Gratka flats challenge

Predict the price of flats in Poznań. Each entry in training data set is described by: Price, Rooms, SqrMeters, Floor, Location, Description. Evaluation metric is RMSE. [ver. 1.0.0]

# submitter when ver. description dev-0 RMSE test-A RMSE
172 [anonymised] 2019-03-19 13:12 1.0.0 test commit N/A N/A
171 [anonymised] 2019-03-19 13:11 1.0.0 test commit N/A N/A
170 [anonymised] 2019-03-05 12:55 1.0.0 testtest N/A N/A
169 [anonymised] 2019-02-26 14:12 1.0.0 test commit N/A N/A
168 [anonymised] 2017-12-17 22:54 1.0.0 Zadniae N/A N/A
110 [anonymised] 2017-12-17 09:28 1.0.0 Wykonane self-made linear-regression 1329.9387232536692 366.5175759271912
48 [anonymised] 2017-12-15 22:05 1.0.0 UMZ-2017-zaoczne-004 gradient descent self-made linear-regression 1328.9613130289044 349.65394912721274
105 [anonymised] 2017-12-13 22:34 1.0.0 Zadanie 004 z kodem programu self-made linear-regression 1329.917349272305 365.6190965465601
167 [anonymised] 2017-12-13 22:30 1.0.0 gradient prosty N/A N/A
47 kubapok 2017-10-06 09:13 1.0.0 mean 1332.8568005121458 347.8067638078092
135 Paweł Skórzewski 2017-09-30 17:59 1.0.0 Naive linear regression 1383.6523126679888 586.5211244969831
140 p/tlen 2017-09-30 15:34 1.0.0 dsdsd N/A 1913.9555625720845
139 p/tlen 2017-09-28 20:22 1.0.0 23 N/A 1815.1928573615749
138 p/tlen 2017-09-28 20:15 1.0.0 22 N/A 1716.5734249755628
137 p/tlen 2017-09-28 19:24 1.0.0 wersja 2100 stupid N/A 1618.1234616827594
136 p/tlen 2017-09-28 19:23 1.0.0 wersja 2000 N/A 1519.875899922114
66 [anonymised] 2016-12-12 20:09 1.0.0 M^2 regresja liniowa 1328.6483035690949 355.53801762906545
28 [anonymised] 2016-12-12 11:14 1.0.0 chyze szynszyle: mean estimator N/A 331.6929603719556
76 [anonymised] 2016-12-12 10:12 1.0.0 chyze szynszyle: ostatni raz k-nn N/A 361.9398271636192
69 [anonymised] 2016-12-12 10:10 1.0.0 chyze szynszyle: ostatni raz random forest N/A 359.3292851475514
90 [anonymised] 2016-12-12 00:26 1.0.0 one more try 1327.5057051760969 363.8063063708608
71 [anonymised] 2016-12-12 00:09 1.0.0 chyze szynszyle: linear model N/A 359.92957099843335
57 [anonymised] 2016-12-12 00:07 1.0.0 Foki: wynik v2 N/A 351.29651780980663
106 [anonymised] 2016-12-11 23:44 1.0.0 one more try 1327.5057051760969 365.73798967528444
92 [anonymised] 2016-12-11 23:38 1.0.0 one more try 1327.5057051760969 363.84987565952815
166 [anonymised] 2016-12-11 23:35 1.0.0 one more try 1327.5057051760969 N/A
73 [anonymised] 2016-12-11 23:35 1.0.0 chyze szynszyle: bez normalizacji na dzielnicach N/A 361.0180616219466
75 [anonymised] 2016-12-11 23:34 1.0.0 chyze szynszyle: k-nn, dodanie nowych cech N/A 361.77708386344295
56 [anonymised] 2016-12-11 23:29 1.0.0 Foki: wyniki N/A 351.29651780980663
79 [anonymised] 2016-12-11 23:28 1.0.0 chyze szynszyle: k-nn bez normalizacji danych, hehe N/A 361.95033283126736
74 [anonymised] 2016-12-11 23:24 1.0.0 chyze szynszyle: k-nn, wszystkie cechy N/A 361.29959857775646
