diff --git a/JenkinsfileNeural b/JenkinsfileNeural
index b42fa9c..89729ab 100644
--- a/JenkinsfileNeural
+++ b/JenkinsfileNeural
@@ -12,7 +12,7 @@ node {
 
         }
         stage('Clone repo') {
-                    docker.image("karopa/ium:10").inside {
+                    docker.image("karopa/ium:11").inside {
                         stage('Test') {
                             checkout([$class: 'GitSCM', branches: [[name: '*/master']], doGenerateSubmoduleConfigurations: false, extensions: [], submoduleCfg: [], userRemoteConfigs: [[url: 'https://git.wmi.amu.edu.pl/s434765/ium_434765']]])
                             copyArtifacts fingerprintArtifacts: true, projectName: 's434765-create-dataset', selector: buildParameter("BUILD_SELECTOR")
diff --git a/data_train b/data_train
index 74dc6e3..7ee80d1 100644
--- a/data_train
+++ b/data_train
@@ -1086,4 +1086,3 @@
 "Q3pUXLQ5G5U","2017-12-30","2017-12-22","0","26","TiffyQuake",34032,1930,14,182,"False","False",5,TRUE,"DOOR TO SPACE - 29 Rooms Vlog","door to space|29 rooms|29|rooms|refinery 29|art|installation|installations|experience|fun|vlog|artist|creative|create|vlogging|cupquake|cupquake vlog|ihascupquake|tiffyquake|tiffyquake vlog","Mario and I were invited to a 29 rooms event and I wanted to bring you guys along! Here are some installations at the show! Which is your favorite?\n\nDOOR TO SPACE - 29 Rooms Vlog\n\nFollow me\n---------------------------------------------------------\nTwitter - https://twitter.com/iHasCupquake\nFacebook - https://www.facebook.com/pages/Tiffyquake/731699746860096\nInstagram - http://instagram.com/tiffyquake\n\n---------------------------------------------",8,8,91,317,20,1325186
 \n
 "yq4mgb1PDTI","2018-02-04","2013-10-25","2","2","Amarullah Rizky",10345,39,0,0,"False","False",2,TRUE,"1980 toyota corolla liftback commercial","Corolla Citytownvillage|Dx|Ke70|Racing|Wrc Rally Magazine Tv Program|Toyota Ae86 Automobile Generation|Slalom|Toyota Corolla Automobile Model|Rally|Retrocar|Rallying Sport|Oldskool|Gt|Car|Retroisme|Toyota|Corolla|Toyota Brand|Youtube Editor","",3,1563,32,70,19,86
-\nSemolina pasta is a southern Italy specialty. From that dough, Luca makes cavatelli, malloreddus, lorighittas, cencioni, capunti, strascinati, culurgionis, and sagne incannulate. From the egg dough, D'Onofrio makes fusilli al ferretto, tagliatelle, tortellini, farfalle, garganelli, anolini, cappelletti, tagliolini, agnolotti, sacchetti. From the spinach dough, Luca makes foglie d'ulivo, trofie, fagiolini, and pappardelle. From the cuttlefish squid ink pasta dough, D'Onofrio makes orecchiette, strichetti, fettuccine, and corzetti.\n\nStill haven’t subscribed to Bon Appetit on YouTube? ►► http://bit.ly/1TLeyPn
diff --git a/neural_network.py b/neural_network.py
index 07de086..6d535e4 100644
--- a/neural_network.py
+++ b/neural_network.py
@@ -9,9 +9,12 @@ def normalize_data(data):
     return (data - np.min(data)) / (np.max(data) - np.min(data))
 
 
-data = pd.read_csv("data_train", sep=',', error_bad_lines=False).dropna()
-X = data.loc[:,data.columns == "2805317"].astype(int)
-y = data.loc[:,data.columns == "198909"].astype(int)
+data = pd.read_csv("data_train", sep=',',  skip_blank_lines=True, nrows=1087, error_bad_lines=False,
+                   names=["video_id", "last_trending_date", "publish_date", "publish_hour", "category_id",
+                          "channel_title", "views", "likes", "dislikes", "comment_count"]).dropna()
+
+X = data.loc[:,data.columns == "views"].astype(int)
+y = data.loc[:,data.columns == "likes"].astype(int)
 
 min_val_sub = np.min(X)
 max_val_sub = np.max(X)
@@ -39,9 +42,13 @@ model.compile(loss='mean_absolute_error', optimizer="Adam", metrics=['mean_absol
 
 model.fit(X, y, epochs=30, validation_split = 0.3)
 
