房租预测-----数据清洗
房租预测-----数据清洗
1. 读入数据
data_train = pd.read_csv('./train_data.csv')
data_train['Type'] = 'Train'
data_test = pd.read_csv('./test_a.csv')
data_test['Type'] = 'Test'
data_all = pd.concat([data_train, data_test], ignore_index=True)
2. 缺失值处理
通过探索性数据分析发现，rentType、buildYear、uv、pv等特征列具有缺失值，采取填充的方式处理，如下表
| 特征列 | 填充方式 |
|---|---|
| rentType | 填充众数 |
| buildYear | 填充众数 |
| uv | 填充平均数 |
| pv | 填充平均数 |
def preprocessingData(data):
# 填充缺失值
data['rentType'][data['rentType'] == '--'] = '未知方式'
# 转换object类型数据
columns = ['rentType','communityName','houseType', 'houseFloor', 'houseToward', 'houseDecoration', 'region', 'plate']
for feature in columns:
data[feature] = LabelEncoder().fit_transform(data[feature])
# 将buildYear列转换为整型数据
buildYearmean = pd.DataFrame(data[data['buildYear'] != '暂无信息']['buildYear'].mode())
data.loc[data[data['buildYear'] == '暂无信息'].index, 'buildYear'] = buildYearmean.iloc[0, 0]
data['buildYear'] = data['buildYear'].astype('int')
# 处理pv和uv的空值
data['pv'].fillna(data['pv'].mean(), inplace=True)
data['uv'].fillna(data['uv'].mean(), inplace=True)
data['pv'] = data['pv'].astype('int')
data['uv'] = data['uv'].astype('int')
3. 时间列处理
提取交易时间的月份和天数
def month(x):
month = int(x.split('/')[1])
return month
def day(x):
day = int(x.split('/')[2])
return day
data['month'] = data['tradeTime'].apply(lambda x: month(x))
data['day'] = data['tradeTime'].apply(lambda x: day(x))
4. 异常值处理
利用孤立深林（Isolation Forest）进行异常值检测，删除异常值
def IF_drop(train):
IForest = IsolationForest(contamination=0.01)
IForest.fit(train["tradeMoney"].values.reshape(-1,1))
y_pred = IForest.predict(train["tradeMoney"].values.reshape(-1,1))
drop_index = train.loc[y_pred==-1].index
print(drop_index)
train.drop(drop_index,inplace=True)
return train
data_train = IF_drop(data_train)
def dropData(train):
# 丢弃部分异常值
train = train[train.area <= 200]
train = train[(train.tradeMoney <=16000) & (train.tradeMoney >=700)]
train.drop(train[(train['totalFloor'] == 0)].index, inplace=True)
return train
#数据集异常值处理
data_train = dropData(data_train)
5. 深度清洗数据
对在不同区域、不同面积、不同交易金额进行可视化分析，删除异常值所在的行
def cleanData(data):
data.drop(data[(data['region']=='RG00001') & (data['tradeMoney']<1000)&(data['area']>50)].index,inplace=True)
data.drop(data[(data['region']=='RG00001') & (data['tradeMoney']>25000)].index,inplace=True)
data.drop(data[(data['region']=='RG00001') & (data['area']>250)&(data['tradeMoney']<20000)].index,inplace=True)
data.drop(data[(data['region']=='RG00001') & (data['area']>400)&(data['tradeMoney']>50000)].index,inplace=True)
data.drop(data[(data['region']=='RG00001') & (data['area']>100)&(data['tradeMoney']<2000)].index,inplace=True)
data.drop(data[(data['region']=='RG00002') & (data['area']<100)&(data['tradeMoney']>60000)].index,inplace=True)
data.drop(data[(data['region']=='RG00003') & (data['area']<300)&(data['tradeMoney']>30000)].index,inplace=True)
data.drop(data[(data['region']=='RG00003') & (data['tradeMoney']<500)&(data['area']<50)].index,inplace=True)
data.drop(data[(data['region']=='RG00003') & (data['tradeMoney']<1500)&(data['area']>100)].index,inplace=True)
data.drop(data[(data['region']=='RG00003') & (data['tradeMoney']<2000)&(data['area']>300)].index,inplace=True)
data.drop(data[(data['region']=='RG00003') & (data['tradeMoney']>5000)&(data['area']<20)].index,inplace=True)
data.drop(data[(data['region']=='RG00003') & (data['area']>600)&(data['tradeMoney']>40000)].index,inplace=True)
data.drop(data[(data['region']=='RG00004') & (data['tradeMoney']<1000)&(data['area']>80)].index,inplace=True)
data.drop(data[(data['region']=='RG00006') & (data['tradeMoney']<200)].index,inplace=True)
data.drop(data[(data['region']=='RG00005') & (data['tradeMoney']<2000)&(data['area']>180)].index,inplace=True)
data.drop(data[(data['region']=='RG00005') & (data['tradeMoney']>50000)&(data['area']<200)].index,inplace=True)
data.drop(data[(data['region']=='RG00006') & (data['area']>200)&(data['tradeMoney']<2000)].index,inplace=True)
data.drop(data[(data['region']=='RG00007') & (data['area']>100)&(data['tradeMoney']<2500)].index,inplace=True)
