diff --git "a/notebooks/Supervised-Unsupervised/customer_churn.ipynb" "b/notebooks/Supervised-Unsupervised/customer_churn.ipynb"
--- "a/notebooks/Supervised-Unsupervised/customer_churn.ipynb"
+++ "b/notebooks/Supervised-Unsupervised/customer_churn.ipynb"
@@ -17,46 +17,31 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Telco customer churn \n",
-    "\n",
-    "https://www.kaggle.com/datasets/blastchar/telco-customer-churn\n",
-    "\n",
-    "- **customerID**: Customer ID\n",
-    "- **gender**: Whether the customer is a male or a femal\n",
-    "- **SeniorCitizen**: Whether the customer is a senior citizen or not (1, 0)\n",
-    "- **Partner**: Whether the customer has a partner or not (Yes, No)\n",
-    "- **Dependents**: Whether the customer has dependents or not (Yes, No)\n",
-    "- **tenure**: Number of months the customer has stayed with the company\n",
-    "- **PhoneService**: Whether the customer has a phone service or not (Yes, No)\n",
-    "- **MultipleLines**: Whether the customer has multiple lines or not (Yes, No, No phone service)\n",
-    "- **InternetService**: Customer’s internet service provider (DSL, Fiber optic, No)\n",
-    "- **OnlineSecurity**: Whether the customer has online security or not (Yes, No, No internet service)\n",
-    "- **OnlineBackup**: Whether the customer has online backup or not (Yes, No, No internet service)\n",
-    "- **DeviceProtection**: Whether the customer has device protection or not (Yes, No, No internet service)\n",
-    "- **TechSupport**: Whether the customer has tech support or not (Yes, No, No internet service)\n",
-    "- **StreamingTV**: Whether the customer has streaming TV or not (Yes, No, No internet service)\n",
-    "- **StreamingMovies**: Whether the customer has streaming movies or not (Yes, No, No internet service)\n",
-    "- **Contract**: The contract term of the customer (Month-to-month, One year, Two year)\n",
-    "- **PaperlessBilling**: Whether the customer has paperless billing or not (Yes, No)\n",
-    "- **PaymentMethod**: The customer’s payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))\n",
-    "- **MonthlyCharges**: The amount charged to the customer monthly\n",
-    "- **TotalCharges**: The total amount charged to the customer\n",
-    "- **Churn**: Whether the customer churned or not (Yes or No)"
+    "## Telco customer churn \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path_data = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\data-hec-AI-DS\\telco-customer-churn.csv\"\n",
+    "path_savedata = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\app-ai-ds-hec\\data\\classification\\churn\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 221,
+   "execution_count": 188,
    "metadata": {},
    "outputs": [],
    "source": [
-    "path_data = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\app-hec-AI-DS\\data\\classification\\telco-customer-churn.csv\"\n",
     "churn_data = pd.read_csv(path_data)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 222,
+   "execution_count": 189,
    "metadata": {},
    "outputs": [
     {
@@ -100,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 223,
+   "execution_count": 190,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
+   "execution_count": 191,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -118,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 225,
+   "execution_count": 192,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -128,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 226,
+   "execution_count": 193,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -139,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 227,
+   "execution_count": 194,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -148,22 +133,110 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 228,
+   "execution_count": 195,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PaymentMethod\n",
+       "Electronic check             2365\n",
+       "Mailed check                 1612\n",
+       "Bank transfer (automatic)    1544\n",
+       "Credit card (automatic)      1522\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 195,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "churn_data[\"PaymentMethod\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def clean_paymentmethod(x):\n",
+    "    if x == \"Bank transfer (automatic)\":\n",
+    "        return \"Bank transfer\"\n",
+    "    elif x == \"Credit card (automatic)\":\n",
+    "        return \"Credit card\"\n",
+    "    else:\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "churn_data[\"PaymentMethod\"] = churn_data[\"PaymentMethod\"].apply(clean_paymentmethod)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<Axes: xlabel='TotalCharges', ylabel='Density'>"
+       "array(['Electronic check', 'Mailed check', 'Bank transfer', 'Credit card'],\n",
+       "      dtype=object)"
       ]
      },
-     "execution_count": 228,
+     "execution_count": 198,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "churn_data[\"PaymentMethod\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "churn_data.dropna(inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "churn_data.head(30).to_pickle(os.path.join(path_savedata, \"churn_train_raw.pkl\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='MonthlyCharges', ylabel='Density'>"
+      ]
+     },
+     "execution_count": 201,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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LQgghLo8kZMLj7c0vadLhynoD2gdTUFLF1qO2Jr+WEEIIzyIJmfB4u/NLmmXOQtcoE0G+XizbkX/xxkIIIcQZJCETHq2sqpZjxU27wrKeQa8jsZ2F76VqvxBCiMskCZnwaPUrLNs2w5AlwID2IewtKOVgYVmzXE8IIYRnkIRMeLS99SUvmmmZde+2ZnwMehm2FEIIcVmkDpnwaHvzS5p8heWZjN4GerUx8f12Kw9d16FZrimEEG6j+AiUn2i+6/mHgiW2+a53FSQhEx5tbzNN6D9T//YhfPjLAU6UVhEa6Nus1xZCiBar+AjMHQg1zbOXJQDefvDIuktOylatWsWrr75KdnY2eXl5fPPNN6SlpTVtjKdIQiY82u78Uvq2szTrNfu1s/CBAst3FXDvAPf4zUwIIZpc+Qk1GRv6OJib4d9G2xH4ebZ63UtMyMrKyujTpw9/+MMfuPvuu5s4QFeSkAmPVVlTx9HiCm7rHd2s17X4+9A5MpBlO/IlIRNCiLOZYyG0k9ZRNOiWW27hlltu0eTaMqlfeKzDReUARJmMzX7t/u2CWbX3OJU1dc1+bSGEEO5HEjLhsepLT0SaNUjI4kKorHHwy97CZr+2EEII9yMJmfBYh4vK8fXSY/HzbvZrt7H4EWMxSvkLIYQQl0QSMuGxDp4oI9JkRKfTaXL9fu2C+XFnPnWy2bgQQoiLkIRMeKyDheVEmrQrOzGgfQgnyqrZdOSkZjEIIYRwD5KQCY916FQPmVY6RwRi9vPmBxm2FEIIt1BaWsqmTZvYtGkTADk5OWzatInDhw83+bWl7IXwSDV1Do4VV3JzT+0SMr1eR99YCz9sz2f6Ld01i0MIIVoU25EWe53169dz4403On+eOnUqAA888ADz589vrMgaJAmZ8EhHT1ZQpyia9pAB9I8LZsWe4+wrKKVTRKCmsQghhKb8Q9XK+T/Pbr5revup171EN9xwA4qizbxfSciERzp4Qi15EaXhHDKA3m0s+Hqpm41LQiaEaNUsseo2RrKXZYMkIRMe6XBROV56HaEB2iZkPl56erc188MOKw/f0FHTWIQQQnOWWLdJkJqbTOoXHulgYTkRJl/0em1KXpypf/tgNh0upqCkUutQhBBCtFCSkAmPdPBEGZFB2s4fq9e3XTA6Hfy4o0DrUIQQQrRQkpAJj3ToRBkRGk/or2cyetMtysQPO6xahyKEEM1Gq8nxzakxP6MkZMLjOBwKR4oqNJ/Qf6b+7YNZva+Q0qparUMRQogm5e2tbldXXl6ucSRNr/4z1n/mqyGT+oXHsdorqa5ztJgeMoAB7YP5v18PsXL3cW7tHa11OEII0WQMBgMWi4WCAnWahr+/v2Zb2DUVRVEoLy+noKAAi8WCwWC46veUhEx4nPqSF1rXIDtThMlIXKg/P2y3SkImhPB4UVFRAM6kzFNZLBbnZ71amidkc+fO5dVXX8VqtdKnTx/eeustBg0adN72ixYt4tlnn+XgwYN07tyZV155hREjRjjPK4rCc889x/vvv09xcTHXXnst77zzDp07d3a2KSoqYvLkyfznP/9Br9dzzz338OabbxIYqNaJqqys5E9/+hPZ2dns3LmT2267jcWLF7vEMW7cOD7++ONz4uvRowfbt28H4Pnnn+eFF15wOd+1a1d27dp12fdJXLrckxUAhAe2nCFLUIctf9ieT3WtAx8vmS0ghPBcOp2O6OhoIiIiqKmp0TqcJuHt7d0oPWP1NP1WWLhwIVOnTuW5555jw4YN9OnTh9TU1PNm1GvWrGH06NFMmDCBjRs3kpaWRlpaGtu2bXO2mTVrFnPmzGHevHlkZWUREBBAamoqlZWnSw6MGTOG7du3s2zZMpYsWcKqVauYOHGi83xdXR1+fn48+uijpKSkNBjLm2++SV5envNx5MgRQkJCuPfee13a9ezZ06XdL7/8cjW3TFyCoycrsPh7t7ikZ0BcCCVVtWTlNGNRRCGE0JDBYMBoNHrkozGTMQCdouEyiKSkJAYOHMjbb78NgMPhIDY2lsmTJ/PUU0+d037kyJGUlZWxZMkS57FrrrmGxMRE5s2bh6IoxMTE8PjjjzNt2jQAbDYbkZGRzJ8/n1GjRrFz50569OjBunXrGDBgAADp6emMGDGC3NxcYmJiXK45btw4iouLz+khO9vixYu5++67ycnJoX379oDaQ7Z48WLnJqWXoqqqiqqqKufPdrud2NhYbDYbJpPpkt+nNZu2aDObjhTz4p29tA7FhaIoTFm4iZt7RvJSWoLW4QghhGhCdrsds9l8yd/fmnUhVFdXk52d7dIDpdfrSUlJITMzs8HXZGZmntNjlZqa6myfk5OD1Wp1aWM2m0lKSnK2yczMxGKxOJMxgJSUFPR6PVlZWVf8eT788ENSUlKcyVi9vXv3EhMTQ4cOHRgzZsxFd4yfOXMmZrPZ+YiNlYrGlyv3ZAWhAT5ah3EOnU5H//bBfL89H4fD85eDCyGEuHSaJWSFhYXU1dURGRnpcjwyMhKrteF6TVar9YLt658v1iYiIsLlvJeXFyEhIee97sUcO3aM//73vzz44IMux5OSkpg/fz7p6em888475OTkMHToUEpKSs77XtOnT8dmszkfR45c/m71rd3Rk+WEB7Ws+WP1BsaFcLykik25xVqHIoQQogXRfFK/J/j444+xWCykpaW5HL/lllucf+7duzdJSUm0b9+eL774ggkTJjT4Xr6+vvj6tsxkwh04HAp5tkpSurfMe9g1Mgiznzffb7fSr12w1uEIIYRoITTrIQsLC8NgMJCfn+9yPD8//7xLSKOioi7Yvv75Ym3OXjRQW1tLUVHRFS1dVRSFjz76iN///vf4+Fx4mMxisdClSxf27dt32dcRl6agpIpah0JYC1thWU+v19GvXTDpW62tooq1EEKIS6NZQubj40P//v3JyMhwHnM4HGRkZJCcnNzga5KTk13aAyxbtszZPj4+nqioKJc2drudrKwsZ5vk5GSKi4vJzs52tlm+fDkOh4OkpKTL/hwrV65k37595+3xOlNpaSn79+8nOlrqUDWVo8Vq1eSwFjpkCTAwLphDReXsyS/VOhQhhBAthKZDllOnTuWBBx5gwIABDBo0iDfeeIOysjLGjx8PwNixY2nTpg0zZ84E4LHHHuP6669n9uzZ3HrrrXz++eesX7+e9957D1AnTU+ZMoWXXnqJzp07Ex8fz7PPPktMTIxzOLF79+4MHz6chx56iHnz5lFTU8OkSZMYNWqUywrLHTt2UF1dTVFRESUlJc6VkomJiS6f4cMPPyQpKYlevc5d0Tdt2jRuv/122rdvz7Fjx3juuecwGAyMHj26ke+kqFdfgywssOVN6q/Xq40ZP28D32+30jUqSOtwhBBCtACaJmQjR47k+PHjzJgxA6vVSmJiIunp6c5J+YcPH0avP92JN3jwYBYsWMAzzzzD008/TefOnVm8eLFLMvTEE09QVlbGxIkTKS4uZsiQIaSnp2M0nq7a/umnnzJp0iSGDRvmLAw7Z84cl9hGjBjBoUOHnD/37dsXcN1I1Gaz8dVXX/Hmm282+Plyc3MZPXo0J06cIDw8nCFDhvDrr78SHh5+FXdNXMjR4goCfA34+7Tc6ZHeBj2JsRb+uy2PR4d1vvgLhBBCeDxN65CJi7vcOiat3dPfbGXNvkJm3t1b61AuKHP/CeYs38vPT9xIbIi/1uEIIYRoZG5Th0yIpnD0ZEWLndB/psRYC94GHd9vv7JSK0IIITyLJGTCo+SeLHeLhMzPx0DvNhb+u00SMiGEEJKQCQ+iKApHi92jhwxgYHwwGw6dpKCk8uKNhRBCeDRJyITHOFleQ2WNg7CglrvC8kz92gWj08GyHfkXbyyEEMKjSUImPMZRZ8kL9+ghCzJ60zPGTLoMWwohRKsnCZnwGPVFYcPdJCEDGBAXTOb+E9jKa7QORQghhIYkIRMeI/dkBb5eeoKMLbcG2dkGtA+hzqHw404ZthRCiNZMEjLhMY4VVxIW6ItOp9M6lEsWEuBDl8gg0qX8hRBCtGqSkAmPYbVXEBrgHhP6zzQgLphVe45TVlWrdShCCCE0IgmZ8BhHiysIdsOEbFBcCFW1DlbsPq51KEIIITQiCZnwGFZbpVv2kEWYjMSHBZC+LU/rUIQQQmhEEjLhEWrrHBwvqSLEDRMygAHtg8nYVUBlTZ3WoQghhNCAJGTCIxwvrcKh4LYJWVJ8KOXVdfyyt1DrUIQQQmhAEjLhEfJs6vZDoW5Ug+xMbYL9aGPxk83GhRCilZKETHiEvGI1IXPXHjKAgXHB/LAjn9o6h9ahCCGEaGaSkAmPkGdTi8IG+Bi0DuWKDYwLwVZRQ1ZOkdahCCGEaGaSkAmPYLVVEhro41ZFYc8WHxZAeKCP7G0phBCtkCRkwiPk2SsJ8Xff4UoAnU7HgLgQ0rdbcTgUrcMRQgjRjCQhEx4hz02Lwp5tUFwIx0uq2HikWOtQhBBCNCNJyIRHOGarJDTAPVdYnqlLZBAWf28pEiuEEK2MJGTC7dU5FI7b3bco7Jn0eh392wWTvs2KosiwpRBCtBaSkAm3V1haRZ2iuOW2SQ0ZGBfCkZMV7Mwr0ToUIYQQzUQSMuH26ovChgR6RkLWM8ZEgI9BisQKIUQrIgmZcHtWWwXg3kVhz+Rl0JPYLph0SciEEKLVkIRMuL1jxZV4G3QE+XppHUqjGRgXzG5rCYdOlGkdihBCiGYgCZlwe1a7usLSnYvCnq1PWws+Br0MWwohRCshCZlwe3mnqvR7EqO3gd5tzfxXqvYLIUSrIAmZcHt5xRVY3LxKf0MGxIWw6XAxBfZKrUMRQgjRxCQhE25PHbL0vISsXzsLOh38uLNA61CEEEI0MUnIhFtTFIUCexXB/t5ah9LogozedI82yTwyIYRoBTRPyObOnUtcXBxGo5GkpCTWrl17wfaLFi2iW7duGI1GEhISWLp0qct5RVGYMWMG0dHR+Pn5kZKSwt69e13aFBUVMWbMGEwmExaLhQkTJlBaWuo8X1lZybhx40hISMDLy4u0tLRz4lixYgU6ne6ch9Xq+uV5uZ9PXJ7i8hqq6xwesY9lQwa0D2b1vkJKKmu0DkUIIUQT0jQhW7hwIVOnTuW5555jw4YN9OnTh9TUVAoKGh6iWbNmDaNHj2bChAls3LiRtLQ00tLS2LZtm7PNrFmzmDNnDvPmzSMrK4uAgABSU1OprDw9D2fMmDFs376dZcuWsWTJElatWsXEiROd5+vq6vDz8+PRRx8lJSXlgp9h9+7d5OXlOR8RERFX/PnE5csvUf+7BnvgHDKA/u1DqHUorNh9XOtQhBBCNCGdouGGeUlJSQwcOJC3334bAIfDQWxsLJMnT+app546p/3IkSMpKytjyZIlzmPXXHMNiYmJzJs3D0VRiImJ4fHHH2fatGkA2Gw2IiMjmT9/PqNGjWLnzp306NGDdevWMWDAAADS09MZMWIEubm5xMTEuFxz3LhxFBcXs3jxYpfjK1as4MYbb+TkyZNYLJZG+XwNsdvtmM1mbDYbJpPpkl7Tmqzcc5wHPlrLnFF9CQ9y/83FG/L0N1vpFWPirfv7aR2KEEKIS3S539+a9ZBVV1eTnZ3t0gOl1+tJSUkhMzOzwddkZmae02OVmprqbJ+Tk4PVanVpYzabSUpKcrbJzMzEYrE4kzGAlJQU9Ho9WVlZl/05EhMTiY6O5qabbmL16tVX9fkAqqqqsNvtLg9xfvm2+h4yz5tDVq9/+2CW7yqgqrZO61CEEEI0Ec0SssLCQurq6oiMjHQ5HhkZec48rHpWq/WC7eufL9bmzGFFAC8vL0JCQs573YZER0czb948vvrqK7766itiY2O54YYb2LBhwxV/PoCZM2diNpudj9jY2EuOqTXKt1di9vPGy6D5dMgmM6B9MGXVdWTuP6F1KEIIIZqI5+w108y6du1K165dnT8PHjyY/fv3849//IP/+7//u+L3nT59OlOnTnX+bLfbJSm7AKu90qN7xwDahfgTEeTLjzvzuaFrxMVfIIQQwu1o1q0QFhaGwWAgPz/f5Xh+fj5RUVENviYqKuqC7eufL9bm7En1tbW1FBUVnfe6l2rQoEHs27cPuLLPB+Dr64vJZHJ5iPPLt1d67IT+ejqdjn7tg1m2Ix8Np3wKIYRoQpolZD4+PvTv35+MjAznMYfDQUZGBsnJyQ2+Jjk52aU9wLJly5zt4+PjiYqKcmljt9vJyspytklOTqa4uJjs7Gxnm+XLl+NwOEhKSrqqz7Rp0yaio6Ov+POJy2e1V3lklf6z9W8XTL69im1HZU6hEEJ4Ik2HLKdOncoDDzzAgAEDGDRoEG+88QZlZWWMHz8egLFjx9KmTRtmzpwJwGOPPcb111/P7NmzufXWW/n8889Zv3497733HqD2JEyZMoWXXnqJzp07Ex8fz7PPPktMTIyzllj37t0ZPnw4Dz30EPPmzaOmpoZJkyYxatQolxWWO3bsoLq6mqKiIkpKSti0aROgTuIHeOONN4iPj6dnz55UVlbywQcfsHz5cn744YdL/nzi6uXbK+kSGah1GE2uW3QQAb4Glu2wktDWrHU4QgghGpmmCdnIkSM5fvw4M2bMwGq1kpiYSHp6unMi/OHDh9HrT3fiDR48mAULFvDMM8/w9NNP07lzZxYvXkyvXr2cbZ544gnKysqYOHEixcXFDBkyhPT0dIxGo7PNp59+yqRJkxg2bBh6vZ577rmHOXPmuMQ2YsQIDh065Py5b9++AM4ho+rqah5//HGOHj2Kv78/vXv35scff+TGG2+85M8nrk5tnYMTpVWEtIIeMi+9nsTYYL7fkc/Um7te/AVCCCHciqZ1yMTFSR2y88u3V5L09wz+cnNX+rUP1jqcJvfrgRO8mbGXn5+4kdgQf63DEUIIcQFuU4dMiKtlra9B5qHbJp2tT1sL3gYdP+7Mv3hjIYQQbkUSMuG28u2eXxT2TH4+BrpHmyQhE0IIDyQJmXBb+SVVGPQ6TH6tIyEDdbVl1oEi7LLZuBBCeBRJyITbyrepRWH1Op3WoTSbvu2CqXUorNojm40LIYQnkYRMuK3WUBT2bOFBvrQP9SdjZ8HFGwshhHAbkpAJt5Vvr8TSSuaPnalvrLrZeG2dQ+tQhBBCNBJJyITbyrO1vh4ygP7tLdgqathwuFjrUIQQQjQSSciE2yooqWo1JS/O1CE8EIu/Nxmy2lIIITyGJGTCLVXW1GGrqGmVPWR6nY7EthaWSUImhBAeQxIy4ZaOl1QBracG2dn6tQvmwPEyDp0o0zoUIYQQjUASMuGWTheFbX09ZAC92pjxNuhYvktWWwohhCeQhEy4pQJnD1nrTMj8fAx0jzJJ+QshhPAQkpAJt1Rgr8TboCPA16B1KJrp285CVs4JSqtqtQ5FCCHEVZKETLil/JIqgv190LWiKv1n69sumJo6hV/2FmodihBCiKskCZlwSwX2qlZZFPZMkSYjbYP9WL5LVlsKIYS7k4RMuKWCkkosfq1z/tiZEmMtLN9VgMOhaB2KEEKIqyAJmXBLVlvr3DbpbH1jLRSWVrP9mF3rUIQQQlwFSciEWzp+ag5Za9clKgh/HwM/7ZbVlkII4c4kIRNup6q2juKKGukhA7z0ehLamMmQeWRCCOHWJCETbqe+Sr9FesgAtfzFliM2TpRWaR2KEEKIKyQJmXA7Ba1826Sz9WlrQQFW7jmudShCCCGukCRkwu0U2Ft3lf6zWfx96BgewE+yjZIQQrgtSciE2ykoqcSg1xFo9NI6lBajT6yFFXuOU1vn0DoUIYQQV0ASMuF2CuxVBPt7o2/FVfrP1jc2mJLKWjYcLtY6FCGEEFdAEjLhdgpKKmVC/1k6hAdg9vNmhZS/EEIItyQJmXA7+fYqLH4yof9Mep2OhDZmqUcmhBBuShIy4Xby7dJD1pDEWAs780rIt1dqHYoQQojLJAmZcDsFJVVS8qIBvdua0etg5W4pfyGEEO5GEjLhVmrqHBSVVUsPWQOCjN50ighkuQxbCiGE25GETLiVwlIpCnshfdpa+GVvITVS/kIIIdyKJGTCrdQXhZUesoYlxlooraol+9BJrUMRQghxGTRPyObOnUtcXBxGo5GkpCTWrl17wfaLFi2iW7duGI1GEhISWLp0qct5RVGYMWMG0dHR+Pn5kZKSwt69e13aFBUVMWbMGEwmExaLhQkTJlBaWuo8X1lZybhx40hISMDLy4u0tLRz4vj666+56aabCA8Px2QykZyczPfff+/S5vnnn0en07k8unXrdpl3SJxJtk26sLiwACz+3rLaUggh3IymCdnChQuZOnUqzz33HBs2bKBPnz6kpqZSUNDwl8maNWsYPXo0EyZMYOPGjaSlpZGWlsa2bducbWbNmsWcOXOYN28eWVlZBAQEkJqaSmXl6ZVnY8aMYfv27SxbtowlS5awatUqJk6c6DxfV1eHn58fjz76KCkpKQ3GsmrVKm666SaWLl1KdnY2N954I7fffjsbN250adezZ0/y8vKcj19++eVqblmrl2+vRK8Dk1ESsobUl79YsUsm9gshhDvRKYqiaHXxpKQkBg4cyNtvvw2Aw+EgNjaWyZMn89RTT53TfuTIkZSVlbFkyRLnsWuuuYbExETmzZuHoijExMTw+OOPM23aNABsNhuRkZHMnz+fUaNGsXPnTnr06MG6desYMGAAAOnp6YwYMYLc3FxiYmJcrjlu3DiKi4tZvHjxRT9Pz549GTlyJDNmzADUHrLFixezadOmK7k9ANjtdsxmMzabDZPJdMXv4yleX7aHf2UeYu6YflqH0mKt2V/IW8v3kTn9N0Sb/bQORwghWqXL/f7WrIesurqa7Oxslx4ovV5PSkoKmZmZDb4mMzPznB6r1NRUZ/ucnBysVqtLG7PZTFJSkrNNZmYmFovFmYwBpKSkoNfrycrKuuLP43A4KCkpISQkxOX43r17iYmJoUOHDowZM4bDhw9f8H2qqqqw2+0uD3Ha8ZJKLDJceUG921ik/IUQQriZK0rIDhw4cNUXLiwspK6ujsjISJfjkZGRWK3WBl9jtVov2L7++WJtIiIiXM57eXkREhJy3uteitdee43S0lLuu+8+57GkpCTmz59Peno677zzDjk5OQwdOpSSkpLzvs/MmTMxm83OR2xs7BXH5IkKSqowS0J2QYFGLzpHBMk2SkII4UauKCHr1KkTN954I//6179c5ma1VgsWLOCFF17giy++cEn2brnlFu6991569+5NamoqS5cupbi4mC+++OK87zV9+nRsNpvzceTIkeb4CG5D3TZJVlheTO+2Zn7eW0h1rZS/EEIId3BFCdmGDRvo3bs3U6dOJSoqij/+8Y8XXR15trCwMAwGA/n5+S7H8/PziYqKavA1UVFRF2xf/3yxNmcvGqitraWoqOi8172Qzz//nAcffJAvvvjivAsA6lksFrp06cK+ffvO28bX1xeTyeTyEKcdL6m8shWWh1bDf5+A9Omw8mUo8+zhvMRYC2XVdVL+Qggh3MQVJWSJiYm8+eabHDt2jI8++oi8vDyGDBlCr169eP311zl+/OJfdj4+PvTv35+MjAznMYfDQUZGBsnJyQ2+Jjk52aU9wLJly5zt4+PjiYqKcmljt9vJyspytklOTqa4uJjs7Gxnm+XLl+NwOEhKSrr0mwB89tlnjB8/ns8++4xbb731ou1LS0vZv38/0dHRl3UdoXI4FE6UVl/+HLLDmbDiZagpB50OrFvh+6ehoqhpAm0B4sICMPt5s2KPDFsKIYQ7uKpJ/V5eXtx9990sWrSIV155hX379jFt2jRiY2MZO3YseXl5F3z91KlTef/99/n444/ZuXMnDz/8MGVlZYwfPx6AsWPHMn36dGf7xx57jPT0dGbPns2uXbt4/vnnWb9+PZMmTQJAp9MxZcoUXnrpJb799lu2bt3K2LFjiYmJcdYS6969O8OHD+ehhx5i7dq1rF69mkmTJjFq1CiXFZY7duxg06ZNFBUVYbPZ2LRpk8tqyQULFjB27Fhmz55NUlISVqsVq9WKzWZztpk2bRorV67k4MGDrFmzhrvuuguDwcDo0aOv5ra3WkXl1dQ6lMsrCntsA6x8BSJ7woAHoc8oGPgQVJfB93+FStvF38MN6XU6ereV8hdCCOEuriohW79+Pf/v//0/oqOjef3115k2bRr79+9n2bJlHDt2jDvvvPOCrx85ciSvvfYaM2bMIDExkU2bNpGenu6clH/48GGXpG7w4MEsWLCA9957jz59+vDll1+yePFievXq5WzzxBNPMHnyZCZOnMjAgQMpLS0lPT0do9HobPPpp5/SrVs3hg0bxogRIxgyZAjvvfeeS2wjRoygb9++/Oc//2HFihX07duXvn37Os+/99571NbW8sgjjxAdHe18PPbYY842ubm5jB49mq5du3LfffcRGhrKr7/+Snh4+JXd8Fauvkr/JQ9ZOmph9ZsQHAcJ94H+1F93/xAY8AcoPwHrP2qaYFuAvrEWdueXkGer0DoUIYQQF3FFdchef/11/vnPf7J7925GjBjBgw8+yIgRI9DrT+d3ubm5xMXFUVtb26gBtzZSh+y0FbsLGPfPdbw1ui9hgb4Xf8H+5fDzbLj2MQhqYH7gwdWweymkzQNzm8YPWGOllbX88V/r+ftdCYwa1E7rcIQQolVpljpk77zzDvfffz+HDh1i8eLF3HbbbS7JGEBERAQffvjhlby9EA2q3zbJ4ncpPWQKbP0Swrs1nIwBxA4C3yDYvKDxgmxB6stfyDZKQgjR8l1RQrZs2TKefPLJcyanK4riLHzq4+PDAw88cPURCnHK8ZIqTEYvvAyX8Nc2dx0UH4L4687fxuANHW6EAyvh5MFGi7Ml6d3WzC9S/kIIIVq8K0rIOnbsSGFh4TnHi4qKiI+Pv+qghGhIgb3y0if0b/0KLO3U+WMX0qY/+AfDloVXHV9L1LddsJS/EEIIN3BFCdn5pp2Vlpa6TJ4XojEVlFRd2nBlqRXyt0H7wWqZiwsxeEFssloao8rztqlqH+qPxd9bqvYLIUQL53U5jadOnQqo5SVmzJiBv7+/81xdXR1ZWVkkJiY2aoBC1Mu3V17atkmHf1WHI8O7Xtobx/SBPf+FnFXQ7barC7KF0et09G5j5qfdBUwf0V3rcIQQQpzHZSVkGzduBNQesq1bt+Ljc3r4yMfHhz59+jBt2rTGjVCIUwpKqugX7H/xhod/hZCO4HWJvbW+QRDWFfZleFxCBpAYG8yc5Xs5VlxBjMVP63CEEEI04LISsp9++gmA8ePH8+abb7b6Mgyi+SiKwvGSqotX6a+yQ/526HHhGnjnaNMXNi0A2xEwe9aG7gltzeh1sGL3ce5PkvIXQgjREl3RHLJ//vOfkoyJZmWvrKWq1nHxjcWPrAPFAeGXOTwX3h18/NVeMg8T6OtF1ygpfyGEEC3ZJfeQ3X333cyfPx+TycTdd999wbZff/31VQcmxJmOl1QCl1Cl/8iv6upKY9DlXcDgBVG91WKy/caC7qo2sWhxere18O2mY1TV1uHrZdA6HCGEEGe55G8ds9mM7tSKNbPZfMGHEI2tftukC5a9qKuGoxsg4gonr0f1VrdTKtx7Za9vwfrGWqioqWNdjpS/EEKIluiSe8j++c9/NvhnIZrD8dL6hOwCPWTWrVBbefnDlfUs7cEnAI5kXfoKTTfRLsSfkAAfVuwuYEjnMK3DEUIIcZYrGpepqKigvLzc+fOhQ4d44403+OGHHxotMCHOVGCvws/bgNH7AsNt+dvVFZOBEVd2Eb0ewrqoCZmH0el09GlrYfkumUcmhBAt0RUlZHfeeSeffPIJAMXFxQwaNIjZs2dz55138s477zRqgEIAFJRUEhxwkflj+dshuP3Fi8FeSER3dRulUuuVv0cL1TfWwoHCMg6fKL94YyGEEM3qihKyDRs2MHToUAC+/PJLoqKiOHToEJ988glz5sxp1ACFgPoq/ReZP1a4GyxxV3ehsM6g91JXa3qYXm3MeOl1stpSCCFaoCtKyMrLywkKUlex/fDDD9x9993o9XquueYaDh061KgBCgFgtV2kSv+JfVBXo/aQXQ0vI4R0ULdS8jB+Pga6R5tk2FIIIVqgK0rIOnXqxOLFizly5Ajff/89N998MwAFBQVSn0w0iYKSKoIvtMIyfwcYfCEo5uovFtFd3QuzuvTq36uFSYy1kLn/BOXVtVqHIoQQ4gxXlJDNmDGDadOmERcXR1JSEsnJyYDaW9a3b99GDVAIgOMlVReuQVawDSyx6sT8qxXeDRx1cGzT1b9XC9O3nYXqOger953QOhQhhBBnuKJvr9/+9rccPnyY9evXk56e7jw+bNgw/vGPfzRacEIAlFfXUlpVe/4aZIpD7SELjmucC/pZ1JWaeRsb5/1akGizH9FmI8t35WsdihBCiDNc1l6WZ4qKiiIqKsrl2KBBg646ICHO5iwK63eeHrLiw1Bd1ngJGaibkx/1vIQM1NWWy3cVoCiKs9izEEIIbV1RQlZWVsbLL79MRkYGBQUFOBwOl/MHDhxolOCEAHX+GHD+OWQFO9StjhpzU/DQzurE/pJjjTMvrQXp2y6Ypdus7Miz0zNGdtYQQoiW4IoSsgcffJCVK1fy+9//nujoaPktWzSpgvp9LM9Xh+z4bjBFg9dFNh6/HCHxapJ3bBN09ayErFtUEH7eBpbvLJCETAghWogrSsj++9//8t1333Httdc2djxCnKPAXoWvlx6/81XpP7Gv8XuxvI3qJuXHNkHXEY373hrzMuhJaGtm2c58Jg/rrHU4QgghuMJJ/cHBwYSEhDR2LEI0KL+kkmB/n4Z7Yuuq1TlkpjaNf+HQTpC3CZS6xn9vjfVvF8yWXJuz91EIIYS2righe/HFF5kxY4bLfpZCNJXj9qrzF4U9maOusjQ1wbBiaCd1sUDhvsZ/b40ltrOg18HynVIkVgghWoIrGrKcPXs2+/fvJzIykri4OLy9Xb8sN2zY0CjBCQFqD9l5V1ie2K/O9QqKavj81TC3VSv3522E8K6N//4aMhm96RIZxI878xk1qJ3W4QghRKt3RQlZWlpaI4chxPkV2KvoGB7Y8Mmi/WoyZrjIxuNXQm+A4HjI2wK9RzX++2usb7tgvtmQS2VNHcbzzc8TQgjRLK4oIXvuuecaOw4hzivfXkn/9sENnyzcB0HRTXfxkHjY96M6V83QiKs4W4D+7YL5bO1hVu8rZFj3SK3DEUKIVu2K95kpLi7mgw8+YPr06RQVFQHqUOXRo0cbLTghKmvqsFeep0q/oxaKDzXN/LF6IR3UZKxwb9NdQyMxFiPRZiM/yjwyIYTQ3BUlZFu2bKFLly688sorvPbaaxQXFwPw9ddfM3369MaMT7Ryx51FYRsYkiw+DHU1TbPCsl5QNHj7gXVr011DIzqdjr6xFn7cmY/DoWgdjhBCtGpXlJBNnTqVcePGsXfvXoxGo/P4iBEjWLVqVaMFJ0R9lf4Ge8iK9gO6ph2y1OvVLZnytjTdNTQ0IC6E4yVVbMot1joUIYRo1a4oIVu3bh1//OMfzznepk0brFbrVQclRL0C+6kq/Q31kBXuUzcBb8wK/Q0JjofjO9ShSw/TNTIIk9GLH7bLZuNCCKGlK0rIfH19sdvt5xzfs2cP4eHhl/Vec+fOJS4uDqPRSFJSEmvXrr1g+0WLFtGtWzeMRiMJCQksXbrU5byiKMyYMYPo6Gj8/PxISUlh717X+T9FRUWMGTMGk8mExWJhwoQJlJaWOs9XVlYybtw4EhIS8PLyOu+q0hUrVtCvXz98fX3p1KkT8+fPv+rPJ1wVlFThZdAR6NvA+pMT+5um3MXZQjqqQ6OFe5r+Ws1Mr9fRr10w32+XX6SEEEJLV5SQ3XHHHfztb3+jpqYGUOeiHD58mCeffJJ77rnnkt9n4cKFTJ06leeee44NGzbQp08fUlNTKShoeJLxmjVrGD16NBMmTGDjxo2kpaWRlpbGtm3bnG1mzZrFnDlzmDdvHllZWQQEBJCamkpl5emK5GPGjGH79u0sW7aMJUuWsGrVKiZOnOg8X1dXh5+fH48++igpKSkNxpKTk8Ott97KjTfeyKZNm5gyZQoPPvgg33///RV/PnGugpJKgv0aqtKvQPFBdQ/LphYUpc4jy/O8eWQAA+NCyCksY19BidahCCFEq6VTFOWyZ/PabDZ++9vfsm7dOkpLS4mJicFqtZKcnMzSpUsJCAi4pPdJSkpi4MCBvP322wA4HA5iY2OZPHkyTz311DntR44cSVlZGUuWLHEeu+aaa0hMTGTevHkoikJMTAyPP/4406ZNc8YaGRnJ/PnzGTVqFDt37qRHjx6sW7eOAQMGAJCens6IESPIzc0lJsZ1xd64ceMoLi5m8eLFLseffPJJvvvuO5dkcNSoURQXF5Oenn5Fnw+gqqqKqqoq5892u53Y2FhsNhsmk+mS7qsn+cuizWw6Uszf7uzleqK0AL4cD/0fgPBuTR/Ihk9A7w3DZzb9tZpZda2DP/5rPZN/05lHbuykdThCCOER7HY7ZrP5kr+/r6iHzGw2s2zZMr777jvmzJnDpEmTWLp0KStXrrzkZKy6uprs7GyXHii9Xk9KSgqZmZkNviYzM/OcHqvU1FRn+5ycHKxWq0sbs9lMUlKSs01mZiYWi8WZjAGkpKSg1+vJysq6tBtwCbFcyecDmDlzJmaz2fmIjY295Jg8UUFJFeaGqvQXH1KfA5thyBJOzSPbqQ5dehgfLz192lpk2FIIITR02YVhHQ4H8+fP5+uvv+bgwYPodDri4+OJiopCUZSGN4BuQGFhIXV1dURGuhakjIyMZNeuXQ2+xmq1Nti+fiFB/fPF2kRERLic9/LyIiQk5LIWJJwvFrvdTkVFBSdPnrzszwcwffp0pk6d6vy5voestbLaK2kX4n/uiZMH1W2NjObmCSSkg5qMndgLET2a55rNaEBcCHN/2keerYJos5/W4QghRKtzWT1kiqJwxx138OCDD3L06FESEhLo2bMnhw4dYty4cdx1111NFWer4evri8lkcnm0Zsft59nHsvgQBEbCJf4CcNWCotUEMH/bxdu6oX7tLHjpdaRvk14yIYTQwmUlZPPnz2fVqlVkZGSwceNGPvvsMz7//HM2b97Mjz/+yPLly/nkk08u6b3CwsIwGAzk57sut8/PzycqquFhqKioqAu2r3++WJuzJ9XX1tZSVFR03uteTiwmkwk/P78r+nzCVXWtg6LyGkICGihrcfKgWvKiuej1ENzeIwvEAvj7eNG7rZnvtuRpHYoQQrRKl5WQffbZZzz99NPceOON55z7zW9+w1NPPcWnn356Se/l4+ND//79ycjIcB5zOBxkZGSQnJzc4GuSk5Nd2gMsW7bM2b5+6PTMNna7naysLGeb5ORkiouLyc7OdrZZvnw5DoeDpKSkS4r9UmK5ks8nXB0vra/Sf1ZC5qgDW27zlLw4U3AcFOxQr++BBsWHkH3oJPn2yos3FkII0aguKyHbsmULw4cPP+/5W265hc2bN1/y+02dOpX333+fjz/+mJ07d/Lwww9TVlbG+PHjARg7dqzLVkyPPfYY6enpzJ49m127dvH888+zfv16Jk2aBKjlN6ZMmcJLL73Et99+y9atWxk7diwxMTHOWmLdu3dn+PDhPPTQQ6xdu5bVq1czadIkRo0a5bLCcseOHWzatImioiJsNhubNm1i06ZNzvN/+tOfOHDgAE888QS7du3if//3f/niiy/485//fMmfT1xYfWIQfHYPWUmeOp8rsJk3xA6Oh5pKKNrXvNdtJv3bh6DX62RyvxBCaOCyJvUXFRWdM0n9TJGRkZw8efKS32/kyJEcP36cGTNmYLVaSUxMJD093XmNw4cPo9efzhkHDx7MggULeOaZZ3j66afp3Lkzixcvplev0yURnnjiCcrKypg4cSLFxcUMGTKE9PR0ly2ePv30UyZNmsSwYcPQ6/Xcc889zJkzxyW2ESNGcOjQIefPffv2BdR5dKD2xn333Xf8+c9/5s0336Rt27Z88MEHpKamXvLnExeWbztPlf76FZbN3UNmagMGb8jfDmFdm/fazSDQ14uENia+25LH2OQ4rcMRQohW5bLqkBkMBqxW63mr8efn5xMTE0NdnWcO6WjhcuuYeJL5q3N46budfPKHQa6rdzctgJ3fwo1PN39Q6z4E/xAY9lzzX7sZ/LSrgPd/PsDav6YQHuSrdThCCOG2Lvf7+7J6yBRFYdy4cfj6NvwP9ZkFTYW4WvklVYQENFClv7kn9J8pOA4OZ4LiAN0VlfFr0frHBfPBL5C+LY/fSy+ZEEI0m8tKyB544IGLthk7duwVByPEmfLtledO6Ad1yyRL+2aPB4CQeNj3o5oUhnTQJoYmZDJ607utmW83H5OETAghmtFlJWT//Oc/myoOIc6Rb6/Ecvb8sbpqsOdB20HaBGWOBb2XOo/MAxMygOQOYbyzcj9HiytoY5EisUII0Rw8b8xFeIx8W9W5PWT2o+pwYXOvsKxn8AZLrMfWIwMYEBeMj0HPks3HtA5FCCFaDUnIRIuVX1LZwArLI+pzQMMLS5qFJQ7ytwKXvB7Grfj7eNG3nYV/b5KETAghmoskZKJFqqiuo6Sy9twaZLZc8AkEnwb2t2wuIfFQaQfbEe1iaGKDO4axI8/OvoJSrUMRQohWQRIy0SIVlNTXIDs7ITsCgRr2jgFY2qkrLK2eua8lQGKsBX8fA9/KsKUQQjQLSchEi2S1nadKv+2wtsOVAF6+YG7r0QmZj5eegXEhfL0hF4fDM4dmhRCiJZGETLRI+SX1+1ieMYdMcYDtKPhrnJCBWo/M6rnzyACu7xJO7skK1h0s0joUIYTweJKQiRapwF6J0VuPn7fh9MGyglN7WGpUFPZMIfFQUaTuq+mhukYFEWny5asNuVqHIoQQHk8SMtEi5dsrCfE/q0q/7VRioPUcMlBXWur0Hl3+Qq/TMaRTOEu25FFeXat1OEII4dEkIRMtUr69CsvZE/qLj6h1wIxmbYI6k7cRTDEePY8M4LrOYZRX15G+zap1KEII4dEkIRMtUoNV+m1H1An9LWUPyeB4sG7Bk+eRRZiMdI8OYtF6GbYUQoim1EK+2YRwZW1oH8viI9qvsDxTSAcoK/ToeWSgTu7PPHCCQyfKtA5FCCE8liRkosVRFIUCewPbJtlaWEIWHOfx88gArukQSoCPgc/Wem4hXCGE0JokZKLFKa2qpaKmjpCAM4YsK21QVdIyVljW8zaCqY3HJ2S+XgaGdg7ni/VHqK51aB2OEEJ4JEnIRIuTb1eLwrpM6re1gD0sGxISD3mePY8MYFj3CIrKqvl+u0zuF0KIpiAJmWhx8k5V6Q85s0q/7SigA/9QbYI6n5B4KD/h8fPI2gb70z06iE+zDmsdihBCeCRJyESL49w26cweMnsu+IeoZS9aklZQj6zeb7pF8uuBE7LhuBBCNAFJyESLY7VVYvbzxsfrjL+etlwIaGG9Y3B6HlneFq0jaXJJ8SFY/Lz55+ocrUMRQgiPIwmZaHHy7JWuw5WgJmQtYQ/LhoR0gLzNePo8Mm+DnpQekXyVnUtxebXW4QghhEeRhEy0OFZbpeum4o5aKM1veRP664V2hIqTUOz586tSukdSpygsWOv5n1UIIZqTJGSixcmzVbjOHyvJB0ddyxyyBAhuD3ovOLZZ60ianNnPm2s7hvHxmoPU1EkJDCGEaCySkIkWJ9921pCl/dS2PS11yNLgA5b2YN2kdSTN4paEaPLtVXy3xbNXlgohRHOShEy0KJU1dRSV1xAaeGbJi1ww+ILRpF1gFxPSQV1p6ajTOpIm1y7En8RYM3N/2ofD4dnz5oQQorlIQiZalAJ7FXB2yYujEBgGOp1GUV2C0I5QXQ4n9mkdSbO4q29b9haU8sMOKRQrhBCNQRIy0aJYT1XpDw3wPX3QdhT8Wuj8sXrmWPAyQt4mrSNpFl0ig+gZY2JOxj4URXrJhBDiaklCJlqUPFsFAMFn7mNpz4XAFjp/rJ5eD8HxrWJif727+rZhR56d5bsKtA5FCCHcniRkokWx2irx8zbg7+OlHqguhYpi8A/TNK5LEtYJju+A2gqtI2kWPaJNdI0K4h/L9shcMiGEuEqSkIkWxWqvdJ3Qbz+qPrfUGmRnCu0MdTVg3aZ1JM1Cp9MxckAs247ZWbJVVlwKIcTVaBEJ2dy5c4mLi8NoNJKUlMTatWsv2H7RokV069YNo9FIQkICS5cudTmvKAozZswgOjoaPz8/UlJS2Lt3r0uboqIixowZg8lkwmKxMGHCBEpLXffo27JlC0OHDsVoNBIbG8usWbNczt9www3odLpzHrfeequzzbhx4845P3z48Cu5Ta2CWhT2zBWWx9TnlrapeEMCwsAvGI5max1Js+kebaJfOwuvpu+iulbqkgkhxJXSPCFbuHAhU6dO5bnnnmPDhg306dOH1NRUCgoanpeyZs0aRo8ezYQJE9i4cSNpaWmkpaWxbdvpXolZs2YxZ84c5s2bR1ZWFgEBAaSmplJZWelsM2bMGLZv386yZctYsmQJq1atYuLEic7zdrudm2++mfbt25Odnc2rr77K888/z3vvveds8/XXX5OXl+d8bNu2DYPBwL333usS8/Dhw13affbZZ411+zxOXkM1yIwmdc/Ilk6ng7DOrSohAxg1sB1Hiyv4NOuQ1qEIIYTb0jwhe/3113nooYcYP348PXr0YN68efj7+/PRRx812P7NN99k+PDh/OUvf6F79+68+OKL9OvXj7fffhtQe8feeOMNnnnmGe6880569+7NJ598wrFjx1i8eDEAO3fuJD09nQ8++ICkpCSGDBnCW2+9xeeff86xY2qPzKeffkp1dTUfffQRPXv2ZNSoUTz66KO8/vrrzlhCQkKIiopyPpYtW4a/v/85CZmvr69Lu+Dg4Ca4k57hnB4y+1H3mD9WL7QL2I9BaespBxEb4s91XcJ5M2Ov7HEphBBXSNOErLq6muzsbFJSUpzH9Ho9KSkpZGZmNviazMxMl/YAqampzvY5OTlYrVaXNmazmaSkJGebzMxMLBYLAwYMcLZJSUlBr9eTlZXlbHPdddfh4+Pjcp3du3dz8uTJBmP78MMPGTVqFAEBAS7HV6xYQUREBF27duXhhx/mxIkT570nVVVV2O12l0drUedQOF5S5dpDZstVhwLdRWhH0Onh6AatI2lW9w2IpbrWwSvpu7UORQgh3JKmCVlhYSF1dXVERka6HI+MjMRqbbiHwWq1XrB9/fPF2kRERLic9/LyIiQkxKVNQ+9x5jXOtHbtWrZt28aDDz7ocnz48OF88sknZGRk8Morr7By5UpuueUW6uoarug+c+ZMzGaz8xEbG9tgO09UWFpFnaKcTsgUh9rb5E49ZN5GCG7X6hKyYH8f7u0fy2drD7PhcMO/sAghhDg/zYcsPcWHH35IQkICgwYNcjk+atQo7rjjDhISEkhLS2PJkiWsW7eOFStWNPg+06dPx2azOR9HjhxphuhbhjybOsfPmZCVn4DaKvdYYXmm0C5qgVhHrdaRNKube0TSISyAp7/eSq1sPC6EEJdF04QsLCwMg8FAfn6+y/H8/HyioqIafE1UVNQF29c/X6zN2YsGamtrKSoqcmnT0HuceY16ZWVlfP7550yYMOHCHxjo0KEDYWFh7NvX8BY7vr6+mEwml0drYbXVV+k/lZDVl7xwpx4ygLAuUFMBBTu0jqRZ6fU6/jAknj35Jby76oDW4QghhFvRNCHz8fGhf//+ZGRkOI85HA4yMjJITk5u8DXJycku7QGWLVvmbB8fH09UVJRLG7vdTlZWlrNNcnIyxcXFZGefXg23fPlyHA4HSUlJzjarVq2ipqbG5Tpdu3Y9Z1L+okWLqKqq4ne/+91FP3Nubi4nTpwgOjr6om1bmzxbBV4GHUHGU0Vh7cfUCvj+brYIwhSjrgw9cuHyLZ6oY3ggt/eJ4fVle9iaa9M6HCGEcBuaD1lOnTqV999/n48//pidO3fy8MMPU1ZWxvjx4wEYO3Ys06dPd7Z/7LHHSE9PZ/bs2ezatYvnn3+e9evXM2nSJEAtVjllyhReeuklvv32W7Zu3crYsWOJiYkhLS0NgO7duzN8+HAeeugh1q5dy+rVq5k0aRKjRo0iJiYGgPvvvx8fHx8mTJjA9u3bWbhwIW+++SZTp0495zN8+OGHpKWlERrqWiurtLSUv/zlL/z6668cPHiQjIwM7rzzTjp16kRqampT3E63lmerJDzQF139JuK2o2r9Mb1B28Aul04HYV3hSJbWkWjit/3a0i7En8cWbqSiuuG5kkIIIVx5aR3AyJEjOX78ODNmzMBqtZKYmEh6erpzAv3hw4fR60/njYMHD2bBggU888wzPP3003Tu3JnFixfTq1cvZ5snnniCsrIyJk6cSHFxMUOGDCE9PR2j8XQtq08//ZRJkyYxbNgw9Ho999xzD3PmzHGeN5vN/PDDDzzyyCP079+fsLAwZsyY4VKrDGD37t388ssv/PDDD+d8NoPBwJYtW/j4448pLi4mJiaGm2++mRdffBFfX99z2rd2R4srzqpBdhT83Gy4sl54d8hdpyaV5jZaR9OsvAx6/t8NHfnrN9t47tttvHJP79NJthBCiAbpFEWRTehaMLvdjtlsxmazefx8srS5qzEZvXj4hk7qga8eVMtIdLv1wi9sieqqYflL0O8B6HmX1tFoYsXuAt5ddYCX0nrxu2vaax2OEEI0q8v9/tZ8yFKIeseKKwgLPNVzWFcDpfnuN6G/nsEHQjq22mFLgBu6RnBzj0ie+3Y7a3OKtA5HCCFaNEnIRItQU+fgeEkVofUJWWm+Wocs0M1KXpwpvBvkb4fq0ou39VC/T25P18ggJv7fevbml2gdjhBCtFiSkIkWwWqrRAHCAt285MWZwruqSWUr29vyTF56PX++qQsmozdjPsjiSFG51iEJIUSLJAmZaBGOFVcAEBpwqofMlgtevuAbpGFUV8nPAua2cGi11pFoKtDXi+m3dEOv1zH6/V85fEKSMiGEOJskZKJFOGY7lZCd2UMWEKaWkHBnkT0hdz3UVWkdiaYs/j48fUt3ah0Kd72zmu3HpEaZEEKcSRIy0SIcK64kyOiF0ftUzTF3Lnlxpohe6vZPrWxvy4aEB/ny/O09sfh5c9+8TDJ25l/8RUII0UpIQiZaBJcVlqDW7wr0gIQsMAwCo1r9sGU9s583z9zag+7RJiZ8vJ6Z/91Jjex7KYQQkpCJluHYmUVhq8ug4qR7T+g/U1RPdRulupqLt20FjN4Gpt7UhTFJ7Xh/1QHumruabUdlCFMI0bpJQiZahNyTFWdsKp6rPge4ccmLM0X0VJNM6xatI2kxdDodt/WO4YU7elFSVcsdb//C899u53hJ655rJ4RovSQhEy1Cnq3ydA0y2zH12T/0/C9wJ0FR6gKFg79oHUmL0ykikJfSejFqYDu+WH+EobOWM3PpTo6eWnUrhBCthSRkQnP2yhpKq2pda5AZTeBtvPAL3YVOB5EJ6jyyumqto2lxvPR6bu8Tw5uj+jK8ZzSfZB5i6CvLmTB/Hd9tyaOsqlbrEIUQoslpvrm4EHnFlQCnJ/Xbc8HfQ4Yr68UkwoGf1NWW7a7ROpoWKdDXi5EDY7kzMYZf9hXy0+4CHlmwAV8vPdd0CCW5YyiD4kPoHmXCz8egdbhCCNGoJCETmjtdFPZUD5ntCAREaBhREwiMAFMM5KyQhOwijN4GUrpHktI9knx7JesOFrH1qI1/LNtDVa0DvQ7iwwLoEB5IXKg/MRY/Ik1GwgJ9sfh7YzJ6E+BrIMDHC73ezevYCSFaDUnIhOaOFleg10Gwv4+61ZDtGIT31DqsxhfdG/ZlQE05ePtrHY1biDQZua13DLf1jqG2zsGhonIOnijj8IlyCkoq2XbUxonSaqrPUzrD26DD6GXA26DD20uPl16Pl0GH96lnL4MeH4MOHy8Dft56jN4G/HwMBPp6EejrhcXfG4ufD6GBPoQH+RJ1KvGTRE8I0dgkIROay7NVEBpw6kuu9Lg6z8oTapCdLaoP7E6Hw79Cx99oHY3b8TLo6RgeSMfwQJfjiqJQVlXnnItYWlVLVY2Dypo6auocVNc5qK1TqHU4qHMo1DoU6s541DoUauoclFbVUlRWTWWt+tqK6jpKq2opr65zjUOvI8biR1yoP/FhAXSKCKRjRCDdokynS7cIIcRlkoRMaO5YcSUhgWeVvPC0OWSg7m0ZEq/OJZOErNHodDoCjV4EGpvmn7NahwN7RS3F5dUUlVdTVFpNQUkVBSWVLN9VwKdZh6l1KABEBPnSK8ZEQlsLvdua6RNrcS14LIQQ5yEJmdDc4aJywp0lL46C3gB+wdoG1VSiE2HHv6H8hOeU