diff --git a/cheese.ipynb b/cheese.ipynb
index df8e66c..fe0d268 100644
--- a/cheese.ipynb
+++ b/cheese.ipynb
@@ -335,6 +335,17 @@
     "    return df.copy()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fda6aaad-7b1e-4daa-8d28-cd049df9cec2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_features=filter_df(data)\n",
+    "data_features"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "d1eb67d7-d16b-4b93-8486-582830ac3903",
@@ -407,9 +418,11 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "48cbb634-6754-4956-a945-539d329812ef",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24e7ff6e-c308-4cc8-aeac-eeb372f4c479",
    "metadata": {},
+   "outputs": [],
    "source": [
     "In this part, we achieved to do two things for the classification: create a decision tree on the database and, given a cheese and its characteristics, find where it originates from. \n",
     "\n"
@@ -507,8 +520,27 @@
    "metadata": {},
    "source": [
     "Not good, even quite bad. \n",
-    "We cannot find the region a cheese originates from given its characteristic. \n",
-    "\n",
+    "In short, it seems that we cannot find the region a cheese originates from given its characteristic. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7fd507d0-1a68-4cd7-a12e-12c9ab1061e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.predict(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9faf2aee-84f5-4633-b3de-039af42d31d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "yprime=pd.DataFrame(model.predict(X),columns=[\"latitude\",\"longitude\"])\n",
     "\n"
    ]
   },
@@ -578,9 +610,11 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "84bf779a-36d0-4aa2-b3a2-0da9bb25fc01",
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78ef08e7-1436-440f-b035-8b480af1cc7b",
    "metadata": {},
+   "outputs": [],
    "source": [
     "For Pattern Mining, we only kept relevant columns (binary attributes) thus dropping RGB colors and any location based information, keeping only information relevant to the final cheese itself.\n",
     "\n",
@@ -588,24 +622,6 @@
     "\n",
     "Est-ce que les fromages artisanaux ont souvent plus de \"goûts\" que les autres ?\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ce4db7c9-8049-4838-af30-b9fe2bca2925",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "len(data_features[data_features[\"pecorino\"] == True]), len(data_features[data_features[\"pecorino\"] == False])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7d9f17c2-6c42-4f24-b0d0-e8640a661801",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {