243AC5', '#2442B5']

j=0

for i in ax:

    if j==0:

        i.set_ylabel('MPG')

    i.scatter(mpg_data[cols[j]], mpg_data['mpg'],  alpha=0.5, color=colors[j])

    i.set_xlabel(cols[j])

    i.set_title('Pearson: %s'%mpg_data.corr().loc[cols[j]]['mpg'].round(2)+' Spearman: %s'%mpg_data.corr(method='spearman').loc[cols[j]]['mpg'].round(2))

    j+=

plt.show()

Using R

my_data <- read.csv(file.choose())

my_data <- mtcars

head(my_data, 6)

library("ggpubr")

ggscatter(my_data, x = "mpg", y = "wt",

          add = "reg.line", conf.int = TRUE,

          cor.coef = TRUE, cor.method = "pearson",

          xlab = "Miles/(US) gallon", ylab = "Weight (1000 lbs)")

library("ggpubr")

ggqqplot(my_data$mpg, ylab = "MPG")

ggqqplot(my_data$wt, ylab = "WT")

res <- cor.test(my_data$wt, my_data$mpg,

                    method = "pearson")

print(res)

Output

In the simulation of correlation and its output, we can see likewise the structure and methods involved in association, R leads because of the richer library. Statistical calculation are the lengthiest in programming because it requires thorough analysis. As R is richer in statistical methods and library, a few line of codes are required to execute a whole application. So execution time will be lesser.

Regression

It is the technique or algorithm to relate the input with target data set. Here the example is shown regarding linear model. Let we see the example.

Using Python

list1=file.iloc[CriminalDamage[0]].tolist()[3:]

plt.figure(figsize=(30,10))

plt.title("Criminal Damage Comparative Analysis",fontsize=28)

for i in range(len(list1)-1):

list4=list1+file.iloc[CriminalDamage[i]].tolist()[3:]

plt.plot(list4,"c",label='Criminal Damage(2016-2018)')

plt.xlabel("Damage quantity")

plt.ylabel("Type of Damage")

plt.legend(loc='upper right',prop={'size': 16})

plt.grid()

list1=file.iloc[FraudorForgery[0]].tolist()[3:]

plt.figure(figsize=(30,10))

plt.title("Fraudor Forgery Comparative Analysis",fontsize=28)

for i in range(len(list1)-1):

    list5=list1+file.iloc[CriminalDamage[i]].tolist()[3:]

plt.plot(list5,"c",label='Fraudor Forgery(2016-2018)')

plt.xlabel("Type of Frauder")

plt.ylabel("Frauder Count")

plt.legend(loc='upper right',prop={'size': 16})

plt.grid()

Using R

library(e1071)

par(mfrow=c(1, 2))  # divide graph area in 2 columns

plot(density(cars$speed), main="Density Plot: Speed", ylab="Frequency", sub=paste("Skewness:", round(e1071::skewness(cars$speed), 2)))  # density plot for 'speed'

polygon(density(cars$speed), col="red")

plot(density(cars$dist), main="Density Plot: Distance", ylab="Frequency", sub=paste("Skewness:", round(e1071::skewness(cars$dist), 2)))  # density plot for 'dist'

polygon(density(cars$dist), col="red")

par(mfrow=c(1, 2))  # divide graph area in 2 columns

boxplot(cars$speed, main="Speed", sub=paste("Outlier rows: ", boxplot.stats(cars$speed)$out))  # box plot for 'speed'

boxplot(cars$dist, main="Distance", sub=paste("Outlier rows: ", boxplot.stats(cars$dist)$out))

Output

Comparative Discussion:

In this operation, preferably statistic is not used or been barely used. So, we can use python as well as R in this domain of work.

Clustering

Clustering is an unsupervised learning where we have to segmentize data which are not known to us. It is basically used for web filtering or stock market analysis or such type of work where the data type may be unknown to us.

Using Python

-------------------------------Importing Packages-------------------------------------

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

get_ipython().run_line_magic('matplotlib', 'inline')

#-------------------------------Creating Data Frame-----------------------------------

df = pd.DataFrame({

    'x': [12, 20, 28, 18, 29, 33, 24, 45, 45, 52, 51, 52, 55, 53, 55, 61, 64, 69, 72,51,12,22,36,45,65,15,19,31,81,26],

    'y': [39, 36, 30, 52, 54, 46, 55, 59, 63, 70, 66, 63, 58, 23, 14, 8, 19, 7, 24,33,43,21,8,34,54,12,11,19,38,49]

})

plt.scatter(df['x'],df['y'])

--------------------------Creating & Assigning Centroid------------------------------

np.random.seed(200)

k = 3

 centroids[i] = [x, y]

centroids = {

 i+1: [np.random.randint(0, 80), np.random.randint(0, 80)]

 for i in range(k)

}

print(centroids)

list1=[]

list1=centroids[1]

list2=centroids[2]

list3=centroids[3]

plt.figure()

a=list1[0]

b=list1[1]

plt.plot(a,b,'*r',0.6)

plt.figure()

a=list2[0]

b=list2[1]

plt.plot(a,b,'*m',0.6)

plt.figure()

a=list3[0]

b=list3[1]

plt.plot(a,b,'*c',0.6)

#-------------------------------Plotting of points(seed)-----------------------------    

fig = plt.figure(figsize=(5, 5))

plt.scatter(df['x'], df['y'], color='k')

colmap = {1: 'r', 2: 'g', 3: 'b'}

for i in centroids.keys():

    plt.scatter(*centroids[i], color=colmap[i])

plt.xlim(0, 80)

plt.ylim(0, 80)

plt.show()

