Prediction Of Used Car Prices Using Artificial Neural Networks And Machine Learning
IEEE BASE PAPER ABSTRACT:
With the extensive growth in usage of cars, the newly produced cars are unable to reach the customers for various reasons like high prices, less availability, financial incapability, and so on. Hence the used car market is escalated across the globe but in India, the used car market is in a very nascent stage and mostly dominated by the unorganized sector. This gives chance for fraud while buying a used car. Hence a high precision model is required which will estimate the price of a used car with none bias towards customer or merchandiser. In this model, A Supervised learning-based Artificial Neural Network model and Random Forest Machine Learning model are developed which can learn from the car dataset provided to it. This project presents a working model for used car price prediction with a low error value. A considerable number of distinct attributes are examined for reliable and accurate predictions. The results obtained agree with theoretical predictions and have shown improvement over models which use simple linear models. An ANN (Artificial Neural Network) is built by using Keras Regression algorithm namely Keras Regressor and other Machine Learning Algorithms namely Random Forest, Lasso, Ridge, Linear regressions are built. These algorithms are tested with the car dataset. Experimental results have shown that the Random Forest model with a Mean Absolute Error value of 1.0970472 and R2 error value of 0.772584 has given the less error among all the other algorithms. The work presented here has shown profound implications for future studies of Used Cars price Prediction using Random Forest and might one day help to solve the problem of frauds with one hundred percent accuracy.
OUR PROPOSED ABSTRACT:
The globe is expanding daily, and with it, so are everyone’s expectations. One of them is going to purchase a car out of all the expectations. However, not everyone can afford to buy a new car every time, so they will get a used one. However, a new person is unaware of the used car market pricing for his or her ideal vehicle. As the price of new cars increased due to higher technology expenses, the value argument of used cars grew stronger. Additionally, during the Covid-19 pandemic epidemic, the lack of public transportation and fear of infection force people to rely on their own means of transportation. However, the increased demand for used cars led some car dealers to overcharge clients by putting their prices higher than usual. The need to develop a model that can forecast the cost of used cars by taking into account the various characteristics and costs of other automobiles already on the market in the nation arises in order to aid consumers in understanding market trends and prices for used cars. Predicting used automobile prices is a challenging but intriguing topic. It is difficult to estimate a car’s resale value. It goes without saying that a variety of factors affect how much secondhand cars are worth. In order to forecast the price of used cars based on various factors, we employed Decision Tree Regressor, a machine learning algorithm, in this study. The resulting model contains more elements of used cars while also being more accurate in its predictions when compared to earlier studies.
PROJECT OUTPUT VIDEO:
ALGORITHM / MODEL USED:
Decision Tree Regressor.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 4 GB
- Operating system : Windows 10.
- Coding Language : Python 3.8
- Web Framework : Flask
Janke Varshitha, K Jahnavi, C. Lakshmi, “Prediction Of Used Car Prices Using Artificial Neural Networks And Machine Learning”, 2022 International Conference on Computer Communication and Informatics (ICCCI), IEEE Conference, 2022.
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