Add my solution

solution.py

@@ -0,0 +1,35 @@
+import numpy as np
+from sklearn import preprocessing
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.pipeline import make_pipeline
+from sklearn.feature_extraction.text import TfidfVectorizer
+import lzma
+
+le=preprocessing.LabelEncoder()
+
+def open_file_xz(file: str):
+    with lzma.open(file, mode='rt') as f:
+        return f.readlines()
+
+def open_file(file: str):
+    with open(file) as f:
+        return f.readlines()
+
+def train_model(train_in, train_exp):
+    train_exp = le.fit_transform(train_exp)
+    pipeline = make_pipeline(TfidfVectorizer(),MultinomialNB())
+    model=pipeline.fit(train_in, train_exp)
+    return model
+
+def make_prediction(catalog: str):
+    train_in = open_file_xz("train/in.tsv.xz")
+    train_exp = open_file("train/expected.tsv")
+    to_be_predict = open_file_xz(catalog + "/in.tsv.xz")
+
+    model = train_model(train_in,train_exp)
+    
+    prediction=model.predict(to_be_predict)
+    np.savetxt(catalog + "/out.tsv", prediction, fmt='%d', delimiter='\n')
+
+make_prediction("dev-0")
+make_prediction("test-A")