diff --git a/Sport/Modelization/3_logit_cross_val_sport.ipynb b/Sport/Modelization/3_logit_cross_val_sport.ipynb
index cf835b7..ef23062 100644
--- a/Sport/Modelization/3_logit_cross_val_sport.ipynb
+++ b/Sport/Modelization/3_logit_cross_val_sport.ipynb
@@ -221,7 +221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 96,
    "id": "639b432a-c39c-4bf8-8ee2-e136d156e0dd",
    "metadata": {},
    "outputs": [
@@ -231,7 +231,7 @@
        "{0.0: 0.5837086520288036, 1.0: 3.486549107420539}"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -247,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 97,
    "id": "34644a00-85a5-41c9-98df-41178cb3ac69",
    "metadata": {},
    "outputs": [
@@ -537,7 +537,7 @@
        "[224213 rows x 14 columns]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -548,7 +548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 98,
    "id": "295676df-36ac-43d8-8b31-49ff08efd6e7",
    "metadata": {},
    "outputs": [],
@@ -586,7 +586,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 99,
    "id": "f46fb56e-c908-40b4-868f-9684d1ae01c2",
    "metadata": {},
    "outputs": [
@@ -606,7 +606,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -617,7 +617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 100,
    "id": "e729781b-4d65-42c5-bdc5-82b4d653aaf0",
    "metadata": {},
    "outputs": [],
@@ -630,7 +630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 101,
    "id": "a7ebbe6f-70ba-4276-be18-f10e7bfd7423",
    "metadata": {},
    "outputs": [],
@@ -666,7 +666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 102,
    "id": "2334eb51-e6ea-4fd0-89ce-f54cd474d332",
    "metadata": {},
    "outputs": [],
@@ -701,7 +701,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 103,
    "id": "83917b97-4d9b-4e3c-ba27-1e546ce885d3",
    "metadata": {},
    "outputs": [],
@@ -738,14 +738,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 104,
    "id": "ba4cde9f-a614-4a43-81b9-e16e78aa6c4c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-11 {\n",
+       "<style>#sk-container-id-5 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -775,15 +775,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 {\n",
+       "#sk-container-id-5 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 pre {\n",
+       "#sk-container-id-5 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 input.sk-hidden--visually {\n",
+       "#sk-container-id-5 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -795,7 +795,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -803,7 +803,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-container {\n",
+       "#sk-container-id-5 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -813,7 +813,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -829,14 +829,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel-item::after {\n",
+       "#sk-container-id-5 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel {\n",
+       "#sk-container-id-5 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -844,28 +844,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel-item {\n",
+       "#sk-container-id-5 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-11 div.sk-serial {\n",
+       "#sk-container-id-5 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -883,14 +883,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-11 div.sk-toggleable {\n",
+       "#sk-container-id-5 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-11 label.sk-toggleable__label {\n",
+       "#sk-container-id-5 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -900,7 +900,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -908,13 +908,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-11 div.sk-toggleable__content {\n",
+       "#sk-container-id-5 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -923,12 +923,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -936,79 +936,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-11 div.sk-label label {\n",
+       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-5 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-11 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-11 div.sk-label label {\n",
+       "#sk-container-id-5 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-label-container {\n",
+       "#sk-container-id-5 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-11 div.sk-estimator {\n",
+       "#sk-container-id-5 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -1018,18 +1018,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-estimator.fitted {\n",
+       "#sk-container-id-5 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-11 div.sk-estimator:hover {\n",
+       "#sk-container-id-5 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -1116,7 +1116,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-11 a.estimator_doc_link {\n",
+       "#sk-container-id-5 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -1131,25 +1131,25 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-11 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-11 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
+       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
        "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
        "                                                  Pipeline(steps=[(&#x27;scaler&#x27;,\n",
        "                                                                   StandardScaler())]),\n",
@@ -1171,7 +1171,7 @@
        "                (&#x27;logreg&#x27;,\n",
        "                 LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
        "                                                  1.0: 3.486549107420539},\n",
-       "                                    max_iter=5000, solver=&#x27;saga&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-68\" type=\"checkbox\" ><label for=\"sk-estimator-id-68\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
+       "                                    max_iter=5000, solver=&#x27;saga&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
        "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
        "                                                  Pipeline(steps=[(&#x27;scaler&#x27;,\n",
        "                                                                   StandardScaler())]),\n",
@@ -1193,7 +1193,7 @@
        "                (&#x27;logreg&#x27;,\n",
        "                 LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
        "                                                  1.0: 3.486549107420539},\n",
-       "                                    max_iter=5000, solver=&#x27;saga&#x27;))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-69\" type=\"checkbox\" ><label for=\"sk-estimator-id-69\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
+       "                                    max_iter=5000, solver=&#x27;saga&#x27;))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
        "                                 Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler())]),\n",
        "                                 [&#x27;nb_tickets&#x27;, &#x27;nb_purchases&#x27;, &#x27;total_amount&#x27;,\n",
        "                                  &#x27;nb_suppliers&#x27;, &#x27;vente_internet_max&#x27;,\n",
@@ -1204,7 +1204,7 @@
        "                                 Pipeline(steps=[(&#x27;onehot&#x27;,\n",
        "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
        "                                                                sparse_output=False))]),\n",
-       "                                 [&#x27;opt_in&#x27;, &#x27;is_email_true&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-70\" type=\"checkbox\" ><label for=\"sk-estimator-id-70\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">num</label><div class=\"sk-toggleable__content \"><pre>[&#x27;nb_tickets&#x27;, &#x27;nb_purchases&#x27;, &#x27;total_amount&#x27;, &#x27;nb_suppliers&#x27;, &#x27;vente_internet_max&#x27;, &#x27;purchase_date_min&#x27;, &#x27;purchase_date_max&#x27;, &#x27;nb_tickets_internet&#x27;, &#x27;nb_campaigns&#x27;, &#x27;nb_campaigns_opened&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-71\" type=\"checkbox\" ><label for=\"sk-estimator-id-71\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content \"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-72\" type=\"checkbox\" ><label for=\"sk-estimator-id-72\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">cat</label><div class=\"sk-toggleable__content \"><pre>[&#x27;opt_in&#x27;, &#x27;is_email_true&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-73\" type=\"checkbox\" ><label for=\"sk-estimator-id-73\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-74\" type=\"checkbox\" ><label for=\"sk-estimator-id-74\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content \"><pre>LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
+       "                                 [&#x27;opt_in&#x27;, &#x27;is_email_true&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">num</label><div class=\"sk-toggleable__content \"><pre>[&#x27;nb_tickets&#x27;, &#x27;nb_purchases&#x27;, &#x27;total_amount&#x27;, &#x27;nb_suppliers&#x27;, &#x27;vente_internet_max&#x27;, &#x27;purchase_date_min&#x27;, &#x27;purchase_date_max&#x27;, &#x27;nb_tickets_internet&#x27;, &#x27;nb_campaigns&#x27;, &#x27;nb_campaigns_opened&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content \"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">cat</label><div class=\"sk-toggleable__content \"><pre>[&#x27;opt_in&#x27;, &#x27;is_email_true&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content \"><pre>LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
        "                                 1.0: 3.486549107420539},\n",
        "                   max_iter=5000, solver=&#x27;saga&#x27;)</pre></div> </div></div></div></div></div></div>"
       ],
@@ -1234,7 +1234,7 @@
        "                                    max_iter=5000, solver='saga'))])"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1252,7 +1252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 105,
    "id": "1e4c1be5-176d-4222-9b3c-fe27225afe36",
    "metadata": {},
    "outputs": [
@@ -1397,24 +1397,24 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>83146</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>37.474040</td>\n",
-       "      <td>37.474040</td>\n",
-       "      <td>1.0</td>\n",
+       "      <th>140473</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>550.000000</td>\n",
+       "      <td>550.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
        "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0</td>\n",
        "      <td>1</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>3.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>11.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>223586</th>\n",
+       "      <th>153768</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1425,65 +1425,65 @@
        "      <td>0.0</td>\n",
        "      <td>True</td>\n",
        "      <td>True</td>\n",
-       "      <td>0</td>\n",
        "      <td>1</td>\n",
-       "      <td>23.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>56489</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>550.000000</td>\n",
-       "      <td>550.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>141236</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>550.000000</td>\n",
-       "      <td>550.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
+       "      <th>110886</th>\n",
+       "      <td>12.0</td>\n",
        "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>430.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>490.688726</td>\n",
+       "      <td>153.686330</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>12.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>6999</th>\n",
+       "      <th>115390</th>\n",
        "      <td>2.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>20.0</td>\n",
+       "      <td>79.9</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>171.446921</td>\n",
-       "      <td>171.446921</td>\n",
+       "      <td>57.498524</td>\n",
+       "      <td>57.498524</td>\n",
        "      <td>0.0</td>\n",
        "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>1</td>\n",
+       "      <td>False</td>\n",
        "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24919</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>149.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>457.437319</td>\n",
+       "      <td>457.437169</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>10000 rows × 14 columns</p>\n",
+       "<p>1000 rows × 14 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -1494,11 +1494,11 @@
        "66956          7.0           2.0         254.0           1.0   \n",
        "116487         0.0           0.0           0.0           0.0   \n",
        "...            ...           ...           ...           ...   \n",
-       "83146          1.0           1.0          35.0           1.0   \n",
-       "223586         0.0           0.0           0.0           0.0   \n",
-       "56489          0.0           0.0           0.0           0.0   \n",
-       "141236         0.0           0.0           0.0           0.0   \n",
-       "6999           2.0           1.0          20.0           1.0   \n",
+       "140473         0.0           0.0           0.0           0.0   \n",
+       "153768         0.0           0.0           0.0           0.0   \n",
+       "110886        12.0           6.0         430.0           1.0   \n",
+       "115390         2.0           1.0          79.9           1.0   \n",
+       "24919          3.0           3.0         149.0           1.0   \n",
        "\n",
        "        vente_internet_max  purchase_date_min  purchase_date_max  \\\n",
        "43000                  0.0         550.000000         550.000000   \n",
@@ -1507,11 +1507,11 @@
        "66956                  1.0         378.343062         370.453947   \n",
        "116487                 0.0         550.000000         550.000000   \n",
        "...                    ...                ...                ...   \n",
-       "83146                  1.0          37.474040          37.474040   \n",
-       "223586                 0.0         550.000000         550.000000   \n",
-       "56489                  0.0         550.000000         550.000000   \n",
-       "141236                 0.0         550.000000         550.000000   \n",
-       "6999                   0.0         171.446921         171.446921   \n",
+       "140473                 0.0         550.000000         550.000000   \n",
+       "153768                 0.0         550.000000         550.000000   \n",
+       "110886                 1.0         490.688726         153.686330   \n",
+       "115390                 0.0          57.498524          57.498524   \n",
+       "24919                  0.0         457.437319         457.437169   \n",
        "\n",
        "        nb_tickets_internet  is_email_true  opt_in  gender_female  \\\n",
        "43000                   0.0           True    True              0   \n",
@@ -1520,11 +1520,11 @@
        "66956                   7.0           True   False              0   \n",
        "116487                  0.0           True   False              1   \n",
        "...                     ...            ...     ...            ...   \n",
-       "83146                   1.0           True   False              0   \n",
-       "223586                  0.0           True    True              0   \n",
-       "56489                   0.0           True    True              0   \n",
-       "141236                  0.0           True   False              0   \n",
-       "6999                    0.0           True    True              1   \n",
+       "140473                  0.0           True    True              1   \n",
+       "153768                  0.0           True    True              1   \n",
+       "110886                 12.0           True   False              1   \n",
+       "115390                  0.0           True   False              0   \n",
+       "24919                   0.0           True   False              0   \n",
        "\n",
        "        gender_male  nb_campaigns  nb_campaigns_opened  \n",
        "43000             1          14.0                 12.0  \n",
@@ -1533,16 +1533,16 @@
        "66956             1           0.0                  0.0  \n",
        "116487            0           5.0                  0.0  \n",
        "...             ...           ...                  ...  \n",
-       "83146             1           9.0                  3.0  \n",
-       "223586            1          23.0                  1.0  \n",
-       "56489             1           4.0                  0.0  \n",
-       "141236            1           6.0                  0.0  \n",
-       "6999              0           0.0                  0.0  \n",
+       "140473            0          83.0                 11.0  \n",
+       "153768            0          12.0                  1.0  \n",
+       "110886            0          40.0                 12.0  \n",
+       "115390            1          11.0                  6.0  \n",
+       "24919             1           0.0                  0.0  \n",
        "\n",
-       "[10000 rows x 14 columns]"
+       "[1000 rows x 14 columns]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1550,14 +1550,14 @@
    "source": [
     "# reduce X_train to reduce the training time\n",
     "\n",
-    "X_train_subsample = X_train.sample(n=10000, random_state=42)\n",
+    "X_train_subsample = X_train.sample(n=1000, random_state=42)\n",
     "y_train_subsample = y_train.loc[X_train_subsample.index]\n",
     "X_train_subsample"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 108,
    "id": "2b09c2cd-fd5c-49b3-be66-cec6c5ec1351",
    "metadata": {},
    "outputs": [
@@ -1587,23 +1587,23 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>64466</th>\n",
+       "      <th>43000</th>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>141327</th>\n",
+       "      <th>183923</th>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>59999</th>\n",
+       "      <th>97373</th>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26882</th>\n",
-       "      <td>0.0</td>\n",
+       "      <th>66956</th>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>62952</th>\n",
+       "      <th>116487</th>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1611,48 +1611,48 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>141318</th>\n",
+       "      <th>140473</th>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>113838</th>\n",
+       "      <th>153768</th>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>184926</th>\n",
+       "      <th>110886</th>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115390</th>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14617</th>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21685</th>\n",
+       "      <th>24919</th>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>10000 rows × 1 columns</p>\n",
+       "<p>1000 rows × 1 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "        y_has_purchased\n",
-       "64466               0.0\n",
-       "141327              0.0\n",
-       "59999               0.0\n",
-       "26882               0.0\n",
-       "62952               0.0\n",
+       "43000               0.0\n",
+       "183923              0.0\n",
+       "97373               0.0\n",
+       "66956               1.0\n",
+       "116487              0.0\n",
        "...                 ...\n",
-       "141318              0.0\n",
-       "113838              0.0\n",
-       "184926              0.0\n",
-       "14617               0.0\n",
-       "21685               0.0\n",
+       "140473              0.0\n",
+       "153768              0.0\n",
+       "110886              1.0\n",
+       "115390              0.0\n",
+       "24919               0.0\n",
        "\n",
-       "[10000 rows x 1 columns]"
