Student_Perfomance
Purity
29/05/2020
Given below are attributes
Attributes for both student-mat.csv (Math course) and student-por.csv (Portuguese language course) datasets:
1 school - student’s school (binary: ‘GP’ - Gabriel Pereira or ‘MS’ - Mousinho da Silveira)
2 sex - student’s sex (binary: ‘F’ - female or ‘M’ - male)
3.age - student’s age (numeric: from 15 to 22)
4.address - student’s home address type (binary: ‘U’ - urban or ‘R’ - rural)
5.famsize - family size (binary: ‘LE3’ - less or equal to 3 or ‘GT3’ - greater than 3)
6.Pstatus - parent’s cohabitation status (binary: ‘T’ - living together or ‘A’ - apart)
7.Medu - mother’s education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
8.Fedu - father’s education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)
9.Mjob - mother’s job (nominal: ‘teacher’, ‘health’ care related, civil ‘services’ (e.g. administrative or police), ‘at_home’ or ‘other’)
10.Fjob - father’s job (nominal: ‘teacher’, ‘health’ care related, civil ‘services’ (e.g. administrative or police), ‘at_home’ or ‘other’)
11.reason - reason to choose this school (nominal: close to ‘home’, school ‘reputation’, ‘course’ preference or ‘other’)
12.guardian - student’s guardian (nominal: ‘mother’, ‘father’ or ‘other’)
13.traveltime - home to school travel time (numeric: 1 - 1 hour)
14.studytime - weekly study time (numeric: 1 - 10 hours)
15.failures - number of past class failures (numeric: n if 1<=n<3, else 4)
16.schoolsup - extra educational support (binary: yes or no)
17.famsup - family educational support (binary: yes or no)
18.paid - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)
19.activities - extra-curricular activities (binary: yes or no)
20.nursery - attended nursery school (binary: yes or no)
21.higher - wants to take higher education (binary: yes or no)
22.internet - Internet access at home (binary: yes or no)
23.romantic - with a romantic relationship (binary: yes or no)
24.famrel - quality of family relationships (numeric: from 1 - very bad to 5 - excellent)
25.freetime - free time after school (numeric: from 1 - very low to 5 - very high)
26.goout - going out with friends (numeric: from 1 - very low to 5 - very high)
27.Dalc - workday alcohol consumption (numeric: from 1 - very low to 5 - very high)
28.Walc - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high)
29.health - current health status (numeric: from 1 - very bad to 5 - very good)
30.absences - number of school absences (numeric: from 0 to 93) These grades are related with the course subject, Math or Portuguese: 1.G1 - first period grade (numeric: from 0 to 20)
2.G2 - second period grade (numeric: from 0 to 20)
3.G3 - final grade (numeric: from 0 to 20, output target)
Additional note: there are several (382) students that belong to both datasets .
These students can be identified by searching for identical attributes
that characterize each student, as shown in the annexed R file. Source Information
P. Cortez and A. Silva. Using Data Mining to Predict Secondary School Student Performance. In A. Brito and J. Teixeira Eds., Proceedings of 5th FUture BUsiness TEChnology Conference (FUBUTEC 2008) pp. 5-12, Porto, Portugal, April, 2008, EUROSIS, ISBN 978-9077381-39-7. Fabio Pagnotta, Hossain Mohammad Amran. Email:fabio.pagnotta@studenti.unicam.it, mohammadamra.hossain ‘@’ studenti.unicam.it University Of Camerino
https://archive.ics.uci.edu/ml/datasets/STUDENT+ALCOHOL+CONSUMPTION
Business Understanding
A Non Governmental Organization has collected data from students doing math and portugese course in high school.They would like to gain insights from the social,gender and study information about them. They would also like you to predict the student final results.
- Solution
Do EDA and get as many insights as possible.Use the suitable supervised machine learning algorithm to predict what is needed.
Data Understanding
Here we load the datasets needed.Know about it all by checking distributions/skewness,shape and skewness.
#load the data sets and libraries
library(tidyverse)##Is a set of packages that include dplyr,tidyr etc## Warning: package 'tidyverse' was built under R version 3.6.3## -- Attaching packages ------------------------ tidyverse 1.3.0 --## v ggplot2 3.2.1 v purrr 0.3.4
## v tibble 2.1.3 v dplyr 0.8.3
## v tidyr 1.0.0 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0## Warning: package 'readr' was built under R version 3.6.3## Warning: package 'purrr' was built under R version 3.6.3## -- Conflicts --------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()d1=read.csv("student-mat.csv",header=TRUE)
d2=read.csv("student-por.csv",header=TRUE)
head(d2)## school sex age address famsize Pstatus Medu Fedu Mjob Fjob
## 1 GP F 18 U GT3 A 4 4 at_home teacher
## 2 GP F 17 U GT3 T 1 1 at_home other
## 3 GP F 15 U LE3 T 1 1 at_home other
## 4 GP F 15 U GT3 T 4 2 health services
## 5 GP F 16 U GT3 T 3 3 other other
## 6 GP M 16 U LE3 T 4 3 services other
## reason guardian traveltime studytime failures schoolsup famsup paid
## 1 course mother 2 2 0 yes no no
## 2 course father 1 2 0 no yes no
## 3 other mother 1 2 0 yes no no
## 4 home mother 1 3 0 no yes no
## 5 home father 1 2 0 no yes no
## 6 reputation mother 1 2 0 no yes no
## activities nursery higher internet romantic famrel freetime goout Dalc
## 1 no yes yes no no 4 3 4 1
## 2 no no yes yes no 5 3 3 1
## 3 no yes yes yes no 4 3 2 2
## 4 yes yes yes yes yes 3 2 2 1
## 5 no yes yes no no 4 3 2 1
## 6 yes yes yes yes no 5 4 2 1
## Walc health absences G1 G2 G3
## 1 1 3 4 0 11 11
## 2 1 3 2 9 11 11
## 3 3 3 6 12 13 12
## 4 1 5 0 14 14 14
## 5 2 5 0 11 13 13
## 6 2 5 6 12 12 13d4=merge(d1,d2,by=c("school","sex","age","address","famsize","Pstatus","Medu","Fedu","Mjob","Fjob","reason","nursery","internet"))
#d4[!duplicated(d4$guardian.x,d4)]
print(nrow(d4))## [1] 382AS seen above the two are merged to give a common dataset with 382 rows
d3<-read.csv("d3.csv")
