diff --git a/lab-dw-aggregating.ipynb b/lab-dw-aggregating.ipynb
index fadd718..0b73cb5 100644
--- a/lab-dw-aggregating.ipynb
+++ b/lab-dw-aggregating.ipynb
@@ -68,6 +68,751 @@
         "4. Find the maximum, minimum, and median customer lifetime value by education level and gender. Write your conclusions."
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": 194,
+      "id": "f99b6c12",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Index(['Unnamed: 0', 'Customer', 'State', 'Customer Lifetime Value',\n",
+              "       'Response', 'Coverage', 'Education', 'Effective To Date',\n",
+              "       'EmploymentStatus', 'Gender', 'Income', 'Location Code',\n",
+              "       'Marital Status', 'Monthly Premium Auto', 'Months Since Last Claim',\n",
+              "       'Months Since Policy Inception', 'Number of Open Complaints',\n",
+              "       'Number of Policies', 'Policy Type', 'Policy', 'Renew Offer Type',\n",
+              "       'Sales Channel', 'Total Claim Amount', 'Vehicle Class', 'Vehicle Size',\n",
+              "       'Vehicle Type'],\n",
+              "      dtype='object')"
+            ]
+          },
+          "execution_count": 194,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pyplot as plt \n",
+        "\n",
+        "url = 'https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv'\n",
+        "df_original = pd.read_csv(url)\n",
+        "\n",
+        "df = df_original.copy()\n",
+        "df.columns\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "32e6b2b1",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Unnamed: 0</th>\n",
+              "      <th>Customer</th>\n",
+              "      <th>State</th>\n",
+              "      <th>Customer Lifetime Value</th>\n",
+              "      <th>Response</th>\n",
+              "      <th>Coverage</th>\n",
+              "      <th>Education</th>\n",
+              "      <th>Effective To Date</th>\n",
+              "      <th>EmploymentStatus</th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>...</th>\n",
+              "      <th>Number of Open Complaints</th>\n",
+              "      <th>Number of Policies</th>\n",
+              "      <th>Policy Type</th>\n",
+              "      <th>Policy</th>\n",
+              "      <th>Renew Offer Type</th>\n",
+              "      <th>Sales Channel</th>\n",
+              "      <th>Total Claim Amount</th>\n",
+              "      <th>Vehicle Class</th>\n",
+              "      <th>Vehicle Size</th>\n",
+              "      <th>Vehicle Type</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>XL78013</td>\n",
+              "      <td>Oregon</td>\n",
+              "      <td>22332.439460</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Extended</td>\n",
+              "      <td>College</td>\n",
+              "      <td>1/11/11</td>\n",
+              "      <td>Employed</td>\n",
+              "      <td>M</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Corporate Auto</td>\n",
+              "      <td>Corporate L3</td>\n",
+              "      <td>Offer2</td>\n",
+              "      <td>Branch</td>\n",
+              "      <td>484.013411</td>\n",
+              "      <td>Four-Door Car</td>\n",
+              "      <td>Medsize</td>\n",
+              "      <td>A</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>8</td>\n",
+              "      <td>FM55990</td>\n",
+              "      <td>California</td>\n",
+              "      <td>5989.773931</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Premium</td>\n",
+              "      <td>College</td>\n",
+              "      <td>1/19/11</td>\n",
+              "      <td>Employed</td>\n",
+              "      <td>M</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Personal Auto</td>\n",
+              "      <td>Personal L1</td>\n",
+              "      <td>Offer2</td>\n",
+              "      <td>Branch</td>\n",
+              "      <td>739.200000</td>\n",
+              "      <td>Sports Car</td>\n",
+              "      <td>Medsize</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>15</td>\n",
+              "      <td>CW49887</td>\n",
+              "      <td>California</td>\n",
+              "      <td>4626.801093</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Basic</td>\n",
+              "      <td>Master</td>\n",
+              "      <td>1/16/11</td>\n",
+              "      <td>Employed</td>\n",
+              "      <td>F</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Special Auto</td>\n",
+              "      <td>Special L1</td>\n",
+              "      <td>Offer2</td>\n",
+              "      <td>Branch</td>\n",
+              "      <td>547.200000</td>\n",
+              "      <td>SUV</td>\n",
+              "      <td>Medsize</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>19</td>\n",
+              "      <td>NJ54277</td>\n",
+              "      <td>California</td>\n",
+              "      <td>3746.751625</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Extended</td>\n",
+              "      <td>College</td>\n",
+              "      <td>2/26/11</td>\n",
+              "      <td>Employed</td>\n",
+              "      <td>F</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Personal Auto</td>\n",
+              "      <td>Personal L2</td>\n",
+              "      <td>Offer2</td>\n",
+              "      <td>Call Center</td>\n",
+              "      <td>19.575683</td>\n",
+              "      <td>Two-Door Car</td>\n",
+              "      <td>Large</td>\n",
+              "      <td>A</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>27</td>\n",
+              "      <td>MQ68407</td>\n",
+              "      <td>Oregon</td>\n",
+              "      <td>4376.363592</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Premium</td>\n",
+              "      <td>Bachelor</td>\n",
+              "      <td>2/28/11</td>\n",
+              "      <td>Employed</td>\n",
+              "      <td>F</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Personal Auto</td>\n",
+              "      <td>Personal L3</td>\n",
+              "      <td>Offer2</td>\n",
+              "      <td>Agent</td>\n",
+              "      <td>60.036683</td>\n",
+              "      <td>Four-Door Car</td>\n",
+              "      <td>Medsize</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10844</th>\n",
+              "      <td>10844</td>\n",
+              "      <td>FM31768</td>\n",
+              "      <td>Arizona</td>\n",
+              "      <td>5979.724161</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Extended</td>\n",
+              "      <td>High School or Below</td>\n",
+              "      <td>2/7/11</td>\n",
+              "      <td>Employed</td>\n",
+              "      <td>F</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Personal Auto</td>\n",
+              "      <td>Personal L1</td>\n",
+              "      <td>Offer2</td>\n",
+              "      <td>Agent</td>\n",
+              "      <td>547.200000</td>\n",
+              "      <td>Four-Door Car</td>\n",
+              "      <td>Medsize</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10852</th>\n",
+              "      <td>10852</td>\n",
+              "      <td>KZ80424</td>\n",
+              "      <td>Washington</td>\n",
+              "      <td>8382.478392</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Basic</td>\n",
+              "      <td>Bachelor</td>\n",
+              "      <td>1/27/11</td>\n",
+              "      <td>Employed</td>\n",
+              "      <td>M</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>Personal Auto</td>\n",
+              "      <td>Personal L2</td>\n",
