Link to my Medium post explaining this project: https://medium.com/@danishariq007/machine-learning-matchmaking-4416579d4d5e
- Using clustering algorithms to cluster people based on their interests.
- Finding people for a random user from his cluster group.
- Ranking them based on how much their personality matches with the user's personality
- Big5 personality dataset
- Interests dataset
- Baby-names dataset
- Combining Big5 personality dataset with personality dataset
- Adding names for ease in identification
- Using PCA for dimention reduction in both Big5 and Interests columns
- Using the PCA'd data to run heirarchical clustering
- Finding the appropriate no of cluster from heirarchical clustering
- Clustering the data using K-Means Clustering
- Attaching cluster assignments to the original PCA'd dataset
- Selecting a user
- Filtering out people within same cluster, country, age-group as user's
- Creating a list of people with personality most similar to user's
library(cluster)
library(dplyr)##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
setwd('E:/Datasets/BIG5/Data')big = read.csv('data.csv', sep= "")
head(big)## race age engnat gender hand source country E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 N1
## 1 3 53 1 1 1 1 US 4 2 5 2 5 1 4 3 5 1 1
## 2 13 46 1 2 1 1 US 2 2 3 3 3 3 1 5 1 5 2
## 3 1 14 2 2 1 1 PK 5 1 1 4 5 1 1 5 5 1 5
## 4 3 19 2 2 1 1 RO 2 5 2 4 3 4 3 4 4 5 5
## 5 11 25 2 2 1 2 US 3 1 3 3 3 1 3 1 3 5 3
## 6 13 31 1 2 1 2 US 1 5 2 4 1 3 2 4 1 5 1
## N2 N3 N4 N5 N6 N7 N8 N9 N10 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 C1 C2 C3 C4 C5 C6
## 1 5 2 5 1 1 1 1 1 1 1 5 1 5 2 3 1 5 4 5 4 1 5 1 5 1
## 2 3 4 2 3 4 3 2 2 4 1 3 3 4 4 4 2 3 4 3 4 1 3 2 3 1
## 3 1 5 5 5 5 5 5 5 5 5 1 5 5 1 5 1 5 5 5 4 1 5 1 5 1
## 4 4 4 2 4 5 5 5 4 5 2 5 4 4 3 5 3 4 4 3 3 3 4 5 1 4
## 5 3 3 4 3 3 3 3 3 4 5 5 3 5 1 5 1 5 5 5 3 1 5 3 3 1
## 6 5 4 5 1 4 4 1 5 2 2 2 3 4 3 4 3 5 5 3 2 5 4 3 3 4
## C7 C8 C9 C10 O1 O2 O3 O4 O5 O6 O7 O8 O9 O10
## 1 4 1 4 5 4 1 3 1 5 1 4 2 5 5
## 2 5 1 4 4 3 3 3 3 2 3 3 1 3 2
## 3 5 1 5 5 4 5 5 1 5 1 5 5 5 5
## 4 5 4 2 3 4 3 5 2 4 2 5 2 5 5
## 5 1 3 3 3 3 1 1 1 3 1 3 1 5 3
## 6 5 3 5 3 4 2 1 3 3 5 5 4 5 3
(Refer to Readme)
str(big)## 'data.frame': 19719 obs. of 57 variables:
## $ race : int 3 13 1 3 11 13 5 4 5 3 ...
## $ age : int 53 46 14 19 25 31 20 23 39 18 ...
## $ engnat : int 1 1 2 2 2 1 1 2 1 1 ...
## $ gender : int 1 2 2 2 2 2 2 1 2 2 ...
## $ hand : int 1 1 1 1 1 1 1 1 3 1 ...
## $ source : int 1 1 1 1 2 2 5 2 4 5 ...
## $ country: chr "US" "US" "PK" "RO" ...
## $ E1 : int 4 2 5 2 3 1 5 4 3 1 ...
## $ E2 : int 2 2 1 5 1 5 1 3 1 4 ...
## $ E3 : int 5 3 1 2 3 2 5 5 5 2 ...
## $ E4 : int 2 3 4 4 3 4 1 3 1 5 ...
## $ E5 : int 5 3 5 3 3 1 5 5 5 2 ...
## $ E6 : int 1 3 1 4 1 3 1 1 1 4 ...
## $ E7 : int 4 1 1 3 3 2 5 4 5 1 ...
## $ E8 : int 3 5 5 4 1 4 4 3 2 4 ...
## $ E9 : int 5 1 5 4 3 1 4 4 5 1 ...
## $ E10 : int 1 5 1 5 5 5 1 3 3 5 ...
## $ N1 : int 1 2 5 5 3 1 2 1 2 5 ...
## $ N2 : int 5 3 1 4 3 5 4 4 4 2 ...
## $ N3 : int 2 4 5 4 3 4 2 4 5 5 ...
## $ N4 : int 5 2 5 2 4 5 4 4 3 2 ...
## $ N5 : int 1 3 5 4 3 1 2 1 3 3 ...
## $ N6 : int 1 4 5 5 3 4 2 1 5 4 ...
## $ N7 : int 1 3 5 5 3 4 3 1 5 3 ...
## $ N8 : int 1 2 5 5 3 1 2 1 4 2 ...
## $ N9 : int 1 2 5 4 3 5 2 1 3 3 ...
## $ N10 : int 1 4 5 5 4 2 2 1 3 4 ...
## $ A1 : int 1 1 5 2 5 2 5 2 1 2 ...
## $ A2 : int 5 3 1 5 5 2 5 5 5 3 ...
## $ A3 : int 1 3 5 4 3 3 1 1 1 1 ...
## $ A4 : int 5 4 5 4 5 4 5 4 5 4 ...
## $ A5 : int 2 4 1 3 1 3 1 3 1 2 ...
## $ A6 : int 3 4 5 5 5 4 5 3 5 4 ...
## $ A7 : int 1 2 1 3 1 3 1 1 1 3 ...
## $ A8 : int 5 3 5 4 5 5 5 3 5 3 ...
## $ A9 : int 4 4 5 4 5 5 4 4 5 3 ...
## $ A10 : int 5 3 5 3 5 3 5 5 4 2 ...
## $ C1 : int 4 4 4 3 3 2 2 4 4 5 ...
## $ C2 : int 1 1 1 3 1 5 4 2 3 2 ...
## $ C3 : int 5 3 5 4 5 4 3 5 5 4 ...
## $ C4 : int 1 2 1 5 3 3 3 1 2 2 ...
## $ C5 : int 5 3 5 1 3 3 3 4 5 3 ...
## $ C6 : int 1 1 1 4 1 4 3 1 2 2 ...
## $ C7 : int 4 5 5 5 1 5 3 4 5 4 ...
## $ C8 : int 1 1 1 4 3 3 3 1 2 2 ...
## $ C9 : int 4 4 5 2 3 5 3 3 4 4 ...
## $ C10 : int 5 4 5 3 3 3 3 5 3 4 ...
## $ O1 : int 4 3 4 4 3 4 3 3 3 4 ...
## $ O2 : int 1 3 5 3 1 2 1 1 3 2 ...
## $ O3 : int 3 3 5 5 1 1 5 5 5 5 ...
## $ O4 : int 1 3 1 2 1 3 1 1 3 2 ...
## $ O5 : int 5 2 5 4 3 3 4 4 5 4 ...
## $ O6 : int 1 3 1 2 1 5 1 1 1 1 ...
## $ O7 : int 4 3 5 5 3 5 4 5 5 4 ...
## $ O8 : int 2 1 5 2 1 4 3 3 3 3 ...
## $ O9 : int 5 3 5 5 5 5 3 2 4 4 ...
## $ O10 : int 5 2 5 5 3 3 4 5 5 4 ...
Removing NAs and unwanted columns
big = big[,-c(5,6)]
head(big)## race age engnat gender country E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 N1 N2 N3 N4 N5
## 1 3 53 1 1 US 4 2 5 2 5 1 4 3 5 1 1 5 2 5 1
## 2 13 46 1 2 US 2 2 3 3 3 3 1 5 1 5 2 3 4 2 3
## 3 1 14 2 2 PK 5 1 1 4 5 1 1 5 5 1 5 1 5 5 5
## 4 3 19 2 2 RO 2 5 2 4 3 4 3 4 4 5 5 4 4 2 4
## 5 11 25 2 2 US 3 1 3 3 3 1 3 1 3 5 3 3 3 4 3
## 6 13 31 1 2 US 1 5 2 4 1 3 2 4 1 5 1 5 4 5 1
## N6 N7 N8 N9 N10 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10
## 1 1 1 1 1 1 1 5 1 5 2 3 1 5 4 5 4 1 5 1 5 1 4 1 4 5
## 2 4 3 2 2 4 1 3 3 4 4 4 2 3 4 3 4 1 3 2 3 1 5 1 4 4
## 3 5 5 5 5 5 5 1 5 5 1 5 1 5 5 5 4 1 5 1 5 1 5 1 5 5
## 4 5 5 5 4 5 2 5 4 4 3 5 3 4 4 3 3 3 4 5 1 4 5 4 2 3
## 5 3 3 3 3 4 5 5 3 5 1 5 1 5 5 5 3 1 5 3 3 1 1 3 3 3
## 6 4 4 1 5 2 2 2 3 4 3 4 3 5 5 3 2 5 4 3 3 4 5 3 5 3
## O1 O2 O3 O4 O5 O6 O7 O8 O9 O10
## 1 4 1 3 1 5 1 4 2 5 5
## 2 3 3 3 3 2 3 3 1 3 2
## 3 4 5 5 1 5 1 5 5 5 5
## 4 4 3 5 2 4 2 5 2 5 5
## 5 3 1 1 1 3 1 3 1 5 3
## 6 4 2 1 3 3 5 5 4 5 3
big = na.omit(big)names(big)## [1] "race" "age" "engnat" "gender" "country" "E1" "E2"
## [8] "E3" "E4" "E5" "E6" "E7" "E8" "E9"
## [15] "E10" "N1" "N2" "N3" "N4" "N5" "N6"
## [22] "N7" "N8" "N9" "N10" "A1" "A2" "A3"
## [29] "A4" "A5" "A6" "A7" "A8" "A9" "A10"
## [36] "C1" "C2" "C3" "C4" "C5" "C6" "C7"
## [43] "C8" "C9" "C10" "O1" "O2" "O3" "O4"
## [50] "O5" "O6" "O7" "O8" "O9" "O10"
dim(big)## [1] 19710 55
Countries of respondents
sort(table(big$country), decreasing = TRUE)##
## US GB IN AU CA PH (nu IT MY PK DE ZA BR ID SE NZ
## 8753 1531 1464 974 924 649 369 277 247 222 191 179 175 172 169 157
## NO RO NL SG FR DK IE AE FI PT BE GR RS ES MX PL
## 147 135 133 133 129 122 107 100 90 88 86 85 85 82 82 79
## TR EG SA BD KE TH AR BG HK LB CH CN HR JP NG HU
## 70 49 45 44 43 42 41 41 41 41 40 40 40 37 35 34
## SI LK KR VN LT CZ JM IL TW EU TT SK LV AT GH AP
## 34 31 30 30 29 28 28 27 26 24 23 22 21 20 20 19
## RU CL CO BZ IR VE PR JO EE IS AL UA MT UG BA NP
## 19 18 18 17 17 17 16 14 13 13 12 12 11 11 10 10
## QA A2 CR MA A1 BH BM CY MU PE MK TN EC KW OM SV
## 10 9 9 9 8 8 8 8 8 8 7 7 6 6 6 6
## BN DO AZ BW DZ GE HN PA BO GT JE KH ME MM MV ZW
## 5 5 4 4 4 4 4 4 3 3 3 3 3 3 3 3
## BB BS CM FJ GG HT IQ LA LS LY MN MP MW MZ NI PG
## 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## PY RW SY TZ UY VC VI ZM AG AO AS BF BT CV ET FO
## 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1
## GD GP GU GY IM KG KY KZ MR PW SD SR TC UZ
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Removing rows with vague age values
unique(big$age)## [1] 53 46 14 19 25 31 20
## [8] 23 39 18 17 15 22 21
## [15] 28 26 29 52 48 37 42
## [22] 27 60 24 47 35 34 36
## [29] 54 45 32 49 13 33 16
## [36] 30 59 41 44 43 38 1997
## [43] 40 55 58 51 57 1995 56
## [50] 68 70 50 76 63 65 1994
## [57] 62 61 67 69 1992 66 1988
## [64] 1990 1999 74 73 64 71 1989
## [71] 72 1984 999999999 1993 208 1996 75
## [78] 1982 1991 1998 1961 92 100 1986
## [85] 1976 412434 1977 77 2000 1968 97
## [92] 80 1974 223 1985 211 266 99
## [99] 188 79 191 78 1964 118
big = big[!(big$age>=120),]
unique(big$age)## [1] 53 46 14 19 25 31 20 23 39 18 17 15 22 21 28 26 29 52 48
## [20] 37 42 27 60 24 47 35 34 36 54 45 32 49 13 33 16 30 59 41
## [39] 44 43 38 40 55 58 51 57 56 68 70 50 76 63 65 62 61 67 69
## [58] 66 74 73 64 71 72 75 92 100 77 97 80 99 79 78 118
set.seed(2)
train = big[sample.int(nrow(big), 5000),]
remove(big)Adding 5000 unique names for easier identification
names=read.csv('baby-names.csv')names = names$name
names = unique(names)
names = names[sample.int(length(names), 5000)]train = data.frame(names,train)
train[1:6,1:6]## names race age engnat gender country
## 12171 Penni 3 21 1 1 US
## 13326 Margaretha 3 18 2 1 IT
## 4831 Isadore 3 34 1 2 US
## 11065 Kinsley 3 25 1 2 US
## 8502 Shad 1 24 1 2 US
## 3293 Sara 13 42 2 1 SA
int = read.csv('interests.csv')
head(int)## Music Classical.music Musical Pop Rock Metal.or.Hardrock Hiphop..Rap
## 1 5 2 1 5 5 1 1
## 2 4 1 2 3 5 4 1
## 3 5 4 5 3 5 3 1
## 4 5 1 1 2 2 1 2
## 5 5 4 3 5 3 1 5
## 6 5 3 3 2 5 5 4
## Rock.n.roll Alternative Techno..Trance Movies Horror Thriller Comedy Romantic
## 1 3 1 1 5 4 2 5 4
## 2 4 4 1 5 2 2 4 3
## 3 5 5 1 5 3 4 4 2
## 4 2 5 2 5 4 4 3 3
## 5 1 2 2 5 4 4 5 2
## 6 4 5 1 5 5 5 5 2
## Sci.fi War Fantasy.Fairy.tales Animated Documentary Action History Psychology
## 1 4 1 5 5 3 2 1 5
## 2 4 1 3 5 4 4 1 3
## 3 4 2 5 5 2 1 1 2
## 4 4 3 1 2 5 2 4 4
## 5 3 3 4 4 3 4 3 2
## 6 3 3 4 3 3 4 5 3
## Politics Mathematics Physics Internet PC Economy.Management Biology Chemistry
## 1 1 3 3 5 3 5 3 3
## 2 4 5 2 4 4 5 1 1
## 3 1 5 2 4 2 4 1 1
## 4 5 4 1 3 1 2 3 3
## 5 3 2 2 2 2 2 3 3
## 6 4 2 3 4 4 1 4 4
## Reading Geography Foreign.languages Medicine Law Cars Art.exhibitions
## 1 3 3 5 3 1 1 1
## 2 4 4 5 1 2 2 2
## 3 5 2 5 2 3 1 5
## 4 5 4 4 2 5 1 5
## 5 5 2 3 3 2 3 1
## 6 3 3 4 4 3 5 2
## Religion Dancing Musical.instruments Writing Passive.sport Active.sport
## 1 1 3 3 2 1 5
## 2 1 1 1 1 1 1
## 3 5 5 5 5 5 2
## 4 4 1 1 3 1 1
## 5 4 1 3 1 3 1
## 6 2 1 5 1 5 4
## Science.and.technology Theatre Adrenaline.sports Pets Appearence.and.gestures
## 1 4 2 4 4 4
## 2 3 2 2 5 4
## 3 2 5 5 5 3
## 4 3 1 1 1 3
## 5 3 2 2 1 3
## 6 3 1 3 2 3
## Happiness.in.life Education
## 1 4 college/bachelor degree
## 2 4 college/bachelor degree
## 3 4 secondary school
## 4 2 college/bachelor degree
## 5 3 secondary school
## 6 3 secondary school
int = na.omit(int)knitr::opts_chunk$set(echo = TRUE,