view(d3)Check The Shape
dim(d3)## [1] 382 35anyDuplicated(colnames(d3))## [1] 0Check the structure
#I normally use glimpse
glimpse(d3)## Observations: 382
## Variables: 35
## $ school <fct> GP, GP, GP, GP, GP, GP, GP, GP, GP, GP, GP, GP, G...
## $ sex <fct> F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F...
## $ age <int> 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 1...
## $ address <fct> R, R, R, R, R, R, R, R, R, U, U, U, U, U, U, U, U...
## $ famsize <fct> GT3, GT3, GT3, GT3, GT3, GT3, GT3, LE3, LE3, GT3,...
## $ Pstatus <fct> T, T, T, T, T, T, T, T, T, A, A, T, T, T, T, T, T...
## $ Medu <int> 1, 1, 2, 2, 3, 3, 3, 2, 3, 3, 4, 1, 1, 1, 1, 1, 2...
## $ Fedu <int> 1, 1, 2, 4, 3, 4, 4, 2, 1, 3, 3, 1, 1, 1, 2, 2, 1...
## $ Mjob <fct> at_home, other, at_home, services, services, serv...
## $ Fjob <fct> other, other, other, health, services, health, te...
## $ reason <fct> home, reputation, reputation, course, reputation,...
## $ nursery <fct> yes, no, yes, yes, yes, yes, yes, yes, no, yes, y...
## $ internet <fct> yes, yes, no, yes, yes, yes, yes, yes, yes, no, y...
## $ traveltime.x <int> 2, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1, 3, 1, 1, 1, 1, 3...
## $ studytime.x <int> 4, 2, 1, 3, 3, 3, 3, 2, 4, 4, 2, 1, 2, 2, 2, 2, 3...
## $ failures.x <int> 1, 2, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0...
## $ schoolsup.x <fct> yes, yes, yes, yes, no, yes, no, yes, no, yes, no...
## $ famsup.x <fct> yes, yes, yes, yes, yes, yes, yes, yes, yes, no, ...
## $ paid.x <fct> yes, no, yes, yes, yes, yes, no, yes, no, no, yes...
## $ activities.x <fct> yes, no, yes, yes, yes, yes, no, no, no, no, yes,...
## $ higher.x <fct> yes, yes, yes, yes, yes, yes, yes, yes, yes, yes,...
## $ romantic.x <fct> no, yes, no, no, yes, no, yes, no, no, no, no, ye...
## $ famrel.x <int> 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 5...
## $ freetime.x <int> 1, 3, 3, 3, 2, 3, 2, 1, 4, 3, 3, 3, 3, 4, 3, 2, 2...
## $ goout.x <int> 2, 4, 1, 2, 1, 2, 2, 3, 2, 3, 2, 3, 2, 2, 2, 3, 2...
## $ Dalc.x <int> 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1...
## $ Walc.x <int> 1, 4, 1, 1, 3, 1, 2, 3, 3, 1, 1, 2, 3, 2, 1, 2, 1...
## $ health.x <int> 1, 5, 2, 5, 3, 5, 5, 4, 3, 4, 1, 4, 4, 5, 5, 1, 4...
## $ absences.x <int> 2, 2, 8, 2, 8, 2, 0, 2, 12, 10, 0, 0, 2, 0, 2, 2,...
## $ G1.x <int> 7, 8, 14, 10, 10, 12, 12, 8, 16, 10, 14, 8, 9, 8,...
## $ G2.x <int> 10, 6, 13, 9, 10, 12, 0, 9, 16, 11, 15, 0, 10, 11...
## $ G3.x <int> 10, 5, 13, 8, 10, 11, 0, 8, 16, 11, 15, 0, 10, 11...
## $ G1.y <int> 13, 13, 14, 10, 13, 11, 10, 11, 15, 10, 15, 11, 1...
## $ G2.y <int> 13, 11, 13, 11, 13, 12, 11, 10, 15, 10, 14, 12, 1...
## $ G3.y <int> 13, 11, 12, 10, 13, 12, 12, 11, 15, 10, 15, 13, 1...next check for missing values
#Check per each column
colSums(is.na(d3))## school sex age address famsize
## 0 0 0 0 0
## Pstatus Medu Fedu Mjob Fjob
## 0 0 0 0 0
## reason nursery internet traveltime.x studytime.x
## 0 0 0 0 0
## failures.x schoolsup.x famsup.x paid.x activities.x
## 0 0 0 0 0
## higher.x romantic.x famrel.x freetime.x goout.x
## 0 0 0 0 0
## Dalc.x Walc.x health.x absences.x G1.x
## 0 0 0 0 0
## G2.x G3.x G1.y G2.y G3.y
## 0 0 0 0 0Have a summary of all
summary(d3)## school sex age address famsize Pstatus
## GP:342 F:198 Min. :15.00 R: 81 GT3:278 A: 38
## MS: 40 M:184 1st Qu.:16.00 U:301 LE3:104 T:344
## Median :17.00
## Mean :16.59
## 3rd Qu.:17.00
## Max. :22.00
## Medu Fedu Mjob Fjob
## Min. :0.000 Min. :0.000 at_home : 53 at_home : 16
## 1st Qu.:2.000 1st Qu.:2.000 health : 33 health : 17
## Median :3.000 Median :3.000 other :138 other :211
## Mean :2.806 Mean :2.565 services: 96 services:107
## 3rd Qu.:4.000 3rd Qu.:4.000 teacher : 62 teacher : 31
## Max. :4.000 Max. :4.000
## reason nursery internet traveltime.x studytime.x
## course :140 no : 72 no : 58 Min. :1.000 Min. :1.000
## home :110 yes:310 yes:324 1st Qu.:1.000 1st Qu.:1.000
## other : 34 Median :1.000 Median :2.000
## reputation: 98 Mean :1.442 Mean :2.034
## 3rd Qu.:2.000 3rd Qu.:2.000
## Max. :4.000 Max. :4.000
## failures.x schoolsup.x famsup.x paid.x activities.x higher.x
## Min. :0.0000 no :331 no :144 no :205 no :181 no : 18
## 1st Qu.:0.0000 yes: 51 yes:238 yes:177 yes:201 yes:364
## Median :0.0000
## Mean :0.2906
## 3rd Qu.:0.0000
## Max. :3.0000
## romantic.x famrel.x freetime.x goout.x Dalc.x
## no :261 Min. :1.00 Min. :1.000 Min. :1.000 Min. :1.000
## yes:121 1st Qu.:4.00 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:1.000
## Median :4.00 Median :3.000 Median :3.000 Median :1.000
## Mean :3.94 Mean :3.223 Mean :3.113 Mean :1.474
## 3rd Qu.:5.00 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:2.000
## Max. :5.00 Max. :5.000 Max. :5.000 Max. :5.000
## Walc.x health.x absences.x G1.x
## Min. :1.00 Min. :1.000 Min. : 0.000 Min. : 3.00
## 1st Qu.:1.00 1st Qu.:3.000 1st Qu.: 0.000 1st Qu.: 8.00
## Median :2.00 Median :4.000 Median : 3.000 Median :10.50
## Mean :2.28 Mean :3.579 Mean : 5.319 Mean :10.86
## 3rd Qu.:3.00 3rd Qu.:5.000 3rd Qu.: 8.000 3rd Qu.:13.00
## Max. :5.00 Max. :5.000 Max. :75.000 Max. :19.00
## G2.x G3.x G1.y G2.y
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 5.00
## 1st Qu.: 8.25 1st Qu.: 8.00 1st Qu.:10.00 1st Qu.:11.00
## Median :11.00 Median :11.00 Median :12.00 Median :12.00
## Mean :10.71 Mean :10.39 Mean :12.11 Mean :12.24
## 3rd Qu.:13.00 3rd Qu.:14.00 3rd Qu.:14.00 3rd Qu.:14.00
## Max. :19.00 Max. :20.00 Max. :19.00 Max. :19.00
## G3.y
## Min. : 0.00
## 1st Qu.:11.00
## Median :13.00
## Mean :12.52
## 3rd Qu.:14.00
## Max. :19.00Remove Some things
since this is a merge from portugese and math course for students who do both they have final grade for the two So we have to have common final grade I decided to add and divide by 2 to get the common final grade for a student
d3<-d3%>%
mutate(G1=(G1.x +G1.y)/2,
G2 = ( G2.x + G2.y )/2,
G3 = ( G3.x + G3.y )/2)know i will drop the grades from both.
dim(d3)## [1] 382 38d3<-select(d3,-c(30:35))str(d3)## 'data.frame': 382 obs. of 32 variables:
## $ school : Factor w/ 2 levels "GP","MS": 1 1 1 1 1 1 1 1 1 1 ...
## $ sex : Factor w/ 2 levels "F","M": 1 1 1 1 1 1 1 1 1 1 ...
## $ age : int 15 15 15 15 15 15 15 15 15 15 ...
## $ address : Factor w/ 2 levels "R","U": 1 1 1 1 1 1 1 1 1 2 ...
## $ famsize : Factor w/ 2 levels "GT3","LE3": 1 1 1 1 1 1 1 2 2 1 ...
## $ Pstatus : Factor w/ 2 levels "A","T": 2 2 2 2 2 2 2 2 2 1 ...
## $ Medu : int 1 1 2 2 3 3 3 2 3 3 ...
## $ Fedu : int 1 1 2 4 3 4 4 2 1 3 ...
## $ Mjob : Factor w/ 5 levels "at_home","health",..: 1 3 1 4 4 4 4 2 3 3 ...
## $ Fjob : Factor w/ 5 levels "at_home","health",..: 3 3 3 2 4 2 5 4 3 2 ...
## $ reason : Factor w/ 4 levels "course","home",..: 2 4 4 1 4 1 1 4 4 4 ...
## $ nursery : Factor w/ 2 levels "no","yes": 2 1 2 2 2 2 2 2 1 2 ...
## $ internet : Factor w/ 2 levels "no","yes": 2 2 1 2 2 2 2 2 2 1 ...
## $ traveltime.x: int 2 1 1 1 2 1 2 2 2 1 ...
## $ studytime.x : int 4 2 1 3 3 3 3 2 4 4 ...
## $ failures.x : int 1 2 0 0 2 0 2 0 0 0 ...
## $ schoolsup.x : Factor w/ 2 levels "no","yes": 2 2 2 2 1 2 1 2 1 2 ...
## $ famsup.x : Factor w/ 2 levels "no","yes": 2 2 2 2 2 2 2 2 2 1 ...
## $ paid.x : Factor w/ 2 levels "no","yes": 2 1 2 2 2 2 1 2 1 1 ...
## $ activities.x: Factor w/ 2 levels "no","yes": 2 1 2 2 2 2 1 1 1 1 ...
## $ higher.x : Factor w/ 2 levels "no","yes": 2 2 2 2 2 2 2 2 2 2 ...
## $ romantic.x : Factor w/ 2 levels "no","yes": 1 2 1 1 2 1 2 1 1 1 ...
## $ famrel.x : int 3 3 4 4 4 4 4 4 4 4 ...
## $ freetime.x : int 1 3 3 3 2 3 2 1 4 3 ...
## $ goout.x : int 2 4 1 2 1 2 2 3 2 3 ...
## $ Dalc.x : int 1 2 1 1 2 1 2 1 2 1 ...
## $ Walc.x : int 1 4 1 1 3 1 2 3 3 1 ...
## $ health.x : int 1 5 2 5 3 5 5 4 3 4 ...
## $ absences.x : int 2 2 8 2 8 2 0 2 12 10 ...
## $ G1 : num 10 10.5 14 10 11.5 11.5 11 9.5 15.5 10 ...
## $ G2 : num 11.5 8.5 13 10 11.5 12 5.5 9.5 15.5 10.5 ...
## $ G3 : num 11.5 8 12.5 9 11.5 11.5 6 9.5 15.5 10.5 ...d3[27:32]## Walc.x health.x absences.x G1 G2 G3
## 1 1 1 2 10.0 11.5 11.5
## 2 4 5 2 10.5 8.5 8.0
## 3 1 2 8 14.0 13.0 12.5
## 4 1 5 2 10.0 10.0 9.0
## 5 3 3 8 11.5 11.5 11.5
## 6 1 5 2 11.5 12.0 11.5
## 7 2 5 0 11.0 5.5 6.0
## 8 3 4 2 9.5 9.5 9.5
## 9 3 3 12 15.5 15.5 15.5
## 10 1 4 10 10.0 10.5 10.5
## 11 1 1 0 14.5 14.5 15.0
## 12 2 4 0 9.5 6.0 6.5
## 13 3 4 2 11.0 11.0 11.0
## 14 2 5 0 10.0 11.5 11.5
## 15 1 5 2 11.5 11.5 12.0
## 16 2 1 2 15.0 14.5 14.5
## 17 1 4 4 10.0 12.0 12.5
## 18 1 3 8 8.5 9.0 9.0
## 19 1 5 0 9.5 9.0 8.5
## 20 1 3 0 8.5 9.0 5.5
## 21 1 2 26 9.0 8.0 8.0
## 22 1 5 2 14.5 14.0 14.5
## 23 3 1 4 14.0 14.5 14.5
## 24 3 1 4 15.0 15.0 15.5
## 25 1 5 0 12.0 7.0 7.5
## 26 3 5 0 10.5 11.0 11.5
## 27 3 5 6 9.5 10.0 10.0
## 28 1 3 2 10.0 9.5 9.5
## 29 2 2 0 12.0 11.0 11.5
## 30 1 5 2 11.0 12.0 12.0
## 31 1 5 0 14.5 15.0 14.5
## 32 4 1 6 11.5 12.5 13.0
## 33 1 1 0 8.5 10.5 11.0
## 34 1 5 8 9.0 9.5 8.5
## 35 3 3 10 9.5 10.5 11.0
## 36 1 5 10 9.5 8.5 8.5
## 37 1 5 2 13.5 13.5 14.5
## 38 4 2 0 11.5 11.0 11.0
## 39 1 5 6 11.5 11.5 12.0
## 40 1 2 0 9.0 10.5 10.5
## 41 3 4 6 13.5 13.5 13.5
## 42 1 2 8 10.0 11.0 11.0
## 43 1 4 0 12.5 13.0 13.5
## 44 2 4 5 9.0 9.0 9.5
## 45 1 5 0 6.5 4.5 5.0
## 46 4 5 16 13.5 12.0 12.5
## 47 4 5 6 10.5 11.0 11.5
## 48 5 5 6 10.0 9.0 13.5
## 49 1 3 0 11.0 10.0 6.5
## 50 1 5 2 10.5 10.5 11.0
## 51 1 5 20 11.0 11.0 11.0
## 52 1 5 6 9.0 11.5 11.5
## 53 1 4 0 9.0 9.5 6.5
## 54 1 5 0 10.0 9.0 9.5
## 55 3 3 0 12.0 12.0 12.0
## 56 4 5 2 13.0 12.5 12.5
## 57 3 5 14 8.0 10.0 10.0
## 58 3 4 6 9.5 11.0 11.0
## 59 1 2 10 11.5 12.0 13.0
## 60 1 3 2 14.5 14.5 15.5
## 61 2 5 4 8.5 8.0 8.0
## 62 2 1 14 13.0 14.0 14.0
## 63 2 5 4 8.5 11.5 11.5
## 64 3 4 0 8.0 9.0 9.5
## 65 1 4 4 10.5 12.0 12.0
## 66 4 5 54 11.0 11.5 11.0
## 67 2 3 12 8.5 8.0 8.0
## 68 1 3 0 15.5 16.0 17.0
## 69 1 5 2 15.5 15.5 16.0
## 70 1 1 0 12.0 12.0 12.0
## 71 5 2 2 15.0 15.0 15.5
## 72 1 3 0 9.5 11.0 10.5
## 73 4 4 2 12.0 11.0 11.0
## 74 1 1 10 12.5 13.5 14.0
## 75 1 3 0 15.0 15.0 15.5
## 76 2 2 4 15.5 15.5 15.5
## 77 2 1 6 11.5 13.5 13.5
## 78 2 2 6 13.0 13.5 14.0
## 79 1 5 0 14.0 14.5 15.0
## 80 4 3 12 12.0 12.0 12.0
## 81 3 1 2 12.0 12.5 12.0
## 82 1 1 4 10.5 11.5 9.5
## 83 2 2 4 10.5 9.0 8.5
## 84 2 5 14 10.0 10.5 10.0
## 85 2 3 25 9.0 10.5 10.5
## 86 3 4 2 13.0 13.0 13.0
## 87 2 5 2 13.5 12.5 13.0
## 88 3 2 4 9.5 9.5 9.0
## 89 1 4 4 12.0 12.5 12.0
## 90 2 1 2 16.0 15.0 15.5
## 91 1 4 4 14.5 15.0 15.0
## 92 2 3 0 10.0 9.5 10.5
## 93 3 2 4 11.5 12.0 13.0
## 94 1 3 2 13.0 12.0 12.0
## 95 2 5 2 8.5 8.0 8.5
## 96 2 3 18 10.0 9.5 9.5
## 97 1 5 7 13.5 14.5 14.5
## 98 5 5 0 13.0 13.0 13.5
## 99 1 4 6 9.0 9.5 9.5
## 100 2 3 3 11.0 11.0 10.5
## 101 1 3 6 18.0 18.5 18.5
## 102 2 3 12 10.0 10.5 10.5
## 103 2 3 10 13.5 12.5 14.0
## 104 2 4 0 9.5 10.0 7.0
## 105 3 3 6 10.0 10.0 11.5
## 106 2 5 23 14.0 13.5 15.0
## 107 1 1 6 8.0 9.0 9.5
## 108 1 3 4 7.0 8.0 8.5
## 109 1 5 0 9.0 8.5 6.0
## 