Renta de bicis

Renta de Bicis

#file.choose()
df <- read.csv("/Users/santiago/Downloads/rentadebicis.csv")

summary(df)

##       hora            dia              mes              año      
##  Min.   : 0.00   Min.   : 1.000   Min.   : 1.000   Min.   :2011  
##  1st Qu.: 6.00   1st Qu.: 5.000   1st Qu.: 4.000   1st Qu.:2011  
##  Median :12.00   Median :10.000   Median : 7.000   Median :2012  
##  Mean   :11.54   Mean   : 9.993   Mean   : 6.521   Mean   :2012  
##  3rd Qu.:18.00   3rd Qu.:15.000   3rd Qu.:10.000   3rd Qu.:2012  
##  Max.   :23.00   Max.   :19.000   Max.   :12.000   Max.   :2012  
##     estacion     dia_de_la_semana     asueto         temperatura   
##  Min.   :1.000   Min.   :1.000    Min.   :0.00000   Min.   : 0.82  
##  1st Qu.:2.000   1st Qu.:2.000    1st Qu.:0.00000   1st Qu.:13.94  
##  Median :3.000   Median :4.000    Median :0.00000   Median :20.50  
##  Mean   :2.507   Mean   :4.014    Mean   :0.02857   Mean   :20.23  
##  3rd Qu.:4.000   3rd Qu.:6.000    3rd Qu.:0.00000   3rd Qu.:26.24  
##  Max.   :4.000   Max.   :7.000    Max.   :1.00000   Max.   :41.00  
##  sensacion_termica    humedad       velocidad_del_viento
##  Min.   : 0.76     Min.   :  0.00   Min.   : 0.000      
##  1st Qu.:16.66     1st Qu.: 47.00   1st Qu.: 7.002      
##  Median :24.24     Median : 62.00   Median :12.998      
##  Mean   :23.66     Mean   : 61.89   Mean   :12.799      
##  3rd Qu.:31.06     3rd Qu.: 77.00   3rd Qu.:16.998      
##  Max.   :45.45     Max.   :100.00   Max.   :56.997      
##  rentas_de_no_registrados rentas_de_registrados rentas_totales 
##  Min.   :  0.00           Min.   :  0.0         Min.   :  1.0  
##  1st Qu.:  4.00           1st Qu.: 36.0         1st Qu.: 42.0  
##  Median : 17.00           Median :118.0         Median :145.0  
##  Mean   : 36.02           Mean   :155.6         Mean   :191.6  
##  3rd Qu.: 49.00           3rd Qu.:222.0         3rd Qu.:284.0  
##  Max.   :367.00           Max.   :886.0         Max.   :977.0

str(df)

## 'data.frame':    10886 obs. of  14 variables:
##  $ hora                    : int  0 1 2 3 4 5 6 7 8 9 ...
##  $ dia                     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ mes                     : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ año                     : int  2011 2011 2011 2011 2011 2011 2011 2011 2011 2011 ...
##  $ estacion                : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ dia_de_la_semana        : int  6 6 6 6 6 6 6 6 6 6 ...
##  $ asueto                  : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ temperatura             : num  9.84 9.02 9.02 9.84 9.84 ...
##  $ sensacion_termica       : num  14.4 13.6 13.6 14.4 14.4 ...
##  $ humedad                 : int  81 80 80 75 75 75 80 86 75 76 ...
##  $ velocidad_del_viento    : num  0 0 0 0 0 ...
##  $ rentas_de_no_registrados: int  3 8 5 3 0 0 2 1 1 8 ...
##  $ rentas_de_registrados   : int  13 32 27 10 1 1 0 2 7 6 ...
##  $ rentas_totales          : int  16 40 32 13 1 1 2 3 8 14 ...

regresion <-  lm(rentas_totales ~ hora + dia + mes + año + estacion + dia_de_la_semana + asueto + temperatura + sensacion_termica + humedad + velocidad_del_viento, data=df)
summary(regresion)

