Cost Estimation And Budget Optimization Using Predictive Analytics

Abstract

Accurate Cost Estimation Plays A Crucial Role In Ensuring Financial Control And Project Success Across Various Domains Such As Construction, Manufacturing, And Software Development. Traditional Methods Are Typically Manual And Error-prone, Leading To Inefficient Budget Planning And Unexpected Cost Overruns. This Paper Proposes A Data-driven Cost Estimation And Budget Optimization System Leveraging Predictive Analytics. The System Categorizes Costs Into Labor, Materials, And Equipment And Forecasts Future Expenses Using Machine Learning Models Trained On Historical Data. Developed Using Python, Streamlit, Scikit-learn, And Data Visualization Libraries, The Proposed Model Ensures Dynamic Updates, Real-time Forecasting, And Automated Reporting In PDF/Excel Formats. This Approach Improves Budgeting Accuracy, Facilitates Transparent Financial Planning, And Supports Informed Decision-making.

Keywords