diff --git a/Exploration_billet_AJ.ipynb b/Exploration_billet_AJ.ipynb
index f1c2e31..b92df45 100644
--- a/Exploration_billet_AJ.ipynb
+++ b/Exploration_billet_AJ.ipynb
@@ -524,6 +524,65 @@
     "export_in_temporary(target_agg, 'Target_kpi_concatenate')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb6f06e6-78de-4b8d-a103-8366eff0493a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5e864b1-adad-4267-b956-3f7ef371d677",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def display_covering_time(df, company, datecover):\n",
+    "    \"\"\"\n",
+    "    This function draws the time coverage of each company\n",
+    "    \"\"\"\n",
+    "    min_date = df['purchase_date'].min().strftime(\"%Y-%m-%d\")\n",
+    "    max_date = df['purchase_date'].max().strftime(\"%Y-%m-%d\")\n",
+    "    datecover[company] = [datetime.strptime(min_date, \"%Y-%m-%d\") + timedelta(days=x) for x in range((datetime.strptime(max_date, \"%Y-%m-%d\") - datetime.strptime(min_date, \"%Y-%m-%d\")).days)]\n",
+    "    print(f'Couverture Company {company} : {min_date} - {max_date}')\n",
+    "    return datecover\n",
+    "\n",
+    "\n",
+    "def compute_time_intersection(datecover):\n",
+    "    \"\"\"\n",
+    "    This function returns the time coverage for all companies\n",
+    "    \"\"\"\n",
+    "    timestamps_sets = [set(timestamps) for timestamps in datecover.values()]\n",
+    "    intersection = set.intersection(*timestamps_sets)\n",
+    "    intersection_list = list(intersection)\n",
+    "    formated_dates = [dt.strftime(\"%Y-%m-%d\") for dt in intersection_list]\n",
+    "    return sorted(formated_dates)\n",
+    "\n",
+    "\n",
+    "def df_coverage_modelization(sport, coverage_features = 0.7):\n",
+    "    \"\"\"\n",
+    "    This function returns start_date, end_of_features and final dates\n",
+    "    that help to construct train and test datasets\n",
+    "    \"\"\"\n",
+    "    datecover = {}\n",
+    "    for company in sport:\n",
+    "        df_products_purchased_reduced = display_input_databases(company, file_name = \"products_purchased_reduced\",\n",
+    "                                                          datetime_col = ['purchase_date'])\n",
+    "        datecover = display_covering_time(df_products_purchased_reduced, company, datecover)\n",
+    "    #print(datecover.keys())\n",
+    "    dt_coverage = compute_time_intersection(datecover)\n",
+    "    start_date = dt_coverage[0]\n",
+    "    end_of_features = dt_coverage[int(0.7 * len(dt_coverage))]\n",
+    "    final_date = dt_coverage[-1]\n",
+    "    return start_date, end_of_features, final_date\n",
+    " "
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "2435097a-95a5-43e1-84d0-7f6b701441ba",