Description

suraj gadapa
library(recommenderlab) #for recommendation
## Loading required package: Matrix
## Loading required package: arules
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## Attaching package: ’arules’
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## abbreviate, write
## Loading required package: proxy
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## as.matrix
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## as.dist, dist
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## as.matrix
## Loading required package: registry
## Registered S3 methods overwritten by ’registry’:
## method from
## print.registry_field proxy
## print.registry_entry proxy
library(reshape2) library(data.table)
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## Attaching package: ’data.table’
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## dcast, melt
library(ggplot2)
#retrieving the data
movie_data<-read.csv(“C:/Users/suraj/Downloads/archive (3)/imdb_top_1000.csv”, str(movie_data)
stringsAsFactors = FALSE)
## $ Overview : chr “Two imprisoned men bond over a number of years, finding solace and eventual r
## $ Meta_score : int 80 100 84 90 96 94 94 94 74 66 …
## $ Director : chr “Frank Darabont” “Francis Ford Coppola” “Christopher Nolan” “Francis Ford Copp
## $ Star1 : chr “Tim Robbins” “Marlon Brando” “Christian Bale” “Al Pacino” …
## $ Star2 : chr “Morgan Freeman” “Al Pacino” “Heath Ledger” “Robert De Niro” …
## $ Star3 : chr “Bob Gunton” “James Caan” “Aaron Eckhart” “Robert Duvall” …
## $ Star4 : chr “William Sadler” “Diane Keaton” “Michael Caine” “Diane Keaton” …
## ’data.frame’: 1000 obs. of 16 variables:
## $ Poster_Link : chr “https://m.media-amazon.com/images/M/MV5BMDFkYTc0MGEtZmNhMC00ZDIzLWFmNTEtODM1Z
## $ Series_Title : chr “The Shawshank Redemption” “The Godfather” “The Dark Knight” “The Godfather: P ## $ Released_Year: chr “1994” “1972” “2008” “1974” …
## $ Certificate : chr “A” “A” “UA” “A” …
## $ Runtime : chr “142 min” “175 min” “152 min” “202 min” …
## $ Genre : chr “Drama” “Crime, Drama” “Action, Crime, Drama” “Crime, Drama” … ## $ IMDB_Rating : num 9.3 9.2 9 9 9 8.9 8.9 8.9 8.8 8.8 …
## $ No_of_Votes : int 2343110 1620367 2303232 1129952 689845 1642758 1826188 1213505 2067042 1854740 ## $ Gross : chr “28,341,469” “134,966,411” “534,858,444” “57,300,000” …
md<- na.omit(movie_data) #gives the data after removing the missing values.
summary(md)
## Poster_Link Series_Title Released_Year Certificate
## Length:843 Length:843 Length:843 Length:843
## Class :character Class :character Class :character Class :character
## Mode :character
##
##
## Mode :character Mode :character Mode :character
## Runtime Genre IMDB_Rating Overview
## Length:843 Length:843 Min. :7.600 Length:843
## Class :character Class :character 1st Qu.:7.700 Class :character
## Mode :character Mode :character Median :7.900 Mode :character
## Mean :7.932
## 3rd Qu.:8.100
## Max. :9.300
## Meta_score Director Star1 Star2
## Min. : 28.00 Length:843 Length:843 Length:843
## 1st Qu.: 70.00 Class :character Class :character Class :character
## Median : 79.00
## Mean : 77.97
## 3rd Qu.: 87.00
## Max. :100.00 Mode :character Mode :character Mode :character
## Star3 Star4 No_of_Votes Gross
## Length:843 Length:843 Min. : 25198 Length:843
## Class :character Class :character 1st Qu.: 71024 Class :character
## Mode :character Mode :character Median : 184966 Mode :character
## Mean : 313187
## 3rd Qu.: 439631
## Max. :2343110
sd(md$IMDB_Rating)
## [1] 0.2837322
sd(md$Meta_score)
## [1] 12.3761
sd(md$No_of_Votes)
## [1] 341798.8
IQR(md$IMDB_Rating)
## [1] 0.4
IQR(md$Meta_score)
## [1] 17
IQR(md$No_of_Votes)
## [1] 368607.5
rating<-table(md$IMDB_Rating)
barplot(rating,main=”movie rating comaprision”,ylab=”number”, xlab=”rating”,col=(“lightblue”),legend=rownames(rating))
movie rating comaprision

