diff --git a/DockerContainers/classifications/classify_terminations.py b/DockerContainers/classifications/classify_terminations.py
new file mode 100644
index 0000000..d2a7702
--- /dev/null
+++ b/DockerContainers/classifications/classify_terminations.py
@@ -0,0 +1,48 @@
+import psycopg2 as psyco
+import pandas as pd
+import nltk
+from nltk.corpus import stopwords
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.cluster import KMeans
+import re
+
+
+def preporcess_text(text):
+    
+    text = text.lower()
+    text = re.sub("[^A-Za-z]+", " ", text)
+    #make tokens
+    tokens = nltk.word_tokenize(text)
+
+    #remove stopwords
+    tokens = [ w for w in tokens if not w in stopwords.words("english")]
+
+    #rejoin
+    return " ".join(tokens).strip()
+
+if __name__ == "__main__":
+    conn = psyco.connect(dbname="aact_db", user="analysis", host="localhost", password="test")
+
+    curse = conn.cursor()
+
+    curse.execute("SELECT why_stopped FROM ctgov.studies WHERE why_stopped IS NOT NULL LIMIT 2000;")
+    results = curse.fetchall()
+
+    curse.close()
+    conn.close()
+
+    data = pd.DataFrame(results, columns = ["corpus"])
+    data["cleaned"] = data.corpus.apply(preporcess_text)
+
+    vectorizer = TfidfVectorizer(sublinear_tf=True)
+
+    X = vectorizer.fit_transform(data.cleaned)
+
+    kmeans = KMeans(n_clusters=3, random_state=11021585)
+    kmeans.fit(X)
+
+    data["cluster"] = kmeans.labels_
+
+    print(data.groupby(["cluster"]).count())
+
+    
\ No newline at end of file
diff --git a/DockerContainers/downloader/db_connection.py b/DockerContainers/downloader/db_connection.py
index e5df6fc..a1da827 100644
--- a/DockerContainers/downloader/db_connection.py
+++ b/DockerContainers/downloader/db_connection.py
@@ -1,10 +1,10 @@
 import psycopg2 as psyco
 
-conn = psyco.connect(dbname="aact_db", user="admin", host="localhost", password="root")
+conn = psyco.connect(dbname="aact_db", user="root", host="localhost", password="root")
 
 curse = conn.cursor()
 
-curse.execute("SELECT * FROM testing")
+curse.execute("SELECT * FROM ctgov.studies LIMIT 2;")
 print(curse.fetchall())
 
 curse.close()