Bike Sharing Demand
By Nihit R Save
This project is maintained by kneehit
Bike Sharing Demand
Author: Nihit R. Save
Date: 23rd March 2017
Problem Statement
We are provided with hourly bike 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.We 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.
Dataset Information
The train and test data, which can be found at the link given above, contain the following variables:
| Variable | Description |
|---|---|
| datetime | hourly date + timestamp |
| season | 1 = spring, 2 = summer, 3 = fall, 4 = winter |
| holiday | whether the day is considered a holiday |
| workingday | whether the day is neither a weekend nor holiday |
| weather | 1: Clear, Few clouds, Partly cloudy, Partly cloudy 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds 4: Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog |
| temp | temperature in Celsius |
| atemp | “feels like” temperature in Celsius |
| humidity | relative humidity |
| windspeed | wind speed |
| casual | number of non-registered user rentals initiated |
| registered | number of registered user rentals initiated |
| count | number of total rentals |
Data Exploration
Loading Dataset from the work directory.
train <- read.csv("train.csv")
test <- read.csv("test.csv")
Exploring data and data types in train dataset.
str(train)
## 'data.frame': 10886 obs. of 12 variables:
## $ datetime : Factor w/ 10886 levels "2011-01-01 00:00:00",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ season : int 1 1 1 1 1 1 1 1 1 1 ...
## $ holiday : int 0 0 0 0 0 0 0 0 0 0 ...
## $ workingday: int 0 0 0 0 0 0 0 0 0 0 ...
## $ weather : int 1 1 1 1 1 2 1 1 1 1 ...
## $ temp : num 9.84 9.02 9.02 9.84 9.84 ...
## $ atemp : num 14.4 13.6 13.6 14.4 14.4 ...
## $ humidity : int 81 80 80 75 75 75 80 86 75 76 ...
## $ windspeed : num 0 0 0 0 0 ...
## $ casual : int 3 8 5 3 0 0 2 1 1 8 ...
## $ registered: int 13 32 27 10 1 1 0 2 7 6 ...
## $ count : int 16 40 32 13 1 1 2 3 8 14 ...
The variables season,holiday,workingday and weather are stored as int and we shall convert them to factor during later stages.
Checking for missing values.
table(is.na(train))
##
## FALSE
## 130632
There are no missing values in our train data set.
Observing the distribution of variables in dataset.
summary(train)
## datetime season holiday
## 2011-01-01 00:00:00: 1 Min. :1.000 Min. :0.00000
## 2011-01-01 01:00:00: 1 1st Qu.:2.000 1st Qu.:0.00000
## 2011-01-01 02:00:00: 1 Median :3.000 Median :0.00000
## 2011-01-01 03:00:00: 1 Mean :2.507 Mean :0.02857
## 2011-01-01 04:00:00: 1 3rd Qu.:4.000 3rd Qu.:0.00000
## 2011-01-01 05:00:00: 1 Max. :4.000 Max. :1.00000
## (Other) :10880
## workingday weather temp atemp
## Min. :0.0000 Min. :1.000 Min. : 0.82 Min. : 0.76
## 1st Qu.:0.0000 1st Qu.:1.000 1st Qu.:13.94 1st Qu.:16.66
## Median :1.0000 Median :1.000 Median :20.50 Median :24.24
## Mean :0.6809 Mean :1.418 Mean :20.23 Mean :23.66
## 3rd Qu.:1.0000 3rd Qu.:2.000 3rd Qu.:26.24 3rd Qu.:31.06
## Max. :1.0000 Max. :4.000 Max. :41.00 Max. :45.45
##
## humidity windspeed casual registered
## Min. : 0.00 Min. : 0.000 Min. : 0.00 Min. : 0.0
## 1st Qu.: 47.00 1st Qu.: 7.002 1st Qu.: 4.00 1st Qu.: 36.0
## Median : 62.00 Median :12.998 Median : 17.00 Median :118.0
## Mean : 61.89 Mean :12.799 Mean : 36.02 Mean :155.6
## 3rd Qu.: 77.00 3rd Qu.:16.998 3rd Qu.: 49.00 3rd Qu.:222.0
## Max. :100.00 Max. :56.997 Max. :367.00 Max. :886.0
##
## count
## Min. : 1.0
## 1st Qu.: 42.0
## Median :145.0
## Mean :191.6
## 3rd Qu.:284.0
## Max. :977.0
##
We notice that our response variables have outliers.