certificate_pie<-table(md$Certificate) library(lessR)
##
## lessR 4.2.4 feedback: gerbing@pdx.edu
## ————————————————————–
## > d <- Read(“”) Read text, Excel, SPSS, SAS, or R data file
## d is default data frame, data= in analysis routines optional ##
## Learn about reading, writing, and manipulating data, graphics,
## testing means and proportions, regression, factor analysis,
## customization, and descriptive statistics from pivot tables.
## Enter: browseVignettes(“lessR”)
##
## View changes in this and recent versions of lessR.
## Enter: news(package=”lessR”)
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## **New Feature**: Interactive analysis of your data
## Enter: interact()
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## Attaching package: ’lessR’
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## set
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## recode
PieChart(certificate_pie,hole = 0,values=”%”,data=md$Certificate,color = rainbow(2),main=” certificiatio
## >>> Note: certificate_pie is not in a data frame (table)
## >>> Note: certificate_pie is not in a data frame (table)
certificiation movie types
R
## >>> suggestions
## piechart(certificate_pie, hole=0) # traditional pie chart
## piechart(certificate_pie, values=”%”) # display %’s on the chart
## piechart(certificate_pie) # bar chart
## plot(certificate_pie) # bubble plot
## plot(certificate_pie, values=”count”) # lollipop plot
##
## — certificate_pie —
##
## certificate_p Count Prop
## ———————-
## 56 0.066
## 16 1 0.001
## A 187 0.222
## Approved 9 0.011
## G 10 0.012
## GP 1 0.001
## Passed 18 0.021
## PG 30 0.036
## PG-13 42 0.050
## R 138 0.164
## TV-PG 3 0.004
## U 202 0.240
## U/A 1 0.001
## UA 145 0.172
## ———————-
## Total 843 1.000
##
## Chi-squared test of null hypothesis of equal probabilities
## Chisq = 1185.738, df = 13, p-value = 0.000
library(plotrix)
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## rescale
lab<-paste0(round(certificate_pie/sum(certificate_pie)*100,2),”%”) pie3D(certificate_pie,col= rainbow(2),labels=lab ,labelcex = 0.75,explode = 0.1)

hist(md$IMDB_Rating, col = “BLUE”, xlab = ” rating”,ylab = “frequency”,labels = TRUE)
Histogram of md$IMDB_Rating

207

129
55

34
6

400
1 1

8.0 8.5 9.0
rating
boxplot(md$IMDB_Rating, col = “red”, border = “black”,main=”boxplot for descriptive analytics of IMDB ra
boxplot for descriptive analytics of IMDB rating

hist(md$Meta_score, col = “BLUE”, xlab = “meta score”,ylab = “frequency”,labels = TRUE)
Histogram of md$Meta_score

meta score
plot(density(md$IMDB_Rating), col=”green”, main=”IMDB rating”, xlab=”rating”, ylab=”density”)
polygon(density(md$IMDB_Rating),col = “black”)
IMDB rating

rating
plot(density(md$Meta_score), col=”green”, main=”density plot based on score”, xlab=”meta score”, ylab=”density”)
polygon(density(md$Meta_score),col = “blue”)
density plot based on score

20 40 60 80 100
meta score
library(factoextra)
## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa
md1<-na.omit(md)
library(dplyr)
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## Attaching package: ’dplyr’
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## recode, rename
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## intersect, recode, setdiff, setequal, union
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md2<-select_if(md1,is.numeric)
library(dplyr)
fviz_nbclust(md2,kmeans,method=”wss”)+labs(subtitle = “elbow method”)
Optimal number of clusters

