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Predicting Shoe Prices Using Machine Learning Algorithms

Akshar S.

This paper investigates the use of machine learning algorithms, particularly Support Vector Regression, to accurately predict shoe prices based on factors like material, color, brand, type, gender, and size, using a dataset of 5000 entries.

This paper explores the application of machine learning algorithms in predicting shoe prices and the correlations between a shoes’ material, color, brand, type, gender, and size and its price. Using a dataset of 5000 shoe entries from Kaggle, the data was preprocessed and turned into numeric values for modeling. The models used include Linear Regression, Decision Tree, Random Forest, Gradient Boosting, and Support Vector Regression. The results show that the Support Vector Regression model had the lowest Mean Squared Error, which means that it can accurately predict shoe prices and find the correlation between a shoes’ make and price.

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Akshar S.
John Basbagill

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