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knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.2     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.4     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(modelr)
bike_data <- read_csv('C:/Users/ADMIN/Documents/data set/db1bike.csv')
## Rows: 199 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (4): Date, Seasons, Holiday, Functioning Day
## dbl (10): Rented_Bike_Count, Hour, Temperature, Humidity, Wind_speed, Visibi...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
glimpse(bike_data)
## Rows: 199
## Columns: 14
## $ Date                    <chr> "01-12-2017", "01-12-2017", "01-12-2017", "01-…
## $ Rented_Bike_Count       <dbl> 254, 204, 173, 107, 78, 100, 181, 460, 930, 49…
## $ Hour                    <dbl> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, …
## $ Temperature             <dbl> -5.2, -5.5, -6.0, -6.2, -6.0, -6.4, -6.6, -7.4…
## $ Humidity                <dbl> 37, 38, 39, 40, 36, 37, 35, 38, 37, 27, 24, 21…
## $ Wind_speed              <dbl> 2.2, 0.8, 1.0, 0.9, 2.3, 1.5, 1.3, 0.9, 1.1, 0…
## $ Visibility              <dbl> 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000…
## $ `Dew point temperature` <dbl> -17.6, -17.6, -17.7, -17.6, -18.6, -18.7, -19.…
## $ `Solar Radiation`       <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00…
## $ Rainfall                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Snowfall                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ Seasons                 <chr> "Winter", "Winter", "Winter", "Winter", "Winte…
## $ Holiday                 <chr> "No Holiday", "No Holiday", "No Holiday", "No …
## $ `Functioning Day`       <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", "Yes…
bike_data <- bike_data %>% 
  mutate(HighDemand = if_else(Rented_Bike_Count > median(Rented_Bike_Count), 1, 0))
bikes_logit <- glm(HighDemand ~ Temperature + Humidity + Wind_speed,  
                   data = bike_data, family = "binomial")
summary(bikes_logit)
## 
## Call:
## glm(formula = HighDemand ~ Temperature + Humidity + Wind_speed, 
##     family = "binomial", data = bike_data)
## 
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  3.414948   0.740193   4.614 3.96e-06 ***
## Temperature  0.271873   0.051551   5.274 1.34e-07 ***
## Humidity    -0.055154   0.009846  -5.601 2.13e-08 ***
## Wind_speed  -0.042782   0.150449  -0.284    0.776    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 275.87  on 198  degrees of freedom
## Residual deviance: 217.36  on 195  degrees of freedom
## AIC: 225.36
## 
## Number of Fisher Scoring iterations: 4
ggplot(bike_data, aes(Temperature, Rented_Bike_Count)) +
  geom_point()

ggplot(bike_data, aes(Humidity, Rented_Bike_Count)) +
  geom_point() 

ggplot(bike_data, aes(Wind_speed, Rented_Bike_Count)) +
  geom_point()