warning = FALSE,
messages = FALSE,
include = TRUE)
heatmap(cor(int[,-51]))
Creating a dataset of 5000 from 800 with replacement
set.seed(2)
intlarge = int[sample.int(nrow(int), 5000, replace = TRUE),]
summary(int)## Music Classical.music Musical Pop
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:5.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.000
## Median :5.000 Median :3.000 Median :3.000 Median :4.000
## Mean :4.735 Mean :2.964 Mean :2.754 Mean :3.466
## 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## Rock Metal.or.Hardrock Hiphop..Rap Rock.n.roll
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000
## Median :4.000 Median :2.000 Median :3.000 Median :3.000
## Mean :3.779 Mean :2.359 Mean :2.896 Mean :3.167
## 3rd Qu.:5.000 3rd Qu.:3.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## Alternative Techno..Trance Movies Horror Thriller
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.00 1st Qu.:1.000 1st Qu.:4.000 1st Qu.:1.000 1st Qu.:3.000
## Median :3.00 Median :2.000 Median :5.000 Median :3.000 Median :4.000
## Mean :2.85 Mean :2.302 Mean :4.603 Mean :2.772 Mean :3.361
## 3rd Qu.:4.00 3rd Qu.:3.000 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.00 Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## Comedy Romantic Sci.fi War
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:4.000 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:2.000
## Median :5.000 Median :4.000 Median :3.000 Median :3.000
## Mean :4.503 Mean :3.483 Mean :3.125 Mean :3.163
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## Fantasy.Fairy.tales Animated Documentary Action
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.752 Mean :3.782 Mean :3.627 Mean :3.527
## 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:5.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## History Psychology Politics Mathematics
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:1.000
## Median :3.000 Median :3.000 Median :2.000 Median :2.000
## Mean :3.194 Mean :3.117 Mean :2.621 Mean :2.375
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## Physics Internet PC Economy.Management
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:4.00 1st Qu.:2.000 1st Qu.:1.000
## Median :2.000 Median :4.00 Median :3.000 Median :2.000
## Mean :2.085 Mean :4.18 Mean :3.119 Mean :2.662
## 3rd Qu.:3.000 3rd Qu.:5.00 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.00 Max. :5.000 Max. :5.000
## Biology Chemistry Reading Geography
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000
## Median :2.000 Median :2.000 Median :3.000 Median :3.000
## Mean :2.641 Mean :2.139 Mean :3.148 Mean :3.094
## 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## Foreign.languages Medicine Law Cars
## Min. :1.000 Min. :1.00 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:1.00 1st Qu.:1.000 1st Qu.:1.000
## Median :4.000 Median :2.00 Median :2.000 Median :2.000
## Mean :3.805 Mean :2.49 Mean :2.256 Mean :2.657
## 3rd Qu.:5.000 3rd Qu.:3.00 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.00 Max. :5.000 Max. :5.000
## Art.exhibitions Religion Dancing Musical.instruments
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000 1st Qu.:1.000
## Median :2.000 Median :2.000 Median :2.000 Median :2.000
## Mean :2.602 Mean :2.267 Mean :2.433 Mean :2.318
## 3rd Qu.:4.000 3rd Qu.:3.000 3rd Qu.:3.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
## Writing Passive.sport Active.sport Science.and.technology
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:1.000 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.00
## Median :1.000 Median :4.000 Median :3.000 Median :3.00
## Mean :1.886 Mean :3.412 Mean :3.282 Mean :3.26
## 3rd Qu.:3.000 3rd Qu.:5.000 3rd Qu.:5.000 3rd Qu.:4.00
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.00
## Theatre Adrenaline.sports Pets Appearence.and.gestures
## Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:2.00 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:3.000
## Median :3.00 Median :3.000 Median :4.000 Median :4.000
## Mean :3.01 Mean :2.923 Mean :3.285 Mean :3.594
## 3rd Qu.:4.00 3rd Qu.:4.000 3rd Qu.:5.000 3rd Qu.:4.000
## Max. :5.00 Max. :5.000 Max. :5.000 Max. :5.000
## Happiness.in.life Education
## Min. :1.000 Length:839
## 1st Qu.:3.000 Class :character
## Median :4.000 Mode :character
## Mean :3.709
## 3rd Qu.:4.000
## Max. :5.000
train = data.frame(train, intlarge)
names(train)## [1] "names" "race"
## [3] "age" "engnat"
## [5] "gender" "country"
## [7] "E1" "E2"
## [9] "E3" "E4"
## [11] "E5" "E6"
## [13] "E7" "E8"
## [15] "E9" "E10"
## [17] "N1" "N2"
## [19] "N3" "N4"
## [21] "N5" "N6"
## [23] "N7" "N8"
## [25] "N9" "N10"
## [27] "A1" "A2"
## [29] "A3" "A4"
## [31] "A5" "A6"
## [33] "A7" "A8"
## [35] "A9" "A10"
## [37] "C1" "C2"
## [39] "C3" "C4"
## [41] "C5" "C6"
## [43] "C7" "C8"
## [45] "C9" "C10"
## [47] "O1" "O2"
## [49] "O3" "O4"
## [51] "O5" "O6"
## [53] "O7" "O8"
## [55] "O9" "O10"
## [57] "Music" "Classical.music"
## [59] "Musical" "Pop"
## [61] "Rock" "Metal.or.Hardrock"
## [63] "Hiphop..Rap" "Rock.n.roll"
## [65] "Alternative" "Techno..Trance"
## [67] "Movies" "Horror"
## [69] "Thriller" "Comedy"
## [71] "Romantic" "Sci.fi"
## [73] "War" "Fantasy.Fairy.tales"
## [75] "Animated" "Documentary"
## [77] "Action" "History"
## [79] "Psychology" "Politics"
## [81] "Mathematics" "Physics"
## [83] "Internet" "PC"
## [85] "Economy.Management" "Biology"
## [87] "Chemistry" "Reading"
## [89] "Geography" "Foreign.languages"
## [91] "Medicine" "Law"
## [93] "Cars" "Art.exhibitions"
## [95] "Religion" "Dancing"
## [97] "Musical.instruments" "Writing"
## [99] "Passive.sport" "Active.sport"
## [101] "Science.and.technology" "Theatre"
## [103] "Adrenaline.sports" "Pets"
## [105] "Appearence.and.gestures" "Happiness.in.life"
## [107] "Education"
sort(table(train$Education), decreasing = TRUE)##
## secondary school college/bachelor degree
## 3130 1001
## masters degree primary school
## 421 387
## currently a primary school pupil doctorate degree
## 37 21
##
## 3
knitr::opts_chunk$set(echo = TRUE,
warning = FALSE,
messages = FALSE,
include = TRUE)
hist(train$age, col = 'red', xlab = 'Age', ylab = 'Frequency', main = 'AGE FREQUENCY')
knitr::opts_chunk$set(echo = TRUE,
warning = FALSE,
messages = FALSE,
include = TRUE)
hist(train$gender, col = 'blue', xlab = 'Gender
1=Male, 2=Female, 3=Other', ylab = 'Frequency', main = 'GENDER FREQUENCY')
pr.out = prcomp(intlarge[,1:50], scale = TRUE)
head(pr.out$rotation[1:20])## [1] -0.06634427 -0.25277250 -0.22282635 0.03677082 -0.12167650 -0.05569898
head(pr.out$x)## PC1 PC2 PC3 PC4 PC5 PC6
