<![CDATA[The automotive industry has experienced significant growth and diversification in recent years. With this growth comes an increase in the demand for used vehicles. However, determining the accurate price of a used vehicle is a complex task due to various factors such as age, mileage, condition, and market trends. Efficient and accurate price prediction and classification are essential for both buyers and sellers in order to make informed decisions. To tackle this challenge, supervised machine learning techniques have gained prominence in recent years. These techniques leverage historical data to train models that can predict and classify the prices of used vehicles. By analyzing large datasets of past sales, these models can identify patterns and relationships between different features of a vehicle and its market value.]]>
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