## 
## Call:
## lm(formula = rentas_totales ~ hora + dia + mes + año + estacion + 
##     dia_de_la_semana + asueto + temperatura + sensacion_termica + 
##     humedad + velocidad_del_viento, data = df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -305.52  -93.64  -27.70   61.85  649.10 
## 
## Coefficients:
##                        Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          -1.661e+05  5.496e+03 -30.217  < 2e-16 ***
## hora                  7.735e+00  2.070e-01  37.368  < 2e-16 ***
## dia                   3.844e-01  2.482e-01   1.549  0.12150    
## mes                   9.996e+00  1.682e+00   5.943 2.89e-09 ***
## año                   8.258e+01  2.732e+00  30.225  < 2e-16 ***
## estacion             -7.774e+00  5.177e+00  -1.502  0.13324    
## dia_de_la_semana      4.393e-01  6.918e-01   0.635  0.52545    
## asueto               -4.864e+00  8.365e+00  -0.582  0.56089    
## temperatura           1.582e+00  1.038e+00   1.524  0.12752    
## sensacion_termica     4.748e+00  9.552e-01   4.971 6.76e-07 ***
## humedad              -2.115e+00  7.884e-02 -26.827  < 2e-16 ***
## velocidad_del_viento  5.582e-01  1.809e-01   3.086  0.00203 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 141.7 on 10874 degrees of freedom
## Multiple R-squared:  0.3891, Adjusted R-squared:  0.3885 
## F-statistic: 629.6 on 11 and 10874 DF,  p-value: < 2.2e-16

regresion <-  lm(rentas_totales ~ hora + mes + año + sensacion_termica + humedad + velocidad_del_viento, data=df)

datos <- data.frame(hora=11.54, mes=1:12, año=2023, sensacion_termica=23.66, humedad=61.89, velocidad_del_viento=12.7999)
predict(regresion, datos)

##        1        2        3        4        5        6        7        8 
## 1100.224 1107.798 1115.372 1122.946 1130.519 1138.093 1145.667 1153.240 
##        9       10       11       12 
## 1160.814 1168.388 1175.962 1183.535

Entender el precio de las casas

1. Importar base de datos

bd <- read.csv("/Users/santiago/Downloads/HousePriceData.csv")
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

2. Entender base de datos

summary(bd)

##   Observation      Dist_Taxi      Dist_Market    Dist_Hospital  
##  Min.   :  1.0   Min.   :  146   Min.   : 1666   Min.   : 3227  
##  1st Qu.:237.0   1st Qu.: 6477   1st Qu.: 9367   1st Qu.:11302  
##  Median :469.0   Median : 8228   Median :11149   Median :13189  
##  Mean   :468.4   Mean   : 8235   Mean   :11022   Mean   :13091  
##  3rd Qu.:700.0   3rd Qu.: 9939   3rd Qu.:12675   3rd Qu.:14855  
##  Max.   :932.0   Max.   :20662   Max.   :20945   Max.   :23294  
##                                                                 
##      Carpet         Builtup        Parking          City_Category     
##  Min.   :  775   Min.   :  932   Length:905         Length:905        
##  1st Qu.: 1317   1st Qu.: 1579   Class :character   Class :character  
##  Median : 1478   Median : 1774   Mode  :character   Mode  :character  
##  Mean   : 1511   Mean   : 1794                                        
##  3rd Qu.: 1654   3rd Qu.: 1985                                        
##  Max.   :24300   Max.   :12730                                        
##  NA's   :7                                                            
##     Rainfall       House_Price       
##  Min.   :-110.0   Min.   :  1492000  
##  1st Qu.: 600.0   1st Qu.:  4623000  
##  Median : 780.0   Median :  5860000  
##  Mean   : 786.9   Mean   :  6083992  
##  3rd Qu.: 970.0   3rd Qu.:  7200000  
##  Max.   :1560.0   Max.   :150000000  
## 

str(bd)