## 849 2.40633523 1.18845622 -2.20520117 0.67070390 -0.4008390 0.9623631
## 925 0.74924685 2.59133550 0.29012247 -0.06594698 -0.7980948 -2.5904208
## 498 -1.71110350 1.84238406 -1.82041733 1.89584090 -0.8066074 1.0725876
## 466 0.08267284 0.08253572 -0.07333943 2.30800138 -0.5239428 1.3757298
## 325 0.49601123 2.03728398 0.82221728 -1.29199027 -0.4204712 0.3998408
## 416 -0.98882514 2.68628023 2.27038431 -2.48013758 0.7205849 -3.4452011
## PC7 PC8 PC9 PC10 PC11 PC12
## 849 3.109552821 -0.6674774 3.0711772 -0.5850462 -0.7363013 1.8818547
## 925 -1.505994366 1.2534910 -1.1161595 0.5383166 1.7365408 -0.4675288
## 498 1.940319388 0.8377373 -0.2676961 -0.4364324 -2.8254240 1.0166555
## 466 -1.699375591 -0.7817496 0.3996406 -1.4110333 0.3824675 -1.8161302
## 325 0.006628133 -0.9736738 0.5380429 0.3662837 -0.1728037 -0.3725905
## 416 0.193662549 -2.7277398 2.5091072 0.4679246 0.4802233 1.4365345
## PC13 PC14 PC15 PC16 PC17 PC18
## 849 0.4826010 0.6609888 -1.9975681 0.8528922 0.18012957 0.9483154
## 925 1.6130843 -1.1724638 -0.8574918 -1.1629262 0.40275968 -1.5147372
## 498 -1.2620974 1.5818881 -2.1051698 1.2337745 1.76217476 -0.5227804
## 466 0.8601170 -0.6864529 0.8235895 0.1501181 1.30775116 -1.6443888
## 325 -0.5157871 -0.4723268 0.1541035 -0.3300593 0.06962588 0.3900671
## 416 -0.8228892 -2.0800021 -1.0992713 0.1605568 -0.70611558 -1.1311807
## PC19 PC20 PC21 PC22 PC23 PC24
## 849 -1.2676371 -0.3616065 1.1676896 1.27376395 0.0544559 -0.21925461
## 925 -0.5204020 0.7815334 -0.4324554 0.17016485 0.2050060 0.08155448
## 498 1.5301582 -0.6192447 -0.8500545 -0.19147989 -1.0873140 0.99838670
## 466 0.0441281 0.2654131 0.1169813 -0.07266223 0.9228714 0.25047310
## 325 -0.5906688 0.3422169 0.2580642 -0.04543875 -1.9387515 0.47572640
## 416 -1.5665277 -0.1885298 -1.2328182 -0.15022279 0.3878765 0.90870086
## PC25 PC26 PC27 PC28 PC29 PC30
## 849 2.04372396 0.16851331 0.71567466 -0.91784809 -0.5954112 1.09093626
## 925 0.07889581 0.08804379 0.54310055 0.25686379 -0.3488732 0.02761916
## 498 0.48515130 -0.85344380 0.55537054 -0.88773647 -0.4510348 0.48507795
## 466 -0.45043636 0.21253488 0.58494838 -0.05180905 0.9369118 0.34782387
## 325 0.98293752 -0.15281424 -0.01630172 0.95195215 -2.2572486 0.04281828
## 416 0.27236859 0.03650196 -0.79860338 -0.09817686 -0.1828348 -0.18950406
## PC31 PC32 PC33 PC34 PC35 PC36
## 849 -1.55931058 0.4594789 0.2902279 -1.2001133 -0.4746006 0.5164593
## 925 0.08615064 -2.0182998 -0.9257653 1.0723580 0.6345827 -2.4738523
## 498 -0.40457091 -1.0498547 0.8103899 0.3627377 2.0688254 -1.1660191
## 466 0.98647323 1.1090505 0.3642609 -0.5349486 -0.4203652 -0.7350462
## 325 0.16927296 0.5503356 0.2791710 -1.4636093 -0.2062266 -0.1613496
## 416 0.50959860 0.7850920 1.5028108 0.6860413 1.2902153 1.4440628
## PC37 PC38 PC39 PC40 PC41 PC42
## 849 -0.27565200 0.9708775 -0.5095193 0.58698679 -0.8746929 -0.9255954
## 925 0.39649642 -0.3045670 0.1300115 0.40125378 -0.4809131 -0.6730388
## 498 0.02044798 -0.9197528 0.2229938 0.73644989 -0.1882511 -0.1568487
## 466 0.68978027 -0.2724562 -0.5911412 -0.08359082 0.8972696 -0.5811108
## 325 -0.36114969 -0.1195906 -0.1020778 -0.06708352 0.5291985 0.2667251
## 416 0.64527418 -1.0964685 -0.1599179 -0.12708545 -0.5940999 -0.8549758
## PC43 PC44 PC45 PC46 PC47 PC48
## 849 0.2992324 0.36451188 0.2445148 0.3520395 0.6069322 0.5084480
## 925 -1.3746090 -0.96708333 -0.7177445 0.3897806 0.1284880 0.5640492
## 498 0.3322456 0.73678396 -0.3011576 -0.2849121 0.5052619 -0.2181399
## 466 -0.4121059 -0.14560662 0.2772995 -0.1517907 -0.8574704 0.1589322
## 325 -0.2291520 0.32761943 1.0040664 -0.9695498 -0.6647947 0.1853779
## 416 0.1713340 -0.07994503 -0.1686825 -1.2252849 0.6345866 0.1117559
## PC49 PC50
## 849 0.6193716 -0.569225246
## 925 0.6475542 0.324285520
## 498 1.1323279 -0.007523632
## 466 0.8239734 0.418511583
## 325 0.2074907 -0.239079499
## 416 0.1297339 0.034554799
par(mfrow=c(1,1))
plot(pr.out$x[,1:2], pch=19, xlab = 'PC1', ylab='PC2')
plot(pr.out$x[,c(1,3)], pch=19, xlab = 'PC1', ylab='PC3')
knitr::opts_chunk$set(echo = TRUE,
warning = FALSE,
messages = FALSE,
include = TRUE)
summary(pr.out)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.2820 2.11769 1.77222 1.61656 1.55213 1.31450 1.26972
## Proportion of Variance 0.1042 0.08969 0.06282 0.05227 0.04818 0.03456 0.03224
## Cumulative Proportion 0.1042 0.19384 0.25666 0.30892 0.35710 0.39166 0.42391
## PC8 PC9 PC10 PC11 PC12 PC13 PC14
## Standard deviation 1.21079 1.17764 1.15138 1.07067 1.06645 1.01904 1.00425
## Proportion of Variance 0.02932 0.02774 0.02651 0.02293 0.02275 0.02077 0.02017
## Cumulative Proportion 0.45323 0.48096 0.50748 0.53040 0.55315 0.57392 0.59409
## PC15 PC16 PC17 PC18 PC19 PC20 PC21
## Standard deviation 0.98683 0.97055 0.9643 0.94761 0.9221 0.91528 0.89675
## Proportion of Variance 0.01948 0.01884 0.0186 0.01796 0.0170 0.01675 0.01608
## Cumulative Proportion 0.61357 0.63240 0.6510 0.66896 0.6860 0.70272 0.71880
## PC22 PC23 PC24 PC25 PC26 PC27 PC28
## Standard deviation 0.87297 0.8544 0.84938 0.83874 0.82079 0.8125 0.78645
## Proportion of Variance 0.01524 0.0146 0.01443 0.01407 0.01347 0.0132 0.01237
## Cumulative Proportion 0.73405 0.7486 0.76307 0.77714 0.79062 0.8038 0.81619
## PC29 PC30 PC31 PC32 PC33 PC34 PC35
## Standard deviation 0.7747 0.76723 0.75284 0.74922 0.73449 0.7247 0.71344
## Proportion of Variance 0.0120 0.01177 0.01134 0.01123 0.01079 0.0105 0.01018
## Cumulative Proportion 0.8282 0.83997 0.85130 0.86253 0.87332 0.8838 0.89400
## PC36 PC37 PC38 PC39 PC40 PC41 PC42
## Standard deviation 0.68603 0.67684 0.66420 0.65213 0.64529 0.63273 0.63028
## Proportion of Variance 0.00941 0.00916 0.00882 0.00851 0.00833 0.00801 0.00794
## Cumulative Proportion 0.90341 0.91258 0.92140 0.92991 0.93823 0.94624 0.95419
## PC43 PC44 PC45 PC46 PC47 PC48 PC49
## Standard deviation 0.59362 0.57297 0.55946 0.54910 0.52573 0.50718 0.50051
## Proportion of Variance 0.00705 0.00657 0.00626 0.00603 0.00553 0.00514 0.00501
## Cumulative Proportion 0.96123 0.96780 0.97406 0.98009 0.98562 0.99076 0.99577
## PC50
## Standard deviation 0.45982
## Proportion of Variance 0.00423
## Cumulative Proportion 1.00000
plot(pr.out$sdev, xlab='Principal Component', ylab='Standard Deviation', main='Standard Deviation explained by each PC')
Proportion of Variance explained by each additional PC
knitr::opts_chunk$set(echo = TRUE,
warning = FALSE,
messages = FALSE,
include = TRUE)
pve = 100*pr.out$sdev^2/sum(pr.out$sdev^2)
par(mfrow=c(1,2))
plot(pve[1:18],type='o',ylab='Prop. var. explained', xlab="Principal Component", col='blue')
plot(cumsum(pve[1:18]),type='o',ylab='Cum. Prop. var. explained', xlab="Principal Component", col='blue')
Setting a cutoff point at 60% cumulative percentage
knitr::opts_chunk$set(echo = TRUE,
warning = FALSE,
messages = FALSE,
include = TRUE)
par(mfrow=c(1,1))
plot(cumsum(pve[1:18]),type='o',ylab='Cum. Prop. var. explained', xlab="Principal Component", col='blue')
abline(h=60)
head(pr.out$x[,1:14])## PC1 PC2 PC3 PC4 PC5 PC6
## 849 2.40633523 1.18845622 -2.20520117 0.67070390 -0.4008390 0.9623631
## 925 0.74924685 2.59133550 0.29012247 -0.06594698 -0.7980948 -2.5904208
## 498 -1.71110350 1.84238406 -1.82041733 1.89584090 -0.8066074 1.0725876
## 466 0.08267284 0.08253572 -0.07333943 2.30800138 -0.5239428 1.3757298
## 325 0.49601123 2.03728398 0.82221728 -1.29199027 -0.4204712 0.3998408
## 416 -0.98882514 2.68628023 2.27038431 -2.48013758 0.7205849 -3.4452011
## PC7 PC8 PC9 PC10 PC11 PC12
## 849 3.109552821 -0.6674774 3.0711772 -0.5850462 -0.7363013 1.8818547
## 925 -1.505994366 1.2534910 -1.1161595 0.5383166 1.7365408 -0.4675288
## 498 1.940319388 0.8377373 -0.2676961 -0.4364324 -2.8254240 1.0166555
## 466 -1.699375591 -0.7817496 0.3996406 -1.4110333 0.3824675 -1.8161302
## 325 0.006628133 -0.9736738 0.5380429 0.3662837 -0.1728037 -0.3725905
## 416 0.193662549 -2.7277398 2.5091072 0.4679246 0.4802233 1.4365345
## PC13 PC14
## 849 0.4826010 0.6609888
## 925 1.6130843 -1.1724638
## 498 -1.2620974 1.5818881
## 466 0.8601170 -0.6864529
## 325 -0.5157871 -0.4723268
## 416 -0.8228892 -2.0800021
pca.int.data = pr.out$x[,1:14]
head(pca.int.data)## PC1 PC2 PC3 PC4 PC5 PC6
