我正在尝试使用线性回归方法预测房价。我从房地产网站收集真实数据。我有一些特征和两个数值，其中价格是要猜测的目标变量。我有大约 3000 个数据，其中第一列是省份，房屋的面积字段为平方米，其次是多少沙龙 + 房间，其他特征为0或1。我试图获得的是一个公式（系数）。然而，我使用的 Orange Toolkit 显示出非常奇怪的猜测。是否有任何错误的步骤或遗漏的步骤？猜测可以改进吗？顺便说一句，可以通过 Box 链接下载数据集。
使用线性回归预测房价
数据挖掘
机器学习
线性回归
橙
2022-03-01 23:45:47
1个回答
需要注意的一些事项：
- 您的数据包含没有变化的指标，删除它们（不确定它们是否会自动删除到您的应用程序中）
- 为“m2”添加多项式以提高拟合度
- 尝试使用“m2”的日志
你的结果只是不合适的结果。查看 R^2 和平均绝对误差。我认为在 OLS 设置中几乎没有进一步改善适合度的空间。
我能做的最好的事情是 mae 为 258434 / R2=0.58。因此，您的预测平均失败了 258434 个单位。
Call:
lm(formula = Fiyat ~ poly(m2, 10, raw = T) + ., data = dat)
Residuals:
Min 1Q Median 3Q Max
-6864176 -190364 301 131575 20452070
Coefficients: (1 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.470e+07 5.729e+06 4.311 1.68e-05 ***
poly(m2, 10, raw = T)1 -1.848e+06 3.749e+05 -4.929 8.76e-07 ***
poly(m2, 10, raw = T)2 5.701e+04 1.015e+04 5.618 2.11e-08 ***
poly(m2, 10, raw = T)3 -9.411e+02 1.513e+02 -6.222 5.63e-10 ***
poly(m2, 10, raw = T)4 9.326e+00 1.380e+00 6.757 1.70e-11 ***
poly(m2, 10, raw = T)5 -5.850e-02 8.090e-03 -7.231 6.12e-13 ***
poly(m2, 10, raw = T)6 2.371e-04 3.100e-05 7.648 2.77e-14 ***
poly(m2, 10, raw = T)7 -6.173e-07 7.706e-08 -8.011 1.65e-15 ***
poly(m2, 10, raw = T)8 9.943e-10 1.195e-10 8.322 < 2e-16 ***
poly(m2, 10, raw = T)9 -8.994e-13 1.048e-13 -8.584 < 2e-16 ***
poly(m2, 10, raw = T)10 3.488e-16 3.964e-17 8.799 < 2e-16 ***
IlceAtasehir -1.855e+05 8.994e+04 -2.062 0.039275 *
IlceBeykoz 4.925e+04 8.370e+04 0.588 0.556325
IlceÇekmeköy -3.554e+05 9.068e+04 -3.919 9.10e-05 ***
IlceKadiköy 2.803e+05 8.855e+04 3.166 0.001564 **
IlceKartal -3.790e+05 8.705e+04 -4.354 1.39e-05 ***
IlceMaltepe -3.065e+05 8.814e+04 -3.478 0.000514 ***
IlcePendik -3.721e+05 9.133e+04 -4.074 4.75e-05 ***
IlceSancaktepe -4.431e+05 9.077e+04 -4.882 1.11e-06 ***
IlceSile -4.746e+05 8.422e+04 -5.636 1.91e-08 ***
IlceSultanbeyli -4.081e+05 9.168e+04 -4.451 8.87e-06 ***
IlceTuzla -3.956e+05 8.975e+04 -4.408 1.08e-05 ***
IlceÜmraniye -2.777e+05 9.185e+04 -3.023 0.002524 **
IlceÜsküdar 6.886e+04 8.704e+04 0.791 0.428931
m2 NA NA NA NA
`Oda Salon`1+1 -1.786e+05 2.131e+05 -0.838 0.401936
`Oda Salon`1+16 1.651e+05 7.199e+05 0.229 0.818646
`Oda Salon`1+2 -6.592e+05 5.347e+05 -1.233 0.217670
`Oda Salon`1+21 -2.802e+05 7.203e+05 -0.389 0.697349
`Oda Salon`1+3 -2.865e+05 4.514e+05 -0.635 0.525770
`Oda Salon`1+5 -3.472e+05 3.536e+05 -0.982 0.326228
`Oda Salon`2+0 -1.754e+05 4.071e+05 -0.431 0.666687
`Oda Salon`2+1 -2.357e+05 2.191e+05 -1.076 0.282167
`Oda Salon`2+2 -2.658e+05 3.176e+05 -0.837 0.402742
`Oda Salon`2+5 -3.400e+05 3.767e+05 -0.903 0.366802
`Oda Salon`3+1 -2.205e+05 2.217e+05 -0.995 0.320057
`Oda Salon`3+2 -2.383e+05 2.362e+05 -1.009 0.313198
`Oda Salon`3+5 -4.054e+05 3.422e+05 -1.184 0.236316
`Oda Salon`4+1 -3.964e+05 2.275e+05 -1.743 0.081513 .