+       "[1000 rows x 1 columns]"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1663,14 +1663,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 109,
    "id": "6c33fcd8-17d8-4390-b836-faec9ada9acd",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-3 {\n",
+       "<style>#sk-container-id-6 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: black;\n",
        "  --sklearn-color-line: gray;\n",
@@ -1700,15 +1700,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 {\n",
+       "#sk-container-id-6 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 pre {\n",
+       "#sk-container-id-6 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 input.sk-hidden--visually {\n",
+       "#sk-container-id-6 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -1720,7 +1720,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -1728,7 +1728,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-container {\n",
+       "#sk-container-id-6 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -1738,7 +1738,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -1754,14 +1754,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item::after {\n",
+       "#sk-container-id-6 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel {\n",
+       "#sk-container-id-6 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -1769,28 +1769,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item {\n",
+       "#sk-container-id-6 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-3 div.sk-serial {\n",
+       "#sk-container-id-6 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -1808,14 +1808,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable {\n",
+       "#sk-container-id-6 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-3 label.sk-toggleable__label {\n",
+       "#sk-container-id-6 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: block;\n",
        "  width: 100%;\n",
@@ -1825,7 +1825,7 @@
        "  text-align: center;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -1833,13 +1833,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content {\n",
+       "#sk-container-id-6 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -1848,12 +1848,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -1861,79 +1861,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-3 div.sk-label label {\n",
+       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-6 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-3 div.sk-label label {\n",
+       "#sk-container-id-6 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-label-container {\n",
+       "#sk-container-id-6 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-3 div.sk-estimator {\n",
+       "#sk-container-id-6 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -1943,18 +1943,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted {\n",
+       "#sk-container-id-6 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-3 div.sk-estimator:hover {\n",
+       "#sk-container-id-6 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -2041,7 +2041,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-3 a.estimator_doc_link {\n",
+       "#sk-container-id-6 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -2056,25 +2056,25 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
+       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
        "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
        "                                                  Pipeline(steps=[(&#x27;scaler&#x27;,\n",
        "                                                                   StandardScaler())]),\n",
@@ -2084,7 +2084,8 @@
        "                                                   &#x27;vente_internet_max&#x27;,\n",
        "                                                   &#x27;purchase_date_min&#x27;,\n",
        "                                                   &#x27;purchase_date_max&#x27;,\n",
-       "                                                   &#x27;nb_tickets_internetnb_campaigns&#x27;,\n",
+       "                                                   &#x27;nb_tickets_internet&#x27;,\n",
+       "                                                   &#x27;nb_campaigns&#x27;,\n",
        "                                                   &#x27;nb_campaigns_opened&#x27;]),\n",
        "                                                 (&#x27;cat&#x27;,\n",
        "                                                  Pipeline(steps=[(&#x27;onehot&#x27;,\n",
@@ -2095,7 +2096,7 @@
        "                (&#x27;logreg&#x27;,\n",
        "                 LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
        "                                                  1.0: 3.486549107420539},\n",
-       "                                    max_iter=5000, solver=&#x27;saga&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
+       "                                    max_iter=5000, solver=&#x27;saga&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
        "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
        "                                                  Pipeline(steps=[(&#x27;scaler&#x27;,\n",
        "                                                                   StandardScaler())]),\n",
@@ -2105,7 +2106,8 @@
        "                                                   &#x27;vente_internet_max&#x27;,\n",
        "                                                   &#x27;purchase_date_min&#x27;,\n",
        "                                                   &#x27;purchase_date_max&#x27;,\n",
-       "                                                   &#x27;nb_tickets_internetnb_campaigns&#x27;,\n",
+       "                                                   &#x27;nb_tickets_internet&#x27;,\n",
+       "                                                   &#x27;nb_campaigns&#x27;,\n",
        "                                                   &#x27;nb_campaigns_opened&#x27;]),\n",
        "                                                 (&#x27;cat&#x27;,\n",
        "                                                  Pipeline(steps=[(&#x27;onehot&#x27;,\n",
@@ -2116,18 +2118,18 @@
        "                (&#x27;logreg&#x27;,\n",
        "                 LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
        "                                                  1.0: 3.486549107420539},\n",
-       "                                    max_iter=5000, solver=&#x27;saga&#x27;))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
+       "                                    max_iter=5000, solver=&#x27;saga&#x27;))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
        "                                 Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler())]),\n",
        "                                 [&#x27;nb_tickets&#x27;, &#x27;nb_purchases&#x27;, &#x27;total_amount&#x27;,\n",
        "                                  &#x27;nb_suppliers&#x27;, &#x27;vente_internet_max&#x27;,\n",
        "                                  &#x27;purchase_date_min&#x27;, &#x27;purchase_date_max&#x27;,\n",
-       "                                  &#x27;nb_tickets_internetnb_campaigns&#x27;,\n",
+       "                                  &#x27;nb_tickets_internet&#x27;, &#x27;nb_campaigns&#x27;,\n",
        "                                  &#x27;nb_campaigns_opened&#x27;]),\n",
        "                                (&#x27;cat&#x27;,\n",
        "                                 Pipeline(steps=[(&#x27;onehot&#x27;,\n",
        "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;,\n",
        "                                                                sparse_output=False))]),\n",
-       "                                 [&#x27;opt_in&#x27;, &#x27;is_email_true&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">num</label><div class=\"sk-toggleable__content \"><pre>[&#x27;nb_tickets&#x27;, &#x27;nb_purchases&#x27;, &#x27;total_amount&#x27;, &#x27;nb_suppliers&#x27;, &#x27;vente_internet_max&#x27;, &#x27;purchase_date_min&#x27;, &#x27;purchase_date_max&#x27;, &#x27;nb_tickets_internetnb_campaigns&#x27;, &#x27;nb_campaigns_opened&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content \"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">cat</label><div class=\"sk-toggleable__content \"><pre>[&#x27;opt_in&#x27;, &#x27;is_email_true&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content \"><pre>LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
+       "                                 [&#x27;opt_in&#x27;, &#x27;is_email_true&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">num</label><div class=\"sk-toggleable__content \"><pre>[&#x27;nb_tickets&#x27;, &#x27;nb_purchases&#x27;, &#x27;total_amount&#x27;, &#x27;nb_suppliers&#x27;, &#x27;vente_internet_max&#x27;, &#x27;purchase_date_min&#x27;, &#x27;purchase_date_max&#x27;, &#x27;nb_tickets_internet&#x27;, &#x27;nb_campaigns&#x27;, &#x27;nb_campaigns_opened&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content \"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">cat</label><div class=\"sk-toggleable__content \"><pre>[&#x27;opt_in&#x27;, &#x27;is_email_true&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content \"><pre>LogisticRegression(class_weight={0.0: 0.5837086520288036,\n",
        "                                 1.0: 3.486549107420539},\n",
        "                   max_iter=5000, solver=&#x27;saga&#x27;)</pre></div> </div></div></div></div></div></div>"
       ],
@@ -2142,7 +2144,8 @@
        "                                                   'vente_internet_max',\n",
        "                                                   'purchase_date_min',\n",
        "                                                   'purchase_date_max',\n",
-       "                                                   'nb_tickets_internetnb_campaigns',\n",
+       "                                                   'nb_tickets_internet',\n",
+       "                                                   'nb_campaigns',\n",
        "                                                   'nb_campaigns_opened']),\n",
        "                                                 ('cat',\n",
        "                                                  Pipeline(steps=[('onehot',\n",
@@ -2156,7 +2159,7 @@
        "                                    max_iter=5000, solver='saga'))])"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2167,7 +2170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 110,
    "id": "710ccccc-50c9-4aba-8cf1-11483dbbdd1c",
    "metadata": {},
    "outputs": [
@@ -2178,10 +2181,10 @@
        "        1.562500e-02, 3.125000e-02, 6.250000e-02, 1.250000e-01,\n",
        "        2.500000e-01, 5.000000e-01, 1.000000e+00, 2.000000e+00,\n",
        "        4.000000e+00, 8.000000e+00, 1.600000e+01]),\n",
-       " 'logreg__penalty': ['l1', 'l2', 'elasticnet']}"
+       " 'logreg__penalty': ['l1']}"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2213,7 +2216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 111,
    "id": "8062169e-8305-42b0-aeff-8f714117da40",
    "metadata": {},
    "outputs": [
@@ -2256,58 +2259,75 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>64466</th>\n",
-       "      <td>11.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>281.4</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>238.330591</td>\n",
-       "      <td>30.285040</td>\n",
+       "      <th>43000</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>550.000000</td>\n",
+       "      <td>550.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>183923</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>550.000000</td>\n",
+       "      <td>550.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>19.0</td>\n",
        "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97373</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>550.000000</td>\n",
+       "      <td>550.000000</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>True</td>\n",
        "      <td>False</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>141327</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>550.000000</td>\n",
-       "      <td>550.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>59999</th>\n",
+       "      <td>7.0</td>\n",
        "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>350.288926</td>\n",
-       "      <td>350.288926</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66956</th>\n",
+       "      <td>7.0</td>\n",
        "      <td>2.0</td>\n",
+       "      <td>254.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>378.343062</td>\n",
+       "      <td>370.453947</td>\n",
+       "      <td>7.0</td>\n",
        "      <td>True</td>\n",
        "      <td>False</td>\n",
-       "      <td>1</td>\n",
        "      <td>0</td>\n",
+       "      <td>1</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>26882</th>\n",
+       "      <th>116487</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -2320,24 +2340,7 @@
        "      <td>False</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>62952</th>\n",
-       "      <td>11.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>325.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>424.486781</td>\n",
-       "      <td>237.282262</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -2358,7 +2361,7 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>141318</th>\n",
+       "      <th>140473</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -2369,74 +2372,74 @@
        "      <td>0.0</td>\n",
        "      <td>True</td>\n",
        "      <td>True</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>153768</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>550.000000</td>\n",
+       "      <td>550.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>16.0</td>\n",
+       "      <td>12.0</td>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>113838</th>\n",
-       "      <td>3.0</td>\n",
+       "      <th>110886</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>430.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>490.688726</td>\n",
+       "      <td>153.686330</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115390</th>\n",
        "      <td>2.0</td>\n",
-       "      <td>15.0</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>79.9</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>153.152945</td>\n",
-       "      <td>90.277099</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>57.498524</td>\n",
+       "      <td>57.498524</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>6.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24919</th>\n",
        "      <td>3.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>14.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>184926</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>550.000000</td>\n",
-       "      <td>550.000000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14617</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>20.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>149.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>239.258970</td>\n",
-       "      <td>239.258970</td>\n",
+       "      <td>457.437319</td>\n",
+       "      <td>457.437169</td>\n",
        "      <td>0.0</td>\n",
        "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21685</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>88.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>240.355162</td>\n",
-       "      <td>240.355162</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
+       "      <td>False</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>0.0</td>\n",
@@ -2444,66 +2447,66 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>10000 rows × 14 columns</p>\n",
+       "<p>1000 rows × 14 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "        nb_tickets  nb_purchases  total_amount  nb_suppliers  \\\n",
-       "64466         11.0           4.0         281.4           1.0   \n",
-       "141327         0.0           0.0           0.0           0.0   \n",
-       "59999          2.0           1.0           0.0           1.0   \n",
-       "26882          0.0           0.0           0.0           0.0   \n",
-       "62952         11.0           3.0         325.0           1.0   \n",
+       "43000          0.0           0.0           0.0           0.0   \n",
+       "183923         0.0           0.0           0.0           0.0   \n",
+       "97373          0.0           0.0           0.0           0.0   \n",
+       "66956          7.0           2.0         254.0           1.0   \n",
+       "116487         0.0           0.0           0.0           0.0   \n",
        "...            ...           ...           ...           ...   \n",
-       "141318         0.0           0.0           0.0           0.0   \n",
-       "113838         3.0           2.0          15.0           1.0   \n",
-       "184926         0.0           0.0           0.0           0.0   \n",
-       "14617          1.0           1.0          20.0           1.0   \n",
-       "21685          4.0           1.0          88.0           1.0   \n",
+       "140473         0.0           0.0           0.0           0.0   \n",
+       "153768         0.0           0.0           0.0           0.0   \n",
+       "110886        12.0           6.0         430.0           1.0   \n",
+       "115390         2.0           1.0          79.9           1.0   \n",
+       "24919          3.0           3.0         149.0           1.0   \n",
        "\n",
        "        vente_internet_max  purchase_date_min  purchase_date_max  \\\n",
-       "64466                  1.0         238.330591          30.285040   \n",
-       "141327                 0.0         550.000000         550.000000   \n",
-       "59999                  1.0         350.288926         350.288926   \n",
-       "26882                  0.0         550.000000         550.000000   \n",
-       "62952                  1.0         424.486781         237.282262   \n",
+       "43000                  0.0         550.000000         550.000000   \n",
+       "183923                 0.0         550.000000         550.000000   \n",
+       "97373                  0.0         550.000000         550.000000   \n",
+       "66956                  1.0         378.343062         370.453947   \n",
+       "116487                 0.0         550.000000         550.000000   \n",
        "...                    ...                ...                ...   \n",
-       "141318                 0.0         550.000000         550.000000   \n",
-       "113838                 1.0         153.152945          90.277099   \n",
-       "184926                 0.0         550.000000         550.000000   \n",
-       "14617                  0.0         239.258970         239.258970   \n",
-       "21685                  0.0         240.355162         240.355162   \n",
+       "140473                 0.0         550.000000         550.000000   \n",
+       "153768                 0.0         550.000000         550.000000   \n",
+       "110886                 1.0         490.688726         153.686330   \n",
+       "115390                 0.0          57.498524          57.498524   \n",
+       "24919                  0.0         457.437319         457.437169   \n",
        "\n",
        "        nb_tickets_internet  is_email_true  opt_in  gender_female  \\\n",
-       "64466                  11.0           True   False              1   \n",
-       "141327                  0.0           True    True              0   \n",
-       "59999                   2.0           True   False              1   \n",
-       "26882                   0.0           True   False              1   \n",
-       "62952                  11.0           True   False              0   \n",
+       "43000                   0.0           True    True              0   \n",
+       "183923                  0.0           True    True              0   \n",
+       "97373                   0.0           True   False              0   \n",
+       "66956                   7.0           True   False              0   \n",
+       "116487                  0.0           True   False              1   \n",
