Data Description You are provided hourly rental data spanning two years. For this competition, the training set is comprised of the first 19 days of each month, while the test set is the 20th to the end of the month. You must predict the total count of bikes rented during each hour covered by the test set, using only information available prior to the rental period. Bike sharing systems are a means of renting bicycles where the process of obtaining membership, rental, and bike return is automated via a network of kiosk locations throughout a city. Using these systems, people are able rent a bike from a one location and return it to a different place on an as-needed basis. Currently, there are over 500 bike-sharing programs around the world.

The data generated by these systems makes them attractive for researchers because the duration of travel, departure location, arrival location, and time elapsed is explicitly recorded. Bike sharing systems therefore function as a sensor network, which can be used for studying mobility in a city. In this competition, participants are asked to combine historical usage patterns with weather data in order to forecast bike rental demand in the Capital Bikeshare program in Washington, D.C.

Bikes

Acknowledgements Kaggle is hosting this competition for the machine learning community to use for fun and practice. This dataset was provided by Hadi Fanaee Tork using data from Capital Bikeshare. We also thank the UCI machine learning repository for hosting the dataset. If you use the problem in publication, please cite:

Fanaee-T, Hadi, and Gama, Joao, Event labeling combining ensemble detectors and background knowledge, Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg.

Reading the bikeshare dataset

bike <- read.csv('bikeshare.csv')
head(bike)
##              datetime season holiday workingday weather temp  atemp humidity
## 1 2011-01-01 00:00:00      1       0          0       1 9.84 14.395       81
## 2 2011-01-01 01:00:00      1       0          0       1 9.02 13.635       80
## 3 2011-01-01 02:00:00      1       0          0       1 9.02 13.635       80
## 4 2011-01-01 03:00:00      1       0          0       1 9.84 14.395       75
## 5 2011-01-01 04:00:00      1       0          0       1 9.84 14.395       75
## 6 2011-01-01 05:00:00      1       0          0       2 9.84 12.880       75
##   windspeed casual registered count
## 1    0.0000      3         13    16
## 2    0.0000      8         32    40
## 3    0.0000      5         27    32
## 4    0.0000      3         10    13
## 5    0.0000      0          1     1
## 6    6.0032      0          1     1

ploting

library(ggplot2)
ggplot(bike,aes(temp,count))+geom_point(aes(color=temp),alpha=0.4)

bike$datetime <- as.POSIXct(bike$datetime)
ggplot(bike,aes(datetime,count)) + geom_point(aes(color=temp),alpha=0.5)  + scale_color_continuous(low='#55D8CE',high='#FF6E2E') +theme_bw()

cor(bike[,c('temp','count')])
##            temp     count
## temp  1.0000000 0.3944536
## count 0.3944536 1.0000000

Let’s explore the season data

ggplot(bike,aes(factor(season),count))+geom_boxplot(aes(color=factor(season)))

Feature Engineering Create an “hour” column that takes the hour from the datetime column. You’ll probably need to apply some function to the entire datetime column and reassign it.

library(only)
bike$hour <- sapply(bike$datetime,only_hour)
head(bike)
##              datetime season holiday workingday weather temp  atemp humidity
## 1 2011-01-01 00:00:00      1       0          0       1 9.84 14.395       81
## 2 2011-01-01 01:00:00      1       0          0       1 9.02 13.635       80
## 3 2011-01-01 02:00:00      1       0          0       1 9.02 13.635       80
## 4 2011-01-01 03:00:00      1       0          0       1 9.84 14.395       75
## 5 2011-01-01 04:00:00      1       0          0       1 9.84 14.395       75
## 6 2011-01-01 05:00:00      1       0          0       2 9.84 12.880       75
##   windspeed casual registered count hour
## 1    0.0000      3         13    16   00
## 2    0.0000      8         32    40   01
## 3    0.0000      5         27    32   02
## 4    0.0000      3         10    13   03
## 5    0.0000      0          1     1   04
## 6    6.0032      0          1     1   05
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
pl <- ggplot(filter(bike,workingday==1),aes(hour,count)) 
pl <- pl + geom_point(position=position_jitter(w=1, h=0),aes(color=temp),alpha=0.5)
pl <- pl + scale_color_gradientn(colours = c('dark blue','blue','light blue','light green','yellow','orange','red'))
pl + theme_bw()

for non working

pl <- ggplot(filter(bike,workingday==0),aes(hour,count)) 
pl <- pl + geom_point(position=position_jitter(w=1, h=0),aes(color=temp),alpha=0.5)
pl <- pl + scale_color_gradientn(colours = c('dark blue','blue','light blue','light green','yellow','orange','red'))
pl + theme_bw()

Building a model

temp.model <- lm(count ~temp,data=bike)
summary(temp.model)
## 
## Call:
## lm(formula = count ~ temp, data = bike)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -293.32 -112.36  -33.36   78.98  741.44 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   6.0462     4.4394   1.362    0.173    
## temp          9.1705     0.2048  44.783   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 166.5 on 10884 degrees of freedom
## Multiple R-squared:  0.1556, Adjusted R-squared:  0.1555 
## F-statistic:  2006 on 1 and 10884 DF,  p-value: < 2.2e-16

prediction

6.0462+9.17*25
## [1] 235.2962
bike$hour <- sapply(bike$hour,as.numeric)

To build a Final model with the below features season holiday workingday weather temp humidity windspeed hour (factor)

final.model <- lm(count~.-casual - registered -datetime -atemp,data=bike)

summary of model

summary(final.model)
## 
## Call:
## lm(formula = count ~ . - casual - registered - datetime - atemp, 
##     data = bike)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -324.61  -96.88  -31.01   55.27  688.83 
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  46.91369    8.45147   5.551 2.91e-08 ***
## season       21.70333    1.35409  16.028  < 2e-16 ***
## holiday     -10.29914    8.79069  -1.172    0.241    
## workingday   -0.71781    3.14463  -0.228    0.819    
## weather      -3.20909    2.49731  -1.285    0.199    
## temp          7.01953    0.19135  36.684  < 2e-16 ***
## humidity     -2.21174    0.09083 -24.349  < 2e-16 ***
## windspeed     0.20271    0.18639   1.088    0.277    
## hour          7.61283    0.21688  35.102  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 147.8 on 10877 degrees of freedom
## Multiple R-squared:  0.3344, Adjusted R-squared:  0.3339 
## F-statistic:   683 on 8 and 10877 DF,  p-value: < 2.2e-16

Thank you !!