-data = pd.read_csv("data_dev", sep=',', error_bad_lines=False, skip_blank_lines=True, nrows=527).dropna()
-X_test = data.loc[:,data.columns == "440265"].astype(int)
-y_test = data.loc[:,data.columns == "21629"].astype(int)
+data = pd.read_csv("data_dev", sep=',', error_bad_lines=False,
+                   skip_blank_lines=True, nrows=527, names=["video_id", "last_trending_date",
+                                                                        "publish_date", "publish_hour", "category_id",
+                                                                        "channel_title", "views", "likes", "dislikes",
+                                                                        "comment_count"]).dropna()
+X_test = data.loc[:,data.columns == "views"].astype(int)
+y_test = data.loc[:,data.columns == "likes"].astype(int)
 
 min_val_sub = np.min(X_test)
 max_val_sub = np.max(X_test)
diff --git a/predictions.txt b/predictions.txt
index 24f80d9..65c9f39 100644
--- a/predictions.txt
+++ b/predictions.txt
@@ -1,406 +1,422 @@
-predicted: 54219.244834486395 expected: 99803
-predicted: 6282.930206091143 expected: 12436
-predicted: 21489.250423718244 expected: 26510
-predicted: 27520.643938072026 expected: 54016
-predicted: 27273.550573628396 expected: 8335
-predicted: 5580.153577243909 expected: 523
-predicted: 13775.527116564102 expected: 11434
-predicted: 16883.396133776754 expected: 6045
-predicted: 21223.035678537562 expected: 17659
-predicted: 6474.914746358991 expected: 6673
-predicted: 34440.94180992991 expected: 230
-predicted: 5783.893453215249 expected: 1395
-predicted: 18985.166732598096 expected: 33556
-predicted: 31993.954921934754 expected: 62338
-predicted: 5207.008885664865 expected: 6490
-predicted: 54967.91182909161 expected: 73435
-predicted: 4175.153009929694 expected: 213
-predicted: 5808.833725125529 expected: 8963
-predicted: 25490.768593208864 expected: 12414
-predicted: 3780.2682943840045 expected: 427
-predicted: 6437.755561393686 expected: 1351
-predicted: 6203.826276916079 expected: 1116
-predicted: 4130.192642907612 expected: 634
-predicted: 35916.17197597399 expected: 13688
-predicted: 4793.699340423103 expected: 4171
-predicted: 53281.93883023411 expected: 100843
-predicted: 3797.0608862375375 expected: 15
-predicted: 12057.913304445334 expected: 2231
-predicted: 4023.8988118525594 expected: 1345
-predicted: 4451.603429270908 expected: 381
-predicted: 4883.495885033626 expected: 3131
-predicted: 4909.072746292688 expected: 1192
-predicted: 7356.25966459699 expected: 8909
-predicted: 4418.829742931761 expected: 2883
-predicted: 4325.145405440591 expected: 2465
-predicted: 5167.413312573917 expected: 3970
-predicted: 12566.450074989349 expected: 6464
-predicted: 15591.273333522491 expected: 14139
-predicted: 9089.553577726707 expected: 8665
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-predicted: 13775.943293368444 expected: 17620
-predicted: 23857.567571550608 expected: 12060
-predicted: 8385.797653574497 expected: 10933
-predicted: 25516.02929281816 expected: 2435
-predicted: 4346.0243984675035 expected: 1633
-predicted: 18237.711295979097 expected: 32256
-predicted: 5012.853400335647 expected: 3331
-predicted: 15632.76777253393 expected: 6686
-predicted: 7518.009291833267 expected: 5293
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-predicted: 15904.430736608803 expected: 2622
-predicted: 7852.983270769939 expected: 5214
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