Cars Prices Prediction Dataset

This dataset contains structured, consumer-facing records for individual cars listed in the marketplace. Each row corresponds to a single vehicle and includes technical specifications, usage indicators, and a market price. The collection is designed to be clean, well-labeled, and immediately useful for exploratory data analysis, feature engineering, and supervised learning.
This dataset provides detailed information about various car models and their market prices. It includes features such as brand, model, engine specifications, fuel type, mileage, and transmission type making it ideal for car price prediction, exploratory data analysis (EDA), and machine learning projects. these are two introductions to a dataset, which one is more professional

💡 Why This Dataset Is Useful

• Directly actionable features are real-world variables commonly used in pricing and valuation models.
• Compact & focused short, high-signal feature set that reduces preprocessing overhead.
• Versatile supports tasks from descriptive analytics and visualization to regression (price prediction) and classification (condition, fuel type).
• Good for demos & teaching small enough for quick iterations, rich enough for meaningful model comparisons.

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📊 Columns (Concise, User-Facing Descriptions)

• Car ID: Unique identifier for each vehicle entry.
• Brand: Manufacturer (e.g., Tesla, BMW, Toyota).
• Model: Specific model name.
• Year: Manufacturing or model year.
• Engine Size: Engine displacement (liters or cc).
• Fuel Type: Petrol, Diesel, Electric, Hybrid, etc.
• Transmission: Manual or Automatic.
• Mileage: Total distance traveled (km or miles).
• Condition: Categorical state (New, Used, Excellent, Fair).
• Price: Listed selling or market price (target variable).

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