110 1 3 0 10.0 9.5 10.5
## 111 3 1 4 11.5 12.0 12.5
## 112 1 3 6 13.0 12.5 13.0
## 113 1 4 4 10.5 11.0 11.5
## 114 2 5 4 11.0 10.5 12.5
## 115 1 3 12 12.5 11.0 11.0
## 116 4 3 3 9.5 10.0 10.5
## 117 1 1 2 12.5 13.0 13.0
## 118 3 5 0 16.5 17.5 17.0
## 119 3 5 1 14.5 16.0 16.0
## 120 2 5 0 17.5 17.5 18.0
## 121 3 3 10 16.5 16.5 16.0
## 122 3 2 0 9.5 10.5 7.5
## 123 1 4 4 12.5 10.5 11.0
## 124 4 5 28 9.0 9.0 8.5
## 125 3 5 16 13.5 14.0 14.5
## 126 1 3 0 15.5 14.0 15.0
## 127 3 4 0 12.0 13.5 14.0
## 128 2 3 0 16.0 16.5 16.5
## 129 4 1 22 8.5 7.5 7.0
## 130 1 4 0 12.0 12.0 12.5
## 131 3 2 24 16.0 16.5 17.0
## 132 3 4 7 10.5 10.0 10.5
## 133 3 4 7 11.5 10.5 11.0
## 134 2 4 4 14.5 14.0 14.5
## 135 3 2 0 15.0 14.5 15.0
## 136 3 5 12 11.5 12.5 12.0
## 137 3 5 6 12.0 12.0 12.0
## 138 1 2 0 10.0 10.0 6.0
## 139 1 5 0 14.0 15.0 15.5
## 140 3 1 2 14.0 15.5 15.0
## 141 3 1 56 11.5 11.0 11.0
## 142 1 4 6 14.0 13.5 15.0
## 143 2 3 14 13.5 12.5 13.0
## 144 1 5 75 11.0 9.0 9.5
## 145 5 4 6 10.0 9.0 10.5
## 146 2 3 0 7.0 4.5 5.0
## 147 1 4 0 12.0 11.0 7.0
## 148 1 4 16 8.5 8.0 7.5
## 149 2 4 8 11.0 10.5 11.5
## 150 1 3 1 14.5 14.5 15.0
## 151 1 3 6 2.5 8.5 8.5
## 152 1 4 4 10.0 11.0 11.5
## 153 1 4 4 11.0 11.5 12.0
## 154 1 3 0 9.5 5.0 5.5
## 155 2 1 0 10.0 10.0 7.5
## 156 2 2 5 18.0 18.0 18.5
## 157 1 3 0 10.0 11.0 6.5
## 158 2 3 4 11.0 11.0 12.0
## 159 3 2 4 15.5 15.0 15.0
## 160 3 1 7 13.5 14.5 15.5
## 161 1 4 0 8.5 5.5 6.0
## 162 1 5 9 11.5 12.5 13.0
## 163 1 3 0 15.0 15.0 15.5
## 164 3 2 21 16.0 16.5 16.5
## 165 1 4 15 11.5 10.0 10.5
## 166 3 5 2 11.5 11.0 12.0
## 167 1 4 14 13.0 12.5 13.0
## 168 1 3 2 12.0 12.0 12.0
## 169 1 2 0 9.0 8.5 6.0
## 170 1 1 7 15.0 14.5 16.0
## 171 2 2 10 11.5 11.0 11.0
## 172 1 5 2 8.0 9.0 10.0
## 173 2 3 10 10.0 10.0 10.0
## 174 3 5 15 10.0 10.5 10.5
## 175 3 2 0 12.0 11.0 6.5
## 176 1 5 8 9.5 9.0 9.5
## 177 1 1 2 9.0 8.0 8.0
## 178 1 5 0 10.5 5.5 5.5
## 179 1 5 0 15.0 15.0 15.5
## 180 5 1 6 11.0 11.5 12.0
## 181 1 3 0 14.0 15.0 15.5
## 182 1 1 0 16.0 17.0 17.0
## 183 5 3 4 12.0 12.5 12.0
## 184 2 5 2 11.5 12.0 11.5
## 185 1 1 0 8.5 9.0 10.5
## 186 2 5 6 15.0 13.5 14.0
## 187 2 5 4 10.5 9.5 10.0
## 188 3 3 2 10.5 11.5 12.0
## 189 1 5 0 13.0 13.5 14.0
## 190 2 4 6 14.0 13.0 13.5
## 191 2 5 0 11.0 11.0 10.5
## 192 1 1 8 11.5 11.0 10.5
## 193 4 1 4 13.0 13.5 13.0
## 194 1 3 0 10.5 9.5 10.0
## 195 2 5 2 13.0 13.5 13.5
## 196 2 3 2 11.0 11.0 12.0
## 197 3 5 6 10.0 10.0 10.5
## 198 1 1 0 12.5 13.5 14.5
## 199 1 3 0 8.0 10.0 5.5
## 200 1 5 0 11.5 13.5 13.5
## 201 4 5 0 9.5 11.0 11.5
## 202 1 2 2 13.5 14.0 15.0
## 203 1 5 4 11.0 13.0 13.0
## 204 1 5 0 16.0 15.5 16.0
## 205 1 5 2 16.5 16.5 16.5
## 206 1 5 4 14.5 15.0 15.5
## 207 5 5 0 10.0 10.0 10.5
## 208 1 1 0 15.5 17.0 18.0
## 209 4 5 6 10.5 10.0 9.5
## 210 1 4 6 15.5 16.5 16.5
## 211 1 5 2 11.5 11.5 11.5
## 212 1 4 6 11.0 12.5 13.0
## 213 3 4 4 14.0 13.5 14.0
## 214 1 2 0 10.5 11.0 12.0
## 215 1 3 10 18.0 18.0 18.5
## 216 1 4 2 14.5 15.0 16.0
## 217 3 5 2 13.0 13.5 13.0
## 218 4 5 8 11.0 11.5 11.5
## 219 1 5 7 14.0 14.5 14.0
## 220 3 3 0 11.0 11.0 10.0
## 221 2 3 2 14.5 12.5 13.0
## 222 4 5 8 9.5 9.5 10.0
## 223 4 3 10 10.0 8.5 10.0
## 224 1 4 2 10.0 13.0 13.0
## 225 5 4 8 16.0 16.0 16.5
## 226 5 3 8 9.5 10.0 11.0
## 227 4 5 0 12.0 13.5 13.0
## 228 1 3 2 14.5 16.0 17.0
## 229 2 3 2 10.5 10.5 11.0
## 230 1 4 6 16.5 17.0 17.0
## 231 4 4 4 9.5 10.0 10.5
## 232 1 3 12 11.5 10.0 10.5
## 233 1 3 0 13.0 13.0 13.0
## 234 2 5 2 12.5 12.5 14.0
## 235 1 5 0 13.0 13.0 13.0
## 236 3 2 10 11.5 10.5 11.0
## 237 1 5 0 12.0 13.0 13.5
## 238 3 2 6 8.0 10.5 10.0
## 239 4 5 4 10.0 11.0 6.5
## 240 1 5 0 13.5 14.0 14.0
## 241 1 5 2 14.5 16.0 16.0
## 242 1 5 2 13.5 15.0 15.5
## 243 2 3 2 12.5 13.5 13.0
## 244 2 3 2 11.5 12.5 12.5
## 245 4 2 0 7.5 7.0 5.0
## 246 1 2 4 18.0 18.0 18.5
## 247 3 3 10 10.0 9.0 9.5
## 248 4 5 18 13.0 12.0 13.0
## 249 4 1 2 13.5 13.0 13.5
## 250 2 5 0 13.5 13.5 13.5
## 251 2 5 0 14.0 12.5 12.5
## 252 5 4 14 8.0 8.0 6.5
## 253 1 4 0 16.0 16.5 16.5
## 254 1 5 0 11.5 10.0 7.0
## 255 1 5 0 8.0 7.5 4.0
## 256 2 5 2 15.5 14.5 15.0
## 257 2 5 2 12.0 12.0 12.0
## 258 5 5 16 11.0 11.5 11.5
## 259 1 5 4 11.5 11.5 12.0
## 260 5 5 18 8.5 7.5 8.5
## 261 1 5 18 9.0 8.0 7.5
## 262 3 5 0 12.0 11.0 11.0
## 263 5 5 6 10.5 11.5 12.0
## 264 2 5 0 14.0 14.5 15.5
## 265 1 3 0 12.5 12.0 12.0
## 266 4 5 0 11.5 11.5 11.0
## 267 2 2 8 10.5 10.0 10.0
## 268 3 5 2 13.5 14.0 15.0
## 269 3 5 4 10.0 11.0 11.0
## 270 2 5 10 13.5 13.5 14.0
## 271 1 3 0 10.0 7.0 7.5
## 272 2 1 2 15.5 15.5 16.0
## 273 5 3 4 8.5 9.0 10.0
## 274 4 5 20 10.5 9.0 9.5
## 275 4 5 0 10.0 12.0 12.5
## 276 1 1 4 11.5 12.0 12.0
## 277 5 4 8 11.0 10.0 10.0
## 278 4 5 0 10.0 4.5 5.0
## 279 3 4 2 11.0 11.5 12.0
## 280 5 5 0 6.5 8.0 7.5
## 281 3 5 8 12.5 10.0 11.5
## 282 2 5 0 7.5 7.0 4.5
## 283 2 4 2 12.0 11.0 11.5
## 284 5 5 12 8.5 9.5 10.0
## 285 2 5 0 7.0 4.5 5.0
## 286 2 5 0 7.0 3.5 3.0
## 287 1 3 2 8.5 8.5 10.0
## 288 1 3 2 8.5 7.5 8.0
## 289 1 2 4 10.5 12.0 13.0
## 290 4 5 2 10.0 10.0 12.0
## 291 4 5 16 7.0 6.5 6.0
## 292 3 5 4 14.0 13.5 13.0
## 293 4 4 4 8.5 7.0 8.0
## 294 5 5 3 15.5 16.5 16.5
## 295 3 4 12 11.5 12.5 12.5
## 296 1 2 4 10.5 10.5 12.0
## 297 2 5 4 15.0 14.0 15.0
## 298 3 2 14 12.0 11.0 11.0
## 299 4 3 19 12.0 11.0 11.5
## 300 4 5 30 9.5 9.5 9.0
## 301 2 5 2 8.0 9.5 9.0
## 302 5 4 4 15.0 14.5 14.5
## 303 3 3 2 11.0 11.0 11.5
## 304 4 5 14 13.5 13.5 13.5
## 305 4 4 4 10.0 10.0 10.0
## 306 2 5 0 11.5 12.0 11.5
## 307 5 3 13 12.5 12.0 12.5
## 308 3 5 0 12.0 10.0 10.0
## 309 1 3 1 11.5 11.5 12.0
## 310 2 4 9 15.5 14.5 16.5
## 311 4 1 13 16.0 16.5 16.5
## 312 1 4 8 14.0 14.0 14.5
## 313 3 5 8 6.5 7.0 7.5
## 314 2 4 8 15.5 15.0 15.0
## 315 5 5 4 6.5 8.5 7.0
## 316 3 2 6 14.5 14.5 14.5
## 317 5 4 0 11.0 11.0 11.5
## 318 5 2 0 7.5 7.0 4.0
## 319 4 5 15 9.0 9.0 9.5
## 320 4 5 0 6.5 8.0 7.5
## 321 3 5 10 10.0 10.5 12.0
## 322 4 5 11 13.5 13.5 13.5
## 323 1 3 2 9.0 9.0 9.5
## 324 3 5 0 12.0 11.5 11.5
## 325 2 3 3 11.0 13.0 12.0
## 326 4 3 22 10.5 10.5 11.0
## 327 4 3 22 10.0 10.5 10.5
## 328 2 2 0 11.0 11.0 6.5
## 329 2 2 0 10.5 11.0 6.0
## 330 1 2 9 16.0 15.0 16.0
## 331 5 4 0 8.5 7.0 0.0
## 332 5 3 14 9.5 9.0 10.0
## 333 4 5 2 10.0 9.5 10.0
## 334 4 4 13 8.0 8.0 9.0
## 335 3 5 11 16.0 15.5 16.0
## 336 2 1 8 10.0 10.5 10.0
## 337 2 1 5 17.0 16.5 16.5
## 338 2 5 0 12.5 11.0 11.5
## 339 1 4 0 8.0 4.5 5.5
## 340 1 1 12 11.0 12.0 12.5
## 341 1 5 0 15.5 16.5 16.5
## 342 5 1 16 6.5 8.0 6.5
## 343 2 1 0 7.5 7.0 4.5
## 344 3 1 14 6.5 6.5 7.0
## 345 2 3 0 12.5 12.0 12.5
## 346 3 1 17 9.0 9.5 9.5
## 347 2 5 4 12.0 12.5 12.5
## 348 1 3 8 11.5 11.0 11.5
## 349 2 5 2 13.5 13.0 13.0
## 350 1 1 0 15.5 14.5 15.0
## 351 2 4 2 9.0 9.5 11.0
## 352 3 4 2 12.0 13.0 12.5
## 353 2 5 10 10.5 8.5 5.5
## 354 2 5 7 6.5 7.0 8.0
## 355 1 1 0 17.0 17.5 18.0
## 356 4 2 4 10.5 11.5 12.0
## 357 1 5 0 6.5 6.5 3.5
## 358 1 5 0 8.5 8.5 4.5
## 359 2 4 0 10.5 10.0 10.0
## 360 2 1 0 15.5 15.5 15.5
## 361 1 5 0 10.0 9.5 9.5
## 362 3 3 0 12.0 11.5 11.5
## 363 1 4 0 18.5 16.5 17.0
## 364 1 1 0 11.0 12.0 12.0
## 365 2 5 0 8.5 8.0 5.0
## 366 3 2 4 11.5 11.0 11.0
## 367 1 3 2 12.5 13.0 13.0
## 368 3 3 2 10.5 11.0 11.5
## 369 4 2 3 12.0 13.0 13.0
## 370 4 3 4 9.0 10.0 9.5
## 371 3 5 5 7.0 6.5 3.5
## 372 5 5 10 10.0 11.5 12.0
## 373 3 3 14 6.5 6.0 2.5
## 374 3 3 3 11.5 11.0 11.0
## 375 4 5 0 10.5 11.5 10.5
## 376 4 2 4 16.0 16.0 16.5
## 377 2 3 4 12.0 11.5 12.0
## 378 3 3 7 8.5 8.5 8.5
## 379 2 5 0 14.0 14.5 14.5
## 380 3 5 4 8.0 8.5 9.0
## 381 3 2 8 9.0 8.0 9.5
## 382 3 5 0 5.5 6.5 0.0now lets set text to be uniform
colnames(d3)<-gsub(".x","",colnames(d3))ncol(d3)## [1] 32colnames(d3)[2]<-"sex"now check place them to same case
colnames(d3)<-tolower(colnames(d3))library(GGally)## Warning: package 'GGally' was built under R version 3.6.3## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2##
## Attaching package: 'GGally'## The following object is masked from 'package:dplyr':
##
## nasacolnames(d3)## [1] "school" "sex" "age" "address" "famsize"
## [6] "pstatus" "medu" "fedu" "mjob" "fjob"
## [11] "reason" "nursery" "internet" "traveltime" "studytime"
## [16] "failures" "schoolsup" "famsup" "paid" "activities"
## [21] "higher" "romantic" "famrel" "freetime" "goout"
## [26] "dalc" "walc" "health" "absences" "g1"
## [31] "g2" "g3"numeral<-d3%>%
select_if(is.factor)
glimpse(numeral)## Observations: 382
## Variables: 16
## $ school <fct> GP, GP, GP, GP, GP, GP, GP, GP, GP, GP, GP, GP, GP,...
## $ sex <fct> F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, F, ...
## $ address <fct> R, R, R, R, R, R, R, R, R, U, U, U, U, U, U, U, U, ...
## $ famsize <fct> GT3, GT3, GT3, GT3, GT3, GT3, GT3, LE3, LE3, GT3, G...
## $ pstatus <fct> T, T, T, T, T, T, T, T, T, A, A, T, T, T, T, T, T, ...
## $ mjob <fct> at_home, other, at_home, services, services, servic...
## $ fjob <fct> other, other, other, health, services, health, teac...
## $ reason <fct> home, reputation, reputation, course, reputation, c...
## $ nursery <fct> yes, no, yes, yes, yes, yes, yes, yes, no, yes, yes...
## $ internet <fct> yes, yes, no, yes, yes, yes, yes, yes, yes, no, yes...
## $ schoolsup <fct> yes, yes, yes, yes, no, yes, no, yes, no, yes, no, ...
## $ famsup <fct> yes, yes, yes, yes, yes, yes, yes, yes, yes, no, ye...
## $ paid <fct> yes, no, yes, yes, yes, yes, no, yes, no, no, yes, ...
## $ activities <fct> yes, no, yes, yes, yes, yes, no, no, no, no, yes, y...
## $ higher <fct> yes, yes, yes, yes, yes, yes, yes, yes, yes, yes, y...
## $ romantic <fct> no, yes, no, no, yes, no, yes, no, no, no, no, yes,... #cor()%>%
#corrplot::corrplot()EDA
Univariate
numerics
#par(mfrow=c(2,2))
numeral_n<-d3%>%
select_if(is.numeric)