+              "      <td>Offer2</td>\n",
+              "      <td>Call Center</td>\n",
+              "      <td>791.878042</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>A</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10872</th>\n",
+              "      <td>10872</td>\n",
+              "      <td>XT67997</td>\n",
+              "      <td>California</td>\n",
+              "      <td>5979.724161</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Extended</td>\n",
+              "      <td>High School or Below</td>\n",
+              "      <td>2/7/11</td>\n",
+              "      <td>Employed</td>\n",
+              "      <td>F</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>Personal Auto</td>\n",
+              "      <td>Personal L3</td>\n",
+              "      <td>Offer2</td>\n",
+              "      <td>Agent</td>\n",
+              "      <td>547.200000</td>\n",
+              "      <td>Four-Door Car</td>\n",
+              "      <td>Medsize</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10887</th>\n",
+              "      <td>10887</td>\n",
+              "      <td>BY78730</td>\n",
+              "      <td>Oregon</td>\n",
+              "      <td>8879.790017</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Basic</td>\n",
+              "      <td>High School or Below</td>\n",
+              "      <td>2/3/11</td>\n",
+              "      <td>Employed</td>\n",
+              "      <td>F</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>7</td>\n",
+              "      <td>Special Auto</td>\n",
+              "      <td>Special L2</td>\n",
+              "      <td>Offer1</td>\n",
+              "      <td>Agent</td>\n",
+              "      <td>528.200860</td>\n",
+              "      <td>SUV</td>\n",
+              "      <td>Small</td>\n",
+              "      <td>A</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10897</th>\n",
+              "      <td>10897</td>\n",
+              "      <td>MM70762</td>\n",
+              "      <td>Arizona</td>\n",
+              "      <td>9075.768214</td>\n",
+              "      <td>Yes</td>\n",
+              "      <td>Basic</td>\n",
+              "      <td>Master</td>\n",
+              "      <td>1/26/11</td>\n",
+              "      <td>Employed</td>\n",
+              "      <td>M</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>Personal Auto</td>\n",
+              "      <td>Personal L1</td>\n",
+              "      <td>Offer1</td>\n",
+              "      <td>Agent</td>\n",
+              "      <td>158.077504</td>\n",
+              "      <td>Sports Car</td>\n",
+              "      <td>Medsize</td>\n",
+              "      <td>A</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>1399 rows × 26 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       Unnamed: 0 Customer       State  Customer Lifetime Value Response  \\\n",
+              "3               3  XL78013      Oregon             22332.439460      Yes   \n",
+              "8               8  FM55990  California              5989.773931      Yes   \n",
+              "15             15  CW49887  California              4626.801093      Yes   \n",
+              "19             19  NJ54277  California              3746.751625      Yes   \n",
+              "27             27  MQ68407      Oregon              4376.363592      Yes   \n",
+              "...           ...      ...         ...                      ...      ...   \n",
+              "10844       10844  FM31768     Arizona              5979.724161      Yes   \n",
+              "10852       10852  KZ80424  Washington              8382.478392      Yes   \n",
+              "10872       10872  XT67997  California              5979.724161      Yes   \n",
+              "10887       10887  BY78730      Oregon              8879.790017      Yes   \n",
+              "10897       10897  MM70762     Arizona              9075.768214      Yes   \n",
+              "\n",
+              "       Coverage             Education Effective To Date EmploymentStatus  \\\n",
+              "3      Extended               College           1/11/11         Employed   \n",
+              "8       Premium               College           1/19/11         Employed   \n",
+              "15        Basic                Master           1/16/11         Employed   \n",
+              "19     Extended               College           2/26/11         Employed   \n",
+              "27      Premium              Bachelor           2/28/11         Employed   \n",
+              "...         ...                   ...               ...              ...   \n",
+              "10844  Extended  High School or Below            2/7/11         Employed   \n",
+              "10852     Basic              Bachelor           1/27/11         Employed   \n",
+              "10872  Extended  High School or Below            2/7/11         Employed   \n",
+              "10887     Basic  High School or Below            2/3/11         Employed   \n",
+              "10897     Basic                Master           1/26/11         Employed   \n",
+              "\n",
+              "      Gender  ...  Number of Open Complaints Number of Policies  \\\n",
+              "3          M  ...                        0.0                  2   \n",
+              "8          M  ...                        0.0                  1   \n",
+              "15         F  ...                        0.0                  1   \n",
+              "19         F  ...                        1.0                  1   \n",
+              "27         F  ...                        0.0                  1   \n",
+              "...      ...  ...                        ...                ...   \n",
+              "10844      F  ...                        0.0                  3   \n",
+              "10852      M  ...                        0.0                  2   \n",
+              "10872      F  ...                        0.0                  3   \n",
+              "10887      F  ...                        0.0                  7   \n",
+              "10897      M  ...                        0.0                  8   \n",
+              "\n",
+              "          Policy Type        Policy  Renew Offer Type  Sales Channel  \\\n",
+              "3      Corporate Auto  Corporate L3            Offer2         Branch   \n",
+              "8       Personal Auto   Personal L1            Offer2         Branch   \n",
+              "15       Special Auto    Special L1            Offer2         Branch   \n",
+              "19      Personal Auto   Personal L2            Offer2    Call Center   \n",
+              "27      Personal Auto   Personal L3            Offer2          Agent   \n",
+              "...               ...           ...               ...            ...   \n",
+              "10844   Personal Auto   Personal L1            Offer2          Agent   \n",
+              "10852   Personal Auto   Personal L2            Offer2    Call Center   \n",
+              "10872   Personal Auto   Personal L3            Offer2          Agent   \n",
+              "10887    Special Auto    Special L2            Offer1          Agent   \n",
+              "10897   Personal Auto   Personal L1            Offer1          Agent   \n",
+              "\n",
+              "       Total Claim Amount  Vehicle Class Vehicle Size Vehicle Type  \n",
+              "3              484.013411  Four-Door Car      Medsize            A  \n",
+              "8              739.200000     Sports Car      Medsize          NaN  \n",
+              "15             547.200000            SUV      Medsize          NaN  \n",
+              "19              19.575683   Two-Door Car        Large            A  \n",
