航空公司客户价值分析（附完整代码）

#-*- coding: utf-8 -*- import pandas as pd import numpy as np from sklearn.cluster import KMeans import matplotlib.pyplot as plt data = pd.read_csv(r'.\\data\\air_data.csv', encoding="utf-8") print(data.shape) print(data.info()) data = data[data["SUM_YR_1"].notnull() & data["SUM_YR_2"].notnull()] index1 = data["SUM_YR_1"] != 0 index2 = data["SUM_YR_2"] != 0 index3 = (data["SEG_KM_SUM"] == 0) & (data["avg_discount"] == 0) data = data[index1 | index2| index3] print(data.shape) filter_data = data[[ "FFP_DATE", "LOAD_TIME", "FLIGHT_COUNT", "SUM_YR_1", "SUM_YR_2", "SEG_KM_SUM", "AVG_INTERVAL" , "MAX_INTERVAL", "avg_discount"]] filter_data[0:5] data["LOAD_TIME"] = pd.to_datetime(data["LOAD_TIME"]) data["FFP_DATE"] = pd.to_datetime(data["FFP_DATE"]) data["入会时间"] = data["LOAD_TIME"] - data["FFP_DATE"] data["平均每公里票价"] = (data["SUM_YR_1"] + data["SUM_YR_2"]) / data["SEG_KM_SUM"] data["时间间隔差值"] = data["MAX_INTERVAL"] - data["AVG_INTERVAL"] deal_data = data.rename( columns = {"FLIGHT_COUNT" : "飞行次数", "SEG_KM_SUM" : "总里程", "avg_discount" : "平均折扣率"}, inplace = False ) filter_data = deal_data[["入会时间", "飞行次数", "平均每公里票价", "总里程", "时间间隔差值", "平均折扣率"]] print(filter_data[0:5]) filter_data['入会时间'] = filter_data['入会时间'].astype(np.int64)/(60*60*24*10**9) print(filter_data[0:5]) print(filter_data.info()) filter_zscore_data = (filter_data - filter_data.mean(axis=0))/(filter_data.std(axis=0)) filter_zscore_data[0:5] def distEclud(vecA, vecB): """ 计算两个向量的欧式距离的平方，并返回 """ return np.sum(np.power(vecA - vecB, 2)) def test_Kmeans_nclusters(data_train): """ 计算不同的k值时，SSE的大小变化 """ data_train = data_train.values nums = range(2, 10) SSE = [] for num in nums: sse = 0 kmodel = KMeans(n_clusters=num, n_jobs=4) kmodel.fit(data_train) # 簇中心 cluster_ceter_list = kmodel.cluster_centers_ # 个样本属于的簇序号列表 cluster_list = kmodel.labels_.tolist() for index in range(len(data)): cluster_num = cluster_list[index] sse += distEclud(data_train[index, :], cluster_ceter_list[cluster_num]) print("簇数是", num, "时； SSE是", sse) SSE.append(sse) return nums, SSE nums, SSE = test_Kmeans_nclusters(filter_zscore_data) #画图，通过观察SSE与k的取值尝试找出合适的k值 # 中文和负号的正常显示 plt.rcParams['font.sans-serif'] = 'SimHei' plt.rcParams['font.size'] = 12.0 plt.rcParams['axes.unicode_minus'] = False # 使用ggplot的绘图风格 plt.style.use('ggplot') ## 绘图观测SSE与簇个数的关系 fig=plt.figure(figsize=(10, 8)) ax=fig.add_subplot(1,1,1) ax.plot(nums,SSE,marker="+") ax.set_xlabel("n_clusters", fontsize=18) ax.set_ylabel("SSE", fontsize=18) fig.suptitle("KMeans", fontsize=20) plt.show() kmodel = KMeans(n_clusters=5, n_jobs=4) kmodel.fit(filter_zscore_data) # 简单打印结果 r1 = pd.Series(kmodel.labels_).value_counts() # 统计各个类别的数目 r2 = pd.DataFrame(kmodel.cluster_centers_) # 找出聚类中心 # 所有簇中心坐标值中最大值和最小值 max = r2.values.max() min = r2.values.min() r = pd.concat([r2, r1], axis=1) # 横向连接（0是纵向），得到聚类中心对应的类别下的数目 r.columns = list(filter_zscore_data.columns) + [u'类别数目'] # 重命名表头 # 绘图 fig = plt.figure(figsize=(10, 8)) ax = fig.add_subplot(111, polar=True) center_num = r.values feature = ["入会时间", "飞行次数", "平均每公里票价", "总里程", "时间间隔差值", "平均折扣率"] N = len(feature) for i, v in enumerate(center_num): # 设置雷达图的角度，用于平分切开一个圆面 angles = np.linspace(0, 2 * np.pi, N, endpoint=False) # 为了使雷达图一圈封闭起来，需要下面的步骤 center = np.concatenate((v[:-1], [v[0]])) angles = np.concatenate((angles, [angles[0]])) # 绘制折线图 ax.plot(angles, center, 'o-', linewidth=2, label="第%d簇人群,%d人" % (i + 1, v[-1])) # 填充颜色 ax.fill(angles, center, alpha=0.25) # 添加每个特征的标签 ax.set_thetagrids(angles * 180 / np.pi, feature, fontsize=15) # 设置雷达图的范围 ax.set_ylim(min - 0.1, max + 0.1) # 添加标题 plt.title('客户群特征分析图', fontsize=20) # 添加网格线 ax.grid(True) # 设置图例 plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1.0), ncol=1, fancybox=True, shadow=True) # 显示图形 plt.show() clu = kmodel.cluster_centers_ x = [1,2,3,4,5,6] colors = ['red','green','yellow','blue','black','purple'] for i in range(5): plt.plot(x,clu[i],label='clustre '+str(i),linewidth=6-i,color=colors[i],marker='o') plt.xlabel('L R F M C C') plt.ylabel('values') plt.show() # 详细输出原始数据及其类别 res = pd.concat([data, pd.Series(kmodel.labels_, index=data.index)], axis=1) # 详细输出每个样本对应的类别 res.columns = list(data.columns) + [u'class'] # 重命名表头 res.to_excel('.\\data\\result2.xls') # 保存结果