Combining the Train and Test dataset for further exploration and manipulation.
test$casual <- NA
test$registered <- NA
test$count <- NA
combined <- rbind(train,test)
Extracting New Features from Date
The library lubridate allows us to extract features from a POSIXct object. Extracting time and day variables.
library(lubridate)
combined$datetime <- ymd_hms(combined$datetime)
combined$hour <- factor(hour(combined$datetime))
combined$weekday <- factor(wday(combined$datetime,label = T),ordered = F)
combined$year <- factor(year(combined$datetime))
Data Manipulation
As we noticed in structure of dataset some variables are stored as integer and we shall convert them to factor.
combined$season <- factor(combined$season, labels = c("Spring","Summer","Fall","Winter"))
combined$weather <- factor(combined$weather, labels = c("Clear","Mist","Light Rain/Snow","Heavy Rain/Snow"))
combined$workingday <- factor(combined$workingday, labels = c("No","Yes"))
On weekends there is holiday and therefore we shall set holiday to 1 if it is a weekend.
library(plyr)
library(dplyr)
combined <- ddply(combined,~datetime,transform,holiday = ifelse(weekday %in% c("Sat","Sun"),"1",holiday))
combined$holiday <- factor(combined$holiday, labels = c("No","Yes"))
Data Visualization
Loading required libraries
library(ggplot2)
library(scales)
library(gridExtra)
ggplot(data = combined, aes(x = count)) + geom_histogram(col = "black",binwidth = 50) + scale_x_continuous(breaks = seq(0,1000,50)) + xlab("") + ylab("Number of Bike Rentals") + ggtitle("Distribution of Response Variable")
The distribution of our response variable is highly skewed and will require transformation while using it in some algorithms.
ggplot(data = combined, aes(x = weather,y = count, fill = weather)) + geom_histogram(stat = "identity") + scale_y_continuous(breaks = seq(0,1500000,100000),labels = comma) + ylab("Total Number of Bikes Rented") + xlab("Weather") + ggtitle("Bike Rentals by Weather") + theme(plot.title = element_text(hjust = 0.5)) + scale_fill_discrete(name = "Weather") + guides(fill=FALSE)
As expected most number of bikes are rented in Clear weather followed by Mist and then Light Rain/Snow. Also it seems like no bikes are rented in Heavy Rain/Snow but on taking a look at our dataset theres only one observation of bikes rented on Heavy Rain/Snow day and it the count corresponds to 164 bikes.
p1 <- ggplot(data = combined,aes(x = registered,y = casual,colour = holiday)) + geom_point() + facet_wrap(~holiday) + xlab("Registered User Bike Rentals") + ylab("Casual User Bike Rentals") + ggtitle("Registered vs Casual User Bike Rentals on Holiday") + theme(plot.title = element_text(hjust = 0.5)) + guides(colour=FALSE)
p2 <- ggplot(data = combined,aes(x = registered,y = casual,colour = workingday)) + geom_point() + facet_wrap(~workingday) + xlab("Registered User Bike Rentals") + ylab("Casual User Bike Rentals") + ggtitle("Registered vs Casual User Bike Rentals on Working Day") + theme(plot.title = element_text(hjust = 0.5)) + guides(colour=FALSE)
library(gridExtra)
grid.arrange(p1,p2)
As we can see casual users rent more bikes compared to registered users if it is holiday. And more bikes are rented by registered users compared to casual users if it is a working day.
This must be due to the fact that registered users ride bikes to their jobs on working days and non registered users have enough leisure to ride bikes on holidays.
ggplot(combined, aes(x = temp , y = count,color = temp)) + geom_point() + scale_color_continuous(name = "Temperature",low = "orange",high = "red") + xlab("Temperature") + ylab("Number of Bike Rentals") + ggtitle("Bike Rentals by Temperature") + theme(plot.title = element_text(hjust = 0.5))
Since the bike rental is taking place in Washington, we can see that bike rentals increase with temperature.
seasonbyyear <- combined %>% group_by(season,year) %>% summarise(count = mean(count,na.rm = T)) %>% as.data.frame()
ggplot(seasonbyyear, aes(x = season,y = count ,fill = year)) + geom_histogram(stat = "identity",position = "dodge") + xlab("Season") + ylab("Average Number of Bike Rentals") + ggtitle("Bike Rentals by Season") + scale_fill_discrete(name = "Year") + theme(plot.title = element_text(hjust = 0.5))
We notice that average number of bikes rented has increased in every season from 2011 to 2012.
humidity_summary <- train %>% group_by(humidity) %>% summarise(count = mean(count,na.rm = T)) %>% as.data.frame()
ggplot(humidity_summary, aes(x = humidity,y = count)) + geom_area(fill = "orange") + geom_smooth() + scale_y_continuous(limits = c(0,360),breaks = seq(0,350,50)) + scale_x_continuous(breaks = seq(0,100,10)) + xlab("Humidity") + ylab("Average Number of Bike Rentals") + ggtitle("Bike Rentals by Humidity") + theme(plot.title = element_text(hjust = 0.5))
On an average, most number of bike rentals occur in the range of 15% to 50% humidity.
This can be due to the fact that very low humidity is associated with dehydration while high humidity leads to more sweating. Both of these negatively impact a bicycle user and thus number of bikes rented decreases at extreme ends.
weekday_summary <- data.frame(combined %>% group_by(weekday,hour) %>% summarise(count = mean(count,na.rm = T)) )
Daily trends of bike rentals can be seen from the following animation.
library(animation)
ani.options(convert = 'C:\\Program Files\\ImageMagick-7.0.5-Q16\\convert.exe')
ani.options(interval = 0.5)
i = 1
saveGIF(while(i < nrow(weekday_summary)) {print( ggplot(data = weekday_summary[1:i,], aes(x = hour,y = count,col = weekday)) + geom_point() + geom_line(aes(group = weekday)) + scale_y_continuous(breaks = seq(0,550,50)) + scale_color_discrete(name = "Day") + xlab("Hour of the Day ") + ylab("Average Number of Bike Rentals") + ggtitle("Daily Trend of Bike Rentals by Time and Day") + theme(plot.title = element_text(hjust = 0.5)) )
i = i + 1},movie.name = "ani_main.gif" ,ani.width = 720,ani.height = 480)
Daily trends of bike rentals can be seen from the above animation.
ggplot(data = weekday_summary,aes(x = hour,y = count,colour = weekday)) + geom_line(aes(group = weekday)) + geom_point(aes(group = weekday)) + scale_y_continuous(breaks = seq(0,550,50)) + scale_color_discrete(name = "Day") + xlab("Hour of the Day ") + ylab("Average Number of Bike Rentals") + ggtitle("Bike Rentals by Time and Day") + theme(plot.title = element_text(hjust = 0.5))
In this graph, we notice 2 peaks : 1) At 8am 2) At 6pm These must be the timings people leave for and from jobs respectively and thus bike rentals increases significantly around this time.
On the other hand, on weekends average number of bikes rented increases around 12pm which could be the time people have more free time.
Data Modelling
Splitting the dataset back to train and test sets.
new_train <- combined[!is.na(combined$count),]
new_test <- combined[is.na(combined$count),]
Using Linear Regression
lmmodel <- lm(count ~ season + holiday + weather + temp + humidity + windspeed + hour + weekday + year,data = new_train)
summary(lmmodel)
##
## Call:
## lm(formula = count ~ season + holiday + weather + temp + humidity +
## windspeed + hour + weekday + year, data = new_train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -370.59 -60.88 -7.13 49.41 464.72
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -78.40179 9.97165 -7.862 4.12e-15 ***