9fBwXno9IQE+hAT40KGB87UOB/n2Kg6fKOdQURmHTpTxz9U52CvV1aFtg/3o3z6Yfu2C6d8+mG5RQXgZZIG7EMKVJGRCc0eKyhnS6dQQpf2oWqFf76FfWFG9YdcS2J8BCfdpHY1oBF56PW0sfrSx+JHcUU2yFUWhsLSKfQWl7C0oZccxO99tyaPWoeDnbSAx1sLAuGD6x4XQt50Fk9Fb408hhNCaJGRCU5U1dRSUVBEWVN9DlqsWUfVU3kaISoC9yyDhXkAmh3sinU5HeJCR8CAjyR3Vv8/VtQ5yCsvYnV/CnvwS/rnmIHOW70MHdI4MZGBciLMnrX2oPzqd/N0QojWRhExoqr4ie4QzITuibjXkydr0h7XvQ/4OiPTwzyqcfLz0dI0KomtUEKD2ouXZKtlzKkFbtec4n2YdBiAkwIe+7SzOYc4+bS1Se00IDycJmdDUkaJy4FRCVlsJZYWe3UMGEBynzh/bu0wSslZMp1NXa8ZY/Lihq1p3r7Syln3HS9iTX8q+glLeXr6Pipo6vPQ6ukUFMSAuhIFxIQyMCybC5CE7WQghAEnIhMaOnFRrkIUE+EJxjnrQUzYVPx+dHmL6wcGfIWmi1CQTToFGLxJjg0mMVRe1OBwKR06Ws7eglD3WEv67zcr8NQcBaBfiT1J8iHMXgxiLn4aRCyGuliRkQlO5J8sJD/LFoNepw5Xg+QkZqMOW+zPUEhhdb9U6GtFC6fU62ocG0D40gJTukQCcLK9mt7WEXdYS1h4sYlG2WiqmfYg/gzuFMbRzGIM7hmLxl5poQrgTSciEpnKLKs4oeZELvkHg0wp6jPzMENEddi6BriOQyf3iUgX7+3BNh1Cu6aCu6CyprGFnXgnbjtlYtec4n609jF4HCW3NXN85nOu7htOnrUVKbQjRwklCJjR1qEjtIQNO7WHZCnrH6rW7BtZ9CNZt6spLIa5AkNGbQfEhDIoPAaCwtIqtuTa2HC3mo9XqSs4goxdDOoVxXZdwrusSThsZ3hSixZGETGgqt6ic7qdWnVF8RN2Eu7UI6ah+3l1LJCETjSYs0Jcbu0VwY7cIHA6F/cdL2XwqQft+uxWHAu1D/RnaOYzkDmEMig85/UuREEIzkpAJzZRW1VJcUaOuFlPq1KKwkb20Dqv56HQQmwS7l0rlftEk9HodnSOD6BwZxG/7t6W0qpbtx2xsO2onY2cB//pVLbMRF+pP//ZqkdqENma6RgVh9JYyG0I0J0nIhGZcSl6U5kNdDQS0oh4yUFdb7vlBTcr6/l7raISHC/T1Iik+lKR4NfkvKqtml9XOLmsJm46c5JuNuTgUMOh0tA/1p0tkIB3CA2kf6k9siD8xZj+izEZJ1oRoApKQCc3UJ2ThQb5QtFM9GNiK5pCBWrm/7QB1cn/CveAltaVE8wkJ8GFwxzAGn7GbwJGT5eQUlnH0ZAW5xeVkHy6msKQK5YzXWfy8CQ/yJdJkJCLIl/BTj0iTkZhT20hFBPmi18tiFSEulSRkQjNHTlbgY9Bj8fNW548ZfMFo0jqs5hd3LRzOhH0/QrfbtI5GtGI+Xno6hgfSMTzQ5XhNnYPCkipOlFVzoqyKk+U1nCyrxlZRw448Oyf311BcXk1VrcP5Gl8vPe1C/OkSGUS3qCB6xJjo2y6YkAApxyFEQyQhE5o5cmqFpU6nU0teBIapRVNbG79giEyA7d9A11tAJ8NBomXxNuiJtvgRfYHVmYqiUF5dx4myagpLqrDaK7HaKzl4ooyVe45TWlULqPPVrj1VL21I53ACfeVrSAiAFvHtN3fuXOLi4jAajSQlJbF27doLtl+0aBHdunXDaDSSkJDA0qVLXc4risKMGTOIjo7Gz8+PlJQU9u7d69KmqKiIMWPGYDKZsFgsTJgwgdLSUpc2W7ZsYejQoRiNRmJjY5k1a5bL+fnz56PT6VweRqPrkNOlxNJa5Z4sJyzo1G/Lxa2s5MXZ4odAiRUO/6p1JEJcEZ1OR4CvF+1C/OnXPpgRCdH84dp4nrm1B+/9vj9vjkxk0o2d6BQRxPJdBfzpXxvo97dljP/nWr7KzqXsVMImRGuleUK2cOFCpk6dynPPPceGDRvo06cPqampFBQUNNh+zZo1jB49mgkTJrBx40bS0tJIS0tj27ZtzjazZs1izpw5zJs3j6ysLAICAkhNTaWystLZZsyYMWzfvp1ly5axZMkSVq1axcSJE53n7XY7N998M+3btyc7O5tXX32V559/nvfee88lHpPJRF5envNx6NAhl/OXEktrdbionPBAI6CA7XDrm9B/JnNbCOkAW74Al9k6Qrg/nU5HhMnItZ3CmDAkntfvS+TNkYmMHBhLnq2Sxxdtpv9Ly3j8i81sOHwSRZH/B0Tro1M0/puflJTEwIEDefvttwFwOBzExsYyefJknnrqqXPajxw5krKyMpYsWeI8ds0115CYmMi8efNQFIWYmBgef/xxpk2bBoDNZiMyMpL58+czatQodu7cSY8ePVi3bh0DBgwAID09nREjRpCbm0tMTAzvvPMOf/3rX7Farfj4qL04Tz31FIsXL2bXrl2A2kM2ZcoUiouLG/xslxLL2aqqqqiqqnL+bLfbiY2NxWazYTJ5zvwqRVHoMeN77urbhtu7+MPCMZA4BqJaUdmLsxXug/Ufwk1/U7dWEqKVOF5Syc97C1m19zj59ip6xph4cGg8t/WOwVt2GBBuym63YzabL/n7W9O/6dXV1WRnZ5OSkuI8ptfrSUlJITMzs8HXZGZmurQHSE1NdbbPycnBarW6tDGbzSQlJTnbZGZmYrFYnMkYQEpKCnq9nqysLGeb6667zpmM1V9n9+7dnDx50nmstLSU9u3bExsby5133sn27dud5y4llrPNnDkTs9nsfMTGxp7n7rm34yVVVNTUEWU2nt7DsjUVhW1IaEewtIdNnyK9ZKI1CQ8ycne/trx+XyJPDu+Kt0HHnxduZugrP/HBzweoqK7TOkQhmpymCVlhYSF1dXVERka6HI+MjMRqtTb4GqvVesH29c8XaxMR4frl7+XlRUhIiEubht7jzGt07dqVjz76iH//+9/861//wuFwMHjwYHJzcy85lrNNnz4dm83mfBw5cqTBdu4up7AMgCjTqYRMpwe/EI2j0phOBx1/A8d3w7GNWkcjRLPT63Qkxgbz5PDuzLqnN12iAvn70p1c+8py3l25XxIz4dFkectVSE5OJjk52fnz4MGD6d69O++++y4vvvjiFb2nr68vvr6ev43JwRNl6IBIkxH2HlYn9BvkryNhncEcC5sWQExfZNNx0VrFhvjz8PWduKdvW/69+Rizvt/N+z8fYPJvOjN6UDt8vGQoU3gWTf9Gh4WFYTAYyM/Pdzmen59PVFRUg6+Jioq6YPv654u1OXvRQG1tLUVFRS5tGnqPM69xNm9vb/r27cu+ffsuOZbWKqewnLBAX/Uf1ZMHZbiynk4HnX4DBTshd73W0QihuQiTkYeGdmD2vX3oEW3i+W+3M2z2Cr7dfEwm/wuPomlC5uPjQ//+/cnIyHAeczgcZGRkuPQ8nSk5OdmlPcCyZcuc7ePj44mKinJpY7fbycrKcrZJTk6muLiY7OxsZ5vly5fjcDhISkpytlm1ahU1NTUu1+natSvBwcENxlZXV8fWrVuJjo6+5Fhaq4OFZUSaT/UEnjwEgZEXfkFrEtYVQuIhez4ojos2F6I1iDQZefiGTrxyT2/Cg3x59LONpM1dzbqDRVqHJkSj0LzPd+rUqbz//vt8/PHH7Ny5k4cffpiysjLGjx8PwNixY5k+fbqz/WOPPUZ6ejqzZ89m165dPP/886xfv55JkyYB6vLqKVOm8NJLL/Htt9+ydetWxo4dS0xMDGlpaQB0796d4cOH89BDD7F27VpWr17NpEmTGDVqFDExMQDcf//9+Pj4MGHCBLZv387ChQt58803mTp1qjOWv/3tb/zwww8cOHCADRs28Lvf/Y5Dhw7x4IMPXnIsrdWBwlJ1/lhFMVTZJSE7k04HnVPVnsOclVpHI0SLEhviz19Su/Hsrd0pqazl3nmZPPyvbOdWbEK4K80n7YwcOZLjx48zY8YMrFYriYmJpKenOyfCHz58GL3+dN44ePBgFixYwDPPPMPTTz9N586dWbx4Mb16nS6X8MQTT1BWVsbEiRMpLi5myJAhpKenuxRt/fTTT5k0aRLDhg1Dr9dzzz33MGfOHOd5s9nMDz/8wCOPPEL//v0JCwtjxowZLrXKTp48yUMPPYTVaiU4OJj+/fuzZs0aevTocVmxtDYOh8LhE+UMiguF4oPqQUnIXAW3h4gesOH/oP0QMHhrHZEQLUqPGDMvpvXil72FLFx/hGGzVzJhaDyTbuxEgFT/F25I8zpk4sIut46JO8izVZA8cznTbu5K//JfYN0HkPIC6DXvsG1ZSgtg9ZswYDz0vFvraIRosSpr6vjP5mMs2ZKH2d+b6bd0Iy2xjWxuLjTlVnXIROvkLHlhNqo9ZIERkow1JDACYgfB5s+g4uTF2wvRShm9Ddw7IJbX7u1Nh7AApn6xmXvmrWHbUZvWoQlxyeRbUDS7g4Xl6HUQGeQLRYda9x6WF9PpJkAHG/9P60iEaPHCg4xMSenCM7d250RpFbe/9QvTv95KUVm11qEJcVGSkIlmd/BEGeFBvngZdOoelkGtuwTIBfn4Q6dhsOcHOCGb0gtxKXrGmPn7Xb0ZmxzHt5uOcsNrP/F/mQepc8gMHdFySUImml1OYZlaELasEKrLZEL/xbRNUpPWNW+DIpXKhbgUBr2O4b2imH1fIv3aBfPsv7dz21s/k31IymSIlkkSMtHsco6XqSUvig+rByQhuzC9HnrcCSf2w66lWkcjhFsx+3nzx+s68uKdPamqdXDPO5k8/sUmjpdUaR2aEC4kIRPNyuFQOFxUfmpC/yEw+ICfReuwWr7g9tAuCTbMh7LjWkcjhNvpFBHEi3f04sEh8fywPZ/fvLaC+atzqK2T4suiZZCETDSrY7YKquscag9ZfYV+nfw1vCSdU9UE9tf/BWQujBCXS6/XMax7JLPv68Og+BBe+M8ObnvrFxnGFC2CfBOKZrX/uFryIsbiB0UHZEL/5fA2Qvc74Mha2Jdx8fZCiAYFGb15cGgH/nZnL6rr1GHMvyzazIlSGcYU2pGETDSrvfkl+HrpCQ8wgO2IJGSXK7IntOkHa99VC8cKIa5Yp4hAXryjF3+4Np7/brNy4+wV/OvXQ7IaU2hCEjLRrPbml9LG4ofengt1NRAUrXVI7qfb7WDwhV/ekM3HhbhKer2Om3pEMvvePvSLDeaZxdtIm7uaLbnFWocmWhlJyESz2p1fcmq4Mkc9IAnZ5fM2Qq97wLoFti7SOhohPILJz5s/Xt+R52/vib2yhjvfXs0zi7diK6/ROjTRSkhCJpqNoijsKyilbfCp+WP+oWpyIS5faEfoeANs/BdYt2odjRAeo2tUEP+TlsDvk9vzVfZRbpy9gi+zc5Ftn0VTk4RMNJt8exWlVbW0CfaDEwfAJL1jV6VjiloOY+UsqCjWOhohPIZBr+OWXtG8dm8fukUFMW3RZu57N5Pd1hKtQxMeTBIy0Wz25Kv/mMUG+0HRfgiUCf1XRa+H3qPAUQM//Y86J08I0WhCAnyY/JvO/HVEd44WVzLizZ95ackOSqtqtQ5NeCBJyESz2VtQio9BT7iuBKpLIShG65Dcn9EEiWOgcI/UJxOiifRqY+aVuxO4d0Bb/u/XQ/zmtRX8Z/MxGcYUjUoSMtFs9uaX0CbYD33xQfWADFk2juD20DMN9v4A2xdrHY0QHsnLoOfOxDa8dm8f2of6M/mzjdz/fhZ782UYUzQOSchEs9njXGF5AHz8wWjWOiTP0aY/xF8P6z6A/T9pHY0QHiss0JepN3XlyeHdyDlRxvA3f+Z/vttBSaVMGRBXRxIy0SwURWFv/hkrLAOjQafTOizP0iUV2g6A1f+A3HVaRyOER0uMtTDrnt78tl9bPsk8xI2vreSbjbIaU1w5SchEsygoqaKkqpa2llMT+qVCf+PT6aDHXRDWVZ3kL0mZEE3K26Anra86jNkpIoA/L9zMPe+sYdtRm9ahCTckCZloFvUrLNsGOKDECqY2GkfkofR66DMaQjvD8pfg8K9aRySExwsL9OWxYV3464juHC+p4va3fuGpr7ZQKHtjissgCZloFnvy1RWWEVUH1QPmWE3j8WgGL0i8HyK6qT1le9K1jkiIVqFXGzMz7+7N2OQ4/rPlGDe8uoL3Vx2gula2OBMXJwmZaBbbj9loF+KHvmgfePtBQIjWIXk2vQF6j4bYQbDmLcj+SPa9FKIZGPQ6hveK4vX7EknuGMrM/+7k5n+s5Mcd+TK/TFyQJGSiWWw7aqN9aIBaL8vcFnTyV6/J6fXQ/Q7odhts/Rp+fE4q+gvRTExGb/5wbTwz7+5NkNGbBz9Zz+8+zGKX1a51aKKFkm9F0eQqa+rYX1BGfFgAHN8DprZah9R66HQQdy0MGK/e+28nQd4mraMSotVoF+LP9Fu6Me3mruQcL2PEmz8z/eutMr9MnEMSMtHkdllLqFMU4gKqoeKk2kMmmldYZxg8GfxC4Pu/wuo3oVoKWgrRHHQ6Hf3bB/PKPb353TXt+c/mY1w/6yfeWbGfypo6rcMTLYQkZKLJbTtqQ6+D2NrD6gGZ0K8NowkG/gF63gU5q+CribDzP7IHphDNxMug55Ze0bx+Xx+Gdg7ntR92M2z2SpZuzZP5ZUISMtH0th+zERvij8/JPeBnAWOQ1iG1Xjq9OtF/yJ8hrAtkvQuL/6SuxKyr1jo6IVqFIKM3DwyOY9Y9vYk0+fL/Pt3Afe9mSv2yVk4SMtHkth61ExcaAMf3Sv2xlsJogoR71GFM/1BY8zZ8+QfY+C8otWodnRCtQozFj7+kdmP6Ld2w2iqlflkrp1Okn7RFs9vtmM1mbDYbJpNJ63AuW02dgx4z0rl/UCzDNz8K8ddBhxu0DkucrfQ4HPoF8jZDbTVE9YL4odBuMPgFax2dEB6vzqHw4858vszORQdMvbkLv7+mPV4G6TdxV5f7/e3VDDGJVmxvfik1dQpxxjKoqZAJ/S1VYLg6t6zrCLBuVR+/zoPMdyC0I7QdCDGJ6rZMBm+toxXC4xj0OlJ7RjG4YyhfrD/C3/6zg8/XHuF/7urFgDip29gatIjUe+7cucTFxWE0GklKSmLt2rUXbL9o0SK6deuG0WgkISGBpUuXupxXFIUZM2YQHR2Nn58fKSkp7N2716VNUVERY8aMwWQyYbFYmDBhAqWlpS5ttmzZwtChQzEajcTGxjJr1iyX8++//z5Dhw4lODiY4OBgUlJSzol93Lhx6HQ6l8fw4cMv9xa5rW3HbOiA9lX71PlLMqG/ZfPyVTcoHzAebnwaEn4LPgGw89/w3yfhs5HqKs3Nn4F1C9TJ0IoQjSnI6M2EIR14Ka0XdYrCb+dl8sSXmykulzmenk7zhGzhwoVMnTqV5557jg0bNtCnTx9SU1MpKChosP2aNWsYPXo0EyZMYOPGjaSlpZGWlsa2bducbWbNmsWcOXOYN28eWVlZBAQEkJqaSmVlpbPNmDFj2L59O8uWLWPJkiWsWrWKiRMnOs/b7XZuvvlm2rdvT3Z2Nq+++irPP/887733nrPNihUrGD16ND/99BOZmZnExsZy8803c/ToUZeYhw8fTl5envPx2WefNdbta/G2H7URY/HD78R2df6Yl6/WIYlL5RMAbfpBn1Fww18heRJ0/A3UVsG2ryB9Onx6H3w3Vd0JIHcdVJdpHbUQHqFDeCAv3N6TCUPi+W5LHsNmr+TbzcdkNaYH03wOWVJSEgMHDuTtt98GwOFwEBsby+TJk3nqqafOaT9y5EjKyspYsmSJ89g111xDYmIi8+bNQ1EUYmJiePzxx5k2bRoANpuNyMhI5s+fz6hRo9i5cyc9evRg3bp1DBgwAID09HRGjBhBbm4uMTExvPPOO/z1r3/FarXi4+MDwFNPPcXixYvZtWtXg5+lrq6O4OBg3n77bcaOHQuoPWTFxcUsXrz4iu6Pu88hu3PuLwT6eDGp8EWI7Aldb9E6JNEYHA4ozYfig1B0CIpzoNKu9oKGdYLovhDTFyK6g15mRghxNU6WV/PxmoNk5RSR0j2Cv9+dQESQUeuwxEVc7ve3pj1k1dXVZGdnk5KS4jym1+tJSUkhMzOzwddkZma6tAdITU11ts/JycFqtbq0MZvNJCUlOdtkZmZisVicyRhASkoKer2erKwsZ5vrrrvOmYzVX2f37t2cPHmywdjKy8upqakhJMR1vH/FihVERETQtWtXHn74YU6cOHHee1JVVYXdbnd5uKuK6jq2H7XTJViB8hNgidM6JNFY9HowRUO7ZEgcBdc/BUMfhx53gsEPdi2B9Kfg89GwYibs/wmqSy/+vkKIcwT7+zAlpQtTU7qw7uBJbnp9Fd9uPqZ1WKKRafqra2FhIXV1dURGRrocj4yMPG8vlNVqbbC91Wp1nq8/dqE2ERERLue9vLwICQlxaRMfH3/Oe9SfCw4+d+XZk08+SUxMjEsyOHz4cO6++27i4+PZv38/Tz/9NLfccguZmZkYDIZz3mPmzJm88MILDX52d7M5t5hah0JX3akh3OD22gYkmo5OBwFh6iN2kLqRuf0YHN+tPg7+ovaURfeG+OvVRM4nQOuohXArA+ND6BodxPzVB3n0s41k7MznxbRemIyy0MYTyFhCI3n55Zf5/PPPWbFiBUbj6a7kUaNGOf+ckJBA79696dixIytWrGDYsGHnvM/06dOZOnWq82e73U5srHtOhM8+dBJ/HwOxpVsgKBp8/LUOSTQXnV5dUWtuC52GQYUNCrZD/jb45R9geBvaXQOdUtShTd25v5wIIc5lMnoz+Ted6Nc+mH+uzmHdwSLm3t+Pvu2kPI2703TIMiwsDIPBQH5+vsvx/Px8oqKiGnxNVFTUBdvXP1+szdmLBmpraykqKnJp09B7nHmNeq+99hovv/wyP/zwA717977gZ+7QoQNhYWHs27evwfO+vr6YTCaXh7tam1NE54hA9AXbpHestfMzQ/vBMGgiXP+kmogd3wPLnoNF42HTv6DsuNZRCuEWdDodQzqF8fLdCQT4evHbeZm8v+qATPh3c5omZD4+PvTv35+MjAznMYfDQUZGBsnJyQ2+Jjk52aU9wLJly5zt4+PjiYqKcmljt9vJyspytklOTqa4uJjs7Gxnm+XLl+NwOEhKSnK2WbVqFTU1NS7X6dq1q8tw5axZs3jxxRdJT093mZN2Prm5uZw4cYLo6OiLtnVnDofChsMn6RLipQ5dBcdf/EWidfCzqAWCr30Mkh9R65xt+1rdKWD5S5C3CZAvFiEuJjzIyIzbenBLryj+Z+lOHv5XNiWVsjetu9K87MXUqVN5//33+fjjj9m5cycPP/wwZWVljB8/HoCxY8cyffp0Z/vHHnuM9PR0Zs+eza5du3j++edZv349kyZNAtTfHKZMmcJLL73Et99+y9atWxk7diwxMTGkpaUB0L17d4YPH85DDz3E2rVrWb16NZMmTWLUqFHExMQAcP/99+Pj48OECRPYvn07Cxcu5M0333QZTnzllVd49tln+eijj4iLi8NqtWK1Wp31zEpLS/nLX/7Cr7/+ysGDB8nIyODOO++kU6dOpKamNsft1czeglJKKmvp6n2qlzE4TtN4RAuk06lDmj3vghuegu53QNEBtc7ZvyfBvmWyv6YQF+Gl1zMmqT2P39SFlXsKuXPuavYVyAIad6R5QjZy5Ehee+01ZsyYQWJiIps2bSI9Pd05gf7w4cPk5eU52w8ePJgFCxbw3nvv0adPH7788ksWL15Mr169nG2eeOIJJk+ezMSJExk4cCClpaWkp6e7zO369NNP6datG8OGDWPEiBEMGTLEpcaY2Wzmhx9+ICcnh/79+/P4448zY8YMl1pl77zzDtXV1fz2t78lOjra+XjttdcAMBgMbNmyhTvuuIMuXbowYcIE+vfvz88//4yvr2fX41p3sAi9DjqWb1KrwBvdd+hVNAMvI7RLUnvNBj4IXn7wyxtqr9nWL6C6ROsIhWjRBsSF8D9pvaiudXDX3NWs2N1wLU/Rcmleh0xcmLvWIfvzwk1syS3mpZrX1O12ut+mdUjC3dTvr3lsI+gN6rZOPe8CP9lGRojzKa+uZe5P+9h0pJhnbu3BH4bIdBGtuFUdMuG51uYU0cUClBVCWBetwxHuqH5/zev+ArHXwO6lao9Z5ttQatU6OiFaJH8fLx6/qSu39Irmb0t28Py326lzSL+LO5CyF6LRHTpRxtHiCkZGFqkbUYfIb2jiKvgGQZdUtX7ZkV8h52fY8z10vAES7pP9UYU4i16v43fXtCfSZGT+mhyOnqxgzui++PlIeZmWTHrIRKNbuec4Br2OnmW/QkgHNSkT4mp5G6HDDXD9X9Thy9xs+OZhWPkyFOVoHZ0QLc5NPSKZdnNXft53nDEf/CoblLdwkpCJRrdi93G6RfjjX7hFhitF4zP4QNy1MHSaulWTdTt8OwkyXlB3BRBCOPVtF8xfR/Rgb0Epv52XSZ6tQuuQxHlIQiYaVVVtHWv2F9LbVAZ1NZKQiaZj8FJXZg6dCgn3wsmD8N1U+P5pqWUmxBk6RQTy/O09sZVXc8//riGnsEzrkEQDJCETjWpdzkkqaxz0cew4vbehEE1Jb4A2/WDwY5B4v1rx//u/wpI/q3toKnVaRyiE5mIsfjx3e090Oh2/fWcNO47ZtQ5JnEUSMtGoVu4pINjfm3b5y9VyF0I0F70eohIgeRL0Hw8OB6yYCV89BDv/AzUyVCNat9BAX2bc3gOzvzej3stk4+GTWockziAJmWhUK3Yfp3eogq7ypPrlKERz0+kgvAsMelDdmikwEta+B4segHUfgP2o1hEKoRmT0Zu/juhOtNmPMR9k8euBE1qHJE6RhEw0mmPFFewtKCWR3eAXDJZ2WockWjtzW0gcrdYya9Mf9n4PX0+EH55VhzNlaybRCvn7ePHULd3oGB7IuI/W8vPe41qHJJCETDSipVvz8NbrSDj5g9o7ptNpHZIQKj8LdL0Frn9KXQBQXqgOZ34xFrLmwfGdyCIA0ZoYvQ1Mu7kr3aNNTJi/nuW78rUOqdWTwrCi0Xy76RiJ4RBQVCjDlaJlMnirCwDa9IPSAjiaDTkr1TlmgeHQbjC0S4aI7qCXfx6FZ/Px0vPnm7rw1vK9TPwkm7fv78fwXlFah9Vqyb84olHkFJax5aiNx9ruBf9QMLXROiQhLiwwQu0165yqlszI3woHfoId/wYff4jqA9F9IKoXWNqDTgYUhOfxNuh5dFhn/ven/Tzy6QbeGJXI7X1itA6rVZKETDSKbzcdw89bT7+i/0JsXxmuFO5Dr4fQDuqj++1gPwbH90DRXli3Fhx1aoIW2hnCOkNwnJqgmWLAy6h19EJcNS+9nkdu7ISXYT+Pfb6R6loH9/Rvq3VYrY4kZOKqKYrC4k1HGRBahc8Jmzp5Wgh3pNOrCwHMbYHfqJP+i4+oD9sR2JcBFWeUCvAPVVdxBkZCQAj4hagLWnyDwNcEvoHgEwjefqCTfQRFy2XQ6/jTdR3xNuh5fNFmqmod3J8kC7OakyRk4qptP2Ynp7CMkSFr1Mr8UgxWeAqDD4R2VB/1airU+WflJ6CsECpPQnEOWLdAlV3doaIhXkY1MfP2Ay+/0392PvzBOwB8/NQkzifg1CNITeyMJjD4Ns/nFq2SXq9jwpB4vPQ6nv5mKxU1dUwYEq91WK2GJGTiqn2ZnYvZV0cv+8/Q7/dahyNE0/L2g+D26uNsiqL2qtVUQHWZ+lxbAbVVUFMJdVXq+dpK9bm6FMqL1D/XVanHayoukNT5qomZr1ntiTOawc8MRov685kP3yBApg6Iy6PX6Rg3OA5fLz0vLtlBaWUtjw7rhE6moTQ5ScjEVbFV1LBw3RFuMR/BUG2BcKnOL1oxnU5Nmrx81VIbV8pRpyZn1RVQWw415eqfa8rVRK+6VH0uK1Cfq0rUpO9Mei81efMLPj2U6mdR/2w0n/7ZaJbkTbjQ6XSMHtQOfx8v/vHjHkoqa3h6RHf0evk70pQkIRNX5fO1h6mtc3BTyTfQKUmdIC2EuDp6w+khy0tVW60mZtWlUFUK1Xb1uf5YifVUIld6bg+c3qDOeTOaTz0sas+br1lN6uqP1ffI+QQiCZxn0+l0pPVtg5+PgQ9+yeFkeTUv39Mbb4P8G99UJCETV6ymzsE/Vx9ksKUYS3k5tB2odUhCtF5ePuAVCgGhF26nKGrvW1UZVJe49rjVP9ty4fhuqClTf1Ycru+h91J71fwsp4ZOLa7Dpf71vXIh6gpVSd7cVmrPKAJ9vXhn5X5Oltfw9v198feR1KEpyF0VV+y/26xY7ZVM8f0a4gZf3m/zQght6HSnFxJwCQtwFMfpOXFnJ29Vp/5cdED9ubJEnQt3Ji8j+IeoK1L9w9TVqP7hauLoHwoB4WpCJ3XeWqxrO4UR6OvFGxl7GPXur3w4biDhQbLApLHpFEWR/UJaMLvdjtlsxmazYTKZtA7Hqc6hcOucn/EqPcZfHe/C0L+oq8OEEK1bbZU6TFr/qLSderarw6iVdvWYo/b0a/ReatIWEH7qEaY++4dBYBgERMg8txYgp7CMV7/fhb+PF/PHD6RzZJDWIbVol/v9LT1k4op8mX2EXdYS/ubzDXQaKsmYEEJVv6jhQuVvFEXtWau0qQlaVTFU2KDKBsWHIW+L+mdH3enXGHxOJ2r1D/9Qdcsr/1D1nMxta1LxYQG8cEcvXv1+F2lzV/PW/X35TbdIrcPyGNJD1sK1xB6yksoabnhtBd11h3nE8RlcN039B1gIIRqL4lDnuVUWn0rcbGf8+VQSV1niOr+tPmnzDzs1JBoCfmGnhkxPPfxC1HbiilVU1/G/K/aRfegk01K78vD1HWUFZgOkh0w0ubk/7ae0vIpR+oXQ93ZJxoQQjU+nB2OQ+iC24TYOh1qMt+rUMGiF7fSfT+ac6mlroFivT+DpeW1nL0KoLxPiH6wW6xXn8PMx8OebuvBldi6vfr+b7EMn+cd9iZj9vbUOza1JQiYuy7ajNj785QB3GLIIjWoHkQlahySEaK30+lMrPS3nb6Mo6qKEKrvao1ZtP2NOW4m6ICGvpOHEzbkgIQT8Qs9YnBB6uifOP1QtG9LK6HU67hsQS6eIQN5ZsZ9b5qzijZF9GRQfonVobkuGLFu4ljRkWVJZo07kL7PynGE+PkMmnZpoK4QQbk5RTi9IqC5Rk7eqU8lblf10bbcKm7qzQj3dqaQwIAICI07tbRoBQVHqIyBCXbTgwY6XVPG/K/ax21rCn27oyJSUzvh6tb4k9WwyZCmahKIoPPnVFgptpfxd9y98EtMkGRNCeA6dDryN6oPw87err+NWP5fNOa+tGGxHwbpN/XP93DadXk3QTDFgaqM+6jewDwjDExYhhAf58uytPfh2yzHeW3WA77dbefnu3tJbdpmkh6yFawk9ZIqiMPuHPbz90z6mGL4kqWcnaD9Yk1iEEKLFc9SpSVp5kboJvcuj8PTqUW8/MMee2hs1DiztIDhencfmpo4UlfP+LwfYm1/K3X3b8MTwbkSZjVqHpYnL/f6WhKyF0zohUxSF137Yzdyf9nO//kdu76CD7rc3exxCCOERHA6oOKnuQ1pWAKX1j/zTc9iMJgjuACHxENIRQuLUxM1Nhj4disLyXQUsWn+E6joHDw3twIQh8Vj8W9fqVknIPIyWCVllTR0vLdnOv7KOMEa/jNs6+UCXVKmoLYQQjc3hgIoiKMmH0jx179ESq9qrBmDwVnvQQjtBSAcI7QiWuFM7LrRM5dW1LN54lB925OOl1/HA4DgeGBxHpKl19JhJQuZhtErI9uaXMPmTTPafqOABww8M69UW2l3TbNcXQggB1FRCyTGw56nPJVYotaoJHDowRasJWki82qsWHKcWy21Bc9OKy6v5bmseGTsLqK5zMKJXFCMHtiO5YygGD65fdrnf3y2iq2Pu3LnExcVhNBpJSkpi7dq1F2y/aNEiunXrhtFoJCEhgaVLl7qcVxSFGTNmEB0djZ+fHykpKezdu9elTVFREWPGjMFkMmGxWJgwYQKlpaUubbZs2cLQoUMxGo3ExsYya9asJomlJbHaKnn6szUMf2MFpSeO8aLp3wwbnCTJmBBCaMHbqCZccddCwr0weDIMewGSJ0Ovu9VeMvtR2PolZLwAX46HBSPhu2mQ+RbsWAxHs9Uh0bM3iW8mFn8fxiS15+37+/K7pHZkHzrJ7z7MYvDLGbzwn+2s2V9IbZ02sbUkmveQLVy4kLFjxzJv3jySkpJ44403WLRoEbt37yYiIuKc9mvWrOG6665j5syZ3HbbbSxYsIBXXnmFDRs20KtXLwBeeeUVZs6cyccff0x8fDzPPvssW7duZceOHRiNalfpLbfcQl5eHu+++y41NTWMHz+egQMHsmDBAkDNbLt06UJKSgrTp09n69at/OEPf+CNN95g4sSJjRrLhTRHD1llTR2rd+by5S+b+fEIGJUq7vBZx80d/fGJvxYM7jFvQQghWi1FURcS1Peglearc9PKjp+em2bwVktxmNpAYNTpEh31W1IZTTRHz5qiKOw/XsrPewvJPnSSE2XVBPgYGBgfQnKHUPrEWujVxkygr3t/97jdkGVSUhIDBw7k7bffBsDhcBAbG8vkyZN56qmnzmk/cuRIysrKWLJkifPYNddcQ2JiIvPmzUNRFGJiYnj88ceZNm0aADabjcjISObPn8+oUaPYuXMnPXr0YN26dQwYMACA9PR0RowYQW5uLjExMbzzzjv89a9/xWq14uOjTkR86qmnWLx4Mbt27Wq0WC6mKROyNTsO8tZ/1pBdHEC14kV7nZWh/oe5Md4P/3Z9ZXsRIYRwd/WLCMoLoaxQfa4ogopi9fiZxXD1XqcK7YaAnxl8LepOCb5B6u4GPoHgE6A+vP3Uwrlep0qFGHy4kmROURQOFJax9aiNHcfs7MkvoarWgQ5oG+xH58ggOoQFEBviTxuLH5EmIxEmX4L9ffDxahGDfOflVnXIqquryc7OZvr06c5jer2elJQUMjMzG3xNZmYmU6dOdTmWmprK4sWLAcjJycFqtZKSkuI8bzabSUpKIjMzk1GjRpGZmYnFYnEmYwApKSno9XqysrK46667yMzM5LrrrnMmY/XXeeWVVzh58iTBwcGNEsvZqqqqqKqqcv5ss9kA9T9sY/tsWSarrd4EUUSi1zFiAvWUGiz85zBweEejX08IIYSWAk492qs/+gC11WpdtdpKqK6G0vrvHwU4eerRPDoC8ejJU0I5qERxyBrCIeuJRr+ODgd6FHSADoUBvkd4aeQ1RHZMbNTr1H9vX2q/l6YJWWFhIXV1dURGuu4WHxkZ6eyFOpvVam2wvdVqdZ6vP3ahNmcPh3p5eRESEuLSJj4+/pz3qD8XHBzcKLGcbebMmbzwwgvnHI+NPc9ebo1E0i8hhBCt0QHgi1ea7v1LSkowm80XbefeA7QeaPr06S69bg6Hg6KiIkJDQ9HpPHc1ytWw2+3ExsZy5MgRzbeXckdy/66e3MOrJ/fw6sj9u3qNfQ8VRaGkpISYmJhLaq9pQhYWFobBYCA/P9/leH5+PlFRUQ2+Jioq6oLt65/z8/OJjo52aZOYmOhsU1BQ4PIetbW1FBUVubxPQ9c58xqNEcvZfH198fX1dTlmsVgabCtcmUwm+YfoKsj9u3pyD6+e3MOrI/fv6jXmPbyUnrF6ms6I8/HxoX///mRkZDiPORwOMjIySE5ObvA1ycnJLu0Bli1b5mwfHx9PVFSUSxu73U5WVpazTXJyMsXFxWRnZzvbLF++HIfDQVJSkrPNqlWrqKmpcblO165dCQ4ObrRYhBBCCCFQNPb5558rvr6+yvz585UdO3YoEydOVCwWi2K1WhVFUZTf//73ylNPPeVsv3r1asXLy0t57bXXlJ07dyrPPfec4u3trWzdutXZ5uWXX1YsFovy73//W9myZYty5513KvHx8UpFRYWzzfDhw5W+ffsqWVlZyi+//KJ07txZGT16tPN8cXGxEhkZqfz+979Xtm3bpnz++eeKv7+/8u677zZ6LOLq2Gw2BVBsNpvWobgluX9XT+7h1ZN7eHXk/l09re+h5gmZoijKW2+9pbRr107x8fFRBg0apPz666/Oc9dff73ywAMPuLT/4osvlC5duig+Pj5Kz549le+++87lvMPhUJ599lklMjJS8fX1VYYNG6bs3r3bpc2JEyeU0aNHK4GBgYrJZFLGjx+vlJSUuLTZvHmzMmTIEMXX11dp06aN8vLLL58Te2PEIq5OZWWl8txzzymVlZVah+KW5P5dPbmHV0/u4dWR+3f1tL6HmtchE0IIIYRo7Vp2VTUhhBBCiFZAEjIhhBBCCI1JQiaEEEIIoTFJyIQQQgghNCYJmXBrc+fOJS4uDqPRSFJSEmvXrtU6JE3MnDmTgQMHEhQUREREBGlpaezevdulTWVlJY888gihoaEEBgZyzz33nFPY+PDhw9x66634+/sTERHBX/7yF2pra13arFixgn79+uHr60unTp2YP39+U3+8Zvfyyy+j0+mYMmWK85jcv4s7evQov/vd7wgNDcXPz4+EhATWr1/vPK8oCjNmzCA6Oho/Pz9SUlLYu3evy3sUFRUxZswYTCYTFouFCRMmUFpa6tJmy5YtDB06FKPRSGxsLLNmzWqWz9fU6urqePbZZ4mPj8fPz4+OHTvy4osvuuyFKPfwtFWrVnH77bcTExODTqdz7iNdrznv1aJFi+jWrRtGo5GEhASWLl16+R9Ik7WdQjSCzz//XPHx8VE++ugjZfv27cpDDz2kWCwWJT8/X+vQml1qaqryz3/+U9m2bZuyadMmZcSIEUq7du2U0tJSZ5s//elPSmxsrJKRkaGsX79eueaaa5TBgwc7z9fW1iq9evVSUlJSlI0bNypLly5VwsLClOnTpzvbHDhwQPH391emTp2q7NixQ3nrrbcUg8GgpKenN+vnbUpr165V4uLilN69eyuPPfaY87jcvwsrKipS2rdvr4wbN07JyspSDhw4oHz//ffKvn37nG1efvllxWw2K4sXL1Y2b96s3HHHHQ3WiOzTp4/y66+/Kj///LPSqVMnlxqRNptNiYyMVMaMGaNs27ZN+eyzzxQ/Pz+XGpHu6n/+53+U0NBQZcmSJUpOTo6yaNEiJTAwUHnzzTedbeQenrZ06VLlr3/9q/L1118rgPLNN9+4nG+ue7V69WrFYDAos2bNUnbs2KE888wz59QkvRSSkAm3NWjQIOWRRx5x/lxXV6fExMQoM2fO1DCqlqGgoEABlJUrVyqKohY69vb2VhYtWuRss3PnTgVQMjMzFUVR/3HT6/XOosyKoijvvPOOYjKZlKqqKkVRFOWJJ55Qevbs6XKtkSNHKqmpqU39kZpFSUmJ0rlzZ2XZsmXK9ddf70zI5P5d3JNPPqkMGTLkvOcdDocSFRWlvPrqq85jxcXFiq+vr/LZZ58piqIoO3bsUABl3bp1zjb//e9/FZ1Opxw9elRRFEX53//9XyU4ONh5T+uv3bVr18b+SM3u1ltvVf7whz+4HLv77ruVMWPGKIoi9/BCzk7ImvNe3Xfffcqtt97qEk9SUpLyxz/+8bI+gwxZCrdUXV1NdnY2KSkpzmN6vZ6UlBQyMzM1jKxlsNlsAISEhACQnZ1NTU2Ny/3q1q0b7dq1c96vzMxMEhISiIyMdLZJTU3Fbrezfft2Z5sz36O+jafc80ceeYRbb731nM8o9+/ivv32WwYMGMC9995LREQEffv25f3333eez8nJwWq1unx+s9lMUlKSyz20WCwMGDDA2SYlJQW9Xk9WVpazzXXXXYePj4+zTWpqKrt37+bkyZNN/TGb1ODBg8nIyGDPnj0AbN68mV9++YVbbrkFkHt4OZrzXjXW/9eSkAm3VFhYSF1dncuXH0BkZCRWq1WjqFoGh8PBlClTuPbaa+nVqxcAVqsVHx+fczaqP/N+Wa3WBu9n/bkLtbHb7VRUVDTFx2k2n3/+ORs2bGDmzJnnnJP7d3EHDhzgnXfeoXPnznz//fc8/PDDPProo3z88cfA6Xtwof9nrVYrERERLue9vLwICQm5rPvsrp566ilGjRpFt27d8Pb2pm/fvkyZMoUxY8YAcg8vR3Peq/O1udx76XVZrYUQLd4jjzzCtm3b+OWXX7QOxW0cOXKExx57jGXLlmE0GrUOxy05HA4GDBjA3//+dwD69u3Ltm3bmDdvHg888IDG0bmHL774gk8//ZQFCxbQs2dPNm3axJQpU4iJiZF72ApID5lwS2FhYRgMhnNWueXn5xMVFaVRVNqbNGkSS5Ys4aeffqJt27bO41FRUVRXV1NcXOzS/sz7FRUV1eD9rD93oTYmkwk/P7/G/jjNJjs7m4KCAvr164eXlxdeXl6sXLmSOXPm4OXlRWRkpNy/i4iOjqZHjx4ux7p3787hw4eB0/fgQv/PRkVFUVBQ4HK+traWoqKiy7rP7uovf/mLs5csISGB3//+9/z5z3929trKPbx0zXmvztfmcu+lJGTCLfn4