Predicting Adults' Income using Naive Bayes Classifier

Abstract Predicting the income level of adults is an important task in understanding the socio-economic dynamics and identifying key factors that contribute to income disparities. This paper proposes to build a predictive model to predict the income levels of adults using the Naive Bayes classifier. To commence the analysis, we preprocess the dataset by addressing missing values and encoding categorical variables, Subsequently, we train a Naive Bayes classifier on a labeled training set, with income levels as the target variable. To evaluate the performance of the Naive Bayes classifier, we employ standard evaluation metrics such as accuracy, precision, recall, and F1-score. Our experimental findings demonstrate the potential of the Naive Bayes classifier in predicting adults' income based on socio-demographic factors. Based on the findings of the experiments, the Naive Bayesian classifier has an accuracy equal to 80.83%.