## 849 2.40633523 1.18845622 -2.20520117 0.67070390 -0.4008390 0.9623631
## 925 0.74924685 2.59133550 0.29012247 -0.06594698 -0.7980948 -2.5904208
## 498 -1.71110350 1.84238406 -1.82041733 1.89584090 -0.8066074 1.0725876
## 466 0.08267284 0.08253572 -0.07333943 2.30800138 -0.5239428 1.3757298
## 325 0.49601123 2.03728398 0.82221728 -1.29199027 -0.4204712 0.3998408
## 416 -0.98882514 2.68628023 2.27038431 -2.48013758 0.7205849 -3.4452011
## PC7 PC8 PC9 PC10 PC11 PC12
## 849 3.109552821 -0.6674774 3.0711772 -0.5850462 -0.7363013 1.8818547
## 925 -1.505994366 1.2534910 -1.1161595 0.5383166 1.7365408 -0.4675288
## 498 1.940319388 0.8377373 -0.2676961 -0.4364324 -2.8254240 1.0166555
## 466 -1.699375591 -0.7817496 0.3996406 -1.4110333 0.3824675 -1.8161302
## 325 0.006628133 -0.9736738 0.5380429 0.3662837 -0.1728037 -0.3725905
## 416 0.193662549 -2.7277398 2.5091072 0.4679246 0.4802233 1.4365345
## PC13 PC14
## 849 0.4826010 0.6609888
## 925 1.6130843 -1.1724638
## 498 -1.2620974 1.5818881
## 466 0.8601170 -0.6864529
## 325 -0.5157871 -0.4723268
## 416 -0.8228892 -2.0800021
dim(train)## [1] 5000 107
pr.out2 = prcomp(train[,7:56], scale = TRUE)
head(pr.out2$rotation[,1:20])## PC1 PC2 PC3 PC4 PC5 PC6
## E1 0.1814075 -0.12312074 0.15068499 -0.09008466 0.15962638 -0.10666695
## E2 -0.1773063 0.15740616 -0.15045741 0.08792998 -0.14436792 -0.12826274
## E3 0.2474366 -0.07720903 0.04944276 -0.11632812 0.06952189 -0.07159757
## E4 -0.2010029 0.09180259 -0.15409936 0.11736287 -0.14349983 -0.14157005
## E5 0.2314176 -0.13805617 0.09546865 -0.06753983 0.14026444 -0.01060677
## E6 -0.1904943 0.11192849 -0.13141372 -0.02142404 -0.08894255 -0.22460688
## PC7 PC8 PC9 PC10 PC11 PC12
## E1 0.01926778 -0.08808511 0.01611768 -0.03520748 0.10295568 -0.063794575
## E2 0.02001374 -0.13621418 0.07198161 0.03249345 0.05134922 -0.201383838
## E3 0.08291197 -0.05874107 0.09548657 -0.01379288 0.02619174 0.054242200
## E4 0.04097756 -0.02756089 0.08728882 -0.08474915 -0.01762125 -0.007102815
## E5 0.07220435 -0.01225390 0.02393521 -0.04445866 -0.04344394 0.094792590
## E6 0.03302689 -0.17099151 0.04199181 0.02760464 0.08399080 -0.242649122
## PC13 PC14 PC15 PC16 PC17 PC18
## E1 0.137089105 0.01754058 -0.04348198 -0.09237452 -0.003105910 0.129867777
## E2 -0.039216664 0.03411028 -0.10172258 0.11260065 0.203328018 0.075542780
## E3 0.042868021 -0.11216921 -0.04939954 -0.05901622 0.206907877 0.049448965
## E4 0.004459078 -0.07580240 -0.08739519 0.06925852 0.015924984 0.017837029
## E5 0.206579182 -0.11375316 -0.08411300 0.02952746 0.005696409 0.009324362
## E6 -0.015094572 -0.02924497 -0.08534992 -0.05876891 0.233342448 0.111503755
## PC19 PC20
## E1 0.134428488 0.201773867
## E2 0.091241551 0.182241918
## E3 0.167431144 -0.009069857
## E4 -0.014024581 0.015964943
## E5 -0.005559828 -0.074931141
## E6 0.111860803 0.239648848
head(pr.out2$x)## PC1 PC2 PC3 PC4 PC5 PC6
## 12171 -4.2281719 0.09414946 -1.3358523 1.0367636 -3.1158100 -0.5579354
## 13326 -1.1591050 0.40162890 2.0486767 1.2753684 3.9863527 0.3281744
## 4831 2.9793370 -1.90827884 -1.6553652 -0.2657698 0.5448155 -1.3335340
## 11065 2.7232585 2.30375915 -0.6191980 0.8428194 2.7204532 1.9388028
## 8502 -0.5840147 1.26746266 1.8184121 0.7904014 -3.6101288 -0.4931417
## 3293 -1.7304459 2.37018711 0.2938001 -1.5130163 0.2905016 1.8268955
## PC7 PC8 PC9 PC10 PC11 PC12
## 12171 0.2497698 -0.53579970 -0.28985466 1.2367214 -2.1649160 -0.4947592
## 13326 -0.4344382 -1.33979430 -1.61670654 1.6336918 0.1593578 1.2330498
## 4831 -1.1218635 0.81310945 -0.13324660 -0.8092402 0.2244848 -0.7121255
## 11065 -0.8875500 1.15160434 0.04443927 1.4714151 0.6759259 0.4525514
## 8502 -0.4901897 -0.11425653 -0.29206909 -0.4725029 -0.7379404 1.5372819
## 3293 -1.6583981 0.07137076 0.65128441 -0.5163869 -0.9335496 -0.8234795
## PC13 PC14 PC15 PC16 PC17 PC18
## 12171 -0.04607323 -0.0561816 0.0006671697 -0.12732146 -1.2949965 0.19732698
## 13326 1.77739261 -0.7340616 0.2374353846 1.80674667 -0.6816483 0.30128877
## 4831 0.50579851 0.9184789 -0.6350679776 0.05617190 0.7869923 -0.07708356
## 11065 0.56815621 -0.1582787 -0.6707577409 1.07633665 0.8272276 0.30490644
## 8502 0.24473280 -0.3744436 -0.1517338728 -0.96654036 -0.6377769 0.06663111
## 3293 -0.41729393 -1.7495846 -0.3671674264 -0.04435093 1.1024128 0.37357408
## PC19 PC20 PC21 PC22 PC23 PC24
## 12171 2.1958304 0.06327232 -1.0322702 0.1689075 -0.2935073 0.9368265
## 13326 0.2309333 -0.51744998 0.3810510 0.4791478 1.9455026 -0.5329160
## 4831 0.3967830 0.25950193 -0.6805522 0.6790635 0.8064277 0.2629281
## 11065 0.4260374 -0.29630587 -1.5836560 -0.5861661 -0.4482428 -0.6250697
## 8502 0.9648619 1.89616376 0.3077211 0.2785895 -0.1913709 0.6014772
## 3293 0.3191259 0.63598246 -0.6330488 1.9904586 -0.4040793 0.6050507
## PC25 PC26 PC27 PC28 PC29 PC30
## 12171 -0.37408181 -1.34955879 -0.1538797 -0.33027654 0.1271963 0.6728096
## 13326 0.16472667 1.23143462 -0.4388517 0.11677837 -0.8859158 0.9799371
## 4831 0.39192287 0.35626950 -0.2980237 -0.45841905 0.6312962 0.2158707
## 11065 -0.05441121 0.07442711 -0.5220421 1.10190840 -1.1936718 1.0715715
## 8502 0.90084730 -0.44447989 0.3666533 -1.08234925 0.7146619 0.8645506
## 3293 1.07100633 -0.66082851 -0.3430444 0.06149232 1.1302729 0.6597747
## PC31 PC32 PC33 PC34 PC35 PC36
## 12171 -1.02764681 0.13250170 0.4659116 -1.37362726 -0.27940673 0.50840788
## 13326 0.14019962 2.28284367 -0.0576576 -0.65195859 -1.27116114 3.15720623
## 4831 -0.18236152 -0.08742827 -0.5297614 -0.05102678 0.37705937 -0.21177401
## 11065 -1.11330391 1.12831119 0.4111681 -0.69193399 0.04732691 0.50767646
## 8502 1.27147411 -0.39405372 -1.0883598 -0.65332280 -0.29666520 1.62545282
## 3293 -0.05417387 -0.17319908 0.1393545 -0.90481846 0.45632760 -0.08106184
## PC37 PC38 PC39 PC40 PC41 PC42
## 12171 -0.26047924 -1.1238449 -0.75034410 0.7425813 0.02383845 -1.01169014
## 13326 0.20791201 -0.2946183 1.72806294 -0.6050935 -1.13347447 -0.41910248
## 4831 0.04377552 -0.5869042 -0.54163004 0.2357573 0.62130407 -0.62349473
## 11065 0.31948445 0.6755323 -0.21434863 0.3638716 0.74819865 0.05293598
## 8502 0.27923794 0.7813368 -0.06250686 0.4061762 -0.17454949 0.07364748
## 3293 0.25418262 -0.3464935 -0.03286990 0.7462930 -0.18161169 0.86026067
## PC43 PC44 PC45 PC46 PC47 PC48
## 12171 1.29924298 -0.8103453 0.2890748 1.1645788 0.658093553 0.112085494
## 13326 0.86491127 -0.2051620 0.5043571 0.7524091 1.065792298 1.490626744
## 4831 0.05428579 -0.1794527 0.1472287 -0.1480209 -0.052683257 0.008162951
## 11065 -0.01803983 -0.1048829 0.6039027 -0.2225279 -0.003864129 0.438556047
## 8502 1.04089473 0.5551200 0.5881510 -0.6462682 0.240949565 -0.907765856
## 3293 -0.21571130 -0.2555440 -0.3784223 -0.5690950 -0.684059595 0.354005806
## PC49 PC50
## 12171 0.45654972 0.4687007
## 13326 -0.12747599 -0.3696767
## 4831 -0.41783371 -0.2238276
## 11065 0.08072017 -0.4759944
## 8502 0.85422051 -2.0360668
## 3293 0.10754078 0.5556684
par(mfrow=c(1,1))
plot(pr.out2$x[,1:2], pch=19, xlab = 'PC1', ylab='PC2')
plot(pr.out2$x[,c(1,3)], pch=19, xlab = 'PC1', ylab='PC3')
knitr::opts_chunk$set(echo = TRUE,
warning = FALSE,
messages = FALSE,
include = TRUE)
summary(pr.out2)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.8450 2.15128 1.95976 1.87808 1.66947 1.23913 1.15664