+              "27              60.036683  Four-Door Car      Medsize          NaN  \n",
+              "...                   ...            ...          ...          ...  \n",
+              "10844          547.200000  Four-Door Car      Medsize          NaN  \n",
+              "10852          791.878042            NaN          NaN            A  \n",
+              "10872          547.200000  Four-Door Car      Medsize          NaN  \n",
+              "10887          528.200860            SUV        Small            A  \n",
+              "10897          158.077504     Sports Car      Medsize            A  \n",
+              "\n",
+              "[1399 rows x 26 columns]"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df_potential_customers = df[(df['Total Claim Amount'] <= 1000)&(df['Response'] == 'Yes')]\n",
+        "df_potential_customers"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "e1f5d0f5",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead tr th {\n",
+              "        text-align: left;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th>Policy Type</th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Monthly Premium Auto</th>\n",
+              "      <th>Total Claim Amount</th>\n",
+              "      <th>Customer Lifetime Value</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th>mean</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>mean</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Personal Auto</td>\n",
+              "      <td>F</td>\n",
+              "      <td>98.998148</td>\n",
+              "      <td>452.965929</td>\n",
+              "      <td>8339.791842</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Corporate Auto</td>\n",
+              "      <td>F</td>\n",
+              "      <td>94.301775</td>\n",
+              "      <td>433.738499</td>\n",
+              "      <td>7712.628736</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Special Auto</td>\n",
+              "      <td>F</td>\n",
+              "      <td>92.314286</td>\n",
+              "      <td>453.280164</td>\n",
+              "      <td>7691.584111</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Corporate Auto</td>\n",
+              "      <td>M</td>\n",
+              "      <td>92.188312</td>\n",
+              "      <td>408.582459</td>\n",
+              "      <td>7944.465414</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Personal Auto</td>\n",
+              "      <td>M</td>\n",
+              "      <td>91.085821</td>\n",
+              "      <td>457.010178</td>\n",
+              "      <td>7448.383281</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Special Auto</td>\n",
+              "      <td>M</td>\n",
+              "      <td>86.343750</td>\n",
+              "      <td>429.527942</td>\n",
+              "      <td>8247.088702</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      Policy Type Gender Monthly Premium Auto Total Claim Amount  \\\n",
+              "                                         mean               mean   \n",
+              "2   Personal Auto      F            98.998148         452.965929   \n",
+              "0  Corporate Auto      F            94.301775         433.738499   \n",
+              "4    Special Auto      F            92.314286         453.280164   \n",
+              "1  Corporate Auto      M            92.188312         408.582459   \n",
+              "3   Personal Auto      M            91.085821         457.010178   \n",
+              "5    Special Auto      M            86.343750         429.527942   \n",
+              "\n",
+              "  Customer Lifetime Value  \n",
+              "                     mean  \n",
+              "2             8339.791842  \n",
+              "0             7712.628736  \n",
+              "4             7691.584111  \n",
+              "1             7944.465414  \n",
+              "3             7448.383281  \n",
+              "5             8247.088702  "
+            ]
+          },
+          "execution_count": 36,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "'''\n",
+        "2. Using the original Dataframe, analyze:\n",
+        "   - the average `monthly_premium` and/or customer lifetime value by `policy_type` and `gender` for customers who responded \"Yes\", and\n",
+        "   - compare these insights to `total_claim_amount` patterns, and discuss which segments appear most profitable or low-risk for the company.\n",
+        "'''\n",
+        "# Segments Corporate Auto&Female and Corporate Auto&M appear most profitable for the company\n",
+        "df_yes = df[df['Response'] == 'Yes']\n",
+        "\n",
+        "summary_df = df_yes.groupby(['Policy Type','Gender'])[['Monthly Premium Auto','Total Claim Amount','Customer Lifetime Value']].agg(['mean']).reset_index()\n",
+        "summary_df = summary_df.sort_values(by=('Monthly Premium Auto', 'mean'), ascending=False)\n",
+        "summary_df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 54,
+      "id": "c3dc732b",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "State\n",
+            "Arizona       1703\n",
+            "California    3150\n",
+            "Nevada         882\n",
+            "Oregon        2601\n",
+            "Washington     798\n",
+            "Name: Customer, dtype: int64\n",
+            "['Arizona', 'California', 'Nevada', 'Oregon', 'Washington']\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "State\n",
+              "Arizona       1703\n",
+              "California    3150\n",
+              "Nevada         882\n",
+              "Oregon        2601\n",
+              "Washington     798\n",
+              "Name: Customer, dtype: int64"
+            ]
+          },
+          "execution_count": 54,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# 3. Analyze the total number of customers who have policies in each state, and then filter the results to only include states where there are more than 500 customers.\n",
+        "\n",
+        "s = df.groupby('State')['Customer'].agg('nunique')\n",
+        "print(s)\n",
+        "filtered_s = s[s>500]\n",
+        "\n",
+        "state_list = filtered_s.index.tolist()\n",
+        "print(state_list)\n",
+        "\n",
+        "filter_df = df[df['State'].isin(state_list)]\n",
+        "filter_df.groupby('State')['Customer'].agg('nunique')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 63,
+      "id": "6c8d1626",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "              Education Gender          max          min       median\n",
+            "0              Bachelor      F  73225.95652  1904.000852  5640.505303\n",
+            "1              Bachelor      M  67907.27050  1898.007675  5548.031892\n",
+            "2               College      F  61850.18803  1898.683686  5623.611187\n",
+            "3               College      M  61134.68307  1918.119700  6005.847375\n",
+            "4                Doctor      F  44856.11397  2395.570000  5332.462694\n",
+            "5                Doctor      M  32677.34284  2267.604038  5577.669457\n",
+            "6  High School or Below      F  55277.44589  2144.921535  6039.553187\n",
+            "7  High School or Below      M  83325.38119  1940.981221  6286.731006\n",
+            "8                Master      F  51016.06704  2417.777032  5729.855012\n",
+            "9                Master      M  50568.25912  2272.307310  5579.099207\n"
+          ]
+        }
+      ],
+      "source": [
+        "# 4. Find the maximum, minimum, and median customer lifetime value by education level and gender. Write your conclusions.\n",
+        "\n",
+        "data = df.groupby(['Education','Gender'])['Customer Lifetime Value'].agg(['max','min','median']).reset_index()\n",
