## 6 Case Study
### Determining Influential Factors Affecting Construction Projects' Profit Using Machine Learning  
As mentioned earlier, the final data set included twenty-eight fields nominated as profit influential factors. The authors selected Rapid Miner software ([[https://docs.rapidminer.com/9.9/studio/installation/](https://docs.rapidminer.com/9.9/studio/installation/)]([https://docs.rapidminer.com/9.9/studio/installation/](https://docs.rapidminer.com/9.9/studio/installation/)), accessed on 19 April 2025) to perform the machine learning process and utilized the random forest algorithm to develop a feature selection model that analyzes factors. Feature selection is a perfect process that can be used before creating a prediction model to reduce the inputs by identifying the most meaningful ones. The \begin{table}
\begin{tabular}{l l} \hline
**Structure/Dimension** & **Metadata Fields** \\ \hline \multirow{6}{*}{OBS} & Company \\  & Division \\  & Business Unit \\  & Portfolio \\  & Program \\  & Scope management \\  & Plamning and scheduling \\  & Actual Duration in Days \\  & Baseline Budget \\  & Baseline Cost \\  & Current Budget \\  & Actual Cost \\  & Profit \% \\  & Project Manager \\  & Owner \\  & Architect (Engineer) \\  & Project Type \\  & Project Category \\  & Complexity Level \\  & Joint Venture \\  & Joint Venture Partner \\  & Late Completion Penalty \\  & Currency \\  & Project Delivery Method (PDM) \\  & Contract Type \\  & Region \\  & Country \\  & Province/State \\  & City \\ \hline \end{tabular}
\end{table} 
Table 3: Metadata fields and their associated structure or dimension. developed feature selection model uses a \"weight by tree importance\" operator [52], which calculates the weight of the attributes by analysing the split points of a random forest model. The attributes with higher weight are considered more relevant and important. This weighting scheme involves using a designated random forest to ascertain the relative importance of the attributes used. To achieve this, each node of each tree is examined to retrieve the benefit generated by the split at that node. Subsequently, the benefits for each attribute that was used for a split are summed. The average benefit across all trees is then regarded as the importance of the attribute. The proposed model examines each profit influential factor as an input and identifies its correlation with the model output, which is profit. Thus, the significant factors can be used to develop a machine learning prediction model to predict profit for future projects. In this research, the developed feature selection model calculated the weights for twenty-three factors out of twenty-eight (Table 4). The most influential attributes with a weight more than 0.5 were baseline budget, approved change orders %, current budget, actual cost, planned duration in days, and baseline cost. The model provided zero weights for the five attributes, namely: country, city, arch/engineer, project manager, and owner, which indicates that these attributes are less relevant and important to the profit. During split point analysis, the random forest model assigned higher weights to factors that most effectively partitioned the data set into distinct profit-based clusters. Specifically, the algorithm evaluates all possible splits across each variable (e.g., baseline budget) to maximize information gain, which is measured by metrics like Gini impurity reduction or entropy. Factors such as baseline budget (weight: 1.00) consistently created the purest subsets, indicating their predictive power. This improvement in split frequency and purity is aggregated across all trees in the forest, with the baseline budget emerging as the dominant splitter. Figure 9: Data visualization using OLAP techniques: Executed projects grouped by their portfolios and divisions. The baseline budget emerged as the most influential factor (weight: 1.00), as it encapsulates the project's initial financial scale and complexity.