哪吒之魔童降世票房超過流浪地球（Python電影票房數據分析-哪吒之魔童降世）

2023-06-04 21:34:12 2

雖然結果出來了，但是，由於數據太少，看不出什麼。所以，我們把機器學習的重點，放在評論上面聚類三個範圍

#!/usr/bin/env Python3# -*- coding: utf-8 -*-# @Software: PyCharm# @virtualenv：workon# @contact: contact information# @Desc：Code descripton__author__ = '未昔/AngelFate/[email protected]'__date__ = '2019/8/24 22:53'k = 3iteration = 500 model = KMeans(n_clusters=k, n_jobs=1, max_iter=iteration) y = model.fit_predict(x)label_pred = model.labels_centroids = model.cluster_centers_ #獲取聚類中心inertia = model.inertia_ print('y:\n',y)print('label_pred:\n',label_pred)print('centroids:\n',centroids)print('inertia:\n',inertia)print('----分類結果----:')result = list(zip(y, x))for i in result: print(i)# 簡單列印結果r1 = pd.Series(model.labels_).value_counts r2 = pd.DataFrame(model.cluster_centers_) # 將二維數組格式的cluster_centers_轉換為DataFrame格式print('r2: \n', r2)r = pd.concat([r2, r1], axis=1) 默認從0開始r.columns = data2.columns.tolist ['類別數目'] # 重命名表頭print('r: \n', r)output_data = pd.concat([data2, pd.Series(model.labels_, index=data2.index)], axis=1) output_data.columns = list(data2.columns) ['聚類類別'] # 重命名表頭# output_data.to_excel(output_path) # 保存結果# 使用TSNE進行數據降維並展示聚類結果tsne = TSNEtsne.fit_transform(data2) # tsne.embedding_可以獲得降維後的數據print('tsne.embedding_: \n', tsne.embedding_)tsn = pd.DataFrame(tsne.embedding_, index=data.index) print('tsne: \n', tsne)import matplotlib.pyplot as pltplt.rcParams['font.sans-serif'] = ['SimHei'] plt.rcParams['axes.unicode_minus'] = False color_style = ['r.', 'go', 'b*']for i in range(k): d = tsn[output_data[u'聚類類別'] == i] plt.plot(d[0], d[1], color_style[i], label='聚類' str(i 1))plt.legendplt.show123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263

分類結果----:(2, array([2.01000e 04, 1.28361e 05, 3.00000e 01, 2.21753e 04]))(1, array([2.301119e 04, 1.567950e 05, 3.900000e 01, 3.459470e 04]))(1, array([2.867732e 04, 1.667870e 05, 4.700000e 01, 4.088600e 04]))(1, array([1.88020e 04, 1.69018e 05, 3.10000e 01, 2.33768e 04]))(1, array([1.934163e 04, 1.783780e 05, 3.000000e 01, 2.363760e 04]))(1, array([1.964646e 04, 1.846340e 05, 3.000000e 01, 2.368560e 04]))(2, array([1.764494e 04, 1.531700e 05, 3.200000e 01, 3.188840e 04]))(2, array([2.019723e 04, 1.453720e 05, 3.800000e 01, 3.594430e 04]))(1, array([3.387688e 04, 1.693550e 05, 5.400000e 01, 5.230970e 04]))(1, array([3.408296e 04, 1.795520e 05, 5.200000e 01, 5.270440e 04]))(1, array([1.668626e 04, 1.726620e 05, 2.700000e 01, 2.785250e 04]))(1, array([1.541501e 04, 1.727600e 05, 2.500000e 01, 2.604260e 04]))(2, array([2.51734e 04, 1.43915e 05, 4.90000e 01, 5.66489e 04]))(2, array([1.221956e 04, 1.446960e 05, 2.400000e 01, 2.578140e 04]))(0, array([1.123695e 04, 1.096660e 05, 2.900000e 01, 3.040230e 04]))(2, array([1.813015e 04, 1.400030e 05, 3.600000e 01, 3.664440e 04]))(0, array([1.790766e 04, 1.065170e 05, 2.900000e 01, 2.748880e 04]))(2, array([8.91428e 03, 1.42105e 05, 1.80000e 01, 1.84165e 04]))(2, array([7.83286e 03, 1.42299e 05, 1.60000e 01, 1.63741e 04]))(2, array([6.97618e 03, 1.41605e 05, 1.40000e 01, 1.46528e 04]))(2, array([6.15493e 03, 1.41263e 05, 1.30000e 01, 1.31160e 04]))(0, array([6.87670e 03, 8.48370e 04, 2.30000e 01, 2.33302e 04]))(0, array([1.108646e 04, 1.065170e 05, 2.900000e 01, 2.748880e 04]))(0, array([1.089267e 04, 1.117420e 05, 2.800000e 01, 2.562580e 04]))(0, array([5.43425e 03, 1.10661e 05, 1.50000e 01, 1.33031e 04]))(0, array([5.01389e 03, 1.11588e 05, 1.40000e 01, 1.20931e 04]))標籤y: [2 1 1 1 1 1 2 2 1 1 1 1 2 2 0 2 0 2 2 2 2 0 0 0 0 0]123456789101112131415161718192021222324252627282930

聚類中心

centroids: [[9.77836857e 03 1.05932571e 05 2.38571429e 01 2.28188714e 04] [2.32821900e 04 1.72215667e 05 3.72222222e 01 3.38988778e 04] [1.43343530e 04 1.42278900e 05 2.70000000e 01 2.71642100e 04]]

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