diff --git a/Gradient Boosting.ipynb b/Gradient Boosting.ipynb
index 5a2f97b..b5e83a2 100644
--- a/Gradient Boosting.ipynb	
+++ b/Gradient Boosting.ipynb	
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -239,7 +239,7 @@
        "[5 rows x 25 columns]"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -251,7 +251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -628,7 +628,7 @@
        "default.payment.next.month      0.00       0.0       0.00        1.0  "
       ]
      },
-     "execution_count": 20,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -731,6 +731,68 @@
     "pyplot.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train/Valid/Test sizes: 16800 4200 9000\n"
+     ]
+    }
+   ],
+   "source": [
+    "#model training and testing \n",
+    "test_data.rename(columns={'default.payment.next.month':'default'}, inplace=True)\n",
+    "\n",
+    "data_x = test_data.drop(['default'], axis = 1)\n",
+    "data_y = test_data.default\n",
+    "\n",
+    "X_temp, X_test, y_temp, y_test = \\\n",
+    "    train_test_split(data_x, data_y, test_size=0.3, random_state=123, stratify=data_y)\n",
+    "\n",
+    "X_train, X_valid, y_train, y_valid = \\\n",
+    "    train_test_split(X_temp, y_temp, test_size=0.2, random_state=123, stratify=y_temp)\n",
+    "\n",
+    "print('Train/Valid/Test sizes:', y_train.shape[0], y_valid.shape[0], y_test.shape[0])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Accuracy: 0.92\n",
+      "Validation Accuracy: 0.81\n",
+      "Test Accuracy: 0.82\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "\n",
+    "\n",
+    "boost = GradientBoostingClassifier(\n",
+    "    learning_rate=0.1,\n",
+    "    n_estimators=100,\n",
+    "    max_depth=8,\n",
+    "    random_state=1)\n",
+    "\n",
+    "boost.fit(X_train, y_train)\n",
+    "    \n",
+    "    \n",
+    "print(\"Training Accuracy: %0.2f\" % boost.score(X_train, y_train))\n",
+    "print(\"Validation Accuracy: %0.2f\" % boost.score(X_valid, y_valid))\n",
+    "print(\"Test Accuracy: %0.2f\" % boost.score(X_test, y_test))"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,