diff --git a/.github/workflows/init-python.yml b/.github/workflows/init-python.yml
new file mode 100644
index 0000000..0995f0f
--- /dev/null
+++ b/.github/workflows/init-python.yml
@@ -0,0 +1,21 @@
+name: Black Python
+
+on: [push,pull_request]
+
+jobs:
+  format:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v2
+
+      - name: Set up Python
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.8
+
+      - name: Install dependencies
+        run: pip install black
+
+      - name: Run Black
+        run: black .
diff --git a/model.ipynb b/model.ipynb
index b6aeb96..0c09680 100644
--- a/model.ipynb
+++ b/model.ipynb
@@ -411,6 +411,57 @@
     "#pd.DataFrame(data={'Actual':test_y,'Predicted':y_pred}).head()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Decision Tree classifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "clf = DecisionTreeClassifier(max_depth=3, random_state=42)\n",
+    "clf.fit(train_X,train_y)\n",
+    "y_pred=clf.predict(test_X)\n",
+    "#find accuracy\n",
+    "ac=accuracy_score(test_y,y_pred)\n",
+    "acc.append(ac)\n",
+    "\n",
+    "#find the ROC_AOC curve\n",
+    "rc=roc_auc_score(test_y,y_pred)\n",
+    "roc.append(rc)\n",
+    "print(\"\\nAccuracy {0} ROC {1}\".format(ac,rc))\n",
+    "\n",
+    "#cross val score\n",
+    "result=cross_validate(clf,train_X,train_y,scoring=scoring,cv=10)\n",
+    "display_result(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#XGBoost Classifier\n",
+    "from xgboost import XGBClassifier\n",
+    "\n",
+    "clf = XGBClassifier(max_depth=3, n_estimators=100, random_state=42)\n",
+    "clf.fit(train_X,train_y)\n",
+    "y_pred=clf.predict(test_X)\n",
+    "#find accuracy\n",
+    "ac=accuracy_score(test_y,y_pred)\n",
+    "acc.append(ac)\n",
+    "\n",
+    "#find the ROC_AOC curve\n",
+    "rc=roc_auc_score(test_y,y_pred)\n",
+    "roc.append(rc)\n",
+    "print(\"\\nAccuracy {0} ROC {1}\".format(ac,rc))\n",
+    "\n",
+    "#cross val score\n",
+    "result=cross_validate(clf,train_X,train_y,scoring=scoring,cv=10)\n",
+    "display_result(result)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,