Feature Importance Analysis and Influence of Indian Knowledge Systems in Concrete Strength Prediction Using Random Forest

Accurate prediction of concrete’s ability to hold weight is very important to ensure that the structures are safe and to get the best use from the materials used in building them. With this study, the process of prediction is improved by the application of feature importance analysis in the Random Forest Repressors system. It finds the features that have the greatest impact, allowing the model to place more emphasis on these key parts. To measure how well the model performed, the accuracy was checked; the results proved that the Mean Squared Error was reduced from 44.10 to 28.08, and the R² scores increased from 0.8288 to 0.8910. Both error and the trustworthiness of the model improved. This work aligns with the views of the Indian Knowledge System (IKS), which calls for the use of practices that do not have a negative impact on the environment and for utilizing what is available while considering how long the structures will last. Traditional building methods in India demonstrated that it was easy to understand how the materials in the structures interacted with the way people used their land and how the structures fit into the landscape. By utilizing these long-standing ways of thinking, this work connects new perspectives on the art of civil engineering with the traditional knowledge of Indian builders. It does this by not only making what we learn here more useful but also by showing how to build sites that harmonize with the land and are beneficial to us.