glimpse(numeral_n)## Observations: 382
## Variables: 16
## $ age <int> 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15, 15,...
## $ medu <int> 1, 1, 2, 2, 3, 3, 3, 2, 3, 3, 4, 1, 1, 1, 1, 1, 2, ...
## $ fedu <int> 1, 1, 2, 4, 3, 4, 4, 2, 1, 3, 3, 1, 1, 1, 2, 2, 1, ...
## $ traveltime <int> 2, 1, 1, 1, 2, 1, 2, 2, 2, 1, 1, 3, 1, 1, 1, 1, 3, ...
## $ studytime <int> 4, 2, 1, 3, 3, 3, 3, 2, 4, 4, 2, 1, 2, 2, 2, 2, 3, ...
## $ failures <int> 1, 2, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...
## $ famrel <int> 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 3, 5, ...
## $ freetime <int> 1, 3, 3, 3, 2, 3, 2, 1, 4, 3, 3, 3, 3, 4, 3, 2, 2, ...
## $ goout <int> 2, 4, 1, 2, 1, 2, 2, 3, 2, 3, 2, 3, 2, 2, 2, 3, 2, ...
## $ dalc <int> 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, ...
## $ walc <int> 1, 4, 1, 1, 3, 1, 2, 3, 3, 1, 1, 2, 3, 2, 1, 2, 1, ...
## $ health <int> 1, 5, 2, 5, 3, 5, 5, 4, 3, 4, 1, 4, 4, 5, 5, 1, 4, ...
## $ absences <int> 2, 2, 8, 2, 8, 2, 0, 2, 12, 10, 0, 0, 2, 0, 2, 2, 4...
## $ g1 <dbl> 10.0, 10.5, 14.0, 10.0, 11.5, 11.5, 11.0, 9.5, 15.5...
## $ g2 <dbl> 11.5, 8.5, 13.0, 10.0, 11.5, 12.0, 5.5, 9.5, 15.5, ...
## $ g3 <dbl> 11.5, 8.0, 12.5, 9.0, 11.5, 11.5, 6.0, 9.5, 15.5, 1...for(i in 1:16) {
hist(numeral_n[,i], main=names(numeral_n)[i],xlab =names(numeral_n)[i],col = "powder blue" )
}
From the above there are lots of conclusion you can make about high school students.
age ranges from 15-20 though there is these student with 22
Most parents for these kids are educated
For absentinsm there seem to a student who has been absent for 60 days do abnormal. As you can also see these students some of them take alcoholo weekdays but more on weekends.
For factors
for(i in 1:16) {
counts <- table(numeral[,i])
name <- names(numeral)[i]
barplot(counts, main=name,col = "red")
}
Multivariate Analysis
Check for multicollinearlity
x=numeral_n[,1:15]
y=numeral_n[,16]
numeral_n%>%
gather(-g3,key = "var",value = "value")%>%
ggplot(aes(x = value,y =g3 ))+
facet_wrap(~var,scales = "free")+
geom_jitter()+
geom_smooth(method = "lm")
I realised the data is given in a nominal form
Even so i didn’t change them factors
I also realised the grades for g1,g2,g3 are very strongly correlated
absentism can obviuosy be a reason for failure
Also the more the failing the lower the grade
Alcohol consumption can greatly influence the final grade
# This sets plot images to a nice size.
options(repr.plot.width = 4, repr.plot.height = 4)
ggplot(d3,aes(x=g3,y =absences))+
geom_col(fill = "blue")+
facet_wrap(~internet+address)+
theme_classic()
*From the plot above:
Most students perfomance is influenced with whether the internet is available
Most students live in the urban settlement these also influences there perfomance
options(repr.plot.width = 4, repr.plot.height = 4)
ggplot(d3,aes(x=g3,y =failures))+
geom_col(fill = "blue")+
facet_wrap(~higher)+
theme_classic()
Those perfoming lower have no ambitions of going to higher learning
library(caret)## Warning: package 'caret' was built under R version 3.6.3## Loading required package: lattice## Warning: package 'lattice' was built under R version 3.6.3##
## Attaching package: 'caret'## The following object is masked from 'package:purrr':
##
## liftset.seed(107)
inTrain <- createDataPartition(
y = d3$g3,
## the outcome data are needed
p = .75,
## The percentage of data in the
## training set
list = FALSE
)
training <- d3[ inTrain,]
testing <- d3[-inTrain,]Modelling
Prepare the data for modelling/preprocessing
Dummyfy the data
training<-model.matrix(~.,data = training)testing <- model.matrix(~.,data = testing)Scale the data
library(caret)
library(gbm)## Warning: package 'gbm' was built under R version 3.6.3## Loaded gbm 2.1.5# load data
d3_t<-preProcess(training,method = "YeoJohnson")
training<-as.data.frame(predict(d3_t,newdata = training))
head(training)## (Intercept) schoolMS sexM age addressU famsizeLE3 pstatusT medu fedu
## 1 1 0 0 15 0 0 1 1 1
## 2 1 0 0 15 0 0 1 1 1
## 4 1 0 0 15 0 0 1 2 4
## 6 1 0 0 15 0 0 1 3 4
## 7 1 0 0 15 0 0 1 3 4
## 8 1 0 0 15 0 1 1 2 2
## mjobhealth mjobother mjobservices mjobteacher fjobhealth fjobother
## 1 0 0 0 0 0 1
## 2 0 1 0 0 0 1
## 4 0 0 1 0 1 0
## 6 0 0 1 0 1 0
## 7 0 0 1 0 0 0
## 8 1 0 0 0 0 0
## fjobservices fjobteacher reasonhome reasonother reasonreputation
## 1 0 0 1 0 0
## 2 0 0 0 0 1
## 4 0 0 0 0 0
## 6 0 0 0 0 0
## 7 0 1 0 0 0
## 8 1 0 0 0 1
## nurseryyes internetyes traveltime studytime failures schoolsupyes
## 1 1 1 2 4 1 1
## 2 0 1 1 2 2 1
## 4 1 1 1 3 0 1
## 6 1 1 1 3 0 1
## 7 1 1 2 3 2 0
## 8 1 1 2 2 0 1
## famsupyes paidyes activitiesyes higheryes romanticyes famrel freetime
## 1 1 1 1 1 0 3 1
## 2 1 0 0 1 1 3 3
## 4 1 1 1 1 0 4 3
## 6 1 1 1 1 0 4 3
## 7 1 0 0 1 1 4 2
## 8 1 1 0 1 0 4 1
## goout dalc walc health absences g1 g2 g3
## 1 2 1 1 1 2 10.0 11.5 11.5
## 2 4 2 4 5 2 10.5 8.5 8.0
## 4 2 1 1 5 2 10.0 10.0 9.0
## 6 2 1 1 5 2 11.5 12.0 11.5
## 7 2 2 2 5 0 11.0 5.5 6.0
## 8 3 1 3 4 2 9.5 9.5 9.5split the data into train and test then train
trainControl <- trainControl(method="repeatedcv", number=5)
set.seed(7)
#usage of caret package and caret available model
fit <- train(g3~g1+g2+absences+failures+studytime+famrel+dalc+activitiesyes+ traveltime+famsupyes, data=training, method="gbm", metric="RMSE", trControl=trainControl)## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 9.2179 nan 0.1000 1.1041
## 2 8.2327 nan 0.1000 0.9184
## 3 7.5088 nan 0.1000 0.7293
## 4 6.7299 nan 0.1000 0.6791
## 5 6.1593 nan 0.1000 0.5277
## 6 5.6840 nan 0.1000 0.5365
## 7 5.2636 nan 0.1000 0.4724
## 8 4.8562 nan 0.1000 0.4289
## 9 4.5388 nan 0.1000 0.1445
## 10 4.2112 nan 0.1000 0.2712
## 20 2.3234 nan 0.1000 0.0322
## 40 1.3288 nan 0.1000 0.0105
## 60 1.1642 nan 0.1000 -0.0084
## 80 1.1168 nan 0.1000 -0.0040
## 100 1.0746 nan 0.1000 -0.0047
## 120 1.0573 nan 0.1000 -0.0007
## 140 1.0451 nan 0.1000 -0.0005
## 150 1.0448 nan 0.1000 -0.0094
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 8.7217 nan 0.1000 1.3625
