DATASET
DATASET
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This project uses datasets on used car listings and U.S. state population estimates from 2010-2019 to explore how population size affects car prices across different states. Below are links to the datasets and an initial exploration of the data.
US Car dataset
US Car dataset
https://www.kaggle.com/datasets/doaaalsenani/usa-cers-dataset
US Census State Population Estimates 2010-2019
US Census State Population Estimates 2010-2019
https://www.kaggle.com/datasets/kbrookshier/us-census-state-population-estimates-20102019
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Data sample US Census State Population Estimates 2010-2019
Data sample US Census State Population Estimates 2010-2019
This dataset provides annual population estimates for U.S. states from 2010 to 2019. It includes numerical values representing the population count for each year, allowing for an analysis of population trends over time. The data can be used to observe growth patterns, demographic shifts, and potential correlations with economic factors such as car pricing.
Data sample: US Car datasetData sample: US Car dataset
Data sample: US Car datasetData sample: US Car dataset
This dataset contains detailed information on used car listings, including variables such as price, brand, model, year, title status, mileage, color, and location. It also includes vehicle identification numbers (VINs), auction lot numbers, and condition details. Additionally, the dataset appears to have multiple columns related to state population data, potentially linking vehicle pricing trends with demographic shifts over time. This data can be useful for analyzing factors influencing car prices across different states and years.
This dataset contains detailed information on used car listings, including variables such as price, brand, model, year, title status, mileage, color, and location. It also includes vehicle identification numbers (VINs), auction lot numbers, and condition details. Additionally, the dataset appears to have multiple columns related to state population data, potentially linking vehicle pricing trends with demographic shifts over time. This data can be useful for analyzing factors influencing car prices across different states and years.
Merged and cleaned data sample: US Car dataset
Merged and cleaned data sample: US Car dataset
To prepare my dataset for analysis, I first merged two datasets using the state column, ensuring a comprehensive view of car data combined with population information. I then removed any duplicate rows to maintain data integrity. To handle missing values, I applied na.omit() to exclude any incomplete entries. Ensuring the accuracy of data types, I converted the year, price, mileage, and Population columns to their appropriate formats. Additionally, I created a new variable, car_age, by subtracting the model year from the current year (2025). Finally, I streamlined the dataset by removing insignificant columns such as vin and lot, focusing on the most relevant information for my analysis.
This boxplot shows the distribution of car prices across different states. It highlights the range of prices in each state, with the middle line indicating the median price. States with higher price variability can be seen, as well as those with lower price ranges. This plot helps us compare how car prices differ depending on the state.
This boxplot shows the distribution of car prices across different states. It highlights the range of prices in each state, with the middle line indicating the median price. States with higher price variability can be seen, as well as those with lower price ranges. This plot helps us compare how car prices differ depending on the state.
This bar chart displays the distribution of car brands across states. It provides a visual representation of how many cars of different brands are listed in each state. For example, we can observe which brands are more common in specific regions, such as Toyota or Ford being popular in certain states.
This bar chart displays the distribution of car brands across states. It provides a visual representation of how many cars of different brands are listed in each state. For example, we can observe which brands are more common in specific regions, such as Toyota or Ford being popular in certain states.
The histogram displays the frequency distribution of car prices in the dataset. It shows the number of cars within different price ranges, helping to identify if most of the cars in the dataset fall into a specific price range, such as lower-priced cars or luxury vehicles.
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