分析目标：
- 通过处理后的房价数据，筛选对房价有显著影响的特征变量。
- 确定特征变量，建立深圳房价预测模型并对假设情景进行模拟
数据预处理
import pandas as pd
import os
file_path="D:\Python数据分析与挖掘实战\深圳二手房价分析\data"
#读取file_path目录下的所有文件
file_name=os.listdir(file_path)
df=pd.DataFrame()
lis=[]
#使用两种方法读取数据
for i in file_name:
file=pd.read_excel(os.path.join(file_path,i))
# lis.append(file)
df=df.append(file)
# df=pd.concat(lis)
#更改第一列的名字
df=df.rename(columns={'Unnamed: 0':'house_id'})
#查看数据描述
print(df.describe())
out:
house_id roomnum ... subway per_price
count 1.851400e+04 18514.000000 ... 18514.000000 18514.000000
mean 2.280900e+08 2.873339 ... 0.504159 6.118192
std 3.031648e+06 1.040839 ... 0.499996 3.050218
min 4.217338e+06 1.000000 ... 0.000000 1.010100
25% 2.276957e+08 2.000000 ... 0.000000 4.052600
50% 2.284619e+08 3.000000 ... 1.000000 5.246300
75% 2.288307e+08 3.000000 ... 1.000000 7.357400
max 2.289965e+08 9.000000 ... 1.000000 26.396800
[8 rows x 8 columns]
#查看数据信息，是否有缺失值,可以看到总共有10个字段，其中house_id字段是没有用的，我们可以删除
df=df.drop(columns='house_id')
print(df.info())
out:
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 district 18514 non-null object
1 roomnum 18514 non-null int64
2 hall 18514 non-null int64
3 AREA 18514 non-null float64
4 C_floor 18514 non-null object
5 floor_num 18514 non-null int64
6 school 18514 non-null int64
7 subway 18514 non-null int64
8 per_price 18514 non-null float64
dtypes: float64(2), int64(5), object(2)
#我们可以看到字段中只有每平米的单价,我们可以加一个字段为总价，多一个维度进行分析,总价为面积乘以每平米单价
df['total_price']=df['AREA']*df['per_price']
print(df['total_price'])
out:
0 632.002890
1 879.995700
2 110.000800
3 93.990400
4 395.998200
...
1487 116.000040
1488 119.999383
1489 145.001298
1490 128.999772
1491 80.999928
Name: total_price, Length: 18514, dtype: float64
#查看是否有重复项
print(df.duplicated().sum())
out:
0
area_map={'baoan':'宝安','dapengxinqu':'大鹏新区','futian':'福田','guangming':'光明',
'longhua':'龙华','luohu':'罗湖','nanshan':'南山','pingshan':'坪山','yantian':'盐田'
,'longgang':'龙岗'}
df['district']=df['district'].apply(lambda x : area_map[x])特征变量分析
1.district特征变量分析
由上图可以看出：
- 南山区二点平均房价最高，大鹏新区最低。
- 平均总价南山区最高，坪山区最低。
- 二手房总数量有18514套，数量最多的为罗湖，接近18%。
- 由箱型图可以看出随着区域不同，箱子中心明显不同，说明房价跟区域有关系。
2.roomnum特征变量分析
由上图可以看出：
- 房间数量为6的平均单价最高。
- 卧室数量对平均单价的影响不明显。
3.hall特征变量分析
由上图可以看出：
- 厅数量为3的平均单价最高。
- 厅数量对平均单价有一定影响。
4.c_floor特征变量分析
由上图可以看出：
1.不同楼层对均价影响不大。
5.school,subway特征变量分析
由上图可以看出：
1.可以看到，靠近地铁站的二手房均价要明显高于不靠近地铁站的二手房。
2.学校对房价的影响没有地铁站大。
5.面积特征变量分析
plt.scatter(df.AREA,df.per_price,marker='x',color='b',alpha=0.5)
plt.title('面积AREA 和 单位面积房价per_price的散点图')
plt.ylabel("单位面积房价")
plt.xlabel("面积（平方米）")
plt.show()
由上图可以看出：
1.二手房以68-102的小户型居多，几乎占到了总数量的一半。
2.面积变化，单价波动明显，说明面积对单价有一定影响。
3.小户型明显比大户型受欢迎。
6.floor特征变量分析
由上图可以看出：
1.随着楼层的变化，平均单价波动较大，所以楼层对单价有影响。
