• Calculating the cost of 🏗️ construction can be an overwhelming task.
• However, with the advancements in machine learning, it is now possible to predict the cost of construction for a particular building with a high degree of accuracy.
• It will be a solution a SCO

• One of the key factors in developing a successful machine learning model for construction cost prediction is training data from real agricultural buildings.
• This means that we need to gather data on the costs of constructing actual buildings and use that data to train our machine-learning model.
• The more data we have, the better our model will be.

• Once we have our training data, we can begin to predict the 💸cost of construction for a particular building based on a set of parameters.
• These parameters can include the size of the building, the type of materials used, the location of the building, and any other relevant factors.

Normally even one basic construction almost 200 cost sub items but we tried to find most important parameters that affect the price

%80- %20

The 80/20 rule, also called the Pareto principle, states that 80% of the outcomes come from 20% of the inputs. This principle applies to many different aspects of practice, including:

• Sales - 80% of sales often come from 20% of customers or products.

• Productivity - 20% of activities often account for 80% of results.

• Problems - 20% of problems often account for 80% of the negative impact.

It’s valid for the cost problem

• Essentially, the 80/20 rule suggests that a small minority of inputs accounts for the vast majority of outputs or consequences.
• There important parameters that effect most of the cost amount.

• In our implementation, we focused on predicting the cost of construction for agricultural buildings.
• By gathering data on the costs of constructing actual agricultural buildings, we were able to train our machine learning model to predict the cost of construction based on various parameters. -Then extracted most important features and trained or ML model*.

• In conclusion, machine learning can be a powerful tool for predicting construction costs.
• By gathering training data from real buildings and using that data to train our models,
• we can predict the cost of construction for a particular building with a high degree of accuracy*.

https://agroai-suatatan.streamlit.app/

Dr. Suat ATAN

Software Engineer