## Proportion of Variance 0.1619 0.09256 0.07681 0.07054 0.05574 0.03071 0.02676
## Cumulative Proportion 0.1619 0.25444 0.33126 0.40180 0.45754 0.48825 0.51501
## PC8 PC9 PC10 PC11 PC12 PC13 PC14
## Standard deviation 1.02914 0.98430 0.96779 0.95148 0.93307 0.92084 0.90414
## Proportion of Variance 0.02118 0.01938 0.01873 0.01811 0.01741 0.01696 0.01635
## Cumulative Proportion 0.53619 0.55557 0.57430 0.59240 0.60982 0.62678 0.64313
## PC15 PC16 PC17 PC18 PC19 PC20 PC21
## Standard deviation 0.87984 0.8661 0.85619 0.84966 0.82899 0.82431 0.8063
## Proportion of Variance 0.01548 0.0150 0.01466 0.01444 0.01374 0.01359 0.0130
## Cumulative Proportion 0.65861 0.6736 0.68827 0.70271 0.71646 0.73004 0.7430
## PC22 PC23 PC24 PC25 PC26 PC27 PC28
## Standard deviation 0.79663 0.77960 0.76689 0.76378 0.75675 0.73671 0.72830
## Proportion of Variance 0.01269 0.01216 0.01176 0.01167 0.01145 0.01085 0.01061
## Cumulative Proportion 0.75574 0.76790 0.77966 0.79133 0.80278 0.81363 0.82424
## PC29 PC30 PC31 PC32 PC33 PC34 PC35
## Standard deviation 0.71113 0.70816 0.70271 0.69560 0.68946 0.67934 0.67148
## Proportion of Variance 0.01011 0.01003 0.00988 0.00968 0.00951 0.00923 0.00902
## Cumulative Proportion 0.83436 0.84439 0.85426 0.86394 0.87345 0.88268 0.89169
## PC36 PC37 PC38 PC39 PC40 PC41 PC42
## Standard deviation 0.66791 0.65350 0.64617 0.64001 0.62737 0.6243 0.61955
## Proportion of Variance 0.00892 0.00854 0.00835 0.00819 0.00787 0.0078 0.00768
## Cumulative Proportion 0.90062 0.90916 0.91751 0.92570 0.93357 0.9414 0.94905
## PC43 PC44 PC45 PC46 PC47 PC48 PC49
## Standard deviation 0.60336 0.59488 0.5829 0.57613 0.57386 0.56660 0.54627
## Proportion of Variance 0.00728 0.00708 0.0068 0.00664 0.00659 0.00642 0.00597
## Cumulative Proportion 0.95633 0.96340 0.9702 0.97684 0.98342 0.98985 0.99581
## PC50
## Standard deviation 0.45752
## Proportion of Variance 0.00419
## Cumulative Proportion 1.00000
plot(pr.out2$sdev, xlab='Principal Component', ylab='Standard Deviation', main='Standard Deviation explained by each PC')
Proportion of Variance explained by each additional PC
knitr::opts_chunk$set(echo = TRUE,
warning = FALSE,
messages = FALSE,
include = TRUE)
pve2= 100*pr.out2$sdev^2/sum(pr.out2$sdev^2)
par(mfrow=c(1,2))
plot(pve2[1:20],type='o',ylab='Prop. var. explained', xlab="Principal Component", col='blue')
plot(cumsum(pve2[1:20]),type='o',ylab='Cum. Prop. var. explained', xlab="Principal Component", col='blue')
Setting a cutoff point at 60% cumulative percentage
knitr::opts_chunk$set(echo = TRUE,
warning = FALSE,
messages = FALSE,
include = TRUE)
par(mfrow=c(1,1))
plot(cumsum(pve2[1:18]),type='o',ylab='Cum. Prop. var. explained', xlab="Principal Component", col='blue')
abline(h=60)
head(pr.out2$x[,1:12])## PC1 PC2 PC3 PC4 PC5 PC6
## 12171 -4.2281719 0.09414946 -1.3358523 1.0367636 -3.1158100 -0.5579354
## 13326 -1.1591050 0.40162890 2.0486767 1.2753684 3.9863527 0.3281744
## 4831 2.9793370 -1.90827884 -1.6553652 -0.2657698 0.5448155 -1.3335340
## 11065 2.7232585 2.30375915 -0.6191980 0.8428194 2.7204532 1.9388028
## 8502 -0.5840147 1.26746266 1.8184121 0.7904014 -3.6101288 -0.4931417
## 3293 -1.7304459 2.37018711 0.2938001 -1.5130163 0.2905016 1.8268955
## PC7 PC8 PC9 PC10 PC11 PC12
## 12171 0.2497698 -0.53579970 -0.28985466 1.2367214 -2.1649160 -0.4947592
## 13326 -0.4344382 -1.33979430 -1.61670654 1.6336918 0.1593578 1.2330498
## 4831 -1.1218635 0.81310945 -0.13324660 -0.8092402 0.2244848 -0.7121255
## 11065 -0.8875500 1.15160434 0.04443927 1.4714151 0.6759259 0.4525514
## 8502 -0.4901897 -0.11425653 -0.29206909 -0.4725029 -0.7379404 1.5372819
## 3293 -1.6583981 0.07137076 0.65128441 -0.5163869 -0.9335496 -0.8234795
pca.big.data = pr.out2$x[,1:12]
head(pca.big.data)## PC1 PC2 PC3 PC4 PC5 PC6
## 12171 -4.2281719 0.09414946 -1.3358523 1.0367636 -3.1158100 -0.5579354
## 13326 -1.1591050 0.40162890 2.0486767 1.2753684 3.9863527 0.3281744
## 4831 2.9793370 -1.90827884 -1.6553652 -0.2657698 0.5448155 -1.3335340
## 11065 2.7232585 2.30375915 -0.6191980 0.8428194 2.7204532 1.9388028
## 8502 -0.5840147 1.26746266 1.8184121 0.7904014 -3.6101288 -0.4931417
## 3293 -1.7304459 2.37018711 0.2938001 -1.5130163 0.2905016 1.8268955
## PC7 PC8 PC9 PC10 PC11 PC12
## 12171 0.2497698 -0.53579970 -0.28985466 1.2367214 -2.1649160 -0.4947592
## 13326 -0.4344382 -1.33979430 -1.61670654 1.6336918 0.1593578 1.2330498
## 4831 -1.1218635 0.81310945 -0.13324660 -0.8092402 0.2244848 -0.7121255
## 11065 -0.8875500 1.15160434 0.04443927 1.4714151 0.6759259 0.4525514
## 8502 -0.4901897 -0.11425653 -0.29206909 -0.4725029 -0.7379404 1.5372819
## 3293 -1.6583981 0.07137076 0.65128441 -0.5163869 -0.9335496 -0.8234795
pcatrain = data.frame(train[,1:6], pca.big.data)
names(pcatrain) ## [1] "names" "race" "age" "engnat" "gender" "country" "PC1"
## [8] "PC2" "PC3" "PC4" "PC5" "PC6" "PC7" "PC8"
## [15] "PC9" "PC10" "PC11" "PC12"
names(pcatrain) = c("names","race","age","engnat","gender","country",
"bigPC1","bigPC2","bigPC3","bigPC4","bigPC5","bigPC6","bigPC7","bigPC8","bigPC9","bigPC10","bigPC11","bigPC12")
head(pcatrain)## names race age engnat gender country bigPC1 bigPC2
## 12171 Penni 3 21 1 1 US -4.2281719 0.09414946
## 13326 Margaretha 3 18 2 1 IT -1.1591050 0.40162890
## 4831 Isadore 3 34 1 2 US 2.9793370 -1.90827884
## 11065 Kinsley 3 25 1 2 US 2.7232585 2.30375915
## 8502 Shad 1 24 1 2 US -0.5840147 1.26746266
## 3293 Sara 13 42 2 1 SA -1.7304459 2.37018711
## bigPC3 bigPC4 bigPC5 bigPC6 bigPC7 bigPC8
## 12171 -1.3358523 1.0367636 -3.1158100 -0.5579354 0.2497698 -0.53579970
## 13326 2.0486767 1.2753684 3.9863527 0.3281744 -0.4344382 -1.33979430
## 4831 -1.6553652 -0.2657698 0.5448155 -1.3335340 -1.1218635 0.81310945
## 11065 -0.6191980 0.8428194 2.7204532 1.9388028 -0.8875500 1.15160434
## 8502 1.8184121 0.7904014 -3.6101288 -0.4931417 -0.4901897 -0.11425653
## 3293 0.2938001 -1.5130163 0.2905016 1.8268955 -1.6583981 0.07137076
## bigPC9 bigPC10 bigPC11 bigPC12
## 12171 -0.28985466 1.2367214 -2.1649160 -0.4947592
## 13326 -1.61670654 1.6336918 0.1593578 1.2330498
## 4831 -0.13324660 -0.8092402 0.2244848 -0.7121255
## 11065 0.04443927 1.4714151 0.6759259 0.4525514
## 8502 -0.29206909 -0.4725029 -0.7379404 1.5372819
## 3293 0.65128441 -0.5163869 -0.9335496 -0.8234795
pcatrain = data.frame(pcatrain, pca.int.data)
names(pcatrain) ## [1] "names" "race" "age" "engnat" "gender" "country" "bigPC1"
## [8] "bigPC2" "bigPC3" "bigPC4" "bigPC5" "bigPC6" "bigPC7" "bigPC8"
## [15] "bigPC9" "bigPC10" "bigPC11" "bigPC12" "PC1" "PC2" "PC3"
## [22] "PC4" "PC5" "PC6" "PC7" "PC8" "PC9" "PC10"
## [29] "PC11" "PC12" "PC13" "PC14"
names(pcatrain) = c("names","race","age","engnat","gender","country",
"bigPC1","bigPC2","bigPC3","bigPC4","bigPC5","bigPC6","bigPC7","bigPC8","bigPC9","bigPC10","bigPC11","bigPC12",
"intPC1","intPC2","intPC3","intPC4","intPC5","intPC6","intPC7","intPC8","intPC9","intPC10","intPC11","intPC12","intPC13","intPC14")head(pcatrain)## names race age engnat gender country bigPC1 bigPC2
## 12171 Penni 3 21 1 1 US -4.2281719 0.09414946
## 13326 Margaretha 3 18 2 1 IT -1.1591050 0.40162890
## 4831 Isadore 3 34 1 2 US 2.9793370 -1.90827884
## 11065 Kinsley 3 25 1 2 US 2.7232585 2.30375915
## 8502 Shad 1 24 1 2 US -0.5840147 1.26746266
## 3293 Sara 13 42 2 1 SA -1.7304459 2.37018711
## bigPC3 bigPC4 bigPC5 bigPC6 bigPC7 bigPC8
## 12171 -1.3358523 1.0367636 -3.1158100 -0.5579354 0.2497698 -0.53579970
## 13326 2.0486767 1.2753684 3.9863527 0.3281744 -0.4344382 -1.33979430
## 4831 -1.6553652 -0.2657698 0.5448155 -1.3335340 -1.1218635 0.81310945
## 11065 -0.6191980 0.8428194 2.7204532 1.9388028 -0.8875500 1.15160434
## 8502 1.8184121 0.7904014 -3.6101288 -0.4931417 -0.4901897 -0.11425653
## 3293 0.2938001 -1.5130163 0.2905016 1.8268955 -1.6583981 0.07137076
## bigPC9 bigPC10 bigPC11 bigPC12 intPC1 intPC2
## 12171 -0.28985466 1.2367214 -2.1649160 -0.4947592 2.40633523 1.18845622
## 13326 -1.61670654 1.6336918 0.1593578 1.2330498 0.74924685 2.59133550
## 4831 -0.13324660 -0.8092402 0.2244848 -0.7121255 -1.71110350 1.84238406
## 11065 0.04443927 1.4714151 0.6759259 0.4525514 0.08267284 0.08253572
## 8502 -0.29206909 -0.4725029 -0.7379404 1.5372819 0.49601123 2.03728398
## 3293 0.65128441 -0.5163869 -0.9335496 -0.8234795 -0.98882514 2.68628023
## intPC3 intPC4 intPC5 intPC6 intPC7 intPC8
## 12171 -2.20520117 0.67070390 -0.4008390 0.9623631 3.109552821 -0.6674774
## 13326 0.29012247 -0.06594698 -0.7980948 -2.5904208 -1.505994366 1.2534910
## 4831 -1.82041733 1.89584090 -0.8066074 1.0725876 1.940319388 0.8377373
## 11065 -0.07333943 2.30800138 -0.5239428 1.3757298 -1.699375591 -0.7817496
## 8502 0.82221728 -1.29199027 -0.4204712 0.3998408 0.006628133 -0.9736738
## 3293 2.27038431 -2.48013758 0.7205849 -3.4452011 0.193662549 -2.7277398
## intPC9 intPC10 intPC11 intPC12 intPC13 intPC14
## 12171 3.0711772 -0.5850462 -0.7363013 1.8818547 0.4826010 0.6609888
## 13326 -1.1161595 0.5383166 1.7365408 -0.4675288 1.6130843 -1.1724638
## 4831 -0.2676961 -0.4364324 -2.8254240 1.0166555 -1.2620974 1.5818881
## 11065 0.3996406 -1.4110333 0.3824675 -1.8161302 0.8601170 -0.6864529
## 8502 0.5380429 0.3662837 -0.1728037 -0.3725905 -0.5157871 -0.4723268
## 3293 2.5091072 0.4679246 0.4802233 1.4365345 -0.8228892 -2.0800021
names(pcatrain)## [1] "names" "race" "age" "engnat" "gender" "country" "bigPC1"