        "...                     ...            ...     ...            ...   \n",
-       "141318                  0.0           True    True              0   \n",
-       "113838                  3.0           True    True              0   \n",
-       "184926                  0.0           True    True              0   \n",
-       "14617                   0.0           True    True              0   \n",
-       "21685                   0.0           True    True              0   \n",
+       "140473                  0.0           True    True              1   \n",
+       "153768                  0.0           True    True              1   \n",
+       "110886                 12.0           True   False              1   \n",
+       "115390                  0.0           True   False              0   \n",
+       "24919                   0.0           True   False              0   \n",
        "\n",
        "        gender_male  nb_campaigns  nb_campaigns_opened  \n",
-       "64466             0           0.0                  0.0  \n",
-       "141327            0          10.0                  0.0  \n",
-       "59999             0           0.0                  0.0  \n",
-       "26882             0           4.0                  1.0  \n",
-       "62952             0           0.0                  0.0  \n",
+       "43000             1          14.0                 12.0  \n",
+       "183923            1          19.0                 11.0  \n",
+       "97373             0           7.0                  2.0  \n",
+       "66956             1           0.0                  0.0  \n",
+       "116487            0           5.0                  0.0  \n",
        "...             ...           ...                  ...  \n",
-       "141318            0          16.0                  1.0  \n",
-       "113838            1          31.0                 14.0  \n",
-       "184926            1          18.0                  0.0  \n",
-       "14617             1           0.0                  0.0  \n",
-       "21685             1           0.0                  0.0  \n",
+       "140473            0          83.0                 11.0  \n",
+       "153768            0          12.0                  1.0  \n",
+       "110886            0          40.0                 12.0  \n",
+       "115390            1          11.0                  6.0  \n",
+       "24919             1           0.0                  0.0  \n",
        "\n",
-       "[10000 rows x 14 columns]"
+       "[1000 rows x 14 columns]"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2514,7 +2517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 112,
    "id": "0270013a-6523-4cf8-8de0-569c0d1c5db5",
    "metadata": {},
    "outputs": [],
@@ -2526,7 +2529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 113,
    "id": "7a49d78a-5a9b-44a9-95cf-3fca1b3febfa",
    "metadata": {},
    "outputs": [
@@ -2534,8 +2537,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Returned hyperparameter: {'logreg__C': 0.03125, 'logreg__penalty': 'l1'}\n",
-      "Best classification accuracy in train is: 0.47785817197986385\n"
+      "Returned hyperparameter: {'logreg__C': 4.0, 'logreg__penalty': 'l1'}\n",
+      "Best classification accuracy in train is: 0.4972844559251812\n"
      ]
     }
    ],
@@ -2548,13 +2551,13 @@
     "\n",
     "# print results\n",
     "print('Returned hyperparameter: {}'.format(logit_grid.best_params_))\n",
-    "print('Best classification accuracy in train is: {}'.format(logit_grid.best_score_))\n",
+    "print('Best classification F1 score in train is: {}'.format(logit_grid.best_score_))\n",
     "# print('Classification accuracy on test is: {}'.format(logit_grid.score(X_test, y_test)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 114,
    "id": "b1d5e71d-1078-4370-86e8-52b1ae378898",
    "metadata": {},
    "outputs": [
@@ -2567,7 +2570,7 @@
        "       4.000000e+00, 8.000000e+00, 1.600000e+01])"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3878,19 +3881,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 115,
    "id": "3573f34e-25d5-4afb-82cc-52323e2f63c6",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 1.34302143,  0.        ,  0.02675567,  0.45036527, -0.05004637,\n",
-       "         0.7663532 , -1.35216757,  0.17404712,  0.13679663,  0.10249737,\n",
-       "         0.40815146, -0.6311938 ,  0.11194512, -0.33498749]])"
+       "array([[ 0.64495866, -0.23909623,  0.54323933,  0.85687092, -0.04235755,\n",
+       "         0.87304348, -1.34756336,  0.21177838,  0.051939  ,  0.04496588,\n",
+       "         0.2103007 , -0.59054784,  0.        ,  0.        ]])"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3902,7 +3905,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 116,
    "id": "0332a814-61fb-4b71-836a-e8ace70b1a44",
    "metadata": {},
    "outputs": [
@@ -3921,13 +3924,13 @@
        "                                                   OneHotEncoder(handle_unknown='ignore',\n",
        "                                                                 sparse_output=False))]),\n",
        "                                  ['opt_in', 'is_email_true'])]),\n",
-       " 'logreg': LogisticRegression(C=0.03125,\n",
+       " 'logreg': LogisticRegression(C=4.0,\n",
        "                    class_weight={0.0: 0.5837086520288036,\n",
        "                                  1.0: 3.486549107420539},\n",
        "                    max_iter=5000, penalty='l1', solver='saga')}"
       ]
      },
-     "execution_count": 125,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3938,17 +3941,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 117,
    "id": "287615b9-e062-4b84-be61-26b9364b2cf4",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([-0.22304234])"
+       "array([-0.38031755])"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4261,29 +4264,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 118,
    "id": "e53b1f79-762d-4f1f-8505-91de1088af42",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "32.0"
+       "0.25"
       ]
      },
-     "execution_count": 122,
+     "execution_count": 118,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# best param : alpha = 32\n",
+    "# best param : alpha = 32 (alpha =1/4 sur le petit subsample)\n",
     "1/logit_grid.best_params_[\"logreg__C\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 119,
    "id": "41bcaaf6-ab58-4004-a3c5-586d77e872d1",
    "metadata": {},
    "outputs": [
@@ -4291,9 +4294,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Accuracy Score: 0.7589597902097902\n",
-      "F1 Score: 0.47078982841737305\n",
-      "Recall Score: 0.7525931336742148\n"
+      "Accuracy Score: 0.7187395937395937\n",
+      "F1 Score: 0.44926236857119567\n",
+      "Recall Score: 0.8052593133674215\n"
      ]
     }
    ],
@@ -4315,13 +4318,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 120,
    "id": "a454bb57-76eb-4a22-9950-0733d39e449f",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -4338,13 +4341,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 121,
    "id": "25ec1701-ade5-4419-8b46-8a1bb109cf84",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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d0pQ9ceIES5cutZzXr18/Tpw4QW5ubp7f1/r16xfo6xQRESkKmiEkIiJSBNq1a8eHH37IvffeS6tWrbjnnnu47LLLyM7OZs2aNXz88cc0btyY/v37U79+ff71r3/x3nvv4efnR+/evdm7dy/PPPMMVatWtTRAbrnlFm6++WbuvfdeBg0axL59+xg3bhzly5cvcI0rV65k5MiRDBkyhAMHDvDUU08RGxvLvffeC5hrurz33nsMHz6chIQEBg8eTExMDMePH2fdunUcP36cDz/88KK+P6+99hpXXHEFL774Iv/3f/8HwLBhw1i9ejVvvPEGe/fu5Y477qBChQps27aN8ePHs2vXLr799ltq1arlep0hQ4bwzDPP8OKLL7J161ZGjBhB7dq1SUtL488//+Sjjz5i6NChF9x6/vfff6dbt248++yz+V5H6GKNHTuWHj160LVrVx555BGCgoL44IMP2LhxIxMnTnTN6Hr66af58ccfueqqq3j22WcJDQ3lv//9r9uaTTVq1OCFF17gmWeeYc+ePfTp04fo6GiOHj3Kn3/+SVhYGC+88EKetZx+7lNPPcXu3bu5+uqrKVOmDEePHuWvv/4iLCyM559/Pt9fm5+fH+PGjeOmm26iX79+3HXXXWRmZvL6669z6tQpXn31Vde5L7zwAn379qVXr1488MAD5Obm8vrrrxMeHk5CQsIFP0///v1p3LgxrVu3pnz58uzbt4+3336b6tWrU7duXQCaNGkCwDvvvMPw4cMJDAykfv36ec7MufXWWxk/fjy33norL7/8MnXr1mX27Nn88ssvbufecsstfPTRR9x8883ceeednDhxgnHjxhEZGWk574YbbuB///sfffr04YEHHqBNmzYEBgZy8OBBfvvtN6699lquu+66fH9vRUREioR961mLiIiUfGvXrjWGDx9uVKtWzQgKCjLCwsKMFi1aGM8++6xx7Ngx13m5ubnGa6+9ZtSrV88IDAw0ypUrZ9x8883GgQMHLK/ndDqNcePGGbVq1TJCQkKM1q1bGwsWLDjvLmOTJ092q+n0LmNz5841brnlFqN06dJGqVKljD59+hg7duxwO//33383+vbta0RHRxuBgYFGbGys0bdv3zxf+2yndxl7/fXX83x8yJAhRkBAgLFz507L+OzZs40+ffoYZcuWdX2+W265xdi0adN5P9fvv/9uDB482KhUqZIRGBhoREZGGu3atTNef/11Iykp6YJ1nv5ejRkz5oLnGYa5y9Rll13mNl69enWjb9++buOA2w5gixcvNq666iojLCzMKFWqlNG2bVvjp59+cnvuH3/8YbRt29YIDg42KlasaDz66KPGxx9/nOfuWdOnTze6du1qREZGGsHBwUb16tWNwYMHG/Pnz3edc+4uYwV5bl7O3WXs7Ne74oorjJCQECMsLMzo1q2b8ccff7g9f9q0aUaTJk2MoKAgo1q1asarr75q3H///UaZMmUs5527y9ibb75ptG/f3ihXrpzruSNGjDD27t1red7o0aONypUrG35+fpY689op7ODBg8agQYOM8PBwIyIiwhg0aJCxdOlSt13GDMMwvvzyS6Nhw4ZGSEiI0ahRI2PSpEluu4wZhmFkZ2cbb7zxhtGsWTMjJCTECA8PNxo0aGDcddddeeZMRESkuDkMI4/51SIiIiIixSg7O5vmzZsTGxvrEbf57d27l5o1a/L555+77QAoIiJSEuiWMREREREpdiNGjKBHjx5UqlSJuLg4JkyYwJYtW3jnnXfsLk1ERMQnqCEkIiIiIsUuOTmZRx55hOPHjxMYGEjLli2ZPXs23bt3t7s0ERERn6BbxkREREREREREfIy2nRcRERERERER8TFqCImIiIiIiIiI+Bg1hEREREREREREfIzPLSrtdDo5fPgwEREROBwOu8sRERERERERESkUhmGQnJxM5cqV8fO78Bwgn2sIHT58mKpVq9pdhoiIiIiIiIhIkThw4ABVqlS54Dk+1xCKiIgAzG9OZGSkzdVcmjlz5nD11VfbXYaIx1AmRKyUCRF3yoWIlTIhYuXtmUhKSqJq1aqu3seF+Ny280lJSURFRZGYmOj1DaGcnBwCAnyupydyXsqEiJUyIeJOuRCxUiZErLw9EwXpeWhRaS82efJku0sQ8SjKhIiVMiHiTrkQsVImRKx8KRNqCImIiIiIiIiI+Bg1hLxYo0aN7C5BxKMoEyJWyoSIO+VCxEqZELHypUyoIeTFoqKi7C5BxKMoEyJWyoSIO+VCxEqZELHypUyoIeTFli1bZncJIh5FmRCxUiZE3CkXIlbKhIiVL2VCDSERERERERERER+jbee92IkTJyhbtqzdZYh4DGVCxEqZEHGnXIhYKRMiVt6eCW077yM2b95sdwkiHkWZELFSJkTcKRciVsqEiJUvZUINIS928OBBu0sQ8SjKhIiVMiHiTrkQsVImRKx8KRNqCHmx0NBQu0sQ8SjKhIiVMiHiTrkQsVImRKx8KRNaQ0hEREREREREpATQGkI+YuLEiXaXIOJRlAkRK2VCxJ1yIWKlTIhY+VIm1BASEREREREREfExagh5sXr16tldgohHUSZErJQJEXfKhYiVMiFi5UuZUEPIi8XExNhdgohHUSZErJQJEXfKhYiVMiFi5UuZUEPIiy1ZssTuEkQ8ijIhYqVMiLhTLkSslAkRK1/KhBpCIiIiIiIiIiI+RtvOe7GjR49SoUIFu8sQ8RjKhIiVMiHiTrkQsVImRKy8PRPadt5H7Nq1y+4SRDyKMiFipUyIuFMuRKyUCRErX8qEGkJebN++fXaXIOJRlAkRK2VCxJ1yIWKlTIhY+VIm1BDyYkFBQXaXIOJRlAkRK2VCxJ1yIWKlTIhY+VImbF1DaNGiRbz++uusWrWKI0eOMG3aNAYMGHDB5/z+++889NBDbNq0icqVK/PYY49x99135/tzlqQ1hERERERERERETvOaNYRSU1Np1qwZ77//fr7O37NnD3369KFjx46sWbOGJ598kvvvv58pU6YUcaWeafLkyXaXIOJRlAkRK2VCxJ1yIWKlTIhY+VImAuz85L1796Z37975Pn/ChAlUq1aNt99+G4CGDRuycuVK3njjDQYNGlREVXqunJwcu0sQ8SjKhIiVMiHiTrkQsVImxNcdS8rgaFImqVk5pGZks/I49M/OJSTQ3+7SipytDaGCWrZsGT179rSM9erVi08//ZTs7GwCAwPdnpOZmUlmZqbrOCkpqcjrLC61atWyuwQRj6JMiFgpEyLulAsRK2VCfIVhGOQ6DXINA6cTlu8+wRdL9/L79uOUSUtk1JJvCc7JYlKfB7g/NYvY0qXsLrnIeVVDKC4ujgoVKljGKlSoQE5ODvHx8VSqVMntOWPHjuX55593G588eTKhoaEMHDiQX3/9lcTERGJiYmjTpg0zZ84EoGXLljidTtauXQvAtddey5IlSzhx4gTR0dF06tSJ6dOnA9C0aVMCAwNZtWoVAH379mXlypUcPXqUyMhIevbsyQ8//ADAZZddRnh4OH/++SdgNrU2btzIoUOHCAsLo1+/fkyaNAmA+vXrU65cOf744w8Aunfvzvbt29m/fz9Op5MrrriCSZMm4XQ6qV27NrGxsSxatAiALl26sH//fnbv3k1AQABDhgxhypQpZGVlUb16dWrXrs2CBQsAuPLKKzl27Bjbt28HYNiwYcyYMYO0tDSqVKlCo0aNmDt3LgDt2rUjMTGRzZs3AzBkyBDmzJlDcnIyFStWpGXLlsyePRuAyy+/nIyMDDZs2ADAddddx8KFCzl58iTlypWjXbt2/PTTTwC0aNECgDVr1gDQv39/li1bRnx8PGXKlKFLly5MmzYNgCZNmhASEsKKFSsA6NOnD6tXryYuLo6IiAiuvvpq11S/Ro0aERUVxbJlywDo2bMnmzdv5uDBg4SGhnLttdcyceJEAOrVq0dMTAxLliwB4KqrrmLXrl3s27ePoKAgBg0axOTJk8nJyaFWrVpUq1aNhQsXAtCpUycOHTrErl278PPzY+jQoUydOpXMzEyqVatGvXr1mD9/PgAdOnQgPj6ebdu2ATB06FBmzpxJamoqsbGxNG7cmF9++QWAK664gpSUFDZt2gTA4MGDmTt3LklJSVSoUIHWrVsza9YsAFq1akV2djbr168HYMCAASxatIiEhATKli3LlVdeyYwZMwBo3rw5fn5+rF69GoB+/frx119/cezYMaKioujWrRtTp04FoHHjxoSGhvLXX38B5uy+devWcfjwYcLDw+nTpw/ff/89AA0aNCA6OpqlS5cC0KNHD7Zu3cqBAwcoVaoUAwYM4LvvvsMwDOrWrUvFihVZvHgxAF27dmXv3r3s2bOHwMBABg8ezA8//EB2djY1a9akRo0a/PbbbwB07NiRuLg4duzYgcPh4IYbbmDXrl3s3r2bqlWr0qBBA+bNmwdA+/btSUhIYOvWrQBcf/31zJ49m5SUFCpXrkyzZs34+eefAWjTpg1paWls3LgRwKvfI4KDgxk4cKDeI/Dd94iwsDCSkpL0HvH3e8T06dNJT0/Xe4SPv0dkZGQQFham9wj9PULvEZjvEUFBQa4/w3qP0N8jLvY9Ijo6mivad2DajJ/IcTqo27AROYaD9Rs3k+10cHnbdqzftIX4k4kEhYRSt0Ejlq9YRY7hoGz5Chj+Aew7cJhsAyrFViPu+AmSU9Mx/AKIii7HobijZDsdBAYF4/DzJyUtDacBIaVCycjKIjsnFzAfT8vIxGmAw+GHE8h1OnEaYODgXIG52YxcNZMHln5HRGYqThz82HUAcUeOcGRngle+R6Slpbl9nedj66LSZ3M4HP+4qHS9evW4/fbbGT16tGvsjz/+4Morr+TIkSNUrFjR7Tl5zRCqWrVqiVhUeuLEiQwbNszuMkQ8hjIhYqVMiLhTLkSslAnvZBgGOU5zxovz75kvTifkOJ2uGTC5hkFurjkj5uzzcp0GGdm57DuRRlpWDpk5TjJznGRk55ofn/7/2WM5uWRmO8n4+/+usbPO8YzOQj4ZBj12/skrS7+kfNwBc6x5c3jrLSbGxXl1JgqyqLRXzRCqWLEicXFxlrFjx44REBBA2bJl83xOcHAwwcHBxVGeiIiIiIiIyCVLzcwhLSuXHKeTjGwnk1ce4Nu/9pOSkUOuYXh88yU4wM/8FehPSKAfwQH+rrGQwNMf+xMcmMfY6eO/H3M9N9CPQH8//B0O/Pwc+J/+5TD/7+c4a+zvcT8/3M7zP3iA0LvuxH+hOWOQChXg5ZfhttvA3x/+ntHlC7yqIXT2lL/T5s6dS+vWrfNcP6ik69Spk90liHgUZULESpkQcadciFgpE5cmMS2bY8kZeT52LDmT7UeTOZacifOcDs6JlCwSUrMwDAOnAc6/mzwGBseSMtlxLOWia3I4cDVNAlyNEfcGStnwIGJLl7I2ZAL9z2nQnN24cW/SnHmeORYS6EeQvx8Oh/vtWR6jYnnYtBGCg+Hhh+GJJyAiwvWwL2XC1oZQSkoKO3fudB3v2bOHtWvXEh0dTbVq1Rg9ejSHDh3iq6++AuDuu+/m/fff56GHHuLOO+9k2bJlfPrpp657Mn3NoUOHiI2NtbsMEY+hTIhYKRMi7pQLEauSkImsHCe741PIyT1za1R2rsGptCxyneatVU7DcD2elpXDybRssnKd5uO5Brl/32p1+jjHaZCQmkV8SibHkzNJTM92NWycBq5GTlEL9HcQ4OdHdFgQD3SvS6e65c1ZLw5z/PQMGD/H380fP4dnN2PskJEB330Hw4eb3bLISPj2W6hbF6pXdzu9JGQiv2xtCK1cuZKuXbu6jh966CEAhg8fzhdffMGRI0fYv3+/6/GaNWsye/ZsRo0axX//+18qV67Mu+++65NbzgPs2rWLNm3a2F2GiMdQJkSslAkRd8rFxUnPyuXXrUfJyHYC5j+Gz2ac58A468Aw8jzlnPF/Pv/cB8/7Wvk5xzJ+/n/dn34oK9dJWlYOOeYKta7XMM66hcf4+/zTX4tr/O8P8nrccJ131tjf56Rl5bo+NiwzSc58XoO8x3OdBhk5ua7x0y9unFXXiYQEyixffE791t8H91rPfC2c9dqn17U5s57Mud+Xc74HF3ids2s0znkdzqnJE26hig4LynO8etlQmlUpTYCftUkT4O9HlTLm7By/v29tcuDA4YCwoABaVCtNdFiQmjuXwjBg8mR4/HHYuxdCQ+H6683Hunc/79N86Tpha0OoS5cuF3zj/eKLL9zGOnfu7NrNwNf5+fnZXYKIR1EmRKyUCRF3ykXBHU3K4IpXfrW7DCky/hxOT7K7iEsWERxAeEiAq7kS4OdHVKlAAv3NWTPmbBpzFk1IoB9lQoMIDvAnwPX42f/3w98PSocGUT48mPIRwUSVCnTNxHE4zIkmDhz4OVDjxhOtWAGjRsHfu2wSGwtBeTftzuVL1wmP2WWsuBRkxW0REREREV/ldBo8OW0D36044BpzOKBT3fKuj8929uHZ/zi2juf9jLPHz3e+45wtoy2Pne+8875ufurL+zkBfg4iQgLx9zsz7vj75NOf22wWnHm90zM/HGde7IKPmw0H8yg4wFxI1/W4w2xCnH7O6Y///s+tYRES6Ie/n5/18531nNNfp+vrOPvxs74Wzlur9djPz0FIgP9ZX8f5v0b+HstPDdb/W79/fg4H5cOD8TtnFo74oIMH4ckn4euvzePQUHjsMXjkEQgLs7e2YlJidxkTq6lTpzJw4EC7yxDxGMqEiJUyIeJOuTClZ+VyLDmDo0mZHE3K4GhSBseSz/o4KZPd8amW5/z4nw40rVLanoKlyCgTUqJcfz0sW2Z+fMst8MorUKVKgV7ClzKhhpAXy8zMtLsEEY+iTIhYKRMi7kp6LjJzcjmWlGlp9pxu9Bw7q/mTlJGTr9crFehP1ehSfHJra6qX9Y2frvuakp4JKeGcTsjNhdO7jr/0Ejz7LIwfD5dfflEv6UuZUEPIi1WrVs3uEkQ8ijIhYqVMiLjz1lxk5zqJT8k80+RJOmt2T3Lm38cZnEzLzvdrhgT6UTEyhJjIECpEhhATEUyFyOC/Pw6hQmQwVcqEEhTgO+tp+CJvzYQIS5fCgw/CtdfCU0+ZY1ddBV27ut//WQC+lAk1hLxYvXr17C5BxKMoEyJWyoSIO2/IRWZOLluOJDNvcxzbj6Ywb/NRHA7yvYtSUIDf380ds6ljNndCXM2eCpHBxESGEBEcoIVwxSsyIWKxb5+5c9ikSebxwYPmGkHBwebxJb6v+VIm1BDyYvPnz2fYsGF2lyHiMZQJEStlQsSdp+Ui12mw41gy6w8ksu7gKdYfTGRrXBLZueds6/73YeWo0zN6Tjd3Qs5q/pjjUaUC1eiRfPO0TIicV3IyjB0Lb70FmZlm42fECHjxxTPNoELgS5lQQ0hEREREpBgYhsH+hDTWHUxk/QGz+bPxcCJpWblu50aHBdG0ShTVokOpXT6cjnXLUaNsmHZREhHfNH8+3HwzHD1qHnftajaGmje3tSxvp4aQF+vQoYPdJYh4FGVCxEqZEHFX3Lk4lpzB1iPJbDiUyNfL9hGXlOF2TliQP02qRNGsSmmaVilN0ypRVClTSrN8pFjoWiFeoUYNSEiAOnXgjTfgmmsu+daw8/GlTKgh5MXi4+N9asErkX+iTIhYKRMi7gozF4ZhkJqVy8nULBLTszmeksmuYykcTcpgy5FktsYlEZ+S5fa8ZlVL06xKFE2rmP+vVT4cf838EZvoWiEeaedOmDsX7r3XPK5TB+bNg3btICioSD+1L2VCDSEvtm3bNlq2bGl3GSIeQ5kQsVImRNxdbC4Mw2D70RSW7Ixn6c541h9K5FRalttaP+dyOKBmuTAaVoykYaUIrm5ckToxERdbvkih07VCPMqpU+bW8e++Czk50LYtnP7z2blzsZTgS5lQQ0hERERESiyn0yA9O5fUrBzSMnM5nO7Hir0JpGXlkpaZQ2pWLmlZOaRm5pKeZT0+/f+NhxLJzHGSlevM83MEBfhRJjSQ0qWCCPB3EBkSyLXNK9OwUiT1KkRQKsi/mL9qEREvk5MDH38MY8ZAfLw5dvXVEB5ub10lnMMw8ruBZcmQlJREVFQUiYmJREZG2l3OJXE6nfj5+dldhojHUCZErJQJKUnSs3LZdTyFHceS2X8i3WzW/N3kSc3KMRs8WbmkZp7++O8mT7b7gs2XomPdclxZpxxtakZTMSqE0qWC1PARr6Zrhdjul1/goYdg82bzuGFDePNN6N3blnK8PRMF6XlohpAXmzlzJtdcc43dZYh4DGVCxEqZEG+UkpnDzmMp7DiabP7/mNkEOngynUv5MabDAWFBAfg5s4mODCM0KICwYH/X/0sFnnUc5E9ocAChQf6EBvkTFhRAgL+DtrXKEhKo5o+ULLpWiK1SU+GWW+D4cShbFp5/Hv71LwgMtK0kX8qEGkJeLDU11e4SRDyKMiFipUyIJ0tMz2bnsWR2HD3d9Elh59FkDie678J1WrnwIOrEhFOjbBgRIQGWpo7ZvLE2eUIDAwgNNhs6IYF+OBwOJk6cyLBh/YrxKxXxbLpWSLE7dQqiov7u1IfB2LGwaRM88wyUKWN3dT6VCTWEvFhsbKzdJYh4FGVCxEqZEE9wMjXLNcvHbP6Y/z+WnHne58REBFO3Qjh1YyJc/68TE0502KXvLKNciFgpE1JssrLggw/ghRfgo49gyBBzfMQIe+s6hy9lQg0hL9a4cWO7SxDxKMqEiJUyIcXFMAziU7LYcezv27z+bvzsPJaS57brp1WOCqFOhQjqxoSbvyqEU6d8BFGhRXergHIhYqVMSJEzDJg5Ex5+GHbsMMe++eZMQ8jD+FIm1BDyYr/88gvDhg2zuwwRj6FMiFgpE1IUcp0G6w+eYs3+U+ZtXseS2XEshVNp2ed9TtXoUuZsn5hw6sSEU7dCBLXLhxERUvxrRCgXIlbKhBSp9evNBaN//dU8jomBl1+G22+3t64L8KVMqCEkIiIiIhaGYZCSmUN8ShbHkzOJT8nkh1UH8XM4WLkvIc/mj58DqkWHUsd1m1c49SpEUKu8uYCziIj4mFdfhaeeAqcTgoLMxtDo0eDlu32XJLo6e7ErrrjC7hJEPIoyIWKlTMg/OZGSyZYjySzcdoy9J1KJT8kiPsVsAGVkO8/7vMiQAK6oVZYGFc21ferGmI0fb9iBS7kQsVImpMi0amU2g4YMgddeg5o17a4oX3wpE2oIebGUlBS7SxDxKMqEiJUyIadl5zrZfTyVLUeS2BKXxJYjyWw5ksTxCyzsDBAW5E+5iGDKhwdTLjyYuKQMnujdgNbVyxDg71dM1Rcu5ULESpmQQmEY8MMPkJgII0eaYz16wIYN4GVr8vhSJtQQ8mKbNm2iadOmdpch4jGUCRErZcI3JaRmmY2fI2caPzuPpZCV6z7jx+GAGmXDaFAxgpiIYBrHRlGrfBjlw0MoFxFUIm/1Ui5ErJQJuWQrV8KoUbBkCYSHQ79+ULGi+ZiXNYPAtzJR8q7yIiIiIj4gJ9fJ7vhUS+Nna1wSR5PynvUTHhxAg4oRNKwUScNKkTSoFEH9ChGEBeuvgyIichEOHYInn4SvvjKPS5Uy1wmKiLC3Lsk3h2EYht1FFKekpCSioqJITEwk0ssXs8rOziYwsPh35xDxVMqEiJUyUbIcSEhj4bZjrNh7kv0JaWw+kkRWTt7r/FQvG0rDimcaP40qRVKlTCkcDkcxV+15lAsRK2VCCiwtDd54w1wXKC3NHLv5ZnjlFaha1d7aCoG3Z6IgPQ/9SMiLzZ07l759+9pdhojHUCZErJQJ75aV42Tl3gR+23aM37YdZ+cx9zUNwoL8aVAp0jLzp37FCMI16+e8lAsRK2VCCuzgQXjxRcjJgXbt4O23oU0bu6sqNL6UCf1twYslJSXZXYKIR1EmRKyUCe+RmpnD7uOp7I5PYdexFLYfTWHxjuOkZuW6zvH3c9Cqehk61S1HzXLhf+/uFY6fn2b9FIRyIWKlTEi+7NlzZpewevXgpZegRg24/npzQboSxJcyoYaQF6tQoYLdJYh4FGVCxEqZ8BxOp8HxlEwOnUrnyKkMDp9KZ39CGruOp7D7eCpxSRl5Pq9ceDBd6pena/0YrqxbjqhS3juF3VMoFyJWyoRc0L598MQT8P335uLRLVqY448/bm9dRciXMqGGkBdr3bq13SWIeBRlQsRKmbBPVo6T9QdPsXz3CZbvTmDVvpOkZ+de8Dllw4KoVT6M2uXDqVU+jMaxUbStWVYzgAqZciFipUxInlJS4NVX4c03ISPDnAX0229nGkIlmC9lQg0hLzZr1iyGDRtmdxkiHkOZELFSJopHrtNgT3wKGw4lsuFgEhsOnWLDoUQysq0LPvs5oGJkCJVLl6Jy6VLElilFrXJh1CofTu3yYZQODbLpK/AtyoWIlTIhFk4nfPmluXtYXJw51rkzjB/vE80g8K1MqCEkIiIiUgDZuU7WHTjFHztPsHRXPBsOJZKW5T77JzosiLa1omlbqyxX1CxL7fJhBPj72VCxiIhIPvXuDXPnmh/Xrg2vvw4DBpS4dYLEpIaQF2vVqpXdJYh4FGVCxEqZuDTZuU62H00mMT2bpPQcNh1OZFtcMkt3nSAlM8dybqlAfxpVjqRJbBRNYqNoWiWK2uW14LMnUi5ErJQJsbj2Wli+HJ55Bu67D4KD7a6o2PlSJtQQ8mLZ2dl2lyDiUZQJEStlomCcToPNR5JYuiuepbtO8NeehDxn/gCUDg2kQ+1ytK9TlstrRFO7fDj+av54BeVCxEqZ8GGJieZuYR06mLOAAP71LxgyBMqXt7U0O/lSJtQQ8mLr16/nsssus7sMEY+hTIhYKRMXdiotiz/3JLD2wCnWHTjF0l0n3M6JDAkgJjKEsOAAIkMCqBQVws1tq9O4cpRm/3gp5ULESpnwQTk58H//B88+C8ePw9Sp0KcPBAVBQIBPN4PAtzKhhpCIiIiUeLlOg+PJmWyJS3I1f1btO0mu03A7t0xoIP/uWof2tcvRoGKEGj8iIlJyzJsHo0bBpk3mcYMG5k5igYH21iW2cBiG4f43oRIsKSmJqKgoEhMTiYyMtLucS5Kenk6pUqXsLkPEYygTIla+mImzd/xas/8UW+OSOZCQxtGkDPLo/VA3JpxW1cvQrGppmlctTd2YcC38XML5Yi5ELkSZ8BE7dsBDD8HMmeZxdDQ8/zzcdZeaQefw9kwUpOehGUJebNGiRfTq1cvuMkQ8hjIhYuULmTAMg53HUpi35SgLtx5n4+G8d/wCc9v3GuXCaF6lNC2qlaZL/RiqRocWc8ViN1/IhUhBKBM+Ys8esxkUEAD/+Y95u1iZMnZX5ZF8KRNqCHmxhIQEu0sQ8SjKhIhVScyE02mwLyGNtQdOsnrfKRbtOM6+E2mWc0oF+nNZ5UgaVY6kaZXS1C4fRmyZUpQNC9bCz1IicyFyKZSJEio7G9avh9M7ZvXsaS4gPWQI1Ktnb20ezpcyoYaQFytbtqzdJYh4FGVCxKokZCIuMYNNhxP5fftxVuw9yZ74FDKynZZzgvz9aF+nLN0aVqBtzWhqaccvuYCSkAuRwqRMlDCGAbNmwSOPwOHD5q1iFSqYjz31lL21eQlfyoTWEPJiaWlphIZqqrvIacqEiJU3ZiIjO5etcclsP5rM53/sZcuRJLdzggL8aFw5kuZVy3B5jTJ0qleesGD9jEvyxxtzIVKUlIkSZONGc52gefPM4/Ll4YcfoFMne+vyMt6eCa0h5CNmzJjBsGHD7C5DxGMoEyJW3pIJwzD4fftx7v5mFU4DsnKsM4AqRYVwVYMYOtYtT/2KEVQpU4pALfwsF8lbciFSXJSJEuDYMXNNoE8+AafT3D7+wQfhySchKsru6ryOL2VCDSEREREpdoZhsPFQEvM2xzFrwxF2HU+1PN6hTlnqxkRQOyacW9pWt6lKERERD5eSAo0awYkT5vHgwfDaa1Crlr11iVdQQ8iLNW/e3O4SRDyKMiFi5WmZSM/KZeW+BP7YeYIf1x7icGKG67HQIH+ubV6ZrvVj6NGoAg6H1gCSouFpuRCxmzLh5cLD4aabYMkSGD9et4cVAl/KhBpCXszPT9PlRc6mTIhYeUomlu8+wfcrDzBj7WFyndalC/s0qUjneuXp06QSESGBNlUovsRTciHiKZQJL7N6tblg9FtvwenGxauvQnAw6PeyUPhSJtQQ8mKrV6+mfv36dpch4jGUCRErOzNxPDmTaWsOMnX1IbbGJVseG9gilitqRdO9YQXKhgfbUp/4Ll0rRKyUCS9x5Ii5S9gXX5g7iT35JMyebT5WqpStpZU0vpQJNYRERESkUDidBrM3HmHa6kP8uvWYa9zhgMurR3Nbhxr0aFRBC0KLiIjkV3q6ORto7FhI/Xu9vZtuMo9FLpG2nfdiycnJRERE2F2GiMdQJkSsiisTKZk5TFt9kG+W72fbUetsoJFX1uSeLrU1E0g8hq4VIlbKhAebNs3cLWz/fvO4bVt4+2244go7qyrxvD0TBel56Ed0Xuyvv/6yuwQRj6JMiFgVZSayc53M33yUsbO30HjMLzwzYxPbjiYTFuTPHR1q8t8bW7JnbB+e7tdIzSDxKLpWiFgpEx7s0CGzGVS1Knz7LSxdqmZQMfClTOiWMS927Nixfz5JxIcoEyJWRZGJ48mZfLF0D//9bZdl3M8BT/ZpyHUtYtUAEo+ma4WIlTLhQQ4cgMOHzzR97rrLXC9o5EitE1SMfCkTagh5saioKLtLEPEoyoSIVWFmIi0rh0krDvDizM2cvVFYbOlSPHfNZXStX54ArQ0kXkDXChErZcIDpKTAuHHw+usQGwubNpm7hgUGwn332V2dz/GlTGgNIS+WmZlJcLB+CitymjIhYnWpmcjIzmXnsRRmbTjCt3/uJzE92/XYG0Oa0aNRBaJKaat48S66VohYKRM2cjrhq6/MHcOOHDHHOnWCiROhcmV7a/Nh3p4JrSHkI6ZOnWp3CSIeRZkQsbqYTKRm5vD9ygOMnrqetmN/pd97S/hw4S4S07OpXjaUMf0bsfPl3gxuVUXNIPFKulaIWCkTNlm8GNq0gdtvN5tBtWrBlCmwcKGaQTbzpUzoljEREREfl5aVw49rDzNj7WFW7ksgO/fM5OHgAD+axEbR67KK3HFlTfz9HDZWKiIiUgKsWWPOBAKIjISnn4b77zdvExMpRmoIebHGjRvbXYKIR1EmRKwulAnDMFi57yRf/LGXeVuOkpXjdD1WNboUdWMi6N24Itc0r0xwgH9xlCtSLHStELFSJoqJ0wl+f9+g06IF9OsHVarA889DTIy9tYmFL2VCDSEvFhoaancJIh5FmRCxyisTSRnZfLJoNzPWHmZ/QpprvEqZUgxpVZVrmlemRtlQHA7NBJKSSdcKEStloojl5sKnn8Ibb8CSJWeaP9Ong79+4OKJfCkTWkPIi/311192lyDiUZQJEatzM7FybwI3fLSc9xbsZH9CGqFB/gxqWYWZ913J4se68kD3utQsF6ZmkJRoulaIWCkTRejXX83ZQHfdBTt2wHvvnXlMzSCP5UuZ0AwhERGREm7pznhen7uNNftPARAU4MeL115G/2aVCQ3SXwVEREQK1fbt8Mgj8NNP5nGZMjBmDNx7r711iZxD2857sVOnTlG6dGm7yxDxGMqEiNWMFbuZsiGeRduPA+Dv5+CaZpX5d9c61IkJt7k6EXvoWiFipUwUIsOAxx+H8eMhJwcCAswm0JgxEB1td3WST96eCW077yPWrVtndwkiHkWZEDElpmXz3I+beGDKFlczqFb5MJaNvorxQ5urGSQ+TdcKEStlohA5HJCRYTaD+vaFDRvgnXfUDPIyvpQJzRP3YocPH7a7BBGPokyIrzuWnMG3f+7n/xbvISUzB4DGsZE82ach7WuXs7k6Ec+ga4WIlTJxCQwDZs+GGjXgssvMsTFjzB3Eeva0tTS5eL6UCTWEvFh4uH7CK3I2ZUJ81YGENCatOMB/F+7k9I3gdWPCuapsEqOHd7S3OBEPo2uFiJUycZE2boSHH4a5c6FbN5g3z5whVLasmkFezpcyoTWEvFhubi7+Wp1exEWZEF8Sn5LJ3E1HmbMpznVb2GkPdKvLf66qgx+GMiFyDl0rRKyUiQI6ftycBfTRR+B0QmAgPPAAvPKK+bF4PW/PhNYQ8hHff/+93SWIeBRlQnzB1rgkHp28jtYvzefJaRssawS9PrgpO1/uzage9Qj091MmRPKgXIhYKRP5lJkJb7wBdevChx+azaCBA2HLFnj9dTWDShBfyoRuGRMREfEC2+KSufWzPzmalOkaq10+jIEtq9C5Xnkax0bZWJ2IiEgJ9/XX8Oij5sctWpg7iXXubG9NIpdIDSEv1qBBA7tLEPEoyoSURBnZuUxeeYBnZmxyjXWuV567O9emXe2yF3yuMiHiTrkQsVImLiA9HUqVMj8ePhy+/RZuuQVuvRW8+JYiuTBfyoQaQl4sWtsXilgoE1KSHE/OZNycrUxbc4gcp7ncX81yYYwf2pzmVUvn6zWUCRF3yoWIlTKRhyNH4Omn4Y8/YP16CAoybwlbsMDuyqQY+FIm1BDyYkuXLqV69ep2lyHiMZQJ8XYJqVl8tmQPW+OS+X37MbJzzUZQ5agQbutQg+HtaxAckP+fSCoTIu6UCxErZeIs6enmrWCvvAKpqebY3LnmNvLiM3wpE2oIiYiI2MwwDF6cuYXJKw+QnJnjGm9WtTSP9qxPhzplcTgcNlYoIiJSghkGTJoEjz8O+/ebY1dcYTaH2rWztzaRIqRt571YfHw85cqVs7sMEY+hTIi3WbXvJP9bvo+paw5Zxq9pVpm7OtfissqXtlC0MiHiTrkQsfL5TCQlQe/esHSpeVylCrz6KgwbBn7alNsXeXsmtO28j9i6davdJYh4FGVCvEFiejajp66nxhOzGPThUkszqFnV0mx76WreHdbikptBoEyI5EW5ELHy+UxERJi/QkPhhRdg2za46SY1g3yYL2VCt4x5sQMHDthdgohHUSbEk+06nsIni3Yzfe0hMrKdrvF2tcry7651uKxyJGXCggr1cyoTIu6UCxErn8tEaiq89RbcfTeULw8OB3z4oblwdGys3dWJB/ClTKgh5MVKnd4CUUQAZUI8U1pWDk9M2cCcjXFk5ZqNoHLhwdzVqRbXtYylXHhwkX1uZULEnXIhYuUzmXA64euv4ckn4fBhcyexDz4wH6tZ097axKP4TCbQGkJ2lyMiIiVUUkY2T03byM8bjri2ja9XIZxHezWgU71yBdotTERERC7BkiUwahSsXGke16wJb7wBAwfaW5dIEdAaQj7iu+++s7sEEY+iTIgncDoNPluyh6bPzeWndYfJcRoEB/gxblBTfnmwEz0aVSi2ZpAyIeJOuRCxKtGZ2LMHrr8eOnY0m0EREeaC0Zs3qxkk51WiM3EO3TLmxXxscpfIP1ImxE5ZOU4m/rWfTxbv5uDJdNd4rfJhzLqvI6WCin9GkDIh4k65ELEq0ZkYPx4mTzYXiB4xAl58ESpUsLsq8XAlOhPnUEPIi9WtW9fuEkQ8ijIhdkjKyOamT/5kw6FEy/h9V9Xh7s61CQu271KrTIi4Uy5ErEpUJnJz4eRJOL1l+LPPwoED8Nxz0KyZraWJ9yhRmfgHagh5sYoVK9pdgohHUSakOJ1MzeKzP/bwf4v3kJ6d6xof1qYa17euQotqZWyszqRMiLhTLkSsSkwmFiww1wmqUAF++cXcPaxcOZg2ze7KxMuUmEzkg9YQ8mKLFy+2uwQRj6JMSHHIynEyacV+ur31O+8t2El6di6VokK4rX0NtrxwNWMHNvGIZhAoEyJ5US5ErLw+Ezt2wLXXQrdusH49rFhhzgoSuUhen4kC0AwhERGRfMjJdfLF0r28NGuLayy2dClG92nA1ZdVJMBfP2MREREpNidPmmsCvf8+ZGeDvz/cey+MGQNly9pdnYhXUEPIi3Xt2tXuEkQ8ijIhReXP3ScY+vFyy9jNbavxaM8GRIUG2lTVP1MmRNwpFyJWXpmJdevMGUEnTpjHffqY28g3bGhvXVIieGUmLpJ+nOnF9u7da3cJIh5FmZDClpiezb+/XW1pBt3TpTa7XunDSwOaeHQzCJQJkbwoFyJWXpmJhg0hOhoaNYI5c2DWLDWDpNB4ZSYukhpCXmzPnj12lyDiUZQJKQyZObnM3RTHv79dTbPn5zJr/REAwoL8mXnflTx+dQP8/Rw2V5k/yoSIO+VCxMorMrF5M9x1F2RlmcdBQebC0evWQa9e9tYmJY5XZKKQ6JYxLxYY6Nk/mRYpbsqEXKzDp9KZuvogP607wrajyZbHQoP8uatTbR7o7n1bkCoTIu6UCxErj85EfLy5ZfyECeaW8g0bwoMPmo/VrGlnZVKCeXQmCpnDMAzD7iKKU1JSElFRUSQmJhIZGWl3OSIiYrNXf97KJ4t3k+u0Xg67N4yhU73y3NimmhaMFhERKU5ZWeZi0S+8AImJ5th118G4cVCnjr21iXi4gvQ89DdcL/bDDz/YXYKIR1EmJL+SM7J5a9526j31MxN+30Wu06B8RDB9m1Ti7aHN2flyb/5v+OXc2q6GVzeDlAkRd8qFiJVHZcIwYMYMuOwyePhhsxnUvDn89htMnapmkBQLj8pEEdMtY14sOzvb7hJEPIoyIf9k9/EUpq89zCeLdpOenesab1W9DD/c3Q6HwzvWBsovZULEnXIhYuVxmXj/fdi5EypUgJdfhttuM7eUFykmHpeJIqSGkBerqftmRSyUCclLdq6TGWsP878/97Fm/ynXeJnQQCpGleL9G1tQu3y4fQUWIWVCxJ1yIWJleybi4sxFoqOjweGAt96CiRNh9GiIiLC3NvFJtmeiGKkh5MVq1KhhdwkiHkWZkHMt3RXPjZ/86TY+blBTBrWq4jW7hV0sZULEnXIhYmVbJjIyYPx4eOUVGD7cnBkE0KSJ+UvEJr50nfDehRGE3377ze4SRDyKMiGnZWTn8tD3ay3NoJvbVmPhI13Y+2pfrr+8aolvBoEyIZIX5ULEqtgzYRjw/ffmjmFPPgkpKbBmDeTkFG8dIufhS9cJzRASEZESIz0rlzE/bmT6msNk5Tpd4x/d0opel1W0sTIRERFhxQoYNQr++MM8jo2FV1+FG28EP81VECluagh5sY4dO9pdgohHUSZ8V2JaNt+vPMDLs7dYxl8a0JibrqhW4haLzi9lQsSdciFiVWyZ+Oor89YwgNBQePxxeOQR82MRD+JL1wk1hLxYXFwcVapUsbsMEY+hTJR8uU6DvSdS2XQ4icXbj7P5SBKbDidZzgkK8KNT3fKM6d+IqtG+/ZdMZULEnXIhYlVsmejdG0qXhmuuMdcNio0t+s8pchF86TqhhpAX27FjB61bt7a7DBGPoUyUTOlZufzvz33MXH+EtQdOnfe84AA/Hu5Zj6GtqxEVGlh8BXowZULEnXIhYlUkmXA64dtv4bff4NNPzbHy5c3t5MuWLdzPJVLIfOk6oYaQF/PVWyBEzkeZKBkS07JZe/AU3/21n583xp33vOiwIOpVCOe6FrE0rBRJk9go/Rk4h74fIu6UCxGrQs/E0qXw4IPmekEAQ4dCz57mx2oGiRfwpeuEwzAMw+4iilNSUhJRUVEkJiYSGRlpdzkiIj7NMAyW705g7uY41h44xZr9p857brMqUTzTrxHNqpYm0F8LT4qIiHiUffvMdYEmTTKPw8PNXcRGjYKQEHtrE/EhBel5aIaQF5s+fToDBgywuwwRj6FMeIdfNsXx2ZI9/Lkn4YLnBfo7aFe7HP/pWofLa5TxqZ/WFBZlQsSdciFidcmZSE+Hl16CN9+EzExwOGDECHjxRaioHT7F+/jSdUINIS+Wnp5udwkiHkWZ8Fxb45KYsHAXGw8nsfNYSp7n9LqsAn2bVqZ19TJULl2qmCssmZQJEXfKhYjVJWfC3x++/95sBnXtCm+9Bc2bF0ptInbwpeuEGkJerGrVqnaXIOJRlAnPs2JvAkMmLHMbb1W9DG1rRdO1fgxNqkQRHOBvQ3UlnzIh4k65ELG6qEwsWQJXXAGBgRAUBB9+CKmp5g5imtErXs6XrhNqCHmxBg0a2F2CiEdRJjzDgYQ0Xp2zlZV7EzialOkaDw3yZ3j7GtzevgYxkVpLoDgoEyLulAsRqwJlYudOePRRmD4d3nkH7r/fHO/evUhqE7GDL10ntCqnF5s3b57dJYh4FGXCXtm5Th76fi0dx/3GrPVHXM2gGmVDmXJPeza/cDWPX91AzaBipEyIuFMuRKzylYlTp+CRR6BRI7MZ5O8Px44VdWkitvCl64RmCImIyCWbsfYQL83awvHkMzOCYkuXYuzAJnSqV97GykREROSi5eTAJ5/As89CfLw5dvXV5gLSjRrZW5uIXDI1hLxY+/bt7S5BxKMoE8VvyqqDPDx5nWXssavrc2+XOjZVJGdTJkTcKRciVhfMxL//DR9/bH7csKG5YPTVVxdPYSI28aXrhG4Z82IJCRfeslnE1ygTxSMrx8mMtYeo+9RsSzOoXHgQq57urmaQB1EmRNwpFyJWbpkwjDMf//vfEBMD778P69erGSQ+wZeuE7Y3hD744ANq1qxJSEgIrVq1YvHixRc8/3//+x/NmjUjNDSUSpUqcfvtt3PixIliqtazbN261e4SRDyKMlG0cnKdPDRpLfWe/pkHvltLdq75F8aGlSL59eHOrHy6B2XDg22uUs6mTIi4Uy5ErFyZOHEC7rvPXCvotKZNYf9+szEUoJtLxDf40nXC1obQpEmTePDBB3nqqadYs2YNHTt2pHfv3uzfvz/P85csWcKtt97KiBEj2LRpE5MnT2bFihWMHDmymCsXEfEdhmHw9fJ91HnqZ6auOeQav751FZY83pWfH+hI7fLhNlYoIiIiF8svJwfGj4c6dcyZQO++CwcOnDkhWD/sESmpHIZx9pzA4nXFFVfQsmVLPvzwQ9dYw4YNGTBgAGPHjnU7/4033uDDDz9k165drrH33nuPcePGceDsN60LSEpKIioqisTERCIjIy/9i7BRbm4u/v7+dpch4jGUicK3fPcJHvhujWX7+JFX1uTRq+sTHKDvtadTJkTcKRcifzMM+OknjEcewbFjhznWtKm5TlC3bvbWJmIjb79OFKTnYdsMoaysLFatWkXPnj0t4z179mTp0qV5Pqd9+/YcPHiQ2bNnYxgGR48e5YcffqBv377n/TyZmZkkJSVZfpUUs2fPtrsEEY+iTBSe1MwcRn65khs+Xu5qBg1sGcvqZ3rwdL9GagZ5CWVCxJ1yIQLs2QM9esC115rNoJgYc/Ho1avVDBKf50vXCdtuBI2Pjyc3N5cKFSpYxitUqEBcXFyez2nfvj3/+9//GDp0KBkZGeTk5HDNNdfw3nvvnffzjB07lueff95tfPLkyYSGhjJw4EB+/fVXEhMTiYmJoU2bNsycOROAli1b4nQ6Wbt2LQDXXnstS5Ys4cSJE0RHR9OpUyemT58OQNOmTQkMDGTVqlUA9O3bl5UrV3L06FEiIyPp2bMnP/zwAwCXXXYZ4eHh/PnnnwD06tWLjRs3cujQIcLCwujXrx+TJk0CoH79+pQrV44//vgDgO7du7N9+3b279/PkSNHAPPWO6fTSe3atYmNjWXRokUAdOnShf3797N7924CAgIYMmQIU6ZMISsri+rVq1O7dm0WLFgAwJVXXsmxY8fYvn07AMOGDWPGjBmkpaVRpUoVGjVqxNy5cwFo164diYmJbN68GYAhQ4YwZ84ckpOTqVixIi1btnSF6PLLLycjI4MNGzYAcN1117Fw4UJOnjxJuXLlaNeuHT/99BMALVq0AGDNmjUA9O/fn2XLlhEfH0+ZMmXo0qUL06ZNA6BJkyaEhISwYsUKAPr06cPq1auJi4sjIiKCq6++msmTJwPQqFEjoqKiWLZsGWA2HTdv3szBgwcJDQ3l2muvZeLEiQDUq1ePmJgYlixZAsBVV13Frl272LdvH0FBQQwaNIjJkyeTk5NDrVq1qFatGgsXLgSgU6dOHDp0iF27duHn58fQoUOZOnUqmZmZVKtWjXr16jF//nwAOnToQHx8PNu2bQNg6NChzJw5k9TUVGJjY2ncuDG//PILYM6kS0lJYdOmTQAMHjyYuXPnkpSURIUKFWjdujWzZs0CoFWrVmRnZ7N+/XoABgwYwKJFi0hISKBs2bJceeWVzJgxA4DmzZvj5+fH6tWrAejXrx9//fUXx44dIyoqim7dujF16lQAGjduTGhoKH/99RcAvXv3Zt26dRw+fJjw8HD69OnD999/D0CDBg2Ijo52NXZ79OjB1q1bOXDgAKVKlWLAgAF89913GIZB3bp1qVixomvtsK5du7J371727NlDYGAggwcP5ocffiA7O5uaNWtSo0YNfvvtNwA6duxIXFwcO3bswOFwcMMNN7Bz504mTpxI1apVadCgAfPmzQPM946EhATX/cDXX389s2fPJiUlhcqVK9OsWTN+/vlnANq0aUNaWhobN24E8Or3iODgYAYOHFig94hvJk3h6TUhnOvdAbWIDUzjlx+nAHqP8Jb3iPj4eJKSkvQe8fd7xPTp00lPT9d7xCW8R5SEv0ccOnTItX6lr79H6O8RvvseMf2XX+i3dCkBgYFs7N6dbYMGkRMaSveEBJ9/jwD9PcLX3yMOHTpEixYtvPY9Ii0tjfyy7Zaxw4cPExsby9KlS2nXrp1r/OWXX+brr7/OcyGnzZs30717d0aNGkWvXr04cuQIjz76KJdffjmffvppnp8nMzOTzMwztzokJSVRtWrVEnHL2O+//07nzp3tLkPEYygTl2bprnhu/ORPy9hz/RtxW4eaNlUkl0qZEHGnXIhPysyE6dNh6NAzYz/9BI0b8/v+/cqEyFm8/TpRkFvGbJshVK5cOfz9/d1mAx07dsxt1tBpY8eOpUOHDjz66KOA2QULCwujY8eOvPTSS1SqVMntOcHBwQSX0IXQmjVrZncJIh5Fmbg48SmZjJmxiVkbjrjGGsdG8tN/rsThcNhYmVwqZULEnXIhPsUwYMoUeOwx8zaxqKgzW8f37w9AszJlbCxQxPP40nXCtjWEgoKCaNWqlWua1Wnz5s2jffv2eT4nLS0NPz9ryacXe7JxbWzbnJ52JiImZaJg9sSn8q+vVtL6pfmuZlD1sqEseLgzM+/rqGZQCaBMiLhTLsRnrFoFnTvDkCFmM6hyZcjOdjtNmRCx8qVM2DZDCOChhx7illtuoXXr1rRr146PP/6Y/fv3c/fddwMwevRoDh06xFdffQWY93neeeedfPjhh65bxh588EHatGlD5cqV7fxSRES8xrHkDIZ+tJw98amW8f7NKvPuDc3VCBIREfFmhw/Dk0/CV1+ZM4RKlYJHHzVnCYWF2V2diHgQWxtCQ4cO5cSJE7zwwgscOXKExo0bM3v2bKpXrw7AkSNH2L9/v+v82267jeTkZN5//30efvhhSpcuzVVXXcVrr71m15dgqzZt2thdgohHUSYuzDAMPvx9F+PmbLOMP9uvEYNaVSGqVKBNlUlRUSZE3CkXUqIZBvTuDX8v+svNN8Mrr0DVqud9ijIhYuVLmbC1IQRw7733cu+99+b52BdffOE2dt9993HfffcVcVXeoSCrh4v4AmXi/NYdOMWdX63kWPKZRfZrlQtjyj3tKRMWZGNlUpSUCRF3yoWUOE6n2Qjy9weHA557Dl5/Hd5+G/LxD1tlQsTKlzJh2xpCculOb1UnIiZlIm9jf97Ctf/9w9UMqlKmFPMf6sSCR7qoGVTCKRMi7pQLKVGWLYN27WDChDNjAwbAH3/kqxkEyoTIuXwpE7bPEBIRkaIzZ2McH/2+G4DgAD9mP9CR2uXDba5KRERELsm+ffDEE/Ddd+bx0aNw110QEGDOEhIRyQeH4WPbcyUlJREVFUViYiKRkZF2l3NJMjMzCQ4OtrsMEY+hTFit3n+SgR8sdR3veqUP/n76S6IvUSZE3CkX4tWSk+HVV+GttyAjw2z+3H47vPQSVKp0US+pTIhYeXsmCtLz0C1jXuzXX3+1uwQRj6JMnPH18n0M/WiZ63jV093VDPJByoSIO+VCvNacOVCvnrlIdEYGdOlibi3/6acX3QwCZULkXL6UCd0y5sUSExPtLkHEoygTMH3NIR6fsp7MHCcA5SOC+fmBjpQN996fcsjFUyZE3CkX4rUqVjRvDatdG954A669tlBuD1MmRKx8KRNqCHmxmJgYu0sQ8Si+nIm1B07x0KS17I5PdY21qRnNJ7e21nbyPsyXMyFyPsqFeI1du2DJEhg+3Dxu3hx+/tmcGVSIt7MoEyJWvpQJrSHkxZKTk4mIiLC7DBGP4YuZ+HnDER6ctNY1I+i0t4c2Z0CLWJuqEk/hi5kQ+SfKhXi8xERzTaB33zW3lN+4EerXL7JPp0yIWHl7JrSGkI+YOXOm3SWIeBRfyoTTafD2/O3c87/VrmZQrXJhTPpXW/a+2lfNIAF8KxMi+aVciMfKyTG3j69b17wlLCsLrroK/Ir2n2zKhIiVL2VCt4yJiHiR3cdTePfXHfy8Mc7VCIoIDuDLEW1oWa2MzdWJiIjIRZk7Fx56CDZtMo/r1zd3EuvdW9vIi0iRUUPIi7Vs2dLuEkQ8SknORFxiBs//tImfN8ZZxvs1rcRb1zcnKEATPsVdSc6EyMVSLsTjnDoFgwebW8pHR8Nzz8Hdd0Ng8awBqEyIWPlSJtQQ8mJOp/OfTxLxISUxEydSMrnm/T84dCrdNVYq0J9+TSsxsmMt6lf03vubpeiVxEyIXCrlQjxCcjKcXqOkdGkYMwYOHIBnnzWbQsVImRCx8qVM6EfKXmzt2rV2lyDiUUpaJvbEp3LFK79amkHdG1Zg0/O9eH1IMzWD5B+VtEyIFAblQmyVnW0uFl29unmb2GkPPwxvv13szSBQJkTO5UuZ0AwhEREPk5iezfsLdvDJ4j2uscevbsBdnWrh56d1BERERLyOYcCsWfDII7Btmzn26afQs6e9dYmIT9O2814sLS2N0NBQu8sQ8Rjenok/dsbzwk+b2XY02TI+b1Qn6lbQbCApOG/PhEhRUC6k2G3caC4YPW+eeVy+vLmt/IgR4O9vb20oEyLn8vZMaNt5H7FkyRK7SxDxKN6aiRMpmTwyeR03/d+flmbQZZUj1QySS+KtmRApSsqFFKsXX4RmzcxmUFAQPPYY7NgB//qXRzSDQJkQOZcvZUK3jHmxEydO2F2CiEfxtkykZOZwx+cr+GtvgmV8/NBmDGgei0PbzMol8rZMiBQH5UKKVaNG4HSau4i99hrUqmV3RW6UCRErX8qEGkJeLNqGRedEPJm3ZGLpznjeXbCD5butjaD7u9VlRIeaRIUWzzazUvJ5SyZEipNyIUXGMGDaNMjKghtuMMcGDoRVq8CDt7FWJkSsfCkTWkPIi6Wnp1OqVCm7yxDxGJ6cidTMHB6bsp5Z64+4PTawRSxvXt9MM4Kk0HlyJkTsolxIkVi9GkaNgkWLoGxZ2LnT3E7eCygTIlbengmtIeQjpk+fbncJIh7FEzOx8VAit3z6J5eN+cWtGXTfVXXY8XJv3hraXM0gKRKemAkRuykXUqgOH4bbb4fWrc1mUEgI3HMPBHrPbF9lQsTKlzKhW8ZERApRVo6TL5fuZeqaQ2w5kuT2eOd65RndpwENKnr3DEURERGflp4Ob74Jr74Kqanm2I03wtixUK2avbWJiOSTGkJerGnTpnaXIOJR7MxEelYuXyzdy2tztub5+N2da/Ng97qEBHrGjiLiG3SdEHGnXEih2LYNnn3WXDeobVsYP978vxdSJkSsfCkTagh5sUAvmooqUhzsyERyRjZvz9/Bp0v2WMZrlQ/jxjbVuK5FLGXDg4u9LhHQdUIkL8qFXLSDB6FKFfPj5s3hySfhssvMBaS9+NZvZULEypcyoYaQF1u1ahX16tWzuwwRj1Gcmdh4KJGXZm122ymsTkw434y4gopRIcVSh8iF6Doh4k65kAI7cACeeAImT4aNG+H0n5+XXrK3rkKiTIhY+VIm1BASEcmn5Ixsvlq2j0krDrA/Ic01HhkSwMCWVXioZz0iQ3znJwoiIiIlWkoKjBsHr78OGRnmLKB58840hEREvJy2nfdiSUlJXv81iBSmosrEsl0neGTyOg6dSreMBwf4MbBlFV65rrF2CROPpOuEiDvlQv6R0wlffw2jR8ORv3cI7dTJXCeoZUt7aysCyoSIlbdnQtvO+4iVK1faXYKIRynsTOw8lszVby9i2CfLLc2gxrGR/PpwZ7a91JuxA5uoGSQeS9cJEXfKhVyQYUC3bnDbbWYzqGZNmDIFFi4skc0gUCZEzuVLmdAtY17s6NGjdpcg4lEKKxPZuU6emLKBKasPWsbHDmzC9a2r4u+nBpB4B10nRNwpF3JBDgf07AmrVsEzz8D990Nwyd4cQpkQsfKlTKgh5MW8eRqbSFEojEwcS8qg9zuLOZGaBUCzqqV54uoGtKtd9pJfW6S46Toh4k65EIukJHj5ZbMJ1K2bOTZqFIwYATEx9tZWTJQJEStfyoTWEPJi2dnZPrUlnsg/udRMrNybwOAJy1zHd3SoybP9GxVGaSK20HVCxJ1yIQDk5sKnn8LTT8Px49C4MaxdC/7+dldW7JQJEStvz4TWEPIRP/zwg90liHiUi81ErtPgqWkbXM2g4AA/3r+xhZpB4vV0nRBxp1wI8+dDixZw111mM6hePRg7Fvx8859GyoSIlS9lQreMiYhPW7UvgUEfnpkVVC48iOn/7kCVMqE2ViUiIiKFbvt2eOQR+Okn87hMGRgzBu69F7x4NoCIyMVSQ8iLXXbZZXaXIOJRCpKJdQdO8dKszazYe9I11qFOWb4ZcYV2DZMSQ9cJEXfKhQ9bt85sBgUEmE2gMWMgOtruqmynTIhY+VIm1BDyYuHh4XaXIOJR8pOJnFwnb8zdzoTfd7nGGlSMYHCrKozsWKsoyxMpdrpOiLhTLnxIdjZs22auDwQweDA88QQMHw4NGthbmwdRJkSsfCkTvnmjbAnx559/2l2CiEf5p0zsPJZCh9cWWJpB911VhzkPdlIzSEokXSdE3CkXPsAwYPZsaNoUunaFxERz3OEw1wpSM8hCmRCx8qVMaIaQiJR4hmHwzq87eHv+DtdYz0YVeOeGFpQK8r3dREREREqsTZvgoYdg7lzzuFw52LwZ2rWzty4REQ+kbee9WEJCAtG671nEJa9MbD6cxPUfLSMlM8c1Nvv+jjSq7N35F8kPXSdE3CkXJdTx4+aaQB99BE6nuUj0gw/CU09BVJTd1Xk0ZULEytszoW3nfcTGjRvtLkHEo5ybiQm/76L/+0tczaC2taLZ8FxPNYPEZ+g6IeJOuSiBEhKgfn348EOzGTRwIGzZAuPGqRmUD8qEiJUvZUK3jHmxQ4cO2V2CiEc5OxP3TVzDT+sOA1AnJpwJN7ekTkyEXaWJ2ELXCRF3ykUJFB0N115r7iL21lvQpYvdFXkVZULEypcyoRlCXiwsLMzuEkQ8yulMvDZnq6sZ1KhSJDPvu1LNIPFJuk6IuFMuSoA1a6BnT9i588zYu+/CihVqBl0EZULEypcyoTWEvJjT6cTPTz09kdOcTic/rjvCg5PWAuYtYhPvbIvD4bC3MBGb6Doh4k658GJHjphrAn3xhbmT2JAh8P33dlfl9ZQJEStvz4TWEPIRkyZNsrsEEY+RlpVDyzGzXM2gkEA/Pr61tZpB4tN0nRBxp1x4ofR0ePllqFsXPv/cbAbdcAO8/rrdlZUIyoSIlS9lQmsIiYjXS83Moftbv3Mq+0yPe+2zPQkJ1JbyIiIiXm3qVBg1CvbvN4+vuALGj9c28iIihUANIS9Wv359u0sQsV12rpPhn/3FkcQMAO7pUpvHetXXzCARdJ0QyYty4WW2bDGbQVWqwKuvwrBh4MW3cngiZULEypcyoYaQFytXrpzdJYjYKiM7lz7vLmb38VQAxvSoyu3dGthclYjn0HVCxJ1y4eEOHjS3kW/a1Dx+6CEICYF77oHQUHtrK6GUCRErX8qE2ute7I8//rC7BBHbnErLovPrv7maQXd0qEnIsU02VyXiWXSdEHGnXHio1FQYMwbq1YNbboHcXHO8VCl4+GE1g4qQMiFi5UuZUENIRLzOzPWHaf7CPI4mZQJwV+daPNOvoc1ViYiISIE5nfDVV2Yj6IUXzAWkIyPhxAm7KxMRKfG07bwXO378OOXLl7e7DJFik5XjZNSktczacMQ1NvHOtrSrXRZQJkTOpUyIuFMuPMiSJeaC0StXmsc1apg7hw0aBFoLsNgoEyJW3p4JbTvvI7Zv3253CSLF5kRKJl3fWOhqBtUqF8bs+zu6mkGgTIicS5kQcadceIg//oCOHc1mUESEuWD0li0weLCaQcVMmRCx8qVMqCHkxfaf3n5TpITLyM6l1UvzOXQqHYBn+zViwSNdaFTZ2vFWJkSslAkRd8qFjc6+MaF9e7Mh9K9/wY4d8Pjj5uLRUuyUCRErX8qEdhnzYsHBwXaXIFLkDp9K5+q3F7mOb+9QgzuurJnnucqEiJUyIeJOubBBbi589hm89555m1hkpDkL6NdfITDQ7up8njIhYuVLmdAaQiLisY4nZ3LFK/Nx/v0uNXZgE4a1qWZvUSIiIpJ/CxaY6wStX28ejx0LTzxhb00iIiWY1hDyEZMmTbK7BJEi8+O6w1z+8plm0E//ufIfm0HKhIiVMiHiTrkoJjt2wIAB0K2b2QwqXRrGj4eHHrK7MjmHMiFi5UuZ0C1jXszpdNpdgkihm7Mxjru/WWUZe3NIM5pUifrH5yoTIlbKhIg75aKIGQY8+ii8+y5kZ4O/P9x7L4wZA2XL/vPzpdgpEyJWvpQJNYS8WO3ate0uQaTQJKZl0+yFuZaxAD8HE//VlstrROfrNZQJEStlQsSdclHEHA44ftxsBvXpA2+8AQ0b2l2VXIAyIWLlS5lQQ8iLxcbG2l2CyCXLyXXyxtztfLJ4t2X8p/9cma9ZQWdTJkSslAkRd8pFEZgzB+rVg1q1zONXXoEbb4ReveytS/JFmRCx8qVMaA0hL7Zo0aJ/PknEg321bC91nvqZCb/vIvfvxYLeur4Ze1/tW+BmECgTIudSJkTcKReFaPNm6N3b/PXoo2fGY2PVDPIiyoSIlS9lQjOERKTY5ToNaj852zLWslppPr+tDVGh2n5WRETEo8XHw3PPwYQJ5pbygYFQsyY4neCnnzeLiHgLNYS8WJcuXewuQaTA9p1I5boPllrGfnmwE/UrRlzyaysTIlbKhIg75eISZGXB++/DCy9AYqI5NmAAvP461Klja2ly8ZQJEStfyoRa+F5s//79dpcgUiCJadl0fn0hCalZAPynax32vtq3UJpBoEyInEuZEHGnXFyCDz6Ahx82m0HNm8OCBTBtmppBXk6ZELHypUyoIeTFdu/e/c8niXiI7UeTLbuIvTGkGY/0ql+on0OZELFSJkTcKRcFlJV15uN//Qsuvxz+7/9g5Uro2tW+uqTQKBMiVr6UCd0y5sUCAvTbJ95h2pqDjJq0znX8XP9GDG5VpdA/jzIhYqVMiLhTLvLp6FF4+mlYswb+/BP8/SE01PzY4bC7OilEyoSIlS9lwmEYhmF3EcUpKSmJqKgoEhMTiYyMtLsckRItMT2b5i/M5ex3meevuYzh7WvYVpOIiIhcQEYGvP22uXV8crI5Nn8+dOtma1kiIpI/Bel56JYxLzZlyhS7SxA5r+/+2k+rF+e5mkGNKkWyfHS3Im0GKRMiVsqEiDvl4jwMAyZPhoYNYfRosxnUpg388YeaQSWcMiFi5UuZ8J25UCVQ1tn3dIt4iEkr9vP4lA2WsVHd6/FA97pF/rmVCRErZULEnXKRhxMnzN3Cliwxj2Nj4dVX4cYbtY28D1AmRKx8KRNqCHmx6tWr212CiMufu08w9OPllrH2tcvyaK/6tKhWplhqUCZErJQJEXfKRR6ioyEnx1wj6LHH4JFHICzM7qqkmCgTIla+lAk1hLxY7dq17S5BBICvl+/jmekbXcfXNKvM030bEhMZUqx1KBMiVsqEiDvlAkhLg3ffhXvvhchIc5Hozz6DiAioUvibPohnUyZErHwpE5oD6sUWLFhgdwni4w4kpDF66npXMygiJID3hrXg3WEtir0ZBMqEyLmUCRF3Pp0LpxO++Qbq1TPXCRo79sxjDRuqGeSjfDoTInnwpUxohpCIFJjTafDID+uYuvqQa6xj3XJ8eHMrwoP1tiIiIuJxli6FBx+EFSvM4+rV4fLLbS1JRETspX+5ebErr7zS7hLEB63Ym8CQCcssYy9f15gb21TD4XDYVJVJmRCxUiZE3PlcLvbtg8cfh0mTzOPwcHjqKbM5FFL8s3nF8/hcJkT+gS9lokANoW3btjFx4kQWL17M3r17SUtLo3z58rRo0YJevXoxaNAggoODi6pWOcexY8eoWrWq3WWIDzh4Mo235+/gt63HOJF6ZtX9Ho0q8PEtrWxvBJ2mTIhYKRMi7nwuF889ZzaDHA4YMQJefBEqVrS7KvEgPpcJkX/gS5nI1xpCa9asoUePHjRr1oxFixZx+eWX8+CDD/Liiy9y8803YxgGTz31FJUrV+a1114jMzOzqOsWYPv27XaXICVYYlo2fd5ZTINnfubK137jh1UHXc2g4AA/fvrPlXxya2uPaQaBMiFyLmVCxF2Jz0VuLiQmnjl+8UXo0wdWr4ZPPlEzSNyU+EyIFJAvZSJfM4QGDBjAo48+yqRJk4iOjj7vecuWLWP8+PG8+eabPPnkk4VWpIgUry/+2MNzP212Gx/WpipDWleleZXS+Pl5TiNIREREgIULYdQoqFMHJk82x6pUgVmzbC1LREQ8k8MwDOOfTsrKyiIoKCjfL1rQ84tTUlISUVFRJCYmEhkZaXc5Ih4lr/WBejeuyDP9GlG5dCmbqhIREZEL2rkTHn0Upk83j0uXhq1boUIFO6sSEREbFKTnka9bxvLb3Dl06FCBzpdLM2PGDLtLkBLiVFoWgz5camkGRZUKZP1zPfnw5lZe0wxSJkSslAkRdyUqF6dOwSOPQKNGZjPI3x/+/W/YsUPNIMm3EpUJkULgS5kolF3G4uLiePnll/m///s/0tPTC+MlJR/S0tLsLkG8XE6uk7E/b+XTJXtcY/5+Dr64/XI61i1vY2UXR5kQsVImRNyVmFysWGGuDRQfbx5ffTW8+abZHBIpgBKTCZFC4kuZyNcMIYBTp05x0003Ub58eSpXrsy7776L0+nk2WefpVatWixfvpzPPvusKGuVc1SpUsXuEsRLOZ0GS3fGU+epny3NoGubV2btsz28shkEyoTIuZQJEXclJheNGkFwMDRsCLNnw88/qxkkF6XEZEKkkPhSJvI9Q+jJJ59k0aJFDB8+nDlz5jBq1CjmzJlDRkYGP//8M507dy7KOiUPjXTRlwLKyM7lyakb+Gn9YbJzzywf1qhSJNP/3YGggHz3iD2SMiFipUyIuPPaXGzZAh99BG+9BX5+EBYGv/4KtWpBYKDd1YkX89pMiBQRX8pEvv/1N2vWLD7//HPeeOMNfvzxRwzDoF69eixYsEDNIJvMnTvX7hLEixxPzqTBM3OYuuaQqxnUsW45Ph3emtkPdPT6ZhAoEyLnUiZE3HldLk6cgPvugyZN4J134KuvzjxWv76aQXLJvC4TIkXMlzKR7xlChw8fdnXKatWqRUhICCNHjiyywkSkcDidBlPXHOLJqRtcYzdcXpUnejegdKgWgBcREfFIWVnwwQfw/PPm4tEA11wD7dvbWpaIiJQc+W4IOZ1OAs/6CYS/vz9hYWFFUpTkT7t27ewuQTzcnvhUur6x0HUc4Odg/NDm9G9W2b6iipAyIWKlTIi48/hcGAbMnAkPP2zuFgbQtCmMHw9XXWVvbVIieXwmRIqZL2Ui3w0hwzC47bbbCA4OBiAjI4O7777brSk0derUwq1QzisxMdHuEsSD7T6ewsAPl7qOO9Qpyzs3tKBceLCNVRUtZULESpkQcecVuXj5ZbMZFBNjfnz77eaW8iJFwCsyIVKMfCkT+V40ZPjw4cTExBAVFUVUVBQ333wzlStXdh2f/iXFZ/PmzXaXIB7qx3WHufrtxZxKywZg2r3t+d/ItiW6GQTKhMi5lAkRdx6Zi6NHISXF/NjhgLffhieeMJtCI0eqGSRFyiMzIWIjX8pEvmcIff7550VZh4gUkrfnb+ft+Ttcx5/d1poW1crYWJGIiIjkKSPDXCj65Zfh/vvhpZfM8bZtzV8iIiJFyGEYhvHPp5n27dvH3Llzyc7OpkuXLl65HVtSUhJRUVEkJiYSGRlpdzmXJCcnh4CAfPf0xAe88cs23v9tJwARwQHMe6gzFaNCbK6q+CgTIlbKhIg7j8iFYcCUKfDYY7Bnjzl25ZXw++/mlvIixcgjMiHiQbw9EwXpeeT7irNo0SIuu+wy7rrrLv7zn//QvHlzJk6ceMnFysWbM2eO3SWIh8jOdfLQ92tdzaDW1cuw/rmePtUMAmVC5FzKhIg723OxahV07gxDhpjNoMqV4csv1QwS29ieCREP40uZyPdV55lnnqFr164cPHiQEydOcMcdd/DYY48VZW3yD5KTk+0uQTxATq6TVi/OY+rqQwB0b1iB7+9qh8PhsLmy4qdMiFgpEyLubM3Fxx9D69aweDGUKgXPPgvbt8Ott6oZJLbRtULEypcyke95UBs2bGDRokVUrmxuV/3mm2/yySefcPLkScqU0fokdqhYsaLdJYjNnE6D4Z//RVJGDgB3dqzJU32971bOwqJMiFgpEyLubM1F794QGgoDB8Irr0DVqvbVIvI3XStErHwpE/luCJ06dYqYmBjXcVhYGKGhoZw6dUoNIZu0bNnS7hLERulZudzw8TLWHTS3RezbtJJPN4NAmRA5lzIh4q7YcuF0wsSJsHIljB9vjlWtCrt2gQ/9Y0M8n64VIla+lIkCzU3dvHkz69evd/0yDIMtW7ZYxqT4zJ492+4SxCZZOU6Gf/6Xqxl0d+fa/PdG33njOh9lQsRKmRBxVyy5WLYM2rWDm282t5BfuvTMY2oGiYfRtULEypcyUaCls7t168a5m5L169cPh8OBYRg4HA5yc3MLtUARsXI6Dfq8u5idx1IAeKhHPe7vVtfmqkRERIR9++CJJ+C778zj8HB48klo0cLeukRERPKQ74bQntNbYorHuPzyy+0uQWzw4KS1rmbQK9c14cYrqtlckedQJkSslAkRd0WSi7Q0c02gN9+EjAxwOOD22+Gll6BSpcL/fCKFSNcKEStfykS+G0JffvkljzzyCKGhoUVZjxRARkaG3SVIMTIMg6enb+THdYcBGHFlTTWDzqFMiFgpEyLuiiQXhgGff242g7p0gbfe0qwg8Rq6VohY+VIm8r2G0PPPP09KSkpR1iIFtGHDBrtLkGL09fJ9/O/P/QB0qleex69uYHNFnkeZELFSJkTcFVou/vzTXDgaICwM/vtfmDYNFixQM0i8iq4VIla+lIl8N4TOXTtIRIpPRnYu/7fYvG2zZbXSfHVHG4ICCrQmvIiIiBSGXbtg0CBo2xa++ebM+IAB5i+Hw67KRERECsRh5LPT4+fnx9GjRylfvnxR11SkkpKSiIqKIjExkcjISLvLuSQZGRmEhITYXYYUg9FTNzDxr/34+zlYNvoqYiL0+54XZULESpkQcXfRuUhMNNcEevddyMoCPz945hl47rlCr1GkOOlaIWLl7ZkoSM+jQFMMunXrRsuWLS/4S4rPwoUL7S5BisFfexKY+Jd5q9h/utZRM+gClAkRK2VCxF2Bc5GTAxMmQN268MYbZjOoZ09Yv17NICkRdK0QsfKlTBRo2/levXoRHh5eVLVIAZ08edLuEqSIZWTnMvTjZQA0qBjBqB71bK7IsykTIlbKhIi7Aufijjvg66/Njxs0MHcS691bt4ZJiaFrhYiVL2WiQA2hRx99lJiYmKKqRQqoXLlydpcgRWzs7C0YBoQG+fPF7W3sLsfjKRMiVsqEiLsC5+Kuu2D2bHM20F13QWBgkdQlYhddK0SsfCkT+W4IOfRTEI/Trl07u0uQIvTrlqN8uWwfAMPaVKNilG4V+yfKhIiVMiHi7oK5SEiA55+HsmXh2WfNsQ4dYN8+cycxkRJI1woRK1/KhHYZ82I//fST3SVIEdl5LJkRX64EICI4gGf6NbK5Iu+gTIhYKRMi7vLMRXa2uVh0nTrm/8eOhePHzzyuZpCUYLpWiFj5Uiby3RDas2ePT02dErHLqn0JdH9rket42r872FiNiIhICWYYMHMmNGkCDzwAJ0+aH8+cCV6+s66IiMg/yVdD6NVXX6V8+fL4+f3z6X/++SezZs3KdwEffPABNWvWJCQkhFatWrF48eILnp+ZmclTTz1F9erVCQ4Opnbt2nz22Wf5/nwlSYsWLewuQQpZdq6Tx6dscB2/N6wFdWK0kHt+KRMiVsqEiDtXLnbtgl69oH9/2LbNbAB99BGsWQPdutlbpEgx0rVCxMqXMpGvNYQ2b95MtWrVGDJkCNdccw2tW7em/N8/NcnJyWHz5s0sWbKEb775hiNHjvDVV1/l65NPmjSJBx98kA8++IAOHTrw0Ucf0bt3b9fny8v111/P0aNH+fTTT6lTpw7Hjh0jJycnn1+uiGf772872XksBYDZ93ekUeVImysSEREpoQICYPFiCAqCBx+EJ5+EqCi7qxIRESk2+Zoh9NVXX7FgwQKcTic33XQTFStWJCgoiIiICIKDg2nRogWfffYZt912G1u3bqVjx475+uRvvfUWI0aMYOTIkTRs2JC3336bqlWr8uGHH+Z5/pw5c/j999+ZPXs23bt3p0aNGrRp04b27dvn/ysuQdasWWN3CVKIUjNzeHv+DgBua19DzaCLoEyIWCkTImfJzIQffzyTi+rV4YsvYMsWeO01NYPEZ+laIWLlS5nI9y5jTZs25aOPPmLChAmsX7+evXv3kp6eTrly5WjevHmB1xfKyspi1apVPPHEE5bxnj17snTp0jyf8+OPP9K6dWvGjRvH119/TVhYGNdccw0vvvgipUqVyvM5mZmZZGZmuo6TkpIKVKdIcXlvwU4AHA54sk9Dm6sREREpIQwDpk6Fxx6D3bsp99xzZx4bOtS2skREROyW74bQaQ6Hg2bNmtGsWbNL+sTx8fHk5uZSoUIFy3iFChWIi4vL8zm7d+9myZIlhISEMG3aNOLj47n33ntJSEg47zpCY8eO5fnnn3cbnzx5MqGhoQwcOJBff/2VxMREYmJiaNOmDTNnzgSgZcuWOJ1O1q5dC8C1117LkiVLOHHiBNHR0XTq1Inp06cDZsMsMDCQVatWAdC3b19WrlzJ0aNHiYyMpGfPnvzwww8AXHbZZYSHh/Pnn38C0KtXLzZu3MihQ4cICwujX79+TJo0CYD69etTrlw5/vjjDwC6d+/O9u3b2b9/P/7+/oB5653T6aR27drExsayaJG5IHGXLl3Yv38/u3fvJiAggCFDhjBlyhSysrKoXr06tWvXZsGCBQBceeWVHDt2jO3btwMwbNgwZsyYQVpaGlWqVKFRo0bMnTsXMLfhS0xMZPPmzQAMGTKEOXPmkJycTMWKFWnZsiWzZ88G4PLLLycjI4MNG8x1ca677joWLlzIyZMnKVeuHO3atXOt4n76Xs3THdn+/fuzbNky4uPjKVOmDF26dGHatGkANGnShJCQEFasWAFAnz59WL16NXFxcURERHD11VczefJkABo1akRUVBTLli0DzKbj5s2bOXjwIKGhoVx77bVMnDgRgHr16hETE8OSJUsAuOqqq9i1axf79u0jKCiIQYMGMXnyZHJycqhVqxbVqlVj4cKFAHTq1IlDhw6xa9cu/Pz8GDp0KFOnTiUzM5Nq1apRr1495s+fD0CHDh2Ij49n27ZtAKRWacOE33cBcEfjYFKSTvHLL78AcMUVV5CSksKmTZsAGDx4MHPnziUpKYkKFSrQunVr17pdrVq1Ijs7m/Xr1wMwYMAAFi1aREJCAmXLluXKK69kxowZADRv3hw/Pz9Wr14NQL9+/fjrr784duwYUVFRdOvWjalTpwLQuHFjQkND+euvvwDo3bs369at4/Dhw4SHh9OnTx++//57ABo0aEB0dLSrsdujRw+2bt3KgQMHKFWqFAMGDOC7777DMAzq1q1LxYoVXWuHde3alb1797Jnzx4CAwMZPHgwP/zwA9nZ2dSsWZMaNWrw22+/AdCxY0fi4uLYsWMHDoeDG264gYCAACZOnEjVqlVp0KAB8+bNA6B9+/YkJCSwdetWwLz1dPbs2aSkpFC5cmWaNWvGzz//DECbNm1IS0tj48aNAF79HhEcHMzAgQP1HoH3v0cMHTqUmTNnkpqaSmxsLI0bN87Xe0R0dDRJSUl6j/j7PWL69Omkp6frPcKH3iM2fvUVtd57j5i/f2/TSpfGLzmZjRs36j1Cf4/QewTme0SLFi1cf4Z97T1Cf4/Qe0Re7xE5OTkcPHjQa98j0tLSyC+HYdN+8ocPHyY2NpalS5fSrl071/jLL7/M119/7fpmnq1nz54sXryYuLg4ov6e1jt16lQGDx5MampqnrOE8pohVLVqVRITE4mM9O5bcubNm0ePHj3sLkMuUXauk7pPmW8QNcqGsuDhLvj5OWyuyjspEyJWyoT4rMOH4amn4MsvzRlCISHw6KPw2GPMW7ZMuRA5i64VIlbenomkpCSioqLy1fMo8AyhwlKuXDn8/f3dZgMdO3bMbdbQaZUqVSI2NtbVDAJo2LAhhmFw8OBB6tat6/ac4OBggoODC7d4DxEfH293CVIIXv35TPPz09suVzPoEigTIlbKhPgkpxO6doW/ZyJw000wdixUrQooFyLnUiZErHwpE/laVLooBAUF0apVK9c0q9PmzZt33kWiO3TowOHDh0lJSXGNbd++HT8/P6pUqVKk9XqiMmXK2F2CXKK98al8uXQvAM/2a0Tt8tpi/lIoEyJWyoT4DMMwG0EAfn4wejS0bQvLl8M337iaQaBciJxLmRCx8qVM2HbLGJhr39xyyy1MmDCBdu3a8fHHH/PJJ5+wadMmqlevzujRozl06JBrG/uUlBQaNmxI27Ztef7554mPj2fkyJF07tyZTz75JF+fsyDTpzxdRkYGISEhdpchl+C+iWv4ad1hmlUtzfR72+NwaHbQpVAmRKyUCfEJy5fDqFFw771wyy3mmNNp7tKQx3VVuRCxUiZErLw9EwXpeVzyDKGkpCSmT5/Oli1bCvzcoUOH8vbbb/PCCy/QvHlzFi1axOzZs6levToAR44cYf/+/a7zw8PDmTdvHqdOnaJ169bcdNNN9O/fn3ffffdSvwyvdHrRM/FOf+1J4Kd1hwEYcWVNNYMKgTIhYqVMSIl24IB5O1i7dmZT6OWXrbOEznNdVS5ErJQJEStfykSB1xC6/vrr6dSpE//5z39IT0+ndevW7N27F8Mw+O677xg0aFCBXu/ee+/l3nvvzfOxL774wm3s7NW8RbxVTq6TJ6aYq/O3qFaa/k0r2VyRiIiIl0hJgXHj4PXXISPDbPwMH242hPxsWw1BRETE6xT4qrlo0SI6duwImJ0zwzA4deoU7777Li+99FKhFyjn16RJE7tLkIv03E+b2B2fCsDrg5tpdlAhUSZErJQJKXFmzYJ69eDFF81mUKdOsHIlfP45VK6cr5dQLkSslAkRK1/KRIEbQomJiURHRwMwZ84cBg0aRGhoKH379mXHjh2FXqCcnzff1+jL9sSn8s1y81bIMf0bUSdGC0kXFmVCxEqZkBInMhKOHIGaNeGHH2DhQmjZskAvoVyIWCkTIla+lIkCN4SqVq3KsmXLSE1NZc6cOfTs2ROAkydP+tQ3zhOsWLHC7hLkIoyeut718W3ta9hXSAmkTIhYKRPi9XbvhsmTzxx37AjTpsGWLTBo0HnXCboQ5ULESpkQsfKlTBS4IfTggw9y0003UaVKFSpXrkyXLl0A81YyX5paJXIxvvhjD8t3JwDw/o0tdKuYiIhIXpKS4PHHoWFDc32gAwfOPDZgAAQH21aaiIhISZGvbeeTkpIs25WtWrWK/fv306NHD8LDzdtdZs2aRenSpenQoUPRVVsIStK284mJiURFRdldhuTTseQM2rz8KwA3XF6VVwc1tbmikkeZELFSJsTr5ObCp5/C00/D8ePmWI8e8OGHULt2oXwK5ULESpkQsfL2TBT6tvNlypTh2LFjAFx11VXUrl2b6667ztUMAujbt6/HN4NKmtWrV9tdghTA6CkbXB8/0L2ujZWUXMqEiJUyIV7l11+hRQu46y6zGVS/PsycCb/8UmjNIFAuRM6lTIhY+VIm8tUQCg8P58SJEwAsXLiQ7OzsIi1K8icuLs7uEiSflu6K59etZlP1vWEtqBRVyuaKSiZlQsRKmRCvcfQo9O0LGzZAmTLwzjvmx337XtQ6QReiXIhYKRMiVr6UiYD8nNS9e3e6du1Kw4YNAbjuuusICgrK89wFCxYUXnVyQREREXaXIPlgGAavzN4CQPeGFejfLH/b4krBKRMiVsqEeLS0NAgNNT+uUMFcM+jUKRgzBv7e0bYoKBciVsqEiJUvZSJfawilp6fz5ZdfsmvXLt58803uvPNOQk9fwM8xfvz4Qi+yMJWkNYRycnIICMhXT09sdOVrCzh4Mh2A2fd3pFFl7/5z58mUCRErZUI8UnY2TJgAzz9v3hLWtm2xfnrlQsRKmRCx8vZMFKTnka+vslSpUtx9990ArFy5ktdee43SpUtfcqFyaSZPnsywYcPsLkMu4K2521zNoIEtY9UMKmLKhIiVMiEexTBg9mx45BHYutUc+/DDYm8IKRciVsqEiJUvZaLAba/ffvutKOoQKXEOnkzj3QU7AWhTM5q3rm9ub0EiIiJ22bgRHn4Y5s41j8uVgxdfhJEj7a1LRETEh+WrIfTQQw/x4osvEhYWxkMPPXTBc996661CKUz+WaNGjewuQc4jMyeX4Z/95Tr+38grbKzGdygTIlbKhHiEZ5+Fl18GpxMCA+GBB+Cpp8Cm2ebKhYiVMiFi5UuZyFdDaM2aNa6dxVavXo2jkHd7kIsTFRVldwlyHv/5dg27jqcC8M2IKwj0z9eGfnKJlAkRK2VCPEKNGmYz6LrrYNw4qFPH1nKUCxErZULEypcyka+G0Nm3iS1cuLCoapECWrZsGTVq1LC7DDnHhoOJzNt8FIAn+zTgyrrlbK7IdygTIlbKhBQ7w4AZM8DfH/r3N8eGD4eGDaFdO3tr+5tyIWKlTIhY+VImCjxt4Y477iA5OdltPDU1lTvuuKNQihLxZv3fXwJArXJh/KtTbZurERERKSZr18JVV5kzge6919xWHszmkIc0g0REROSMAjeEvvzyS9LT093G09PT+eqrrwqlKMmfnj172l2CnOP37cddHz/cs76NlfgmZULESpmQYhEXZy4O3bIlLFwIISHmrCAPpVyIWCkTIla+lIl8N4SSkpJITEzEMAySk5NJSkpy/Tp58iSzZ88mJiamKGuVc2zevNnuEuQcD3+/DoBy4cH0bVrJ5mp8jzIhYqVMSJFKT4dXXoG6deHTT83bxW64wdxS/qWXIDTU7grzpFyIWCkTIla+lIl8bztfunRpHA4HDoeDevXquT3ucDh4/vnnC7U4ubCDBw/aXYKcZc7GOOJTMgH4ekQbm6vxTcqEiJUyIUVq1SpztzCANm1g/Hho397emvJBuRCxUiZErHwpE/luCP32228YhsFVV13FlClTiI6Odj0WFBRE9erVqVy5cpEUKXkL9dCfvPkiwzB47sdNAFxeowwNK0XaXJFvUiZErJQJKXRHj0KFCubHV14J//kPtG0Lw4aBn3fsqKlciFgpEyJWvpQJh2EYRkGesG/fPqpVq+a1W88nJSURFRVFYmIikZH6R7sUjkcmr+OHVWYnef5DnakTE25zRSIiIoXowAEYPRqmT4dt2yA21u6KREREJA8F6Xnk60c569evx+l0ApCYmMiGDRtYv359nr+k+EycONHuEgTYeCjR1Qy6vUMNNYNspEyIWCkTcslSU2HMGKhfH/73P/P455/truqSKBciVsqEiJUvZSJft4w1b96cuLg4YmJiaN68OQ6Hg7wmFjkcDnJzcwu9SBFP9vb87a6Pn+rT0MZKREREConTCV9/DU8+CYcPm2NXXmmuE9S6tb21iYiISKHIV0Noz549lC9f3vWxeIa8FveW4vXxol3M33IMgJeva0yAv3esn1BSKRMiVsqEXBSnEzp3hiVLzOOaNWHcOBg0CLx0yYCzKRciVsqEiJUvZSJfDaHq1avn+bHYKyYmxu4SfNqBhDTe+MWcHdSwUiQ3XaFs2E2ZELFSJuSi+PlBhw6wbh08/TTcfz+EhNhdVaFRLkSslAkRK1/KRIGnM3z55ZfMmjXLdfzYY49RunRp2rdvz759+wq1OLmwJad/cie2uOOLFWTlOokMCeD7u9raXY6gTIicS5mQfElKMheM/uuvM2NPPQU7dsBjj5WoZhAoFyLnUiZErHwpEwVuCL3yyiuUKlUKgGXLlvH+++8zbtw4ypUrx6hRowq9QBFPNGXVQXYcSwFgws2tiAgJtLkiERGRAsrNhU8+gbp14dVX4cEH4fQakRERZ7aXFxERkRIpX7eMne3AgQPUqVMHgOnTpzN48GD+9a9/0aFDB7p06VLY9ckFXHXVVXaX4JOOJ2fy6A/rAOjfrDLt65SzuSI5TZkQsVIm5LwWLIBRo+D0DrH16pkLSPsA5ULESpkQsfKlTBR4hlB4eDgnTpwAYO7cuXTv3h2AkJAQ0tPTC7c6uaBdu3bZXYJPGvnVSpwGBAf48cp1je0uR86iTIhYKRPiZscOGDAAunUzm0GlS5s7h23YAP36lYhFo/+JciFipUyIWPlSJgrcEOrRowcjR45k5MiRbN++nb59+wKwadMmatSoUdj1yQVozabi9878Haw7cAqAN4Y0061iHkaZELFSJsTN77/DjBng7w/33Qc7d5q3igUF2V1ZsVEuRKyUCRErX8pEgRtC//3vf2nXrh3Hjx9nypQplC1bFoBVq1YxbNiwQi9Qzi/Ih/7y5gmOJmUwfr65q1hs6VL0b1bZ5orkXMqEiJUyIeTkwPbtZ45vv91sBG3YAO++C3//Pc6XKBciVsqEiJUvZcJhGKdXD/QNSUlJREVFkZiYSGRkpN3liJdYte8kwz5eTlauk+AAPzY934sA/wL3U0VERIrPnDnw0EOQkgLbtsHfm4KIiIhIyVWQnsdF/Yv21KlTvPnmm4wcOZI777yTt956i8TExIsqVi7e5MmT7S7BJ2TlOLnr61Vk5ToB+HrEFWoGeShlQsRKmfBRmzdD797mry1bIC0NNm2yuyqPoVyIWCkTIla+lIkC/6t25cqV1K5dm/Hjx5OQkEB8fDzjx4+ndu3arF69uihqlPPIycmxu4QSzzAM/vX1SuJTMgGYfHc72tSMtrkqOR9lQsRKmfAx8fHwn/9A06bm7KDAQHj4YXOdoNat7a7OYygXIlbKhIiVL2WiwNvOjxo1imuuuYZPPvmEgADz6Tk5OYwcOZIHH3yQRYsWFXqRkrdatWrZXUKJ9/xPm1m47TgArw1qwuU11AzyZMqEiJUy4UPi4qBhQzh1yjweMABefx3q1LGzKo+kXIhYKRMiVr6UiQI3hFauXGlpBgEEBATw2GOP0Vo/fSpW1apVs7uEEm3prni+WLoXgFvbVWfo5fp+ezplQsRKmfAhFStC166wZw+89Zb5seRJuRCxUiZErHwpEwW+ZSwyMpL9+/e7jR84cICIiIhCKUryZ+HChXaXUGIdPpXOXV+vch0/06+RjdVIfikTIlbKRAm2bh306weHDp0Z++wzWLlSzaB/oFyIWCkTIla+lIkCN4SGDh3KiBEjmDRpEgcOHODgwYN89913jBw5UtvOS4nx7IyNJGfkEBEcwIqnuhOoRaRFRMQTxMXBnXdCixYwaxY8++yZx0qXBn9/20oTERER71LgW8beeOMNHA4Ht956q2uxpcDAQO655x5effXVQi9Qzq9Tp052l1Aifb/iAPO3HAPg/ZtaUj4i2OaKJL+UCRErZaIEyciA8ePhlVfMbeQBhg6FZ56xty4vpFyIWCkTIla+lIkCT3sICgrinXfe4eTJk6xdu5Y1a9aQkJDA+PHjCQ7WP5yL06Gzp4lLoTAMgy+X7QWgc73ydK5X3t6CpECUCRErZaKEmDrVXDD6ySfNZtDll8OSJfDdd1Cjht3VeR3lQsRKmRCx8qVMXPR9MKGhoZQuXZro6GhCQ0MLsybJp127dtldQonz9fJ9bDqcBMDTfRvaXI0UlDIhYqVMlBDLl8PevRAbC19/bR536GB3VV5LuRCxUiZErHwpEwVuCOXk5PDMM88QFRVFjRo1qF69OlFRUTz99NNkZ2cXRY1yHn5+WtemMO0+nsKzMzYBcFv7GtStoEXSvY0yIWKlTHipgwdh27Yzx089Zd4qtm0b3Hwz6Pf1kigXIlbKhIiVL2XCYRiGUZAn3H333UybNo0XXniBdu3aAbBs2TKee+45rr32WiZMmFAkhRaWpKQkoqKiSExMJDIy0u5yxEPsjU+l5/hFZOU6CQ3yZ+2zPQkK8J03AhER8QBpafD66/Daa9CyJSxeDA6H3VWJiIiIFylIz6PA/+KdOHEiX3zxBXfddRdNmzaladOm3HXXXXz22WdMnDjxoouWgps6dardJZQIp9Ky6PLGQrJynQBMuLmVmkFeSpkQsVImvITTCd98A/XqwXPPQXq6OX7ypK1llVTKhYiVMiFi5UuZKPC/ekNCQqiRxwKGNWrUICgoqDBqknzKzMy0uwSvZxgGvd9Z7Dqedf+VdNJC0l5LmRCxUia8wNKl0LYt3HILHDoE1avDpEnm7KDoaLurK5GUCxErZULEypcyUeCG0L///W9efPFFyzcpMzOTl19+mf/85z+FWpxcWLVq1ewuwevd+tlfHEnMAMxFpC+rHGVzRXIplAkRK2XCw82fby4OvWIFhIfD2LGwdStcf71uFStCyoWIlTIhYuVLmQgo6BPWrFnDr7/+SpUqVWjWrBkA69atIysri27dujFw4EDXub401coO9erVs7sEr/bjusMs3hEPQK3yYYzsWMvmiuRSKRMiVsqEBzKMM82erl2heXNo3RpefBEqVrS1NF+hXIhYKRMiVr6UiQLPECpdujSDBg2iX79+VK1alapVq9KvXz8GDhxIVFSU5ZcUrfnz59tdgtf66Pdd3D9xDQB1YsKZ+2AnmyuSwqBMiFgpEx4kNxf+7/+gXTvIMGem4u9vbiH/ySdqBhUj5ULESpkQsfKlTBR4htDnn39eFHWIFJmcXCdT1xxi/uaj7E9II9Dfjw2HEgHoXK88H9zUkgB/LSItIiJF5LffYNQoWLfOPP74Y7j/fvPj4GD76hIRERGfVuCGkHiODh062F2Cx9t0+P/Zu+/wKMr1jePf9EpCJ5TQe+8IUqUIKIJIscs5ogf1iNiwomDBCvjjeECPqBw9Cog0RaSoVEXpUkKTXgIIgSQQ0uf3x8jiuKEsJDu7mftzXbnYnZ2deXbDvSEP77xvMjeMX5HnY70alWP8rY0J0DwNhYYyIWKlTNhs50548kmYM8e8HxsLL7wAQ4bYW5fDKRciVsqEiJWTMqGGkB87fvy4oya8yktqehZ7j6eRmZPDmYwcjp/OIOlMJmczc/j14Cm+23rMsv+tLeKpHVeERvFFaRxfVM2gQkaZELFSJmySkwPDh8O//gVZWealYf/4B4waBSVL2l2d4ykXIlbKhIiVkzKhhpAf2759O02bNrW7DK86k5HN9DUHmLr6ANuOpF72874c0prmlbV8b2HnxEyIXIwyYZOgINi1y2wGde8OY8ZA3bp2VyV/UC5ErJQJESsnZUINIfFphmHwzaZEDp08y6KEo6zZdzLP/QICoFqpaNIysgkPCaJoZAhxseFEhwVzf/uqVC9dxMuVi4iIoyxYAA0bQtmy5v233zZHBfXoYW9dIiIiIhcQYBiGcbUHOXXqFEWLFs2HcgpeSkoKsbGxJCcnExMTY3c5VyU3N5fAwMI7GfLOo6l0Hbcsz8eurV6C8kUjuL1VJRrHF/VuYeKzCnsmRDylTHjB1q3wxBMwbx787W/w0Ud2VySXoFyIWCkTIlb+nglPeh4ev8o33niDadOmue4PGDCAEiVKUL58eX49t3qGeMXcuXPtLiHfpWVmM27RDpq/8p1bM+iOVhV5/oY6bHu5O58NvoY3+zVSM0gsCmMmRK6GMlGATpyAhx+GBg3MZlBwMBQvDlf//2xSwJQLEStlQsTKSZnw+JKx999/n//9738ALFq0iEWLFvHtt9/yxRdf8OSTT7Jw4cJ8L1LydubMGbtLyFe/HjhF73//6LZ91E31uKdNZe8XJH6nsGVC5GopEwUgMxMmTDAniD51ytzWuze89RbUqGFraXJ5lAsRK2VCxMpJmfC4IZSYmEh8fDxgds4GDBhAt27dqFy5Mq1atcr3AuXCypcvb3cJ+WbP8TOWZlDtuCL0aVKeQW0qEx4SZGNl4k8KUyZE8oMyUQDeeguef9683bAhjBsH111nb03iEeVCxEqZELFyUiY8bggVK1aMAwcOEB8fz/z583nllVcAc/LfnJycfC9QLqx+/fp2l5AvcnIN7vlolev+D493oGqpaBsrEn9VWDIhkl+UiXySnW1eEgbw0EMwZQo88gj8/e/mimLiV5QLEStlQsTKSZnweA6hvn37cvvtt9O1a1dOnDhBjz9Wz9iwYQPVq1fP9wLlwhYsWGB3Cfni5gk/sj8pDYAFw9qrGSRXrLBkQiS/KBNX6ehRuP9+6Nr1/NxARYvCpk1w331qBvkp5ULESpkQsXJSJjweITRu3DgqV67MgQMHePPNN4mONn95T0xM5MEHH8z3AqVw+z01g40HkwF4tEtNasVpeXgREbFZejr83//Bq69Caqq5beVKaNPGvB0QYF9tIiIiIvnE44ZQSEgITzzxhNv2YcOG5Uc94oHCMGfT/32/w3V7aGeNMJOrUxgyIZKflAkPGQZ8+SUMHw5795rbmjUz5wk61wwSv6dciFgpEyJWTsrEZTWEvvrqK3r06EFISAhfffXVRfe96aab8qUwubTTp0/bXcJVOXkmky/WHATgqe61CdD/uMpV8vdMiOQ3ZcIDR49Cv36wYoV5v1w5eO01uPNOCPT4CnvxYcqFiJUyIWLlpExcVkOoT58+HDlyhNKlS9OnT58L7hcQEKCJpb1oy5YtNGzY0O4yrohhGDR7ZRG5BpSMDuNv11a2uyQpBPw5EyIFQZnwQIkS5jLyERHw5JPmKKGoKLurkgKgXIhYKRMiVk7KxGU1hHJzc/O8LXKlpq0+QO4f83OOuLGOlpYXERHvSkuD996DBx+E8HBzFbFPPzUbQ/HxdlcnIiIiUuACDOPcshnOkJKSQmxsLMnJycTExNhdzlXJysoiJCTE7jI8lpaZTYe3lvB7agaVSkSy9MlOdpckhYS/ZkKkoCgTecjNNZeNf/ppOHgQXn8dnnrK7qrEi5QLEStlQsTK3zPhSc/jii6KP3PmDPPmzeO9995j/Pjxli/xnoULF9pdwhV5b+lufk/NAOCLf7S2uRopTPw1EyIFRZn4i3Mrhd15p9kMqlgRatSwuyrxMuVCxEqZELFyUiY8XmVs/fr19OzZk7S0NM6cOUPx4sU5fvw4kZGRlC5dmqFDhxZEnZKHlJQUu0vwmGEYzFhrTiT9cp/6lIkJt7kiKUz8MRMiBUmZ+MO+feaIoKlTzfvR0fDMM/Doo+acQeIoyoWIlTIhYuWkTHg8QujRRx+lV69eJCUlERERwc8//8y+ffto1qwZb7/9dkHUKBdQpkwZu0vw2IrfjnPo1FlCgwPp07ic3eVIIeOPmRApSMrEHx5/3GwGBQTA3/8OO3bAs8+qGeRQyoWIlTIhYuWkTHjcENqwYQOPP/44QUFBBAUFkZGRQXx8PG+++SbPPvtsQdQoF9C8eXO7S/DYhMW7AOhRP44i4f57Xab4Jn/MhEhBcmwmcnLgz0vGjh4NXbvC2rXw4YdQtqx9tYntHJsLkQtQJkSsnJQJjxtCISEhBAQEAGbnbP/+/QDExsa6bot3fPPNN3aX4JFjqems3H0CgLtbV7a3GCmU/C0TIgXNkZlYuhRatIBhw85vq1kTFi6EJk1sK0t8hyNzIXIRyoSIlZMy4fEcQk2aNGHNmjXUrFmTTp068cILL3D8+HE+/fRTGjRoUBA1SiExa90hAOqUjaFZpWI2VyMiIoXKrl0wfDjMnGne37sX3noLiunnjYiIiEhePB4hNHr0aMr+MdT65ZdfpkSJEjzwwAMcO3aM//znP/leoFxYs2bN7C7BI1+sOQBA17rOuSZTvMvfMiFS0ByRieRksxFUt67ZDAoMhAcegO3b1QySPDkiFyIeUCZErJyUCY9GCBmGQalSpahXrx4ApUqVYt68eQVSmFxaVlaW3SVctgNJaez6/QwAbauXtLkaKaz8KRMi3lDoM/HTT9CnD/z+u3m/WzcYOxb++HeKSF4KfS5EPKRMiFg5KRMejRAyDIMaNWpw8ODBgqpHPLBx40a7S7hsE5eak0mHBQfSorL+x1YKhj9lQsQbCn0m6tSB3FyoXRu++Qbmz1czSC6p0OdCxEPKhIiVkzLhUUMoMDCQGjVqcOLEiYKqRwqh9KwcPv/FnHD86R61XZOSi4iIeGTbNnjuOTAM836xYrB4MWzcCD17msvKi4iIiMhlCTCMc/+qujzffPMNr7/+OhMnTqR+/foFVVeBSUlJITY2luTkZGJiYuwu56qcPXuWiIgIu8u4pH8v/o23FmwHYNPIblpuXgqMv2RCxFsKTSaSkmDUKJgwAbKzzbmCbr7Z7qrETxWaXIjkE2VCxMrfM+FJz8PjSaXvvPNOVq1aRaNGjYiIiKB48eKWL/GeZcuW2V3CJeXmGvz3p70AjLixrppBUqD8IRMi3uT3mcjKgvHjoXp188/sbOjVS5eFyVXx+1yI5DNlQsTKSZnweNn5cePG6ZIfH5GUlGR3CZe09UgKx1IzALijVUWbq5HCzh8yIeJNfpsJwzDnBHriCXO1MIAGDcwJo7t0sbc28Xt+mwuRAqJMiFg5KRMeN4QGDRpUAGXIlShRooTdJVzS/3425w66pmpxwkOCbK5GCjt/yISIN/ltJnJz4emnzWZQqVLwyitw770QpJ8jcvX8NhciBUSZELFyUiY8nkOoU6dO3HnnnfTr14/Y2NiCqqvAFKY5hNLS0oiMjLS7jAvKyTWo9uw8AP7v1sb0blze5oqksPP1TIh4m19l4vffoUgRCA837y9aBN99B88+C3747w3xXX6VCxEvUCZErPw9EwU6h1CDBg14/vnniYuL45ZbbmH27NlkZmZecbFy5ebMmWN3CRc1ZdV+1+0udcrYWIk4ha9nQsTb/CITGRnw1lvn5wk6p2tXeOMNNYMk3/lFLkS8SJkQsXJSJjxuCI0fP55Dhw4xZ84cihQpwj333ENcXBz3338/S5cuLYgaxU+N+noLAAOaVyAqzOOrE0VEpDAzDHO1sLp1YfhwSEmB+fPPLykvIiIiIgXK44YQQGBgIN26dWPy5MkcPXqU999/n1WrVnHdddfld31yEY0bN7a7hAs6lZZJVo75j3pdKibe4suZELGDz2Zi3Tro1AluuQV274ayZWHyZPMSMS1cIQXMZ3MhYhNlQsTKSZm4qmEbR44cYerUqfzvf/9j48aNtGjRIr/qkssQGHhF/TyvmL3+EGD+u751VedMyiX28uVMiNjBJzPx7rswdKg5Eig8HJ580hwhFB1td2XiED6ZCxEbKRMiVk7KhMevNCUlhY8//piuXbsSHx/PxIkT6dWrFzt27OCXX34piBrlAtatW2d3CRc064+G0ONdaxIYqP/tFe/w5UyI2MEnM9GlCwQHw+23m6uIvfSSmkHiVT6ZCxEbKRMiVk7KhMcjhMqUKUOxYsUYMGAAo0eP1qggcbNu/0l+PZgMQK9G5WyuRkREbGMYMHUqbN1qNn4AateG336DihXtrU1ERETE4Txedn7hwoV06dLFb4dRFaZl51NTUylSpIjdZbi5/5M1LEw4SrsaJfn03lZ2lyMO4quZELGLrZn4+Wd49FHzz4AA2LABGja0pxaRP9HPChErZULEyt8zUaDLznfr1s1vm0GFzapVq+wuwU3y2SwWJhwF4O9tq9hcjTiNL2ZCxE62ZOLAAbjjDmjd2mwGRUWZo4Nq1PB+LSJ50M8KEStlQsTKSZnQWuB+7NixY3aX4GbmuoOu222rl7SxEnEiX8yEiJ28mokzZ+CNN+CttyA93RwVNGgQvPIKlNPlw+I79LNCxEqZELFyUibUEPJjsbGxdpfgZtTXCQDc3boSIUEaSSbe5YuZELGTVzORmQn//rfZDGrfHsaNg6ZNvXd+kcuknxUiVsqEiJWTMuHxHEL+rjDNIZSRkUFYWJjdZbi8t3QXr3+7DYDPB7eijUYIiZf5WiZE7FbgmVi/Hho3NkcDAXz2GUREwM03n98m4mP0s0LESpkQsfL3TBToHEJ/lp6efjVPl6s0c+ZMu0uw+HTlPgDaVCuhZpDYwtcyIWK3AsvE7t3Qr585Auirr85vv+MO6NtXzSDxafpZIWKlTIhYOSkTHjeEcnNzefnllylfvjzR0dHs3r0bgBEjRvDhhx/me4HiH9buO8mhU2cBeLt/I5urERGRApGSAk89BXXqwIwZEBgImzbZXZWIiIiIXAGPG0KvvPIKkydP5s033yQ0NNS1vUGDBkyaNClfi5OLq1+/vt0luExc8hsAlUtEUq5ohM3ViFP5UiZEfEG+ZSInB/7zH6heHd5805wvqGtXcyn555/Pn3OIeIl+VohYKRMiVk7KhMcNoU8++YT//Oc/3HHHHQQFBbm2N2zYkG3btuVrcXJxkZGRdpcAQGp6Fr/sTgLgXi01LzbylUyI+Ip8y8Rtt8E//gG//w41a8LcubBgATRokD/HF/Ei/awQsVImRKyclAmPG0KHDh2ievXqbttzc3PJysrKl6Lk8qxatcruEgB4cc4WUjOyiS8ewe2tKtldjjiYr2RCxFfkWyb+9jcoVgzeeQc2b4YbbtA8QeK39LNCxEqZELFyUiY8Xna+Xr16LF++nEqVrL/4T58+nSZNmuRbYeIf0rNymLn+EAC3tqhIUKB+QRAR8WtJSfDSS1ClCjzyiLmtRw/Yuxf8fHVOERERETnP44bQiy++yF133cWhQ4fIzc1l5syZbN++nU8++YS5c+cWRI1yAT169LC7BD5Zudd1W5eLid18IRMivsSjTGRlwXvvwciRZlMoJgbuuQeKFjUfVzNICgn9rBCxUiZErJyUCY8vGevVqxfTpk1j3rx5BAQE8MILL7B161a+/vprunbtWhA1ygX8+uuvdpfA6HnmvFHd68URHhJ0ib1FCpYvZELEl1xWJgwD5s2Dhg1h6FCzGVS/Pnz55flmkEghop8VIlbKhIiVkzLh8QghgOuvv57rr78+v2sRDx0+fNjW809csst1+8Wb6tpYiYjJ7kyI+JpLZmLnTnj4YXOCaICSJeHll2HwYAi+on8iiPg8/awQsVImRKyclAn9a8+PRUdH23bus5k5vDHfHB3UpU4ZysZqqXmxn52ZEPFFl8xEdjZ89x2EhMCwYfDccxAb65XaROyinxUiVsqEiJWTMhFgGIZxqZ2KFStGwGWuJpKUlHTVRRWklJQUYmNjSU5OJsbP50PIyckhKMiey7TmbUrkwc/WAfDbqz0IDvL46kORfGdnJkR8kVsmMjJg2TL48yXeH38M7dtDtWreL1DEBvpZIWKlTIhY+XsmPOl5XNZv8e+88w7jxo1j3LhxPP/884B52djIkSMZOXKk6/KxESNGeFzshAkTqFKlCuHh4TRr1ozly5df1vN+/PFHgoODady4scfnLCy++OIL2849b1MiAB1qllIzSHyGnZkQ8UWuTBgGzJoF9epB9+6wadP5nf72NzWDxFH0s0LESpkQsXJSJi7rkrF77rnHdfuWW27hpZde4p///Kdr29ChQ3n33Xf57rvvePTRRy/75NOmTWPYsGFMmDCBa6+9lvfff58ePXqQkJBAxYoVL/i85ORk7r77bjp37szRo0cv+3ySf1b8dhyAbvXK2FyJiIhc1Pr18NhjsGSJeT8uDg4fhgYNbC1LREREROzl8dCOBQsW0L17d7ft119/Pd99951Hxxo7diz33nsvgwcPpk6dOrzzzjvEx8czceLEiz7vH//4B7fffjutW7f26HyFTe3atW0576FTZzmVlgVA17pqCInvsCsTIj4pMZFu06ZBs2ZmMyg83JwjaMcO0MIQ4mD6WSFipUyIWDkpEx43hEqUKMGsWbPcts+ePZsSJUpc9nEyMzNZu3Yt3bp1s2zv1q0bP/300wWf9/HHH7Nr1y5efPHFyzpPRkYGKSkplq/Conjx4rac9/Nf9gHQtGJRShcJt6UGkbzYlQkRn5OdDddcQ4k5c8zLxW69FbZtg1degSJF7K5OxFb6WSFipUyIWDkpEx6vMjZq1CjuvfdelixZ4hqh8/PPPzN//nwmTZp02cc5fvw4OTk5lCljHWFSpkwZjhw5kudzdu7cydNPP83y5csJvszlcF977TVGjRrltn369OlERkbSt29fvv/+e5KTkyldujQtW7Zk7ty5ADRt2pTc3Fw2bNgAQO/evVmxYgUnTpygePHitG/fntmzZwPQsGFDQkJCWLt2LQA33HADa9as4ejRo8TExNCtWze+/PJLAOrVq0d0dDS//PILYI6u2rx5M4cOHSIqKoobb7yRadOmAVCrVi1KlizJjz/+CECXLl3YsWMH+/fvJzExkccee4xp06aRm5tLtWrVKF++PMuWLQOgY8eO7N+/n927dxMcHEz//v2ZMWMGmZmZVKpUiWrVqvHDDz8A0LZtW44dO8aOHTsAuO2225gzZw5paWlUqFCBunXrsnDhQgD+/as5MVWxjKNMmTKF/v37M3/+fFJTU4mLi6Np06bMmzcPgBYtWpCens6mP+aruPnmm1myZAknT56kZMmStG7dmq+//hqAJk2aALB+/XoAevXqxcqVKzl+/DjFihWjY8eOrmZkgwYNCA8PZ/Xq1QD07NmTdevWceTIEYoUKUL37t2ZPn06AHXr1iU2NpaVK1cCZtMxISGBgwcPEhkZSe/evZkyZQoANWvWpHTp0qxYsQKA6667jl27drFv3z5CQ0O55ZZbmD59OtnZ2VStWpWKFSuy5I/LMNq3b8+hQ4fYtWsXgYGBDBw4kJkzZ5KRkUHFihWpWbOmaxTdtddey/Hjx9m+fTsAAwcOZO7cuZw5c4by5ctTv359FvyxDHSrVq04ffo0W7ZsAaBfv34sXLiQlJQUypQpQ/Pmzfnmm28AaNasGVlZWWzcuBGAPn36sGzZMpKSkihRogRt27Zlzpw5ADRu3JjAwEDWrTMnB7/xxhtZtWoVx44dIzY2ls6dOzNz5kwA6tevT2RkJKtWrQKgR48e/Prrrxw+fJjo6Gh69uzputa2du3aFC9e3NXY7dq1K9u2bePAgQNERETQp08fpk6dimEY1KhRg7i4ONfcYZ06dWLv3r3s2bOHkJAQ+vXrx5dffklWVhZVqlShcuXKLF68GIB27dpx5MgRdu7cSUBAALfeeiszZ84kLi6O+Ph4ateuzaJFiwBo06YNSUlJbNtmrow3YMAA5s2bx+nTpylXrhyNGjXi22+/BaBly5akpaWxefNmAL/+jAgLC6Nv375e/4xo3bo1ycnJJCQkAOgzwlufEX/8ne3Xvz8LFy6kbIcOlFu2jCKTJvHV77/DTz/RLCPD0Z8Rs2fP5uzZs/qMcPhnxKFDh+jevbvzPiP07wh9RlzgM2LDhg1ERJgrBuszwsH/jtBnhOsz4tChQ9x6661++xmRlpbG5bqsVcb+6pdffmH8+PFs3boVwzCoW7cuQ4cOpVWrVpd9jMOHD1O+fHl++ukny6Vfr776Kp9++qnrzTwnJyeHa665hnvvvZchQ4YAMHLkSGbPnu16g/KSkZFBRkaG635KSgrx8fGFYpWxKVOmcNttt3n1nLt+P03nMUsBWD68E/HFI716fpGLsSMTIj5h1Sp49FF46im46SZzW04OU6ZO5bY77rC3NhEfo58VIlbKhIiVv2fCk1XGrqghlB8yMzOJjIxk+vTp3Hzzza7tjzzyCBs2bGDp0qWW/U+dOkWxYsUsy7/l5uZiGAZBQUEsXLiQ66677pLnLUzLzh8/fpySJUt69ZzvL93Fa99uo2GFWL76Z1uvnlvkUuzIhIitDh6EZ56B//3PvN+kCaxdCwEBgDIhkhflQsRKmRCx8vdM5Puy8wUhNDSUZs2auYZZnbNo0SLatGnjtn9MTAybNm1iw4YNrq8hQ4a4hjl6MjqpsPjrKCpv+N8f8wd1rFXa6+cWuRQ7MiFiizNn4MUXoWbN882gQYNg7lxXMwiUCZG8KBciVsqEiJWTMuHxHEL56bHHHuOuu+6iefPmtG7dmv/85z/s37/fdUnYM888w6FDh/jkk08IDAykfv36lueXLl2a8PBwt+1OceDAAa+eLzM7lwNJZwHoUkcNIfE93s6EiC2+/hqGDDGXjgdo1w7GjTNXE/sLZULEnXIhYqVMiFg5KRO2NoQGDhzIiRMneOmll0hMTKR+/frMmzePSpUqAZCYmMj+/fvtLNGnnZv8zVvW7Ety3a5fLtar5xa5HN7OhIgtAgPNZlCVKvDWW9C3r2VU0J8pEyLulAsRK2VCxMpJmbBtDiG7FKY5hLxt9Lyt/GfZbrrUKcOke5rbXY6IiDPs2QMJCXDDDeZ9w4CpU+HmmyE83N7aRERERMSnFOgcQkePHr3gY+eWnxPvmDp1qtfOlZtr8NUG8/KE6+uV8dp5RTzhzUyIFLiUFHPC6Dp14I474Phxc3tAANx222U1g5QJEXfKhYiVMiFi5aRMeNwQatCgAV999ZXb9rffftuREzvbyZuDuxYmHOFISjqhwYH0aFDWa+cV8YTDBjxKYZWTAx98ADVqwOuvQ0YGNG8OqakeH0qZEHGnXIhYKRMiVk7KhMcNoaeeeoqBAwcyZMgQzp49y6FDh7juuut46623mDZtWkHUKBdQo0YNr53rq1/N0UHNKhYjOszWqadELsibmRApED/8AE2bwv33w7Fj5ipiX30FixaZcwZ5SJkQcadciFgpEyJWTsqEx7/ZP/7443Tp0oU777yThg0bkpSUxDXXXMPGjRspU0aXEnlTXFycV85jGAbzNh0BoJsuFxMf5q1MiBSI/fuhWzdzhFDRouay8g8+CKGhV3xIZULEnXIhYqVMiFg5KRMejxACqFq1KvXq1WPv3r2kpKQwYMAANYNssHz5cq+cZ8fR067bNzcp75VzilwJb2VCJN9kZJy/XbEi/POf8PDD8NtvMGzYVTWDQJkQyYtyIWKlTIhYOSkTHjeEfvzxRxo2bMhvv/3Gxo0bmThxIg8//DADBgzg5MmTBVGj2GzpjmMANK1YlKKRV/fLiYiIANnZ8O9/Q6VKsHnz+e3jxsH48VCihH21iYiIiIgjeNwQuu666xg4cCArV66kTp06DB48mPXr13Pw4EEaNGhQEDXKBXTq1Mkr5/l5dxIA3es7Z+ic+CdvZULkqsyfDw0bmqOBjh6Fd989/1hAQL6eSpkQcadciFgpEyJWTsqExw2hhQsX8vrrrxMSEuLaVq1aNVasWME//vGPfC1OLm7v3r1eOc/Gg6cAqFs21ivnE7lS3sqEyBVJSIAePcyvrVvNUUATJlgbQvlMmRBxp1yIWCkTIlZOyoTHDaEOHTrkfaDAQEaMGHHVBcnl27NnT4GfY8vhZI6fziQgAJpWKlrg5xO5Gt7IhMgVee45c1TQ/PkQEgKPP27OE/TAAxBccCs3KhMi7pQLEStlQsTKSZnw+F+hL7300kUff+GFF664GPHMn0dpFZQFW44C0LZ6SSJDtdy8+DZvZELkipQsaa4e1qcPvPUWVK/uldMqEyLulAsRK2VCxMpJmQgwDMPw5AlNmjSx3M/KymLPnj0EBwdTrVo11q1bl68F5reUlBRiY2NJTk4mJibG7nJ8Xrs3f+BA0lme6l6bBzpWs7scERHfZxjw1VdQpAhcd525LTMTfvkF2rWztzYRERERKdQ86Xl4fMnY+vXrLV+bN28mMTGRzp078+ijj15x0eK5L7/8skCPn56Vw4GkswDUK6fmmfi+gs6EyCX9+it07myOBHrwQcjKMreHhtrSDFImRNwpFyJWyoSIlZMy4XFDKC8xMTG89NJLmkPIy7LO/aJRQBISU1y3r61eskDPJZIfCjoTIhd05Ajcdx80aQKLF0NYGPTtay4vbyNlQsSdciFipUyIWDkpE/k2KcypU6dITk7Or8PJZahSpUqBHn/P72fM85SMIigwf5dCFikIBZ0JETfp6TBuHIweDadPm9sGDoTXX4fKlW0tDZQJkbwoFyJWyoSIlZMy4XFDaPz48Zb7hmGQmJjIp59+Svfu3fOtMLm0ygX8y8aOo6kAtKpSvEDPI5JfCjoTIm6WLIFnnzVvt2xpNofatLG1pD9TJkTcKRciVsqEiJWTMuHxJWPjxo2zfI0fP54lS5Zwzz338J///KcgapQLWLx4cYEef9Mhc8RXgwqxBXoekfxS0JkQASAp6fzt66+He+6BTz+FlSt9qhkEyoRIXpQLEStlQsTKSZnweITQnj17CqIO8TGGYbD9iDlCqFaZIjZXIyLiAw4dMkcDzZ0LO3ZAiRIQEACTJ9tdmYiIiIiIx/JlUmmxR7sCXLHm14PJnDiTSVhwIPXKaYSQ+IeCzIQ4WFoajBoFNWvCJ5+YI4S++cbuqi6LMiHiTrkQsVImRKyclIkrmlR69erVTJ8+nf3795OZmWl5bObMmflSmFzakSNHqFChQoEce8n2YwB0rFWKiNCgAjmHSH4ryEyIA+Xmwuefw9NPm6ODwLwkbNw4c74gP6BMiLhTLkSslAkRKydlwuMRQlOnTuXaa68lISGBWbNmkZWVRUJCAj/88AOxsRpJ4k07d+4ssGMnHDaXnG9RWRNKi/8oyEyIw2RlQdu2cNddZjOoUiWYNg1WrPCbZhAoEyJ5US5ErJQJESsnZcLjhtDo0aMZN24cc+fOJTQ0lP/7v/9j69atDBgwgIoVKxZEjXIBAQEFtxT8woSjAFQuEVVg5xDJbwWZCXGYkBBo1Aiio80l5bdtgwEDzDmD/IgyIeJOuRCxUiZErJyUiQDDMAxPnhAVFcWWLVuoXLkyJUuWZPHixTRo0ICtW7dy3XXXkZiYWFC15ouUlBRiY2NJTk4mJibG7nJ80rHUdFq++j0AvzzbmTIx4TZXJCJSwFJT4bXX4M47oW5dc9uJE+ZIobg4e2sTEREREblMnvQ8PB4hVLx4cVJTzdWnypcvz+bNmwE4deoUaWlpV1CuXKnZs2cXyHGX7TgOQIViEWoGiV8pqExIIZaTAx9+CDVqmA2hxx8//1iJEn7fDFImRNwpFyJWyoSIlZMycdkNob///e+kpqbSrl07Fi1aBMCAAQN45JFHuO+++7jtttvo3LlzgRUq7s6ePVsgx1256wQAXeqUKZDjixSUgsqEFFKLF0Pz5jB4MBw9CtWrw5Ah4NnAWZ+mTIi4Uy5ErJQJESsnZeKyVxn773//y+uvv867775Leno6AM888wwhISGsWLGCvn37MmLEiAIrVNzFx8cXyHG3HE4GoGEFTRIu/qWgMiGFzG+/wZNPwrn//YmNhRdfhIcegtBQW0vLb8qEiDvlQsRKmRCxclImLrshdG6qoeLFz686FRgYyPDhwxk+fHj+VyaXVLt27Xw/ZlpmNjuOmpcEXlO1RL4fX6QgFUQmpBCaM8dsBgUFmSOCRo6EkiXtrqpAKBMi7pQLEStlQsTKSZnwaA4hJ8227Q/OXbqXnzYdTCbXgNiIEMoVjcj344sUpILIhBQC2dmwd+/5+w8/bF4mtnEjvPtuoW0GgTIhkhflQsRKmRCxclImLnuEEEDNmjUv2RRKSkq6qoLEXueWm+9Qs5TNlYiI5IMFC+CxxyA312wAhYSYl4V98IHdlYmIiIiI2MqjhtCoUaOIjdW8Mr6iTZs2+X7M6WsOANCuRuH9H3MpvAoiE+Kntm41Vwz79lvzfvHi5raGDe2ty8uUCRF3yoWIlTIhYuWkTHjUELr11lspXbp0QdUiHkpKSqJSpUr5dryzmTmkpGcD0KxSsXw7roi35HcmxA+dOGHOCTRxormkfHCweYnYiBFQzHmfa8qEiDvlQsRKmRCxclImLnsOIc0f5Hu2bduWr8ebtf6Q63aVklH5emwRb8jvTIif2bvXXDr+3XfNZtBNN8GWLTB2rCObQaBMiORFuRCxUiZErJyUCY9XGZPCa/Mfy813qVNaDUAR8T+VKkHTpnD8uNkE6tzZ7opERERERHxWgOGwTk9KSgqxsbEkJycTExNjdzlXJScnh6CgoHw7XpVnvsEwYEz/RtzSrEK+HVfEW/I7E+LjNm40Lw+bNMmcIwjg99/N2/p7ACgTInlRLkSslAkRK3/PhCc9D4+WnRffMm/evHw7Vk6uQUig+dehRpnofDuuiDflZybEhx09CvffD02awKxZ8NJL5x8rVUrNoD9RJkTcKRciVsqEiJWTMuHRpNLiW06fPp1vx9r9+2kyc3IBqFvWv0dOiXPlZybEB6Wnw//9H7z6KqSmmtv694dHHrG3Lh+mTIi4Uy5ErJQJESsnZUINIT9Wrly5fDvWku2/A1A7rgjBQRo4Jv4pPzMhPmbmTHjiCdizx7zfvDmMGwdt29pbl49TJkTcKRciVsqEiJWTMqHf/P1Yo0aN8u1Ya/YlARAbEZJvxxTxtvzMhPiYBQvMZlC5cvDf/8Ivv6gZdBmUCRF3yoWIlTIhYuWkTKgh5Me+/fbbfDvW8p3HAWhRuXi+HVPE2/IzE2Kzw4fNZeTPeeklGDUKduyAu++GQP34uhzKhIg75ULESpkQsXJSJvQvauFsZg5pmTkA9GninOFxIuKD0tLM5k+NGvDQQ+e3lykDL7wAUVH21SYiIiIiUohoDiE/1rJly3w5TkJisut2tVJaYUz8V35lQmyQmwtTpsDTT8PBg+a2kyfNyaOLFLG3Nj+mTIi4Uy5ErJQJESsnZUIjhPxYWlpavhxn8TZzQukapaMJCAjIl2OK2CG/MiFetnIltG4Nd95pNoMqVoSpU+HHH9UMukrKhIg75ULESpkQsXJSJtQQ8mObN2/Ol+N8t/UoAE0qFs2X44nYJb8yIV709dfQpg2sWgXR0eaS8tu2wcCBoAb1VVMmRNwpFyJWyoSIlZMyoUvGHC4zO5dtR1IBuLdtVZurERHH6dYNqleH9u3hlVegbFm7KxIRERERcYQAwzAMu4vwppSUFGJjY0lOTiYmJsbucq5KRkYGYWFhV3WM346dpsvYpQDsea2nLhkTv5YfmZAClJtrLhn/2Wcwfz4E//F/EmlpEBlpb22FlDIh4k65ELFSJkSs/D0TnvQ8dMmYH/v++++v+hhbDpsTSheLDFEzSPxefmRCCsiyZdCiBfz97/D99/DJJ+cfUzOowCgTIu6UCxErZULEykmZUEPIjyUnJ196p0tIOJwCQLe6cVd9LBG75UcmJJ/t2gW33AIdOsC6dRAbC2+/DXfcYXdljqBMiLhTLkSslAkRKydlQnMI+bHSpUtf9TG++vUwALXLaiUf8X/5kQnJJ1lZ8Nxz8H//B5mZEBgI//gHjBoFpUrZXZ1jKBMi7pQLEStlQsTKSZlQQ8iPtWzZ8qqebxgGicnpANSO8+/5lETg6jMh+Sg4GFavNptB3brBmDFQv77dVTmOMiHiTrkQsVImRKyclAldMubH5s6de1XP3/X7adftRvGxV1uOiO2uNhNylRYtgqQk83ZAAIwfD998Y04grWaQLZQJEXfKhYiVMiFi5aRMqCHkYIu3/Q5Ao/iiRIZqsJiIXKFt2+DGG82RQC+/fH57gwbQs6fZHBIREREREZ+ihpAfa9q06VU9f9Ve83/yu9ZxzjWSUrhdbSbEQ0lJ8MgjZuPnm2/My8RCQuyuSv5EmRBxp1yIWCkTIlZOyoSGhfix3Nzcq3r+ooSjADSpWCw/yhGx3dVmQi5TVhZMnAgjR8LJk+a2Xr3grbegVi1bSxMrZULEnXIhYqVMiFg5KRMaIeTHNmzYcMXPTc/KcV3FUa1UdP4UJGKzq8mEeGDkSHNk0MmT5uigRYvgq6/UDPJByoSIO+VCxEqZELFyUibUEHKo3b+fwTAgMjSIuNhwu8sREV/35/8pGToUqleH99+H9euhSxf76hIRERERkSsSYBiGYXcR3pSSkkJsbCzJycnExPj3UutpaWlERkZe0XPfW7qL17/dRssqxfniH63zuTIRe1xNJuQCjh2DF16AI0dg9uzz23NzIVD/p+DrlAkRd8qFiJUyIWLl75nwpOehf837sRUrVlzxc3/adQKAImGaRkoKj6vJhPxFRoY5J1CNGuZIoDlz4M/DZ9UM8gvKhIg75ULESpkQsXJSJvQvej924sSJK35uclomAPHF/bfzKfJXV5MJ+YNhwIwZULcuDB8OKSnQtCksXQqNG9tdnXhImRBxp1yIWCkTIlZOyoSGh/ix4sWLX/Fzj6ZkANCmWon8KkfEdleTCQEOHYLbb4dly8z7ZcvC6NFw990aEeSnlAkRd8qFiJUyIWLlpExoDiE/dvbsWSIiIjx+3qm0TBq/tAiAdSO6UjwqNL9LE7HFlWZC/pCRAXXqQGIiPPmkOUIoWqsQ+jNlQsSdciFipUyIWPl7JjSHkEPM/vMErx5ISEwBoHzRCDWDpFC50kw41tmz8O67kJ1t3g8Lg88/h+3b4aWX1AwqBJQJEXfKhYiVMiFi5aRM6JIxB/r5jwmla8cVsbkSEbGFYcDUqfDUU3DgAAQFwQMPmI9dc429tYmIiIiIiFeoIeTHGjZseEXPOzd/UOmYsPwsR8R2V5oJR/n5Z3j0UfNPgPh4iIuztyYpMMqEiDvlQsRKmRCxclImdMmYHwsJCbmi5/168BQAcTH+e12kSF6uNBOOcOAA3HEHtG5tNoOiouCVV8zLw26+2e7qpIAoEyLulAsRK2VCxMpJmVBDyI+tXbv2ip6XdMZccr5uOf+eVFvkr640E45w333m/EABAfC3v8HOnfDcc+DHE+bJpSkTIu6UCxErZULEykmZ0CVjDpOba3DqbBYAlUtE2lyNiBSY3FzIzITwcPP+6NHmKmJjxkDTpvbWJiIiIiIittOy834sJSXF49dwNCWdVqO/B2DHKz0IDdYgMSk8riQThdKyZeY8QR07mg0gcSxlQsSdciFipUyIWPl7JrTsvEOsWbPG4+ds/WPJ+bKx4WoGSaFzJZkoVHbvhn79oEMHWLcOPvkETp+2uyqxkeMzIZIH5ULESpkQsXJSJtQR8GNHjx71/Dkp6QBk5zpqYJg4xJVkolBITobhw6FOHZgxAwID4R//gC1bIDra7urERo7NhMhFKBciVsqEiJWTMqE5hPzYlQxj23ciDYDOtUvndzkitvPnoZ1XbOlS6N8ffv/dvN+lC4wdCw0a2FuX+ARHZkLkEpQLEStlQsTKSZnQHEJ+LCsry+Ml8Xr833K2JqbwVPfaPNCxWgFVJmKPK8mE30tMhJo1oVw5c76gG24wVxITwaGZELkE5ULESpkQsfL3TGgOIYf48ssvPX7OgSRzhJDmD5LC6Eoy4Xd27IDXXjt/v2xZ+OEH2LwZbrxRzSCxcEQmRDykXIhYKRMiVk7KhLoCDlMsyux0asl5ET9z8qS5cli9evDss/Ddd+cfa9EC/Ph/MURERERExPs0h5Afq1evnkf7G4bB8dRMACqpISSFkKeZ8AtZWfDeezByJCQlmdtuuAEqVrS1LPEPhTITIldJuRCxUiZErJyUCTWE/Fi0h6sH7U9K42xWDoEBEF9cDSEpfDzNhE8zDPj2W3j8cdi2zdxWv745YXTXrvbWJn6jUGVCJJ8oFyJWyoSIlZMyoUvG/Ngvv/zi0f5bE1MAiIkIISw4qCBKErGVp5nwaVlZ8NBDZjOoZEmYOBHWr1czSDxSqDIhkk+UCxErZULEykmZ0AghB9lz3JxQupJGB4n4puPHoWhRCA6G0FBz1bCVK+G558ztIiIiIiIi+UQjhPzY9ddf79H+Gw6cBKBr3TIFUY6I7TzNhM/IyIC334Zq1eDDD89v79sX3npLzSC5Yn6bCZECpFyIWCkTIlZOyoQaQn5s8+bNHu1/8ORZAGrHxRREOSK28zQTtjMMmDXLXDnsySchJcW8L5JP/C4TIl6gXIhYKRMiVk7KhBpCfuzQoUMe7b/lsDmHUKkiYQVRjojtPM2Erdavh06dzFFAu3ZBXBx89BF8843dlUkh4leZEPES5ULESpkQsXJSJjSHkB+Lioq67H1T07Nct+NiwwuiHBHbeZIJW40ZY44IMgwIDzdXEnv6aXDQigbiHX6TCREvUi5ErJQJESsnZSLAMAzD7iK8KSUlhdjYWJKTk4mJ8e9Lp3JzcwkMvLxBXj/vPsGt//mZsOBAtr/So4ArE7GHJ5mw1Zo10LIl3HorvP46VKxod0VSSPlNJkS8SLkQsVImRKz8PROe9Dz891UK06ZNu+x9fzt2GoCgwICCKkfEdp5kwmsMA6ZNMyeNPqd5c9i5Ez7/XM0gKVA+mQkRmykXIlbKhIiVkzKhS8YcYsvhZACaVixmcyUiDrJqFTz6KPz0E4SEwM03myuJwfk/RUREREREbKARQn6sVq1al73vlFUHALipUbmCKkfEdp5kokAdPAh33QWtWpnNoMhIGDECypa1uzJxGJ/JhIgPUS5ErJQJESsnZUIjhPxYyZIlL2u/zOxcggMDyM41aFGleAFXJWKfy81EgTlzBt56C958E86eNbfdcw+8+iqUL29vbeJItmdCxAcpFyJWyoSIlZMyoRFCfuzHH3+8rP12HkslO9cgIiSIyiUiC7gqEftcbiYKTHKyOVfQ2bPQti2sXg2TJ6sZJLaxPRMiPki5ELFSJkSsnJQJjRBygG2JqQDkGgYBAZpUWiRfJSRA3brm7XLlzCXlS5SAW24B5U1ERERERHyURgj5sS5dulzWfonJ5qUrpWPCCrIcEdtdbibyxZ49MGAA1KsHixef3/6Pf0C/fmoGiU/waiZE/IRyIWKlTIhYOSkTagj5sR07dlzWflsOpwBwS9MKBVmOiO0uNxNXJSUFnnkG6tSB6dMhMNBcTUzEB3klEyJ+RrkQsVImRKyclAk1hPzY/v37L2u/bzcfAaBO2ZiCLEfEdpebiSuSkwOTJkGNGvD665CRAZ07w/r18NRTBXdekatQoJkQ8VPKhYiVMiFi5aRMaA4hPxYWdulLwM5kZLtu1yunhpAUbpeTiSvWty989ZV5u2ZNc/LoG2/UpWHi0wo0EyJ+SrkQsVImRKyclIkAwzAMu4vwppSUFGJjY0lOTiYmpvA3SBZsOcI/Pl1LaHAg21/urkmlRa7U1KnwwAPw4ovw4IMQGmp3RSIiIiIiIhae9Dx0yZgfmzZt2iX32XQwGYCY8GA1g6TQu5xMXJaTJ+Gxx+Djj89vGzgQdu+GYcPUDBK/kW+ZEClElAsRK2VCxMpJmdAlY34sNzf3kvukZeYAmj9InOFyMnFR2dnw/vvmKKATJ6B0aXMlsago89KwYsXyp1ARL7nqTIgUQsqFiJUyIWLlpExohJAfq1at2iX3WbDFnFD6mqolCrocEdtdTiYuaP58aNgQ/vlPsxlUty588onZDBLxU1eVCZFCSrkQsVImRKyclAk1hPxY+fLlL/q4YRic/mNS6cbxRb1QkYi9LpWJPO3YAT16mF9bt0KJEjBhAvz6K1x/ff4XKeJFV5QJkUJOuRCxUiZErJyUCTWE/NiyZcsu+vix1AySz2YB0KySLnWRwu9SmchTcrI5OigkBB5/HH77zZw8OlhX1Ir/u6JMiBRyyoWIlTIhYuWkTOg3nkJs25FUAMJDAgkPCbK5GhEfkZkJv/wC7dqZ91u0gH/9C7p3h+rV7a1NRERERETESzRCyI917Njxoo8v/GP+oKolo71QjYj9LpoJw4A5c6BePejSxVwx7Jx//lPNICmULvVzQsSJlAsRK2VCxMpJmVBDyI/t37//oo//evAUAN3rx3mhGhH7XTATv/4KnTtDnz7mJWHFisGePV6tTcQOl/o5IeJEyoWIlTIhYuWkTKgh5Md2/3mEw18kn81i86EUAPo2dc6kWOJsbpk4ehTuuw+aNIHFiyEsDJ59FnbuNBtEIoXcxX5OiDiVciFipUyIWDkpE7Y3hCZMmECVKlUIDw+nWbNmLF++/IL7zpw5k65du1KqVCliYmJo3bo1CxYs8GK1viX4IpPebk00m0GRoUFUKBbprZJEbGXJREYGNG4MkyaZl4sNHAjbtsGrr0KRIrbVKOJNF/s5IeJUyoWIlTIhYuWkTNjaEJo2bRrDhg3jueeeY/369bRr144ePXpccIjWsmXL6Nq1K/PmzWPt2rV06tSJXr16sX79ei9X7hv69+9/wcc2H0oGoGR0mLfKEbFd/379zt8JCzNXC2vRAlasgKlToXJl22oTscPFfk6IOJVyIWKlTIhYOSkTtjaExo4dy7333svgwYOpU6cO77zzDvHx8UycODHP/d955x2GDx9OixYtqFGjBqNHj6ZGjRp8/fXXXq7cN8yYMeOCjy3beRyALnXKeKscEXutXs3xunVhyZLz255+Gn7+Ga691rayROx0sZ8TIk6lXIhYKRMiVk7KhG0NoczMTNauXUu3bt0s27t168ZPP/10WcfIzc0lNTWV4sWLX3CfjIwMUlJSLF+FRWZm5gUfSzqTAUDF4hHeKkfEHgcPwt13Q8uWlNy2DZ5//vxjoaEQaPuVsSK2udjPCRGnUi5ErJQJESsnZcK2i+OOHz9OTk4OZcpYR7CUKVOGI0eOXNYxxowZw5kzZxgwYMAF93nttdcYNWqU2/bp06cTGRlJ3759+f7770lOTqZ06dK0bNmSuXPnAtC0aVNyc3PZsGEDAL1792bFihWcOHGC4sWL0759e2bPng1Aw4YNCQkJYe3atQDccMMNrFmzhqNHjxITE0O3bt348ssvAahXrx7R0dH88ssvAFx//fVs3ryZQ4cOERUVxY033si0adMAqFWrFiVLluTHH38EoEuXLuzYsYP9+/e7mlvTpk0jNzeXatWqUb58eRYvXcb2w0WAAKLTDjNlys8EBwfTv39/ZsyYQWZmJpUqVaJatWr88MMPALRt25Zjx46xY8cOAG677TbmzJlDWloaFSpUoG7duixcuBCA1q1bk5ycTEJCAmAOqZs/fz6pqanExcXRtGlT5s2bB0CLFi1IT09n06ZNANx8880sWbKEkydPUrJkSVq3bu0a4dWkSRMA1yWAvXr1YuXKlRw/fpxixYrRsWNHZs2aBUCDBg0IDw9n9erVAPTs2ZN169Zx5MgRihQpQvfu3Zk+fToAdevWJTY2lpUrVwJm0zEhIYGDBw8SGRlJ7969mTJlCgA1a9akdOnSrFixAoDrrruOXbt2sW/fPkJDQ7nllluYPn062dnZVK1alYoVK7LkjxEp7du359ChQ+zatYvAwEAGDhzIzJkzycjIoGLFitSsWZPvvvsOgGuvvZbjx4+zfft2AAYOHMjcuXM5c+YM5cuXp379+q75sVq1asXp06fZsmULAP369WPhwoWkpKRQpkwZmjdvzjfffANAs2bNyMrKYuPGjQD06dOHZcuWkZSURIkSJWjbti1z5swBoHHjxgQGBrJu3ToAbrzxRlatWsWxY8eIjY2lc+fOzJw5E4D69esTGRnJqlWrAOjRowe//vorhw8fJjo6mp49e/LFF18AULt2bYoXL+5q7Hbt2pVt27Zx4MABIiIi6NOnD1OnTsUwDGrUqEFcXJxr7rBOnTqxd+9e9uzZQ0hICP369ePLL78kKyuLKlWqULlyZRYvXmy+382awZgxlJk8meA/PrR3XHMNCbfeSskVK6hduzaLFi0CoE2bNiQlJbFt2zYABgwYwLx58zh9+jTlypWjUaNGfPvttwC0bNmStLQ0Nm/eDODXnxFhYWH07dvX7TNi2bJlgLmk5v79+9m9e7c+IwrpZ0RWVhYpKSmO/Ixo164dR44cYefOnQQEBHDrrbcye/Zszp49S3x8vD4jHPwZkZSUxObNm/UZ4fB/R+gz4vxnRExMjOvvsD4j9O8IfUYsJikpiYMHD/rtZ0RaWhqXK8AwDOOy985Hhw8fpnz58vz000+0bt3atf3VV1/l008/db2ZFzJlyhQGDx7MnDlz6NKlywX3y8jIICMjw3U/JSWF+Ph4kpOTiYmJufoXYqOjR4+6NdQAfjuWSpexywgNDmTbS90JDAywoTqRAjR7Nvzzn3DokHn/2mth3DiOVqyYZyZEnOpCPydEnEy5ELFSJkSs/D0TKSkpxMbGXlbPw7ZrKUqWLElQUJDbaKBjx45d8s2fNm0a9957L1988cVFm0EAYWFhxMTEWL4Ki3Md97/67dgZAOKLRagZJIVTWprZDKpUCaZNg+XLoUWLC2ZCxKmUCRF3yoWIlTIhYuWkTNjWEAoNDaVZs2auYVbnLFq0iDZt2lzweVOmTGHQoEF8/vnn3HDDDQVdpl86kGQOEQsMUDNICol9++CPId4A3HYbfPihuYz8gAGgv+siIiIiIiIesW0OIYDHHnuMu+66i+bNm9O6dWv+85//sH//foYMGQLAM888w6FDh/jkk08Asxl0991383//939cc801rtFFERERxMbG2vY67NK2bds8t285bC45XyTc1m+vyNVLTYXXXoOxY6FoUdi5E4oUMRtAf/+72+4XyoSIUykTIu6UCxErZULEykmZsHX5nYEDB/LOO+/w0ksv0bhxY5YtW8a8efOoVKkSAImJiezfv9+1//vvv092djYPPfQQZcuWdX098sgjdr0EWx07dizP7QF/jJYoGhnqzXJE8k9OjjkCqEYNsyGUkQF168LJkxd92oUyIeJUyoSIO+VCxEqZELFyUiZsX4/5wQcfZO/evWRkZLB27Vrat2/vemzy5MmuWdUBlixZgmEYbl+TJ0/2fuE+4Nws/X91OiMbgLbVS3qzHJH8sXgxNG8OgwfD0aNQvbo5ifT330PFihd96oUyIeJUyoSIO+VCxEqZELFyUiZ0TVEhtHT77wCUKxphcyUiHtqxA667zrwdGwsvvGCuJhaq0W4iIiIiIiL5ybZl5+3iyRJs/qrpy4tIOpPJtPuvoVXVEnaXI3JxWVkQEnL+/qBBEBUFo0ZBSY1yExERERERuVx+sey8XL05c+a4bcvJNTiZlglA+WIaISQ+LDsbJkyAKlVgz57z2z/+GP797ytqBuWVCREnUyZE3CkXIlbKhIiVkzKhhpAfS0tLc9t2Mi2Tc2O+4mLCvVyRyGVasAAaNYKHHoJDh2D8+POPXcUS8nllQsTJlAkRd8qFiJUyIWLlpEyoIeTHKlSo4LZt7/EzrtvBQfr2io/ZuhVuuAG6d4eEBChRAt59F958M18On1cmRJxMmRBxp1yIWCkTIlZOyoQmlfZjdevWddv227HTNlQichmefhrefttcUj44GB5+GEaMgGLF8u0UeWVCxMmUCRF3yoWIlTIhYuWkTGgIiR9buHCh27asXPN6sUolIr1djsjFRUSYzaDevc3RQWPH5mszCPLOhIiTKRMi7pQLEStlQsTKSZnQCKFCJj0zB4BaZYrYXIk4mmHA3LlQujS0amVue/JJaN8eOnWytzYRERERERHRCCF/1rp1a7dtP+8+AUC5olphTGyycSN07Qo33WROGp2ba26PjCzwZlBemRBxMmVCxJ1yIWKlTIhYOSkTagj5seTkZLdtCYkpAESFBXm7HHG6o0fh/vuhSRP4/nsICzMbQ1lZXishr0yIOJkyIeJOuRCxUiZErJyUCTWE/FhCQoLbtmKRoQBUKxXt7XLEqdLT4Y03oEYN+OADc0RQ//7mimKvvWY2hrwkr0yIOJkyIeJOuRCxUiZErJyUCc0hVMicGyGkSaXFa776ylxBDKB5cxg3Dtq2tbcmERERERERuagAwzAMu4vwppSUFGJjY0lOTiYmJsbucq5KdnY2wcHWnl7lp78BYMGw9tSK08TSUkBSUuBcfnJzoV8/6NMH7rwTAu0beJhXJkScTJkQcadciFgpEyJW/p4JT3oeumTMj82fP99yPz0rx3U7Libc2+WIExw+DIMGQZ06kJpqbgsMhJkz4e67bW0GgXsmRJxOmRBxp1yIWCkTIlZOyoQaQn4s9dwv5H84eDLNdTsmwn87muKD0tLg5ZfNeYL++1+zMeSDH5R/zYSI0ykTIu6UCxErZULEykmZUNfAj8XFxVnur9xlLjkfERJEQECAHSVJYZObC1OmmHMEHTxobmvTxpwnqGVLe2vLw18zIeJ0yoSIO+VCxEqZELFyUibUEPJjTZs2tdxPOmMu7121VJQd5Uhhk54OnTrBzz+b9ytVMlcTGzAAfLTh+NdMiDidMiHiTrkQsVImRKyclAldMubH5s2bZ7m/ePsxALrUKWNHOVLYhIdDlSoQHQ2jR5vLyA8c6LPNIHDPhIjTKRMi7pQLEStlQsTKSZlQQ6gQOfd7eskiYfYWIv7p9GkYMQL27j2/bcwY2LkTnnkGIiJsK01ERERERETyly4Z82MtWrRw3c7NNVi//5S5vXIxmyoSv5Sba04U/eyzcOSI2QCaOtV8rGxZe2vz0J8zISLKhEhelAsRK2VCxMpJmVBDyI+lp6e7bh9JOX+7WqloO8oRf7R0KTz6KKxfb96vVs28LMxP/TkTIqJMiORFuRCxUiZErJyUCV0y5sc2bdrkur33xBnX7ZAgfVvlEnbtgltugY4dzWZQbCy8/TZs2QI332x3dVfsz5kQEWVCJC/KhYiVMiFi5aRMaIRQIXHyjxXGQtUMksvxyScwcyYEBsI//gGjRkGpUnZXJSIiIiIiIl4SYBiGYXcR3pSSkkJsbCzJycnExMTYXc5VSU9PJzw8HIBJy3fzyjdb6dkgjgl3NLO5MvE52dlw7BiUK2feP30ahgyBp5+G+vXtrS0f/TkTIqJMiORFuRCxUiZErPw9E570PDScxI8tWbLEdftUmjlCqGS0VhiTv1i0CJo0gd69zQmkwVxK/n//K1TNILBmQkSUCZG8KBciVsqEiJWTMqGGkB87efKk6/bRPyaVLq0l5+WcbdvgxhuhWzfYvBl27zZXECvE/pwJEVEmRPKiXIhYKRMiVk7KhBpCfqxkyZKu28t2/g5A6Rj/Hdom+SQpCR55BBo0gG++geBgGDYMfvsNatWyu7oC9edMiIgyIZIX5ULESpkQsXJSJjSptB9r3bq16/a5S8aiQvUtdbTt26F1azjX1e7VC956q9A3gs75cyZERJkQyYtyIWKlTIhYOSkTGiHkx77++msAcnMNMrLNuWGql462sySxW40aUK2aOTfQokXw1VeOaQbB+UyIiEmZEHGnXIhYKRMiVk7KhBpChcCOY6mu21VLRdlYiXjd5s1w++3mqmFgLiM/Zw6sXw9duthbm4iIiIiIiPgsNYT8WJMmTQA4fOqsa1tIkL6ljnDsmLlsfKNGMGUKvPnm+cfKlTPnDXKgc5kQEZMyIeJOuRCxUiZErJyUCWf+1ljILNxyFIAudcrYXIkUuIwMGD8eXnkFUlLMbf36waBBtpYlIiIiIiIi/kXDSfzY+vXrAQgIMO9nZOfYWI0UuJkzoW5dGD7cbAY1bQpLl8L06VC1qt3V+YRzmRARkzIh4k65ELFSJkSsnJQJNYQKgV8PJAPQqEJRewuRgjV9OuzeDWXLwuTJsHo1tG9vd1UiIiIiIiLihwIMwzDsLsKbUlJSiI2NJTk5mZiYGLvLuSqnT58mOjqaas/OIyfXYNRN9binTWW7y5L8kpho/lm2rPnnvn3w0Ufw5JMQrdXk8nIuEyJiUiZE3CkXIlbKhIiVv2fCk56HRgj5sZUrVwKQk2v29KqV8t+/tPInZ8+acwTVqAGPP35+e6VKMGqUmkEXcS4TImJSJkTcKRciVsqEiJWTMqFJpf3Y8ePHXc0ggBpl1Cjwa4YBU6fCU0/BgQPmtr17IT0dwsNtLc1fHD9+3O4SRHyKMiHiTrkQsVImRKyclAmNEPJjxYoVI/ls1vn7kaE2ViNX5eefoU0buP12sxkUHw+ffQY//qhmkAeKFStmdwkiPkWZEHGnXIhYKRMiVk7KhOYQ8mPp6ensPZVJ93eWA7D39RtsrkiuyBdfwMCB5u2oKHj6aXjsMYiMtLcuP5Senk64GmgiLsqEiDvlQsRKmRCx8vdMaA4hh5g1axaJp9IBqFPWv5tbjtazJ5QvD4MGwY4d8PzzagZdoVmzZtldgohPUSZE3CkXIlbKhIiVkzKhOYT83J7jZwAoXzTC5krksuTmwiefwOzZMHMmBAaak0QnJICfj1gTERERERER/6ERQn6sQYMGLN5+DICqpaJsrkYuadkyaNEC/vY3mDMHvvzy/GNqBuWLBg0a2F2CiE9RJkTcKRciVsqEiJWTMqGGkB8LDw8nIysXwLLamPiY3buhXz/o0AHWrTObP2++Cb17211ZoePP1/qKFARlQsSdciFipUyIWDkpE2oI+bHVq1dz6mwmANVLa8l5n5Oebi4hX6cOzJhhXh42ZAjs3AlPPglhYXZXWOisXr3a7hJEfIoyIeJOuRCxUiZErJyUCc0h5OeCAs2eXpFwfSt9TmgofPcdZGZC164wZgw4aPihiIiIiIiI+C4tO+/HkpOTafTaCgCm3HcNrauVsLki4YcfoFUrc/l4gF9+gePHzZXEAgLsrc0BkpOTiY2NtbsMEZ+hTIi4Uy5ErJQJESt/z4SWnXeIdevWUSIqFIDwEH0rbbV9O/TqBZ07w1tvnd/eqhXccIOaQV6ybt06u0sQ8SnKhIg75ULESpkQsXJSJtRF8GNHjhzhxBlzDqESUZqPxhZJSTBsGNSvD3PnQlCQOXeQ2OLIkSN2lyDiU5QJEXfKhYiVMiFi5aRMaOIZPxYWWcR1OzYixMZKHCgrC957D0aONJtCYI4EevttqF3b1tKcrEiRIpfeScRBlAkRd8qFiJUyIWLlpExoDiE/tv94Ku3fXgbA7tE9CQzUZUle8+ij8M475u169WDsWOjWzdaSBLKzswkOVp9b5BxlQsSdciFipUyIWPl7JjSHkEPMnjvPdVvNIC/4c+906FCoUAEmToQNG9QM8hHTp0+3uwQRn6JMiLhTLkSslAkRKydlwn/bXkJ2rvlnyehQewsp7H7/HV54AdLS4L//NbdVqQJ79oAfd45FRERERETEuTRCyI9VqFQFgLDgIJsrKaQyMsw5gapXN+cL+uQT2LHj/ONqBvmcunXr2l2CiE9RJkTcKRciVsqEiJWTMqGGkD8LjQTgZFqmzYUUMoYBs2aZcwM9+SSkpECTJrBkCdSsaXd1chGxsbF2lyDiU5QJEXfKhYiVMiFi5aRMqCHkxzZv2QpAsUhdMpZv9u+H666Dvn1h1y6Ii4OPPoLVq6FDB7urk0tYuXKl3SWI+BRlQsSdciFipUyIWDkpE7rmxY+dzjYnkq5aKsrmSgqRYsVg61YID4fHH4enn4boaLurEhEREREREclXGiHkx6LLVgWgRJRGCF2xs2dh0iTI/WOG7iJF4PPPYds2eOUVNYP8TDet9iZioUyIuFMuRKyUCRErJ2VCDSE/lnDgdwACArTkvMcMA6ZOhdq14b77zNvnXHcdVKpkX21yxRISEuwuQcSnKBMi7pQLEStlQsTKSZnQJWN+7OzZs0AI4SFaZcwjq1bBo4/CTz+Z9ytUgChddlcYHDx40O4SRHyKMiHiTrkQsVImRKyclAmNEPJjB9LMfl710rqs6bIcOAB33gmtWpnNoMhIeOkl2L4deve2uzrJB5GRkXaXIOJTlAkRd8qFiJUyIWLlpEwEGIZh2F2EN6WkpBAbG0tycjIxMTF2l3NVOry1mH0n0nirX0P6N4+3uxzf164drFhh3r7nHnj1VShf3t6aRERERERERPKJJz0PjRDyY/tOpAEQX9w5HUyP5OZCZub5+6+8YjaFVq+GyZPVDCqEpkyZYncJIj5FmRBxp1yIWCkTIlZOyoQaQn4sKshcGSsmPMTmSnzQjz+al4a9/vr5bR06wNKl0Ly5fXWJiIiIiIiI+AA1hPzYmRzz21ckXHODu+zdCwMHQtu2sGYNTJwIGRnnH9eKbIVazZo17S5BxKcoEyLulAsRK2VCxMpJmVBDyE+dzcxx3VZDCEhJgWeeMZeR/+ILCAyE+++HDRsgLMzu6sRLSpcubXcJIj5FmRBxp1yIWCkTIlZOyoQaQn7q99Tzo15iIxx+ydh330GNGublYRkZ0LkzrF8P778PZcrYXZ140Ypzk4aLCKBMiORFuRCxUiZErJyUCQ0t8VOZObmu2wFOvwyqalU4dcpsCo0ZAzfeqEvDRERERERERC5CI4T8VGa22RAqGe3Ay6F27oTx48/fr1oVvv8eNm+GXr3UDHKw6667zu4SRHyKMiHiTrkQsVImRKyclAk1hPzUuRFCYcEO+haePAmPPQb16sEjj8CqVecfa9sWQkPtq018wq5du+wuQcSnKBMi7pQLEStlQsTKSZlwUDehcDmZlglYLx0rtLKy4N13zUvCxo0z7/foAUWL2l2Z+Jh9+/bZXYKIT1EmRNwpFyJWyoSIlZMyoTmE/Nzp9Gy7SyhY334Ljz8OW7ea9+vWhbFj4frr7a1LfFKoRomJWCgTIu6UCxErZULEykmZCDAMw7C7CG9KSUkhNjaW5ORkYmJi7C7niv3v5308P3szLSoXY/qQNnaXUzDS0qBKFTh2DEqUgJdfhvvug2D1MUVERERERET+ypOehy4Z81NBgebEyYdPpdtcST5LSoJzPcrISHjjDXOE0G+/wQMPqBkkFzV9+nS7SxDxKcqEiDvlQsRKmRCxclIm1BDyUzuOpgLQOL6ovYXkl8xM81KwatVg2rTz2wcNgrff1nxBclmyswv5JZQiHlImRNwpFyJWyoSIlZMyoYaQn4qNCAHgSIqfjxAyDJgzx1w57PHH4dQp+Pxzu6sSP1W1alW7SxDxKcqEiDvlQsRKmRCxclIm1BDyU5nZ5upiDcrH2lzJVfj1V+jcGfr0MS8JK1MGJk2CWbPsrkz8VMWKFe0uQcSnKBMi7pQLEStlQsTKSZlQQ8hPZf2x3HxYsJ9+C994A5o0gcWLISwMnnkGdu6Ee++FoCC7qxM/tWTJErtLEPEpyoSIO+VCxEqZELFyUiY0Q6+f2nH0NACh/toQatXKvFxs4EB4/XWoXNnuikREREREREQcQw0hP1UiOhSA31MzbK7kMhgGfPEFnDwJQ4aY2zp2hC1boG5dW0uTwqV9+/Z2lyDiU5QJEXfKhYiVMiFi5aRMqCHk5yqWiLS7hItbvRoefRR+/NFcRv6mm6BcOfMxNYMknx06dIjy5cvbXYaIz1AmRNwpFyJWV5OJnJwcsrKy8rkiEXsdOnSIEiVK2F3GRYWGhhIYePVXC6kh5KeycgwAwoN9dL6dgwfh2Wfh00/N+5GRMHw4xPrxJNji83bt2kXLli3tLkPEZygTIu6UCxGrK8mEYRgcOXKEU6dOFUxRIjYKDAxkz549dpdxUYGBgVSpUoXQ0NCrOo4aQn4q+49JpUOCAmyu5C/S0uCtt8xJo8+eNbfddReMHg0VKthbmxR6+dElFylMlAkRd8qFiNWVZOJcM6h06dJERkYSEOBjv5OIXIVTp05RtGhRu8u4oNzcXA4fPkxiYiIVK1a8qvwFGIZh5GNtPi8lJYXY2FiSk5OJiYmxu5wr1nXsUnYeO83rfRtwa0sfWhZv1y7zUrDMTLj2Whg3Dlq0sLsqERERERHJBzk5OezYsYPSpUv7/GU1IoVVcnIyhw8fpnr16oSEhFge86Tnof8i8VORYebgrrTMHJsrAX777fztatXM0UBffAHLl6sZJF41c+ZMu0sQ8SnKhIg75ULEytNMnJszKDLSx+cyFblCJ0+etLuESzp3qVhOztX1A9QQ8lNhQea37txqY7bYu9dcNr5mTViz5vz2xx+H/v1BQ0fFyzIy/GDVPREvUiZE3CkXIlZXmgldJiaFVW5urt0lXFJ+5U8NIT+V8cccQpGhNkwDlZpqThhdu7Y5Eghg2TLv1yHyFxUr+tDlkyI+QJkQcadciFgpEyJWYWFhdpfgNWoI+alfD5wCIDTYi9/CnByYNAlq1IDXXoOMDOjUCdatg8ce814dIhdQs2ZNu0sQ8SnKhIg75ULESpmQvHz44Yd069bN7jJsYXdDKCMjg4oVK7J27doCP5caQn6qQrEIwFzy0Wt69oT77oOjR6F6dZg9G77/Hho39l4NIhfx3Xff2V2CiE9RJkTcKRciVk7KxKBBgwgICCAgIIDg4GAqVqzIAw88kOecMT/99BM9e/akWLFihIeH06BBA8aMGZPnnC2LFy+mZ8+elChRgsjISOrWrcvjjz/OoUOHvPGy8l1GRgYvvPACI0aMsLuUAmMYBiNHjqRcuXJERETQsWNHtmzZApiTMl/IO++8Q61atYiIiCA+Pp5HH32U9PT0PPd97bXXCAgIYNiwYZbtR48eZdCgQZQrV47IyEi6d+/Ozp07XY+HhYXxxBNP8NRTT139C70ENYT8VE6u2QgqEeXF7uUtt0BsLIwZA1u2QO/emidIRERERET8Rvfu3UlMTGTv3r1MmjSJr7/+mgcffNCyz6xZs+jQoQMVKlRg8eLFbNu2jUceeYRXX32VW2+91fKf8u+//z5dunQhLi6OGTNmkJCQwHvvvUdycjJjxozx2uvKzMzMt2PNmDGD6Oho2rVrd1XHOTcBuS968803GTt2LO+++y6rV68mLi6Orl27kpqaesHnfPbZZzz99NO8+OKLbN26lQ8//JBp06bxzDPPuO27evVq/vOf/9CwYUPLdsMw6NOnD7t372bOnDmsX7+eSpUq0aVLF86cOePa74477mD58uVs3bo1/150XgyHSU5ONgAjOTnZ7lKuSqWn5hqVnppr7DiSUjAnOHnSMB5/3DC+/PL8tuxsw/j994I5n0g+2Ldvn90liPgUZULEnXIhYuVpJs6ePWskJCQYZ8+edW3Lzc01zmRk2fKVm5t72bXfc889Ru/evS3bHnvsMaN48eKu+6dPnzZKlChh9O3b1+35X331lQEYU6dONQzDMA4cOGCEhoYaw4YNy/N8J0+evGAtJ0+eNO677z6jdOnSRlhYmFGvXj3j66+/NgzDMF588UWjUaNGlv3HjRtnVKpUye21jB492ihbtqxRqVIl4+mnnzZatWrldq4GDRoYL7zwguv+Rx99ZNSuXdsICwszatWqZfz73/+27N+rVy/jiSeesGxbtWqV0aVLF6NEiRJGTEyM0b59e2Pt2rWWfQBj4sSJxk033WRERka6zvnVV18ZTZs2NcLCwowqVaoYI0eONLKyslzPGzNmjFG/fn0jMjLSqFChgvHAAw8YqampF3zvrlZubq4RFxdnvP76665t6enpRmxsrPHee+8Z6enpeT7voYceMq677jrLtscee8xo27atZVtqaqpRo0YNY9GiRUaHDh2MRx55xPXY9u3bDcDYvHmza1t2drZRvHhx44MPPrAcp2PHjsaIESPyrCWvHJ7jSc/DhhmJJT/l+xxC2dnwwQfwwgtw/DhUqgQ33ghhYRAUBCVL5u/5RPLR8ePHNTGiyJ8oEyLulAsRq/zIxNmsHOq+sCCfKvJMwkvXX/FCO7t372b+/PmEhIS4ti1cuJATJ07wxBNPuO3fq1cvatasyZQpUxg4cCDTp08nMzOT4cOH53n8okWL5rk9NzeXHj16kJqayv/+9z+qVatGQkICQUFBHtX//fffExMTw6JFi1yjll5//XV27dpFtWrVANiyZQubNm3iyy+/BOCDDz7gxRdf5N1336VJkyasX7+e++67j6ioKO655x4Ali9fzh133GE5V2pqKvfccw/jx48HYMyYMfTs2ZOdO3dSpEgR134vvvgir732GuPGjSMoKIgFCxZw5513Mn78eNq1a8euXbu4//77XfsCBAYGMn78eCpXrsyePXt48MEHGT58OBMmTLjga+/RowfLly+/6Ptz+vTpPLfv2bOHI0eOWOZICgsLo0OHDvz000/ceeedec4j1LZtW/73v/+xatUqWrZsye7du5k3b57rfTvnoYce4oYbbqBLly688sorlsfOreoXHh7u2hYUFERoaCgrVqxg8ODBru0tW7a85Gu8WmoI+aHc3PNDFKPD8vFbuGCBuWT8H9dOUqeOeXmYg2ZZF/+2fft2mjZtancZIj5DmRBxp1yIWDktE3PnziU6OpqcnBzX3C9jx451Pb5jxw4A6tSpk+fza9eu7dpn586dxMTEULZsWY9q+O6771i1ahVbt251TepdtWpVj19LVFQUkyZNIjQ01LWtYcOGfP755675fz777DNatGjhOs/LL7/MmDFj6Nu3LwBVqlQhISGB999/n3vuuYdTp05x6tQpypUrZznXddddZ7n//vvvU6xYMZYuXcqNN97o2n777bfz97//3XX/rrvu4umnn3Y1TapWrcrLL7/M8OHDXQ2hP8+xU6VKFV5++WUeeOCBizaEJk2axNmzZy/7vfqzI0eOAFCmTBnL9jJlyrBv3z7S09OJiopye96tt97K77//Ttu2bTEMg+zsbB544AGefvpp1z5Tp05l3bp1rF69Os9z165dm0qVKvHMM8/w/vvvExUVxdixYzly5AiJiYmWfcuXL8/evXuv6DVeLjWE/FBWbq7rdkh+jBDasQMefRTmzTPvlygBo0bB/ffDn7rlIiIiIiIifxUREkTCS9fbdm5PdOrUiYkTJ5KWlsakSZPYsWMHDz/8sNt+xgUW7zEMg4A/5lH9821PbNiwgQoVKlz1Cm8NGjSwNIPAnHvmo48+YsSIERiGwZQpU1wNl99//50DBw5w7733ct9997mek52dTWxsLICryfLnESwAx44d44UXXuCHH37g6NGj5OTkkJaWxv79+y37NW/e3HJ/7dq1rF69mldffdW17VwzLi0tjcjISBYvXszo0aNJSEggJSWF7Oxs0tPTOXPmTJ6NGTCbJVfrr9+7S30/lyxZwquvvsqECRNo1aoVv/32G4888ghly5ZlxIgRHDhwgEceeYSFCxe6vX/nhISEMGPGDO69916KFy9OUFAQXbp0oUePHm77RkREkJaWdnUv8hLUEPJDWTnnP5xCg/KhIXTggNkMCg6Ghx+GESOgWLGrP66Ilw0cONDuEkR8ijIh4k65ELHKj0wEBARc8WVb3hYVFUX16tUBGD9+PJ06dWLUqFG8/PLLAK4mzdatW2nTpo3b87dt20bdunVd+yYnJ5OYmOjRKKGIiIiLPh4YGOjWkMprgua8miW33347Tz/9NOvWrePs2bMcOHCAW2+9FTAvVQPzsrFWrVpZnnfucrUSJUoQEBDgtvLaoEGD+P3333nnnXeoVKkSYWFhtG7d2m0y67/WlJuby6hRo1wjkv4sPDycffv20bNnT4YMGcLLL79M8eLFWbFiBffee+9FJ6W+mkvG4uLiAHOk0J+/b8eOHaNMmTIUL148z+eNGDGCu+66y3VZV4MGDThz5gz3338/zz33HGvXruXYsWM0a9bM9ZycnByWLVvGu+++S0ZGBkFBQTRr1owNGzaQnJxMZmYmpUqVolWrVm7NtKSkJEqVKnXR13i1tMqYH8rIOr/UYciVNIQyM2HVqvP3O3eG0aPNS8XGjlUzSPzW3Llz7S5BxKcoEyLulAsRK6dn4sUXX+Ttt9/m8OHDAHTr1o3ixYvnuULYV199xc6dO7ntttsA6NevH6Ghobz55pt5HvvUqVN5bm/YsCEHDx50XXr2V6VKleLIkSOWptCGDRsu6/VUqFCB9u3b89lnn/HZZ5/RpUsX16VRZcqUoXz58uzevZvq1atbvqpUqQJAaGgodevWJSEhwXLc5cuXM3ToUHr27Em9evUICwvj+PHjl6ynadOmbN++3e181atXJzAwkDVr1pCdnc2YMWO45pprqFmzput7cTGTJk1iw4YNF/26kCpVqhAXF8eiRYtc2zIzM1m6dClt2rQhOTk5z+elpaURGGj9/TsoKAjDMDAMg86dO7Np0yZLDc2bN+eOO+5gw4YNbnNExcbGUqpUKXbu3MmaNWvo3bu35fHNmzfTpEmTS74XV8M/2rhikZF9/pKxoEAPhigaBnz9NTzxBBw+DDt3wrmOaB5L5Yn4mz8v1SgiyoRIXpQLESunZ6Jjx47Uq1eP0aNH8+677xIVFcX777/Prbfeyv33388///lPYmJi+P7773nyySfp168fAwYMACA+Pp5x48bxz3/+k5SUFO6++24qV67MwYMH+eSTT4iOjs6zsdShQwfat2/PLbfcwtixY6levTrbtm0jICCA7t2707FjR37//XfefPNN+vXrx/z58/n222+JiYm5rNd0xx13MHLkSDIzMxk3bpzlsZEjRzJ06FBiYmLo0aMHGRkZrFmzhpMnT/LYY48BcP3117NixQrL3D7Vq1fn008/pXnz5qSkpPDkk09ecqQTwAsvvMCNN95IfHw8/fv3JzAwkI0bN7Jp0yZeeeUVqlWrRnZ2Nv/617/o1asXP/74I++9994lj3s1l4wFBAQwbNgwRo8eTY0aNahRowajR48mMjKS22+/3TXq6e6776Z8+fK89tprgDmp+NixY2nSpInrkrERI0Zw0003ERQURJEiRahfv77lXFFRUZQoUcKyffr06ZQqVYqKFSuyadMmHnnkEfr06WOZ5BrMJty5kWsFRSOE/FDmHw2hcE/aeRs3Qpcu0Lu32QiKijLnDhIpRPLjWmKRwkSZEHGnXIhYKRPw2GOP8cEHH3DgwAHAHPmzePFiDhw4QPv27alVqxZjx47lueeeY+rUqZZ5Zh588EEWLlzIoUOHuPnmm6lduzaDBw8mJiYmz5XKzpkxYwYtWrTgtttuo27dugwfPpycHPNKkDp16jBhwgT+/e9/06hRI1atWnXRY/1V//79OXHiBGlpafTp08fy2ODBg5k0aRKTJ0+mQYMGdOjQgcmTJ7tGCAHcd999zJs3zzJS5qOPPuLkyZM0adKEu+66i6FDh1K6dOlL1nL99dczd+5cFi1aRIsWLbjmmmsYO3YslSpVAqBx48aMHTuWN954g/r16/PZZ5+5GjAFafjw4QwbNowHH3yQ5s2bc+jQIRYuXEiRIkVc8zLt37/fMtHz888/z+OPP87zzz9P3bp1uffee7n++ut5//33PTp3YmIid911F7Vr12bo0KHcddddTJkyxbLPypUrSU5Opl+/flf/Yi8iwLjQbFmFVEpKCrGxsSQnJ192h9XX7DyaStdxyygaEcyGFy8xedvRo+acQB9+CLm55ophjz5qjgjy09cvciFJSUkXvOZXxImUCRF3yoWIlaeZSE9PZ8+ePVSpUuWCE+eK/xswYABNmjThGQdeSZKdnU1wsL0XU/Xv358mTZrw7LPP5vn4xXLoSc9DI4T8UEp6NgA5WZkX3/HMGahXDz74wGwG9e8PW7fCa6+pGSSF0oIFC+wuQcSnKBMi7pQLEStlQvLy1ltvER0dbXcZtrjQHELekpGRQaNGjXj00UcL/FyaQ8gvmYO6UrMv0c+LioJ77oFly2DcOGjb1gu1iYiIiIiIiD+rVKkSDz/8sN1lOFJYWBjPP/+8V86lEUJ+6Nyy8/GxodYH1q6Fjh1h3brz2159FX75Rc0gcYS/Lp8p4nTKhIg75ULESpkQsXLSyCjbG0ITJkxwXffWrFkzli9fftH9ly5dSrNmzQgPD6dq1aqXNQN5YZOTazaEAv8YKcThwzBoELRoAUuXwp+vMwwPh0Dbv80iXnH69Gm7SxDxKcqEiDvlQsRKmRCxOje5txPY2imYNm0aw4YN47nnnmP9+vW0a9eOHj16sH///jz337NnDz179qRdu3asX7+eZ599lqFDhzJjxgwvV26vrBxzlbHA08nw8stQowb897/msvJ33mnOGSTiQFu2bLG7BBGfokyIuFMuRKyUCRGrs2fP2l2C19g6h9DYsWO59957GTx4MADvvPMOCxYsYOLEiXkuNffee+9RsWJF3nnnHcBcjm/NmjW8/fbb3HLLLd4s3VbZOQbX7/iJl75/H1JOmBtbt4Z33oGWLW2tTURERERERER8n20jhDIzM1m7di3dunWzbO/WrRs//fRTns9ZuXKl2/7XX389a9asISsrK8/nZGRkkJKSYvnyd9m5uZQ6c4oyKSegYkWYOhV+/FHNIHG8fv362V2CiE9RJkTcKRciVsqEiFWxYsXsLsFrbBshdPz4cXJycihTpoxle5kyZThy5Eiezzly5Eie+2dnZ3P8+HHKli3r9pzXXnuNUaNGuW2fPn06kZGR9O3bl++//57k5GRKly5Ny5YtmTt3LgBNmzYlNzeXDRs2ANC7d29WrFjBiRMnKF68OO3bt2f27NkANGzYkJCQENauXQvADTfcwJo1azh69CgxMTF069aNL7/8EoB69eoRHR3NL7/8AphNrc2bN3Po0CGioqK48cYbmTZtGgC1atWiZMmS/PjjjwB06dKFA7u2s7J1Z94LTWPIFxOY9tVX5E6dSrVq1ShfvjzLli0DoGPHjuzfv5/du3cTHBxM//79mTFjBpmZmVSqVIlq1arxww8/ANC2bVuOHTvGjh07ALjtttuYM2cOaWlpVKhQgbp167Jw4UIAWrduTXJyMgkJCQD079+f+fPnk5qaSlxcHE2bNmXevHkAtGjRgvT0dDZt2gTAzTffzJIlSzh58iQlS5akdevWfP311wA0adIEgPXr1wPQq1cvVq5cyfHjxylWrBgdO3Zk1qxZADRo0IDw8HBWr14NQM+ePVm3bh1HjhyhSJEidO/enenTpwNQt25dYmNjWblyJWA2HRMSEjh48CCRkZH07t2bKVOmAFCzZk1Kly7NihUrALjuuuvYtWsX+/btIzQ0lFtuuYXp06eTnZ1N1apVqVixIkuWLAGgffv2HDp0iF27dhEYGMjAgQOZOXMmGRkZVKxYkZo1a/Ldd98BcO2113L8+HG2b98OwMCBA5k7dy5nzpyhfPny1K9f37UEaKtWrTh9+rRrOG+/fv1YuHAhKSkplClThubNm/PNN98A0KxZM7Kysti4cSMAffr0YdmyZSQlJVGiRAnatm3LnDlzAGjcuDGBgYGs+2MS8htvvJFVq1Zx7NgxYmNj6dy5MzNnzgSgfv36REZGsmrVKgB69OjBr7/+yuHDh4mOjqZnz5588cUXANSuXZvixYu7Grtdu3Zl27ZtHDhwgIiICPr06cPUqVMxDIMaNWoQFxfnmjusU6dO7N27lz179hASEkK/fv348ssvycrKokqVKlSuXJnFixcD0K5dO44cOcLOnTsJCAjg1ltv5f3336dEiRLEx8dTu3ZtFi1aBECbNm1ISkpi27ZtAAwYMIB58+Zx+vRpypUrR6NGjfj2228BaNmyJWlpaWzevBnALz8jduzYwf79+wkLC6Nv375MmzaN3NxcfUY48DMiNTWVW2+9VZ8Rf3xGzJ49m7Nnz+ozwuGfEUePHqVLly76jNC/I/QZgfkZsWPHDgzDnJv0cj4jVq5cSeXKlcnMzCQ7O5uMjAwCAgIoXrw4J0+eJDc3l7CwMMLCwlz/GV+kSBGysrJIT08HoESJEpw6dYqcnBxCQ0OJiIhwLfUdHR1NTk6O67Kd4sWLk5ycTE5ODiEhIURGRrr2jYqKwjAM0tLSAPMX+dTUVLKzswkJCSEqKopTp04BEBkZCeDat2jRopw5c4asrCyCg4MpUqQIJ0+edO0bEBDAmTNnAIiNjSUtLY2srCyCgoKIjY0lKSkJgIiICIKCglxzMcXGxnL27FkyMzMJCgqiaNGinDhhXtURHh5OSEgIqampAMTExJCRkUFGRgaBgYEUK1aMpKQkDMMgLCyM0NBQ175/fg8v9X5HR0eTnZ3ter///B5e6v0uVqwYKSkprvf7z+/hxd7v4OBgoqOjLe/3n9/Di73fERERBAYGWt7vP7+HF3u/Y2JiSE9PJzMz0+09vNj7fe49/PP7fe49zM7OplixYhd8v0NDQwkPD7e83xf6O/vX9zsqKorc3FzL+32hv7N/fb+LFi3K6dOnyc7OJjc3F8MwmDt3LllZWZbPiHP7X44A41z6vezw4cOUL1+en376idatW7u2v/rqq3z66aeuD9w/q1mzJn/729945plnXNt+/PFH2rZtS2JiInFxcW7POfcNPyclJYX4+HiSk5OJiYnJ51flXVOmTOG2226zuwwRn6FMiFgpEyLulAsRK08zkZ6ezp49e1wLA4kUNidOnKBEiRJ2l3FRF8thSkoKsbGxl9XzsO2SsZIlSxIUFOQ2GujYsWNuo4DOiYuLy3P/4ODgC37DwsLCiImJsXwVFhd6n0ScSpkQsVImRNwpFyJWyoT3VK5c2TUfrhN17NiRYcOGue776vsREhJidwleY1tDKDQ0lGbNmrmGYp6zaNEi2rRpk+dzWrdu7bb/woULad68uaO+aec0b97c7hJEfIoyIWKlTIi4Uy5ErJyUiUGDBhEQEEBAQADBwcFUrFiRBx54wHXpUmE1cuRI1+sOCAggNjaWdu3asXTpUlvrWr16Nffff7+tNeQlKirK7hK8xtZl5x977DEmTZrERx99xNatW3n00UfZv38/Q4YMAeCZZ57h7rvvdu0/ZMgQ9u3bx2OPPcbWrVv56KOP+PDDD3niiSfsegm2One9t4iYlAkRK2VCxJ1yIWLltEx0796dxMRE9u7dy6RJk/j666958MEH7S6rwNWrV4/ExEQSExNZuXIlNWrU4MYbb3TNbWOHUqVKueZ28iXn5vBxAlsbQgMHDuSdd97hpZdeonHjxixbtox58+ZRqVIlABITE9m/f79r/ypVqjBv3jyWLFlC48aNefnllxk/fryjlpwXERERERHxSWfOXPjrjwmOL2vfPybcveS+VyAsLIy4uDgqVKhAt27dGDhwoGvSe4CcnBzuvfdeqlSpQkREBLVq1eL//u//LMcYNGgQffr04e2336Zs2bKUKFGChx56yLLy9bFjx+jVqxcRERFUqVKFzz77zK2W/fv307t3b6Kjo4mJiWHAgAEcPXrU9fjIkSNp3LgxH330ERUrViQ6OpoHHniAnJwc3nzzTeLi4ihdujSvvvrqJV93cHAwcXFxxMXFUbduXUaNGsXp06ddiwEAjB07lgYNGhAVFUV8fDwPPviga/JmgH379tGrVy+KFStGVFQU9erVcy0CAJCQkEDPnj2Jjo6mTJky3HXXXRw/fvyCNf31krGAgAAmTZrEzTffTGRkJDVq1OCrr76yPMfTc8jF2doQAnjwwQfZu3cvGRkZrF27lvbt27semzx5smvlhXM6dOjAunXryMjIYM+ePa7RRE7UrFkzu0sQ8SnKhIiVMiHiTrkQscrXTERHX/jrr/+JX7r0hfft0cO6b+XKee93lXbv3s38+fMt04/k5uZSoUIFvvjiCxISEnjhhRd49tlnXSvhnbN48WJ27drF4sWL+e9//8vkyZOZPHmy6/FBgwaxd+9efvjhB7788ksmTJjAsWPHXI8bhkGfPn1ISkpi6dKlLFq0iF27djFw4EDLeXbt2sW3337L/PnzmTJlCh999BE33HADBw8eZOnSpbzxxhs8//zz/Pzzz5f9ujMyMpg8eTJFixalVq1aru2BgYGMHz+ezZs389///pcffviB4cOHux5/6KGHyMjIYNmyZWzatIk33niD6D++D4mJiXTo0IHGjRuzZs0a5s+fz9GjRxkwYMBl1wUwatQoBgwYwMaNG+nZsyd33HGHa4Wx/DrHpTjpkjHblp2Xq/fnDrSIKBMif6VMiLhTLkSsnJaJuXPnupYJP7cs+9ixY12Ph4SEMGrUKNf9KlWq8NNPP/HFF19YGg/FihXj3XffJSgoiNq1a3PDDTfw/fffc99997Fjxw6+/fZbfv75Z1q1agXAhx9+SJ06dVzP/+6779i4cSN79uwhPj4egE8//ZR69eqxevVqWrRoAZgNqo8++ogiRYpQt25dOnXqxPbt25k3bx6BgYHUqlWLN954gyVLlnDNNddc8HVv2rTJ1bxJS0ujSJEiTJs2zbLo0p8nfK5SpQovv/wyDzzwABMmTADMEU233HILDRo0AKBq1aqu/SdOnEjTpk0ZPXq0a9tHH31EfHw8O3bsoGbNmhf9vpwzaNAg16p3o0eP5l//+herVq2ie/fu+XaOS7FpIXZbqCHkxzZu3Ei9evXsLkPEZygTIlbKhIg75ULEKl8z8afLi9wEBVnv/2m0jJvAv1zIsnfvFZf0V506dWLixImkpaUxadIkduzYwcMPP2zZ57333mPSpEns27ePs2fPkpmZSePGjS371KtXj6A/vaayZcuyadMmALZu3UpwcLBlwu7atWtTtGhR1/2tW7cSHx/vagYB1K1bl6JFi7J161ZXQ6hy5coUKVLEtU+ZMmUICgoi8E/vUZkyZSyjj/JSq1Yt1+VXqampTJs2jf79+7N48WJXnYsXL2b06NEkJCSQkpJCdnY26enpnDlzhqioKIYOHcoDDzzAwoUL6dKlC7fccgsNGzYEYO3atSxevNjVdPqzXbt2XXaz5tzxwBypU6RIEddry69zXEpaWhoRERH5cixfZ/slYyIiIiIiIlIIREVd+Cs8/PL3/esv4xfa74pKjKJ69eo0bNiQ8ePHk5GRYRkR9MUXX/Doo4/y97//nYULF7Jhwwb+9re/kZmZaTnOX1e5DggIIDc3Fzg/wiQgIOCCdRiGkefjf92e13kudu4LCQ0NpXr16lSvXp0mTZrw+uuvU758edccPvv27aNnz57Ur1+fGTNmsHbtWv79738D50eRDR48mN27d3PXXXexadMmmjdvzr/+9S/AHMnUq1cvNmzYYPnauXOnZVqYS7nYa8uvc8h5GiHkx/r06WN3CSI+RZkQsVImRNwpFyJWTs/Eiy++SI8ePXjggQcoV64cy5cvp02bNpaVx3bt2uXRMevUqUN2djZr1qyhZcuWAGzfvt2yelXdunXZv38/Bw4ccI0SSkhIIDk52XJpWUEKCgri7B8TeK9Zs4bs7GzGjBnjGn3013mTAOLj4xkyZAhDhgzhmWee4YMPPuDhhx+madOmzJgxg8qVKxMcXDBtBm+cA8zLAZ1CI4T82LJly+wuQcSnKBMiVsqEiDvlQsTK6Zno2LEj9erVc81LU716ddasWcOCBQvYsWMHI0aMYPXq1R4ds1atWnTv3p377ruPX375hbVr1zJ48GDLZUhdunShYcOG3HHHHaxbt45Vq1Zx991306FDB8ulZvklOzubI0eOcOTIEXbu3Mkrr7xCQkICvXv3BqBatWpkZ2fzr3/9i927d/Ppp5/y3nvvWY4xbNgwFixYwJ49e1i3bh0//PCDq3n10EMPkZSUxG233caqVavYvXs3Cxcu5O9//zs5OTn58hq8cQ4wL6lzCjWE/Ni52dZFxKRMiFgpEyLulAsRK2UCHnvsMT744AMOHDjAkCFD6Nu3LwMHDqRVq1acOHHCMlrocn388cfEx8fToUMH+vbty/3330/p0qVdjwcEBDB79myKFStG+/bt6dKlC1WrVmXatGn5+dJctmzZQtmyZSlbtiyNGzfmiy++YOLEidx9990ANG7cmLFjx/LGG29Qv359PvvsM1577TXLMXJycnjooYeoU6cO3bt3p1atWq4Jp8uVK8ePP/5ITk4O119/PfXr1+eRRx4hNjbWMt/R1fDGOcBsnjlFgOGkKbSBlJQUYmNjSU5Otsyo7o8WLlxIt27d7C5DxGcoEyJWyoSIO+VCxMrTTKSnp7Nnzx6qVKlC+F/nBRIpBJKTk4mNjbW7jIu6WA496XlohJAfa9u2rd0liPgUZULESpkQcadciFgpEyJWea1iVlipIeTH5syZY3cJIj5FmRCxUiZE3CkXIlbKhIjVnyf/LuzUEBIRERERERERcRg1hPxY48aN7S5BxKcoEyJWyoSIO+VCxEqZELGKjIy0uwSvUUPIj+XnTOoihYEyIWKlTIi4Uy5ErK40Ew5bm0gcJCAgwO4SLim/8qefiH5s3bp1dpcg4lOUCRErZULEnXIhYuVpJkJCQgBIS0sriHJEbHfmzBm7S7ikzMxMAIKCgq7qOMH5UYyIiIiIiIgUfkFBQRQtWpRjx44B5uU1/jCiQuRyZWZmkp6ebncZM+lR+wAAFutJREFUF5Sbm8vvv/9OZGQkwcFX19IJMBw21i8lJYXY2FiSk5OJiYmxu5yrkpqaSpEiRewuQ8RnKBMiVsqEiDvlQsTqSjJhGAZHjhxx1GpM4hy5ubk+f3lxYGAgVapUITQ01O0xT3oeGiHkx1atWkXnzp3tLkPEZygTIlbKhIg75ULE6koyERAQQNmyZSldujRZWVkFVJmIPVauXEnr1q3tLuOiQkND86VppYaQHzs3TFNETMqEiJUyIeJOuRCxuppMBAUFXfUcJiK+5siRI4SHh9tdhlf49jgouajY2Fi7SxDxKcqEiJUyIeJOuRCxUiZErJyUCc0h5McyMjIICwuzuwwRn6FMiFgpEyLulAsRK2VCxMrfM+FJz0MjhPzYzJkz7S5BxKcoEyJWyoSIO+VCxEqZELFyUiYcN4fQuQFRKSkpNldy9dLS0grF6xDJL8qEiJUyIeJOuRCxUiZErPw9E+dqv5yLwRx3ydjBgweJj4+3uwwRERERERERkQJx4MABKlSocNF9HNcQys3N5fDhwxQpUoSAgAC7y7liKSkpxMfHc+DAAb+fC0kkPygTIlbKhIg75ULESpkQsSoMmTAMg9TUVMqVK3fJpekdd8lYYGDgJbtk/iQmJsZv/6KKFARlQsRKmRBxp1yIWCkTIlb+nonLXSlNk0qLiIiIiIiIiDiMGkIiIiIiIiIiIg6jhpCfCgsL48UXXyQsLMzuUkR8gjIhYqVMiLhTLkSslAkRK6dlwnGTSouIiIiIiIiIOJ1GCImIiIiIiIiIOIwaQiIiIiIiIiIiDqOGkIiIiIiIiIiIw6ghJCIiIiIiIiLiMGoI+bAJEyZQpUoVwsPDadasGcuXL7/o/kuXLqVZs2aEh4dTtWpV3nvvPS9VKuIdnmRi5syZdO3alVKlShETE0Pr1q1ZsGCBF6sVKXie/pw458cffyQ4OJjGjRsXbIEiXuZpJjIyMnjuueeoVKkSYWFhVKtWjY8++shL1Yp4h6e5+Oyzz2jUqBGRkZGULVuWv/3tb5w4ccJL1YoUrGXLltGrVy/KlStHQEAAs2fPvuRzCvPv2WoI+ahp06YxbNgwnnvuOdavX0+7du3o0aMH+/fvz3P/PXv20LNnT9q1a8f69et59tlnGTp0KDNmzPBy5SIFw9NMLFu2jK5duzJv3jzWrl1Lp06d6NWrF+vXr/dy5SIFw9NMnJOcnMzdd99N586dvVSpiHdcSSYGDBjA999/z4cffsj27duZMmUKtWvX9mLVIgXL01ysWLGCu+++m3vvvZctW7Ywffp0Vq9ezeDBg71cuUjBOHPmDI0aNeLdd9+9rP0L++/ZWnbeR7Vq1YqmTZsyceJE17Y6derQp08fXnvtNbf9n3rqKb766iu2bt3q2jZkyBB+/fVXVq5c6ZWaRQqSp5nIS7169Rg4cCAvvPBCQZUp4jVXmolbb72VGjVqEBQUxOzZs9mwYYMXqhUpeJ5mYv78+dx6663s3r2b4sWLe7NUEa/xNBdvv/02EydOZNeuXa5t//rXv3jzzTc5cOCAV2oW8ZaAgABmzZpFnz59LrhPYf89WyOEfFBmZiZr166lW7dulu3dunXjp59+yvM5K1eudNv/+uuvZ82aNWRlZRVYrSLecCWZ+Kvc3FxSU1P1j34pFK40Ex9//DG7du3ixRdfLOgSRbzqSjLx1Vdf0bx5c958803Kly9PzZo1eeKJJzh79qw3ShYpcFeSizZt2nDw4EHmzZuHYRgcPXqUL7/8khtuuMEbJYv4nML+e3aw3QWIu+PHj5OTk0OZMmUs28uUKcORI0fyfM6RI0fy3D87O5vjx49TtmzZAqtXpKBdSSb+asyYMZw5c4YBAwYURIkiXnUlmdi5cydPP/00y5cvJzhYP/6lcLmSTOzevZsVK1YQHh7OrFmzOH78OA8++CBJSUmaR0gKhSvJRZs2bfjss88YOHAg6enpZGdnc9NNN/Gvf/3LGyWL+JzC/nu2Rgj5sICAAMt9wzDctl1q/7y2i/grTzNxzpQpUxg5ciTTpk2jdOnSBVWeiNddbiZycnK4/fbbGTVqFDVr1vRWeSJe58nPidzcXAICAvjss89o2bIlPXv2ZOzYsUyePFmjhKRQ8SQXCQkJDB06lBdeeIG1a9cyf/589uzZw5AhQ7xRqohPKsy/Z+u/CH1QyZIlCQoKcuvcHzt2zK07eU5cXFye+wcHB1OiRIkCq1XEG64kE+dMmzaNe++9l+nTp9OlS5eCLFPEazzNRGpqKmvWrGH9+vX885//BMxfhg3DIDg4mIULF3Ldddd5pXaRgnAlPyfKli1L+fLliY2NdW2rU6cOhmFw8OBBatSoUaA1ixS0K8nFa6+9xrXXXsuTTz4JQMOGDYmKiqJdu3a88sorfj8aQsRThf33bI0Q8kGhoaE0a9aMRYsWWbYvWrSINm3a5Pmc1q1bu+2/cOFCmjdvTkhISIHVKuINV5IJMEcGDRo0iM8//1zXvkuh4mkmYmJi2LRpExs2bHB9DRkyhFq1arFhwwZatWrlrdJFCsSV/Jy49tprOXz4MKdPn3Zt27FjB4GBgVSoUKFA6xXxhivJRVpaGoGB1l8Rg4KCgPOjIkScpND/nm2IT5o6daoREhJifPjhh0ZCQoIxbNgwIyoqyti7d69hGIbx9NNPG3fddZdr/927dxuRkZHGo48+aiQkJBgffvihERISYnz55Zd2vQSRfOVpJj7//HMjODjY+Pe//20kJia6vk6dOmXXSxDJV55m4q9efPFFo1GjRl6qVqTgeZqJ1NRUo0KFCka/fv2MLVu2GEuXLjVq1KhhDB482K6XIJLvPM3Fxx9/bAQHBxsTJkwwdu3aZaxYscJo3ry50bJlS7tegki+Sk1NNdavX2+sX7/eAIyxY8ca69evN/bt22cYhvN+z1ZDyIf9+9//NipVqmSEhoYaTZs2NZYuXep67J577jE6dOhg2X/JkiVGkyZNjNDQUKNy5crGxIkTvVyxSMHyJBMdOnQwALeve+65x/uFixQQT39O/JkaQlIYeZqJrVu3Gl26dDEiIiKMChUqGI899piRlpbm5apFCpanuRg/frxRt25dIyIiwihbtqxxxx13GAcPHvRy1SIFY/HixRf9HcFpv2cHGIbG/omIiIiIiIiIOInmEBIRERERERERcRg1hEREREREREREHEYNIRERERERERERh1FDSERERERERETEYdQQEhERERERERFxGDWEREREREREREQcRg0hERERERERERGHUUNIRERERERERMRh1BASERERnzV58mSKFi161ccZOXIkZcqUISAggNmzZ1/18XzV3r17CQgIYMOGDRfdr2PHjgwbNsx1Py0tjVtuuYWYmBgCAgI4derUFZ3/rrvuYvTo0Vf03KvxxBNPMHToUK+fV0RExJ+pISQiIuJAAQEBF/0aNGiQ3SXmm61btzJq1Cjef/99EhMT6dGjh90lFZj4+HgSExOpX78+AEuWLMmzwTNz5kxefvll1/3//ve/LF++nJ9++onExERiY2M9PvfGjRv55ptvePjhh13bOnbsmOffr+zsbLfHw8LCqFmzJqNHjyYnJ8dS/7mvEiVKcN111/Hjjz9azj18+HA+/vhj9uzZ43HdIiIiTqWGkIiIiAMlJia6vt555x1iYmIs2/7v//7P7hLzza5duwDo3bs3cXFxhIWF2VxRwQkKCiIuLo7g4OCL7le8eHGKFCniur9r1y7q1KlD/fr1iYuLIyAgwONzv/vuu/Tv399yXID77rvP8ncrMTHRUt+5x7dv387QoUN5/vnnefvtty3H2L59O4mJiSxZsoRSpUpxww03cOzYMdfjpUuXplu3brz33nse1y0iIuJUagiJiIg4UFxcnOsrNjaWgIAA1/2QkBCGDBlChQoViIyMpEGDBkyZMsXy/MqVK/POO+9YtjVu3JiRI0cC5siO0NBQli9f7np8zJgxlCxZksTExAvWNXnyZCpWrEhkZCQ333wzJ06ccNvn66+/plmzZoSHh1O1alVGjRrlGnHyVyNHjqRXr14ABAYGuhodq1evpmvXrpQsWZLY2Fg6dOjAunXrXM/L69KrU6dOERAQwJIlSwB46aWXKFeunKXGm266ifbt25Obm5tnPYMGDaJPnz6MGjWK0qVLExMTwz/+8Q8yMzNd+2RkZDB06FBKly5NeHg4bdu2ZfXq1a7HT548yR133EGpUqWIiIigRo0afPzxx2517927l06dOgFQrFgxy8ivP18y1rFjR8aMGcOyZcsICAigY8eOAEyYMIEaNWoQHh5OmTJl6NevX56vCSA3N5fp06dz0003uT0WGRlp+fsWFxeX5+OVK1fmn//8J507d3a7rK906dLExcXRoEEDnn/+eZKTk/nll18s+9x0001uf09FRETkwtQQEhEREYv09HSaNWvG3Llz2bx5M/fffz933XWX2y/gF3Ou4XDXXXeRnJzMr7/+ynPPPccHH3xA2bJl83zOL7/8wt///ncefPBBNmzYQKdOnXjllVcs+yxYsIA777yToUOHkpCQwPvvv8/kyZN59dVX8zzmE0884WqWnBudApCamso999zD8uXL+fnnn6lRowY9e/YkNTX1sl/jc889R+XKlRk8eDAA7733HsuWLePTTz8lMPDC/8T6/vvv2bp1K4sXL2bKlCnMmjWLUaNGuR4fPnw4M2bM4L///S/r1q2jevXqXH/99SQlJQEwYsQIEhIS+Pbbb9m6dSsTJ06kZMmSbueJj49nxowZwPkRNnmN/Jo5cyb33XcfrVu3JjExkZkzZ7JmzRqGDh3KSy+9xPbt25k/fz7t27e/4GvauHEjp06donnz5pf35l1EREQEWVlZeT6Wlpbm+n6GhIRYHmvZsiUHDhxg3759V12DiIiIIxgiIiLiaB9//LERGxt70X169uxpPP744677lSpVMsaNG2fZp1GjRsaLL77oup+RkWE0adLEGDBggFGvXj1j8ODBFz3HbbfdZnTv3t2ybeDAgZba2rVrZ4wePdqyz6effmqULVv2gsedNWuWcal/8mRnZxtFihQxvv76a8MwDGPPnj0GYKxfv961z8mTJw3AWLx4sWvbrl27jCJFihhPPfWUERkZafzvf/+76Hnuueceo3jx4saZM2dc2yZOnGhER0cbOTk5xunTp42QkBDjs88+cz2emZlplCtXznjzzTcNwzCMXr16GX/729/yPP5f6168eLEBGCdPnrTs16FDB+ORRx5x3X/kkUeMDh06uO7PmDHDiImJMVJSUi76es6ZNWuWERQUZOTm5rqdJyQkxIiKinJ9PfbYY3nWkZOTY3z77bdGaGioMXz4cEv9554bEBBgAEazZs2MzMxMy7mSk5MNwFiyZMll1SwiIuJ0F7/AXERERBwnJyeH119/nWnTpnHo0CEyMjLI+P/27i6kyfaPA/hXl85akUGS9MLIVmKS2ijZerHQUpMiTbCDuVqJyWiYHUhJ5stBZJI1SiRKUtRQT6RggmmIuJDCspK5URnM7KAy7AWbPi63/0E4ut1MfXr+xfPs+wEP7vu67vu6rtsD8cf1+11//QWJRDKn9/j7+6Ourg4RERGQSqVuKWZTWSwWpKSkCO4plUq0tLS4rh8/fozu7m7BjqCJiQmMjY3BZrNhwYIFs5rb+/fvUVBQgPb2drx79w4TExOw2Wx4/fr17BcIICQkBBcvXkRWVhYOHjwIlUo14zORkZGCeSqVSoyMjGBwcBCfP3+G3W7H1q1bXe1+fn6Ijo6GxWIBAGi1WqSmpqKnpwfx8fFITk7Gli1b5jTvmezevRtSqRQhISFITExEYmIiUlJSpv2+o6OjEIvFHmsPqVQqnDlzxnU99dS4iooKVFZWutLm1Go1CgsLBX2MRiMkEgmePHmCU6dOobq62m2H0Pz58wF830VEREREM2NAiIiIiATKyspw+fJl6PV6bNiwARKJBDk5OYI6N76+vnA6nYLnPKX5dHV1AQCGh4cxPDz806DS1Pd54nA4UFxcjAMHDri1BQQEzPj8JI1Gg6GhIej1ekilUojFYiiVStcaJ1O+fpzTdGlMnZ2dEIlEsFqt+Pbt24wFnafj4+PjGm9qYMXpdLru7dmzBwMDA2hubsa9e/cQFxeH48ePuxVi/hWLFi1CT08POjo60NraioKCAhQVFaG7u9stoAMAS5cuhc1mw/j4OPz9/QVtixcvhkwmm3asyYCRWCzG8uXLIRKJ3PqsXr0agYGBWLduHcbGxpCSkgKTySQoED6ZUhcUFPQ3V01ERORdWEOIiIiIBIxGI/bv34/09HRERkYiJCQEL1++FPQJCgoSFIf+8uWL25Hfr169wsmTJ3Hjxg0oFAocOnRo2mLLALB+/Xo8ePBAcG/qtVwux/PnzyGTydx+fla3x9Mas7OzkZSUhPDwcIjFYnz48EGwPgCCNf5YYHpSY2Mjmpqa0NHRgcHBQcFR7tN59uwZRkdHBWtcuHAhVq5cCZlMBn9/f9y/f9/Vbrfb8ejRI4SFhQnmp9FoUFdXB71ej+vXr3scazI4M3mM+1zMmzcPu3btQmlpKXp7e2G1WtHe3u6xb1RUFADAbDbPeZzJgNGqVas8BoOmUqvVcDgcqKioENw3mUzw8/NDeHj4nOdARETkjRgQIiIiIgGZTIa2tjZ0dXXBYrEgKysLb9++FfSJjY1FbW0tjEYjTCYTDh8+LPhnfmJiAmq1GvHx8Thy5AiqqqpgMplQVlY27bjZ2dloaWlBaWkpXrx4gfLyckG6GAAUFBSgpqYGRUVF6Ovrg8ViQWNjI/Lz8+e8xtraWlgsFjx8+BAqlcqVcgR8Tz9SKBQoKSmB2WxGZ2en2xhv3ryBVqvFhQsXsG3bNlRXV+P8+fNuQaypxsfHkZGR4SoMXVhYCJ1OB19fX0gkEmi1WuTm5qKlpQVmsxmZmZmw2WzIyMhwfYM7d+6gv78ffX19MBgMgmDRj6RSKXx8fGAwGDA0NISRkZFZfR+DwYArV67g6dOnGBgYQE1NDRwOB0JDQz32DwoKglwuFwSy/l98fX2Rk5ODkpISQXqY0WjE9u3bBb9HIiIimh4DQkRERCRw9uxZyOVyJCQkYOfOnQgODkZycrKgT15eHmJiYrB3714kJSUhOTkZa9ascbWfO3cOVqvVtXMlODgYlZWVyM/P97jTBgAUCgUqKytx9epVREVFobW11S0Ik5CQAIPBgLa2NmzevBkKhQKXLl2CVCqd0xpv3ryJjx8/YuPGjVCr1a5j3qf2sdvt2LRpE06cOCE48czpdEKj0SA6Oho6nQ7A97o7Op0O6enpPw28xMXFYe3atYiJiUFaWhr27duHoqIiV3tJSQlSU1OhVqshl8vR39+Pu3fvYsmSJQC+7/rJy8tDREQEYmJiIBKJ0NDQ4HGsFStWoLi4GKdPn8ayZctcc51JYGAgmpqaEBsbi7CwMFy7dg319fU/3X1z7Ngx3Lp1a1bv/1VHjx6F3W5HeXm56159fT0yMzN/y/hERET/BT7O2STsExEREdEv02g0+PTpE27fvv2np/KPGxsbQ2hoKBoaGqBUKn/r2M3NzcjNzUVvb+/fruFERETkbbhDiIiIiIh+WUBAAGpqagS1mH6Xr1+/oqqqisEgIiKiOeBfTSIiIiL6R+zYseOPjJuWlvZHxiUiIvo3Y8oYEREREREREZGXYcoYEREREREREZGXYUCIiIiIiIiIiMjLMCBERERERERERORlGBAiIiIiIiIiIvIyDAgREREREREREXkZBoSIiIiIiIiIiLwMA0JERERERERERF6GASEiIiIiIiIiIi/zP9WnlQVy2xk0AAAAAElFTkSuQmCC",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1400x800 with 1 Axes>"
       ]
@@ -4375,6 +4378,1065 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "3b5c9485-511b-4f6b-b667-154f4f519682",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# utilisation d'une métrique plus adaptée aux modèles de marketing : courbe de lift\n",
+    "\n",
+    "# Tri des prédictions de probabilités et des vraies valeurs\n",
+    "sorted_indices = np.argsort(y_pred_prob)[::-1]\n",
+    "y_pred_prob_sorted = y_pred_prob[sorted_indices]\n",
+    "y_test_sorted = y_test.iloc[sorted_indices]\n",
+    "\n",
+    "# Calcul du gain cumulatif\n",
+    "cumulative_gain = np.cumsum(y_test_sorted) / np.sum(y_test_sorted)\n",
+    "\n",
+    "# Tracé de la courbe de lift\n",
+    "plt.plot(np.linspace(0, 1, len(cumulative_gain)), cumulative_gain, label='Courbe de lift')\n",
+    "plt.xlabel('Part de clients identifiés sans modèle ')\n",
+    "plt.ylabel('Part de clients identifiés avec modèle')\n",
+    "plt.title('Courbe de Lift')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "6e7cfb6c-8049-4bd1-8d82-61a2e97b257d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# look at the distribution of the score \n",
+    "\n",
+    "plt.hist(y_pred_prob, bins=20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "99f7f70e-c3bb-445e-8889-e7547f6ebd1e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# number of observations\n",
+    "N = len(y_pred_prob)\n",
+    "\n",
+    "# sort the data in ascending order \n",
+    "y_pred_prob_sorted = np.sort(y_pred_prob) \n",
+    "\n",
+    "# get the cdf values of y \n",
+    "steps = np.arange(N) / N\n",
+    "  \n",
+    "# plotting \n",
+    "plt.xlabel('X') \n",
+    "plt.ylabel('P(score<=X)') \n",
+    "  \n",
+    "plt.title('CDF curve of the predicted probability of purchase (score) for sports companies') \n",
+    "  \n",
+    "plt.plot(y_pred_prob_sorted, steps) \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "id": "dd7a4a9c-d7e3-4747-ae59-b2a5a0b77260",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-9 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-9 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-9 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-9 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-9 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-9 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-9 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-9 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=3, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" checked><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KMeans<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(n_clusters=3, random_state=0)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=3, random_state=0)"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# K-means clustering \n",
+    "\n",
+    "kmeans = KMeans(n_clusters=3, random_state=0)\n",
+    "\n",
+    "kmeans.fit(y_pred_prob.reshape(-1,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "id": "10b6ece7-adcf-41c0-884b-a4aef42af378",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 0, 2, ..., 0, 2, 0], dtype=int32)"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_clusters = kmeans.predict(y_pred_prob.reshape(-1,1))\n",
+    "y_clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "id": "e4b3b16e-03b8-4883-9788-cb7296fe56cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "seuil cluster 0 : 0.3666817620198657 (55.46%)\n",
+      "seuil cluster 2 : 0.7518681604748351 (34.86%)\n",
+      "seuil cluster 1 : 1.0 (9.68%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# seuils des clusters et part de clients dans chacun d'eux\n",
+    "\n",
+    "print(f\"seuil cluster 0 : {y_pred_prob[y_clusters==0].max()} ({round(100 * (y_clusters==0).mean(), 2)}%)\")\n",
