Leveraging Urban Water Distribution Systems with Smart Sensors for Sustainable Cities

. 2024 Nov 12;24(22):7223. doi: 10.3390/s24227223

Algorithm 2 Linear Regression Model Training and Prediction

1:
Load Data:
2:
$d f \leftarrow$ pd.read_excel(’city.xlsx’)
3:
$d i a m e t e r \leftarrow d f [^{'} d i a m e t e r^{'}] . v a l u e s$
4:
$b a s e_d e m a n d \leftarrow d f [^{'} b a s e_d e m a n d^{'}] . v a l u e s$
5:
Create Linear Regression Model:
6:
$m o d e l \leftarrow$ LinearRegression()
7:
Train the Model:
8:
$m o d e l . f i t (d i a m e t e r . r e s h a p e (- 1, 1), b a s e_d e m a n d)$
9:
Extract Coefficients:
10:
print( $m o d e l o . c o e f_$ )
11:
Extract Intercept:
12:
print( $m o d e l o . i n t e r c e p t_$ )
13:
Generate Prediction Data:
14:
$l i n e_d i a m e t e r s \leftarrow$ np.linspace(min(diameter), max(diameter), 100)
15:
$p r e d i c t i o n s \leftarrow$ model.predict(diameters_line.reshape(−1,1))