## [8] "bigPC2" "bigPC3" "bigPC4" "bigPC5" "bigPC6" "bigPC7" "bigPC8"
## [15] "bigPC9" "bigPC10" "bigPC11" "bigPC12" "intPC1" "intPC2" "intPC3"
## [22] "intPC4" "intPC5" "intPC6" "intPC7" "intPC8" "intPC9" "intPC10"
## [29] "intPC11" "intPC12" "intPC13" "intPC14"
remove(pr.out)
remove(pr.out2)
remove(pca.big.data)
remove(pca.int.data)head(pcatrain[,19:32])## intPC1 intPC2 intPC3 intPC4 intPC5 intPC6
## 12171 2.40633523 1.18845622 -2.20520117 0.67070390 -0.4008390 0.9623631
## 13326 0.74924685 2.59133550 0.29012247 -0.06594698 -0.7980948 -2.5904208
## 4831 -1.71110350 1.84238406 -1.82041733 1.89584090 -0.8066074 1.0725876
## 11065 0.08267284 0.08253572 -0.07333943 2.30800138 -0.5239428 1.3757298
## 8502 0.49601123 2.03728398 0.82221728 -1.29199027 -0.4204712 0.3998408
## 3293 -0.98882514 2.68628023 2.27038431 -2.48013758 0.7205849 -3.4452011
## intPC7 intPC8 intPC9 intPC10 intPC11 intPC12
## 12171 3.109552821 -0.6674774 3.0711772 -0.5850462 -0.7363013 1.8818547
## 13326 -1.505994366 1.2534910 -1.1161595 0.5383166 1.7365408 -0.4675288
## 4831 1.940319388 0.8377373 -0.2676961 -0.4364324 -2.8254240 1.0166555
## 11065 -1.699375591 -0.7817496 0.3996406 -1.4110333 0.3824675 -1.8161302
## 8502 0.006628133 -0.9736738 0.5380429 0.3662837 -0.1728037 -0.3725905
## 3293 0.193662549 -2.7277398 2.5091072 0.4679246 0.4802233 1.4365345
## intPC13 intPC14
## 12171 0.4826010 0.6609888
## 13326 1.6130843 -1.1724638
## 4831 -1.2620974 1.5818881
## 11065 0.8601170 -0.6864529
## 8502 -0.5157871 -0.4723268
## 3293 -0.8228892 -2.0800021
summary(pcatrain[,19:32])## intPC1 intPC2 intPC3 intPC4
## Min. :-6.15531 Min. :-6.6050 Min. :-5.0356 Min. :-4.45407
## 1st Qu.:-1.67308 1st Qu.:-1.5446 1st Qu.:-1.2614 1st Qu.:-1.02977
## Median :-0.01012 Median : 0.1526 Median : 0.1416 Median : 0.02759
## Mean : 0.00000 Mean : 0.0000 Mean : 0.0000 Mean : 0.00000
## 3rd Qu.: 1.63923 3rd Qu.: 1.4387 3rd Qu.: 1.3402 3rd Qu.: 1.13046
## Max. : 6.57355 Max. : 5.0302 Max. : 5.4538 Max. : 4.84087
## intPC5 intPC6 intPC7 intPC8
## Min. :-5.28843 Min. :-3.94786 Min. :-3.934797 Min. :-4.74929
## 1st Qu.:-1.00202 1st Qu.:-0.85142 1st Qu.:-0.883630 1st Qu.:-0.79510
## Median : 0.05238 Median :-0.09942 Median : 0.005552 Median : 0.03857
## Mean : 0.00000 Mean : 0.00000 Mean : 0.000000 Mean : 0.00000
## 3rd Qu.: 1.09424 3rd Qu.: 0.90053 3rd Qu.: 0.895008 3rd Qu.: 0.75753
## Max. : 3.86125 Max. : 4.25887 Max. : 4.233777 Max. : 5.02253
## intPC9 intPC10 intPC11 intPC12
## Min. :-4.21023 Min. :-3.43964 Min. :-3.96774 Min. :-3.17733
## 1st Qu.:-0.73270 1st Qu.:-0.76225 1st Qu.:-0.70676 1st Qu.:-0.72962
## Median :-0.04246 Median :-0.02173 Median : 0.00916 Median :-0.04405
## Mean : 0.00000 Mean : 0.00000 Mean : 0.00000 Mean : 0.00000
## 3rd Qu.: 0.82123 3rd Qu.: 0.77060 3rd Qu.: 0.66332 3rd Qu.: 0.68411
## Max. : 3.55215 Max. : 3.47732 Max. : 3.65949 Max. : 3.90112
## intPC13 intPC14
## Min. :-5.190609 Min. :-3.144010
## 1st Qu.:-0.634196 1st Qu.:-0.704728
## Median :-0.002093 Median : 0.009765
## Mean : 0.000000 Mean : 0.000000
## 3rd Qu.: 0.661558 3rd Qu.: 0.705060
## Max. : 3.054591 Max. : 3.086630
distances = dist(pcatrain[,19:32], method = "euclidean")
hc = hclust(distances, method = 'ward.D')
remove(distances)Plotting the dendogram
knitr::opts_chunk$set(echo = TRUE,
warning = FALSE,
messages = FALSE,
include = TRUE)
plot(hc)
abline(h=500, col = 'red')
k.max <- 15
data <- pcatrain[,19:32]wss <- sapply(1:k.max, function(k){kmeans(data, k, nstart=50,iter.max = 15 )$tot.withinss})
wss## [1] 148492.44 131151.09 120232.47 113390.30 108691.24 104751.28 101589.00
## [8] 98891.29 96364.51 94664.70 92871.99 91267.72 89703.13 88514.72
## [15] 87436.42
bss <- sapply(1:k.max, function(k){kmeans(data, k, nstart=50,iter.max = 15 )$betweenss})
bss## [1] 1.658918e-09 1.733674e+04 2.826365e+04 3.509917e+04 3.980048e+04
## [6] 4.367044e+04 4.686375e+04 4.957283e+04 5.205421e+04 5.401076e+04
## [11] 5.561663e+04 5.721925e+04 5.899675e+04 6.007405e+04 6.111143e+04
par(mfrow=c(1,2))
plot(1:k.max, wss, type="b", pch = 19, frame = FALSE,
xlab="Number of clusters K",
ylab="Total within-clusters sum of squares")
plot(1:k.max, bss, type="b", pch = 19, frame = FALSE,
xlab="Number of clusters K",
ylab="Between clusters sum of squares")
set.seed(2)
kmc = kmeans(pcatrain[,19:32], centers = 6, iter.max = 20)kmc$size## [1] 741 938 676 713 840 1092
kmc$cluster[1:10]## 12171 13326 4831 11065 8502 3293 3470 15729 17157 11904
## 2 6 1 2 6 3 2 6 3 1
kmc$withinss## [1] 16397.44 21521.65 14349.41 13665.49 18327.25 20854.34
kmc$tot.withinss## [1] 105115.6
cluster = kmc$clusterJoining the cluster assigned with the PCA'd data
cluster = kmc$cluster
pcatrain = data.frame(pcatrain, cluster)
names(pcatrain)## [1] "names" "race" "age" "engnat" "gender" "country" "bigPC1"
## [8] "bigPC2" "bigPC3" "bigPC4" "bigPC5" "bigPC6" "bigPC7" "bigPC8"
## [15] "bigPC9" "bigPC10" "bigPC11" "bigPC12" "intPC1" "intPC2" "intPC3"
## [22] "intPC4" "intPC5" "intPC6" "intPC7" "intPC8" "intPC9" "intPC10"
## [29] "intPC11" "intPC12" "intPC13" "intPC14" "cluster"
head(pcatrain[,1:6])## names race age engnat gender country
## 12171 Penni 3 21 1 1 US
## 13326 Margaretha 3 18 2 1 IT
## 4831 Isadore 3 34 1 2 US
## 11065 Kinsley 3 25 1 2 US
## 8502 Shad 1 24 1 2 US
## 3293 Sara 13 42 2 1 SA
Let's take 'Penni' as our user
user = pcatrain[pcatrain$names == 'Penni',]
user## names race age engnat gender country bigPC1 bigPC2 bigPC3
## 12171 Penni 3 21 1 1 US -4.228172 0.09414946 -1.335852
## bigPC4 bigPC5 bigPC6 bigPC7 bigPC8 bigPC9 bigPC10
## 12171 1.036764 -3.11581 -0.5579354 0.2497698 -0.5357997 -0.2898547 1.236721
## bigPC11 bigPC12 intPC1 intPC2 intPC3 intPC4 intPC5
## 12171 -2.164916 -0.4947592 2.406335 1.188456 -2.205201 0.6707039 -0.400839
## intPC6 intPC7 intPC8 intPC9 intPC10 intPC11 intPC12
## 12171 0.9623631 3.109553 -0.6674774 3.071177 -0.5850462 -0.7363013 1.881855
## intPC13 intPC14 cluster
## 12171 0.482601 0.6609888 2
closecluster = pcatrain %>% filter(kmc$cluster == user$cluster)
refined = as.data.frame(subset(closecluster,closecluster$country == user$country & closecluster$gender != user$gender & (closecluster$age >= (user$age-3) & closecluster$age <= (user$age+3))))
head(refined)## names race age engnat gender country bigPC1 bigPC2 bigPC3