1 2 3 4 5 6 7 8 9 10
Number of clusters k
library(purrr)
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## Attaching package: ’purrr’
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## transpose
set.seed(123)
#functionto calculate total intra-cluster sum of squares(euclidean distance) ics<-function(k){ kmeans(md2[,1:3],k,iter.max =100,nstart = 100,algorithm = “Lloyd” )$tot.withinss
} k_values<-1:3 ics_values<-map_dbl(k_values,ics)
plot(k_values,ics_values, type=”b”,pch=19,frame=FALSE, xlab=”no of clusters”, ylab=”total instra-clusters sum of squares”
)
1.0 1.5 2.0 2.5 3.0
no of clusters
library(cluster) library(gridExtra)
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## Attaching package: ’gridExtra’
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## combine
library(grid) k1<-kmeans(md2[,1:3],2,iter.max=100,nstart=50,algorithm=”Lloyd”) s1<-plot(silhouette(k1$cluster,dist(md2[,1:3],”euclidean”)))
Silhouette plot of (x = k1$cluster, dist = dist(md2[, 1:3], “euclid

Silhouette width si Average silhouette width : 0.7
k2<-kmeans(md2[,1:3],3 ,iter.max = 100,nstart = 50,algorithm = “Lloyd”) s2<-plot(silhouette(k2$cluster,dist(md2[,1:3],”euclidean”)))
Silhouette plot of (x = k2$cluster, dist = dist(md2[, 1:3], “euclid

Silhouette width si
Average silhouette width : 0.67
#we make use of the fviz_nbcluster() function #to determine and visualize the optimal number of cluster
library(NbClust) library(factoextra) fviz_nbclust(md2[,1:3], kmeans, method = “silhouette”)+ labs(subtitle = “Silhouette method”)
Optimal number of clusters

#gap statastic method
set.seed(123) stat_gap<-clusGap(md2[,1:3],FUN=kmeans,nstart=25,K.max = 10,B=50) fviz_gap_stat(stat_gap)
Optimal number of clusters