机器学习预测房价
由上面的分析可以看出（区域、房间数量、学校、楼层数、是否靠近地铁站、面积、厅数）等7个特征对房价有影响，因此将这些特征作为作为机器算法的输入，经过训练拟合后输出预测的房价。
首先使用one-hot编码将类别变量（区域，房间数量，厅数）转化为数值型变量，学校和地铁已经转化过了不需要转化，连续变量可以不转为数值型变量。
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.svm import SVR
import xgboost as xgb
from sklearn.preprocessing import StandardScaler
# 学校和是否靠近地铁不需要转化成数值型变量
Roomnum = pd.get_dummies(df['roomnum'])
Roomnum.rename(columns={i: 'roomnum_' + str(i) for i in Roomnum.columns}, inplace=True)
District = pd.get_dummies(df['district'])
District.rename(columns={i: 'district_' + str(i) for i in District.columns}, inplace=True)
Hall = pd.get_dummies(df['hall'])
Hall.rename(columns={i: 'hall_' + str(i) for i in Hall.columns}, inplace=True)
data_new = pd.concat([Roomnum, District, Hall, df], axis=1)
data_new = data_new.drop(columns=['district', 'hall', 'roomnum', 'C_floor', 'total_price'], axis=1)
# 确定数据中的特征与标签
x = data_new.loc[:, data_new.columns != "per_price"]
fea_imp = x.columns
y = data_new.loc[:, 'per_price']
# 数据分割，随机采样30%作为测试样本，其余作为训练样本
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=10, test_size=0.3)
# print(x_train.shape, x_test.shape, y_train.shape, y_test.shape)
# reshape(-1,1)表示任意行，一列
y_train = y_train.values.reshape(-1, 1)
y_test = y_test.values.reshape(-1, 1)
# 数据标准化处理
ss_x = StandardScaler()
ss_y = StandardScaler()
# fit_transform是fit和transform的组合，既包括了训练又包含了转换。
# transform()和fit_transform()二者的功能都是对数据进行某种统一处理
# （比如标准化~N(0,1)，将数据缩放(映射)到某个固定区间，归一化，正则化等）
x_train = ss_x.fit_transform(x_train)
x_test = ss_x.transform(x_test)
mean_y = np.mean(y_train)
s_y = np.var(y_train)
y_train = ss_y.fit_transform(y_train)
y_test = ss_y.transform(y_test)
# 线性回归
lr = LinearRegression()
# 支持向量机回归
svr = SVR(kernel="rbf")
param = {'max_depth': 3,
'learning_rate': 0.1,
'n_estimators': 100,
'objective': 'reg:linear', # 此默认参数与 XGBClassifier 不同
'booster': 'gbtree',
'gamma': 0,
'min_child_weight': 1,
'subsample': 1,
'colsample_bytree': 1,
'reg_alpha': 0,
'reg_lambda': 1,
'random_state': 2}
dtrain = xgb.DMatrix(x_train, label=y_train, feature_names=fea_imp)
dtest = xgb.DMatrix(x_test, label=y_test, feature_names=fea_imp)
num_round = 100
watchlist = [(dtrain, 'train'), (dtest, 'test')]
lr.fit(x_test, y_test)
svr.fit(x_test, y_test)
xg = xgb.train(param, dtrain, num_round, evals=watchlist, early_stopping_rounds=10)
out:
[0] train-rmse:1.04640 test-rmse:1.04475
[1] train-rmse:0.98451 test-rmse:0.98363
[2] train-rmse:0.93073 test-rmse:0.93074
[3] train-rmse:0.88439 test-rmse:0.88517
[4] train-rmse:0.84244 test-rmse:0.84438
[5] train-rmse:0.80710 test-rmse:0.81022
[6] train-rmse:0.77707 test-rmse:0.78133
[7] train-rmse:0.75142 test-rmse:0.75731
[8] train-rmse:0.72903 test-rmse:0.73546
