Najmeh Dehghani, Analytics Engineer, JLL
The request for quotation (RFQ) process can be optimized using machine learning algorithms to predict the price of pre-engineered metal buildings (PEMBs) based on historical data. This process involves collecting data on PEMBs, preprocessing it by removing missing values and handling outliers, selecting relevant features through feature engineering, and splitting the data into training and testing sets. Popular machine learning algorithms such as linear regression, decision trees, random forest, or neural networks can be used to build the model, and performance metrics like mean squared error (MSE) and root mean squared error (RMSE) are used to assess the model.
Python and JMP are two tools that can be used for this predictive modeling. Python is a popular programming language with data analysis and machine learning libraries. At the same time, JMP is a commercial software package for statistical analysis and data visualization. Choosing between the two depends on specific project requirements, user skills, and available resources.
By building a predictive model for PEMBs, organizations can more accurately estimate the weight of future projects, leading to better budgeting, planning, and resource allocation.
