Folding Bicycle Prospective Buyer Prediction Model

Abstract

The trend of bicycle exercise during the pandemic has resulted in increased sales and even scarcity of bicycle stock in some shops. The phenomenon has raised attention from both the bicycle industry and government to provide necessary responses toward the trends. Even though it is a trend, many prospective buyers are still confused about their choices. The types of bicycles that sell the most on the market are folding bikes, mountain bikes, and racing bikes. The research data were collected from 242 bicycle users who came from various bicycle communities in major cities of Java Island, Indonesia. Some of the predictors used were age, gender, height, weight, and cycling speed. The target variable is the type of bicycle whose data is categorical. Predictor variables consist of nominal and ordinal variables, so preprocessing needs to be done using Python's Sklearn library. To test the accuracy of the model, the data was broken down into training data and test data with a test size of 20%. Several methods are used to form a classification model, including K-NN, Naive Bayes, Support Vector Machine, Decision Tree, and Random Forest. The results of the classification model evaluation show that the Support Vector Machine and Decision Tree have the highest accuracy of 90%, while Naive Bayes has the lowest accuracy of 73%. The model formed can be a predictive tool for potential bicycle buyers in order to be able to choose the right type of bicycle.