We compiled the experimental mechanical property data, such as tensile strength and yield strength data for known Niobium alloys . This dataset consists of 18 unique Nb alloys where mechanical strength is reported at varying temperatures ranging from 24 o C to 1871 o C. This leads to a total of 140 yield strength and 144 ultimate tensile strength data points. The goal was to train a machine learning model to accurately predict the targeted mechanical property based on the composition/feature-property relationship and evaluate its performance on unseen test data. To use the machine learning algorithms and to evaluate their performance, we split the data into train and test sets with 80:20 train-test split ratio. We ensured that data splitting was carried out not only to prevent data leakage from duplicate data points but also to ensure that the whole alloy families were grouped into either train or test to maximize model generalizability.