Developed a machine learning model using boosting regression techniques, such as Gradient Boosting Regression (GBR), to predict quotation prices for painting jobs.
Collected a dataset containing input parameters like painting area size, paint type, number of coats, and labor hours, along with corresponding quotation prices.
Preprocessed the dataset by handling missing values, encoding categorical variables, and scaling numerical features.
Trained the boosting regression model on the preprocessed dataset, focusing on minimizing prediction errors.
Evaluated the model's performance using metrics like Mean Absolute Error (MAE) and Mean Squared Error (MSE) to ensure accuracy.