用户流失预警项目实战[大数据培训]

#TODO:用户流失预警
#1.导包
from __future__ import division
import pandas as pd
import numpy as np
#2.加载数据与观察数据
churn_df = pd.read_csv('UserDrain_data/churn.csv')
col_names = churn_df.columns.tolist()
# print("Column names:\n",col_names)
# print(churn_df.shape) #(3333, 21)
# Column names:['State', 'Account Length', 'Area Code', 'Phone', "Int'l Plan", 'VMail Plan',
'VMail Message', 'Day Mins', 'Day Calls', 'Day Charge', 'Eve Mins', 'Eve Calls', 'Eve Charge',
'Night Mins', 'Night Calls', 'Night Charge', 'Intl Mins', 'Intl Calls', 'Intl Charge', 'CustServ
Calls', 'Churn?']
to_show = col_names[:6] + col_names[-6:] #前6 列与后6 列
# print(len(to_show))#共12 列
# print ("\nSample data:\n",churn_df[to_show].head(6))
# State Account Length Area Code ... Intl Charge CustServ Calls Churn?
# 0 KS 128 415 ... 2.70 1 False.
# 1 OH 107 415 ... 3.70 1 False.
#2.1 类别编码
churn_result = churn_df['Churn?']
y = np.where(churn_result == 'True.',1,0)
#2.2 删除不需要对应列数据
to_drop = ['State','Area Code','Phone','Churn?']
churn_feat_space = churn_df.drop(to_drop,axis=1)
#yes 或者no 需要转化为布尔类型数据
yes_no_cols = ["Int'l Plan","VMail Plan"]
churn_feat_space[yes_no_cols] = churn_feat_space[yes_no_cols] == 'yes'
# print(churn_feat_space[yes_no_cols])
# Int'l Plan VMail Plan
# 0 False True
# 1 False True
# 获取数据属性名
features = churn_feat_space.columns
# print("churn_feat_space:\n",churn_feat_space.head())
# Account Length Int'l Plan ... Intl Charge CustServ Calls
# 0 128 False ... 2.7 1
# 1 107 False ... 3.7 1
#将dataframe 转化为ndarray 数组，同时数组中的元素类型为float 类型
#对应的布尔类型的值，True 为1，False 为0
X = churn_feat_space.as_matrix().astype(np.float)
np.set_printoptions(threshold=np.NaN)
# print("churn_feat_space.as_matrix().astype(np.float):\n",X)
# [[128. 0. 1.... 3. 2.7 1.]
# [107. 0. 1.... 3. 3.7 1.]
# 2.3 数据标准化
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X = scaler.fit_transform(X)
# print("Feature space holds %d observations and %d features" % X.shape) #3333 行17 列
# Feature space holds 3333 observations and 17 features
# print("Unique target labels:", np.unique(y))# [0 1]
# print(X[0])
#标准化之后的第一行数据
#[ 0.67648946 -0.32758048 1.6170861 1.23488274 1.56676695 0.47664315
# 1.56703625 -0.07060962 -0.05594035 -0.07042665 0.86674322 -0.46549436
# 0.86602851 -0.08500823 -0.60119509 -0.0856905 -0.42793202]
# print(len(y[y == 0])) #2850
#3.KFold K 折交叉验证
from sklearn.cross_validation import KFold
def run_cv(X,y,clf_class,**kwargs):
#创建kfold 对象
kf=KFold(len(y),n_folds=5,shuffle=True)
y_pred=y.copy()
#迭代
count=0
for train_index,test_index in kf:
count=count+1
#y 对应的标签数量：3333
# print("train_index 数量:",len(train_index)) #train_index 数量: 2666
# print("test_index 数量:", len(test_index)) # test_index 数量: 667
# print(test_index)
X_train,X_test=X[train_index],X[test_index]
y_train=y[train_index]
#初始化一个分类器模型
clf=clf_class(**kwargs)
clf.fit(X_train,y_train)
y_pred[test_index] = clf.predict(X_test)
# print("迭代次树:", count)#5
return y_pred
#4.建模
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier as RF
from sklearn.neighbors import KNeighborsClassifier as KNN
def accuracy(y_true,y_pred):
# NumPy interprets True and False as 1. and 0.
return np.mean(y_true == y_pred)
#4.1 SVM / RF /KNN 三种算法预测准确率
print("Support vector machines:","%.3f" % accuracy(y, run_cv(X,y,SVC)))
print("Random forest:","%.3f" % accuracy(y, run_cv(X,y,RF)))
print("K-nearest-neighbors:","%.3f" % accuracy(y,run_cv(X,y,KNN)))