+NC/f38yMjKcxxwOBxkZGSQnJ2sYmTYURWHSpEl88803LF++nPj4eJfz/fv3x9vb2+V+7d69m8OHDzvvV3JyMlu3bv3/7dx/TNT1Hwfw5wfo7jjhDhfCgQYInqDmAnfKTlq6lFS2dDhna86wQpeopxNxtfJHy0tzSKkzf6GQeP7+UbqKhElzsvmzu9DJyEx06q2WiYdhwuT1/eM7P/kpVK6CK3w+tvfG+/15fT7v9+fNuHvtzefz1nxAVVRUwGQyqV+0drtdc417Mf/1OR8xYgTOnDkDj8ejFpvNhkmTJqk/c/4eLiMj409brXz33XeIj48HAPTu3RsWi0Vz/z6fD8ePH9fMYUNDA06fPq3GHD58GK2trUhPT1djjhw5gpaWFjWmoqICycnJ6N69e4fdX2doampCUJD2azk4OBitra0AOIf+6My5+sf+rv16BYDoX2THjh2i1+ultLRUzp07J9OmTZOIiAjNW26Pi+nTp4vZbJavv/5avF6vWpqamtSYN954Q+Li4uTw4cNy6tQpsdvtYrfb1eP3tm144YUXxOPxSHl5ufTo0aPNbRsKCgqktrZW1qxZ02W2bfij+9+yFOH8PcqJEyckJCREnE6nnD9/XlwulxiNRtm6dasas2zZMomIiJDPPvtMampqZNy4cW1uQ5CWlibHjx+Xo0ePitVq1WxD0NDQINHR0TJ58mQ5e/as7NixQ4xG439uy4a25OTkSM+ePdVtL/bt2yeRkZEyf/58NYZz+LvGxkZxu93idrsFgBQVFYnb7ZZLly6JSOfNVXV1tYSEhEhhYaHU1tbKokWLuO0FPX5Wr14tcXFxotPpZMiQIXLs2LFADykgALRZSkpK1Jjbt29LXl6edO/eXYxGo2RnZ4vX69Vcp76+XsaMGSOhoaESGRkp+fn50tLSoompqqqS1NRU0el0kpiYqOmjK/ljQsb5e7SDBw/K008/LXq9XlJSUmTDhg2a462trbJgwQKJjo4WvV4vI0aMkLq6Ok3M9evX5eWXX5awsDAxmUzy6quvSmNjoybm22+/lWeffVb0er307NlTli1b1uH31hl8Pp/Mnj1b4uLixGAwSGJiorz99tuaLRc4h7+rqqpq83MvJydHRDp3rnbt2iV9+/YVnU4nAwYMkM8//9zv+1FE7tsCmIiIiIg6HZ8hIyIiIgowJmREREREAcaEjIiIiCjAmJARERERBRgTMiIiIqIAY0JGREREFGBMyIiIiIgCjAkZERERUYAxISMi+psURcGnn37q1zmlpaWIiIjokPEQ0X8PEzIi6jIURXloWbx48QPPra+vh6Io8Hg8/8hYqqqqkJWVhSeffBJGoxH9+/dHfn4+rl69+o9cn4i6FiZkRNRleL1etXz00UcwmUyatnnz5nXKONavX4+RI0fCYrFg7969OHfuHNatW4ebN29ixYoVHdp3S0tLh16fiDoGEzIi6jIsFotazGYzFEVR61FRUSgqKkKvXr2g1+uRmpqK8vJy9dzevXsDANLS0qAoCoYPHw4AOHnyJDIzMxEZGQmz2Yxhw4bhm2++eeAYrly5AofDAYfDgc2bN2P48OFISEjAc889h+LiYixcuFAT/9VXX6Ffv34ICwvD6NGj4fV61WPt6VtRFKxduxZjx45Ft27d4HQ6AQBLlixBVFQUwsPDkZubizfffBOpqamac4uLi9GvXz8YDAakpKTg448/Vo81Nzdj5syZiImJgcFgQHx8PJYuXdr+XwYR+YUJGRE9FlauXIkVK1agsLAQNTU1GDVqFMaOHYvz588DAE6cOAEAqKyshNfrxb59+wAAjY2NyMnJwdGjR3Hs2DFYrVZkZWWhsbGxzX52796N5uZmzJ8/v83j9z831tTUhMLCQpSVleHIkSO4fPmyZhWvvX0vXrwY2dnZOHPmDF577TW4XC44nU588MEHOH36NOLi4rB27VrNOS6XCwsXLoTT6URtbS3ef/99LFiwAJ988gkAYNWqVThw4AB27dqFuro6uFwuJCQktH/Cicg/QkTUBZWUlIjZbFbrsbGx4nQ6NTGDBw+WvLw8ERG5ePGiABC32/3Q6969e1fCw8Pl4MGDahsA2b9/v4iITJ8+XUwmU7vGB0C+//57tW3NmjUSHR3td99z5szRxKWnp8uMGTM0bRkZGfLMM8+o9aSkJNm2bZsm5r333hO73S4iIrNmzZLnn39eWltbH3kvRPT3cYWMiLo8n8+Ha9euISMjQ9OekZGB2trah577448/YurUqbBarTCbzTCZTLh16xYuX77cZryIQFGUdo3LaDQiKSlJrcfExOCnn37yu2+bzaap19XVYciQIZq2++u//vorLly4gNdffx1hYWFqWbJkCS5cuAAAmDJlCjweD5KTk+FwOHDo0KF23RMR/TUhgR4AEdG/WU5ODq5fv46VK1ciPj4eer0edrsdzc3Nbcb37dsXN2/ehNfrRUxMzEOv/cQTT2jqiqJARPzuu1u3bn7d061btwAAGzduRHp6uuZYcHAwAGDQoEG4ePEivvzyS1RWVmLixIkYOXIk9uzZ41dfRNQ+XCEjoi7PZDIhNjYW1dXVmvbq6mr0798fAKDT6QAAd+/e/VOMw+FAVlYWBgwYAL1ej59//vmBfU2YMAE6nQ7Lly9v83hDQ0O7x+1v3/ckJyfj5MmTmrb769HR0YiNjcUPP/yAPn36aMq9lxuA/8/bSy+9hI0bN2Lnzp3Yu3cvfvnll3aPn4jajytkRPRYKCgowKJFi5CUlITU1FSUlJTA4/HA5XIBAKKiohAaGory8nL06tULBoMBZrMZVqsVZWVlsNls8Pl8KCgoQGho6AP7eeqpp/Dhhx9i5syZ8Pl8eOWVV5CQkIArV65gy5YtCAsLa/fWF/72fc+sWbMwdepU2Gw2DB06FDt37kRNTQ0SExPVmHfffRcOhwNmsxmjR4/GnTt3cOrUKdy4cQNz585FUVERYmJikJaWhqCgIOzevRsWi4Wb2RJ1EK6QEdFjweFwYO7cucjPz8fAgQNRXl6OAwcOwGq1AgBCQkKwatUqrF+/HrGxsRg3bhwAYNOmTbhx4wYGDRqEyZMnw+FwICoq6qF95eXl4dChQ7h69Sqys7ORkpKC3NxcmEwmv/ZC+yt9A8CkSZPw1ltvYd68eeq/HqdMmQKDwaDG5Obmori4GCUlJRg4cCCGDRuG0tJSdYUsPDwcy5cvh81mw+DBg1FfX48vvvgCQUH82iDqCIrc/8ACERF1SZmZmbBYLCgrKwv0UIioDfyXJRFRF9PU1IR169Zh1KhRCA4Oxvbt21FZWYmKiopAD42IHoArZEREXczt27fx4osvwu1247fffkNycjLeeecdjB8/PtBDI6IHYEJGREREFGB8OpOIiIgowJiQEREREQUYEzIiIiKiAGNCRkRERBRgTMiIiIiIAowJGREREVGAMSEjIiIiCjAmZEREREQB9j9J/HAnV5MaowAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -173,19 +246,19 @@
     }
    ],
    "source": [
-    "sns.kdeplot(data=churn_data, x=\"TotalCharges\", hue=\"Churn\", fill=\"Churn\")"
+    "sns.kdeplot(data=churn_data, x=\"MonthlyCharges\", hue=\"Churn\", fill=\"Churn\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 229,
+   "execution_count": 202,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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BmjJlikJDQzV8+HAtXrxYd955p5o3b67XXntNXl5eGjhwoI4dO5bva1CQHJuWlqZOnTpp8+bNeuKJJ/T888/r+++/17PPPpvv+JYMGDBAV65c0QcffGDWfu7cOX3xxRfq0aOH3N3db3h8ACgryEXFl4t69uwpBwcHffzxx6a25cuXKzw8XM2aNcvVPykpSW3atNEXX3yhxx9/XK+88oouX76se++9V2vWrJF0dcZ9+/btc+UvSVq1apUcHR3Vu3dvizF99dVXateunVJTUzVhwgRNnjxZ58+fV6dOnbRr1y5Tv0cffVTz5s1TdHS05s6dq6eeekru7u765Zdfrv9iwn4YgJ2JjY01rv1of/PNN4YkY9myZWb9Nm7caNZ+/vx5w8vLy2jVqpXx77//mvXNzs42/dy+fXtDkrF06VJTW3p6uhEQEGBER0eb2hITE41q1aoZkozw8HDj0UcfNZYvX26cP38+V8whISFGTExMrvb27dsb7du3N93fsmWLIcmoXr26kZqaamr/4IMPDEnGrFmzcsU5bdo0szibNGli+Pn5GRkZGYZhGMbMmTMNScb7779v6peRkWG0bt3a8PT0NO3n2LFjhiTD29vbSE5ONotz9+7dhiRj0aJFuZ4DAKDgvvzyS8PR0dFwdHQ0WrdubTzzzDPGF198YfrOzuHh4ZFn3pgwYYIhyejfv79Z+/Hjxw1HR0fjlVdeMWs/cOCA4eTkZNZ+6dKlXOPGx8cbDg4OxokTJ0xtMTExhiRj8uTJprZ//vnHcHd3NxwcHIyVK1ea2g8dOmRIMiZMmGBqy8lpW7ZsMbUVNMdOmzbNkGSsXbvW1Pbvv/8a4eHhuca8VoMGDczy6rWuXLliBAYGGq1btzZrnz9/viHJ+OKLL/J8HADYG3KR9XNRTEyM4eHhYRiGYfTq1cvo3LmzYRiGkZWVZQQEBBgTJ040HW9NnTrV9LhRo0YZkoxvvvnG1HbhwgUjLCzMCA0NNbKysgzDMIy33nrLkGQcOHDA7DWrX7++0alTJ4vPNzs726hdu7YRFRVldsx76dIlIywszOjSpYupzcfHx4iNjTVQfjEDEXZv9erV8vHxUZcuXXT27FnTLSIiQp6entqyZYskadOmTbpw4YLGjh2ba10IBwcHs/uenp5m6ye5uLioZcuW+v33301t/v7+2r9/vx599FH9888/mj9/vu6//375+fnppZdekmEYN/ycBg4cKC8vL9P9Xr16KTAwUJ9//rlZPycnJ7P/BLq4uGjYsGFKTk7Wnj17JEmff/65AgIC1L9/f1M/Z2dnPfHEE7p48aK2bdtmNmZ0dLSqVat2w7EDACzr0qWLtm/frnvvvVf79+/XlClTFBUVperVq2vdunUFHufRRx81u//xxx8rOztbffr0McuFAQEBql27tikXSjKbZZeWlqazZ8+qTZs2MgxDP/74Y659Pfzww6affX19VbduXXl4eJit71S3bl35+vqa5UlLCpJjN27cqOrVq5udUuzm5maa4XIjHB0d1a9fP23fvl3Hjx83tS9fvlz+/v7q3LnzDY8NAGUJuah4c9H999+vrVu3KjExUV999ZUSExMtnr78+eefq2XLlmangHt6emro0KE6fvy4fv75Z0lXZzY6OTlp1apVpn4HDx7Uzz//rL59+1qMZd++fabTp//++2/Te5KWlqbOnTvr66+/Nq157+vrq507d+rMmTPXfX6wXxQQYfeOHDmilJQU+fn5qVq1ama3ixcvmtY6Onr0qCSpYcOG+Y5500035SoqVqpUKdeaioGBgZo3b57+/PNPHT58WG+88YaqVaum8ePH65133rnh51S7dm2z+w4ODqpVq5bZAY8kBQUF5VqsuE6dOpJk6nvixAnVrl1bFSqYfx3kXLX6xIkTZu1hYWE3HDcAIH8tWrTQxx9/rH/++Ue7du1SXFycLly4oF69epkOFPLz3+/qI0eOyDAM1a5dO1cu/OWXX8zW/Tt58qQGDRqkypUry9PTU9WqVVP79u0lSSkpKWbjurm55fqnko+PT5550sfHJ8+1h/+rIDn2xIkTqlmzZq5+tWrVynf868m5SErOGll//PGHvvnmG/Xr10+Ojo5FGhsAyhJyUfHlorvvvlteXl5atWqVli1bphYtWlh8zIkTJ1S3bt1c7f89Vqtatao6d+5sdhrzqlWr5OTkpJ49e1qM5ciRI5KkmJiYXO/J22+/rfT0dNPrPWXKFB08eFDBwcFq2bKlXnzxxQIVY2E/uAoz7F52drb8/Py0bNmyPLffyGw6SwcRlmYVOjg4qE6dOqpTp466du2q2rVra9myZab/lP036eTIysoqdQcsrP8EACXDxcVFLVq0UIsWLVSnTh0NHjxYq1ev1oQJE/J97H+/q7Ozs+Xg4KANGzbkmVc8PT0lXc07Xbp00blz5/Tss88qPDxcHh4eOn36tAYNGmSahZDDUo4qbJ601mOLKiIiQuHh4VqxYoWee+45rVixQoZhcPVlAOUWuajwj82Pq6urevbsqSVLluj333/Xiy++WOQxJalfv34aPHiw9u3bpyZNmuiDDz5Q586dVbVqVYuPyXktp06dqiZNmuTZJ+d96dOnj26//XatWbNGX375paZOnarXXntNH3/8se666y6rPAeUbhQQYfdq1qypzZs3q23bttctftWsWVPS1aneRZ3BcD0333yzKlWqpD///NPUVqlSJbNFg3OcOHFCN998c672nP8U5TAMQ7/99ptuueUWs/YzZ84oLS3NbBbir7/+KunqlZ+lq4sk//TTT8rOzjabhXjo0CHT9vxYKoACAKyjefPmkmTKHYX93q1Zs6YMw1BYWJhpJnpeDhw4oF9//VVLliwxW6h+06ZNNxB18QkJCdHPP/8swzDMXovffvutyGMPGDBA48aN008//aTly5erdu3aatGiRZHHBYCyjlxkrii56P7779e7776rChUqqF+/ftfdx+HDh3O153Ws1r17dw0bNsx0GvOvv/6quLi468aRcwzs7e2tyMjIfOMODAzU448/rscff1zJyclq1qyZXnnlFQqI5QSnMMPu9enTR1lZWXrppZdybbty5YqpcHfHHXfIy8tL8fHxunz5slm/G/lP086dO5WWlparfdeuXfr777/NpqLXrFlTO3bsUEZGhqnt008/1alTp/Ice+nSpbpw4YLp/ocffqg///wz1xf3lStX9NZbb5nuZ2Rk6K233lK1atUUEREh6eoU+sTERLP1Mq5cuaLZs2fL09PTdKrA9eQUKPMqggIACm7Lli155pycNW5zcoeHh0ehvnN79uwpR0dHTZw4Mdf4hmHo77//lvR/My6u7WMYhmbNmlWo51HcoqKidPr0abO1uC5fvqyFCxcWeeyc2Ybjx4/Xvn37mH0IoNwhFxVMUXJRx44d9dJLL+nNN99UQECAxX533323du3ape3bt5va0tLStGDBAoWGhqp+/fqmdl9fX0VFRemDDz7QypUr5eLiou7du183joiICNWsWVOvv/66Ll68mGv7X3/9JenqrND/njru5+enoKAgpaen5/t8YR+YgQi71759ew0bNkzx8fHat2+f7rjjDjk7O+vIkSNavXq1Zs2apV69esnb21szZszQww8/rBYtWuj+++9XpUqVtH//fl26dElLliwp1H7fe+89LVu2TD169FBERIRcXFz0yy+/6N1335Wbm5uee+45U9+HH35YH374oe6880716dNHR48e1fvvv2/6j9B/Va5cWbfddpsGDx6spKQkzZw5U7Vq1cq1YG9QUJBee+01HT9+XHXq1NGqVau0b98+LViwQM7OzpKkoUOH6q233tKgQYO0Z88ehYaG6sMPP9R3332nmTNnml2sxZKaNWvK19dX8+fPl5eXlzw8PNSqVSvWSwSAQhoxYoQuXbqkHj16KDw8XBkZGfr++++1atUqhYaGavDgwZKu/sG/efNmTZ8+XUFBQQoLC1OrVq0sjluzZk29/PLLiouL0/Hjx9W9e3d5eXnp2LFjWrNmjYYOHaqnnnpK4eHhqlmzpp566imdPn1a3t7e+uijjwq0XlRJGjZsmN588031799fI0eOVGBgoJYtW2a6CFpRZsaHhYWpTZs2+uSTTySJAiKAcodcVDBFyUUVKlTQCy+8kO8+xo4dqxUrVuiuu+7SE088ocqVK2vJkiU6duyYPvroo1zr2Pft21cPPPCA5s6dq6ioKPn6+l53/AoVKujtt9/WXXfdpQYNGmjw4MGqXr26Tp8+rS1btsjb21vr16/XhQsXdNNNN6lXr15q3LixPD09tXnzZu3evVvTpk3L/8WCfSihqz0DJSY2NtbI66O9YMECIyIiwnB3dze8vLyMRo0aGc8884xx5swZs37r1q0z2rRpY7i7uxve3t5Gy5YtjRUrVpi2t2/f3mjQoEGu8WNiYoyQkBDT/Z9++sl4+umnjWbNmhmVK1c2nJycjMDAQKN3797G3r17cz1+2rRpRvXq1Q1XV1ejbdu2xg8//GC0b9/eaN++vanPli1bDEnGihUrjLi4OMPPz89wd3c3unbtapw4ccJsvJw4f/jhB6N169aGm5ubERISYrz55pu59p2UlGQMHjzYqFq1quHi4mI0atTIWLRokVmfY8eOGZKMqVOn5nq8YRjGJ598YtSvX99wcnIyJOV6PAAgfxs2bDAeeughIzw83PD09DRcXFyMWrVqGSNGjDCSkpJM/Q4dOmS0a9fOcHd3NyQZMTExhmEYxoQJEwxJxl9//ZXn+B999JFx2223GR4eHoaHh4cRHh5uxMbGGocPHzb1+fnnn43IyEjD09PTqFq1qvHII48Y+/fvz/XdHhMTY3h4eOTah6U8GRISYnTt2tV0PyenbdmyJd/H/jfHGoZh/P7770bXrl0Nd3d3o1q1asaTTz5pfPTRR4YkY8eOHXk+/wYNGpjlVUvmzJljSDJatmyZb18AsDfkIuvnIktxXsvS8dbRo0eNXr16Gb6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",
       "text/plain": [
-       "<Figure size 1600x1600 with 15 Axes>"
+       "<Figure size 2000x1200 with 15 Axes>"
       ]
      },
      "metadata": {},
@@ -197,23 +270,27 @@
     "\n",
     "cat_columns_plot = [col for col in churn_data.select_dtypes(include=[\"object\",\"int\"]).columns if col not in [\"Churn\",\"tenure\"]]\n",
