# Predicted model for weight gained using calories consumed
calories<-read.csv("E:\\Data science\\calories_consumed.csv")
View(calories)
attach(calories)
summary(calories)

##        wg               cc      
##  Min.   :  62.0   Min.   :1400  
##  1st Qu.: 114.5   1st Qu.:1728  
##  Median : 200.0   Median :2250  
##  Mean   : 357.7   Mean   :2341  
##  3rd Qu.: 537.5   3rd Qu.:2775  
##  Max.   :1100.0   Max.   :3900

qqnorm(wg)

windows()
plot(wg,cc)

windows()
cor(wg,cc)

## [1] 0.946991

m1<-lm("wg~cc",data=calories)
m1

## 
## Call:
## lm(formula = "wg~cc", data = calories)
## 
## Coefficients:
## (Intercept)           cc  
##   -625.7524       0.4202

pv<-predict(m1,calories)
pv

##           1           2           3           4           5           6 
##    4.482599  340.607908  802.780209  298.592245  424.639236   46.498263 
##           7           8           9          10          11          12 
##  -37.533065  172.545254  550.686227 1012.858527   75.909227  172.545254 
##          13          14 
##  508.670563  634.717554

pv1<-as.data.frame(pv)
pv1

##             pv
## 1     4.482599
## 2   340.607908
## 3   802.780209
## 4   298.592245
## 5   424.639236
## 6    46.498263
## 7   -37.533065
## 8   172.545254
## 9   550.686227
## 10 1012.858527
## 11   75.909227
## 12  172.545254
## 13  508.670563
## 14  634.717554

final<-cbind(calories,pv)
final

##      wg   cc          pv
## 1   108 1500    4.482599
## 2   200 2300  340.607908
## 3   900 3400  802.780209
## 4   200 2200  298.592245
## 5   300 2500  424.639236
## 6   110 1600   46.498263
## 7   128 1400  -37.533065
## 8    62 1900  172.545254
## 9   600 2800  550.686227
## 10 1100 3900 1012.858527
## 11  100 1670   75.909227
## 12  150 1900  172.545254
## 13  350 2700  508.670563
## 14  700 3000  634.717554