I’ve always been intereted in photography but, i’ve never actually tried it. When I did research on camera’s, I found so many sources and reading reviews took a long time. Since, I’m an amateur photgrapher, my budget is around 500-700 Dollars. I’ve accumulated the top camera’s that is in my budget but it’s hard to distinquish between them. I’ve accumalted a list of ratings from various retail websites for the cameras I am most interested in. From this, I wil build a recommender system that will help narrow my decission.
dslr <- read.csv("https://raw.githubusercontent.com/mgankhuyag/data618/master/dslr.csv",header = TRUE)
dslr %>% kable(caption = "DSLR Camera Ratings by Retail Users") %>% kable_styling("striped", full_width = TRUE)| X | Amazon | Best.Buy | Walmart | Bhphotovideo | Techradar | Adorama | cnet |
|---|---|---|---|---|---|---|---|
| Nikon D3500 | 4.6 | 4.8 | 5.0 | 4.4 | 4.0 | 4.7 | NA |
| Nikon D5600 | 4.4 | NA | NA | 4.7 | 4.0 | 4.7 | 4.0 |
| Nikon D5300 | 4.4 | 4.8 | NA | 4.8 | 4.0 | 4.8 | 4.0 |
| Nikon D610 | 4.3 | 4.8 | 4.4 | 4.5 | NA | 4.5 | 4.0 |
| Canon EOS Rebel T6 | 4.5 | 4.7 | 4.4 | 4.8 | 4.0 | 4.7 | 3.0 |
| Canon EOS Rebel T7 | 4.7 | 4.7 | 5.0 | 4.5 | 3.5 | 4.7 | NA |
| Canon EOS Rebel T7i | 4.7 | 4.9 | 4.6 | 4.8 | 4.0 | 4.9 | 3.5 |
| Canon EOS Rebel T6i | 4.6 | 4.8 | 4.6 | 4.8 | 4.0 | 4.7 | 3.5 |
paste("Training Data")## [1] "Training Data"print(train)## Amazon Best.Buy Walmart Bhphotovideo Techradar Adorama cnet
## [1,] 4.7 4.7 5.0 4.5 3.5 4.7 NA
## [2,] 4.6 4.8 5.0 4.4 4.0 4.7 NA
## [3,] 4.4 4.8 NA 4.8 4.0 4.8 4.0
## [4,] 4.4 NA NA 4.7 4.0 4.7 4.0
## [5,] 4.7 4.9 4.6 4.8 4.0 4.9 3.5
## [6,] 4.6 4.8 4.6 4.8 4.0 4.7 3.5paste("Testing Data")## [1] "Testing Data"print(test)## Amazon Best.Buy Walmart Bhphotovideo Techradar Adorama cnet
## [1,] 4.5 4.7 4.4 4.8 4 4.7 3
## [2,] 4.3 4.8 4.4 4.5 NA 4.5 4#Mean of Training
mean_train <- mean(train, na.rm = TRUE)
paste("Mean =" ,print(round(mean_train,2)))## [1] 4.48## [1] "Mean = 4.48"error <- mean_train - train
paste("error =" ,print(round(error,2)))## Amazon Best.Buy Walmart Bhphotovideo Techradar Adorama cnet
## [1,] -0.22 -0.22 -0.52 -0.02 0.98 -0.22 NA
## [2,] -0.12 -0.32 -0.52 0.08 0.48 -0.22 NA
## [3,] 0.08 -0.32 NA -0.32 0.48 -0.32 0.48
## [4,] 0.08 NA NA -0.22 0.48 -0.22 0.48
## [5,] -0.22 -0.42 -0.12 -0.32 0.48 -0.42 0.98
## [6,] -0.12 -0.32 -0.12 -0.32 0.48 -0.22 0.98## [1] "error = -0.22" "error = -0.12" "error = 0.08" "error = 0.08"
## [5] "error = -0.22" "error = -0.12" "error = -0.22" "error = -0.32"
## [9] "error = -0.32" "error = NA" "error = -0.42" "error = -0.32"
## [13] "error = -0.52" "error = -0.52" "error = NA" "error = NA"
## [17] "error = -0.12" "error = -0.12" "error = -0.02" "error = 0.08"
## [21] "error = -0.32" "error = -0.22" "error = -0.32" "error = -0.32"
## [25] "error = 0.98" "error = 0.48" "error = 0.48" "error = 0.48"
## [29] "error = 0.48" "error = 0.48" "error = -0.22" "error = -0.22"
## [33] "error = -0.32" "error = -0.22" "error = -0.42" "error = -0.22"
## [37] "error = NA" "error = NA" "error = 0.48" "error = 0.48"
## [41] "error = 0.98" "error = 0.98"rmse_train <- sqrt(mean((error^2),na.rm=TRUE))
paste("RMSE =" ,print(round(rmse_train,2)))## [1] 0.42## [1] "RMSE = 0.42"bias_camera <- round(((rowMeans(train, na.rm=TRUE))-mean_train),2)
bias_publisher <-round(((colMeans(train,na.rm = TRUE))-mean_train),2)bias_camera## [1] 0.04 0.11 -0.01 -0.12 0.01 -0.05bias_publisher## Amazon Best.Buy Walmart Bhphotovideo Techradar
## 0.09 0.32 0.32 0.19 -0.56
## Adorama cnet
## 0.27 -0.73