Machine Learning-based Tender Price Prediction in a Construction Industry

Student: Danila Dergachev

Educational Programme: Master of Data Science (Master)

Year of Graduation: 2024

The purpose of this work is to build a mathematical model that estimates the tender price of a given construction project. This study explores the existing traditional, as well as machine learning techniques for tender price prediction. The real dataset of a Russian construction company was analyzed to identify correlations and validate assumptions. Several machine learning models were trained and tested to assess their prediction accuracy compared to the true tender costs. The results demonstrate that machine learning models significantly enhance prediction accuracy, providing construction firms with robust tools for making informed bidding decisions.

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