`Oda Salon`4+2 -8.005e+05 2.383e+05 -3.360 0.000790 ***
`Oda Salon`5+1 -2.213e+05 2.468e+05 -0.896 0.370068
`Oda Salon`5+2 -8.853e+05 2.731e+05 -3.242 0.001200 **
`Oda Salon`6+1 -1.228e+06 3.856e+05 -3.186 0.001461 **
`Oda Salon`6+2 -1.075e+06 3.246e+05 -3.311 0.000941 ***
`Oda Salon`6+3 -3.735e+06 7.681e+05 -4.862 1.23e-06 ***
`Oda Salon`7+2 -6.971e+07 9.975e+06 -6.989 3.44e-12 ***
`Oda Salon`7+3 -1.982e+06 7.255e+05 -2.732 0.006338 **
Bati 4.756e+04 2.866e+04 1.659 0.097145 .
Dogu -3.334e+04 2.762e+04 -1.207 0.227453
Güney -4.931e+04 2.943e+04 -1.675 0.094008 .
Kuzey -1.060e+05 3.521e+04 -3.011 0.002623 **
`Akilli Ev` 1.898e+05 5.759e+04 3.296 0.000993 ***
`Amerikan Mutfak` -5.887e+04 4.319e+04 -1.363 0.173001
`Beyaz Esya` 2.681e+05 4.909e+04 5.462 5.11e-08 ***
Dusakabin -2.155e+04 3.629e+04 -0.594 0.552626
`Ebeveyn Banyosu` 8.674e+04 3.529e+04 2.458 0.014025 *
Kiler -1.156e+05 4.324e+04 -2.673 0.007552 **
Küvet 7.295e+04 4.786e+04 1.524 0.127554
Mobilya -1.255e+05 5.194e+04 -2.416 0.015741 *
`Parke Zemin` 8.113e+03 2.762e+04 0.294 0.769021
`Seramik Zemin` 1.968e+04 2.886e+04 0.682 0.495326
Vestiyer -2.499e+04 3.240e+04 -0.771 0.440650
Deniz 3.070e+05 3.833e+04 8.011 1.64e-15 ***
Doga 1.926e+04 2.834e+04 0.679 0.496936
Sehir 3.760e+04 3.175e+04 1.184 0.236481
ADSL -1.644e+04 3.094e+04 -0.531 0.595204
`Fiber Internet` -2.553e+04 3.498e+04 -0.730 0.465493
`Kablo TV` -1.419e+04 3.141e+04 -0.452 0.651406
Uydu 2.616e+04 3.133e+04 0.835 0.403767
`Wi-Fi` -4.504e+04 3.611e+04 -1.247 0.212455
Hidrofor 1.551e+04 3.754e+04 0.413 0.679614
Jeneratör 6.466e+04 4.022e+04 1.608 0.108010
Otopark 3.620e+03 3.139e+04 0.115 0.908216
`Ses Yalitimi` 1.325e+04 3.176e+04 0.417 0.676645
`Su Deposu` 3.149e+04 3.593e+04 0.877 0.380817
Cami -7.882e+04 4.203e+04 -1.876 0.060813 .
Kilise 5.621e+04 4.429e+04 1.269 0.204515
Market -4.442e+04 5.364e+04 -0.828 0.407649
Park 3.590e+04 3.884e+04 0.924 0.355344
`Saglik Ocagi` 2.112e+04 4.679e+04 0.451 0.651778
`Semt Pazari` -7.069e+04 4.379e+04 -1.614 0.106543
Sauna 2.789e+04 5.311e+04 0.525 0.599491
`Spor Salonu` -5.249e+04 3.349e+04 -1.567 0.117194
`Tenis Kortu` 4.304e+04 5.482e+04 0.785 0.432419
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 679000 on 2862 degrees of freedom
Multiple R-squared: 0.5908, Adjusted R-squared: 0.5791
F-statistic: 50.39 on 82 and 2862 DF, p-value: < 2.2e-16
前 20 个预测：
V1 pred
1 1200000 881787.6
2 1100000 862002.8
3 245000 339582.8
4 1890000 2160635.7
5 1360000 1036269.9
6 2400000 3067823.0
7 1280000 926335.9
8 575000 411630.6
9 390000 706514.2
10 1300000 1140435.6
11 460000 677953.1
12 920000 1287126.6
13 850000 1614840.1
14 1200000 166346.9
15 1500000 1172148.9
16 1200000 393769.3
17 3000000 1157697.3
18 1500000 1082589.2
19 490000 561175.0
20 3350000 3212890.7