diff --git a/run2.py b/run2.py
index 3b48aeb..aa68789 100644
--- a/run2.py
+++ b/run2.py
@@ -10,8 +10,8 @@ df = pd.read_csv("train/in.tsv", names=in_columns, sep="\t")
 df_test = pd.read_csv("dev-0/in.tsv", names=in_columns, sep="\t")
 
 df = pd.concat([df, df_test])
-df = df.drop(["nazwa_stacji", "typ_zbioru"], axis=1)
-x = pd.get_dummies(df, columns=["id_stacji", "rok", "miesiąc"])
+df = df.drop(["nazwa_stacji"], axis=1)
+x = pd.get_dummies(df, columns=["id_stacji", "typ_zbioru", "rok", "miesiąc"])
 
 x = x.iloc[:-600]
 y = pd.read_csv("train/expected.tsv", sep="\t", names=["rainfall"])
@@ -24,7 +24,7 @@ df = poly.fit_transform(x)
 
 model = Sequential(
     [
-        Dense(512, activation="relu", input_dim=2702),
+        Dense(512, activation="relu", input_dim=2927),
         tensorflow.keras.layers.BatchNormalization(),
         Dense(512 // 2, activation="relu"),
         tensorflow.keras.layers.BatchNormalization(),
@@ -41,14 +41,14 @@ model = Sequential(
 model.compile(
     loss="mean_squared_error", optimizer="adam", metrics=["mean_squared_error"]
 )
-model.fit(df, y, epochs=60)
+model.fit(df, y, epochs=100)
 
 x_test = pd.read_csv("test-A/in.tsv", sep="\t", names=in_columns)
 df_train = pd.read_csv("train/in.tsv", names=in_columns, sep="\t")
 
 x_test = pd.concat([x_test, df_train])
-x_test = x_test.drop(["nazwa_stacji", "typ_zbioru"], axis=1)
-x_test = pd.get_dummies(x_test, columns=["id_stacji", "rok", "miesiąc"])
+x_test = x_test.drop(["nazwa_stacji"], axis=1)
+x_test = pd.get_dummies(x_test, columns=["id_stacji", "typ_zbioru", "rok", "miesiąc"])
 
 x_test = x_test.iloc[:-8760]
 poly = PolynomialFeatures(2, interaction_only=True)
diff --git a/test-A/out.tsv b/test-A/out.tsv
index b9a07bf..58c0346 100644
--- a/test-A/out.tsv
+++ b/test-A/out.tsv
@@ -1,720 +1,720 @@
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