PROJECT TITLE
Business Analytics Honors - Final Project
TEAM NAME
DATE_HERE
About this Project
Brief description of your project.
Resources: Packages Use on this notebook
- watson
- tableau
Install required packages
- Package: tidyverse, corrplot
# Here we are checking if the package is installed
if(!require("tidyverse")){
# If the package is not in the system then it will be install
install.packages("tidyverse", dependencies = TRUE)
# Here we are loading the package
library("tidyverse")
}
# Here we are checking if the package is installed
if(!require("corrplot")){
# If the package is not in the system then it will be install
install.packages("corrplot", dependencies = TRUE)
# Here we are loading the package
library("corrplot")
}
Data Collection: INDUSTRY - TITLE_OF_YOUR_DATASET
Read the csv file into R Studio and explore the dataset (extract variables)
summary(mydata)
title genres director actor1
Length:5043 Length:5043 Length:5043 Length:5043
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
actor2 actor3 length budget
Length:5043 Length:5043 Min. : 7.0 Min. :2.180e+02
Class :character Class :character 1st Qu.: 93.0 1st Qu.:6.000e+06
Mode :character Mode :character Median :103.0 Median :2.000e+07
Mean :107.2 Mean :3.510e+07
3rd Qu.:118.0 3rd Qu.:4.400e+07
Max. :511.0 Max. :2.128e+09
NA's :15 NA's :496
director_fb_likes actor1_fb_likes actor2_fb_likes actor3_fb_likes
Min. : 0.0 Min. : 0 Min. : 0 Min. : 0.0
1st Qu.: 7.0 1st Qu.: 614 1st Qu.: 281 1st Qu.: 133.0
Median : 49.0 Median : 988 Median : 595 Median : 371.5
Mean : 686.5 Mean : 6560 Mean : 1652 Mean : 645.0
3rd Qu.: 194.5 3rd Qu.: 11000 3rd Qu.: 918 3rd Qu.: 636.0
Max. :23000.0 Max. :640000 Max. :137000 Max. :23000.0
NA's :104 NA's :7 NA's :13 NA's :23
total_cast_likes fb_likes critic_reviews users_reviews
Min. : 0 Min. : 0 Min. : 1.0 Min. : 1.0
1st Qu.: 1411 1st Qu.: 0 1st Qu.: 50.0 1st Qu.: 65.0
Median : 3090 Median : 166 Median :110.0 Median : 156.0
Mean : 9699 Mean : 7526 Mean :140.2 Mean : 272.8
3rd Qu.: 13756 3rd Qu.: 3000 3rd Qu.:195.0 3rd Qu.: 326.0
Max. :656730 Max. :349000 Max. :813.0 Max. :5060.0
NA's :50 NA's :21
users_votes score aspect_ratio gross
Min. : 5 Min. :1.600 Min. : 1.18 Min. : 162
1st Qu.: 8594 1st Qu.:5.800 1st Qu.: 1.85 1st Qu.: 5340988
Median : 34359 Median :6.600 Median : 2.35 Median : 25517500
Mean : 83668 Mean :6.442 Mean : 2.22 Mean : 48468408
3rd Qu.: 96309 3rd Qu.:7.200 3rd Qu.: 2.35 3rd Qu.: 62309438
Max. :1689764 Max. :9.500 Max. :16.00 Max. :760505847
NA's :329 NA's :884
year genres_count
