Price estimation of pre-owned automotive vehicles

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Problem statement

The client was a pre-owned automotive car sales showroom and they were in search of an agency to modernise their business process. They wanted to build a website where new customers can login to their website, register with their pre-owned car details and the system could generate an automatic price estimate for the car under the current market conditions. We were tasked with the goal of developing a price estimation model for any pre-owned car that a new customer might want to sell on their website. The automatic price estimation process enables a touch free process for the customer and also free up the manpower requirements on the car sales process. To solve this problem, we analysed various parameters about the car, namely, make, model, year of manufacture, mileage, its previous owner profile like gender, age group and also the current market conditions.

Our solution

To develop the price estimation ML model, we trained it with a dataset of past sales records. We collected the data about the car details of the previous few years of car sales, the sale price of the car along with the buyer and seller customer profiles. We converted the car sale details, car make and model details, the car buyer details and market condition details into a multi feature dataset to make it amenable to ML training. The price estimation problem is a type of Regression problem in Machine Learning. We studied the performance of various Regression algorithms like Linear, Ridge, SVR and MLP Regressor. It was observed that the MLP regression performed the best in terms of both accuracy and execution time. We packaged the ML model as a cloud function which ingests the new car details and generates a price estimate of the car. The cloud function exposed a HTTP API that was integrated into their new e-commerce website.

Key metrics

We were able to help our clients eliminate the manual effort involved on the car sale price estimation process and also increased the conversion on their website by 35%. This project was developed within a 6 week time frame. The ML Regressor based ML model was predicting pre-owned car prices at an accuracy of 95%.

Technology stack

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