+        "print(data)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "491ba182",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "\n",
+        "\n",
+        "sns.set(style=\"white\")\n",
+        "\n",
+        "# Plot 1: Max\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "sns.barplot(data=data, x='Education', y='max', hue='Gender')\n",
+        "plt.title('Maximum Customer Lifetime Value by Education and Gender')\n",
+        "plt.xticks(rotation=45)\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "# the group of High School or Below Male shows unusually high max customer liftime value\n",
+        "# the group of Bachlor Female have highest max customer liftime value\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "dd0846ab",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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9+yRJWbJkUaFChbR69WpJUmRkpH766aentl5LUu3atbV8+fJH/ouf32H//v1ydHS0Cpzxw1eeV3BwsMqUKWPV8lqgQAFt27ZNOXPmVOnSpTV//nw5Ozvr5MmT2rp1q+VLrGd9/k+fPq0rV64k6OHz3nvvqUCBAtq7d6/V8gevwfjg/qT3x+e5Rp703rt//35J1r1w7OzsntqK+3DX/Hi7du1S7ty5rf7Ffym7a9cumUwmlS1bNsHr9sqVKzpx4oTlOHnz5rW8v8bXLEkRERGKjIzUkSNH9OGHH1o99qeffpqglmd9nebMmfOJ5wsg8aEFG0CS9+mnn6pdu3YKCQmRs7Ozdu3apU6dOj3z/i4uLla/29nZPfUWVvGtRw9ydXV95sd83mPHj5V+VvGB9r///rO0sj4sNDRUnp6ez/VFRFBQkGbPnq1Zs2YpKChIadOmVatWrRKMsY1369YtZcyYMcHytGnTSpLCwsIsy573vB+cYOxRHhy3mixZsscex0jxNcW3qD/s8uXLz3W8573Obt68qStXrih37tyPXH/lyhWlTJnykescHBxUtWpVrVixQr1795adnZ3Wrl2rzz//XI6OjpLud+Xu27evtmzZIpPJpEyZMllau1/mtm83b97U2bNnH1t3RETEY887derUll4Q8Tw8PKyurVq1aunbb7/VpUuXFBwcrDt37qhatWpPrcvT01N58+Z94ja3bt1SqlSpEryO0qVL99TjP+zmzZuWL1IeJS4uTqNHj9aCBQt09+5dpU+fXr6+vo/tCv24x5D+9xp8UNq0aROMe3/e98fnuUaedOz4kBn/JVK8pz2v77zzjiQlmO/C19dXy5cvt/zet29fy883b96U2Wx+bI+Gy5cvW4Luw9dh/LUXFxenW7duyWw2J6j54W76z/M6fd73fgC2R8AGkOSVKVNGyZIl04YNG+Tm5qZ3331XefLksXVZVuI/fD/cunLnzp1X8nilS5eWJG3fvv2RATsmJkaff/65ChYsqMmTJ1vqi42NtbTOPKo2Pz8/S9fJ3bt3a+7cuRo4cKDy5cv3yK7wKVOm1JUrVxIsj1/28AfR5xHfgjhnzpxHBuj4D9qvU3xNI0eOfOTkco8KNUZKnjy5MmfO/Nh7ND9tyECtWrU0c+ZM/fbbb7K3t9eVK1cs3aql+2Nm//33X82ePVsFChSQk5OTIiIitHTp0ice9+GeFA+3gCZPnlxFixa1dE1+2JN6H9y+fVtms9kq4F69etVq1uxPPvlEAwcO1IYNG7R//36VKlXKsNtspU6dWjdu3LB67Uj/C7IPepbn4fr16wn22759u3LmzKkVK1Zo9uzZ6tevnypUqKDkyZNLkmVM/7NIlSqVpPvP0cOuXLnyUq9J6cWvkYfF13H16lWr1/KjntcHpUmTRgUKFNCWLVvUtWtXy/8Td3d3qy9LHnzPSJ48udzc3Czj2B+WKVOmZ6o5VapUsrOzS/DcPlzzy75OASRudBEHkOQ5OTnpo48+0saNG/XTTz89tkXTluJbIh+cjO3UqVNP/bD4orJnz64yZcpo2rRpOn/+fIL1gYGBunHjhqpWrfrY+h7sYi1Jw4YNU82aNWU2m+Xq6qoPP/zQMrnPf//9J0kJWhKLFCmiAwcO6OLFi1bL16xZo3Tp0j3zB9dHiW8Vu3HjhvLmzWv5d/36dY0bN+6VPbdPki9fPjk6Oio0NNSqJgcHB40ePdrSqvbw82SUokWL6tKlS/Lw8LB6/J07d2r69OlWAfBRsmbNqgIFCmjTpk366aefVLBgQWXNmtWyPjg4WBUqVFCxYsUsoffXX3+V9Piuue7u7lbXVfxxHq779OnTypIli1Xdq1ev1vLly59Yd/yXPfHu3LmjX375RcWKFbMsc3NzU6VKlbRu3Trt3LnzmbqHP6sSJUooJiZGW7ZssSyLjo5OMBzgWZ6HwoULa+fOnVZdqY8ePaoWLVroyJEjCg4OVrZs2VSzZk1LuA4NDdXx48etnv8nXV9ZsmRRunTptG7dOqvl58+f18GDB586Lv1pXuQaeZT4e64/fJeIn3/++an7tm3bVufPn9fw4cMf2dp+69Ytq94kRYsW1d27d2U2m62uv+PHj2vSpElWk9o9ibOzs+X18+Djbtu2zWq7l32dAkjcaMEG8EaoVKmSWrZsKTs7O8NuvWOkYsWKycXFRUOHDlXHjh11584djR8/3tKa9Cr069dPjRo1Uu3atfXVV18pX758unPnjjZs2KAff/xRdevWtUwMVbZsWQ0ZMkTfffedmjZtqkuXLmnSpElWrTzFixfXrFmz1KNHD1WtWlX37t3T9OnTlSpVKsuH4RQpUujAgQPatWuXcuXKpcaNG2vNmjX6+uuv1a5dO6VKlUqrVq3S7t27NXjw4JcKmj4+Pqpatar69OmjixcvKk+ePDp9+rTGjBmjd99995EtyM/ihx9+SNCN2s7OTl999dVT902dOrWaNWumcePGKTw8XMWKFVNoaKjGjRsnk8mkHDlySPpfS/e6deuUL1++R3ajfxE1atTQ/Pnz1bhxY7Vq1Urp06fX77//rmnTpqlBgwaWrt5PEn9PbElWsyNL97vZrl27Vrlz55a3t7f++OMPBQUFyWQyWd3y6EEffvihtm3bpiFDhsjf31/79+9PMDPz119/rdWrV+vrr79WkyZNlDp1aq1fv15Lly5Vz549n1ivo6Ojvv32W3Xu3Fnu7u4KCgpSZGSkZWK2eLVq1VKdOnWUMmVKffTRR099HqT7XzgdPHjwketcXV3l4+OjEiVKqHTp0urdu7euXbumDBkyaO7cubp+/bpVd+8PP/xQgYGBCgwMVL58+bRt2zarLwYkqU2bNqpTp45atmypr776SpGRkRo7dqx8fX1VqlQpHT58WJMnT1ZQUJDy58+vs2fPKjAwUNHR0VbPf4oUKXT06FHt3bs3Qc8SOzs7de7cWT179lSXLl1UtWpV3bhxQxMnTlTKlClfekKtF7lGHiVTpkyqU6eOxowZo5iYGOXMmVOrV6/WP//889R9/fz81KdPHw0ZMkQHDx5U9erVlSVLFt29e1d79+7VDz/8oKioKMtrumzZsipSpIjatGmjNm3aKGvWrDp06JDGjx8vPz+/57qHeOfOndWoUSO1a9dOderU0enTpzV16lSrbYx4nQJIvAjYAN4IJUuWVIoUKZQ+fXqrFrfEIkWKFJowYYJGjRqltm3bKkOGDGrXrl2CoGGkd955R0uWLNGcOXO0bt06BQUFycnJSe+//75GjRplmS1Xut+qNWzYME2ZMkUtWrRQ1qxZNWDAAA0YMMCyTdmyZTVy5EjNnDnTMrFZoUKFNHfuXMsXBfXr19fhw4fVvHlzDRkyRJ999pkWLVqkUaNGaeDAgbp3755y5MihyZMnq3z58i99jkOGDFFgYKAWL16skJAQeXh4qFKlSurUqdMLtwI9atZoe3v7ZwrYktSpUyelS5dOCxcu1PTp05UyZUqVKFFCnTt3trQ6VqhQQatXr1aPHj1Uq1Ytff/99y9U68Pc3Ny0YMECjRo1SiNGjNDt27eVIUMGdenSRU2aNHmmY3z66acaPHiwTCaT5QuYeEOHDrW6LjJnzqx+/fppzZo1lkmpHlazZk2dO3dOK1eu1OLFi1WkSBGNHz/eMjO0JHl5eWnx4sUaNWqUvv/+e0VFRSlz5swaNGjQU7s/p0mTRl26dNHo0aN15coV5cuXT/Pnz9f7779vtV3+/PmVKlUqVapU6ZknvIuf0OxRcuTIYZk4beLEiRo5cqTGjx+vqKgoVapUSbVr17a633vLli11/fp1zZgxQ/fu3VO5cuU0aNAgtW7d2rJNrly5NG/ePI0aNUqdOnWSu7u7ypYtq65du8rJyUktW7bUjRs3NHfuXMu9nj///HPLzPlhYWFKkSKFmjRposGDB6tp06aaNWtWgtpr1KihZMmSKTAwUG3btpW7u7v8/PzUuXPnFxo7/qAXuUYep2/fvkqbNq3mz5+vW7duyc/PT61atdLYsWOfum/9+vVVtGhRLVq0SLNmzVJISIjs7e2VJUsWNWjQQHXq1LEME7Czs1NQUJDGjRunwMBAXbt2TV5eXmrcuLFllvNnVbhwYU2bNk2jR49Wu3bt9O6772rw4MFq1aqVZRsjXqcAEi+T+WVmJQEAAEgC/vzzT9WuXVurV6+29CQAAMBotGADAIA31p49e7Rnzx6tWrVKpUuXJlwDAF4pJjkDAABvrBs3bmjWrFlKmzatBg4caOtyAABvOLqIAwAAAABgAFqwAQAAAAAwAAEbAAAAAAADELABAAAAADAAs4i/gMKFCys6Ovql7xUJAAAAAEjcrly5IicnJ+3fv/+p2xKwX0BUVJRiY2NtXQYAAAAA4BWLiYnRs84NbvOAffPmTY0ePVq//PKLwsPD5ePjoy5duqhw4cKSpMaNG+v333+32qdo0aKaN2+epPthd+jQodqwYYMiIyPl7++vXr16KU2aNJbtd+3apREjRujUqVNKnz692rdvr8qVK79wzZ6enpKkrVu3vvAxAAAAAACJX/ny5Z95W5sH7M6dO+vKlSsaPXq0PDw8NG/ePDVt2lQrV67U+++/r3/++Ufff/+9PvroI8s+jo6Olp+///577d+/XxMmTJCTk5P69u2rDh06aP78+ZKkU6dOqWXLlmrcuLFGjBihX375Rd27d1eaNGlUokSJ136+AAAAAIA3k00D9tmzZ7Vz504tXLhQhQoVkiT16dNHO3bs0Nq1a9WgQQNdu3ZN+fLle+R459DQUK1atUpTp061tHiPHj1an3zyiQ4cOKACBQpozpw58vHxUUBAgCQpa9asOnr0qKZPn07ABgAAAAAYxqaziKdOnVpBQUHKmzevZZnJZJLJZFJYWJj++ecfmUwmZcmS5ZH7BwcHS5KKFy9uWZYlSxZ5eXlp3759kqT9+/cnCNLFixdXcHDwM/ejBwAAAADgaWwasFOkSKGyZcvKycnJsmzjxo06e/as/Pz8dPz4cSVPnlz9+/dXmTJl9Mknn2js2LGKjo6WdL8FO3Xq1HJ2drY6rqenp0JCQiRJISEh8vb2TrA+IiJCN27ceMVnCAAAAAB4WySq+2D/8ccf6tmzpypUqKBy5crp+PHjioqKkq+vr6ZPn67WrVtr2bJl6t27tyQpIiLCKpzHc3Z2VlRUlCQpMjIywTbxv8cHdQAAAAAAXpbNJzmLt2XLFnXt2lUFCxbUyJEjJUn9+/fXN998o5QpU0qSPvjgAzk6OiogIEDdu3eXi4vLI0NyVFSUXF1dJd0P2w9vE/97/DavSmxsrO7du/dKHyMxc3R0lL29va3LAAAAAIDXIlEE7Pnz52vQoEH65JNPNGzYMEsLs4ODgyVcx8uePbuk/3X9vnnzpqKjo61aqS9fviwvLy9JUvr06XX58mWrY1y+fFlubm5Knjz5Kzkfs9mskJAQ3bx585UcPylJlSqVvL29ZTKZbF0KAAAAALxSNg/YCxcu1IABA9SwYUP16tXLKog1bNhQ7777roYMGWJZ9tdff8nR0VGZM2dWunTpFBcXp+DgYMtEZqdPn1ZoaKiKFCkiSSpcuLD27t1r9Zi7d+9WwYIFZWf3anrIx4drT09Pubm5vZXh0mw26+7du5YvN9KnT2/jigAAAADg1bJpwD59+rQGDx6sjz/+WC1bttTVq1ct61xcXFSxYkUNHjxYvr6+Kl26tP766y8NHz5cTZs2lbu7u9zd3VW5cmX17t1bgwcPlqurq/r27auiRYsqf/78ku6H9OrVq2vkyJGqXr26tm/frg0bNmj69Omv5JxiY2Mt4drDw+OVPEZSEd8F//Lly/L09KS7OAAAAIA3mk0D9saNG3Xv3j1t3rxZmzdvtlpXvXp1DR06VCaTSfPmzdPgwYOVLl06ff3112rRooVluwEDBmjw4MFq166dJKlMmTKWSdCk+13KJ0+erBEjRmjOnDl69913NWLEiFd2D+z4Mddubm6v5PhJTfzzcO/ePQI2AAAAgDeayczNoJ9b+fLlJUlbt25NsC4yMlKnT59WlixZ5OLi8rpLS3R4PgAAAAAkZU/Kfw9LVLfpAgAAAAAgqSJg29CpU6c0YMAAVaxYUfny5VOhQoVUt25dLVy4UDExMa+tDh8fH02YMOG1PR4AAAAAvIlsPov422r9+vXq2bOnsmbNqsaNGytLliyKjIzU9u3bNXjwYO3YsUOTJ09+K2cgBwAAAICkiIBtA6dOnVLPnj3l5+ensWPHysHhf/8bypYtq2LFiqlDhw766aefVKlSJRtWCgAAAAB4VnQRt4Hp06fLzs5O/fr1swrX8SpWrKhq1apZfo+Li1NQUJA+/vhj5cmTRxUrVtS8efOs9om/j3hQUJDKlSunvHnzqm7dujp06JDVdnv37lWdOnWUL18+VaxYUb///nuCx4+KitLw4cNVtmxZ5cmTR5999pnWr19vtY2/v78GDx6sRo0aydfXV7169XqJZwQAAAAAkj5asG1g69atKl68+BPvkz1s2DDLz99//71WrFihli1bqkCBAtq3b58GDx6ssLAwtW3b1rLdxo0blTVrVvXu3Vtms1nDhg1T+/bttW3bNtnb2+vIkSNq0qSJihcvrvHjx+vChQvq3Lmz1eOazWa1bdtWf/zxhzp06KCsWbNq8+bNCggIUHR0tFXwX7BggRo3bqzmzZsrWbJkxj1BAAAAAJAEEbBfs1u3bunWrVvKnDlzgnUPT2xmMpl07tw5LV26VJ07d7bc/7t06dIymUwKDAzUl19+qdSpU1v2nzFjhtzd3SVJd+7c0TfffKO///5befLkUWBgoDw8PDRlyhQ5OjpKklKnTq2AgADLY/7+++/asWOHxowZY+me7ufnp4iICI0cOVJVqlSxtLq/88476tq1q7FPEAAAAPCQuDiz7OyYm+hReG4SFwL2axYXF/fI5WfPnlWFChWslmXIkEHNmzeX2WyWv7+/VQD39/fXlClTFBwcrI8++kiSlC1bNku4liQvLy9JUkREhCQpODhYH374oSVcS1KFChVkb29v+X3Xrl0ymUwqW7Zsgsdbs2aNTpw4oZw5c0qS5b8AAADAq2RnZ9KkRTt18fItW5eSqGTwTKm29UrZugw8gID9mqVOnVpubm66ePGi1fL06dNr+fLllt8nTZqk48eP6+bNm5KkypUrP/J4oaGhlp9dXV2t1tnZ3R9iHx/qb926ZWntjufg4GC17ObNmzKbzSpYsOAjH+/y5cuWYO3m5vbY8wQAAACMdPHyLZ25eMPWZQBPRMC2AX9/f/38888KDw+3tDg7OTkpb968lm1SpUolSUqRIoUkac6cOY8c5/zOO+888+OmSpVKV69etVpmNpt169b/vglMnjy53NzcNHfu3EceI1OmTM/8eAAAAADwNmEWcRto0aKFYmJi1Lt3b0VHRydYHxkZqfPnz0uSChcuLEm6ceOG8ubNa/l3/fp1jRs3ztLC/SxKlCihX3/91dJlXJJ27Nihe/fuWX4vWrSo7t69K7PZbPV4x48f16RJkxKMEweAN01cnNnWJSRqPD8AADweLdg24OPjoxEjRqhnz56qUaOGatWqJR8fH8XExOjAgQNavny5rl69qmbNmsnHx0dVq1ZVnz59dPHiReXJk0enT5/WmDFj9O677z5ysrTHadu2rbZs2aKmTZuqWbNmun79usaOHWs1Jrts2bIqUqSI2rRpozZt2ihr1qw6dOiQxo8fLz8/P6VJk+YVPCMAkHgwzu/xGOsHAMCTEbBtpGLFisqTJ48WLVqk5cuX6+LFizKbzcqYMaMqVaqkunXrWsLzkCFDFBgYqMWLFyskJEQeHh6qVKmSOnXqZDVB2dNkzpxZ8+fP19ChQxUQECAPDw998803Gjp0qGUbOzs7BQUFady4cQoMDNS1a9fk5eWlxo0bW90SDADeZIzzAwAAL8JkNpvp6/WcypcvL+n+/awfFhkZqdOnTytLlixycXF53aUlOjwfAJKib8etJ2A/QuYMqTW4YyVblwHgLcV7c0K8L78eT8p/D2MMNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAB1sX8LaJizPLzs6UZB63YcOG2rt37yPXNWnSRN98883LlgYAAAAAbwQC9mtmZ2fSpEU7dfHyrdf2mBk8U6ptvVIvvP+nn36qXr16JVju6ur6MmUBAAAAwBuFgG0DFy/f0pmLN2xdxjNzcXFRunTpbF0GAAAAACRqjMEGAAAAAMAABGwAAAAAAAxAF3E81dq1a7Vx40arZYUKFdL06dNtVBEAAACAlMldZI6Lk8mOdtPHed3PDwEbT+Xv76+uXbtaLXNxcbFRNQAAW+GD3NPx/AB4nZK5OMlkZ6fT66Yp4tolW5eT6Lh6pFeWKs1f62MSsPFUyZIlU6ZMmWxdBgDAxvgg92S2+CAHAJIUce2SIkLP2boMiIANAACeEx/kAAB4NPowAQAAAABgAFqwbSCDZ8o3+vEAAAAA4G1EwH7N4uLMaluvlE0e187O9Nz7zZs37xVUAwAAAABvHrqIv2YvEnKT8uMCAAAAwNuCgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAQjYAAAAAAAYgIANAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGICAjSfy9/eXj4+PZs2a9cj13333nXx8fDRhwoTXXBkAAAAAJC4E7NfMHBeX5B7X0dFRGzduTLA8JiZGmzZtkslkepnSAAAAAOCN4GDrAt42Jjs7nV43TRHXLr22x3T1SK8sVZq/8P4lSpTQjh07FBISIm9vb8vy3bt3y83NTa6urkaUCQAAAABJGgHbBiKuXVJE6Dlbl/HMfH19derUKW3YsEFff/21Zfn69ev16aef6qeffrJdcQAAAACQSNBFHM/k008/1YYNGyy/R0dHa8uWLapcubINqwIAAI8TF2e2dQmJGs8PgFeBFmw8k08//VQzZsxQaGiovLy8tHPnTqVJk0a5cuWydWkAAOAR7OxMmrRopy5evmXrUhKdDJ4p1bZeKVuXAeANRMDGM8mTJ48yZsyojRs36quvvtL69etpvQYAIJG7ePmWzly8YesyAOCtQRdxPLP4buJRUVHaunWrKlWqZOuSAAAAACDRIGDjmX366af6448/9MMPPyhjxozKmjWrrUsCAAAAgESDgI1nljNnTmXKlEmjRo2iezgAAAAAPIQx2Dbg6pE+yT7ep59+qilTptA9HAAAAAAeQsB+zcxxccpSpblNHtdk9/wdFrZt22b1e6dOndSpU6cnbgMAAAAAbyO6iL9mLxJyk/LjAgAAAMDbgtQFAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGICA/YqYzWZbl5Ao8DwAAAAAeFsQsA3m4HD/zmcxMTE2riRxiH8e4p8XAAAAAHhTEbANZm9vL3t7e4WFhdm6lEQhLCzM8pwAAAAAwJuMZkWDmUwmeXp66tKlS3J2dlayZMlkMplsXdZrZzabdefOHYWFhSl9+vRv5XMAAAAA4O1i84B98+ZNjR49Wr/88ovCw8Pl4+OjLl26qHDhwpKkXbt2acSIETp16pTSp0+v9u3bq3Llypb9o6KiNHToUG3YsEGRkZHy9/dXr169lCZNGss2TzuG0VKmTKmIiAhdvXpVV65ceWWPk9iZTCalSpVKKVOmtHUpAAAAAPDK2Txgd+7cWVeuXNHo0aPl4eGhefPmqWnTplq5cqXMZrNatmypxo0ba8SIEfrll1/UvXt3pUmTRiVKlJAkff/999q/f78mTJggJycn9e3bVx06dND8+fMlSadOnXrqMYxmMpmUPn16eXp66t69e6/kMZICR0dHuoYDAAAAeGvYNGCfPXtWO3fu1MKFC1WoUCFJUp8+fbRjxw6tXbtW165dk4+PjwICAiRJWbNm1dGjRzV9+nSVKFFCoaGhWrVqlaZOnWpp8R49erQ++eQTHThwQAUKFNCcOXOeeIxXibHHAAAAAPD2sOkkZ6lTp1ZQUJDy5s1rWWYymWQymRQWFqb9+/cnCMHFixdXcHCwzGazgoODLcviZcmSRV5eXtq3b58kPfUYAAAAAAAYwaYBO0WKFCpbtqycnJwsyzZu3KizZ8/Kz89PISEh8vb2ttrH09NTERERunHjhkJDQ5U6dWo5Ozsn2CYkJESSnnoMAAAAAACMkKhu0/XHH3+oZ8+eqlChgsqVK6fIyEir8C3J8nt0dLQiIiISrJckZ2dnRUVFSdJTjwEAAAAAgBESTcDesmWLmjRpovz582vkyJGS7gflh0Nw/O+urq5ycXF5ZEiOioqSq6vrMx0DAAAAAAAjJIqAPX/+fLVv314ffvihpk6daunynT59el2+fNlq28uXL8vNzU3JkyeXt7e3bt68mSBAX758WV5eXs90DAAAAAAAjGDzgL1w4UINGDBA9evX1+jRo626cxcuXFh79+612n737t0qWLCg7OzsVKhQIcXFxVkmO5Ok06dPKzQ0VEWKFHmmYwAAAAAAYASbJszTp09r8ODB+vjjj9WyZUtdvXpVV65c0ZUrV3T79m01bNhQhw4d0siRI3Xq1CnNnDlTGzZsULNmzSRJXl5eqly5snr37q09e/bo0KFD6ty5s4oWLar8+fNL0lOPAQAAAACAEWx6H+yNGzfq3r172rx5szZv3my1rnr16ho6dKgmT56sESNGaM6cOXr33Xc1YsQIq9tuDRgwQIMHD1a7du0kSWXKlFHv3r0t67Nnz/7UYwAAAAAA8LJsGrBbtWqlVq1aPXGbMmXKqEyZMo9d7+bmpoEDB2rgwIEvfAwAAAAAAF4Wg5ABAAAAADAAARsAAAAAAAMQsAEAAAAAMAABGwAAAG+VlMldZI6Ls3UZiRrPD/BibDrJGQAAAPC6JXNxksnOTqfXTVPEtUu2LifRcfVIryxVmtu6DCBJImADAADgrRRx7ZIiQs/ZugwAbxC6iAPAa0BXu8fjuQEAAG8KWrAB4DWgK+Kj0Q0RAAC8SQjYAPCa0BURAADgzUYXcRuLizPbuoREi26jT5bYnh+uZQAAALztaMG2MTs7kyYt2qmLl2/ZupREJZ/PO6rzSX661D5GYuxWy7X8ePHXMwAAAN5sBOxE4OLlWzpz8Yaty0hU3kmXQhJdapMaruVHi7+eAQAA8GajizgAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGCARBWwAwMD1bBhQ6tlvXv3lo+Pj9U/f39/y/q4uDiNHz9efn5+yp8/v5o3b67z589bHePvv/9WgwYNlD9/fvn7+2vu3Lmv5XwAAAAAAG+PRBOwFyxYoLFjxyZY/s8//6hVq1b67bffLP+WL19uWT958mQtXLhQAwYM0OLFixUXF6dmzZopOjpaknTjxg01btxY7733nn744Qe1bdtWI0eO1A8//PC6Tg0AAAAA8BZwsHUBoaGh6tu3r/bs2aPMmTNbrTObzTp58qRatGihdOnSJdg3OjpaM2fOVNeuXVWuXDlJ0pgxY+Tn56dNmzapSpUqWrp0qRwdHdW/f385ODgoa9asOnv2rIKCglSzZs3XcIYAAAAAgLeBzVuwjxw5IkdHR61Zs0b58uWzWnfu3DndvXtX77///iP3PXbsmO7cuaMSJUpYlqVIkUK5cuXSvn37JEn79+9X0aJF5eDwv+8SihcvrjNnzujq1auv4IwAAAAAAG8jm7dg+/v7W42pftDx48clSfPmzdOvv/4qOzs7lSlTRgEBAUqePLlCQkIkSenTp7faz9PT07IuJCREH3zwQYL1knTp0iWlTZvW0PMBAAAAALydbN6C/STHjx+XnZ2dPD09NXXqVPXo0UO//fab2rRpo7i4OEVEREiSnJycrPZzdnZWVFSUJCkyMvKR6yVZtgEAAAAA4GXZvAX7SVq3bq0vv/xSqVOnliR98MEHSpcunWrXrq2//vpLLi4uku6PxY7/WbofnF1dXSVJLi4ulgnPHlwvSW5ubq/jNAAAAAAAb4FE3YJtZ2dnCdfxsmfPLul+1+/4ruGXL1+22uby5cvy8vKSJHl7ez9yvSTLNgAAAAAAvKxEHbC7d++ur7/+2mrZX3/9JUnKli2bcuTIIXd3d+3Zs8eyPiwsTEePHlWRIkUkSUWKFFFwcLBiY2Mt2+zevVtZsmSRh4fHqz8JAAAAAMBbIVEH7IoVK2rXrl2aOHGizp07p+3bt+vbb79VlSpVlDVrVjk5OalBgwYaOXKktm7dqmPHjikgIEDe3t6qUKGCJKlmzZoKDw9Xr169dPLkSa1YsUKzZ89Wy5YtbXx2AAAAAIA3SaIeg12+fHmNHTtWQUFBmjZtmpInT67PPvtMnTp1smzToUMHxcTEqHfv3oqMjFSRIkU0Y8YMOTo6SpI8PDw0ffp0DRo0SNWrV1e6dOnUvXt3Va9e3UZnBQAAAAB4EyWqgD106NAEyz799FN9+umnj93H3t5e3bp1U7du3R67ja+vr5YsWWJIjQAAAAAAPEqi7iIOAAAAAEBSQcAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAADi+yU2RkpKZMmaKff/5ZERERiouLs1pvMpm0ZcsWQwoEAAAAACApeKGAPWjQIC1fvlxFixZVzpw5ZWdHQzgAAAAA4O32QgF706ZNCggIUIsWLYyuBwAAAACAJOmFmp7v3bsnX19fo2sBAAAAACDJeqGAXbp0af36669G1wIAAAAAQJL1Ql3EK1WqpL59++r69evKly+fXF1dE2xTrVq1l60NAAAAAIAk44UCdqdOnSRJq1at0qpVqxKsN5lMBGwAAAAAwFvlhQL21q1bja4DAAAAAIAk7YUCdoYMGYyuAwAAAACAJO2ZA3bPnj3Vpk0bZcyYUT179nzitiaTSYMHD37p4gAAAAAASCqeOWDv2bNHjRo1svwMAAAAAAD+55kD9rZt2x75MwAAAAAAeMEx2Ldu3dL48eP1xx9/KCwsLMF6k8mkLVu2vHRxAAAAAAAkFS8UsPv06aOtW7fKz89POXLkMLomAAAAAACSnBcK2L///rt69+6tevXqGV0PAAAAAABJkt2L7JQsWTK9++67RtcCAAAAAECS9UIBu379+poxY4bu3LljdD0AAAAAACRJL9RFvEGDBlq5cqXKli2rLFmyyNXV1Wq9yWTSnDlzDCkQAAAAAICk4IVasL/77judPn1anp6ecnFxkdlstvoXFxdndJ0AAAAAACRqL9SCvW3bNnXp0kXNmzc3uh4AAAAAAJKkF2rBdnJyUp48eYyuBQAAAACAJOuFAvbnn3+uRYsW0RUcAAAAAID/90JdxJMnT67ly5fL399fvr6+SpYsmdV6k8mkwYMHG1IgAAAAAABJwQsF7BUrVihlypSSpMOHDydYbzKZXq4qAAAAAACSmBee5AwAAAAAAPzPC43BBgAAAAAA1gjYAAAAAAAYgIANAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAQjYAAAAAAAYgIANAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAQjYAAAAAAAYgIANAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAQjYAAAAAAAYgIANAAAAAIABElXADgwMVMOGDa2W/f3332rQoIHy588vf39/zZ0712p9XFycxo8fLz8/P+XPn1/NmzfX+fPnn+sYAAAAAAC8rEQTsBcsWKCxY8daLbtx44YaN26s9957Tz/88IPatm2rkSNH6ocffrBsM3nyZC1cuFADBgzQ4sWLFRcXp2bNmik6OvqZjwEAAAAAwMtysHUBoaGh6tu3r/bs2aPMmTNbrVu6dKkcHR3Vv39/OTg4KGvWrDp79qyCgoJUs2ZNRUdHa+bMmeratavKlSsnSRozZoz8/Py0adMmValS5anHAAAAAADACDZvwT5y5IgcHR21Zs0a5cuXz2rd/v37VbRoUTk4/O97gOLFi+vMmTO6evWqjh07pjt37qhEiRKW9SlSpFCuXLm0b9++ZzoGAAAAAABGsHkLtr+/v/z9/R+5LiQkRB988IHVMk9PT0nSpUuXFBISIklKnz59gm3i1z3tGGnTpn35kwAAAAAAvPVs3oL9JJGRkXJycrJa5uzsLEmKiopSRESEJD1ym6ioqGc6BgAAAAAARkjUAdvFxcUyWVm8+FDs5uYmFxcXSXrkNq6urs90DAAAAAAAjJCoA7a3t7cuX75stSz+dy8vL0vX8Edt4+Xl9UzHAAAAAADACIk6YBcpUkTBwcGKjY21LNu9e7eyZMkiDw8P5ciRQ+7u7tqzZ49lfVhYmI4ePaoiRYo80zEAAAAAADBCog7YNWvWVHh4uHr16qWTJ09qxYoVmj17tlq2bCnp/tjrBg0aaOTIkdq6dauOHTumgIAAeXt7q0KFCs90DAAAAAAAjGDzWcSfxMPDQ9OnT9egQYNUvXp1pUuXTt27d1f16tUt23To0EExMTHq3bu3IiMjVaRIEc2YMUOOjo7PfAwAAAAAAF5WogrYQ4cOTbDM19dXS5Yseew+9vb26tatm7p16/bYbZ52DAAAAAAAXlai7iIOAAAAAEBSQcAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAEkiYIeGhsrHxyfBvxUrVkiS/v77bzVo0ED58+eXv7+/5s6da7V/XFycxo8fLz8/P+XPn1/NmzfX+fPnbXEqAAAAAIA3lIOtC3gWx44dk7Ozs7Zs2SKTyWRZnjx5ct24cUONGzeWv7+/+vXrp4MHD6pfv35KliyZatasKUmaPHmyFi5cqKFDh8rb21sjRoxQs2bNtHbtWjk5OdnqtAAAAAAAb5AkEbCPHz+uzJkzy9PTM8G6OXPmyNHRUf3795eDg4OyZs2qs2fPKigoSDVr1lR0dLRmzpyprl27qly5cpKkMWPGyM/PT5s2bVKVKlVe89kAAAAAAN5ESaKL+D///KOsWbM+ct3+/ftVtGhROTj877uC4sWL68yZM7p69aqOHTumO3fuqESJEpb1KVKkUK5cubRv375XXjsAAAAA4O2QJAL28ePHdf36ddWvX18lS5ZUvXr19Ouvv0qSQkJC5O3tbbV9fEv3pUuXFBISIklKnz59gm3i1wEAAAAA8LISfcCOiYnRv//+q1u3bql9+/YKCgpS/vz51aJFC+3atUuRkZEJxlE7OztLkqKiohQRESFJj9wmKirq9ZwEAAAAAOCNl+jHYDs4OGjPnj2yt7eXi4uLJClPnjw6ceKEZsyYIRcXF0VHR1vtEx+c3dzcLPtER0dbfo7fxtXV9TWdBQAAAADgTZfoW7AlKVmyZFbhWJKyZ8+u0NBQeXt76/Lly1br4n/38vKydA1/1DZeXl6vsGoAAAAAwNsk0QfsEydOqGDBgtqzZ4/V8sOHDytbtmwqUqSIgoODFRsba1m3e/duZcmSRR4eHsqRI4fc3d2t9g8LC9PRo0dVpEiR13YeAAAAAIA3W6IP2FmzZtX777+v/v37a//+/Tp16pSGDBmigwcPqnXr1qpZs6bCw8PVq1cvnTx5UitWrNDs2bPVsmVLSffHXjdo0EAjR47U1q1bdezYMQUEBMjb21sVKlSw8dkBAAAAAN4UiX4Mtp2dnaZOnapRo0apU6dOCgsLU65cuTRr1ix98MEHkqTp06dr0KBBql69utKlS6fu3burevXqlmN06NBBMTEx6t27tyIjI1WkSBHNmDFDjo6OtjotAAAAAMAbJtEHbElKmzathgwZ8tj1vr6+WrJkyWPX29vbq1u3burWrdurKA8AAAAAgMTfRRwAAAAAgKSAgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAQjYAAAAAAAYgIANAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAQjYAAAAAAAYgIANAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAQjYAAAAAAAYgIANAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAQjYAAAAAAAYgIANAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAQjYAAAAAAAYgIANAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAQjYAAAAAAAYgIANAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAQjYAAAAAAAYgIANAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAQjYAAAAAAAYgIANAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAQjYAAAAAAAYgIANAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAQjYAAAAAAAYgIANAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAQjYAAAAAAAYgIANAAAAAIAB3pqAHRcXp/Hjx8vPz0/58+dX8+bNdf78eVuXBQAAAAB4Q7w1AXvy5MlauHChBgwYoMWLFysuLk7NmjVTdHS0rUsDAAAAALwB3oqAHR0drZkzZ6pDhw4qV66ccuTIoTFjxigkJESbNm2ydXkAAAAAgDfAWxGwjx07pjt37qhEiRKWZSlSpFCuXLm0b98+G1YGAAAAAHhTvBUBOyQkRJKUPn16q+Wenp6WdQAAAAAAvAyT2Ww227qIV2316tXq3r27/v77b9nZ/e87he7du+vy5cuaPXv2cx0vb968io2NTRDYX1RYeKRi4uIMOdabwtnRQclcnRRz97bMcbG2LifRMdnZy8Etua3LSIBr+dG4nh+Pazlp4Vp+ssR4PXMtPxrX8pMlxmtZ4np+FK7lJzPqWr506ZLs7e31119/PXVbh5d+tCTAxcVF0v2x2PE/S1JUVJRcXV2f+3jOzs6GTo6Wwt3l6Ru9pRLjmzsej2v5ybiekw6u5SfjWk46uJafjGs5aeF6fjyu5VfLwcFBTk5Oz7btK64lUYhvab58+bLee+89y/LLly/Lx8fnuY+3f/9+w2oDAAAAALwZ3oox2Dly5JC7u7v27NljWRYWFqajR4+qSJEiNqwMAAAAAPCmeCtasJ2cnNSgQQONHDlSadKkUYYMGTRixAh5e3urQoUKti4PAAAAAPAGeCsCtiR16NBBMTEx6t27tyIjI1WkSBHNmDFDjo6Oti4NAAAAAPAGeCtmEQcAAAAA4FV7K8ZgAwAAAADwqhGwAQAAAAAwAAEbAAAAAAADELABAAAAADAAARsAAAAAAAMQsAEAAAAAMAABGwAAAAAAAxCwAQAAAAAwAAEbAAAASALMZrPV73FxcTaqBMDjELABAACAJMBkMkmSZs+erfPnz8vOzi5B6AZgWwRsvLH4g4M3AdcxkiquXeDVCA8P14YNGzRx4kRFR0dbQjfwLOLfm6Ojo21cyZuLgI03lslk0u7du7V+/Xq6UCHJePha5YMTkiqTyaTffvtNu3fvtnUpQJIWGxtr9XuyZMn02Wef6fz58zp//rwkuorj2ZlMJm3btk1jx45VeHi4rct5IxGw8cY6ePCg2rVrJzs7uwR/nIDEKC4uTnZ299+WN23apClTpui7777T1q1b+fCEJCO+deTvv/9WixYtdPr0acXExNi4KiDpsre3lyTt2LFDx44dk8lkUt26dXX37l1NnDhRkix/O4Cn+eeffzRkyBD5+PjIycnJ1uW8kXg14o10/PhxzZ49W1WrVtUnn3xCKyCShPgPSMOHD9ewYcN06NAhRUVFqW3btpo8ebIiIiJsXCHwdCaTSb///rvWrl2r5s2bq169enJwcLB1WUCStmvXLjVv3lzt2rXTypUrZW9vr/79++vMmTNav369rctDEnHs2DENHjxYWbNm1aeffmr58gbG4i8e3jhRUVGaO3euduzYoWzZskmSHBwcFBsbyxsJEr2ffvpJa9as0aRJk5QvXz798ssvWr16tfLmzaubN2/K1dVVZrOZL42QaEVHR2v9+vVavny5SpQooaioKDk7O9u6LCBJebBHkySVKFFC5cqV0+HDh9WzZ0/t2bNHBQoUUN68eXXy5ElLLydasvE4ZrNZf/75p65cuaIrV64oIiJCKVOmVExMDF+CGoxXId44zs7Oat26tapXr66///5bo0ePlnS/ixXdbJHYXbhwQaVKlVK+fPm0ceNGBQQEqF+/fsqRI4e6d++uQ4cOEa6RqDk5OSkgIEBffvml9u3bp23bttm6JCDJiQ/KoaGhku4H7o8//lilSpXS6NGjdffuXW3ZskUbNmzQvHnzdPDgQcI1nshkMql69epq27atnJ2d1bJlS925c8fSCAXj8EpEkhc/3i80NFRnz57VqVOnlCFDBnXq1Ek1a9bU5s2bNWnSJEliPDYSlQdnWY7/8ic8PFx37tzR+vXr1aNHD3Xv3l116tTR3bt3dfz4cZ05c8ZG1QKPFn8dX716VWfPntXZs2fl4eGhb7/9VpUrV9Y333yjX375xbZFAknQmjVrVLVqVS1dulSxsbGWic0OHTqk8ePHq2HDhipYsKBu376tNWvW2LpcJDLx783//vuvDh48qJ9++klXr15V5cqV9e233yosLEzt27fX3bt3ZW9vz1wZBqI/AJK0+K6yW7Zs0ejRoxUREaHQ0FD5+fmpWbNm6t69u4YPH64ff/xRdnZ2at26Nd3EkSg82M1706ZNunbtmurUqaPcuXNryZIl2rFjhzp37qx69epJklxcXOTh4aG0adPasmzAyoPvwZMnT9bly5dlb2+vTJkyacyYMerXr5/i4uLUoUMHTZgwQWXLlrV1yUCiFd8tPP51lT59elWvXl0DBgzQ9u3bVbNmTY0ZM0YNGzaUr6+vKlWqpDJlymjx4sWqVauWrctHIhJ/DW3evFlDhgxR2rRpdf78eaVOnVo1atRQ06ZNFR0drWnTpqlDhw4aN26ckiVLZuuy3xi0YCNJim+Fjr8VV48ePVS7dm2NGzdOI0aMUFhYmIYOHapjx46pU6dOKlGihBYsWKDp06fbuHLg/oeo+HB96NAhTZ48WcuWLdO2bdtUoUIFVaxYUVFRUZLuz/Z58uRJfffdd0qRIoWKFStmy9IBK/ETmnXt2lWVK1fW4MGD1a5dO125ckUNGzbUvXv3NGTIEFWuXFktW7bUb7/9ZuuSgUTpwTHX586d06lTp/Tee++pR48eWrFihVKkSKFhw4apZ8+eKlSokH7++WddvnxZklS3bl05ODjQAgkLk8mk4OBgffvtt2revLmWLl2qcePG6d9//5W9vb2io6NVrVo1tWrVSv/884+++eYbq151eDkmM88mkpB9+/apSJEikqR79+7J0dFR33//vWJjYzVgwADLdnv27NGIESOUIUMGjRs3TufPn9eCBQtUv359ZcyY0VblA1aGDh2qQ4cO6fbt2/rvv/+UNm1adevWTR999JF69+6t33//Xf/9959y5swpFxcXzZ07V46OjkzYB5uJjo6Wk5OTzGaz4uLiLO+9yZMnV/fu3S3bHT16VAEBAcqUKZOCgoJ0/fp1TZw4UfXr11fWrFlteAZA4vNgj6axY8dqw4YNunbtmiIjI/XZZ5+pRYsWeu+993TixAkNHTpUR44cUVhYmAIDA+kVAkn3h0mmTp1aTk5OlvfpoKAgHTx4UJMnT9a5c+fUpEkTlS5dWs2aNdOCBQvUqFEjpUmTRps2bVK+fPn4fGwguogjyfjtt9/Uo0cP1axZUwEBAXJ0dNS9e/d07Ngx5c2bV9L/QnexYsVUt25d9e3bV+fPn1fGjBnVtWtXZklEorFq1SqtXbtWQUFBeuedd2Rvb6/u3bsrKChIJpNJAwcO1L///qtz584pbdq0ypUrl+zs7JjtEzZz584dLVq0SBUrVlTGjBllb28ve3t7nThxQrly5bLaNleuXKpXr57mzp2rkJAQeXt7q1evXnwxBDzkwZbradOmafny5erfv7+8vb114MABzZkzR2FhYerYsaN8fHw0a9YsrV+/XocOHVKpUqVsXD0Si99++0329vaqVq2aIiIi5OTkpAsXLih9+vSKjo5WgwYNVK5cOX3//fc6f/685s2bpzx58qhy5cqqUqWKrct/4/ApDUlGtmzZVL58ef3888+yt7dXhw4d5OjoqIwZM+q3335TWFiYUqRIYQnZnp6eypQpk2VMCaEEicnp06eVO3du5cqVS7GxsXJwcNCwYcPUrl07DRkyRNHR0apQoYLef/99yz5xcXFcx7CZe/fuafv27Tpw4IAKFSqk8PBwtWvXTi4uLvrvv/8UFRUlR0dHS1jw9PSUo6OjJVQTroH/2b17t4oXL26ZfDU6Olo7d+5U27Zt5e/vL+n+F1WpUqXSoEGDtHXrVmXPnl2SVKlSJVWqVEmS+NIViomJ0ebNm3Xu3DkdPnxYf/zxh1asWKHcuXPr+++/19q1a1WjRg117dpV0v334syZMzOnyyvEGGwkCWazWd7e3mrbtq0KFy6sTZs2acyYMZJkmdijd+/eun37thwdHSVJu3btUooUKeTk5GSzuoGHxY+RCw0NVXh4uEwmkxwcHBQdHa2UKVMqICBAFy5c0Lx587RlyxarMVHcggW2lCpVKtWsWVMnT57U8OHDZWdnJzs7O7Vo0UK//vqrJk6caJk7QJIOHjyoDBkyyM3NzYZVA4nPpEmT1KdPH61cuVLS/cBz69YtHTlyRKlTp5Z0/wstSapcubI++eQTLVu2TJGRkQluN0q4hoODg6ZOnSoHBwfNnz9fuXPnlnT/i5gKFSooJiZG5cuXl4ODg6KiorRs2TJFREQoc+bMti38DcarEkmCyWRSXFycPD091bJlS0nS5s2b5eLiotatW+urr77S3LlzVa1aNcstK/bv36/58+fL3d3dxtXjbfZg9z/pfx+G4sfVzZs3Tw0bNrR8EXT37l0VK1ZM4eHhmjVrlsqUKSNXV1eb1A7Ei7+OfXx8dPPmTaVPn15///23zp49q5IlS6pnz54aPHiwjhw5Ig8PD0nStm3btGDBAmamBR5SoUIFHThwQEuWLJHZbFaNGjXk7e2tLFmy6Mcff1SZMmXk5uZmGUubKlUqZcmSRS4uLrYuHYlQVFSU7t27pytXrihz5sz666+/tHr1alWtWlVNmjRRZGSkGjVqpA8++EBOTk66ePGipk2bJi8vL1uX/sZikjMkeg9O/hH/Ie/q1auaOnWqduzYoZo1a6pFixY6ePCgVqxYocuXL+u9995TnTp1mEwHNvVguN6yZYtOnz6tkJAQffjhh8qTJ4+mTJmiH374QS1btlTNmjV1584d9e/fX9myZVOTJk3k5+eniRMn6qOPPrLxmeBt9uB78PXr13Xu3DldunRJ06ZNk6enp/r06aMMGTJo7969mj17tqKjo/XOO+/oq6++UrZs2WxcPZC4xP9d+PfffzVo0CCFh4erdu3aqlmzpmbPnq3Fixfro48+UpcuXWQymRQTE6PmzZsrY8aM6t+/v63LRyLy4Hvzg8saNWqk69evq2XLlqpSpYpu376trVu36vjx43r33XdVpkwZJjR7xQjYSNTi3zz27dunffv26dChQ/Lz81Px4sWVPn16jRo1Sjt37lTVqlXVpk0bq32AxGL48OHatGmTsmTJInd3d/3000/69ttvVaxYMW3evFnTpk1TsmTJ5OzsrNSpU2vBggWKjY1Vo0aN1LdvX/n6+tr6FPCWin8//f3337Vu3TqFh4fr448/1meffaYlS5Zo2bJlSps2rb799lu99957lhY3xoUCjxcfsk+dOqXBgwfr1q1batasmSpUqKChQ4dq69atcnV1Vc6cOXXmzBndvXtXq1evloODA59xIOl/78179+7Vtm3bFBISoqZNmypv3rwKDw9XmzZtdOPGDTVv3lxVq1ZVeHg4PTpfIwI2Er1NmzapT58+Kl26tJIlS6aNGzfKy8tLc+bMUWxsrKZMmaK9e/dauikCiUH8H7/169dr8ODBmjRpkvLly6eff/5ZrVu31rRp05Q7d26lSZNG58+f17///isnJyeVKFFCkjRu3DitX79ec+fOpRsXbGrr1q3q3LmzateurWvXrql06dKqVq2a7OzstHz5ci1cuFDJkiVT2rRplSFDBnXt2pUQADzk4eFC8bdbPHPmjAYMGKAbN26odevW+vjjj7V161