## seasonSummer 47.84652 3.60931 13.256 < 2e-16 ***
## seasonFall 34.25734 4.62064 7.414 1.32e-13 ***
## seasonWinter 68.35700 2.97696 22.962 < 2e-16 ***
## holidayYes -8.77363 6.12871 -1.432 0.152298
## weatherMist -11.50188 2.39497 -4.803 1.59e-06 ***
## weatherLight Rain/Snow -69.28823 4.04705 -17.121 < 2e-16 ***
## weatherHeavy Rain/Snow -186.51579 101.23066 -1.842 0.065432 .
## temp 5.49803 0.22447 24.494 < 2e-16 ***
## humidity -0.61595 0.06866 -8.972 < 2e-16 ***
## windspeed -0.54154 0.12869 -4.208 2.60e-05 ***
## hour1 -17.09565 6.70177 -2.551 0.010758 *
## hour2 -28.42833 6.72654 -4.226 2.40e-05 ***
## hour3 -39.79459 6.78950 -5.861 4.73e-09 ***
## hour4 -40.63524 6.76021 -6.011 1.90e-09 ***
## hour5 -24.46941 6.72379 -3.639 0.000275 ***
## hour6 35.08567 6.71490 5.225 1.77e-07 ***
## hour7 169.91893 6.70712 25.334 < 2e-16 ***
## hour8 313.90875 6.70055 46.848 < 2e-16 ***
## hour9 165.67465 6.70724 24.701 < 2e-16 ***
## hour10 111.03071 6.72945 16.499 < 2e-16 ***
## hour11 138.04417 6.76812 20.396 < 2e-16 ***
## hour12 178.47718 6.81114 26.204 < 2e-16 ***
## hour13 174.13870 6.85698 25.396 < 2e-16 ***
## hour14 157.24082 6.89193 22.815 < 2e-16 ***
## hour15 168.02257 6.90118 24.347 < 2e-16 ***
## hour16 230.81109 6.88850 33.507 < 2e-16 ***
## hour17 388.03975 6.85824 56.580 < 2e-16 ***
## hour18 354.46691 6.82390 51.945 < 2e-16 ***
## hour19 242.65099 6.76204 35.884 < 2e-16 ***
## hour20 160.59993 6.73145 23.858 < 2e-16 ***
## hour21 109.23662 6.70926 16.281 < 2e-16 ***
## hour22 73.22677 6.70004 10.929 < 2e-16 ***
## hour23 33.81752 6.69605 5.050 4.48e-07 ***
## weekdayMon -2.17731 6.32176 -0.344 0.730540
## weekdayTues 0.52658 7.12129 0.074 0.941056
## weekdayWed 3.07457 7.03428 0.437 0.662060
## weekdayThurs 5.58561 7.12052 0.784 0.432800
## weekdayFri 8.78643 6.95876 1.263 0.206745
## weekdaySat 20.60748 3.59599 5.731 1.03e-08 ***
## year2012 87.76699 1.96185 44.737 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 101 on 10845 degrees of freedom
## Multiple R-squared: 0.6902, Adjusted R-squared: 0.6891
## F-statistic: 604.1 on 40 and 10845 DF, p-value: < 2.2e-16
We get R squared value of 0.69 which tells us that about 69% of variation in variable count is explained by the predictor variables.
As we saw earlier, Count is highly skewed and thus we will use it’s log transformation as response variable.
lmmodel2 <- lm(log(count) ~ season + holiday + weather + temp + humidity + windspeed + hour + weekday + year,data = new_train)
summary(lmmodel2)
##
## Call:
## lm(formula = log(count) ~ season + holiday + weather + temp +
## humidity + windspeed + hour + weekday + year, data = new_train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2693 -0.2931 0.0278 0.3708 2.5744
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.676e+00 6.128e-02 43.670 < 2e-16 ***
## seasonSummer 4.031e-01 2.218e-02 18.173 < 2e-16 ***
## seasonFall 3.415e-01 2.839e-02 12.027 < 2e-16 ***
## seasonWinter 5.759e-01 1.829e-02 31.480 < 2e-16 ***
## holidayYes -3.086e-03 3.766e-02 -0.082 0.9347
## weatherMist -6.020e-02 1.472e-02 -4.090 4.34e-05 ***
## weatherLight Rain/Snow -5.645e-01 2.487e-02 -22.700 < 2e-16 ***
## weatherHeavy Rain/Snow -1.963e-01 6.221e-01 -0.316 0.7524
## temp 3.527e-02 1.379e-03 25.569 < 2e-16 ***
## humidity -1.872e-03 4.219e-04 -4.437 9.21e-06 ***
## windspeed -3.783e-03 7.908e-04 -4.783 1.75e-06 ***
## hour1 -6.472e-01 4.118e-02 -15.715 < 2e-16 ***
## hour2 -1.203e+00 4.134e-02 -29.110 < 2e-16 ***
## hour3 -1.751e+00 4.172e-02 -41.976 < 2e-16 ***
## hour4 -2.052e+00 4.154e-02 -49.384 < 2e-16 ***
## hour5 -9.626e-01 