Scikit-Learn
import numpy as npfrom sklearn.preprocessing import MinMaxScalerdata = np.random.randint(0,100,(10,2))dataarray([[90, 77],
[82, 35],
[ 9, 71],
[20, 64],
[39, 42],
[74, 45],
[35, 37],
[92, 64],
[49, 0],
[11, 63]])import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
x=data[:,0]
y=data[:,1]
plt.figure(figsize=(8,6))
plt.plot(x,y,'r')
plt.xlabel('x')
plt.ylabel('y')
plt.title(r"Plot of y")
plt.show()
scaler_model=MinMaxScaler()scaler_model.fit(data)MinMaxScaler(copy=True, feature_range=(0, 1))
result=scaler_model.transform(data)#scaler_model_fit_transform(data) Alternative to the above 2 stepsresultarray([[0.97590361, 1. ],
[0.87951807, 0.45454545],
[0. , 0.92207792],
[0.13253012, 0.83116883],
[0.36144578, 0.54545455],
[0.78313253, 0.58441558],
[0.31325301, 0.48051948],
[1. , 0.83116883],
[0.48192771, 0. ],
[0.02409639, 0.81818182]])x=result[:,0]
y=result[:,1]
plt.figure(figsize=(8,6))
plt.plot(x,y,'r')
plt.xlabel('x')
plt.ylabel('y')
plt.title(r"Plot of y")
plt.show()
dataarray([[90, 77],
[82, 35],
[ 9, 71],
[20, 64],
[39, 42],
[74, 45],
[35, 37],
[92, 64],
[49, 0],
[11, 63]])import pandas as pddata= pd.DataFrame(data=np.random.randint(0,101,(50,4)),columns=['f1','f2','f3','label'])data.head()| f1 | f2 | f3 | label | |
|---|---|---|---|---|
| 0 | 59 | 38 | 9 | 67 |
| 1 | 33 | 33 | 57 | 42 |
| 2 | 0 | 73 | 68 | 56 |
| 3 | 75 | 27 | 41 | 86 |
| 4 | 33 | 20 | 0 | 100 |
x=data[['f1','f2','f3']]y=data['label']from sklearn.model_selection import train_test_splitX_train,X_test,Y_train,Y_test=train_test_split(x,y,test_size=0.3,random_state=101)X_train.shape
(35, 3)
X_test.shape(15, 3)
Y_train.shape(35,)
Y_test.shape(15,)
