Used Car Price Prediction Dataset

Dataset Description Title: Used Car Price Prediction Dataset Description This dataset has been developed to support research and development in the areas of machine learning, predictive analytics, and automotive market intelligence. The dataset contains information on used vehicles collected from publicly available online sources and has been carefully preprocessed to facilitate depreciation prediction and resale value analysis. The dataset includes 5997 vehicle-specific attributes such as title, brand, model, transmission type, fuel type, manufacturing year, energy capacity, kilometers driven, vehicle age, and second-hand selling price. These features capture both the technical characteristics and market-related factors that influence vehicle depreciation over time. Data preprocessing and feature engineering were primarily conducted using the R programming language. This process included data cleaning, missing value handling, duplicate removal, feature standardization, and derivation of additional variables such as vehicle age. The processed data were subsequently validated and prepared using Python to ensure consistency, data quality, and suitability for machine learning applications. Researchers may utilize this dataset for a variety of tasks, including vehicle depreciation prediction, resale price estimation, regression modeling, feature importance analysis, automotive market trend analysis, and benchmarking of machine learning algorithms. The dataset is suitable for educational, academic, and industrial research purposes. Dataset Features * title * brand * model * transmission * fuel_type * price * year_of_manufacture * energy_capacity * kilometers_run * car_age Potential Applications * Used Car Depreciation Prediction * Resale Value Estimation * Machine Learning Regression Tasks * Automotive Market Analytics * Predictive Modeling Research * Data Science Education and Benchmarking