63 [anonymised] 2016-12-11 23:22 1.0.0 chyze szynszyle: jeszcze więcej słów w bag of words N/A 354.2042311088168
78 [anonymised] 2016-12-11 23:14 1.0.0 chyze szynszyle: ostatni krzyk szynszyli N/A 361.95007934207376
55 [anonymised] 2016-12-11 23:12 1.0.0 testoza N/A 351.29651780980663
141 [anonymised] 2016-12-11 23:11 1.0.0 chyze szynszyle: bez usuwania outliers N/A 3818.6669411345397
65 [anonymised] 2016-12-11 23:10 1.0.0 chyze szynszyle: nie pamiętam co zmieniłem N/A 355.1413019820511
165 [anonymised] 2016-12-11 23:09 1.0.0 testoza N/A N/A
59 [anonymised] 2016-12-11 23:06 1.0.0 chyze szynszyle: k-nn using bag of words, more words, more train data N/A 351.71261766533803
70 [anonymised] 2016-12-11 23:04 1.0.0 chyze szynszyle: k-nn using all features and bag of words, more train data N/A 359.46585695328304
164 [anonymised] 2016-12-11 23:02 1.0.0 Foki: prace nad wynikiem N/A N/A
101 [anonymised] 2016-12-11 22:59 1.0.0 chyze szynszyle: k-nn using bag of words, less words N/A 364.87618001757806
77 [anonymised] 2016-12-11 22:58 1.0.0 chyze szynszyle: k-nn using all features and bag of words N/A 361.95007934207376
68 [anonymised] 2016-12-11 22:56 1.0.0 chyze szynszyle: k-nn using bag of words, more words N/A 358.3581782433502
60 [anonymised] 2016-12-11 22:53 1.0.0 chyze szynszyle: k-nn using bag of words N/A 352.6069548339578
158 [anonymised] 2016-12-11 22:53 1.0.0 Foki: wyjście N/A N/A
133 [anonymised] 2016-12-11 22:52 1.0.0 chyze szynszyle: regresja liniowa usuniete dane odstajace N/A 584.4432387046777
72 [anonymised] 2016-12-11 22:50 1.0.0 chyze szynszyle: random forest N/A 360.7676814116051
163 [anonymised] 2016-12-11 22:31 1.0.0 Foki: liczba wyników N/A N/A
162 [anonymised] 2016-12-11 22:27 1.0.0 Foki: prace nad wynikiem N/A N/A
161 [anonymised] 2016-12-11 22:23 1.0.0 Foki: konwertowanie N/A N/A
160 [anonymised] 2016-12-11 22:07 1.0.0 Foki: testowanie zbioru N/A N/A
159 [anonymised] 2016-12-11 21:37 1.0.0 Foki: zapis do pliku N/A N/A
157 [anonymised] 2016-12-11 21:35 1.0.0 Foki: zapis do pliku N/A N/A
1 [anonymised] 2016-12-11 21:33 1.0.0 zpd: En final N/A 292.0697770526135
51 [anonymised] 2016-12-11 20:47 1.0.0 darkside: Lr + 1 additional feature N/A 349.8628821621377
50 [anonymised] 2016-12-11 20:39 1.0.0 LR with other feature N/A 349.8628821621377
82 [anonymised] 2016-12-11 20:32 1.0.0 chyże szynszyle: k-nn add new feature 1328.0201297150877 363.2784649327984
54 [anonymised] 2016-12-11 20:31 1.0.0 LR + 1 feature N/A 351.2965178098064
156 [anonymised] 2016-12-11 20:26 1.0.0 Foki: pierwsze przymiarki N/A N/A
128 [anonymised] 2016-12-11 19:54 1.0.0 chyże szynszyle: k-nn, add more train examples 1332.2639850037515 378.8273866325867
38 [anonymised] 2016-12-11 19:30 1.0.0 chyze szynszyle: regresja liniowa glm N/A 338.1134778534156
53 [anonymised] 2016-12-11 19:08 1.0.0 LR + 2 new features N/A 351.2542022156315