Zt3LhRJpNJGTJkUJs2bSz3ueaLK8TbtGmTevTooSJFisjT01MtWrSwtEzHT0L533//KXny5Lpz546WLVumZMmSMZ/La0DARqJ29uxZNW3aVC1atFDt2rUVFxenwoULq23btqpQoYJSp06tqKgojRgxQmfOnNHkyZOVJk0aW5eNt9SoUaNUrlw5FSpUyLJs6tSpunDhggYOHKiNGzeqR48e6tGjh/z8/NSjRw998803yp07t+Li4rR7926tWLFCd+/e1R9//KGZM2cmuP0R8DrduHFDLVu21EcffaQWLVpY7n9tb2+v8+fP691339X69eu1du1aHT9+XOPGjbPcNhHAfQ+G61WrVunkyZM6cuSIPvvsM/n7+ysiIkK9e/fWzZs31aRJE1WuXDnBMeIDOSBJJ0+eVJMmTdS+fXt98cUXunnzpkJCQvTLL7/Iy8tL1atXV3h4uCZOnKirV6+qWbNmypEjh63LfmvwNRgStcjISKVIkUK1a9fW6dOn1ahRI1WsWFFffvmlevbsqSJFiqh+/frq0qWLJBGuYTPXr1/XtGnTtG/fPn377beWbt23bt3S7du3tWHDBvXs2VPdu3dXnTp1dOrUKR07dkxnz55V7ty5ZTKZlDp1aoWHhytPnjzq2rWr1S26AFu4c+eOQkJCrEKzvb297t69q969e6tUqVJq0aKFypYtK0l0QQQeIT5cDx8+XBs2bFCxYsX0/vvv69tvv9Vnn32mESNGqE+fPho4cKDmzZunGzduqEGDBlbHIFzjQeHh4cqWLZs+/vhjXblyRWPGjNGff/6pGzdu6MaNG7p69aqaN2+u7t27KyYmhjvqvGb0EUCiFhISoosXL+ro0aNq3ry5ypYtq0GDBsnV1VXXrl3Tnj17FBcXp3Tp0ildunS2LhdvqRMnTihNmjTaunWrLly4oEGDBunQoUOSpJw5c2rfvn3q0aOHAgICVK9ePUmSs7OzPDw8LDMux287depUtWvXjnCNRMHd3V2Ojo7at2+fJFm6fTs6OiouLk4hISGW7QjXwOP9/PPPWrt2rSZMmKAhQ4aoRo0aMplMqlq1qk6dOqXMmTOrZ8+eio6O1vHjx0UHUzxJ/NwYPXr0UPny5XX69GlVr15dW7du1eeff66DBw8qNjZWdnZ2hGsbIGAjUbh3757lj0lERITlPo9ly5ZV5syZVaNGDeXPn18DBgyw7OPg4CAfHx/GksCmpkyZoi+++EJ79+5VhgwZtGTJEp0/f14DBw7UsWPHVLVqVZUsWVL37t2Tg4ODTp06pVOnTqlfv35KlSqVChcuLEmMV4XNxb8Hx8XFKTo6WpKUMmVKlShRQtu2bdNPP/1k2dbR0VFeXl5KmTKl1b4AHu369evKmzevcufOrR9//FENGjRQ7969lSdPHvXo0UN79uxR1qxZNXHiRH3//fcymUy8riDpf++voaGhOnfunMLDw5UvXz6NGDFCKVOmVKdOnTR9+nQ1bdpUrq6uioiIUMaMGen1YEOMwYZN/fHHHypYsKDl9+3bt2vmzJmyt7dX3rx5FRAQoPXr12vSpElKkyaNvvvuO4WHh+uXX37RkiVLtGjRImXJksWGZ4C33c6dOzVv3jz9+++/GjBggIoVK6aLFy/qiy++UPr06TV8+HBlzZpVAQEB+vPPP3Xp0iXlyJFDzs7OmjdvnhwdHRlbB5uLn5Rs586d2rBhg44cOaI8efKofPnyKly4sFq1aqXw8HCVLFlShQsX1s6dO7V69WotWbKE3hbAA1avXq0bN24obdq0KlOmjFKkSCFJCgoK0o8//qj27dvrm2++UZcuXfTll18qOjpa5cuXV5MmTdS4cWPLcR6eFA1vp/j35i1btmjEiBG6d++e3NzcNGvWLKVLl05ms1nh4eH677//ZDab9dNPP2nBggVavHgxt0m0IQI2bGb79u3q1auX6tWrp7Zt22rfvn366quv9MUXX+jixYs6c+aMSpYsqQEDBmj9+vWaMWOGjhw5ovfee0/Ozs4aNmwYE0AhUdi/f7+mTZumkydPatCgQSpevLj+++8/ffHFF/Ly8tKIESOUNWtWHTt2TBcuXJCnp6fy5MkjOzs7ZoVFohE/W3iVKlVkZ2enY8eO6dKlSwoICNAnn3yiMWPGaNeuXYqNjZWHh4f69OnDpDnAA5o0aaLQ0FAlS5ZMhw4dUoUKFdSvXz+lTp1ahw4dUs+ePXX69Gl169bNEqavXr2qhg0bqkuXLvroo49sfAZIDO7du2cZhmNnZ6ctW7YoICBAzZs3V4YMGTRjxgzFxsZq4cKF8vDwsMzx4uXlJXt7e40cOVI5c+a09Wm81QjYsJnz589r6tSpOnTokD766CPdvXtX77zzjho1aqTw8HCtWLFCs2bNUunSpS1dw3fv3q306dMrefLkTGgGm3vwVkR79+7V9OnTderUqQQhO3369Orfv3+CL4RooYAtPHjdxl+DoaGhat++vapXr26ZJ+Ds2bOaP3++tm7dqkGDBqlEiRKKjo5WWFiYXF1dlSxZMlueBpCo1KtXT1FRUQoKClKaNGl07NgxffHFFxo4cKCqV6+umJgYTZgwQWvXrlWxYsX09ddfKywsTDNmzNC1a9e0ePFiejJBCxYsUEREhOrWrSt3d3eFhISoTZs2qlmzpurXr6/Lly+rXr16unnzppIlS6aVK1fKw8NDf/75p9zc3JQmTRqruV1gG3yyg02YzWZlzJhR7dq1U4ECBbR9+3atX79emTJlknR/wpzq1aurUaNG2rlzp3r06CFJKl68uDJlykS4hk3FzxHw4LjpokWLql27dsqcObN69eql3bt365133tGyZcsUGhqqDh066OTJk1bHIVzDFu7cuaMrV64oMjJSMTExku63mFy9elWZM2e2jPfLlCmT6tevL3d3d23fvl2S5OTkpLRp0xKugQfEfyk1a9YspU2bVjExMcqVK5fKly9vmaj16tWrCggIUN26dfXXX3/p888/V//+/RUZGamFCxfK3t5esbGxNj4T2NK+ffs0YMAALVu2TCtXrlRUVJTu3bunkJAQlS5dWnfv3tWYMWNUpkwZzZo1S3Fxcfr666914MAB+fr6Knv27ITrRIJ+ibCp9OnTq2XLlgoKCtKyZcv0+++/q1y5cpKk5MmTq1atWrK3t9e4ceP03XffqX///rYtGG+9B1ud165dqzNnzui///5TqVKl9PHHH6tnz54aMmSIevXqpcGDB6tYsWJauHChBg0axHwBsLlZs2Zp27ZtOn78uJydnVW4cGHVq1dPmTJl0qVLlxQXFyeTyaTo6Gg5OTkpc+bMypIli06dOmXV8g3gvrFjx+rAgQPav3+/3N3ddffuXbm5uUmS/v77bx07dkyTJ0+Wp6enKleurG7duumrr77SkSNH5OnpqQwZMjBcCJIkb29veXp66ty5c1q/fr1MJpMKFCigUqVK6caNG9q/f79iYmJUs2ZN5c6dWxkyZNCff/6pjh076scff1Ty5MltfQr4f3QRx2sX/yHt3LlzioyMlNls1nvvvaehQ4dq9+7dqlatmlq3bm3Z/vbt21q3bp1KlixpaeEGbG348OFasWKFChcurFOnTikiIkLZs2fX2LFjdfHiRY0YMUJnz55Vnz595OfnZ9mPCc1gK8OGDdPatWvVpEkTvfPOO/r777+1Y8cOHT9+XMOHD9dvv/2mHTt2aP78+Vbvte3bt1eWLFnUuXNnG1YPJD6xsbHatm2bZTbn+fPny9nZWZLUoUMHHTt2TL169ZKbm5tWr16trVu3asSIESpdurTVcRguhPjPxmvXrtX8+fMVGxuru3fvqnbt2ipYsKB8fX3VtGlT+fr6qmPHjrp37566deummjVrKleuXLRcJzJ8VYbXKv4NZNOmTRo5cqRiY2Pl5uamESNGqFWrVjKbzVq3bp0kWUJ28uTJVbduXVpOkGhs2bJFmzdv1syZMy3jqhcvXqxFixbpu+++0/Dhw9W6dWsNGzZMy5Ytk5+fn+UDFOEatjBo0CCtWrVKM2fOVN68eSVJn3zyicqXL69Zs2apc+fOqlu3rvLnz6/69eurb9++MplMOnDggPbs2aNOnTrZ9gSARMje3l4ffvihXF1d1bdvX9WvX1/Lly9Xly5d9O+//2rmzJl69913JUkpUqTQypUrdfXq1QTHIVy/3R7sHeTj46PMmTOratWqWrdunebPny8HBwelS5dOt27dkru7u+7du6fAwEAdOXJE3bp1I1wnQgRsvFYmk0nBwcH69ttv1bt3b2XNmlUuLi7y9PRUypQp1apVKwUGBmr9+vWKjIxUQECAZT/AFq5du6YrV67I3t5e2bNnlySdO3dO6dOnV9asWS2zfdaoUUO3bt3S4sWLdfbsWRUsWFDff/+9fHx8JPEBCrYzZswYLV++XKtWrVKmTJkscwjY2dnJ19dX7du3l52dndatW6d27dopefLkCggIUIYMGZQyZUrNmTNHWbNmtfFZAImTg4ODihcvrn79+um7776Tr6+v3nvvPS1atEjJkye3fLnq4uKibNmyyd3d3dYlI5G4cuWK0qVLJ5PJZOnd9sEHH8jNzU1TpkzR/Pnz1bNnT82ePVtRUVFydXXVwoULtWTJEoWHh2vGjBnKkCGDrU8Dj0DAxmsXHBys/Pnz67PPPrNqzTt06JB2796tbt266fvvv9eePXt048YNpU6d2obV4m327bff6ty5c9q/f7/s7e3VtWtXNW7cWGfOnNGtW7csXQHjx6vWrFlTY8eO1bFjx/T+++9bbpNB9z/YSmhoqAIDA1W5cmXL+LyHr8X3339ftWrV0uHDh3Xz5k0NHjxYTZo0UZo0aeTg4GC5jy+AhMxmsyVk9+3bV5MmTdKdO3cSvN6GDBmiZMmS6cMPP7RluUgkJk+erF9//VVly5ZVixYtrD4Pd+/eXS1atNCPP/6oIUOGqEOHDtqwYYP8/f2VLFkypU2bVnny5NF7771nwzPAk/CJD69N/HD/48eP686dO5Y3k/hZM2/duqXx48crKipKXbp00YQJEwjXsJnGjRvr33//VZMmTTR37lyNHTtWlSpVkiRVqVJFp06dUmBgoKT7MytL96/hHDlyKGPGjFbHIlzDVry8vDR//nwFBwcrMDBQ58+ft1of/75cokQJ5cmTR7/++quk+6E7TZo0hGvgIfE9QOLF97BzcHBQyZIl1b59e0VERKh69eqWbZo2barTp09rzpw5zBYOXb58WePHj9fBgwc1ZcoUffHFF1qwYIHOnj0r6f61VKRIEW3btk2SNH78eKVPn14LFy6Uq6urKlWqRLhO5PjUh1fqwTn04v8IlS1bVseOHdOmTZskyRK042esjYmJUfr06ZUuXbrXXzAgacKECYqIiNDkyZPl7++vokWLqnz58vLy8pLZbJavr68aNmyomTNnavTo0bp48aL++ecfjRgxQi4uLsqdO7etTwGwKFy4sEaNGqUff/xRCxYssArZJpPJ8j7t4eGhlClTSuJLIeBRzGaz5bVx8uRJRUdHW613dHRUiRIl1K9fP4WFhalWrVpq1qyZLly4oHXr1snR0VExMTHMxfGW8/T01JIlS5QmTRpVqlRJqVOn1s8//6yvv/5ay5Yt0+3bt/X1119r3759Wrx4saT7Ibt06dIqWrSojavHs+AvKF6Z+EkbDh8+rCVLlmj79u26ceOGSpUqpZIlS2rSpEnauHGjJCkmJkb79u2Ti4uLpdstYCtHjx7VRx99ZHW/9fgPVSaTSS4uLnJ1dVXz5s21bNkyVa5cWR06dFBYWJjmzp0rOzu7BK0cgC0VKlRI48aN07p16xKEbOl+T6Jr167J19dXkvWXowBkuYWdJI0YMUI9evRQZGRkgu0cHBxUokQJ9e/fX1evXlVoaKhVuOZWXJCkfPnyafz48QoODlaePHn05ZdfqlGjRho7dqw6duyoFStWqFWrVvrjjz9048YNSfeHGXA3naSB23ThlYgP1xs3btR3330nJycnxcXF6ZNPPlFAQIAuXLigwMBAbdu2TVmzZpWrq6tOnjyp2bNnW8atAq9bXFycrly5oqpVq2rcuHEqXrz4I8dP37hxQ3Xr1rWMnTpy5Ig8PDyUK1cu7meKRC04OFgdO3ZUlSpVVL9+fctwhjFjxmjp0qVauHAh92sHnuDQoUMaPny42rRpoxIlSjx2EtZ79+7p8OHD8vX1lb29PX8X8Ej79+9X586dValSJQUEBOjSpUvatm2bZs+erdu3bysiIkLLly9Xnjx5bF0qngOvdBjiwVsMxP+8c+dO9ejRQ126dFGDBg00aNAgrVmzRpLUsWNHDRgwQNWqVdOuXbuUIUMGlSlThm/mYFN2dnZKmzat3N3dtWvXLhUvXjxBuDabzUqdOrWyZMmi0NBQpU2bVmXLlrWsj4uL40MUEq34luyOHTtKklq0aKElS5Zo1qxZWrRoEeEaeIItW7bo22+/lYODgzw9PS1DLB4O2WazWY6OjipQoICk+z1E+LuARylcuLBGjx6tDh06KC4uTs2bN1eTJk30xRdfKDAwUHv27GHm+SSIFmwY4uFvZiMiItSzZ09lzJhRXbp0UXh4uJo0aSLp/kRQpUqVUuvWrZUuXTpmWEaiYTabFRUVpfbt2yssLExDhw59bOBo3ry50qVLp8GDB7/mKoGXFxwcrK5du8rd3V3nz5/X/PnzaSEBHvKo8NyrVy/98MMPatKkiVq1aqUUKVI8cjvgeQQHB6tDhw6qUqWKvvzyS8stFeNvz4WkhVSDl7Zw4UL16NFD7dq105QpUyRJrq6uCgkJUbJkyXT37l2NHz9eefLk0dKlS1W4cGEtXbpUHTt21D///EO4RqIRP766Xbt2Onr0qKZPn64rV64k2O769eu6ffu2cuXKZYMqgZdXqFAhDR8+XNevX9fixYsJ18BDHhxzLUl3796VJA0aNEhffPGF1q5dqx9++EG3b9+2miwQeBGFChXS+PHjtXHjRs2YMUPnz5+XnZ0d4TqJItngpQwbNkyTJ09WxowZFRMTo9WrV6tPnz6SpFKlSilHjhz666+/dOnSJZUqVUqSlD17dmXIkEFeXl5MaIZEKV++fPruu++0evVqDRgwQLt27ZJ0v6fGf//9p2+//VZms1n16tWzcaXAiytSpIi2bt2qHDly2LoUIFF5sGfd/Pnz1bFjR9WtW1d9+vTRv//+qwEDBqhs2bKaN2+eVcgGXkahQoU0bNgw7d27l2CdxNFFHC9s0KBBWrVqlWbNmqU8efIoJiZG06dP16ZNm7RixQrLdiNHjtTRo0c1Y8YMmUwm9evXT66urmrTpg3jSpBomc1mbd68WX379pW9vb28vLxkMpnk4OAgOzs7zZkzR46OjoqNjeWWKwDwBhozZoyWL1+uSpUqyd7eXmvXrlWaNGnUp08fFS1aVD179lRwcLA+//xzNW7cWG5ubrYuGW+AiIgIAnYSR8DGCxkzZozmzp2rlStXKnPmzIqKipKzs7OOHTum5s2bq0OHDgoJCVGVKlU0duxYhYaGqmPHjtq2bZvWrFmjxYsXM5kOkoT//vtPv//+uw4dOqTUqVPLx8dHFStWZFZYAHiD/fnnn+ratauGDBmiwoULS5IuX76sJk2ayMXFRXPmzFGyZMnUsmVLOTs7a9y4cbRiA5BEwMYLCA0NVdmyZVW5cmX16tXL6l7BEyZMUGBgoLJnz66QkBA5Ojqqdu3aWr9+ve7evStXV1eNGjWKsatI8mi5BoA315YtWzRy5EitWLFCbm5uunfvnhwdHXXhwgVVrlxZnTt3VqNGjST