## 2 7.5077 nan 0.1000 1.2001
## 3 6.5780 nan 0.1000 0.8739
## 4 5.8405 nan 0.1000 0.7041
## 5 5.2226 nan 0.1000 0.5399
## 6 4.6335 nan 0.1000 0.5857
## 7 4.1586 nan 0.1000 0.3573
## 8 3.7708 nan 0.1000 0.4062
## 9 3.4391 nan 0.1000 0.3563
## 10 3.1347 nan 0.1000 0.2485
## 20 1.6477 nan 0.1000 0.0843
## 40 1.0394 nan 0.1000 0.0001
## 60 0.9464 nan 0.1000 -0.0126
## 80 0.9016 nan 0.1000 -0.0035
## 100 0.8610 nan 0.1000 -0.0084
## 120 0.8352 nan 0.1000 -0.0058
## 140 0.8059 nan 0.1000 -0.0028
## 150 0.7951 nan 0.1000 -0.0032
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 8.6770 nan 0.1000 1.6362
## 2 7.5802 nan 0.1000 1.0998
## 3 6.4551 nan 0.1000 1.1241
## 4 5.5511 nan 0.1000 0.8751
## 5 4.8043 nan 0.1000 0.7729
## 6 4.1757 nan 0.1000 0.5172
## 7 3.6268 nan 0.1000 0.5003
## 8 3.2034 nan 0.1000 0.3544
## 9 2.8703 nan 0.1000 0.3601
## 10 2.5578 nan 0.1000 0.2270
## 20 1.2382 nan 0.1000 0.0181
## 40 0.8940 nan 0.1000 -0.0076
## 60 0.7979 nan 0.1000 -0.0075
## 80 0.7431 nan 0.1000 -0.0038
## 100 0.6981 nan 0.1000 -0.0102
## 120 0.6716 nan 0.1000 -0.0155
## 140 0.6383 nan 0.1000 -0.0053
## 150 0.6239 nan 0.1000 -0.0047
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 9.3883 nan 0.1000 1.1244
## 2 8.3620 nan 0.1000 0.9685
## 3 7.6131 nan 0.1000 0.7319
## 4 6.9909 nan 0.1000 0.7130
## 5 6.3335 nan 0.1000 0.5563
## 6 5.7741 nan 0.1000 0.4540
## 7 5.3309 nan 0.1000 0.4800
## 8 4.9335 nan 0.1000 0.4143
## 9 4.5703 nan 0.1000 0.3625
## 10 4.2265 nan 0.1000 0.3406
## 20 2.2879 nan 0.1000 0.1248
## 40 1.2685 nan 0.1000 0.0068
## 60 1.0726 nan 0.1000 -0.0082
## 80 1.0119 nan 0.1000 -0.0070
## 100 0.9939 nan 0.1000 -0.0044
## 120 0.9855 nan 0.1000 -0.0067
## 140 0.9732 nan 0.1000 -0.0075
## 150 0.9653 nan 0.1000 -0.0068
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 9.1257 nan 0.1000 1.4302
## 2 7.8954 nan 0.1000 1.2163
## 3 6.9389 nan 0.1000 1.0461
## 4 6.0678 nan 0.1000 0.6646
## 5 5.3645 nan 0.1000 0.7035
## 6 4.7546 nan 0.1000 0.5490
## 7 4.2550 nan 0.1000 0.4127
## 8 3.7808 nan 0.1000 0.4509
## 9 3.3595 nan 0.1000 0.3876
## 10 3.0089 nan 0.1000 0.2869
## 20 1.4316 nan 0.1000 0.0702
## 40 0.9797 nan 0.1000 -0.0080
## 60 0.9091 nan 0.1000 -0.0051
## 80 0.8572 nan 0.1000 -0.0109
## 100 0.8164 nan 0.1000 -0.0111
## 120 0.7890 nan 0.1000 -0.0082
## 140 0.7583 nan 0.1000 -0.0039
## 150 0.7479 nan 0.1000 -0.0171
##
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 9.0238 nan 0.1000 1.7174
## 2 7.6956 nan 0.1000 1.2191
## 3 6.6582 nan 0.1000 1.1433
## 4 5.7240 nan 0.1000 0.9069
## 5 5.0198 nan 0.1000 0.7482
## 6 4.3710 nan 0.1000 0.6335
## 7 3.8432 nan 0.1000 0.4798
## 8 3.4111 nan 0.1000 0.4090
## 9 2.9999 nan 0.1000 0.3822
## 10 2.6496 nan 0.1000 0.2778
## 20 1.2599 nan 0.1000 0.0428
## 40 0.8945 nan 0.1000 -0.0068
## 60 0.8195 nan 0.1000 -0.0028
## 80 0.7461 nan 0.1000 -0.0122
## 100 0.6978 nan 0.1000 -0.0014
## 120 0.6662 nan 0.1000 -0.0141
## 140 0.6358 nan 0.1000 -0.0141
## 150 0.6247 nan 0.1000 -0.0074
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 9.8006 nan 0.1000 1.1438
## 2 8.7274 nan 0.1000 0.9454
## 3 7.9378 nan 0.1000 0.7479
## 4 7.2064 nan 0.1000 0.7096
## 5 6.6049 nan 0.1000 0.4816
## 6 6.0547 nan 0.1000 0.4769
## 7 5.5998 nan 0.1000 0.4877
## 8 5.1781 nan 0.1000 0.4218
## 9 4.7982 nan 0.1000 0.4208
## 10 4.4991 nan 0.1000 0.1695
## 20 2.4310 nan 0.1000 0.0931
## 40 1.3586 nan 0.1000 0.0158
## 60 1.1791 nan 0.1000 -0.0124
## 80 1.1106 nan 0.1000 -0.0015
## 100 1.0770 nan 0.1000 -0.0076
## 120 1.0562 nan 0.1000 -0.0198
## 140 1.0486 nan 0.1000 -0.0204
## 150 1.0451 nan 0.1000 -0.0059
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 9.7283 nan 0.1000 1.2514
## 2 8.5555 nan 0.1000 1.2616
## 3 7.4052 nan 0.1000 1.0389
## 4 6.4760 nan 0.1000 0.9198
## 5 5.7567 nan 0.1000 0.6154
## 6 5.0256 nan 0.1000 0.6178
## 7 4.4465 nan 0.1000 0.5622
## 8 4.0060 nan 0.1000 0.3248
## 9 3.5567 nan 0.1000 0.3826
## 10 3.1931 nan 0.1000 0.3687
## 20 1.5781 nan 0.1000 0.0688
## 40 1.0446 nan 0.1000 -0.0128
## 60 0.9441 nan 0.1000 -0.0064
## 80 0.8980 nan 0.1000 -0.0092
## 100 0.8675 nan 0.1000 -0.0076
## 120 0.8385 nan 0.1000 -0.0099
## 140 0.8135 nan 0.1000 -0.0060
## 150 0.8002 nan 0.1000 -0.0046
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 9.2337 nan 0.1000 1.6978
## 2 8.0247 nan 0.1000 1.1792
## 3 6.9207 nan 0.1000 1.2295
## 4 5.9696 nan 0.1000 1.0311
## 5 5.2091 nan 0.1000 0.8800
## 6 4.6573 nan 0.1000 0.5471
## 7 4.1035 nan 0.1000 0.5924
## 8 3.7185 nan 0.1000 0.4487
## 9 3.3291 nan 0.1000 0.4008
## 10 3.0259 nan 0.1000 0.3310
## 20 1.4952 nan 0.1000 0.0643
## 40 0.9540 nan 0.1000 0.0005
## 60 0.8677 nan 0.1000 -0.0190
## 80 0.8024 nan 0.1000 -0.0055
## 100 0.7612 nan 0.1000 -0.0160
## 120 0.7154 nan 0.1000 -0.0250
## 140 0.6718 nan 0.1000 -0.0071
## 150 0.6604 nan 0.1000 -0.0090
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 8.8363 nan 0.1000 1.0813
## 2 7.8597 nan 0.1000 0.9008
## 3 7.1161 nan 0.1000 0.7202
## 4 6.4852 nan 0.1000 0.6185
## 5 5.8800 nan 0.1000 0.5274
## 6 5.4491 nan 0.1000 0.3741
## 7 5.0096 nan 0.1000 0.4232
## 8 4.6050 nan 0.1000 0.3634
## 9 4.2144 nan 0.1000 0.3848
## 10 3.9147 nan 0.1000 0.2350
## 20 2.2128 nan 0.1000 0.0810
## 40 1.2920 nan 0.1000 0.0059
## 60 1.1221 nan 0.1000 -0.0130
## 80 1.0603 nan 0.1000 0.0017
## 100 0.9993 nan 0.1000 -0.0024
## 120 0.9721 nan 0.1000 0.0003
## 140 0.9575 nan 0.1000 -0.0020
## 150 0.9473 nan 0.1000 -0.0092
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 8.5738 nan 0.1000 1.1197