4.132e-02 -23.297 < 2e-16 ***
## hour6 2.729e-01 4.126e-02 6.614 3.92e-11 ***
## hour7 1.270e+00 4.122e-02 30.805 < 2e-16 ***
## hour8 1.905e+00 4.118e-02 46.273 < 2e-16 ***
## hour9 1.576e+00 4.122e-02 38.245 < 2e-16 ***
## hour10 1.247e+00 4.135e-02 30.164 < 2e-16 ***
## hour11 1.365e+00 4.159e-02 32.823 < 2e-16 ***
## hour12 1.552e+00 4.186e-02 37.082 < 2e-16 ***
## hour13 1.525e+00 4.214e-02 36.190 < 2e-16 ***
## hour14 1.441e+00 4.235e-02 34.028 < 2e-16 ***
## hour15 1.499e+00 4.241e-02 35.350 < 2e-16 ***
## hour16 1.762e+00 4.233e-02 41.617 < 2e-16 ***
## hour17 2.176e+00 4.214e-02 51.634 < 2e-16 ***
## hour18 2.093e+00 4.193e-02 49.921 < 2e-16 ***
## hour19 1.801e+00 4.155e-02 43.331 < 2e-16 ***
## hour20 1.501e+00 4.137e-02 36.294 < 2e-16 ***
## hour21 1.242e+00 4.123e-02 30.123 < 2e-16 ***
## hour22 9.988e-01 4.117e-02 24.259 < 2e-16 ***
## hour23 5.993e-01 4.115e-02 14.564 < 2e-16 ***
## weekdayMon -7.167e-02 3.885e-02 -1.845 0.0651 .
## weekdayTues -8.623e-02 4.376e-02 -1.971 0.0488 *
## weekdayWed -7.583e-02 4.323e-02 -1.754 0.0794 .
## weekdayThurs -7.632e-05 4.376e-02 -0.002 0.9986
## weekdayFri 9.903e-02 4.276e-02 2.316 0.0206 *
## weekdaySat 1.178e-01 2.210e-02 5.330 1.00e-07 ***
## year2012 4.930e-01 1.206e-02 40.891 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6207 on 10845 degrees of freedom
## Multiple R-squared: 0.8269, Adjusted R-squared: 0.8262
## F-statistic: 1295 on 40 and 10845 DF, p-value: < 2.2e-16
We get a significant increase in R squared value and now it is 0.82, denoting that about 82% of variation is explained.
We shall compute RMSE to compare the result of linear regression model with other models.
library(Metrics)
selfpredictlm <- predict(lmmodel2,newdata = new_train)
new_train$lmmodelpredict <- round(exp(selfpredictlm))
paste("Rmse for Linear Regression Model is:" ,rmse(new_train$count,new_train$lmmodelpredict))
## [1] "Rmse for Linear Regression Model is: 98.5790446089307"
Using Decision Tree
Lets try using decision tree algorithm to predict count.
library(rpart)
library(rpart.plot)
library(rattle)
library(RColorBrewer)
Treemodel <- rpart(count ~ season + holiday + weather + temp + humidity + windspeed + hour + weekday + year, data = new_train, method = "class")
printcp(Treemodel)
##
## Regression tree:
## rpart(formula = count ~ season + holiday + weather + temp + humidity +
## windspeed + hour + weekday + year, data = new_train, method = "class")
##
## Variables actually used in tree construction:
## [1] holiday hour season temp year
##
## Root node error: 357172914/10886 = 32810
##
## n= 10886
##
## CP nsplit rel error xerror xstd
## 1 0.360230 0 1.00000 1.00020 0.0174137
## 2 0.102632 1 0.63977 0.64235 0.0127945
## 3 0.058803 2 0.53714 0.54582 0.0101152
## 4 0.040309 3 0.47834 0.47094 0.0084452
## 5 0.030748 4 0.43803 0.42627 0.0077568
## 6 0.026609 5 0.40728 0.40294 0.0071259
## 7 0.021815 6 0.38067 0.37330 0.0066517
## 8 0.019199 7 0.35885 0.35175 0.0064923
## 9 0.018747 8 0.33966 0.33962 0.0062437
## 10 0.015147 10 0.30216 0.32013 0.0058132
## 11 0.014120 11 0.28702 0.29481 0.0055542
## 12 0.013656 12 0.27290 0.28506 0.0054558
## 13 0.011181 13 0.25924 0.26279 0.0051215
## 14 0.010000 14 0.24806 0.25459 0.0050235
To avoid overfitting we must prune our decision tree. A rule of thumb is to choose the lowest level where the rel error + xstd is less than xerror which is 0.01. The optimum value of complexity parameter can also be found out from cp graph.
plotcp(Treemodel)
As also evident from cp graph, complexity parameter of 0.01 is optimum for pruning the decision tree.