125 [anonymised] 2016-12-11 18:02 1.0.0 test 1327.5057051760969 374.0284911395515
37 [anonymised] 2016-12-11 17:54 1.0.0 chyze szynszyle: regresja liniowa N/A 338.1134778534156
155 [anonymised] 2016-12-11 17:47 1.0.0 chyze szynszyle: regresja liniowa N/A N/A
52 [anonymised] 2016-12-11 17:33 1.0.0 test 1327.5057051760969 350.7309289590218
46 [anonymised] 2016-12-11 17:18 1.0.0 mean 1327.5057051760969 347.75058462465176
81 [anonymised] 2016-12-11 16:43 1.0.0 test 1327.5057051760969 363.04982737268864
132 [anonymised] 2016-12-11 16:38 1.0.0 test 1327.5057051760969 420.4795898961904
134 [anonymised] 2016-12-11 16:37 1.0.0 test 1327.5057051760969 585.6428197176216
93 [anonymised] 2016-12-11 16:07 1.0.0 test 1329.505828593376 363.9546262608084
113 [anonymised] 2016-12-11 14:40 1.0.0 chyze_szynszyle: solution using svm 1332.2639850037515 366.6514188616088
109 [anonymised] 2016-12-11 14:34 1.0.0 chyze szynszyle: first solution using k-NN 1328.4599597449453 366.1710954626986
13 [anonymised] 2016-12-11 12:30 1.0.0 En3 + new feature N/A 295.6836138214144
97 [anonymised] 2016-12-11 02:20 1.0.0 linear model, fixed bug 1329.616224645464 364.39422791956554
49 [anonymised] 2016-12-10 19:56 1.0.0 LR no1 N/A 349.85164999195206
154 [anonymised] 2016-12-10 19:25 1.0.0 LR N/A N/A
102 [anonymised] 2016-12-10 16:31 1.0.0 words counting N/A 364.90878916892314
10 [anonymised] 2016-12-10 12:52 1.0.0 En3 N/A 294.7376696265167
9 [anonymised] 2016-12-10 12:31 1.0.0 En2 N/A 293.7742009899776
12 [anonymised] 2016-12-10 12:27 1.0.0 En1 N/A 295.31322637037925
153 [anonymised] 2016-12-10 12:21 1.0.0 En1 N/A N/A
40 [anonymised] 2016-12-10 12:00 1.0.0 LR + multi N/A 338.54886464751775
39 [anonymised] 2016-12-10 11:48 1.0.0 LR + 2 new features N/A 338.4197524203438
108 [anonymised] 2016-12-09 22:42 1.0.0 submission N/A 365.9453347939574
107 [anonymised] 2016-12-09 20:48 1.0.0 N/A 365.76184294457374
111 [anonymised] 2016-12-09 20:39 1.0.0 submission N/A 366.58788736362294
18 [anonymised] 2016-12-09 17:39 1.0.0 MLP + new feature N/A 318.98492916093164
17 [anonymised] 2016-12-09 17:31 1.0.0 MLP N/A 317.4839843506789
27 [anonymised] 2016-12-09 17:27 1.0.0 SVR N/A 331.5277362395748
35 [anonymised] 2016-12-09 17:15 1.0.0 LR + new feature (no noise) N/A 336.8815753908138
36 [anonymised] 2016-12-09 17:05 1.0.0 LR + new feature N/A 337.3735483512773
152 [anonymised] 2016-12-09 16:49 1.0.0 MLP N/A N/A
32 [anonymised] 2016-12-08 21:59 1.0.0 LR2 N/A 335.4422247101907
151 [anonymised] 2016-12-08 21:52 1.0.0 LR N/A N/A
150 [anonymised] 2016-12-08 21:26 1.0.0 test5 N/A N/A
149 [anonymised] 2016-12-08 16:48 1.0.0 test4 N/A N/A
148 [anonymised] 2016-12-08 16:06 1.0.0 test3 N/A N/A
147 [anonymised] 2016-12-08 15:55 1.0.0 test2 N/A N/A
146 [anonymised] 2016-12-08 15:40 1.0.0 test N/A N/A
145 [anonymised] 2016-12-08 12:04 1.0.0 LP N/A N/A
103 [anonymised] 2016-12-07 17:21 1.0.0 falstart N/A 365.16076725827764
144 [anonymised] 2016-12-07 17:10 1.0.0 first submission N/A N/A
3 [anonymised] 2016-12-07 16:25 1.0.0 code, no data N/A 292.22184602569035
119 [anonymised] 2016-12-06 05:26 1.0.0 experiment 1329.6603072386258 368.6754311609567
85 [anonymised] 2016-12-06 04:11 1.0.0 test 1329.6603072386258 363.59180218454554
87 [anonymised] 2016-12-06 01:52 1.0.0 test 1329.659719498618 363.59623819598266
120 [anonymised] 2016-12-06 00:52 1.0.0 random experiment 1327.7318270046173 369.173547818869
99 [anonymised] 2016-12-06 00:49 1.0.0 random experiment 1329.1373665362448 364.56332530555443
86 [anonymised] 2016-12-06 00:20 1.0.0 test 1329.6597218389416 363.59574257045915
84 [anonymised] 2016-12-05 23:50 1.0.0 test 1329.6600205143388 363.5073862555861
43 [anonymised] 2016-12-05 23:42 1.0.0 test 1332.3102422325658 339.82592555904745
44 [anonymised] 2016-12-05 22:43 1.0.0 test 3 0.0 340.7950072240639
42 [anonymised] 2016-12-05 22:39 1.0.0 test 2 0.0 339.82592555904745
41 [anonymised] 2016-12-05 22:37 1.0.0 test 1 N/A 339.82592555904745
80 [anonymised] 2016-12-05 22:26 1.0.0 trivial linear model N/A 362.4348555413414
123 [anonymised] 2016-12-05 22:18 1.0.0 first test, poor solution N/A 372.7609380275838
2 [anonymised] 2016-12-04 17:23 1.0.0 even closer to mean + 510 N/A 292.22184602569035
62 [anonymised] 2016-12-04 17:15 1.0.0 even closer to mean N/A 352.8661218224335
61 [anonymised] 2016-12-04 16:56 1.0.0 simple solution, closer to mean N/A 352.74576996991243
122 [anonymised] 2016-12-04 16:40 1.0.0 simple solution, rm only N/A 370.5591403336245
100 [anonymised] 2016-12-04 16:36 1.0.0 simple solution, mtr only N/A 364.5747308803998
95 [anonymised] 2016-12-04 16:31 1.0.0 simple solution, mtr+fltTp N/A 364.17863255958116
94 [anonymised] 2016-12-04 16:22 1.0.0 simple solution, warm start N/A 364.06053397788173
98 [anonymised] 2016-12-04 16:18 1.0.0 simple solution, learningRate = optimal N/A 364.48804081473656
126 [anonymised] 2016-12-04 16:13 1.0.0 simple solution, learningRate = constant N/A 374.50148708155734
104 [anonymised] 2016-12-04 16:10 1.0.0 simple solution, power_t = 0.7 N/A 365.3526163354973
83 [anonymised] 2016-12-04 16:03 1.0.0 simple solution, penalty = elastic net N/A 363.3668235609241
91 [anonymised] 2016-12-04 15:58 1.0.0 simple solution, penalty = l1 N/A 363.81478077037843
88 [anonymised] 2016-12-04 15:50 1.0.0 simple solution, penalty = l2 N/A 363.6112304616613
121 [anonymised] 2016-12-04 15:44 1.0.0 simple solution, loss method = eps_ins_sqr N/A 370.29496922318157
89 [anonymised] 2016-12-04 15:38 1.0.0 simple solution, loss method = eps_insens N/A 363.6631027727898
96 [anonymised] 2016-12-04 15:30 1.0.0 simple solution, loss method = huber N/A 364.17868976209684
118 [anonymised] 2016-12-04 15:23 1.0.0 simple solution, l1=0.8 N/A 368.15445413529716
116 [anonymised] 2016-12-04 14:38 1.0.0 simple solution, l1=0.4 N/A 368.0086017841721
124 [anonymised] 2016-12-04 14:09 1.0.0 simple solution, but more epochs N/A 373.5684309782851
