I have separated a dataset between train and test like this. By selecting the 80%th index of rows and spliting it.

# Eliminamos variables no útiles
df <- df[,!(colnames(df) %in% c("sqm_lot", "sql_lot15"))]

df_train <- df[1:as.integer(nrow(df)*0.8),]
df_test <- df[as.integer(nrow(df)*0.8):nrow(df),]

nrow(df_test)
## [1] 4324
nrow(df_train)
## [1] 17290

This dataset, by the way, has the following info

## 'data.frame':    21613 obs. of  16 variables:
##  $ price_eur   : num  206367 500340 167400 561720 474300 ...
##  $ bedrooms    : int  3 3 2 4 3 4 3 3 3 3 ...
##  $ bathrooms   : int  1 2 1 3 2 4 2 1 1 2 ...
##  $ sqm_living  : num  109.6 238.8 71.5 182.1 156.1 ...
##  $ sqm_lot     : num  525 673 929 464 751 ...
##  $ floors      : int  1 2 1 1 1 1 2 1 1 2 ...
##  $ waterfront  : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ view        : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ condition   : int  3 3 3 5 3 3 3 3 3 3 ...
##  $ sqm_basement: num  0 37.2 0 84.5 0 ...
##  $ yr_built    : int  1955 1951 1933 1965 1987 2001 1995 1963 1960 2003 ...
##  $ yr_renovated: int  0 1991 0 0 0 0 0 0 0 0 ...
##  $ sqm_living15: num  124 157 253 126 167 ...
##  $ sqm_lot15   : num  525 710 749 464 697 ...

Then I trained a linear model like this.

x_train <- df_train[,!(colnames(df) %in% "price_eur")]
y_train <- df_train$price_eur

# Entrenamos el modelo

modelo.mlineal <- lm(formula = price_eur ~ ., data = df_train)
modelo.mlineal

## 
## Call:
## lm(formula = price_eur ~ ., data = df_train)
## 
## Coefficients:
##  (Intercept)      bedrooms     bathrooms    sqm_living       sqm_lot  
##    5.696e+06    -4.366e+04     5.886e+04     2.371e+03     3.063e-01  
##       floors    waterfront          view     condition  sqm_basement  
##    2.524e+04     5.327e+05     4.727e+04     2.125e+04    -3.477e+02  
##     yr_built  yr_renovated  sqm_living15     sqm_lot15  
##   -3.000e+03     1.571e+01     1.039e+03    -8.048e+00

Then I used my df_test dataset to predict and compare with my

# Tomamos los valores de y_test y x_test
y_test <- df_test$price_eur
x_test <- df_test[,!(colnames(df_test) %in% "price_eur")]

# Restamos sqm_living al modelo y lo metemos con tubería a predict.lm
prediccion_prueba <- modelo.mlineal %>% predict.lm( data = df_test )

print(length(prediccion_prueba))
## [1] 17290
print(length(y_test))
## [1] 4324

As you can see I have different lengths in my y_test and y_pred, though I am parsing my df_test in the predict.lm func. Instead, it is giving me the length of the original df_train that I used to train it with. Why is this happening?

I was expecting to have, well… obviously the same lengths for my variable y_test and prediccion_prueba.

My desired output would look like this.

print(length(prediccion_prueba))
## [1] 4324
print(length(y_test))
## [1] 4324