Question

我想根据客户的年龄，余额以及是否已经有贷款对客户进行分类。

为什么我无法绘制结果？没有打印错误。
为每个班级分配颜色的最简单方法是什么？

这就是my dataset的样子：

    cid     age job         marital education   default balance housing loan
0   C00004  47  blue-collar married NaN         no      1506    yes     0
1   C00005  33  #           single  NaN         no      1       no      0
2   C00009  58  retired     married 1.0         no      121     yes     0
3   C00021  28  blue-collar married 2.0         no      723     yes     1
4   C00025  40  retired     married 1.0         no      0       yes     1

这是我的代码：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import neighbors
from sklearn.neighbors import KNeighborsClassifier

n_neighbors = 15


# import data
df = pd.read_csv('/Users/Soul.C/PycharmProjects/prac01/bank_customer.csv')
df.head()
df['loan'].replace('no','0')
df['loan'].replace('yes','1')

# set variables
X = np.array(df[['age','balance']])
y = df['loan']

#split train, test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.33)

knn=neighbors.KNeighborsClassifier()

# we create an instance of Neighbours Classifier and fit the data.
knn.fit(X_train, y_train)

# Plot the decision boundary. 
# point in the mesh [x_min, m_max]x[y_min, y_max].
x_min, x_max = X_train[:,0].min() - .5, X_train[:,0].max() + .5
y_min, y_max = X_train[:,1].min() - .5, X_train[:,1].max() + .5

# Create a mesh with bottom-left corner: (x_min,y_min) & 
#   top-right corner: (x_max,y_max). 
#     cell width & height: h. (Larger h leads to coarser class-boundaries)
#      'meshgrid' :to evaluate functions on a grid.
h = 0.02 
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))

#flatten the multidimensional arrays of xx & yy and make class predictions on those coordinates
Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])

# Reshape Z as per xx array
Z = Z.reshape(xx.shape)

# Create figure in memory with default parameter values
plt.figure(1, figsize=(4, 3))

# Create list of three colors (corresponding to three class values)
light_colors =  ListedColormap(['blue', 'c', 'g'])  # 'c' for cyan and 'g' for green
bold_colors  =  ListedColormap(['r', 'k', 'yellow'])    # 'r' for red  and 'k' for black 

plt.pcolormesh(xx, yy, Z,  cmap=light_colors) 


# Plot also the training points
plt.scatter(X_train[:,0], X_train[:,1],c=y, cmap=bold_colors)
plt.xlabel('age,balance')
plt.ylabel('loan')
plt.show()

我无法绘制k-最近邻居的结果

0 个答案: