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.

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Mily   2016-12-12 20:09
submitted a solution:M^2 regresja liniowa
biesek   2016-12-12 11:14
submitted a solution:chyze szynszyle: mean estimator
biesek   2016-12-12 10:12
submitted a solution:chyze szynszyle: ostatni raz k-nn
biesek   2016-12-12 10:10
submitted a solution:chyze szynszyle: ostatni raz random forest
Durson   2016-12-12 00:26
submitted a solution:one more try
biesek   2016-12-12 00:09
submitted a solution:chyze szynszyle: linear model
tamazaki   2016-12-12 00:07
submitted a solution:Foki: wynik v2
Durson   2016-12-11 23:44
submitted a solution:one more try
Durson   2016-12-11 23:38
submitted a solution:one more try
Durson   2016-12-11 23:35
submitted a solution:one more try
biesek   2016-12-11 23:35
submitted a solution:chyze szynszyle: bez normalizacji na dzielnicach
biesek   2016-12-11 23:34
submitted a solution:chyze szynszyle: k-nn, dodanie nowych cech
[name not given]   2016-12-11 23:29
submitted a solution:Foki: wyniki
biesek   2016-12-11 23:28
submitted a solution:chyze szynszyle: k-nn bez normalizacji danych, hehe
biesek   2016-12-11 23:24
submitted a solution:chyze szynszyle: k-nn, wszystkie cechy
biesek   2016-12-11 23:22
submitted a solution:chyze szynszyle: jeszcze więcej słów w bag of words
biesek   2016-12-11 23:14
submitted a solution:chyze szynszyle: ostatni krzyk szynszyli
[name not given]   2016-12-11 23:12
submitted a solution:testoza
biesek   2016-12-11 23:11
submitted a solution:chyze szynszyle: bez usuwania outliers
biesek   2016-12-11 23:10
submitted a solution:chyze szynszyle: nie pamiętam co zmieniłem
[name not given]   2016-12-11 23:09
submitted a solution:testoza
biesek   2016-12-11 23:06
submitted a solution:chyze szynszyle: k-nn using bag of words, more words, more train data
biesek   2016-12-11 23:04
submitted a solution:chyze szynszyle: k-nn using all features and bag of words, more train data
tamazaki   2016-12-11 23:02
submitted a solution:Foki: prace nad wynikiem
biesek   2016-12-11 22:59
submitted a solution:chyze szynszyle: k-nn using bag of words, less words
biesek   2016-12-11 22:58
submitted a solution:chyze szynszyle: k-nn using all features and bag of words
biesek   2016-12-11 22:56
submitted a solution:chyze szynszyle: k-nn using bag of words, more words
biesek   2016-12-11 22:53
submitted a solution:chyze szynszyle: k-nn using bag of words
tamazaki   2016-12-11 22:53
submitted a solution:Foki: wyjście
[name not given]   2016-12-11 22:52
submitted a solution:chyze szynszyle: regresja liniowa usuniete dane odstajace
biesek   2016-12-11 22:50
submitted a solution:chyze szynszyle: random forest
tamazaki   2016-12-11 22:31
submitted a solution:Foki: liczba wyników
tamazaki   2016-12-11 22:27
submitted a solution:Foki: prace nad wynikiem
tamazaki   2016-12-11 22:23
submitted a solution:Foki: konwertowanie
tamazaki   2016-12-11 22:07
submitted a solution:Foki: testowanie zbioru
tamazaki   2016-12-11 21:37
submitted a solution:Foki: zapis do pliku
tamazaki   2016-12-11 21:35
submitted a solution:Foki: zapis do pliku
s384026   2016-12-11 21:33
submitted a solution:zpd: En final
Luke   2016-12-11 20:47
submitted a solution:darkside: Lr + 1 additional feature
Luke   2016-12-11 20:39
submitted a solution:LR with other feature
biesek   2016-12-11 20:32
submitted a solution:chyże szynszyle: k-nn add new feature
Luke   2016-12-11 20:31
submitted a solution:LR + 1 feature
tamazaki   2016-12-11 20:26
submitted a solution:Foki: pierwsze przymiarki
biesek   2016-12-11 19:54
submitted a solution:chyże szynszyle: k-nn, add more train examples
[name not given]   2016-12-11 19:30
submitted a solution:chyze szynszyle: regresja liniowa glm
Luke   2016-12-11 19:08
submitted a solution:LR + 2 new features
Durson   2016-12-11 18:02
submitted a solution:test
[name not given]   2016-12-11 17:54
submitted a solution:chyze szynszyle: regresja liniowa
[name not given]   2016-12-11 17:47
submitted a solution:chyze szynszyle: regresja liniowa
Durson   2016-12-11 17:33
submitted a solution:test
Durson   2016-12-11 17:18
submitted a solution:mean
Durson   2016-12-11 16:43
submitted a solution:test
Durson   2016-12-11 16:38
submitted a solution:test
Durson   2016-12-11 16:37
submitted a solution:test
Durson   2016-12-11 16:07
submitted a solution:test
biesek   2016-12-11 14:40
submitted a solution:chyze_szynszyle: solution using svm
biesek   2016-12-11 14:34
submitted a solution:chyze szynszyle: first solution using k-NN
s384026   2016-12-11 12:30
submitted a solution:En3 + new feature
Durson   2016-12-11 02:20
submitted a solution:linear model, fixed bug
Luke   2016-12-10 19:56
submitted a solution:LR no1
Luke   2016-12-10 19:25
submitted a solution:LR
Przemysław Nowaczyk   2016-12-10 16:31
submitted a solution:words counting
s384026   2016-12-10 12:52
submitted a solution:En3
s384026   2016-12-10 12:31
submitted a solution:En2
s384026   2016-12-10 12:27
submitted a solution:En1
s384026   2016-12-10 12:21
submitted a solution:En1
s384026   2016-12-10 12:00
submitted a solution:LR + multi
s384026   2016-12-10 11:48
submitted a solution:LR + 2 new features
[name not given]   2016-12-09 22:42
submitted a solution:submission
[name not given]   2016-12-09 20:48
submitted a solution:
[name not given]   2016-12-09 20:39
submitted a solution:submission
s384026   2016-12-09 17:39
submitted a solution:MLP + new feature
s384026   2016-12-09 17:31
submitted a solution:MLP
s384026   2016-12-09 17:27
submitted a solution:SVR
s384026   2016-12-09 17:15
submitted a solution:LR + new feature (no noise)
s384026   2016-12-09 17:05
submitted a solution:LR + new feature
s384026   2016-12-09 16:49
submitted a solution:MLP
s384026   2016-12-08 21:59
submitted a solution:LR2
s384026   2016-12-08 21:52
submitted a solution:LR
s384026   2016-12-08 21:26
submitted a solution:test5
s384026   2016-12-08 16:48
submitted a solution:test4
s384026   2016-12-08 16:06
submitted a solution:test3
s384026   2016-12-08 15:55
submitted a solution:test2
s384026   2016-12-08 15:40
submitted a solution:test
s384026   2016-12-08 12:04
submitted a solution:LP
[name not given]   2016-12-07 17:21
submitted a solution:falstart
[name not given]   2016-12-07 17:10
submitted a solution:first submission
Przemysław Nowaczyk   2016-12-07 16:25
submitted a solution:code, no data
Durson   2016-12-06 05:26
submitted a solution:experiment
Durson   2016-12-06 04:11
submitted a solution:test
Durson   2016-12-06 01:52
submitted a solution:test
Durson   2016-12-06 00:52
submitted a solution:random experiment
Durson   2016-12-06 00:49
submitted a solution:random experiment
Durson   2016-12-06 00:20
submitted a solution:test
Durson   2016-12-05 23:50
submitted a solution:test
Durson   2016-12-05 23:42
submitted a solution:test
Durson   2016-12-05 22:43
submitted a solution:test 3
Durson   2016-12-05 22:39
submitted a solution:test 2
Durson   2016-12-05 22:37
submitted a solution:test 1
Durson   2016-12-05 22:26
submitted a solution:trivial linear model
Durson   2016-12-05 22:18
submitted a solution:first test, poor solution
Przemysław Nowaczyk   2016-12-04 17:23
submitted a solution:even closer to mean + 510
Przemysław Nowaczyk   2016-12-04 17:15
submitted a solution:even closer to mean
Przemysław Nowaczyk   2016-12-04 16:56
submitted a solution:simple solution, closer to mean
Przemysław Nowaczyk   2016-12-04 16:40
submitted a solution:simple solution, rm only
Przemysław Nowaczyk   2016-12-04 16:36
submitted a solution:simple solution, mtr only
Przemysław Nowaczyk   2016-12-04 16:31
submitted a solution:simple solution, mtr+fltTp
Przemysław Nowaczyk   2016-12-04 16:22
submitted a solution:simple solution, warm start
Przemysław Nowaczyk   2016-12-04 16:18
submitted a solution:simple solution, learningRate = optimal
Przemysław Nowaczyk   2016-12-04 16:13
submitted a solution:simple solution, learningRate = constant
Przemysław Nowaczyk   2016-12-04 16:10
submitted a solution:simple solution, power_t = 0.7
Przemysław Nowaczyk   2016-12-04 16:03
submitted a solution:simple solution, penalty = elastic net
Przemysław Nowaczyk   2016-12-04 15:58
submitted a solution:simple solution, penalty = l1
Przemysław Nowaczyk   2016-12-04 15:50
submitted a solution:simple solution, penalty = l2
Przemysław Nowaczyk   2016-12-04 15:44
submitted a solution:simple solution, loss method = eps_ins_sqr
Przemysław Nowaczyk   2016-12-04 15:38
submitted a solution:simple solution, loss method = eps_insens
Przemysław Nowaczyk   2016-12-04 15:30
submitted a solution:simple solution, loss method = huber
Przemysław Nowaczyk   2016-12-04 15:23
submitted a solution:simple solution, l1=0.8
Przemysław Nowaczyk   2016-12-04 14:38
submitted a solution:simple solution, l1=0.4
Przemysław Nowaczyk   2016-12-04 14:09
submitted a solution:simple solution, but more epochs
Przemysław Nowaczyk   2016-12-04 13:38
submitted a solution:simple solution
Przemysław Nowaczyk   2016-12-04 13:28
submitted a solution:Also mean, but better
Przemysław Nowaczyk   2016-12-03 21:15
submitted a solution:Klol
Przemysław Nowaczyk   2016-12-03 21:15
submitted a solution:Klol
Przemysław Nowaczyk   2016-12-03 20:51
submitted a solution:idk
Przemysław Nowaczyk   2016-12-03 20:18
submitted a solution:0.1kmr
Przemysław Nowaczyk   2016-12-03 19:52
submitted a solution:nowy regresor
Przemysław Nowaczyk   2016-12-03 00:48
submitted a solution:bigger lower
Przemysław Nowaczyk   2016-12-03 00:45
submitted a solution:stupid RMSE prediction * p
Przemysław Nowaczyk   2016-12-03 00:42
submitted a solution:stupid RMSE prediction * 1,36
Przemysław Nowaczyk   2016-12-03 00:36
submitted a solution:stupid RMSE prediction * 1,36
Przemysław Nowaczyk   2016-12-03 00:35
submitted a solution:stupid RMSE prediction
Przemysław Nowaczyk   2016-12-03 00:33
submitted a solution:stupid RMSE prediction
Przemysław Nowaczyk   2016-12-03 00:32
submitted a solution:stupid RMSE prediction
Przemysław Nowaczyk   2016-12-03 00:03
submitted a solution:xxx
Przemysław Nowaczyk   2016-12-03 00:02
submitted a solution:xxx
Przemysław Nowaczyk   2016-12-03 00:02
submitted a solution:xxx
Przemysław Nowaczyk   2016-12-02 23:59
submitted a solution:xxx
Przemysław Nowaczyk   2016-12-02 23:41
submitted a solution:xxx
Przemysław Nowaczyk   2016-12-02 23:40
submitted a solution:xxx
Przemysław Nowaczyk   2016-12-02 23:35
submitted a solution:xxx
Przemysław Nowaczyk   2016-12-02 23:31
submitted a solution:xxx
Przemysław Nowaczyk   2016-12-02 23:30
submitted a solution:xxx
Przemysław Nowaczyk   2016-12-02 23:30
submitted a solution:xxx
Przemysław Nowaczyk   2016-12-02 23:26
submitted a solution:means increased
Przemysław Nowaczyk   2016-12-02 23:25
submitted a solution:means increased
Przemysław Nowaczyk   2016-12-02 23:24
submitted a solution:means increased
Przemysław Nowaczyk   2016-12-02 23:23
submitted a solution:means increased
Przemysław Nowaczyk   2016-12-02 23:22
submitted a solution:means increased
Przemysław Nowaczyk   2016-12-02 23:05
submitted a solution:means unfiltered
Przemysław Nowaczyk   2016-12-02 23:01
submitted a solution:at_least_means
Przemysław Nowaczyk   2016-12-01 19:22
submitted a solution:at_least_we_tried team first solution
Maxi   2016-11-23 21:24
submitted a solution:Instance Monad Siekiera - Vowpal Wabbit with options --passes 500 --ngram 2 -b 20 --l1 0.0000001 --l2 0.0000001 --nn 10 --cubic mld
Maxi   2016-11-23 13:05
submitted a solution:VW with NN, ngrams = 3, passes = 100
Maxi   2016-11-23 11:12
submitted a solution:pitu pitu
romang   2016-11-16 09:36
submitted a solution:admin: always mean price