上海餐饮数据集+基于Python的上海餐饮数据可视化分析+聚类分析

[{"title":"（ 26 个子文件 4.98MB ） 上海餐饮数据集+基于Python的上海餐饮数据可视化分析+聚类分析","children":[{"title":"餐饮数据清洗.py <span style='color:#111;'> 1.65KB </span>","children":null,"spread":false},{"title":"原始数据层","children":[{"title":"上海餐饮数据.csv <span style='color:#111;'> 5.61MB </span>","children":null,"spread":false},{"title":"上海餐饮数据_utf8.csv <span style='color:#111;'> 6.25MB </span>","children":null,"spread":false}],"spread":true},{"title":"餐饮数据可视化.py <span style='color:#111;'> 4.91KB </span>","children":null,"spread":false},{"title":"餐饮数据分析.py <span style='color:#111;'> 2.59KB </span>","children":null,"spread":false},{"title":"结果输出层","children":[{"title":"数据可视化结果图","children":[{"title":"cluster_visualization.png <span style='color:#111;'> 42.95KB </span>","children":null,"spread":false},{"title":"category_avg_scores.png <span style='color:#111;'> 44.44KB </span>","children":null,"spread":false},{"title":"category_avg_consumption.png <span style='color:#111;'> 40.32KB </span>","children":null,"spread":false},{"title":"district_frequency_wordcloud.png <span style='color:#111;'> 182.08KB </span>","children":null,"spread":false},{"title":"elbow_method.png <span style='color:#111;'> 24.49KB </span>","children":null,"spread":false},{"title":"district_avg_consumption.png <span style='color:#111;'> 36.31KB </span>","children":null,"spread":false},{"title":"category_total_comments.png <span style='color:#111;'> 45.95KB </span>","children":null,"spread":false},{"title":"category_frequency_wordcloud.png <span style='color:#111;'> 197.61KB </span>","children":null,"spread":false},{"title":"district_avg_scores.png <span style='color:#111;'> 41.33KB </span>","children":null,"spread":false},{"title":"district_total_comments.png <span style='color:#111;'> 38.43KB </span>","children":null,"spread":false}],"spread":true},{"title":"数据分析结果数据","children":[{"title":"district_sum_comment.csv <span style='color:#111;'> 356B </span>","children":null,"spread":false},{"title":"district_avg_consumption.csv <span style='color:#111;'> 282B </span>","children":null,"spread":false},{"title":"category_analysis.csv <span style='color:#111;'> 368B </span>","children":null,"spread":false},{"title":"cluster_analysis.csv <span style='color:#111;'> 7.47MB </span>","children":null,"spread":false},{"title":"district_avg_score.csv <span style='color:#111;'> 479B </span>","children":null,"spread":false},{"title":"category_avg_consumption.csv <span style='color:#111;'> 331B </span>","children":null,"spread":false},{"title":"category_avg_score.csv <span style='color:#111;'> 625B </span>","children":null,"spread":false},{"title":"category_sum_comment.csv <span style='color:#111;'> 442B </span>","children":null,"spread":false},{"title":"district_analysis.csv <span style='color:#111;'> 310B </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"k均值聚类分析.py <span style='color:#111;'> 2.74KB </span>","children":null,"spread":false},{"title":"中间处理层","children":[{"title":"清洗后数据.csv <span style='color:#111;'> 5.59MB </span>","children":null,"spread":false}],"spread":true}],"spread":true}]