#------------------------------ Centroid Assignment Stage(Euclidian Distance) ---------------------------

def assignment(df, centroids):

    for i in centroids.keys():

        # sqrt((x1 - x2)^2 - (y1 - y2)^2)

        df['distance_from_{}'.format(i)] = (

            np.sqrt(

                (df['x'] - centroids[i][0]) ** 2

                + (df['y'] - centroids[i][1]) ** 2

    centroid_distance_cols = ['distance_from_{}'.format(i) for i in centroids.keys()]

    df['closest'] = df.loc[:, centroid_distance_cols].idxmin(axis=1)

    df['closest'] = df['closest'].map(lambda x: int(x.lstrip('distance_from_')))

    df['color'] = df['closest'].map(lambda x: colmap[x])

    return dfds with Centroid-------------------------

fig = plt.figure(figsize=(5, 5))

plt.scatter(df['x'], df['y'], color=df['color'], alpha=0.5, edgecolor='k')

for i in centroids.keys():

    plt.scatter(*centroids[i], color=colmap[i])

plt.xlim(0, 80)

plt.ylim(0, 80)

plt.show()---

def update(k):

    for i in centroids.keys():

        centroids[i][0] = np.mean(df[df['closest'] == i]['x'])

        centroids[i][1] = np.mean(df[df['closest'] == i]['y'])

    return k

centroids = update(centroids)

#--------------------------------Plot updated seeds in cluster form--------------------    

fig = plt.figure(figsize=(5, 5))

ax = plt.axes()

plt.scatter(df['x'], df['y'], color=df['color'], alpha=0.5, edgecolor='k')

for i in centroids.keys():

    plt.scatter(*centroids[i], color=colmap[i])

plt.xlim(0, 80)

plt.ylim(0, 80)

for i in old_centroids.keys():

    old_x = old_centroids[i][0]

    old_y = old_centroids[i][1]

    dx = (centroids[i][0] - old_centroids[i][0]) * 0.75

    dy = (centroids[i][1] - old_centroids[i][1]) * 0.75

    ax.arrow(old_x, old_y, dx, dy, head_width=2, head_length=3, fc=colmap[i], ec=colmap[i])

plt.show()

Using R

library(VIM)

data14$Date.Time <- mdy_hms(data14$Date.Time)

data14$Year <- factor(year(data14$Date.Time))

data14$Month <- factor(month(data14$Date.Time))

data14$Day <- factor(day(data14$Date.Time))

data14$Weekday <- factor(wday(data14$Date.Time))

data14$Hour <- factor(hour(data14$Date.Time))

data14$Minute <- factor(minute(data14$Date.Time))

data14$Second <- factor(second(data14$Date.Time))

set.seed(10)

clusters <- kmeans(data14[,2:3], 5)

library(ggmap)

NYCMap <- get_map("New York", zoom = 10)

ggmap(NYCMap) + geom_point(aes(x = Lon[], y = Lat[], colour = as.factor(Borough)),data = data14) +

  ggtitle("NYC Boroughs using KMean")

Comparative Discussion:

In this operation, preferably statistic is not used or been barely used. So, we can use python as well as R in this domain of work. But if there is a necessity of using the statistical concept in it, we should go for R rather by using Python.

Anomaly Detection

This is type of outlier detection where in the data set , may be known or unknown to us, some parameters are changed suspiciously. This is one of the important aspect of our consideration because those parameters are need to know to catch the entire simulation process and for future aspects.

Using Python

import matplotlib.pyplot as plt

import numpy as np

%matplotlib inline

from numpy import genfromtxt

from scipy.stats import multivariate_normal

from sklearn.metrics import f1_score

def selectThresholdByCV(probs,gt):

    best_epsilon = 0

    best_f1 = 0

    f = 0

    stepsize = (max(probs) - min(probs)) / 1000;

    epsilons = np.arange(min(probs),max(probs),stepsize)

    for epsilon in np.nditer(epsilons):

        predictions = (probs < epsilon)

        f = f1_score(gt, predictions, average = "binary")

        if f > best_f1:

            best_f1 = f

            best_epsilon = epsilon

    return best_f1, best_epsilon

tr_data = read_dataset('tr_server_data.csv')

cv_data = read_dataset('cv_server_data.csv')

gt_data = read_dataset('gt_server_data.csv')

n_training_samples = tr_data.shape[0]

n_dim = tr_data.shape[1]

plt.figure()

plt.xlabel("Latency (ms)")

plt.ylabel("Throughput (mb/s)")

plt.plot(tr_data[:,0],tr_data[:,1],"bx")

plt.show()

mu, sigma = estimateGaussian(tr_data)

p = multivariateGaussian(tr_data,mu,sigma)

p_cv = multivariateGaussian(cv_data,mu,sigma)

fscore, ep = selectThresholdByCV(p_cv,gt_data)

outliers = np.asarray(np.where(p < ep))

plt.figure()

plt.xlabel("Latency (ms)")

plt.ylabel("Throughput (mb/s)")

plt.plot(tr_data[:,0],tr_data[:,1],"bx") plt.plot(tr_data[outliers,0],tr_data[outliers,1],"ro")

plt.show()

Comparative Discussion:

In anomaly detection, statistical analysis is essentially required. In the example program the simulation of bitcoin is specie which one of the most hot topic on which we can concentrate because there are many parameters available for which the demand of bitcoin goes ups and down.

Reference list

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