#select cluster2
k2
## K-means clustering with 3 clusters of sizes 62, 557, 224
##
## Cluster means:
## IMDB_Rating Meta_score No_of_Votes
## 1 8.451613 78.37097 1220051.7
## 2 7.854937 78.42370 122482.7
## 3 7.978571 76.73661 536387.0
## ## Clustering vector:
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
## 1 1 1 1 3 1 1 1 1 1 1 1 3 1 1 1
## 17 18 19 20 22 23 24 25 26 27 28 29 30 31 32 33
## 1 1 2 3 1 3 3 1 1 3 1 1 1 2 2 3
## 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
## 1 3 3 1 1 3 1 1 1 1 1 1 2 2 1 2
## 50 51 52 53 54 57 59 60 61 62 63 64 65 67 68 69
## 3 3 2 2 2 2 3 3 3 3 1 1 3 1 3 3
## 70 71 73 74 75 76 79 81 82 85 89 90 91 94 95 96
## 1 3 1 1 3 3 3 2 3 3 2 2 2 1 1 3
## 97 98 99 100 101 102 103 104 105 107 108 109 110 111 112 113
## 3 3 1 3 2 1 1 1 3 3 3 3 1 2 3 2
## 114 115 116 117 118 119 120 121 124 125 127 132 133 136 139 141
## 3 3 2 2 2 2 3 2 2 3 2 3 3 2 2 2 ## 144 145 146 147 148 149 150 151 152 153 156 158 159 160 162 163
## 2 3 1 1 1 2 3 3 3 1 1 3 3 3 3 3
## 165 166 168 169 171 172 173 174 179 180 181 183 184 185 186 187
## 3 3 3 3 2 3 2 2 3 2 2 2 2 2 2 2
## 188 190 191 192 193 196 198 200 201 203 204 205 206 207 210 211
## 2 2 2 2 2 2 2 2 2 3 3 2 2 2 3 3
## 212 213 214 216 217 218 219 220 223 224 226 227 228 231 232 233
## 2 3 3 3 3 2 3 2 3 1 3 3 2 3 3 3
## 235 236 237 238 240 242 243 244 245 246 248 249 250 251 252 253
## 3 3 2 2 2 1 1 3 2 3 2 1 2 1 2 3
## 254 255 256 260 261 262 263 264 267 268 269 270 271 272 273 275
## 2 3 3 2 2 2 3 2 3 3 3 2 2 3 2 2
## 276 277 278 279 280 281 282 284 285 287 289 290 292 294 296 299
## 3 2 3 2 3 2 2 2 2 2 2 2 2 2 2 2
## 301 305 306 307 308 310 312 313 314 315 317 319 324 328 329 330
## 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 3
## 331 333 334 335 337 338 339 340 341 342 343 344 345 346 347 348
## 3 2 2 2 2 2 3 1 3 3 3 3 3 2 3 3
## 349 350 352 353 355 356 357 358 359 360 361 362 363 364 365 366
## 1 2 2 2 3 3 2 1 2 3 3 3 3 2 3 2
## 368 369 370 371 372 373 374 375 376 377 378 379 381 383 384 385
## 3 3 3 2 2 2 2 2 2 1 3 3 2 3 2 2
## 386 388 389 390 392 393 394 395 396 397 398 399 400 401 402 403
## 2 2 2 2 2 2 3 2 2 3 2 2 3 2 3 2
## 404 405 407 408 409 410 411 412 413 414 415 416 417 418 419 420
## 2 3 3 2 2 2 3 2 2 2 2 3 2 2 2 3
## 422 423 424 425 426 427 428 429 430 431 432 433 435 436 437 438
## 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 439 440 441 442 443 446 447 448 451 453 455 457 458 459 461 462
## 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2
## 463 464 466 467 468 469 470 471 472 473 474 475 476 477 478 479
## 2 3 2 2 2 2 2 2 2 2 3 3 2 3 3 2
## 480 481 482 483 484 485 486 487 488 489 490 492 493 494 495 497
## 3 2 2 3 2 2 2 3 2 3 2 3 2 3 3 2
## 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513
## 2 3 2 2 2 1 3 2 3 3 2 2 2 2 3 2
## 514 515 516 517 518 519 520 521 522 524 525 526 527 528 529 530
## 3 2 2 3 2 2 2 2 2 2 3 2 2 2 2 2
## 531 532 533 534 535 536 537 538 540 542 543 544 545 547 548 549
## 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 550 554 555 556 557 559 560 563 565 567 568 570 572 573 574 576
## 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2
## 577 579 581 582 583 584 585 586 587 588 589 590 591 593 594 596
## 2 2 2 2 3 3 3 2 2 2 2 2 2 3 2 2
## 597 598 599 600 601 603 604 605 607 608 609 610 611 612 613 614
## 2 2 2 2 3 2 3 3 2 2 2 3 2 2 2 2
## 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630
## 3 3 2 2 3 2 2 3 3 1 2 2 3 3 3 2
## 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646
## 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2
## 647 648 649 650 651 652 653 