Min. :1916 Min. :1.000
1st Qu.:1999 1st Qu.:2.000
Median :2005 Median :3.000
Mean :2002 Mean :2.853
3rd Qu.:2011 3rd Qu.:4.000
Max. :2016 Max. :8.000
NA's :108 ## Data Preparation: Cleaning and preparing the data for analysis
Replace strings
lapply(genres[1:5], length) %>% unlist()
[1] 4 3 3 2 1doc_true[1:10]
[1] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSESave the new clean data
write_csv( newdata, "data/clean_data.csv")Data Analysis: Descriptive Statistics, Correlations
Basic descriptive statistics of the dataset (write down any observations)
summary(newdata)
title genres director actor1
Length:49 Length:49 Length:49 Length:49
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
actor2 actor3 length budget
Length:49 Length:49 Min. : 41.00 Min. : 218
Class :character Class :character 1st Qu.: 89.00 1st Qu.: 700000
Mode :character Mode :character Median : 93.00 Median : 2000000
Mean : 99.04 Mean : 5254317
3rd Qu.:106.00 3rd Qu.: 5000000
Max. :215.00 Max. :60000000
director_fb_likes actor1_fb_likes actor2_fb_likes actor3_fb_likes
Min. : 0.0 Min. : 0.0 Min. : 0.0 Min. : 0.00
1st Qu.: 9.0 1st Qu.: 56.0 1st Qu.: 7.0 1st Qu.: 0.00
Median : 23.0 Median : 191.0 Median : 44.0 Median : 13.00
Mean : 153.2 Mean : 602.6 Mean :156.6 Mean : 81.49
3rd Qu.: 120.0 3rd Qu.: 773.0 3rd Qu.:208.0 3rd Qu.: 67.00
Max. :1000.0 Max. :11000.0 Max. :892.0 Max. :748.00
total_cast_likes fb_likes critic_reviews users_reviews
Min. : 0.0 Min. : 0 Min. : 4.00 Min. : 4.0
1st Qu.: 68.0 1st Qu.: 0 1st Qu.: 30.00 1st Qu.: 31.0
Median : 338.0 Median : 0 Median : 72.00 Median : 69.0
Mean : 946.3 Mean : 3212 Mean : 92.82 Mean : 143.8
3rd Qu.: 1252.0 3rd Qu.: 588 3rd Qu.:123.00 3rd Qu.: 134.0
Max. :11218.0 Max. :62000 Max. :288.00 Max. :1416.0
users_votes score aspect_ratio gross
Min. : 131 Min. :1.60 Min. : 1.330 Min. : 1111
1st Qu.: 2482 1st Qu.:6.70 1st Qu.: 1.780 1st Qu.: 592014
Median : 7721 Median :7.40 Median : 1.850 Median : 3713002
Mean : 21676 Mean :6.98 Mean : 2.133 Mean : 17885042
3rd Qu.: 23836 3rd Qu.:7.70 3rd Qu.: 1.850 3rd Qu.: 21244913
Max. :123090 Max. :8.50 Max. :16.000 Max. :119078393
year genres_count
Min. :1970 Min. :1
1st Qu.:2004 1st Qu.:1
Median :2007 Median :1
Mean :2006 Mean :1
3rd Qu.:2010 3rd Qu.:1
Max. :2013 Max. :1 sum(newdata$director_fb_likes,newdata$actor1_fb_likes[1], newdata$total_cast_likes[1])
[1] 7726Correlation table ( only numeric data )
data_corr
length budget director_fb_likes actor1_fb_likes
length 1.00000000 0.07155201 0.16101077 -0.10143280
budget 0.07155201 1.00000000 0.05289809 0.06737129
