000			02110nam a2200289Ia 4500
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005			20251010171712.0
008			230718n 000 0 eng d
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041			_aenglish
050			_aT58.6 An3 2022
082			_a.
100			_a Ancheta, Jherimae, Lazo, Primo Ivan, Medina, Llovelyn.
245		0	_aThe development of construction estimation of road projects in Manila City using Multiple Linear Regression with machine learning algorithm /
264			_aManila: _bPLM, _cc2022
300			_a. _bUndergraduate Thesis: (Bachelor of Science in Information Technology) - Pamantasan ng Lungsod ng Maynila, 2023.
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505			_aABSTRACT: Estimatingf cost in construction is important to the city’s design and planning management hence, costg estimate must not be overpriced which may cause corruption or underpricing that leads to unreliable or low-quality road projects. The total estimated cost is only valid in the same year it was proposed because of the inflation rate the costs may change. The researchers applied Multiple Linear Regression technique in predicting total estimated cost for road construction analysis. The model is evaluated by the means of R-squared to determine the variables if they are correlated or overfitting. The calculated R-squared is equal to 0.696598 with the predictor variables (x1 & x2) Roadbed width and Net length and it means that the predictors (Xi) explain 69.7% of the variance of Y. The higher the R-squared result, the better fit it is for the Multiple Linear Regression model. It also shows that X1 and X2 are significant predictor variables. The coefficient of multiple correlation (R) is equals to 0.834624 and it means that there is a very strong correlation between the predicted data and the observed data whereas the dependent variable (y) is the Estimated cost.
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