data.drop(data[(data['region']=='RG00010') & (data['area']>200)&(data['tradeMoney']>25000)].index,inplace=True)
data.drop(data[(data['region']=='RG00010') & (data['area']>400)&(data['tradeMoney']<15000)].index,inplace=True)
data.drop(data[(data['region']=='RG00010') & (data['tradeMoney']<3000)&(data['area']>200)].index,inplace=True)
data.drop(data[(data['region']=='RG00010') & (data['tradeMoney']>7000)&(data['area']<75)].index,inplace=True)
data.drop(data[(data['region']=='RG00010') & (data['tradeMoney']>12500)&(data['area']<100)].index,inplace=True)
data.drop(data[(data['region']=='RG00004') & (data['area']>400)&(data['tradeMoney']>20000)].index,inplace=True)
data.drop(data[(data['region']=='RG00008') & (data['tradeMoney']<2000)&(data['area']>80)].index,inplace=True)
data.drop(data[(data['region']=='RG00009') & (data['tradeMoney']>40000)].index,inplace=True)
data.drop(data[(data['region']=='RG00009') & (data['area']>300)].index,inplace=True)
data.drop(data[(data['region']=='RG00009') & (data['area']>100)&(data['tradeMoney']<2000)].index,inplace=True)
data.drop(data[(data['region']=='RG00011') & (data['tradeMoney']<10000)&(data['area']>390)].index,inplace=True)
data.drop(data[(data['region']=='RG00012') & (data['area']>120)&(data['tradeMoney']<5000)].index,inplace=True)
data.drop(data[(data['region']=='RG00013') & (data['area']<100)&(data['tradeMoney']>40000)].index,inplace=True)
data.drop(data[(data['region']=='RG00013') & (data['area']>400)&(data['tradeMoney']>50000)].index,inplace=True)
data.drop(data[(data['region']=='RG00013') & (data['area']>80)&(data['tradeMoney']<2000)].index,inplace=True)
data.drop(data[(data['region']=='RG00014') & (data['area']>300)&(data['tradeMoney']>40000)].index,inplace=True)
data.drop(data[(data['region']=='RG00014') & (data['tradeMoney']<1300)&(data['area']>80)].index,inplace=True)
data.drop(data[(data['region']=='RG00014') & (data['tradeMoney']<8000)&(data['area']>200)].index,inplace=True)
data.drop(data[(data['region']=='RG00014') & (data['tradeMoney']<1000)&(data['area']>20)].index,inplace=True)
data.drop(data[(data['region']=='RG00014') & (data['tradeMoney']>25000)&(data['area']>200)].index,inplace=True)
data.drop(data[(data['region']=='RG00014') & (data['tradeMoney']<20000)&(data['area']>250)].index,inplace=True)
data.drop(data[(data['region']=='RG00005') & (data['tradeMoney']>30000)&(data['area']<100)].index,inplace=True)
data.drop(data[(data['region']=='RG00005') & (data['tradeMoney']<50000)&(data['area']>600)].index,inplace=True)
data.drop(data[(data['region']=='RG00005') & (data['tradeMoney']>50000)&(data['area']>350)].index,inplace=True)
data.drop(data[(data['region']=='RG00006') & (data['tradeMoney']>4000)&(data['area']<100)].index,inplace=True)
data.drop(data[(data['region']=='RG00006') & (data['tradeMoney']<600)&(data['area']>100)].index,inplace=True)
data.drop(data[(data['region']=='RG00006') & (data['area']>165)].index,inplace=True)
data.drop(data[(data['region']=='RG00012') & (data['tradeMoney']<800)&(data['area']<30)].index,inplace=True)
data.drop(data[(data['region']=='RG00007') & (data['tradeMoney']<1100)&(data['area']>50)].index,inplace=True)
data.drop(data[(data['region']=='RG00004') & (data['tradeMoney']>8000)&(data['area']<80)].index,inplace=True)
data.loc[(data['region']=='RG00002')&(data['area']>50)&(data['rentType']=='合租'),'rentType']='整租'
data.loc[(data['region']=='RG00014')&(data['rentType']=='合租')&(data['area']>60),'rentType']='整租'
data.drop(data[(data['region']=='RG00008')&(data['tradeMoney']>15000)&(data['area']<110)].index,inplace=True)
data.drop(data[(data['region']=='RG00008')&(data['tradeMoney']>20000)&(data['area']>110)].index,inplace=True)
data.drop(data[(data['region']=='RG00008')&(data['tradeMoney']<1500)&(data['area']<50)].index,inplace=True)
data.drop(data[(data['region']=='RG00008')&(data['rentType']=='合租')&(data['area']>50)].index,inplace=True)
data.drop(data[(data['region']=='RG00015') ].index,inplace=True)
data.reset_index(drop=True, inplace=True)
return data
data_train = cleanData(data_train)
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