diff --git a/sklearn-gridsearch/grid-search.ipynb b/sklearn-gridsearch/grid-search.ipynb
index 112b76d..64a083e 100644
--- a/sklearn-gridsearch/grid-search.ipynb
+++ b/sklearn-gridsearch/grid-search.ipynb
@@ -2,9 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "# Hyperparamerter optimisation with Dotscience\n",
     "\n",
@@ -17,6 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+       "import sklearn\n",
     "from sklearn import datasets\n",
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.model_selection import GridSearchCV\n",
@@ -28,9 +27,7 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "Let's load the digits dataset and apply a classifier on this data."
    ]
@@ -54,9 +51,7 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "Split the dataset in two equal parts\n"
    ]
@@ -73,9 +68,7 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "Set the parameters by cross-validation"
    ]
@@ -93,9 +86,7 @@
   },
   {
    "cell_type": "markdown",
-   "execution_count": null,
    "metadata": {},
-   "outputs": [],
    "source": [
     "Let's tune hyper-parameters for the scores. \n",
     "\n",
@@ -126,6 +117,7 @@
     "    print()\n",
     "    means = clf.cv_results_['mean_test_score']\n",
     "    stds = clf.cv_results_['std_test_score']\n",
+    "    ds.sklearn_model(sklearn, clf, \"grid search\", \"model_%s.joblib\" % score)\n",
     "    for mean, std, params in zip(means, stds, clf.cv_results_['params']):\n",
     "        ds.start()\n",
     "        ds.add_parameters(**params)\n",
@@ -143,7 +135,6 @@
     "    print()\n",
     "    y_true, y_pred = y_test, clf.predict(X_test)\n",
     "    print(classification_report(y_true, y_pred))\n",
-    "    print()\n",
     "\n",
     "# Note the problem is too easy: the hyperparameter plateau is too flat and the\n",
     "# output model is the same for precision and recall with ties in quality."
@@ -166,9 +157,10 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.2"
+   "version": "3.6.8"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
+ "nbformat_minor": 4
+}
+   
\ No newline at end of file