Bike Share Demand Challenge
Akash
IMPORT DATA
bike <- read.csv("bikeshare.csv")
print(head(bike))## datetime season holiday workingday weather temp atemp
## 1 2011-01-01 00:00:00 1 0 0 1 9.84 14.395
## 2 2011-01-01 01:00:00 1 0 0 1 9.02 13.635
## 3 2011-01-01 02:00:00 1 0 0 1 9.02 13.635
## 4 2011-01-01 03:00:00 1 0 0 1 9.84 14.395
## 5 2011-01-01 04:00:00 1 0 0 1 9.84 14.395
## 6 2011-01-01 05:00:00 1 0 0 2 9.84 12.880
## humidity windspeed casual registered count
## 1 81 0.0000 3 13 16
## 2 80 0.0000 8 32 40
## 3 80 0.0000 5 27 32
## 4 75 0.0000 3 10 13
## 5 75 0.0000 0 1 1
## 6 75 6.0032 0 1 1EXPLORATORY DATA ANALYSIS
1.Count vs temp - Scatterplot
ggplot(bike,aes(temp,count))+ geom_point(alpha=0.3,aes(color=temp))+theme_bw()
2.Count vs Datetime - Scatterplot
bike$datetime <- as.POSIXct(bike$datetime)
pl <- ggplot(bike,aes(datetime,count))+geom_point(aes(color=temp),alpha=0.5)3.Customize the color using scale_color_continous
pl + scale_color_continuous(low='#55D8CE',high='#FF6E2E')+theme_bw()
2011 we have less bike rentals and 2012 we have more bike rental trends. Winter bike rental is less and summer bike rentals are more.
CORRELATIONS
Correlation between temp and count
cor(bike$temp,bike$count)## [1] 0.3944536BOXPLOT
Count vs Season is a factor data
ggplot(bike,aes(factor(season),count))+geom_boxplot(aes(color=factor(season)))+theme_bw()
More rentals in winter than spring. Highest rentals in summer and fall.
FEATURE ENGINEERING
An “hour” column that takes the hour from the datetime column.
bike$hour <- sapply(bike$datetime,function(x){format(x,"%H")})
head(bike)## datetime season holiday workingday weather temp atemp
## 1 2011-01-01 00:00:00 1 0 0 1 9.84 14.395
## 2 2011-01-01 01:00:00 1 0 0 1 9.02 13.635
## 3 2011-01-01 02:00:00 1 0 0 1 9.02 13.635
## 4 2011-01-01 03:00:00 1 0 0 1 9.84 14.395
## 5 2011-01-01 04:00:00 1 0 0 1 9.84 14.395
## 6 2011-01-01 05:00:00 1 0 0 2 9.84 12.880
## humidity windspeed casual registered count hour
## 1 81 0.0000 3 13 16 00
## 2 80 0.0000 8 32 40 01
## 3 80 0.0000 5 27 32 02
## 4 75 0.0000 3 10 13 03
## 5 75 0.0000 0 1 1 04
## 6 75 6.0032 0 1 1 05sapply() and as.numeric to change the hour column to a column of numeric values.
bike$hour <- sapply(bike$hour,as.numeric)VISUALIZATIONS
1.Scatterplot - Count vs hour and color scale as temperature
pl <- ggplot(filter(bike,workingday==0),aes(hour,count))lowest at in 3 at morning,major bike rental at 6 oclock
2.color scale as temp and jitter with the width at x-axis and we don’t add jitter to height
pl <- pl + geom_point(position=position_jitter(w=1, h=0),aes(color=temp),alpha=0.5)
pl
extra colorgradients
pl <- pl + scale_color_gradientn(colours = c('dark blue','blue','light blue','light green','yellow','orange','red'))
pl
pl+theme_bw()
MODEL BUILDING
1.Building Model with Single Feature
temp.model <- lm(count~temp,bike)summary
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-16bike rentals if the temperature was 25 degrees Celsius
temp.test <- data.frame(temp=c(25))
temp.test## temp
## 1 25predict
predict(temp.model,temp.test)## 1
## 235.30972.Model with all the features
model <- lm(count~.-casual-registered-datetime-atemp,bike)summary
summary(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