[9] train-rmse:0.71017 test-rmse:0.71765
[10] train-rmse:0.69146 test-rmse:0.69960
[11] train-rmse:0.67692 test-rmse:0.68635
[12] train-rmse:0.66298 test-rmse:0.67298
[13] train-rmse:0.65210 test-rmse:0.66320
[14] train-rmse:0.64173 test-rmse:0.65345
[15] train-rmse:0.63378 test-rmse:0.64664
[16] train-rmse:0.62579 test-rmse:0.63936
[17] train-rmse:0.61993 test-rmse:0.63410
[18] train-rmse:0.61405 test-rmse:0.62849
[19] train-rmse:0.60906 test-rmse:0.62385
[20] train-rmse:0.60482 test-rmse:0.62023
[21] train-rmse:0.60097 test-rmse:0.61680
[22] train-rmse:0.59790 test-rmse:0.61411
[23] train-rmse:0.59470 test-rmse:0.61117
[24] train-rmse:0.59193 test-rmse:0.60857
[25] train-rmse:0.58939 test-rmse:0.60670
[26] train-rmse:0.58688 test-rmse:0.60459
[27] train-rmse:0.58516 test-rmse:0.60302
[28] train-rmse:0.58327 test-rmse:0.60164
[29] train-rmse:0.58163 test-rmse:0.60073
[30] train-rmse:0.58036 test-rmse:0.59952
[31] train-rmse:0.57869 test-rmse:0.59794
[32] train-rmse:0.57696 test-rmse:0.59644
[33] train-rmse:0.57596 test-rmse:0.59559
[34] train-rmse:0.57463 test-rmse:0.59455
[35] train-rmse:0.57330 test-rmse:0.59346
[36] train-rmse:0.57212 test-rmse:0.59236
[37] train-rmse:0.57119 test-rmse:0.59174
[38] train-rmse:0.57041 test-rmse:0.59106
[39] train-rmse:0.56929 test-rmse:0.59012
[40] train-rmse:0.56834 test-rmse:0.58927
[41] train-rmse:0.56733 test-rmse:0.58862
[42] train-rmse:0.56628 test-rmse:0.58777
[43] train-rmse:0.56491 test-rmse:0.58683
[44] train-rmse:0.56391 test-rmse:0.58618
[45] train-rmse:0.56332 test-rmse:0.58569
[46] train-rmse:0.56253 test-rmse:0.58515
[47] train-rmse:0.56154 test-rmse:0.58434
[48] train-rmse:0.56082 test-rmse:0.58371
[49] train-rmse:0.56017 test-rmse:0.58302
[50] train-rmse:0.55971 test-rmse:0.58265
[51] train-rmse:0.55917 test-rmse:0.58233
[52] train-rmse:0.55806 test-rmse:0.58155
[53] train-rmse:0.55745 test-rmse:0.58125
[54] train-rmse:0.55672 test-rmse:0.58080
[55] train-rmse:0.55569 test-rmse:0.57971
[56] train-rmse:0.55514 test-rmse:0.57927
[57] train-rmse:0.55455 test-rmse:0.57894
[58] train-rmse:0.55408 test-rmse:0.57853
[59] train-rmse:0.55361 test-rmse:0.57818
[60] train-rmse:0.55305 test-rmse:0.57795
[61] train-rmse:0.55269 test-rmse:0.57773
[62] train-rmse:0.55180 test-rmse:0.57714
[63] train-rmse:0.55151 test-rmse:0.57698
[64] train-rmse:0.55115 test-rmse:0.57673
[65] train-rmse:0.55049 test-rmse:0.57638
[66] train-rmse:0.54969 test-rmse:0.57585
[67] train-rmse:0.54928 test-rmse:0.57555
[68] train-rmse:0.54904 test-rmse:0.57539
[69] train-rmse:0.54829 test-rmse:0.57457
[70] train-rmse:0.54804 test-rmse:0.57442
[71] train-rmse:0.54737 test-rmse:0.57405
[72] train-rmse:0.54685 test-rmse:0.57380