director_fb_likes 0.16101077 0.05289809 1.00000000 0.02609618
actor1_fb_likes -0.10143280 0.06737129 0.02609618 1.00000000
actor2_fb_likes 0.09005790 0.12778240 0.35184349 0.17359325
actor3_fb_likes 0.12845460 0.07481820 0.26296159 0.14794872
total_cast_likes -0.05884588 0.10133039 0.11683803 0.95142980
fb_likes 0.07960336 0.03894341 0.04583538 -0.05343623
critic_reviews 0.12889690 0.17629284 0.47632480 0.15000723
users_reviews 0.20296368 0.07294848 0.62394343 0.15510807
users_votes 0.21743822 0.14351307 0.58671621 0.15475257
score 0.11019499 -0.10990129 0.11807291 -0.02906095
aspect_ratio -0.02948317 -0.05001503 -0.08010282 -0.01951027
gross 0.14669378 0.47931328 0.25540764 0.35986081
year -0.67426761 0.17327838 -0.15972997 -0.02006917
actor2_fb_likes actor3_fb_likes total_cast_likes fb_likes
length 0.090057898 0.12845460 -0.05884588 0.07960336
budget 0.127782398 0.07481820 0.10133039 0.03894341
director_fb_likes 0.351843492 0.26296159 0.11683803 0.04583538
actor1_fb_likes 0.173593252 0.14794872 0.95142980 -0.05343623
actor2_fb_likes 1.000000000 0.83002137 0.44505783 -0.15045265
actor3_fb_likes 0.830021372 1.00000000 0.43361069 -0.11901935
total_cast_likes 0.445057828 0.43361069 1.00000000 -0.09193344
fb_likes -0.150452655 -0.11901935 -0.09193344 1.00000000
critic_reviews 0.134061968 0.12619893 0.18491380 0.17762914
users_reviews 0.201427943 0.19625651 0.20508101 0.07557735
users_votes 0.176643997 0.17002324 0.20628669 0.28102803
score -0.157769992 -0.24756783 -0.09436891 -0.35490438
aspect_ratio 0.008564091 -0.01889250 -0.02383234 -0.02995331
gross 0.290829251 0.30754525 0.43816420 0.15681634
year -0.011731671 -0.00547031 -0.01419498 0.19141490
critic_reviews users_reviews users_votes score
length 0.12889690 0.20296368 0.21743822 0.11019499
budget 0.17629284 0.07294848 0.14351307 -0.10990129
director_fb_likes 0.47632480 0.62394343 0.58671621 0.11807291
actor1_fb_likes 0.15000723 0.15510807 0.15475257 -0.02906095
actor2_fb_likes 0.13406197 0.20142794 0.17664400 -0.15776999
actor3_fb_likes 0.12619893 0.19625651 0.17002324 -0.24756783
total_cast_likes 0.18491380 0.20508101 0.20628669 -0.09436891
fb_likes 0.17762914 0.07557735 0.28102803 -0.35490438
critic_reviews 1.00000000 0.67203644 0.76042212 0.30222208
users_reviews 0.67203644 1.00000000 0.86347911 0.09576719
users_votes 0.76042212 0.86347911 1.00000000 0.03199929
score 0.30222208 0.09576719 0.03199929 1.00000000
aspect_ratio -0.12132492 -0.08777527 -0.10820789 -0.01380318
gross 0.38946540 0.56748782 0.59163181 -0.21334622
