COMPARATIVE ANALYSIS OF DATA CLASSIFICATION METHODS FOR PREDICTION OF TRADE-IN AUTO PRICES

This article implements and analyzes machine-learning algorithms, for predicting carsprices. Predicting prices is one of the most challenging but interesting tasks. There are so many factors involved in the prediction - year of manufacture, condition, mileage, engine size, etc. All these aspects combine to make auto prices volatile and very difficult to predict with a high degree of accuracy. Machine learning techniques can uncover patterns and ideas that we have not seen before, and can be used to predict and classify data accurately and accurately. The choice of the proper data classification algorithm, which would be suitable for a given task, depends on the volume, quality and nature of the data, on the computing resources of the computer, and how you plan to use the result. Each classification algorithm has its own characteristics and is based on certain assumptions. Also requires practical skills. In practice, it is always recommended to compare the quality of at least several different learning algorithms in order to choose the best model for a particular task, since the most experienced data scientists will not be able to tell which algorithm is more efficient. Algorithms can differ in the number of features or samples, the noise level in the dataset, and whether the classes are linearly separable or not. Ultimately, the quality of the classifier, its computational and predictive power, depends on the underlying data intended for training the algorithm. The purpose of this article is to consider the stages of pre-processing training data, and show how machine learning in particular and information technology in general have succeeded in developing tools for modeling, designing, predicting, planning and decision support in the field of auto sales. This study proposes a hybrid approach to forecasting problems, that is, solving forecasting problems using statistical analysis and machine learning methods.

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