[73] train-rmse:0.54622 test-rmse:0.57343
[74] train-rmse:0.54584 test-rmse:0.57330
[75] train-rmse:0.54572 test-rmse:0.57320
[76] train-rmse:0.54557 test-rmse:0.57312
[77] train-rmse:0.54502 test-rmse:0.57257
[78] train-rmse:0.54446 test-rmse:0.57215
[79] train-rmse:0.54392 test-rmse:0.57191
[80] train-rmse:0.54342 test-rmse:0.57153
[81] train-rmse:0.54309 test-rmse:0.57132
[82] train-rmse:0.54299 test-rmse:0.57130
[83] train-rmse:0.54251 test-rmse:0.57103
[84] train-rmse:0.54239 test-rmse:0.57095
[85] train-rmse:0.54197 test-rmse:0.57077
[86] train-rmse:0.54146 test-rmse:0.57042
[87] train-rmse:0.54137 test-rmse:0.57035
[88] train-rmse:0.54091 test-rmse:0.57010
[89] train-rmse:0.54067 test-rmse:0.56994
[90] train-rmse:0.54059 test-rmse:0.56993
[91] train-rmse:0.54031 test-rmse:0.56971
[92] train-rmse:0.54009 test-rmse:0.56960
[93] train-rmse:0.53972 test-rmse:0.56913
[94] train-rmse:0.53932 test-rmse:0.56885
[95] train-rmse:0.53903 test-rmse:0.56882
[96] train-rmse:0.53881 test-rmse:0.56873
[97] train-rmse:0.53849 test-rmse:0.56849
[98] train-rmse:0.53814 test-rmse:0.56835
[99] train-rmse:0.53806 test-rmse:0.56828
print('逻辑回归模型的r2-score为:', r2_score(lr.predict(x_test), y_test))
print('支持向量机模型的r2-score为:',r2_score(svr.predict(x_test), y_test))
print('xgboost模型的r2-score为:',r2_score(xg.predict(dtest), y_test))
out:
逻辑回归模型的r2-score为: 0.3927518839179279
支持向量机模型的r2-score为: 0.47270739927209093
xgboost模型的r2-score为: 0.5097990013836289
# 新版本将get_fscore()替换了旧的feature_importance_
im = pd.DataFrame({'importance': xg.get_fscore().values(), 'var': xg.get_fscore().keys()})
im = im.sort_values(by='importance', ascending=False)
print(im.head(10))
out;
importance var
22 201.0 AREA
23 168.0 floor_num
24 36.0 school
9 34.0 district_南山
14 29.0 district_福田
25 28.0 subway
12 25.0 district_宝安
17 25.0 district_龙岗
10 20.0 district_坪山
11 15.0 district_大鹏新区
xgb.plot_importance(xg, max_num_features=10, importance_type='gain')
plt.show()
# (三) 假想情形，做预测，x_new是新的自变量
'''
预测要找一个条件为：
1.南山区
2.有3个房间
3.面积大概再80㎡左右
4.有地铁
5.学区房
的房子的大概花费
'''
room = Roomnum.loc[Roomnum['roomnum_3'] == 1].head(1).reset_index(drop=True)
dis = District.loc[District['district_南山'] == 1].head(1).reset_index(drop=True)
hal = Hall.loc[Hall['hall_3'] == 1].head(1).reset_index(drop=True)
x_new1 = pd.concat([room, dis, hal], axis=1)
x_new1['AREA'] = 80
x_new1['floor_num'] = 3
x_new1['school'] = 1
x_new1['subway'] = 1
x_new1_scale = ss_x.transform(x_new1)
dtt = xgb.DMatrix(x_new1_scale, feature_names=fea_imp)
p=xg.predict(dtt)
# 反标准化
per_price=p*s_y+mean_y
print("单位面积房价：", per_price)
print("总价：", per_price * 80)
out：
单位面积房价： [15.402145]
总价： [1232.1716]
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