## 3 Pattie 1 20 1 2 US -1.3591848 -0.4410222 -2.0629725
## 4 Shelia 1 23 1 2 US 1.1214095 0.5534369 -1.8178033
## 15 Teri 13 20 1 2 US -0.2839822 -1.5404516 -0.1379372
## 17 Gaige 3 20 1 2 US 3.5917136 0.9297529 0.4709359
## 21 Adaline 13 20 1 2 US 0.5138417 -4.6030577 1.8177538
## 24 Moe 3 23 1 2 US -0.9600468 0.8772718 -1.7421830
## bigPC4 bigPC5 bigPC6 bigPC7 bigPC8 bigPC9
## 3 1.361873920 0.9788897 -0.7674841 1.4658995 -0.7795143 -0.3176327
## 4 0.835951460 -0.3425387 0.2541555 -0.4395472 -0.4535604 0.2157490
## 15 0.098565505 -2.1292169 0.2986199 0.4924998 -0.2385092 0.4378537
## 17 -0.492982912 -0.4661128 0.2313610 0.5908614 0.9495662 0.6761938
## 21 0.544240305 -0.6789188 0.6020035 -0.9770587 0.6461576 -0.3821976
## 24 0.009323049 1.3004048 0.6743873 -1.4561606 0.7077245 0.8280518
## bigPC10 bigPC11 bigPC12 intPC1 intPC2 intPC3
## 3 2.0305558 -0.39186230 0.213804058 2.9400556 -1.81406215 0.454633
## 4 -0.7289876 -0.02728458 1.544471611 2.0042566 1.90034249 -3.597985
## 15 0.4686442 0.48644158 -0.888025970 0.0303163 0.47771472 -1.258392
## 17 -0.4676101 1.22780088 0.586652268 3.5404318 0.12670785 3.231298
## 21 1.8547801 -0.63440053 0.007361884 3.1804805 0.01808245 -0.444194
## 24 0.4501704 -0.45035885 0.006148293 2.6272822 1.16178403 -1.983430
## intPC4 intPC5 intPC6 intPC7 intPC8 intPC9
## 3 0.9714420 -1.3388389 0.001766073 -0.6417052 2.4901381 -0.3664859
## 4 0.6884702 -3.2919456 1.681655777 -0.9191112 -1.7187045 -4.0334402
## 15 1.3994289 -0.5987709 -0.201098757 1.7393151 -0.9227329 0.2531125
## 17 2.2869994 0.2372757 -3.082327007 0.7077357 -1.0607778 -0.8295813
## 21 0.1631865 -1.7095435 0.760249556 -1.0678127 0.0678897 -0.3083916
## 24 -1.0713924 1.8109613 0.605498463 -0.4845007 -1.8077732 1.8870192
## intPC10 intPC11 intPC12 intPC13 intPC14 cluster
## 3 1.9920381 1.7659955 0.9100315 -1.32180547 0.3852264 2
## 4 1.2024824 1.7023286 -0.6160232 -1.10857334 -0.7198607 2
## 15 0.6955775 -1.4696346 0.5545520 -1.82050032 -1.8930884 2
## 17 -0.2614229 0.5036463 1.0025457 0.01459203 1.1291965 2
## 21 2.8924941 1.6430890 -0.8658139 -0.13037245 1.1646183 2
## 24 -1.2414851 0.5321581 -1.3933501 -1.43915252 -1.1400259 2
for(i in c(1:nrow(refined))) {refined$sumdifference[i] = sum(sqrt((refined[i,7:18]-user[,7:18])^2))}
selected = head(refined[order(refined$sumdifference),],10)$names
selected## [1] "Brody" "Alissa" "Young" "Lizabeth" "Reinhold" "Liza"
## [7] "Polly" "Ephram" "Keenen" "Pattie"
head(refined[order(refined$sumdifference),c(1,33)],10)## names cluster
## 64 Brody 2
## 79 Alissa 2
## 69 Young 2
## 204 Lizabeth 2
## 728 Reinhold 2
## 734 Liza 2
## 281 Polly 2
## 617 Ephram 2
## 110 Keenen 2
## 3 Pattie 2
train[train$names==user$names,]## names race age engnat gender country E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 N1 N2
## 12171 Penni 3 21 1 1 US 1 5 1 5 2 5 1 4 1 5 5 4
## N3 N4 N5 N6 N7 N8 N9 N10 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 C1 C2 C3 C4 C5 C6
## 12171 5 1 4 4 3 2 2 4 1 1 2 5 2 5 2 4 4 3 4 5 5 4 1 4
## C7 C8 C9 C10 O1 O2 O3 O4 O5 O6 O7 O8 O9 O10 Music Classical.music Musical
## 12171 1 3 3 3 2 1 4 3 4 2 5 4 4 4 5 3 1
## Pop Rock Metal.or.Hardrock Hiphop..Rap Rock.n.roll Alternative
## 12171 3 5 3 1 3 5
## Techno..Trance Movies Horror Thriller Comedy Romantic Sci.fi War
## 12171 2 3 1 2 3 4 4 2
## Fantasy.Fairy.tales Animated Documentary Action History Psychology
## 12171 1 2 1 3 1 5
## Politics Mathematics Physics Internet PC Economy.Management Biology
## 12171 2 1 2 5 3 5 2
## Chemistry Reading Geography Foreign.languages Medicine Law Cars
## 12171 2 1 2 3 1 2 1
## Art.exhibitions Religion Dancing Musical.instruments Writing
## 12171 2 1 2 2 1
## Passive.sport Active.sport Science.and.technology Theatre
## 12171 5 1 3 1
## Adrenaline.sports Pets Appearence.and.gestures Happiness.in.life
## 12171 4 2 5 5
## Education
## 12171 college/bachelor degree
#Following are the potential matches for the user
train[train$names %in% selected,]## names race age engnat gender country E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 N1
## 3470 Pattie 1 20 1 2 US 2 3 2 4 3 2 3 4 2 4 4
## 17908 Brody 3 18 1 2 US 1 5 1 5 1 5 1 4 1 5 3
## 162 Young 3 22 1 2 US 1 5 3 5 1 5 1 5 1 5 5
## 18736 Alissa 1 22 1 2 US 1 5 2 5 1 4 1 5 1 5 5
## 7796 Keenen 3 18 1 2 US 2 4 2 4 1 4 1 5 1 5 3
## 17916 Lizabeth 3 21 2 2 US 3 5 2 4 4 5 1 4 2 5 4
## 14206 Polly 3 23 1 2 US 3 4 2 3 1 3 1 4 4 4 3
## 1382 Ephram 13 18 1 2 US 1 3 1 5 3 5 2 5 3 5 5
## 12162 Reinhold 8 21 1 2 US 1 3 2 5 2 3 2 3 4 5 4
## 6464 Liza 1 22 1 3 US 1 4 1 5 1 4 1 4 2 5 3
## N2 N3 N4 N5 N6 N7 N8 N9 N10 A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 C1 C2 C3 C4 C5
## 3470 3 4 2 5 3 3 4 4 4 5 3 1 5 2 4 3 4 5 4 5 4 5 3 3
## 17908 4 3 2 4 3 3 2 2 3 1 5 1 5 2 5 1 3 4 1 3 4 4 2 2
## 162 3 5 2 3 5 4 2 3 2 1 2 1 5 2 5 3 5 4 3 1 5 4 5 2
## 18736 3 5 2 3 5 5 3 4 3 1 2 3 5 3 5 4 5 5 4 4 3 4 4 1
## 7796 4 4 3 2 2 4 4 3 3 1 4 1 5 1 4 1 5 4 4 4 4 3 2 3
## 17916 2 4 1 4 4 4 2 3 4 2 4 1 3 3 5 2 3 3 2 4 5 2 3 1
## 14206 5 5 2 1 1 2 2 3 3 1 1 1 5 1 4 4 4 5 4 3 1 3 2 2
## 1382 5 5 1 3 4 5 4 4 4 1 5 1 5 3 5 1 4 5 3 1 4 4 3 1
## 12162 2 5 2 4 5 5 4 3 5 1 4 1 5 1 5 2 4 4 3 3 5 3 5 1
## 6464 3 3 1 4 4 3 4 4 5 2 4 4 4 3 3 3 4 4 1 2 5 4 4 2
## C6 C7 C8 C9 C10 O1 O2 O3 O4 O5 O6 O7 O8 O9 O10 Music Classical.music
## 3470 4 5 2 5 4 4 2 4 1 4 4 5 3 5 4 3 1
## 17908 5 3 3 3 3 4 2 4 1 4 2 4 3 4 4 4 3
## 162 5 3 3 1 2 3 3 5 1 4 1 3 1 5 4 5 5
## 18736 5 2 1 2 4 4 4 4 2 3 1 4 4 5 4 5 4
## 7796 2 3 1 4 4 4 3 5 2 4 1 4 3 4 4 4 2
## 17916 5 5 5 3 4 3 3 5 1 5 2 2 4 5 5 4 3
## 14206 4 4 3 2 3 2 1 5 3 4 1 4 1 5 4 4 3
## 1382 5 5 3 4 3 5 1 5 1 4 1 4 5 5 5 5 3
## 12162 5 4 3 1 5 5 1 5 1 4 2 4 4 5 5 5 1
## 6464 4 2 4 1 2 4 2 4 2 3 2 4 4 5 4 5 3
## Musical Pop Rock Metal.or.Hardrock Hiphop..Rap Rock.n.roll Alternative
## 3470 1 2 3 2 2 2 2
## 17908 1 5 4 1 1 2 1
## 162 3 2 1 1 5 1 1
## 18736 3 4 4 1 4 3 2
## 7796 1 3 3 3 4 2 1
## 17916 1 3 5 4 1 5 4
## 14206 1 5 3 3 2 2 2
## 1382 1 2 3 2 5 3 2
## 12162 1 3 1 1 1 1 1
## 6464 4 4 4 2 4 4 1
## Techno..Trance Movies Horror Thriller Comedy Romantic Sci.fi War
## 3470 2 3 3 3 5 3 3 4
## 17908 1 5 1 2 5 2 4 5
## 162 2 5 1 1 5 1 4 5
## 18736 1 5 5 5 5 3 3 4
## 7796 4 5 5 5 5 3 5 5
## 17916 1 5 1 4 4 2 2 3
## 14206 3 5 3 5 3 3 3 5
## 1382 2 5 3 2 4 2 3 5
## 12162 1 3 5 5 5 5 3 1
## 6464 2 4 3 3 4 2 2 4
## Fantasy.Fairy.tales Animated Documentary Action History Psychology
## 3470 5 5 4 4 3 3
## 17908 3 3 5 5 3 2
## 162 3 1 4 5 4 3
## 18736 3 4 3 4 4 4
## 7796 4 4 3 5 5 2
## 17916 1 1 1 3 4 2
## 14206 3 3 5 4 3 3
## 1382 2 3 5 3 4 4
## 12162 2 1 2 2 1 3
## 6464 2 3 2 4 5 2
## Politics Mathematics Physics Internet PC Economy.Management Biology
## 3470 5 2 2 5 5 4 2
## 17908 4 4 2 5 5 1 1
## 162 5 1 1 4 3 4 1
## 18736 4 1 1 5 5 5 1
## 7796 3 2 1 5 3 2 1
## 17916 5 1 1 5 5 1 1
## 14206 2 4 2 4 4 3 2
## 1382 1 3 3 4 3 2 2
## 12162 1 1 5 5 5 1 3
## 6464 5 1 2 4 4 4 2
## Chemistry Reading Geography Foreign.languages Medicine Law Cars
## 3470 2 1 3 3 2 4 5
## 17908 1 3 3 3 1 1 3
## 162 1 1 4 5 1 3 5
## 18736 1 4 3 3 1 5 1
## 7796 1 1 5 3 3 1 2
## 17916 1 4 3 2 1 4 4
## 14206 1 2 2 3 1 1 1
## 1382 2 4 3 1 2 4 1
## 12162 1 1 1 2 2 1 5
## 6464 2 2 4 4 3 5 3
## Art.exhibitions Religion Dancing Musical.instruments Writing
## 3470 3 1 1 1 1
## 17908 1 1 1 1 1
## 162 3 2 1 5 3
## 18736 1 1 1 1 4
## 7796 1 2 1 1 1
## 17916 2 1 1 1 1
## 14206 1 3 1 1 1
## 1382 1 3 1 1 1
## 12162 1 5 2 1 1
## 6464 1 1 1 2 1
## Passive.sport Active.sport Science.and.technology Theatre
## 3470 5 5 3 2
## 17908 2 1 5 1
## 162 2 5 2 2
## 18736 4 4 5 3
## 7796 3 3 2 1
## 17916 3 2 3 2
## 14206 1 5 3 1
## 1382 5 5 2 3
## 12162 5 2 5 1
## 6464 5 4 3 4
## Adrenaline.sports Pets Appearence.and.gestures Happiness.in.life
## 3470 5 3 3 4
## 17908 1 1 4 4
## 162 4 3 4 3
## 18736 4 1 5 2
## 7796 3 3 3 3
## 17916 1 4 2 3
## 14206 2 3 3 3
## 1382 4 3 4 4
## 12162 1 1 5 3
## 6464 5 4 2 4
## Education
## 3470 college/bachelor degree
## 17908 secondary school
## 162 masters degree
## 18736 secondary school
## 7796 primary school
## 17916 secondary school
## 14206 secondary school
## 1382 masters degree
## 12162 primary school
## 6464 college/bachelor degree