## 2 7.4225 nan 0.1000 1.2319
## 3 6.4868 nan 0.1000 0.9760
## 4 5.7223 nan 0.1000 0.7124
## 5 5.0167 nan 0.1000 0.7839
## 6 4.4570 nan 0.1000 0.6171
## 7 3.9672 nan 0.1000 0.5032
## 8 3.5341 nan 0.1000 0.4064
## 9 3.1900 nan 0.1000 0.3374
## 10 2.8914 nan 0.1000 0.3136
## 20 1.4335 nan 0.1000 0.0509
## 40 0.9929 nan 0.1000 -0.0048
## 60 0.8625 nan 0.1000 0.0000
## 80 0.8017 nan 0.1000 -0.0017
## 100 0.7671 nan 0.1000 -0.0003
## 120 0.7424 nan 0.1000 -0.0133
## 140 0.7224 nan 0.1000 -0.0042
## 150 0.7015 nan 0.1000 -0.0040
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 8.3390 nan 0.1000 1.4731
## 2 7.1187 nan 0.1000 1.1991
## 3 6.0723 nan 0.1000 0.9027
## 4 5.2558 nan 0.1000 0.7689
## 5 4.5884 nan 0.1000 0.6344
## 6 3.9887 nan 0.1000 0.5521
## 7 3.5070 nan 0.1000 0.5002
## 8 3.0626 nan 0.1000 0.3710
## 9 2.7191 nan 0.1000 0.2524
## 10 2.4431 nan 0.1000 0.2653
## 20 1.2382 nan 0.1000 0.0422
## 40 0.8782 nan 0.1000 0.0039
## 60 0.7827 nan 0.1000 -0.0101
## 80 0.7250 nan 0.1000 -0.0053
## 100 0.6847 nan 0.1000 -0.0142
## 120 0.6405 nan 0.1000 -0.0102
## 140 0.6103 nan 0.1000 -0.0094
## 150 0.5950 nan 0.1000 -0.0031
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 9.2987 nan 0.1000 1.1402
## 2 8.4369 nan 0.1000 0.9491
## 3 7.6948 nan 0.1000 0.7641
## 4 7.0177 nan 0.1000 0.7054
## 5 6.3600 nan 0.1000 0.6591
## 6 5.8544 nan 0.1000 0.5731
## 7 5.4152 nan 0.1000 0.4587
## 8 4.9715 nan 0.1000 0.4001
## 9 4.6121 nan 0.1000 0.3575
## 10 4.2380 nan 0.1000 0.2617
## 20 2.3206 nan 0.1000 0.1067
## 40 1.2981 nan 0.1000 0.0073
## 60 1.0610 nan 0.1000 0.0010
## 80 1.0081 nan 0.1000 0.0015
## 100 0.9749 nan 0.1000 -0.0026
## 120 0.9448 nan 0.1000 -0.0020
## 140 0.9314 nan 0.1000 -0.0047
## 150 0.9291 nan 0.1000 -0.0108
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 9.0487 nan 0.1000 1.5056
## 2 7.8248 nan 0.1000 1.2401
## 3 6.8498 nan 0.1000 0.9066
## 4 6.0832 nan 0.1000 0.8568
## 5 5.3886 nan 0.1000 0.5665
## 6 4.7040 nan 0.1000 0.7033
## 7 4.1840 nan 0.1000 0.5053
## 8 3.7722 nan 0.1000 0.4425
## 9 3.3554 nan 0.1000 0.4083
## 10 3.0421 nan 0.1000 0.2746
## 20 1.4851 nan 0.1000 0.0730
## 40 0.9504 nan 0.1000 0.0068
## 60 0.8358 nan 0.1000 -0.0089
## 80 0.7806 nan 0.1000 -0.0058
## 100 0.7567 nan 0.1000 -0.0030
## 120 0.7262 nan 0.1000 -0.0000
## 140 0.6878 nan 0.1000 -0.0054
## 150 0.6822 nan 0.1000 -0.0106
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 9.0076 nan 0.1000 1.6057
## 2 7.6013 nan 0.1000 1.3534
## 3 6.5499 nan 0.1000 1.1515
## 4 5.5812 nan 0.1000 0.8376
## 5 4.7840 nan 0.1000 0.7651
## 6 4.2053 nan 0.1000 0.5301
## 7 3.6959 nan 0.1000 0.4956
## 8 3.2578 nan 0.1000 0.4020
## 9 2.8951 nan 0.1000 0.2692
## 10 2.6011 nan 0.1000 0.2631
## 20 1.2436 nan 0.1000 0.0306
## 40 0.8679 nan 0.1000 -0.0030
## 60 0.7500 nan 0.1000 -0.0155
## 80 0.7076 nan 0.1000 -0.0066
## 100 0.6692 nan 0.1000 -0.0108
## 120 0.6193 nan 0.1000 -0.0080
## 140 0.5759 nan 0.1000 -0.0058
## 150 0.5578 nan 0.1000 -0.0012
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## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 9.0833 nan 0.1000 1.2400
## 2 7.8713 nan 0.1000 1.0788
## 3 6.9392 nan 0.1000 0.9326
## 4 6.1054 nan 0.1000 0.8671
## 5 5.3683 nan 0.1000 0.8161
## 6 4.7250 nan 0.1000 0.5750
## 7 4.2501 nan 0.1000 0.4253
## 8 3.7735 nan 0.1000 0.4610
## 9 3.3661 nan 0.1000 0.3800
## 10 3.0903 nan 0.1000 0.3286
## 20 1.5074 nan 0.1000 0.0594
## 40 0.9958 nan 0.1000 -0.0057
## 50 0.9419 nan 0.1000 -0.0083check the tuning
fit$finalModel$tuneValue## n.trees interaction.depth shrinkage n.minobsinnode
## 4 50 2 0.1 10fit$modelInfo$parameters## parameter class label
## 1 n.trees numeric # Boosting Iterations
## 2 interaction.depth numeric Max Tree Depth
## 3 shrinkage numeric Shrinkage
## 4 n.minobsinnode numeric Min. Terminal Node Sizefit$results## shrinkage interaction.depth n.minobsinnode n.trees RMSE Rsquared
## 1 0.1 1 10 50 1.188607 0.8707311
## 4 0.1 2 10 50 1.065813 0.8921385
## 7 0.1 3 10 50 1.089577 0.8889983
## 2 0.1 1 10 100 1.141892 0.8763186
## 5 0.1 2 10 100 1.080946 0.8904279
## 8 0.1 3 10 100 1.115851 0.8848588
## 3 0.1 1 10 150 1.156052 0.8737196
## 6 0.1 2 10 150 1.107978 0.8865094
## 9 0.1 3 10 150 1.150548 0.8806178
## MAE RMSESD RsquaredSD MAESD
## 1 0.7932506 0.1615309 0.02986635 0.13371955
## 4 0.7187926 0.1708549 0.03295292 0.11416189
## 7 0.7425992 0.1370138 0.02646134 0.08945598
## 2 0.7958403 0.1415568 0.02685797 0.10329428
## 5 0.7462661 0.1476166 0.02902839 0.09253360
## 8 0.7666287 0.1145936 0.02325612 0.07649601
## 3 0.8089657 0.1454722 0.02652464 0.08583505
## 6 0.7631962 0.1342080 0.02674470 0.08677796
## 9 0.7909464 0.1183977 0.02337154 0.08898527fit$bestTune## n.trees interaction.depth shrinkage n.minobsinnode
## 4 50 2 0.1 10Predict the future data/testing data
testing$pred<-predict(fit,testing)## Warning in testing$pred <- predict(fit, testing): Coercing LHS to a listhead(testing$pred)## [1] 13.586503 11.326598 14.978721 10.921632 12.115943 7.757402check for variample importance
library(caret)
summary(fit)
## var rel.inf
## g2 g2 80.21622642
## g1 g1 13.40116248
## absences absences 3.25669394
## failures failures 2.17794453
## famrel famrel 0.53431073
## dalc dalc 0.32920213
## studytime studytime 0.04306847
## traveltime traveltime 0.04139129
## activitiesyes activitiesyes 0.00000000
## famsupyes famsupyes 0.00000000Conclusion
1.The second grade is the most important factor that would determine a student’s final. 2.Absentism is a factor also the students who are mostly absent are the likely ones to fail in their final grade. 3.A factor like how students use their time also can be a concern in perfomance.
Recommendations
Students should work on every exam and perfom well so that it can influence their final grade.Teachers should know what makes their students be absent and help them. They should also advise them on time management.Also parents should be concerned on their children perfomance.For example they should organize good transport so that the travel time should be little.