year 0.07304008 -0.09208640 -0.06017546 -0.20168869
aspect_ratio gross year
length -0.029483170 0.14669378 -0.67426761
budget -0.050015032 0.47931328 0.17327838
director_fb_likes -0.080102817 0.25540764 -0.15972997
actor1_fb_likes -0.019510275 0.35986081 -0.02006917
actor2_fb_likes 0.008564091 0.29082925 -0.01173167
actor3_fb_likes -0.018892501 0.30754525 -0.00547031
total_cast_likes -0.023832344 0.43816420 -0.01419498
fb_likes -0.029953312 0.15681634 0.19141490
critic_reviews -0.121324922 0.38946540 0.07304008
users_reviews -0.087775268 0.56748782 -0.09208640
users_votes -0.108207891 0.59163181 -0.06017546
score -0.013803184 -0.21334622 -0.20168869
aspect_ratio 1.000000000 -0.08506091 0.10744772
gross -0.085060913 1.00000000 0.04006751
year 0.107447717 0.04006751 1.00000000Correlation Plot ( only numeric data )
Visual Analytics: Use Table or R to create plots
Predictive Analytics: Create a Predictive Model
Based on your hypothesis create a predictive mode
LS0tCnRpdGxlOiAiUFJPSkVDVCBUSVRMRSIKYXV0aG9yOiAiVEVBTSBOQU1FIgpvdXRwdXQ6CiAgaHRtbF9ub3RlYm9vazogZGVmYXVsdAogIGh0bWxfZG9jdW1lbnQ6IGRlZmF1bHQKZGF0ZTogIkRBVEVfSEVSRSIKc3VidGl0bGU6ICJCdXNpbmVzcyBBbmFseXRpY3MgSG9ub3JzIC0gRmluYWwgUHJvamVjdCIKLS0tCgoKLS0tLS0tLS0tLS0tLS0KCiMjIEFib3V0IHRoaXMgUHJvamVjdAoKLS0tLS0tLS0tLS0tLS0KCkJyaWVmIGRlc2NyaXB0aW9uIG9mIHlvdXIgcHJvamVjdC4KCi0tLS0tLS0tLS0tLS0tCgojIyBSZXNvdXJjZXM6IFBhY2thZ2VzIFVzZSBvbiB0aGlzIG5vdGVib29rCgotLS0tLS0tLS0tLS0tLQoKKiB3YXRzb24KKiB0YWJsZWF1CgoqKkluc3RhbGwgcmVxdWlyZWQgcGFja2FnZXMqKgoKKiBQYWNrYWdlOiB0aWR5dmVyc2UsIGNvcnJwbG90CgpgYGB7cn0KCiMgSGVyZSB3ZSBhcmUgY2hlY2tpbmcgaWYgdGhlIHBhY2thZ2UgaXMgaW5zdGFsbGVkCmlmKCFyZXF1aXJlKCJ0aWR5dmVyc2UiKSl7CiAgCiAgIyBJZiB0aGUgcGFja2FnZSBpcyBub3QgaW4gdGhlIHN5c3RlbSB0aGVuIGl0IHdpbGwgYmUgaW5zdGFsbAogIGluc3RhbGwucGFja2FnZXMoInRpZHl2ZXJzZSIsIGRlcGVuZGVuY2llcyA9IFRSVUUpCiAgCiAgIyBIZXJlIHdlIGFyZSBsb2FkaW5nIHRoZSBwYWNrYWdlCiAgbGlicmFyeSgidGlkeXZlcnNlIikKfQoKIyBIZXJlIHdlIGFyZSBjaGVja2luZyBpZiB0aGUgcGFja2FnZSBpcyBpbnN0YWxsZWQKaWYoIXJlcXVpcmUoImNvcnJwbG90IikpewogIAogICMgSWYgdGhlIHBhY2thZ2UgaXMgbm90IGluIHRoZSBzeXN0ZW0gdGhlbiBpdCB3aWxsIGJlIGluc3RhbGwKICBpbnN0YWxsLnBhY2thZ2VzKCJjb3JycGxvdCIsIGRlcGVuZGVuY2llcyA9IFRSVUUpCiAgCiAgIyBIZXJlIHdlIGFyZSBsb2FkaW5nIHRoZSBwYWNrYWdlCiAgbGlicmFyeSgiY29ycnBsb3QiKQp9CgpgYGAKCgotLS0tLS0tLS0tLS0tLQoKIyMgRGF0YSBDb2xsZWN0aW9uOiBJTkRVU1RSWSAtIFRJVExFX09GX1lPVVJfREFUQVNFVCAKCi0tLS0tLS0tLS0tLS0tCgojIyMjIFJlYWQgdGhlIGNzdiBmaWxlIGludG8gUiBTdHVkaW8gYW5kIGV4cGxvcmUgdGhlIGRhdGFzZXQgKGV4dHJhY3QgdmFyaWFibGVzKQoKYGBge3J9CgpteWRhdGEgPC0gcmVhZF9jc3YoImRhdGEvcm90dGVudG9tYXRvZXMuY3N2IikKCmhlYWQobXlkYXRhKQpzdW1tYXJ5KGFzLmZhY3RvcihteWRhdGEkZGlyZWN0b3IpKQoKc3VtbWFyeShteWRhdGEpCmBgYAoKLS0tLS0tLS0tLS0tLS0KCiMjIERhdGEgUHJlcGFyYXRpb246IENsZWFuaW5nIGFuZCBwcmVwYXJpbmcgdGhlIGRhdGEgZm9yIGFuYWx5c2lzCi0tLS0tLS0tLS0tLS0tCgoKIyMjIyBSZXBsYWNlIHN0cmluZ3MKCmBgYHtyfQoKdGl0bGUgPC0gbXlkYXRhJHRpdGxlWzE6MTBdCgp0aXRsZSA8LSBzdHJfcmVwbGFjZSh0aXRsZSwKICAgICAgICAgICAgcGF0dGVybiA9ICLDgiIsCiAgICAgICAgICAgIHJlcGxhY2VtZW50ID0gIiIpCgp0aXRsZSA8LSBzdHJfcmVwbGFjZV9hbGwodGl0bGUsCiAgICAgICAgICAgIHBhdHRlcm4gPSAiw4IiLAogICAgICAgICAgICByZXBsYWNlbWVudCA9ICIiKQoKZ2VucmVzIDwtIG15ZGF0YSRnZW5yZXMKZ2VucmVzWzE6MTBdCgpnZW5yZXMgPC0gc3RyX3NwbGl0KGdlbnJlcywgIlxcfCIpCgpnZW5yZXNfY291bnQgPC0gbGFwcGx5KGdlbnJlcywgbGVuZ3RoKSAlPiUgdW5saXN0KCkKCm15ZGF0YSRnZW5yZXNfY291bnQgPC0gZ2VucmVzX2NvdW50CgpgYGAKCmBgYHtyfQoKZG9jX3RydWUgPC0gc3RyX2RldGVjdChnZW5yZXMsICJEb2N1bWVudGFyeSIpCgpkb2NfdHJ1ZVsxOjEwXQoKZ2VucmVzW2RvY190cnVlXSA8LSAiRG9jdW1lbnRhcnkiCgpgYGAKCmBgYHtyfQoKbmV3ZGF0YSA8LSBuYS5vbWl0KG15ZGF0YVtkb2NfdHJ1ZSxdKQoKbmV3ZGF0YSR0aXRsZSA8LSBzdHJfcmVwbGFjZV9hbGwobmV3ZGF0YSR0aXRsZSwKICAgICAgICAgICAgcGF0dGVybiA9ICLDgiIsCiAgICAgICAgICAgIHJlcGxhY2VtZW50ID0gIiIpCgpuZXdkYXRhJGdlbnJlcyA8LSAiRG9jdW1lbnRhcnkiCgpoZWFkKG5ld2RhdGEpCnRhaWwobmV3ZGF0YSkKCmBgYAoKCgojIyMjIFNhdmUgdGhlIG5ldyBjbGVhbiBkYXRhCgpgYGB7cn0KCndyaXRlX2NzdiggbmV3ZGF0YSwgImRhdGEvY2xlYW5fZGF0YS5jc3YiKQoKYGBgCgoKLS0tLS0tLS0tLS0tLS0KCiMjIERhdGEgQW5hbHlzaXM6IERlc2NyaXB0aXZlIFN0YXRpc3RpY3MsIENvcnJlbGF0aW9ucwoKLS0tLS0tLS0tLS0tLS0KCiMjIyMgQmFzaWMgZGVzY3JpcHRpdmUgc3RhdGlzdGljcyBvZiB0aGUgZGF0YXNldCAod3JpdGUgZG93biBhbnkgb2JzZXJ2YXRpb25zKQoKYGBge3J9CgpzdW1tYXJ5KG5ld2RhdGEpCgpgYGAKCmBgYHtyfQoKbmV3ZGF0YVtuZXdkYXRhJGJ1ZGdldCA+IDUwMDAwMDAwLCBdCgpuZXdkYXRhW25ld2RhdGEkZ3Jvc3MgPT0gMTE5MDc4MzkzLCBdCgpuZXdkYXRhW25ld2RhdGEkeWVhciA9PSAxOTcwLCBdCgpzdW0obmV3ZGF0YSRkaXJlY3Rvcl9mYl9saWtlcyxuZXdkYXRhJGFjdG9yMV9mYl9saWtlc1sxXSwgbmV3ZGF0YSR0b3RhbF9jYXN0X2xpa2VzWzFdKQoKYGBgCgoKIyMjIyBDb3JyZWxhdGlvbiB0YWJsZSAoIG9ubHkgbnVtZXJpYyBkYXRhICkKCmBgYHtyfQoKZGF0YV9jb3JyIDwtIGNvciggbmV3ZGF0YVstYygxLDIsMyw0LDUsNiwyMildICkKZGF0YV9jb3JyCgpgYGAKCgojIyMjIENvcnJlbGF0aW9uIFBsb3QgKCBvbmx5IG51bWVyaWMgZGF0YSApCgpgYGB7cn0KCmNvcnJwbG90KGRhdGFfY29ycikKCmBgYAoKCi0tLS0tLS0tLS0tLS0tCgojIyBWaXN1YWwgQW5hbHl0aWNzOiBVc2UgVGFibGUgb3IgUiB0byBjcmVhdGUgcGxvdHMgCgotLS0tLS0tLS0tLS0tLQoKCmBgYHtyfQoKcGxvdChuZXdkYXRhWy1jKDEsMiwzLDQsNSw2LDIyKV1bMTo1XSkKCmBgYAoKCi0tLS0tLS0tLS0tLS0tCgojIyBQcmVkaWN0aXZlIEFuYWx5dGljczogQ3JlYXRlIGEgUHJlZGljdGl2ZSBNb2RlbAoKLS0tLS0tLS0tLS0tLS0KCkJhc2VkIG9uIHlvdXIgaHlwb3RoZXNpcyBjcmVhdGUgYSBwcmVkaWN0aXZlIG1vZGUgCgo=