The goal of my analysis is to evaluate car preferences and acceptance levels based on attributes like buying price, maintenance cost, number of doors, seating capacity, luggage space, and safety. I'll create dashboards to identify the most and least accepted car categories, investigate why some cars are rated as "non-acceptable," and highlight the safest 2-door cars ordered by price. Additionally, I'll explore other insights to provide a deeper understanding of user preferences and car evaluations.
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What is the most accepted category of cars among those with 2, 3, 4, and 5 doors? What is the least accepted category (i.e., cars rated as "non-acceptable")? Additionally, how many 2-person cars were evaluated as "Acceptable"?
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Investigate the main reasons why some cars are evaluated as "non-acceptable." Consider factors such as buying price, safety, luggage space, seating capacity, and maintenance costs.
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For 2-door cars, identify the safest options (with a safety rating of "high") and order them by buying price.
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Explore and highlight any other interesting findings from this data to enhance the overall analysis and storytelling.
I've imported the dataset to PostgreSQL database and cleaned that before visualization to deliver high-quality data into my BI tool.
Key steps for data cleaning:
- Remove Duplicates: Ensure there are no duplicate records.
- Handle Missing Values: Impute or remove missing data
- Correct Data Types: Ensure all columns have appropriate data types.
- Remove Outliers: Identify and handle outliers to avoid skewing results.
- Standardize and Normalize Data: Especially for numerical data.
- Fix Inconsistencies: Address inconsistent entries (e.g., spelling variations).
- Resolve Data Entry Errors: Correct obvious typos and errors.
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Open Interactive Dashboards on Tableau Public
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Download and Open the Tableau Workbook file on your own Tableau: ../Car-Evaluation-Story.twbx