Prunedtree <- prune(Treemodel,cp = 0.01)
fancyRpartPlot(Prunedtree,sub = "Bike Rental Decision Tree",cex = 0.5)
Lets compute the RMSE of pruned decision tree model
selfpredictdt <- predict(Prunedtree,newdata = new_train)
paste("RMSE for decision tree is:",rmse(new_train$count,round(selfpredictdt)))
## [1] "RMSE for decision tree is: 90.2161376768925"
We get a minor improvement in RMSE using decision tree as compared to linear regression model.
Using Random Forest
Lets try predicting count using random forest.
library(randomForest)
set.seed(999)
rfmodel <- randomForest(data = new_train,importance = TRUE,ntree = 250,count ~ season + holiday + weather + temp + humidity + windspeed + hour + year )
rfmodel
##
## Call:
## randomForest(formula = count ~ season + holiday + weather + temp + humidity + windspeed + hour + year, data = new_train, importance = TRUE, ntree = 250)
## Type of random forest: regression
## Number of trees: 250
## No. of variables tried at each split: 2
##
## Mean of squared residuals: 3584.52
## % Var explained: 89.08
varImpPlot(rfmodel)
We can see that Year, Hour,Temperature and Holiday are some of the important variables.
selfpredictrf <- predict(rfmodel,newdata = new_train)
new_train$rfmodelpredict <- round(selfpredictrf)
paste("RMSE for random Forest is:", rmse(new_train$count,new_train$rfmodelpredict))
## [1] "RMSE for random Forest is: 40.1412339892129"
Using random model our RMSE has improved substantially.
That is why we will use random forest algorithm to predict the number of casual and registered users.
rfmodelcas <- randomForest(data = new_train,importance = TRUE,ntree = 250,casual ~ season + holiday + weather + temp + humidity + windspeed + hour + year )
rfmodelcas
##
## Call:
## randomForest(formula = casual ~ season + holiday + weather + temp + humidity + windspeed + hour + year, data = new_train, importance = TRUE, ntree = 250)
## Type of random forest: regression
## Number of trees: 250
## No. of variables tried at each split: 2
##
## Mean of squared residuals: 324.7255
## % Var explained: 86.99
varImpPlot(rfmodelcas)
From above plot we can see that for casual users holiday is the most important variable (as was evident from graphs also) followed by hour.
rfmodelreg <- randomForest(data = new_train,importance = TRUE,ntree = 250,registered ~ season + holiday + weather + temp + humidity + windspeed + hour + year )
rfmodelreg
##
## Call:
## randomForest(formula = registered ~ season + holiday + weather + temp + humidity + windspeed + hour + year, data = new_train, importance = TRUE, ntree = 250)
## Type of random forest: regression
## Number of trees: 250
## No. of variables tried at each split: 2
##
## Mean of squared residuals: 2074.822
## % Var explained: 90.9
varImpPlot(rfmodelreg)
For Registered users hour seems to be most important variable in determining bike demand as they must be consisting of daily office goers.
caspredict <- predict(rfmodelcas,newdata = new_train)
new_train$rfmodelcaspredict <- round(caspredict)
regpredict <- predict(rfmodelreg,newdata = new_train)
new_train$rfmodelregpredict <- round(regpredict)
new_train$rfmodelcasregpredict <- new_train$rfmodelcaspredict + new_train$rfmodelregpredict
rmse(new_train$count,new_train$rfmodelcasregpredict)
## [1] 36.91844
We can see that our rmse has decreased even further and thus we shall use this model for prediction.
Predicting the Bike Demand for Test dataset using Random Forest Models
caspredict <- predict(rfmodelcas,newdata = new_test)
caspredict <- round(caspredict)
regpredict <- predict(rfmodelreg,newdata = new_test)
regpredict <- round(regpredict)
prediction <- caspredict + regpredict
sub_file <- data.frame(datetime = new_test$datetime,count = prediction)
write.csv(sub_file, "Predicted_Testrfcasreg.csv",row.names = FALSE,quote = FALSE)