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Airbnb数据集-价格因素分析

import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport seaborn as snscalendar = pd.read_csv("./data/madrid-airbnb-data/calendar.csv")calendar.head()

calendar['price'] = calendar['price'].str.replace(r"[$,]","",regex=True).astype(np.float32)calendar['adjusted_price'] = calendar['adjusted_price'].str.replace(r"[$,]","",regex=True).astype(np.float32)

calendar['date'] = pd.to_datetime(calendar['date'])calendar['date'].head()

calendar['weekday'] = calendar['date'].dt.weekdaycalendar['month'] = calendar['date'].dt.monthcalendar['month'].head()

month_price = calendar.groupby("month")['price'].mean()sns.barplot(month_price.index,month_price.values)

weekday_price = calendar.groupby("weekday")['price'].mean()sns.barplot(weekday_price.index,weekday_price.values)

sns.distplot(calendar[calendar['price']<300]['price'])

Airbnb数据集-房屋数据预处理

listings_detailed = pd.read_csv("./data/madrid-airbnb-data/listings_detailed.csv")

listings_detailed.columns.values.tolist()

listings_detailed['price'] = listings_detailed['price'].str.replace(r"[$,]","",regex=True).astype(np.float32)listings_detailed['cleaning_fee'] = listings_detailed['cleaning_fee'].str.replace(r"[$,]","",regex=True).astype(np.float32)

listings_detailed['cleaning_fee'].fillna(0,inplace=True)listings_detailed['cleaning_fee'].head()

# 添加一个新的字段：最低消费listings_detailed['minimum_cost'] = (listings_detailed['price']+listings_detailed['cleaning_fee'])*listings_detailed['minimum_nights']listings_detailed['minimum_cost'].head()

# 设施的数量listings_detailed['n_amenities'] = listings_detailed['amenities'].str[1:-1].str.split(",").apply(len)

# 根据可容纳的人数，添加一个新的列，用来表示类型：Single(1)、Couple(2)、Family(5)、Group(100)# accommodates/listings_detailed['accommodates_type'] = listings_detailed['accommodates_type'] = pd.cut(listings_detailed['accommodates'],bins=[1,2,3,5,100],include_lowest=True,right=False,labels=['Single','Couple','Family','Group'])

listings_detailed['neighbourhood_group_cleansed'].head()

listings_detailed['review_scores_rating'].head()

listings_detailed_df = listings_detailed[['id','host_id','listing_url','room_type',                                          'neighbourhood_group_cleansed','price','cleaning_fee','amenities','n_amenities',                                         'accommodates','accommodates_type','minimum_nights','minimum_cost']]listings_detailed_df.head()

Airbnb数据集-房间类型和社区分析

# 房间类型的情况room_type_counts = listings_detailed_df['room_type'].value_counts()fig,axes = plt.subplots(1,2,figsize=(10,5))axes[0].pie(room_type_counts.values,autopct="%.2f%%",labels=room_type_counts.index)sns.barplot(room_type_counts.index,room_type_counts.values)plt.tight_layout()

neighbourhood_counts = listings_detailed_df['neighbourhood_group_cleansed'].value_counts()sns.barplot(y=neighbourhood_counts.index,x=neighbourhood_counts.values,orient='h')

Airbnb数据集-房间类型和社区对比分析

neighbour_room_type = listings_detailed_df.groupby(['neighbourhood_group_cleansed','room_type']) \    .size() \    .unstack('room_type') \    .fillna(0) \    .apply(lambda row: row/row.sum(),axis=1) \    .sort_values("Entire home/apt",ascending=True)neighbour_room_type

columns = neighbour_room_type.columnsplt.figure(figsize=(10,5))index = neighbour_room_type.indexplt.barh(index,neighbour_room_type[columns[0]])left = neighbour_room_type[columns[0]].copy()plt.barh(index,neighbour_room_type[columns[1]],left=left)left += neighbour_room_type[columns[1]].copy()plt.barh(index,neighbour_room_type[columns[2]],left=left)left += neighbour_room_type[columns[2]].copy()plt.barh(index,neighbour_room_type[columns[3]],left=left)plt.legend(columns)

fig,ax = plt.subplots(figsize=(10,5))neighbour_room_type.plot(kind="barh",stacked=True,width=0.75,ax=ax)

Airbnb数据集-房东房源数量分析

host_number = listings_detailed_df.groupby('host_id').size()sns.distplot(host_number[host_number<10])

# 1,2,3,5+#[1,2),[2,3),[3,4),5+host_number_bins = pd.cut(host_number,bins=[1,2,3,5,1000],include_lowest=True,right=False,labels=['1','2','3-4','5+']).value_counts()plt.pie(host_number_bins,autopct="%.2f%%",labels=host_number_bins.index)