655 656 657 658 659 660 661 662 663
## 2 3 2 2 2 3 1 2 3 2 2 2 2 2 2 2
## 664 665 668 669 670 671 672 673 674 675 676 677 678 679 680 681
## 2 2 2 2 2 2 2 2 2 2 3 2 3 2 2 2 ## 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697
## 2 2 3 2 3 2 2 3 2 2 2 2 2 2 2 2
## 698 699 700 701 702 703 704 705 706 707 708 713 714 715 716 717
## 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733
## 2 2 2 2 2 3 2 3 2 2 2 2 3 3 3 2
## 734 735 736 737 738 740 741 742 743 744 745 746 747 748 749 750
## 3 2 2 2 3 2 3 2 2 3 2 3 3 2 3 3
## 751 752 753 754 755 756 757 758 759 760 761 764 765 766 767 768
## 3 3 3 3 3 3 2 2 2 2 2 2 3 2 2 3
## 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784
## 2 2 2 3 2 2 2 3 2 3 3 2 3 3 3 2
## 785 786 787 788 789 790 791 792 793 794 795 796 797 798 800 801
## 3 2 2 2 2 2 3 2 2 2 2 3 2 2 2 2
## 802 803 804 806 807 808 809 810 811 812 814 815 816 817 818 819
## 2 3 2 2 2 3 2 2 2 2 2 2 2 2 2 2
## 820 821 822 823 824 825 826 827 828 829 831 832 833 834 835 836
## 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 837 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853
## 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 854 855 856 857 859 860 861 862 863 864 866 867 868 869 870 871
## 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 874 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890
## 2 2 2 2 2 3 2 2 2 2 2 2 2 3 3 2
## 891 892 893 894 895 896 897 898 899 901 902 903 904 905 906 907
## 2 2 2 3 2 2 2 2 3 2 2 2 3 2 2 3
## 908 909 911 912 913 915 916 917 918 919 920 921 922 923 924 925
## 2 3 3 2 3 3 2 2 2 2 2 2 3 2 2 2
## 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941
## 3 2 3 3 2 3 2 3 2 2 2 2 2 2 2 2
## 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957
## 2 3 3 2 2 2 3 3 2 2 3 2 3 2 2 2
## 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973
## 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2 2
## 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989
## 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 990 991 992 993 994 995 996 997 998 999 1000
## 2 2 2 2 2 2 2 2 2 2 2
## ## Within cluster sum of squares by cluster:
## [1] 7418707707912 3814453102634 4729359642907
## (between_SS / total_SS = 83.8 %)
## ## Available components:
##
## [1] “cluster” “centers” “totss” “withinss” “tot.withinss”
## [6] “betweenss” “size” “iter” “ifault”
pcluster<-prcomp(md2[,1:3],scale. = FALSE) summary(pcluster)
## Importance of components:
## PC1 PC2 PC3
## Standard deviation 341799 12.37 0.2157
## Proportion of Variance 1 0.00 0.0000
## Cumulative Proportion 1 1.00 1.0000
pcluster$rotation[,1:2]
## PC1 PC2
## IMDB_Rating 0.0000004869986 -0.0064092136358
## Meta_score -0.0000006701138 -0.9999794607791
## No_of_Votes 0.9999999999997 -0.0000006669787
set.seed(123) ggplot(md2,aes(x=IMDB_Rating,y=No_of_Votes))+ geom_point(stat=”identity”,aes(color=as.factor(k2$cluster)))+ scale_color_discrete(name=” “,breaks=c(“1″,”2″,”3”), labels=c(“cluster1″,”cluster2″,”cluster3”), ggtitle(
“segments of demographics”,su

set.seed(123)
ggplot(md2,aes(x=IMDB_Rating,y=No_of_Votes))+ geom_point(stat=”identity”,aes(color=as.factor(k2$cluster)))+ scale_color_discrete(name=” “,breaks=c(“1″,”2″,”3”), labels=c(“cluster1″,”cluster2″,”cluster3”), ggtitle(
“segments of demographics”,su

kcols=function(vec){cols=rainbow(length(unique(vec))) return (cols[as.numeric(as.factor(vec))])} digCluster<-k2$cluster; dignm<-as.character(digCluster); plot(pcluster$x[,1:2],col=kcols(digCluster),pch=19,xlab=”kmeans”,ylab=”classes”) legend(“bottomleft”,unique(dignm),fill = unique(kcols(digCluster)))

kmeans