+    "print(f\"seuil cluster 2 : {y_pred_prob[y_clusters==2].max()} ({round(100 * (y_clusters==2).mean(), 2)}%)\")\n",
+    "print(f\"seuil cluster 1 : {y_pred_prob[y_clusters==1].max()} ({round(100* (y_clusters==1).mean(), 2)}%)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "id": "3e404a5e-6734-4d98-8853-48b09c96e7e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>nb_tickets</th>\n",
+       "      <th>nb_purchases</th>\n",
+       "      <th>total_amount</th>\n",
+       "      <th>nb_suppliers</th>\n",
+       "      <th>vente_internet_max</th>\n",
+       "      <th>purchase_date_min</th>\n",
+       "      <th>purchase_date_max</th>\n",
+       "      <th>nb_tickets_internet</th>\n",
+       "      <th>is_email_true</th>\n",
+       "      <th>opt_in</th>\n",
+       "      <th>gender_female</th>\n",
+       "      <th>gender_male</th>\n",
+       "      <th>nb_campaigns</th>\n",
+       "      <th>nb_campaigns_opened</th>\n",
+       "      <th>cluster</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.177187</td>\n",
+       "      <td>5.177187</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>426.265613</td>\n",
+       "      <td>426.265613</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>80.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>436.033437</td>\n",
+       "      <td>436.033437</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>120.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.196412</td>\n",
+       "      <td>5.196412</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>34.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>416.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>478.693148</td>\n",
+       "      <td>115.631470</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   nb_tickets  nb_purchases  total_amount  nb_suppliers  vente_internet_max  \\\n",
+       "0         4.0           1.0         100.0           1.0                 0.0   \n",
+       "1         1.0           1.0          55.0           1.0                 0.0   \n",
+       "2        17.0           1.0          80.0           1.0                 0.0   \n",
+       "3         4.0           1.0         120.0           1.0                 0.0   \n",
+       "4        34.0           2.0         416.0           1.0                 0.0   \n",
+       "\n",
+       "   purchase_date_min  purchase_date_max  nb_tickets_internet  is_email_true  \\\n",
+       "0           5.177187           5.177187                  0.0           True   \n",
+       "1         426.265613         426.265613                  0.0           True   \n",
+       "2         436.033437         436.033437                  0.0           True   \n",
+       "3           5.196412           5.196412                  0.0           True   \n",
+       "4         478.693148         115.631470                  0.0           True   \n",
+       "\n",
+       "   opt_in  gender_female  gender_male  nb_campaigns  nb_campaigns_opened  \\\n",
+       "0   False              1            0           0.0                  0.0   \n",
+       "1    True              0            1           0.0                  0.0   \n",
+       "2    True              1            0           0.0                  0.0   \n",
+       "3   False              1            0           0.0                  0.0   \n",
+       "4   False              1            0           0.0                  0.0   \n",
+       "\n",
+       "   cluster  \n",
+       "0        2  \n",
+       "1        0  \n",
+       "2        2  \n",
+       "3        2  \n",
+       "4        1  "
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# les individus des clusters sont-ils semblables ? def des marketing personae\n",
+    "\n",
+    "X_test_clustered = X_test.assign(cluster = y_clusters)\n",
+    "X_test_clustered.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "id": "b6f4638d-23c4-427a-88a4-b09528b3f91b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>nb_tickets</th>\n",
+       "      <th>nb_purchases</th>\n",
+       "      <th>total_amount</th>\n",
+       "      <th>nb_suppliers</th>\n",
+       "      <th>vente_internet_max</th>\n",
+       "      <th>purchase_date_min</th>\n",
+       "      <th>purchase_date_max</th>\n",
+       "      <th>nb_tickets_internet</th>\n",
+       "      <th>is_email_true</th>\n",
+       "      <th>opt_in</th>\n",
+       "      <th>gender_female</th>\n",
+       "      <th>gender_male</th>\n",
+       "      <th>nb_campaigns</th>\n",
+       "      <th>nb_campaigns_opened</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cluster</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>550.000000</td>\n",
+       "      <td>550.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>59.000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>232.198352</td>\n",
+       "      <td>225.296614</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>205.075</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>416.542519</td>\n",
+       "      <td>60.404957</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         nb_tickets  nb_purchases  total_amount  nb_suppliers  \\\n",
+       "cluster                                                         \n",
+       "0               0.0           0.0         0.000           0.0   \n",
+       "2               2.0           1.0        59.000           1.0   \n",
+       "1              12.0           4.0       205.075           1.0   \n",
+       "\n",
+       "         vente_internet_max  purchase_date_min  purchase_date_max  \\\n",
+       "cluster                                                             \n",
+       "0                       0.0         550.000000         550.000000   \n",
+       "2                       1.0         232.198352         225.296614   \n",
+       "1                       1.0         416.542519          60.404957   \n",
+       "\n",
+       "         nb_tickets_internet  is_email_true  opt_in  gender_female  \\\n",
+       "cluster                                                              \n",
+       "0                        0.0            1.0     1.0            0.0   \n",
+       "2                        1.0            1.0     0.0            0.0   \n",
+       "1                        4.0            1.0     0.0            0.0   \n",
+       "\n",
+       "         gender_male  nb_campaigns  nb_campaigns_opened  \n",
+       "cluster                                                  \n",
+       "0                0.0           7.0                  0.0  \n",
+       "2                1.0           3.0                  0.0  \n",
+       "1                1.0          16.0                  1.0  "
+      ]
+     },
+     "execution_count": 182,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test_clustered.groupby(\"cluster\").median().iloc[[0,2,1], :]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "id": "f80474be-c897-47f9-8fdd-f2fb8d724ee2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>nb_tickets</th>\n",
+       "      <th>nb_purchases</th>\n",
+       "      <th>total_amount</th>\n",
+       "      <th>nb_suppliers</th>\n",
+       "      <th>vente_internet_max</th>\n",
+       "      <th>purchase_date_min</th>\n",
+       "      <th>purchase_date_max</th>\n",
+       "      <th>nb_tickets_internet</th>\n",
+       "      <th>is_email_true</th>\n",
+       "      <th>opt_in</th>\n",
+       "      <th>gender_female</th>\n",
+       "      <th>gender_male</th>\n",
+       "      <th>nb_campaigns</th>\n",
+       "      <th>nb_campaigns_opened</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cluster</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.132484</td>\n",
+       "      <td>0.067283</td>\n",
+       "      <td>0.950238</td>\n",
+       "      <td>0.025292</td>\n",
+       "      <td>0.007149</td>\n",
+       "      <td>545.999770</td>\n",
+       "      <td>545.961714</td>\n",
+       "      <td>0.015142</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.522619</td>\n",
+       "      <td>0.240389</td>\n",
+       "      <td>0.431319</td>\n",
+       "      <td>12.712442</td>\n",
+       "      <td>2.241721</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2.956270</td>\n",
+       "      <td>1.396973</td>\n",
+       "      <td>77.660347</td>\n",
+       "      <td>0.999164</td>\n",
+       "      <td>0.659682</td>\n",
+       "      <td>235.984535</td>\n",
+       "      <td>229.598802</td>\n",
+       "      <td>1.620787</td>\n",
+       "      <td>0.991373</td>\n",
+       "      <td>0.255246</td>\n",
+       "      <td>0.258321</td>\n",
+       "      <td>0.558162</td>\n",
+       "      <td>10.610967</td>\n",
+       "      <td>2.741799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>42.274898</td>\n",
+       "      <td>10.682943</td>\n",
+       "      <td>1859.028185</td>\n",
+       "      <td>1.481824</td>\n",
+       "      <td>0.750376</td>\n",
+       "      <td>386.850491</td>\n",
+       "      <td>96.427147</td>\n",
+       "      <td>12.382663</td>\n",
+       "      <td>0.973220</td>\n",
+       "      <td>0.163261</td>\n",
+       "      <td>0.197892</td>\n",
+       "      <td>0.609378</td>\n",
+       "      <td>19.805442</td>\n",
+       "      <td>7.528286</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         nb_tickets  nb_purchases  total_amount  nb_suppliers  \\\n",
+       "cluster                                                         \n",
+       "0          0.132484      0.067283      0.950238      0.025292   \n",
+       "2          2.956270      1.396973     77.660347      0.999164   \n",
+       "1         42.274898     10.682943   1859.028185      1.481824   \n",
+       "\n",
+       "         vente_internet_max  purchase_date_min  purchase_date_max  \\\n",
+       "cluster                                                             \n",
+       "0                  0.007149         545.999770         545.961714   \n",
+       "2                  0.659682         235.984535         229.598802   \n",
+       "1                  0.750376         386.850491          96.427147   \n",
+       "\n",
+       "         nb_tickets_internet  is_email_true    opt_in  gender_female  \\\n",
+       "cluster                                                                \n",
+       "0                   0.015142       1.000000  0.522619       0.240389   \n",
+       "2                   1.620787       0.991373  0.255246       0.258321   \n",
+       "1                  12.382663       0.973220  0.163261       0.197892   \n",
+       "\n",
+       "         gender_male  nb_campaigns  nb_campaigns_opened  \n",
+       "cluster                                                  \n",
+       "0           0.431319     12.712442             2.241721  \n",
+       "2           0.558162     10.610967             2.741799  \n",
+       "1           0.609378     19.805442             7.528286  "
+      ]
+     },
+     "execution_count": 183,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_test_clustered.groupby(\"cluster\").mean().iloc[[0,2,1], :]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "d2d5aca0-7e8b-4039-9bb2-ff5011c436a6",
@@ -7033,6 +8095,795 @@
     "plt.legend()\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7cbb1fec-97b9-4780-9488-5b8eff5aee0d",
+   "metadata": {},
+   "source": [
+    "## From model to segmentation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "d97ca3df-3778-469c-a077-495b3ee25051",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([9.0362e+04, 2.7200e+02, 1.6700e+02, 1.0000e+02, 8.6000e+01,\n",
+       "        5.7000e+01, 6.6000e+01, 6.3000e+01, 4.5000e+01, 5.1000e+01,\n",
+       "        5.4000e+01, 3.6000e+01, 5.3000e+01, 5.3000e+01, 5.3000e+01,\n",
+       "        5.1000e+01, 7.7000e+01, 1.1800e+02, 1.2700e+02, 4.2050e+03]),\n",
+       " array([8.76852176e-09, 5.00000083e-02, 1.00000008e-01, 1.50000007e-01,\n",
+       "        2.00000007e-01, 2.50000007e-01, 3.00000006e-01, 3.50000006e-01,\n",
+       "        4.00000005e-01, 4.50000005e-01, 5.00000004e-01, 5.50000004e-01,\n",
+       "        6.00000004e-01, 6.50000003e-01, 7.00000003e-01, 7.50000002e-01,\n",
+       "        8.00000002e-01, 8.50000001e-01, 9.00000001e-01, 9.50000000e-01,\n",
+       "        1.00000000e+00]),\n",
+       " <BarContainer object of 20 artists>)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(y_pred_prob, bins=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "b4ae4508-d5ac-4b22-a546-6c724278f8c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([8.76852176e-09, 8.76852176e-09, 8.76852176e-09, ...,\n",
+       "       1.00000000e+00, 1.00000000e+00, 1.00000000e+00])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sort(y_pred_prob)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "ace9c778-0ab4-4e28-8ca0-364040d122e6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4527"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(y_pred_prob>0.8).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "4a202a7e-e7fe-479c-8be3-7b2b93fe9d7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# number of observations\n",
+    "N = len(y_pred_prob)\n",
+    "\n",
+    "# sort the data in ascending order \n",
+    "y_pred_prob_sorted = np.sort(y_pred_prob) \n",
+    "\n",
+    "# get the cdf values of y \n",
+    "steps = np.arange(N) / N\n",
+    "  \n",
+    "# plotting \n",
+    "plt.xlabel('X') \n",
+    "plt.ylabel('P(score<=X)') \n",
+    "  \n",
+    "plt.title('CDF curve of the predicted probability of purchasec(score) for sports companies') \n",
+    "  \n",
+    "plt.plot(y_pred_prob_sorted, steps) \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "e87efb96-71e6-4571-9a48-576ff5ebcbdc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.  , 0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 ,\n",
+       "       0.55, 0.6 , 0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1.  ])"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# on regarde de plus près les quantiles (on identifie 2 clusters, où est le cut-off ?)\n",
+    "\n",
+    "np.linspace(0,1, 21)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "ccd8373c-85c4-451d-b918-7bb84713c9ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(90634,)"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred_prob_sorted[y_pred_prob < 0.1].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "75a2c582-3020-4e2e-9a41-0da75c5dbbed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "score du quantile 0.0 : 1.0\n",
+      "score du quantile 0.05 : 1.1703610048497538e-08\n",
+      "score du quantile 0.1 : 1.1916538583855572e-08\n",
+      "score du quantile 0.15000000000000002 : 1.672960453020865e-08\n",
+      "score du quantile 0.2 : 2.261530896018714e-08\n",
+      "score du quantile 0.25 : 4.429426100901144e-08\n",
+      "score du quantile 0.30000000000000004 : 5.527720441770875e-08\n",
+      "score du quantile 0.35000000000000003 : 6.583003552085313e-08\n",
+      "score du quantile 0.4 : 1.0150014636815537e-07\n",
+      "score du quantile 0.45 : 1.045553983975125e-07\n",
+      "score du quantile 0.5 : 1.8254643649033717e-07\n",
+      "score du quantile 0.55 : 1.0036337913333724e-06\n",
+      "score du quantile 0.6000000000000001 : 3.6006418270834777e-06\n",
+      "score du quantile 0.65 : 8.750051427856617e-06\n",
+      "score du quantile 0.7000000000000001 : 1.7761176996762073e-05\n",
+      "score du quantile 0.75 : 3.658511676930477e-05\n",
+      "score du quantile 0.8 : 7.449089979671675e-05\n",
+      "score du quantile 0.8500000000000001 : 0.0001599334998042523\n",
+      "score du quantile 0.9 : 0.0006156933309033692\n",
+      "score du quantile 0.9500000000000001 : 0.5161846499348189\n",
+      "score du quantile 1.0 : 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "for step in np.linspace(0,1, 21) :\n",
+    "    score_reached = y_pred_prob_sorted[int(step*N)-1]\n",
+    "    print(f\"score du quantile {step} : {score_reached}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "3e7d04c4-1add-4ef3-bca5-c2f68356b669",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "score du quantile 0.94 : 0.046364832132301186\n",
+      "score du quantile 0.941 : 0.060426331367796585\n",
+      "score du quantile 0.942 : 0.07560789365683944\n",
+      "score du quantile 0.943 : 0.0961854989484283\n",
+      "score du quantile 0.944 : 0.12036366182214445\n",
+      "score du quantile 0.945 : 0.15326229828189683\n",
+      "score du quantile 0.946 : 0.20141929276940546\n",
+      "score du quantile 0.947 : 0.26129057078459816\n",
+      "score du quantile 0.948 : 0.34459110917836233\n",
+      "score du quantile 0.949 : 0.42441766527261676\n",
+      "score du quantile 0.95 : 0.5161846499348189\n",
+      "score du quantile 0.951 : 0.6281715747542238\n",
+      "score du quantile 0.952 : 0.7161294443763133\n",
+      "score du quantile 0.953 : 0.8098274658632696\n",
+      "score du quantile 0.954 : 0.8628210594682936\n",
+      "score du quantile 0.955 : 0.9031546758694196\n",
+      "score du quantile 0.956 : 0.9406325197642711\n",
+      "score du quantile 0.957 : 0.9717094630837765\n",
+      "score du quantile 0.958 : 0.9853416074407844\n",
+      "score du quantile 0.959 : 0.99263528504162\n",
+      "score du quantile 0.96 : 0.9965103675841931\n"
+     ]
+    }
+   ],
+   "source": [
+    "# le saut survient entre le quantile 0.94 et 0.955\n",
+    "# on peut prendre le quantile 0.95 / score = 0.52 comme cut-off approximatif\n",
+    "for step in np.linspace(0.94,0.96, 21) :\n",
+    "    score_reached = y_pred_prob_sorted[int(step*N)-1]\n",
+    "    print(f\"score du quantile {step} : {score_reached}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "5d8bb4ea-0030-4d23-8cff-26c9ed54ca71",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=2, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KMeans<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(n_clusters=2, random_state=0)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "KMeans(n_clusters=2, random_state=0)"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# simple K-means pour déterminer le seuil qui sépare les 2 clusters apparents\n",
+    "\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "kmeans = KMeans(n_clusters=2, random_state=0)\n",
+    "\n",
+    "kmeans.fit(y_pred_prob.reshape(-1,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "afbf8247-4cb1-455b-96df-7e9a87407413",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, ..., 0, 0, 0], dtype=int32)"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_clusters = kmeans.predict(y_pred_prob.reshape(-1,1))\n",
+    "y_clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "e4747b82-1967-4043-bcd1-7659dbd87a2a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4846"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_clusters[y_clusters==1].size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "2853083a-99a4-4ae9-9e8d-ddf175cca7ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9495712620712621"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 5% des individus sont dans le cluster 1\n",
+    "1 - y_clusters.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d18c8a4c-7d19-4d24-a304-cb26a533303e",
+   "metadata": {},
+   "source": [
+    "Intérêt du K-means : permet d'identifier un seuil de passage d'un cluster à l'autre quand le cluster est restreint, comme ici où on isole les clients avec la proba d'achat dans le quantile 0.95, et on les sépare des 95% restant"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "77f59f30-1dc6-43b8-98b7-d179a966786a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "part d'individus dans le cluster 0 : 0.9495712620712621\n",
+      "seuil de passage du cluster 0 au cluster 1 : 0.4855790414879801\n"
+     ]
+    }
+   ],
+   "source": [
+    "# seuil de split \n",
+    "\n",
+    "size_cluster_0 = 1 - y_clusters.mean()\n",
+    "seuil_cluster = y_pred_prob_sorted[int(1 - y_clusters.mean()*N)]\n",
+    "\n",
+    "print(f\"part d'individus dans le cluster 0 : {size_cluster_0}\")\n",
+    "print(f\"seuil de passage du cluster 0 au cluster 1 : {seuil_cluster}\")"
+   ]
   }
  ],
  "metadata": {