Airbnb数据集-评论数量与时间分析

reviews = pd.read_csv("./data/madrid-airbnb-data/reviews_detailed.csv",parse_dates=['date'])reviews.head()

reviews['year'] = reviews['date'].dt.yearreviews['month'] = reviews['date'].dt.month

n_reviews_year = reviews.groupby("year").size()sns.barplot(n_reviews_year.index,n_reviews_year.values)

n_reviews_month = reviews.groupby("month").size()sns.barplot(n_reviews_month.index,n_reviews_month.values)

Airbnb数据集-评论数量与时间综合分析

year_month_reviews = reviews.groupby(['year','month']).size().unstack("month").fillna(0)year_month_reviews

fig,ax = plt.subplots(figsize=(10,5))for index in year_month_reviews.index:    series = year_month_reviews.loc[index]    sns.lineplot(x=series.index,y=series.values,ax=ax)ax.legend(labels=year_month_reviews.index)ax.grid()_ = ax.set_xticks(list(range(1,13)))

'host_is_superhost','host_identity_verified','neighbourhood_group_cleansed','latitude','longitude','property_type','room_type','accommodates','bathrooms','bedrooms','cleaning_fee','minimum_nights','maximum_nights','availability_90','number_of_reviews','review_scores_rating','is_business_travel_ready','n_amenities','price'

Airbnb数据集-房屋价格预测（1）

from sklearn.preprocessing import StandardScalerml_listings = listings_detailed[listings_detailed['price']<300][[    'host_is_superhost',    'host_identity_verified',    'neighbourhood_group_cleansed',    'latitude',    'longitude',    'property_type',    'room_type',    'accommodates',    'bathrooms',    'bedrooms',    'cleaning_fee',    'minimum_nights',    'maximum_nights',    'availability_90',    'number_of_reviews',#     'review_scores_rating',    'is_business_travel_ready',    'n_amenities',    'price']]# 删除异常值ml_listings.dropna(axis=0,inplace=True)# 分割特征值和目标值features = ml_listings.drop(columns=['price'])targets = ml_listings['price']# 针对离散型进行one-hot编码disperse_columns = [    'host_is_superhost',    'host_identity_verified',    'neighbourhood_group_cleansed',    'property_type',    'room_type',    'is_business_travel_ready']disperse_features = features[disperse_columns]disperse_features = pd.get_dummies(disperse_features)# 对连续性数据进行标准化（因为特征值之间相差并不是很大，所以标准化的可能对预测结果影响不是很大）continuouse_features = features.drop(columns=disperse_columns)scaler = StandardScaler()continuouse_features = scaler.fit_transform(continuouse_features)# 对处理后的特征进行组合feature_array = np.hstack([disperse_features,continuouse_features])

Airbnb数据集-房屋价格预测（2）

from sklearn.model_selection import train_test_splitfrom sklearn.linear_model import LinearRegressionfrom sklearn.ensemble import RandomForestRegressorfrom sklearn.metrics import mean_absolute_error,r2_scoreX_train,X_test,y_train,y_test = train_test_split(feature_array,targets,test_size=0.25)regressor = RandomForestRegressor()regressor.fit(X_train,y_train)y_predict = regressor.predict(X_test)print("平均误差：",mean_absolute_error(y_test,y_predict))print("R2评分：",r2_score(y_test,y_predict))

Airbnb数据集-评论数量预测

ym_reviews = reviews.groupby(['year','month']).size().reset_index().rename(columns={
   0:"count"})features = ym_reviews[['year','month']]targets = ym_reviews['count']# X_train,X_test,y_train,y_test = train_test_split(features,targets,test_size=0.25)# regressor = RandomForestRegressor(n_estimators=100)# regressor.fit(X_train,y_train)# y_predict = regressor.predict(X_test)# print("平均误差：",mean_absolute_error(y_test,y_predict))# print("R2评分：",r2_score(y_test,y_predict))

regressor = RandomForestRegressor(n_estimators=100)regressor.fit(features,targets)y_predict = regressor.predict([    [2019,10],    [2019,11],    [2019,12]])y_predict

Airbnb数据集-预测结果可视化

predict_reviews = pd.DataFrame([[2019,10+index,x] for index,x in enumerate(y_predict)],columns=['year','month','count'])final_reviews = pd.concat([ym_reviews,predict_reviews]).reset_index()years = final_reviews['year'].unique()fig,ax = plt.subplots(figsize=(10,5))for year in years:    df = final_reviews[final_reviews['year']==year]    sns.lineplot(x="month",y='count',data=df)ax.legend(labels=year_month_reviews.index)ax.grid()_ = ax.set_xticks(list(range(1,13)))

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