Data Analysis on Vehicle Recalls
The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
Introduction
Vehicle Safety is important when considering to buy or make a new vehicle. Most accidents do occur due to human error, but 2% can occur from a vehicle component defect or issue. In 2016, vehicle recalls cost companies $22 billion in claims and warranty accruals. In this study, I will be data analyzing vehicle recalls to see if there is any progress or change in recalls over time, have cars become safer, and what kind of car might have a low chance of having a recall.
Data
For this study, I will be using a dataset provided by the NHTSA on Kaggle. I will be looking at recalls from 1966-2016, for 2017, there were only recalls from the first few months that were provided. The dataset contains thousands of recalls from a variety of companies. The recalls are broken down into several different components, such as air bags, seat belts, tires, and more. The dataset also provided the Model Year, Date of Recall, Estimated Units, and more.
Data Analysis
Chart 1
Chart 2
When starting to look into the data, I focused on the top recalled components by the number of recalls. On Chart 1, the top two recalled components were air bags and tires. However, I noticed that there was a great difference between the top two components and the rest.
So in Chart 2, I looked at the top ten recalled components over time and noticed some huge outliers, especially in air bags. In 2014, there was a massive recall of Takata air bags that made up a good portion of air bag recalls that year. From this outlier, I felt that looking at the number of recalls might not be a great way of analyzing recalls, and I decided to look in the frequency of recalls.
Chart 3
Chart 4
From Chart 3, there are several great outliers for each recalled component that confirmed my suspicions earlier. When looking at the frequency of recall for each recalled component in Chart 4, there were not as great of outliers as Chart 3. From there on, I decided to look at the frequency of recalls to go deeper into my analysis.
Chart 5
Chart 6
On Chart 5, these are the top most frequently recalled components, and the top two items are Equipment and Service Breaks. This chart shows the top recalled components to be aware of when making or buying a vehicle. Also, on Chart 6, shows the top companies to be aware when deciding on a brand. Even with these charts, have newer cars shown any signs of improvement?
Chart 7
Chart 7 shows the frequency of recalls for each Model Year. From the late 1990s to late 2000s, there was a great increase of frequency in recalls. This might have been due to stricter guidelines or changes, such as in 1999, dual air bags were required for all vehicles. However, in the 2010s, there has been signs of a decrease in recalls. This might indicate that companies have improved safety measures in newer models.
Chart 8
Finally, in Chart 8, I wanted to see how soon vehicles are recalled from their Model Year. It looks like most vehicles are recalled within the first few years from the Model Year, especially the same year. So it seems like it might be risky to buy a new car. The dotted line is the 3rd Quartile of the frequency of recalls, that lies just around 4 years. So, I would say that the newest car to get with less chances of a recall, is at least 4 years from the Model Year.
Conclusion
From my analysis, it appears that vehicles have seen improvement or at least less recalls in the newer models. When looking to get a new car, it seems that the newest release models have the most recalls, and car models from 4 years ago have a smaller chance of getting a recall. Finally, the most frequently recalled component is the Equipment, and the most frequently recalled company is Ford.
I realized when doing this that the most recalled component might not be the most important component for some people. For example, for families, air bags might be a more important component to focus on. I created a Shiny App to look at different components to see what are the top most frequently recalled companies for the selected component. Also, on the Shiny app, you can see the whole dataset to find any specific vehicles were recalled and the NHTSA Campaign ID that online goes into more detail of the recall.
Future Considerations
I would love to look at the most up-to-date dataset to see the change in the frequency of recalls, as it started to show in this analysis. Also, the dataset provided, did not categorize the vehicles, such as buses, trailers, or motorcycles. This would help different industries on which companies have the most recalls.
Safety is always a major concern for customers and companies. With newer technology, hopefully recalls can continue to be minimized to maximize the safety for drivers.
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