     "\n",
-    "plt.figure(figsize=(16,16))\n",
+    "plt.figure(figsize=(20,12))\n",
     "for i,col in enumerate(cat_columns_plot):\n",
-    "    plt.subplot(5,3,i+1)\n",
+    "    plt.subplot(4,4,i+1)\n",
     "\n",
-    "    df_plot = pd.crosstab(churn_data[col], churn_data[\"Churn\"].map({0:\"No Churn\", 1:\"Churn\"}), normalize=\"index\").apply(lambda x: np.round(100*x)).reset_index(drop=False)\n",
+    "    df_plot = pd.crosstab(churn_data[col], churn_data[\"Churn\"].map({0:\"No\", 1:\"Yes\"}), normalize=\"index\").apply(lambda x: np.round(100*x)).reset_index(drop=False)\n",
     "    df_plot.rename_axis(None, axis=1, inplace=True)\n",
     "    df_plot = df_plot.melt(id_vars=[col])\n",
+    "    df_plot.rename({\"variable\":\"Churn\"},axis=1, inplace=True)\n",
     "\n",
-    "    sns.barplot(data = df_plot, x=col, y=\"value\", hue=\"variable\")\n",
+    "    sns.barplot(data = df_plot, x=col, y=\"value\", hue=\"Churn\")\n",
     "    plt.xlabel(\"\")\n",
     "    plt.ylabel(\"\")\n",
-    "    plt.title(str(col))"
+    "    #plt.xticks(rotation=10)\n",
+    "    plt.title(str(col))\n",
+    "\n",
+    "plt.tight_layout()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 203,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -228,7 +305,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": 204,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save raw test data for app \n",
+    "X_test_save = X_test.copy()[:1000]\n",
+    "X_test_save.to_pickle(os.path.join(path_savedata,\"churn_test_raw.pkl\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -242,7 +330,7 @@
     "\n",
     "# Build data processing pipeline\n",
     "ct = ColumnTransformer(\n",
-    "    [(\"numerical\", MinMaxScaler(), num_columns), \n",
+    "    [(\"numerical\", RobustScaler(), num_columns), \n",
     "     (\"categorical\", OneHotEncoder(sparse_output=False), cat_columns)], \n",
     "      remainder='passthrough')\n",
     "\n",
@@ -252,15 +340,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
+   "execution_count": 206,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Save preprocessed test data \n",
+    "columns_transform = [col.split(\"__\")[1] for col in ct.get_feature_names_out()]\n",
+    "\n",
+    "X_test_save_pp = X_test_pp.copy()[:1000]\n",
+    "X_test_save_pp = pd.DataFrame(X_test_save_pp, columns=columns_transform)\n",
+    "X_test_save_pp.to_pickle(os.path.join(path_savedata,\"churn_test_pp.pkl\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Train Accuracy 0.8759318423855165\n",
-      "Test Accuracy 0.7757274662881476\n"
+      "Train Accuracy 0.8922666666666667\n",
+      "Test Accuracy 0.7995735607675906\n"
      ]
     }
    ],
@@ -268,7 +370,7 @@
     "from sklearn.ensemble import RandomForestClassifier\n",
     "from sklearn.metrics import accuracy_score, roc_auc_score\n",
     "\n",
-    "rf = RandomForestClassifier(max_depth=12, class_weight=\"balanced_subsample\")\n",
+    "rf = RandomForestClassifier(max_depth=12, class_weight=\"balanced\", min_samples_split=5)\n",
     "rf.fit(X_train_pp, y_train)\n",
     "\n",
     "y_pred_train = rf.predict(X_train_pp)\n",
@@ -280,22 +382,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": 208,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "\n",
+    "path_model = r\"C:\\Users\\LaurèneDAVID\\Documents\\Teaching\\Educational_apps\\app-ai-ds-hec\\pretrained_models\\supervised_learning\"\n",
+    "with open(os.path.join(path_model,'churn_model.pkl'),'wb') as f:\n",
+    "    pickle.dump(rf,f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x255137ceb20>"
+       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x29666b72f10>"
       ]
      },
-     "execution_count": 204,
+     "execution_count": 209,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -307,519 +422,197 @@
    "source": [
     "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
     "\n",
-    "cm = confusion_matrix(y_test, y_pred_test, normalize=\"true\")*100\n",
+    "cm = confusion_matrix(y_train, y_pred_train, normalize=\"true\")*100\n",
     "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=rf.classes_)\n",
     "disp.plot()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
+   "execution_count": 178,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
-       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan])"
-      ]
-     },
-     "execution_count": 205,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "rf.feature_importances_"
+    "df_cm = pd.DataFrame(cm, columns=[\"No Churn\",\"Churn\"])\n",
+    "#df_cm.insert(0,\"Label\",[\"Good\",\"Poor\",\"Standard\"])\n",
+    "df_cm.to_pickle(os.path.join(path_savedata,\"churn_cm_train.pkl\"))"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 179,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "### LightGBM"
+    "columns_transform = [col.split(\"__\")[1] for col in ct.get_feature_names_out()]\n",
+    "\n",
+    "df_features_imp = pd.DataFrame({\"variable\":columns_transform,\n",
+    "                               \"importance\":rf.feature_importances_})\n",
+    "\n",
+    "df_features_imp[\"variable\"] = df_features_imp[\"variable\"].apply(lambda x: x.split(\"_\")[0] if \"_\" in x else x)\n",
+    "df_features_imp = df_features_imp.groupby(\"variable\")[\"importance\"].mean().to_frame().reset_index()\n",
+    "df_features_imp.sort_values(by=\"importance\", ascending=False, inplace=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 206,
+   "execution_count": 180,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[LightGBM] [Warning] feature_fraction is set=0.9, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9\n",
-      "[LightGBM] [Warning] feature_fraction is set=0.9, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9\n",
-      "[LightGBM] [Info] Number of positive: 1495, number of negative: 4139\n",
-      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000387 seconds.\n",
-      "You can set `force_row_wise=true` to remove the overhead.\n",
-      "And if memory is not enough, you can set `force_col_wise=true`.\n",
-      "[LightGBM] [Info] Total Bins 627\n",
-      "[LightGBM] [Info] Number of data points in the train set: 5634, number of used features: 25\n",
-      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.265353 -> initscore=-1.018328\n",
-      "[LightGBM] [Info] Start training from score -1.018328\n"
-     ]
-    },
     {
      "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>LGBMClassifier(feature_fraction=0.9, learning_rate=0.05, num_leaves=30)</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;LGBMClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LGBMClassifier(feature_fraction=0.9, learning_rate=0.05, num_leaves=30)</pre></div> </div></div></div></div>"
+       "<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>variable</th>\n",
+       "      <th>importance</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>tenure</td>\n",
+       "      <td>0.150953</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>TotalCharges</td>\n",
+       "      <td>0.136729</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>MonthlyCharges</td>\n",
+       "      <td>0.135956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Contract</td>\n",
+       "      <td>0.078704</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>InternetService</td>\n",
+       "      <td>0.029338</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>OnlineSecurity</td>\n",
+       "      <td>0.023258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>PaperlessBilling</td>\n",
+       "      <td>0.021521</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>PaymentMethod</td>\n",
+       "      <td>0.017772</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>TechSupport</td>\n",
+       "      <td>0.017222</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Partner</td>\n",
+       "      <td>0.016507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Dependents</td>\n",
+       "      <td>0.014212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>OnlineBackup</td>\n",
+       "      <td>0.014165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>MultipleLines</td>\n",
+       "      <td>0.014120</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>StreamingMovies</td>\n",
+       "      <td>0.013998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>StreamingTV</td>\n",
+       "      <td>0.013701</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>SeniorCitizen</td>\n",
+       "      <td>0.013402</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>DeviceProtection</td>\n",
+       "      <td>0.011463</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>PhoneService</td>\n",
+       "      <td>0.007577</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
       "text/plain": [
-       "LGBMClassifier(feature_fraction=0.9, learning_rate=0.05, num_leaves=30)"
+       "            variable  importance\n",
+       "17            tenure    0.150953\n",
+       "16      TotalCharges    0.136729\n",
+       "4     MonthlyCharges    0.135956\n",
+       "0           Contract    0.078704\n",
+       "3    InternetService    0.029338\n",
+       "7     OnlineSecurity    0.023258\n",
+       "8   PaperlessBilling    0.021521\n",
