Car Data Analysis

Description:

• This dataset is a collection of over 90,000 used cars spanning from the year 1970 to 2024. This dataset offers a comprehensive glimpse into the world of automobiles, providing valuable insights for researchers, enthusiasts, and industry professionals alike.

Features Included:

This data set contains 90,000+ car data From 1970 to 2024, the dataset contains the following columns:

• Model: The model of the car
• Year: The manufacturing year of the car.
• Price: The price of the car.
• Transmission: The type of transmission used in the car.
• Mileage: The mileage of the car.
• FuelType: The type of fuel used by the car.
• Tax: The tax rate applicable to the car.
• MPG: The miles per gallon efficiency of the car.
• EngineSize: The size of the car's engine.
• Manufacturer: The manufacturer of the car.

Project Steps:

Suppose we work for a company that imports and exports cars throughout Europe. The company is considering a significant investment in purchasing used cars for resale. Among these cars, some are vintage, and there's a particular interest in their profitability. The project manager has approached us to research this matter:

1. Step 1: First we need to understand the problem:
   • What does the company want?
   • How can we solve the matter?
   • What do the stakeholders state as a problem?
2. Step 2: We need to prepare our tools and decide what we need to do.
   • What research is needed?
   • What needs to be figured out how to solve this problem?
   • Where is the data located (files, database, external system, internal system)?
3. Step 3: We need to process our data
   • We have to study our dataset and process it, by cleaning, fixing, or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset.
   • We have to identify if our data are bias
   • We need to use proper tools to find incorrect and incomplete data.
   • We also need to remove inconsistencies in data.
4. Step 4: Now we can start the analysis:
   • We have to perform different calculations and get additional metrics
   • We can create different views for the data. Like tables with our results, filter and pivot them.
   • Make charts and viz
   • What story is our data telling us?
5. Step 5: Share our data!
   • After the feedback we have to improve the general outcome. From one angle, the decision will most likely be more informed and better, but also the transparency will grant that there is more support to the findings.
   • What would help us understand the viz if we were the listeners?
   • We need to wait for the company's feedback.
6. Step 6: Act! We know our results, let's start!
   • What potential solutions to the outlined problem could there be?
   • Is this problem worth solving? (Yes, that is also a potential outcome)
   • How can the feedback received during the sharing phase (step 5) be used to meet the stakeholder’s needs and expectations?

Project Objective

Before we start, we need to define some things first:

• Define Vintage: We'll define vintage cars as those that are at least 25 years old from the current year (2024), so cars manufactured in 1999 or earlier.
• Calculate Potential Profitability: We'll calculate the average price for vintage cars compared to newer ones, consider tax and MPG as part of the operational costs, and if possible, deduce any available data on maintenance or additional costs.
• Identify Trends and Demand: We'll look at price trends over the past years for vintage cars to see if there's an appreciation in value, and try to identify the most sought-after models/manufacturers.

Why do we perform these analyses?

We can gain:

• Strategic Insights: Help automotive businesses, analysts, and enthusiasts understand market trends, consumer preferences, and competitive landscapes.
• Historical Trends: Analyzing data from 1970 to 2024 provides valuable insights into how the automotive industry has evolved over more than five decades.
• Decision Support: For businesses, these insights can support strategic decisions related to inventory management, marketing strategies, and future investments. For consumers, it can inform buying decisions.
• Market Segmentation: Understanding different segments (e.g., vintage cars, high MPG vehicles) enables targeted analysis, which is crucial for marketing, restoration projects, and more.
• Technological and Environmental Impact: Analyzing trends in engine size, MPG, and the popularity of fuel types can provide insights into the automotive industry's impact on the environment and its response to technological advancements and environmental regulation

Source/Credits

https://www.kaggle.com/datasets/meruvulikith/90000-cars-data-from-1970-to-2024 by MERUVU LIKITH