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The major factor in the success of most projects is accurate cost estimation at an early stage of production. Cost forecasting relates to cost prediction and typically involves calculating costs for supplies, labor, sales, overhead, and extra expenses. The project manager drafts a bid including the specifications and anticipated cost of construction projects. The contractor evaluates the bid and calculates the project completion costs. Here, we propose a cost calculating strategy for the early stages that is based on machine learning methods like regression and linear modeling. The goal of the project is to create a machine learning-based system that can deliver precise cost estimates and efficient bidding techniques for engineering and construction projects. In order to anticipate expenses and offer advice on bidding methods, the system will specifically use a linear regression algorithm to examine historical data and other project characteristics. Prior projectssize, length, location, materials utilized, labor prices, and other pertinent information will be first gathered by the system.In order to anticipate the costs of upcoming projects, a linear regression model will be trained using this data. The technology will also take into account information about the bidding procedure, such as client preferences and competitive analyses. As a result, the algorithm will be able to offer competitive bidding tactics that are customized for particular projects. A variety of measures, such as the precision of cost projections, the effectiveness of bidder strategies, and ultimate project profitability, will be used to assess the system. The ultimate objective of this project is to create a potent tool that may assist engineering and construction companies in streamlining their cost estimation and bidding procedures, which will increase productivity and profitability.

Copyright © 2023 RAAVI VENKATA SAI CHAITANYA. This is an open access article distributed under the Creative Commons Attribution License.