I have csv file,
Data :
yield : 1172.4 , 1712.7 , 863.88 , 2731.34 , 5220
Rain(mm): 113.6 , 152.3 , 181.9 , 152.3 , 125.3
dummy(types_of_soil) : 1 , 0 , 0 , 2 , 1
dummy variable : 1 ==> Medium black soil
0 ==> deep black
2 ==> Radish brown
Dependent variable ( y) : yield
Independent variable : Rain , dummy(types of soil)
So, I want to normalize this data, How to scale data in range of 1 to 10 ?
I have try to use formula : (xi - min ) / (max - min ) Is it correct ?
and how to scale data for binary variable(dummy)?
you can use this code to normalize data
import pandas
import scipy
import numpy
from sklearn.preprocessing import MinMaxScaler
url = "filename.csv"
names = ['yield','Rain','types of soil']
dataframe = pandas.read_csv(url, names=names)
array = dataframe.values
# separate array into input and output components
X = array[:,] **select x independent variable**
Y = array[:,] **select y dependent variable**
scaler = MinMaxScaler(feature_range=(0, 1))
rescaledX = scaler.fit_transform(X)
# summarize transformed data
numpy.set_printoptions(precision=3)
print(rescaledX[0:5,:])
for more details . see this link http://machinelearningmastery.com/prepare-data-machine-learning-python-scikit-learn/