117 [anonymised] 2016-12-04 13:38 1.0.0 simple solution N/A 368.1409953155817
5 [anonymised] 2016-12-04 13:28 1.0.0 Also mean, but better N/A 292.3098465176517
115 [anonymised] 2016-12-03 21:15 1.0.0 Klol N/A 367.77012621849826
114 [anonymised] 2016-12-03 21:15 1.0.0 Klol N/A 367.77012621849826
127 [anonymised] 2016-12-03 20:51 1.0.0 idk N/A 375.61632519866384
142 [anonymised] 2016-12-03 20:18 1.0.0 0.1kmr N/A 16807.21832927793
64 [anonymised] 2016-12-03 19:52 1.0.0 nowy regresor N/A 354.9719069452974
7 [anonymised] 2016-12-03 00:48 1.0.0 bigger lower N/A 292.96246525926296
25 [anonymised] 2016-12-03 00:45 1.0.0 stupid RMSE prediction * p N/A 329.5661679702471
130 [anonymised] 2016-12-03 00:42 1.0.0 stupid RMSE prediction * 1,36 N/A 397.80434024566523
45 [anonymised] 2016-12-03 00:36 1.0.0 stupid RMSE prediction * 1,36 N/A 341.5498570301739
20 [anonymised] 2016-12-03 00:35 1.0.0 stupid RMSE prediction N/A 321.05805283132815
21 [anonymised] 2016-12-03 00:33 1.0.0 stupid RMSE prediction N/A 322.3068282333366
143 [anonymised] 2016-12-03 00:32 1.0.0 stupid RMSE prediction N/A N/A
4 [anonymised] 2016-12-03 00:03 1.0.0 xxx N/A 292.30299093634284
11 [anonymised] 2016-12-03 00:02 1.0.0 xxx N/A 295.23101622964475
6 [anonymised] 2016-12-03 00:02 1.0.0 xxx N/A 292.4225707805701
8 [anonymised] 2016-12-02 23:59 1.0.0 xxx N/A 293.68486969043715
14 [anonymised] 2016-12-02 23:41 1.0.0 xxx N/A 298.09489572927316
15 [anonymised] 2016-12-02 23:40 1.0.0 xxx N/A 305.40191720846076
16 [anonymised] 2016-12-02 23:35 1.0.0 xxx N/A 315.40465311938704
19 [anonymised] 2016-12-02 23:31 1.0.0 xxx N/A 319.29612681795334
22 [anonymised] 2016-12-02 23:30 1.0.0 xxx N/A 323.450054865558
24 [anonymised] 2016-12-02 23:30 1.0.0 xxx N/A 327.8564615561483
29 [anonymised] 2016-12-02 23:26 1.0.0 means increased N/A 332.02980082598793
31 [anonymised] 2016-12-02 23:25 1.0.0 means increased N/A 333.46328891501844
26 [anonymised] 2016-12-02 23:24 1.0.0 means increased N/A 330.15120790315325
23 [anonymised] 2016-12-02 23:23 1.0.0 means increased N/A 327.8564615561483
30 [anonymised] 2016-12-02 23:22 1.0.0 means increased N/A 332.5053093947581
33 [anonymised] 2016-12-02 23:05 1.0.0 means unfiltered N/A 336.50967015783175
67 [anonymised] 2016-12-02 23:01 1.0.0 at_least_means N/A 356.876236925264
112 [anonymised] 2016-12-01 19:22 1.0.0 at_least_we_tried team first solution N/A 366.6414339725286
58 [anonymised] 2016-11-23 21:24 1.0.0 Instance Monad Siekiera - Vowpal Wabbit with options --passes 500 --ngram 2 -b 20 --l1 0.0000001 --l2 0.0000001 --nn 10 --cubic mld 1331.8335741552555 351.31511293654745
131 [anonymised] 2016-11-23 13:05 1.0.0 VW with NN, ngrams = 3, passes = 100 1339.3346346443586 406.5652039389791
129 [anonymised] 2016-11-23 11:12 1.0.0 pitu pitu 1342.5609010811486 383.29382114352774
34 [anonymised] 2016-11-16 09:36 1.0.0 admin: always mean price N/A 336.50967280038304