I want to predict yield(y), I have independent variables are rain(x1) and Soil(x2)
yield(y) : 25000, 26000, 27000, 28000, 29000
Rain_mm (x1) : 1000, 875, 852, 1005, 1250
Soil ( x2) : 0, 0, 1, 1, 2,
Next, I was normalize this data to scale in same range using this code :
from sklearn.preprocessing import Normalizer import pandas import numpy url = "data.csv" dataframe = pandas.read_csv(url) array = dataframe.values # separate array into input and output components X = array[:,0:3] scaler = Normalizer().fit(X) normalizedX = scaler.transform(X)
next, I have use regression formula ,using this normalizedX values ,
suppose now we have bo , b1 and b2 value . I want to predict yield using regression model.
yield = b0 + b1 * rain + b2 * soil
for example : bo = 1.25 , b1 =0.45 , b2 =-0.36
Now, I am confusing when I use this regression equation for predict yield(y), I get all value in the scale range data, (in normalize data), I want to convert this numbers in original yield.
for example : if I have plug value rain 1000, and soil 0 , then yield will be comes 25000 or any predicted number but not in 0 to 1 range.
Any one help me how to get that? or I am doing wrong something? any hints?