diff --git a/dev-0/out.tsv b/dev-0/out.tsv
new file mode 100644
index 0000000..765b967
--- /dev/null
+++ b/dev-0/out.tsv
@@ -0,0 +1,87 @@
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diff --git a/solution-dev0.py b/solution-dev0.py
new file mode 100644
index 0000000..2dedafe
--- /dev/null
+++ b/solution-dev0.py
@@ -0,0 +1,23 @@
+from sklearn.feature_extraction.text import TfidfVectorizer
+import numpy as np
+import sklearn.metrics
+from sklearn.cluster import KMeans
+
+
+def main():
+    with open("dev-0/in.tsv") as in_file:
+        documents = in_file.readlines()
+
+        vectorizer = TfidfVectorizer(ngram_range=(1, 3), use_idf=False)
+        vectorizer = TfidfVectorizer()
+
+        document_vectors = vectorizer.fit_transform(documents)
+        predictions = KMeans(n_clusters=45).fit_predict(document_vectors)        
+
+        with open("dev-0/out.tsv", "w") as out_file:
+            for prediction in predictions:
+                out_file.write(str(prediction) + '\n')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/solution-testA.py b/solution-testA.py
new file mode 100644
index 0000000..e1e321a
--- /dev/null
+++ b/solution-testA.py
@@ -0,0 +1,23 @@
+from sklearn.feature_extraction.text import TfidfVectorizer
+import numpy as np
+import sklearn.metrics
+from sklearn.cluster import KMeans
+
+
+def main():
+    with open("test-A/in.tsv") as in_file:
+        documents = in_file.readlines()
+
+        vectorizer = TfidfVectorizer(ngram_range=(1, 3), use_idf=False)
+        vectorizer = TfidfVectorizer()
+
+        document_vectors = vectorizer.fit_transform(documents)
+        predictions = KMeans(n_clusters=45).fit_predict(document_vectors)        
+
+        with open("test-A/out.tsv", "w") as out_file:
+            for prediction in predictions:
+                out_file.write(str(prediction) + '\n')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/test-A/out.tsv b/test-A/out.tsv
new file mode 100644
index 0000000..8834240
--- /dev/null
+++ b/test-A/out.tsv
@@ -0,0 +1,691 @@
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