ML之FE：利用FE特征工程(单个特征及其与标签关系的可视化)对RentListingInquries(Kaggle竞赛)数据集实现房屋感兴趣程度的多分类预测（四）

2.7、词云图可视化：

【display_address】、【street_address】、【features】

设计思路

核心代码

from wordcloud import WordCloud

text = ''

text_da = ''

text_street = ''

#text_desc = ''

for ind, row in train.iterrows():

   for feature in row['features']:

       text = " ".join([text, "_".join(feature.strip().split(" "))])

   text_da = " ".join([text_da,"_".join(row['display_address'].strip().split(" "))])

   text_street = " ".join([text_street,"_".join(row['street_address'].strip().split(" "))])

   #text_desc = " ".join([text_desc, row['description']])

text = text.strip()

text_da = text_da.strip()

text_street = text_street.strip()

#text_desc = text_desc.strip()

plt.figure(figsize=(12,6))

wordcloud = WordCloud(background_color='white', width=600, height=300, max_font_size=50, max_words=40).generate(text)

wordcloud.recolor(random_state=0)

plt.imshow(wordcloud)

plt.title("features: Wordcloud for features", fontsize=30)

plt.axis("off")

plt.show()

# wordcloud for display address

plt.figure()

wordcloud = WordCloud(background_color='white', width=600, height=300, max_font_size=50, max_words=40).generate(text_da)

wordcloud.recolor(random_state=0)

plt.imshow(wordcloud)

plt.title("display_address: Wordcloud for Display Address", fontsize=30)

plt.axis("off")

plt.show()

# wordcloud for street address

plt.figure()

wordcloud = WordCloud(background_color='white', width=600, height=300, max_font_size=50, max_words=40).generate(text_street)

wordcloud.recolor(random_state=0)

plt.imshow(wordcloud)

plt.title("street_address: Wordcloud for Street Address", fontsize=30)

plt.axis("off")

plt.show()