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BIKE

SHARING DATASET

Pain Point – it is to forecast the future requirement of bike for sharin !ur!ose "ue to its

increasin "eman"

Summar# of number of renta$ bikes a%ai$ab$e

Sample Size 17379Median &'(Average &)*Confdence interval o

Average

&)+,-- to

&*(,&+Standard deviation &)&Minimum &Maximum *--Sum .(*(+-*

• Bike shares/count0 are corre$ate" 1ith Seasons2 There is increase in the count of bikesharin in summer than in 1inter "ue to more usae of bike Thus it can be sai" that 3ount

is !ositi%e$# corre$ate" 1ith season

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 There 1ere %arious conc$usion that 1ere inferre" from the "ataset

Pain Point2 There are %arious factors 1hich a4ect the bike renta$s $ike

•  Tem!erature

• 5hether e4ect an" ho$i"a#

• Humi"it#

An" I am focusin to 6n" out 1hich are the most im!ortant factors an" corre$ation that a4ects

the bike renta$s /Due to constrain of 1 Page I am not able to share more but will try to cover

as much as possible)7arious Tem!erature across a$$ seasons

Season mean Temp.S!rin &(,(&Summer ((,.(8a$$ (),*+5inter &-,.'

• Because of the mild temperatures in spring and winter and

the warm weather in summer and fall,temperature should

be highly correlated with the total amount of bikerentals

all three kinds of temperatures are positive correlated with the

total amount of bike rentals. We also see that the correlation

value is not different across the three types of temperatures(cor=0.63). Anyway the analysis clearly shows, that there is a

relationship between those two variables.

• I had done a linear regression, because the weather and

holiday would be good predictors of bike rentals. It clearly shows, that holiday is a significant negative predictor

(estimate = -929.5). It also shows that

weather nice compared to weather cloudy

(default) is a significant positive

predictor (estimate = 848.5) and

weather wet compared to weather coludy

is a significant negative predictor

(estimate = -2255.2) of bike rentals.

• I also plotted the mean humidity, the mean temperature, the mean wind speed and the mean total rentals per

months. As we can see thetotal amount of bike rentals increases with the temperature per month. Whereas it

seems that therentals are independent of the wind speed and the humidity, because they are almost constant

over the months. This also confirms on the one hand thehigh correlation between rentals and temperature and

on the other hand that nice weather could be a good predictor

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