## 'data.frame':    905 obs. of  10 variables:
##  $ Observation  : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ Dist_Taxi    : int  9796 8294 11001 8301 10510 6665 13153 5882 7495 8233 ...
##  $ Dist_Market  : int  5250 8186 14399 11188 12629 5142 11869 9948 11589 7067 ...
##  $ Dist_Hospital: int  10703 12694 16991 12289 13921 9972 17811 13315 13370 11400 ...
##  $ Carpet       : int  1659 1461 1340 1451 1770 1442 1542 1261 1090 1030 ...
##  $ Builtup      : int  1961 1752 1609 1748 2111 1733 1858 1507 1321 1235 ...
##  $ Parking      : chr  "Open" "Not Provided" "Not Provided" "Covered" ...
##  $ City_Category: chr  "CAT B" "CAT B" "CAT A" "CAT B" ...
##  $ Rainfall     : int  530 210 720 620 450 760 1030 1020 680 1130 ...
##  $ House_Price  : int  6649000 3982000 5401000 5373000 4662000 4526000 7224000 3772000 4631000 4415000 ...

count(bd, Parking, sort = TRUE)

##        Parking   n
## 1         Open 355
## 2 Not Provided 225
## 3      Covered 184
## 4   No Parking 141

count(bd, City_Category, sort = TRUE)

##   City_Category   n
## 1         CAT B 351
## 2         CAT A 320
## 3         CAT C 234

3. Caja de bigotes, datos outliers

sum(is.na(bd))

## [1] 7

sapply(bd, function(x)sum(is.na(bd)))

##   Observation     Dist_Taxi   Dist_Market Dist_Hospital        Carpet 
##             7             7             7             7             7 
##       Builtup       Parking City_Category      Rainfall   House_Price 
##             7             7             7             7             7

bd <- na.omit(bd)
bd <- bd[bd$House_Price<15000000,]
bd <- bd[bd$Rainfall > 0,]

4. Generar regresión lineal

regresion2 <- lm(House_Price ~ Dist_Taxi + Dist_Market + Dist_Hospital + Carpet + Builtup + Parking + City_Category + Rainfall, data = bd)
summary(regresion2)

## 
## Call:
## lm(formula = House_Price ~ Dist_Taxi + Dist_Market + Dist_Hospital + 
##     Carpet + Builtup + Parking + City_Category + Rainfall, data = bd)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -3572286  -803711   -64861   759084  4399052 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          5.611e+06  3.681e+05  15.246  < 2e-16 ***
## Dist_Taxi            3.041e+01  2.684e+01   1.133   0.2575    
## Dist_Market          1.248e+01  2.083e+01   0.599   0.5492    
## Dist_Hospital        4.862e+01  3.009e+01   1.616   0.1065    
## Carpet              -7.734e+02  3.478e+03  -0.222   0.8241    
## Builtup              1.315e+03  2.902e+03   0.453   0.6506    
## ParkingNo Parking   -6.046e+05  1.390e+05  -4.351 1.52e-05 ***
## ParkingNot Provided -4.898e+05  1.236e+05  -3.963 8.00e-05 ***
## ParkingOpen         -2.635e+05  1.126e+05  -2.340   0.0195 *  
## City_CategoryCAT B  -1.875e+06  9.607e+04 -19.517  < 2e-16 ***
## City_CategoryCAT C  -2.890e+06  1.059e+05 -27.291  < 2e-16 ***
## Rainfall            -1.260e+02  1.558e+02  -0.809   0.4187    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1223000 on 883 degrees of freedom
## Multiple R-squared:  0.5005, Adjusted R-squared:  0.4943 
## F-statistic: 80.43 on 11 and 883 DF,  p-value: < 2.2e-16

5. Construir el modelo predictivo

datos2 <- data_frame(Dist_Taxi=8278, Dist_Market=16251, Dist_Hospital=13857,Carpet=1455, Builtup=1764,Parking="Covered", City_Category="CAT A", Rainfall= 390)

## Warning: `data_frame()` was deprecated in tibble 1.1.0.
## ℹ Please use `tibble()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

predict(regresion2, datos2)

##       1 
## 7884599

Conclusiones

R Studio es una gran alternativa para poder realizar un análisis de regresión líneal cuando se busca realizar una predicción sobre una base de datos con una gran cantidad de variables y observaciones disponibles, antes de construir el modelo predictivo es importante identificar posibles datos atípicos que puedan comprometer la precisión del análisis, el código de R te permite conocer cuáles son las variables más influyentes para el modelo y cuales no son necesarias. Por último se genera el análisis de regresión líneal con los las variables influyentes y a partir de eso se genera el modelo predictivo que nos permite realizar proyecciones de variables que desamos conocer.