+       "10     PaymentMethod    0.017772\n",
+       "15       TechSupport    0.017222\n",
+       "9            Partner    0.016507\n",
+       "1         Dependents    0.014212\n",
+       "6       OnlineBackup    0.014165\n",
+       "5      MultipleLines    0.014120\n",
+       "13   StreamingMovies    0.013998\n",
+       "14       StreamingTV    0.013701\n",
+       "12     SeniorCitizen    0.013402\n",
+       "2   DeviceProtection    0.011463\n",
+       "11      PhoneService    0.007577"
       ]
      },
-     "execution_count": 206,
+     "execution_count": 180,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "import lightgbm as lgb\n",
-    "\n",
-    "params = {\n",
-    "'boosting_type': 'gbdt',\n",
-    "'num_leaves': 30,\n",
-    "'learning_rate': 0.05,\n",
-    "'feature_fraction': 0.9\n",
-    "}\n",
-    "\n",
-    "clf = lgb.LGBMClassifier(**params)\n",
-    "clf.fit(X_train_pp, y_train)"
+    "df_features_imp"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[LightGBM] [Warning] feature_fraction is set=0.9, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9\n",
-      "[LightGBM] [Warning] feature_fraction is set=0.9, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=0.9\n",
-      "Train Accuracy 0.8473553425630103\n",
-      "Test Accuracy 0.7913413768630234\n"
-     ]
-    }
-   ],
-   "source": [
-    "y_pred_lgb_train = clf.predict(X_train_pp)\n",
-    "y_pred_lgb_test = clf.predict(X_test_pp)\n",
-    "\n",
-    "print(\"Train Accuracy\", accuracy_score(y_train, y_pred_lgb_train))\n",
-    "print(\"Test Accuracy\", accuracy_score(y_test, y_pred_lgb_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 208,
+   "execution_count": 181,
    "metadata": {},
    "outputs": [
     {
@@ -828,13 +621,13 @@
        "<Axes: xlabel='importance', ylabel='variable'>"
       ]
      },
-     "execution_count": 208,
+     "execution_count": 181,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -844,1075 +637,48 @@
     }
    ],
    "source": [
-    "columns_transform = [col.split(\"__\")[1] for col in ct.get_feature_names_out()]\n",
-    "\n",
-    "df_features_imp = pd.DataFrame({\"variable\":columns_transform,\n",
-    "                               \"importance\":clf.feature_importances_})\n",
-    "\n",
     "sns.barplot(data=df_features_imp, x=\"importance\", y=\"variable\")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### CatBoost"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 182,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.model_selection import train_test_split\n",
-    "\n",
-    "X = churn_data.drop(columns=[\"Churn\"])\n",
-    "y = churn_data[\"Churn\"]\n",
-    "\n",
-    "# Split train/test set\n",
-    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y)\n",
-    "\n",
-    "# Split train/validation set\n",
-    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, stratify=y)"
+    "df_features_imp.to_pickle(os.path.join(path_savedata, \"churn_feature_importance.pkl\"))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 123,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Learning rate set to 0.048569\n",
-      "0:\tlearn: 0.6627881\ttest: 0.6623821\tbest: 0.6623821 (0)\ttotal: 32.5ms\tremaining: 32.5s\n",
-      "1:\tlearn: 0.6372805\ttest: 0.6359148\tbest: 0.6359148 (1)\ttotal: 62.2ms\tremaining: 31.1s\n",
-      "2:\tlearn: 0.6118743\ttest: 0.6094715\tbest: 0.6094715 (2)\ttotal: 97.7ms\tremaining: 32.5s\n",
-      "3:\tlearn: 0.5898587\ttest: 0.5870121\tbest: 0.5870121 (3)\ttotal: 123ms\tremaining: 30.6s\n",
-      "4:\tlearn: 0.5729321\ttest: 0.5694306\tbest: 0.5694306 (4)\ttotal: 141ms\tremaining: 28.1s\n",
-      "5:\tlearn: 0.5563865\ttest: 0.5527201\tbest: 0.5527201 (5)\ttotal: 166ms\tremaining: 27.5s\n",
-      "6:\tlearn: 0.5406930\ttest: 0.5370096\tbest: 0.5370096 (6)\ttotal: 194ms\tremaining: 27.5s\n",
-      "7:\tlearn: 0.5268732\ttest: 0.5229710\tbest: 0.5229710 (7)\ttotal: 223ms\tremaining: 27.7s\n",
-      "8:\tlearn: 0.5149236\ttest: 0.5106099\tbest: 0.5106099 (8)\ttotal: 255ms\tremaining: 28.1s\n",
-      "9:\tlearn: 0.5067475\ttest: 0.5020982\tbest: 0.5020982 (9)\ttotal: 271ms\tremaining: 26.9s\n",
-      "10:\tlearn: 0.4972163\ttest: 0.4926949\tbest: 0.4926949 (10)\ttotal: 301ms\tremaining: 27s\n",
-      "11:\tlearn: 0.4896058\ttest: 0.4852133\tbest: 0.4852133 (11)\ttotal: 327ms\tremaining: 26.9s\n",
-      "12:\tlearn: 0.4825590\ttest: 0.4780927\tbest: 0.4780927 (12)\ttotal: 357ms\tremaining: 27.1s\n",
-      "13:\tlearn: 0.4756378\ttest: 0.4711545\tbest: 0.4711545 (13)\ttotal: 383ms\tremaining: 27s\n",
-      "14:\tlearn: 0.4698411\ttest: 0.4662418\tbest: 0.4662418 (14)\ttotal: 410ms\tremaining: 26.9s\n",
-      "15:\tlearn: 0.4652496\ttest: 0.4619031\tbest: 0.4619031 (15)\ttotal: 438ms\tremaining: 27s\n",
-      "16:\tlearn: 0.4611658\ttest: 0.4577089\tbest: 0.4577089 (16)\ttotal: 465ms\tremaining: 26.9s\n",
-      "17:\tlearn: 0.4574404\ttest: 0.4541869\tbest: 0.4541869 (17)\ttotal: 500ms\tremaining: 27.3s\n",
-      "18:\tlearn: 0.4528654\ttest: 0.4497378\tbest: 0.4497378 (18)\ttotal: 531ms\tremaining: 27.4s\n",
-      "19:\tlearn: 0.4482357\ttest: 0.4459038\tbest: 0.4459038 (19)\ttotal: 564ms\tremaining: 27.6s\n",
-      "20:\tlearn: 0.4447377\ttest: 0.4426946\tbest: 0.4426946 (20)\ttotal: 597ms\tremaining: 27.8s\n",
-      "21:\tlearn: 0.4414096\ttest: 0.4397617\tbest: 0.4397617 (21)\ttotal: 630ms\tremaining: 28s\n",
-      "22:\tlearn: 0.4383806\ttest: 0.4370010\tbest: 0.4370010 (22)\ttotal: 666ms\tremaining: 28.3s\n",
-      "23:\tlearn: 0.4357141\ttest: 0.4346098\tbest: 0.4346098 (23)\ttotal: 710ms\tremaining: 28.9s\n",
-      "24:\tlearn: 0.4340841\ttest: 0.4333834\tbest: 0.4333834 (24)\ttotal: 739ms\tremaining: 28.8s\n",
-      "25:\tlearn: 0.4316206\ttest: 0.4315814\tbest: 0.4315814 (25)\ttotal: 775ms\tremaining: 29s\n",
-      "26:\tlearn: 0.4291486\ttest: 0.4294672\tbest: 0.4294672 (26)\ttotal: 809ms\tremaining: 29.2s\n",
-      "27:\tlearn: 0.4272860\ttest: 0.4279721\tbest: 0.4279721 (27)\ttotal: 845ms\tremaining: 29.3s\n",
-      "28:\tlearn: 0.4255243\ttest: 0.4265458\tbest: 0.4265458 (28)\ttotal: 879ms\tremaining: 29.4s\n",
-      "29:\tlearn: 0.4237533\ttest: 0.4252464\tbest: 0.4252464 (29)\ttotal: 919ms\tremaining: 29.7s\n",
-      "30:\tlearn: 0.4220748\ttest: 0.4242429\tbest: 0.4242429 (30)\ttotal: 954ms\tremaining: 29.8s\n",
-      "31:\tlearn: 0.4208790\ttest: 0.4230531\tbest: 0.4230531 (31)\ttotal: 989ms\tremaining: 29.9s\n",
-      "32:\tlearn: 0.4196280\ttest: 0.4219187\tbest: 0.4219187 (32)\ttotal: 1.02s\tremaining: 30s\n",
-      "33:\tlearn: 0.4188299\ttest: 0.4213500\tbest: 0.4213500 (33)\ttotal: 1.06s\tremaining: 30.1s\n",
-      "34:\tlearn: 0.4175882\ttest: 0.4202557\tbest: 0.4202557 (34)\ttotal: 1.09s\tremaining: 30s\n",
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-      "992:\tlearn: 0.2580069\ttest: 0.4326181\tbest: 0.4098372 (105)\ttotal: 41.2s\tremaining: 291ms\n",
-      "993:\tlearn: 0.2579531\ttest: 0.4326442\tbest: 0.4098372 (105)\ttotal: 41.3s\tremaining: 249ms\n",
-      "994:\tlearn: 0.2577599\ttest: 0.4324447\tbest: 0.4098372 (105)\ttotal: 41.3s\tremaining: 208ms\n",
-      "995:\tlearn: 0.2576217\ttest: 0.4324315\tbest: 0.4098372 (105)\ttotal: 41.3s\tremaining: 166ms\n",
-      "996:\tlearn: 0.2575789\ttest: 0.4324588\tbest: 0.4098372 (105)\ttotal: 41.4s\tremaining: 124ms\n",
-      "997:\tlearn: 0.2574557\ttest: 0.4326492\tbest: 0.4098372 (105)\ttotal: 41.4s\tremaining: 82.9ms\n",
-      "998:\tlearn: 0.2573330\ttest: 0.4326792\tbest: 0.4098372 (105)\ttotal: 41.4s\tremaining: 41.5ms\n",
-      "999:\tlearn: 0.2571166\ttest: 0.4327456\tbest: 0.4098372 (105)\ttotal: 41.5s\tremaining: 0us\n",
-      "\n",
-      "bestTest = 0.4098372131\n",
-      "bestIteration = 105\n",
-      "\n",
-      "Shrink model to first 106 iterations.\n",
-      "CatBoost model parameters:\n",
-      "{}\n"
-     ]
+     "data": {
+      "text/plain": [
+       "array([[-0.85      , -0.37066418, -0.5106383 , ...,  0.        ,\n",
+       "         0.        ,  1.        ],\n",
+       "       [ 0.34537037, -0.13512675, -0.40425532, ...,  1.        ,\n",
+       "         1.        ,  1.        ],\n",
+       "       [-0.00555556,  0.83906518,  0.68085106, ...,  0.        ,\n",
+       "         1.        ,  1.        ],\n",
+       "       ...,\n",
+       "       [-0.83611111, -0.35361638, -0.46808511, ...,  0.        ,\n",
+       "         0.        ,  0.        ],\n",
+       "       [-0.84537037, -0.01647167,  0.4893617 , ...,  0.        ,\n",
+       "         0.        ,  0.        ],\n",
+       "       [-0.26481481, -0.30179342, -0.4893617 , ...,  0.        ,\n",
+       "         0.        ,  0.        ]])"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "from catboost import CatBoostClassifier\n",
-    "\n",
-    "cat_features = [col for col in X_train.select_dtypes(include=[\"object\",\"int\"]) if col != \"tenure\"]\n",
-    "\n",
-    "clf = CatBoostClassifier()\n",
-    "\n",
-    "\n",
-    "clf.fit(X_train, y_train, \n",
-    "        cat_features=cat_features, \n",
-    "        eval_set=(X_val, y_val), \n",
-    "        verbose=True\n",
-    ")\n",
-    "\n",
-    "print('CatBoost model parameters:')\n",
-    "print(clf.get_params())"
+    "X_test_pp"
    ]
   },
   {