The goal of mercedestrenzr is to inspect and analyze used Mercedes Benz car prices. The package helps users to get simple answers on how to choose the used Mercedes Benz car in the market. The package also includes useful visualization tool and trained model to serve buyers and sellers.
Kelly Wu, Morris Zhao, Spencer Gerlach, Ty Andrews
Our package is unique in the r package ecosystem. It provides an easy way to investigate used Mercedes Benz car prices. It provide people a big picture about the market. The package is rely on the real market data set to plot, filter and predict. It also gives advice to buyers and seller when they try to make a decision.
Our package is the first package in cran that focus on analyzing the market trend of Mercedes-Benz. This is a pilot project on streamlining the data analysis process of used cars market. This package could serve as the foundation for bigger projects in the future that extend the scope beyond a single car brand and leverage web scrapping techniques to enhance the dataset.
The package contains the following functions:
-
plot_mercedes_price: Plot a density plot of a Mercedes-Benz model to see where the current vehicle’s price falls for that same model in the market. -
listing_search: Retrieves the top listings that are within the budget range specified by the user. -
listing_summary: Summarize mileage, price, and rarity information in the dataset. -
predict_mercedes_price: Predicts the price in USD of a Mercedes-Benz given the year, model, condition, and number of cylinders.
For a more detailed walkthrough of function usage, the package vignette is available here
The package contains a static dataset for Craiglist used-car listings that were previously web scraped. Several key attributes about the used-car are available in the dataset, such as vehicle prices, models, car conditions, odometer readings, VINs, regions and transmission. The package’s dataset was adapted from verison 10 of the raw dataset created by AustinReese.
You can install the development version of mercedestrenzr from GitHub with:
# install.packages("devtools")
devtools::install_github("UBC-MDS/mercedestrenzr")This is a basic example which shows you how to solve a common problem:
library(mercedestrenzr)
# load sample data from package
summary(mercedes_data)
#> ...1 price_USD condition paint_color
#> Min. : 0 Min. :0.000e+00 Length:8553 Length:8553
#> 1st Qu.:2138 1st Qu.:6.500e+03 Class :character Class :character
#> Median :4276 Median :1.790e+04 Mode :character Mode :character
#> Mean :4276 Mean :7.273e+05
#> 3rd Qu.:6414 3rd Qu.:3.000e+04
#> Max. :8552 Max. :3.025e+09
#>
#> model odometer_mi year num_cylinders
#> Length:8553 Min. : 0 Min. :1929 Length:8553
#> Class :character 1st Qu.: 36525 1st Qu.:2011 Class :character
#> Mode :character Median : 68376 Median :2014 Mode :character
#> Mean : 75356 Mean :2013
#> 3rd Qu.: 99838 3rd Qu.:2017
#> Max. :9999999 Max. :2021
#> NA's :150
#> fuel transmission drive size
#> Length:8553 Length:8553 Length:8553 Length:8553
#> Class :character Class :character Class :character Class :character
#> Mode :character Mode :character Mode :character Mode :character
#>
#>
#>
#>
#> type state VIN title_status
#> Length:8553 Length:8553 Length:8553 Length:8553
#> Class :character Class :character Class :character Class :character
#> Mode :character Mode :character Mode :character Mode :character
#>
#>
#>
#>
#> description
#> Length:8553
#> Class :character
#> Mode :character
#>
#>
#>
#> summary_result<- listing_summary(data = mercedes_data, model="all")
head(summary_result)
#> # A tibble: 6 × 7
#> model avg_mileage_mi median_mileage_mi avg_price_USD media…¹ perce…² rarity
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 a-class 14456 10512 32094 31590 0.3 Rare
#> 2 amg 21997 26191 50325 44990 1.8 Rare
#> 3 b-class 25819 26803 16797 15590 0.7 Rare
#> 4 c-class 75869 74235 14783 12950 20.1 Common
#> 5 cl-class 65209 61445 19324 20888 9 Uncom…
#> 6 d-class 163542 150000 8994 7100 0.2 Rare
#> # … with abbreviated variable names ¹median_price_USD, ²percent_of_marketresult <- listing_search(mercedes_data, budget=c(0, 30000),
model = "any", sort_feature = "odometer_mi",
ascending = TRUE)
head(result[, 1:5], 5)
#> # A tibble: 5 × 5
#> price_USD model odometer_mi ...1 condition
#> <dbl> <chr> <dbl> <dbl> <chr>
#> 1 0 e-class 0 1599 excellent
#> 2 0 sl-class 0 3528 used
#> 3 4999 c-class 0 2493 excellent
#> 4 4999 c-class 0 2494 excellent
#> 5 11900 m-class 0 686 excellentplot_mercedes_price('c-class', 30000, mercedes_data)
price <- predict_mercedes_price("e-class", 2015, 55000, "fair", "silver")
price
#> # A tibble: 1 × 1
#> predicted_price_USD
#> <dbl>
#> 1 11069.Please note that the mercedestrenzr project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.