Analysis of Car Selling Prediction Based On AIML

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Analysis of Car Selling Prediction Based On AIML

Kale Dnyaneshwar , Jabhade Tushar, Kangude Shivam,Thamke Sagar

DOI : 10.46335/IJIES.2023.8.2.3

Abstract – The purpose of this research paper is to develop a predictive model for car sales using machine learning techniques. We explore various factors that affect car sales and use them as features to train and test our model. We collected data from various sources, including online car listings, car dealerships, and demographic data. Our findings show that demographic factors, such as age, income, and education, play a significant role in predicting car sales. Additionally, car features, such as make, model, and year, also influence sales. Using these features, we developed a model that accurately predicts car sales and can be used by car dealerships to make informed decisions.

Predicting car sales is crucial for automotive manufacturers, dealerships, and market analysts to make informed decisions about inventory management, marketing strategies, and overall business performance. In this paper, we present a comparative study on the analysis of car selling prediction using multilayer perception (MLP).We begin by collecting a dataset of historical car sales data, including features such as car make, model, year, mileage, price, and other relevant factors. The dataset is preprocessed to handle missing values, outliers, and categorical variables. We then split the dataset into training and testing sets to train and evaluate the MLP model.