9r0s5k50BkBiDjRfg5eWl+fPnKzg4WFOmTNGFCxckSUFBQZo9e7bGjh2roKAgbd68WZkyZdJvv/2mtWvXasaMGVq4cCHhGknOg99Dxv9MuAaAN5ednZ1CQ0N18uRJSbIMCUqXLp0yZsyo6Ohoq20fnhQNwNuLvo14IYULF9aoUaPUsWNHJU+eXFFRUVq+fLnGjRun0qVLW7bLkyePfvvtN5nNZmXNmtWGFQMv7sEPTXyAAoA3w5OG+WTMmFHvvvuuFi5cKBcXF33wwQeyt7dXRESEnJyc5OXlZbU9d0QBEI+AjRdWqFAhjR07Vq1bt1ZkZKQGDBig0qVLW1r4TCaTbt68qSxZsiguLs7G1QIAANzXoUMHFShQQPXr15eTk1OC9dmzZ9eXX36pqVOn6ubNm6pYsaJSp06tefPmyWw2q3LlyjaoGkBSwNdteCmFCxfW9OnT5ebmpuPHj+v8+fMymUwymUwaN26cNm/erPbt2z/yjxcAAIAtpEmTRqNGjdLKlSutuntL/xsKVK9ePXXq1EkxMTHq06ePJkyYIHt7ey1dulT29vaKjY21RekAEjkmOYMh9u/fr06dOqlKlSpq3ry5li5dqsmTJ2vRokXKkyePrcsDAACwMmrUKM2cOVN9+vRRjRo1rBoDHrwX9t27d3Xt2jW5ubkpTZo0MplM3EUCwGMRsGGY4OBgde3aVe7u7jp//rzmz59PuAYAAInWyJEjNWvWrEeG7MfNCv5g+AaAhxGwYah9+/apU6dOmjFjhnLkyGHrcgAAACQ9PhgPGzZMc+fOVe/evVWzZk2GtQF4KQRsGC4yMlIuLi62LgMAAECSdbj+888/devWLUVFRalcuXJydHTU6NGjNX36dPXp04eQDeClMHgEhiNcAwCAxMJsNlvC9ahRo7Rp0yaFhYUpLi5OqVKl0rBhw9S5c2fFxsZq0KBBsrOzU/Xq1QnZAF4IARsAAABvrPhx1HPnztUPP/ygsWPHKl26dLp9+7aGDx+uTp06ae7cuerWrZskqW/fvvLw8NBHH31ky7IBJFF0EQcAAMAby2w2KyYmRj169FCuXLnUtGlTy7qrV6+qWbNmSp48uebNmydJWrRokb744gtmCQfwQpgCEQAAAG8sk8kks9ms48ePKzQ01LI8Li5OadOmVa1atXThwgWFhIRIun//awcHB8XExNiqZABJGAEbAAAAb4y4uLgEy2JjY5UhQwb9/fff+u+//6zGZbu7u8vDw0Nubm5W+9CCDeBFELABAADwRnhwtvAjR45o165d2r9/v+zt7dWtWzf99ddfGj16tE6fPq24uDiFh4dr7dq1eu+995Q8eXIbVw/gTcAYbAAAACR5ZrPZMqHZqFGj9OOPP+revXu6evWqChYsqFatWsnNzU3NmzeXt7e3nJyc5OzsrLt372rFihVydHS0OgYAvAgCNgAAAN4Y8+fPV1BQkIYPH66sWbPqn3/+0YwZMxQWFqa+ffsqXbp0WrNmjcLCwpQhQwbVrl3bMuaabuEAXhYBGwAAAG8Es9msjh076oMPPlC7du0sy/fv36+BAwcqW7ZsGjlyZIKW6tjYWNnb29uiZABvGMZgAwAAIMmLi4vTnTt39Ndff8nFxUWSdO/ePZnNZhUuXFjVqlXTli1bdPXq1QT7Eq4BGIWADQAAgCTFbDYnmC3czs5O7u7uyps3r1avXq0rV67I0dHRcrstd3d35cyZUylSpGCcNYBXhoANAACAJMVkMllmC9+wYYMWL16slStXSrp/H2uz2aw+ffro+vXrcnR0VFRUlH766Sd5e3vL0dHRlqUDeMMxBhsAAABJwqBBg+To6Kju3btLkgYOHKg1a9bIxcVFsbGx8vf314ABA7Ry5UpNnz5dV69eVbZs2XTnzh3FxsYyWziAV46pEgEAAJDo3bhxQ9euXdOJEyeUMmVKFS5cWPv379fcuXOVPHlybdmyRXPmzFGvXr00aNAgFSpUSGvWrFFERIS8vb1Vr149ZgsH8MrRgg0AAIAkISQkRIGBgfrzzz+VKlUqvfPOOxo4cKAkKTw8XCtWrNDs2bNVpEgRDRs2LMH+zBYO4FVjDDYAAAAStbi4OJnNZnl7e6tZs2bKmzevDh48qDNnzli2cXd3V40aNdS4cWMdPHhQTZs2TXAcwjWAV42ADQAAgEQrLi5OdnZ2MplMio2NVYYMGdSzZ09Vq1ZNp06d0sSJEy3buru7q3r16qpRo4bc3d0TzDQOAK8aA1AAAACQKMWHa0maPXu2du/ercuXL6tdu3Zq27atYmNjtXHjRklSu3btJN0P2V999ZVcXFxkMpmsjgEArxrvNgAAAEiU4oPxmDFjNH36dOXJk0f+/v6KiIiQh4eHWrdurUKFCmnDhg2aMmWKZT9XV1eZTCaZzWbCNYDXihZsAAAAJFqnTp3Shg0bNGTIEPn5+Vmtu3Hjhtq0aaPAwEDNnj1bXl5eqlGjhmU9t+IC8LoRsAEAAJBo3blzR7dv31bGjBklSfE3wLl165bq1aunYcOGqXXr1nrnnXf0+eef27JUACBgAwAAwPaCgoJ0+vRpnT59WmXKlFHDhg2VPHlyJU+eXLdu3dLBgweVOXNmy7hqNzc3ZcuWTZcuXVLatGkts4ZzKy4AtkTABgAAgE01atRIt2/fVt68eeXo6KibN2/K2dlZcXFx8vb2VrVq1RQYGCg3NzdVqFBBdnZ2sre3l8lkUrJkySTdb9k2mUyEawA2RcAGAACAzXTo0EF3797VjBkzlDp16gTrXV1d1aRJE924cUP9+/fX4cOH5enpqW3btik6Otoy5prx1gASAwI2AAAAbOLw4cMKCQnR4MGDLeE6JiZGERERmjVrls6ePStHR0e1bt1agwcP1vz58/XDDz8oY8aMSps2rQIDA2Vvb0+3cACJBgEbAAAANnHz5k1dv35dKVOmlHR/4rJ169Zp8eLFOnHihNKmTStHR0dt3rxZy5cvV7t27dS0aVM5ODjI0dFR0v1A7uDAR1oAiQPvRgAAALAJDw8PhYaGasGCBcqYMaOWLl2qQ4cOKWfOnBo9erQKFy6sO3fuqG/fvvr999+VJUsWOTs7W+5tbTabCdcAEhXekQAAAPDamc1m5cyZUx07dtTEiRMVExOjjBkzqkmTJurSpYuly3d8l/GbN29KkiVcS4y7BpD4ELABAADw2sWH42bNmqls2bK6deuWMmbMKC8vL0n/6/p99epVpUiRQtmzZ7dluQDwTExms9ls6yIAAADwdoq/vVa869evK02aNIqNjVVERIQ6deqksLAwLVq0iInMACR6tGADAADAZh4M19u3b9eMGTNUokQJXb16VSdPnlRYWJiWLl3KbOEAkgS7p28CAAAAvLiHO0w+rgNl2rRplSFDBi1YsEBnzpyRr6+vli1bJkdHR8XExBCuASR6dBEHAADAK/Ngq/PNmzfl4uIiFxeXJ+4THh4ud3f3Rx4DABIzWrABAABguL///tuq1Xnq1Klq3769atWqpfXr1+v27duP3dfNzc3ys9lsJlwDSDII2AAAADBUUFCQqlevrt27d0uSJk2apJkzZyp//vzy8vJS9+7d9cMPPygsLOyR+3MrLgBJFZOcAQAAwFCNGjXSoUOH1K1bN/Xr108nTpzQlClTVKhQIUnSsGHDNGzYMJnNZtWsWVMpUqSwccUAYAzGYAMAAMAwcXFxsrOzU1RUlDp27KidO3cqWbJkCgoKkq+vr2W7YcOGae7cuerWrZuqVaumVKlS2a5oADAIXcQBAABgmPju3c7Ozpo6daoqVaqkmzdv6sKFC4qNjbXMIP7NN9/o66+/1tChQ/X777/bsmQAMAwt2AAAAHhp8S3XkrRs2TL99ddf6t+/v+Li4tSmTRsdOHBAo0aNUsmSJa3GWC9YsEB16tSRgwMjFwEkfbRgAwAA4KU8GK537typP/74Q0uXLtXEiRNlZ2enqVOnqkCBAurSpYt27txpdR/s+vXry8HBQTExMbYqHwAMQws2AAAAXpjZbLbM9D1s2DD9/PPPypYtm06cOKGbN2+qWrVq6tmzpySpVatWOnTokPr376/y5cszQziANw4BGwAAAM/t7t27Vver/vnnn9WnTx9NmDBBBQoU0PXr17V27VrNnTtXFSpU0DfffCNJqlu3rpInT65p06bZqnQAeGUY7AIAAIDnUr9+fX355ZeqXLmyZdnZs2fl5eVlmSk8TZo0+uyzzxQWFqbp06fL3d1dbdu21eLFixUdHW2r0gHglWIMNgAAAJ5LyZIl9fHHH0uSoqKiJEkuLi66e/euQkJCLNulSZNG5cqVU0xMjGbNmqXRo0dLkpycnBQbG/v6CweAV4yADQAAgGeyadMmSVLbtm3l5OSkqVOnat68eYqKilK+fPl06dIlLVu2TGFhYZZ9XF1dVbRoUdWuXVvbt2/Xvn37JEn29vY2OQcAeJXoIg4AAICnWrBggSZOnKjz58+radOmkqRTp05p7dq1cnV1Vf369dWzZ0/17dtX4eHh8vPzk7e3t4YPH64UKVKoTp06Wr16tc6ePasiRYrY+GwA4NUgYAMAAOCpSpYsqaNHj2rVqlWKjY1VixYtNGLEiP9r7+5imr7+OI5/WoFMkVUSmmG0PscapwWWaYwPQafRaYhZIsRMJSImMMOWzbkQUIYPdTDZcFmAMmbUC+dj0Ej1Qi+cRlymBoNP0QsyxgB1CjKmqBvMdhem/f+7gkpWKM73K+lFzznt75zeffo9v/NTRESEPvvsM7lcLiUnJ2vAgAEqLi6W0+lUZGSkIiMjVVpaqrCwMI0aNUrh4eHBXgoA9BgCNgAAAJ7K7XZr5MiReu+99+RwOHTkyBG53W6lp6crNzdXjx8/Vn5+viQpOTlZU6dOVVtbm+7fv6/Ro0crLCxMW7duVX19vWJiYoK8GgDoOTymCwAAAF1yuVwyGo3e5103NTWpuLhYZ86c0dKlS5WamipJWr9+vcrLy7V27VolJCTIZDLpxx9/1J49e9TU1KTGxkZ9++23Gj9+fJBXBAA9hwo2AAAA/Pzwww+aNm2ajEajXC6XXC6XQkJCZDab1djYqPv376u8vFwdHR1KT0/Xxo0bZTQaZbfbFRUVpXnz5slkMmnMmDGaNGmSZs6cqWHDhgV7WQDQo6hgAwAAwMc333yjAwcOKD09XYsXL/bp++CDD9TQ0KB169bJ6XSqurpaCxcuVFpamiRp+/btWr58uUJCqOMAePkQsAEAAODjl19+UX5+vlpaWvTOO+9oyZIlkp6E67q6OjkcDlksFtXX16usrExXrlzRrFmztHr1au93dHR0KDQ0NFhLAICgIGADAADAT2NjozZv3qyWlhYtWbJE33//verq6lRSUiKLxeK9J7uhoUEFBQV65ZVXVFBQIIPBEOypA0DQELABAADQKU/IvnLligwGg8rLyxUdHe138Nnt27dlNpt92gDgZWQM9gQAAADQNw0dOlS5ubmy2WyKiopSZWWlJHkPPvME6ddee82vDQBeRlSwAQAA8FSeSnZzc7OSkpK8B59RrQYAXwRsAAAAPJMnZP/222+aP3++UlJSgj0lAOhz2CIOAACAZxo6dKhycnJkMBhUU1MjajQA4I8KNgAAAJ7bnTt3FBUVxYFmANAJAjYAAAC6zXOSOADgfwjYAAAAAAAEAH87AgAAAAAQAARsAAAAAAACgIANAAAAAEAAELABAAAAAAgAAjYAAAAAAAFAwAYAAD2CB5UAAF42BGwAAPqwrKwsWa3WLl/Tpk3r8rPnzp2T1WrVuXPnenHGT9TU1Ojdd9/1abNarSoqKur1uQAA0FtCgj0BAADwdGazWcXFxZ32hYaG9vJsns+xY8dUXV3t07Z//35FR0cHaUYAAPQ8AjYAAH1cWFiYYmNjgz2Nf+2/sAYAAJ6GLeIAAPxH7Nu3T/PmzZPNZtOyZct08+ZNn/6ioiJZrVa/z/1z63ZbW5vsdrtmzJih2NhYLVq0SKdOnfL2//HHHyosLNTcuXM1YcIEvfHGG1qxYoWuX7/uvY6n4v7/3/3P69y5c0fZ2dmKj4+XzWZTYmKiTpw44Te33bt3a926dZo8ebLi4uL04Ycfqrm5+d/9WAAA9AACNgAAL4C//vqr05fnILHvvvtO69evV3x8vBwOh2JiYvTpp592+zqPHz9Wamqqjhw5ovT0dDkcDo0aNUoZGRmqqqqSJGVmZurgwYNKS0vTjh07lJ2drZqaGq1Zs0Zut1tJSUlKTEyU9GRbeFJSkt91mpublZiYqKqqKq1evVpFRUUaMmSIMjIy5HQ6fcZ+9dVXcrlc2rp1qzIzM3Xy5Enl5eV1e20AAPQ0togDANDH3bhxQ6+//nqnfZmZmUpNTZXD4dCCBQu0du1aSdL06dPV1tamffv2detap0+f1qVLl1RSUqI5c+ZIkqZMmaKGhgadPXtWNptNDx48UE5OjhYsWCBJmjx5stra2vT555+rublZ0dHR3nutu9oWvnPnTrW0tOj48eMaMmSIJCk+Pl4pKSkqKChQQkKCjMYndYCxY8cqPz/f+9nLly/r2LFj3VoXAAC9gYANAEAfZzabVVpa2mnf4MGDVVtbq7t372rWrFk+ffPnz+92wL5w4YJCQ0P11ltveduMRqPP92zfvl2SdPv2bf3888+qq6vTyZMnJUnt7e3PdZ3z588rLi7OG649Fi5cqOzsbNXW1mrMmDGS/EN6dHS0Hj161K11AQDQGwjYAAD0cWFhYZo4cWKX/fX19ZKkyMhIn3az2dzta7W2tmrQoEHe6nFnKisrlZeXp9raWoWHh2vcuHEaMGCApOd/9vXvv/8ui8Xi1x4VFSVJunfvnretf//+PmOMRiPP2AYA9EkEbAAAXnCeYH337l2f9tbWVp/3BoNB0pP7rPv16ydJevDggc+YiIgItba2yu12e8dL0rVr1+R2uxUREaGMjAzNmTNHZWVlslgsMhgM2r17tyorK597ziaTSU1NTX7tnrZ//lkAAMCLgEPOAAB4wY0YMUKDBw/2uy/Zs23bY+DAgZKkX3/91dt24cIFnzFvvvmmOjo6dPr0aW+b2+1Wdna2ysrKdPXqVf35559KS0vTsGHDvCHcE649leWnVcAladKkSaqurtaNGzd82p1Op8xms4YPH/7MdQMA0NdQwQYAoI9rb2/XxYsXu+y3Wq365JNPtGbNGuXk5Ojtt9/WxYsXtXfvXp9x8fHxys/PV25urlauXKlbt26ppKRE4eHh3jEzZ85UXFycsrKy9NFHH8lisaiiokI//fST7Ha7TCaTQkJC9MUXXyg1NVXt7e06dOiQ9zFeDx8+lCS9+uqrkqSjR48qJibGbzv4ihUr5HQ6lZKSovfff1+DBg3S4cOHdfbsWeXl5T0zoAMA0BcRsAEA6OOampq0ePHiLvsPHz7sPXXb4XCooqJCY8eO1aZNm/Txxx97x40cOVJbtmxRaWmp0tLSNHr0aNntdtntdu+Yfv36adu2bfryyy/19ddf69GjR7JardqxY4dsNpskqbCwUMXFxVq1apVMJpNiY2O1a9cuJScnq6qqSlarVXPnzlVFRYWysrKUmJioDRs2+MzZbDZr7969Kiws1ObNm9XR0aFx48bJ4XBo9uzZgf0BAQDoJQY3p4QAAAAAAPCvsf8KAAAAAIAAIGADAAAAABAABGwAAAAAAAKAgA0AAAAAQAAQsAEAAAAACAACNgAAAAAAAUDABgAAAAAgAAjYAAAAAAAEAAEbAAAAAIAAIGADAAAAABAABGwAAAAAAAKAgA0AAAAAQAD8DTB4JWkeqcagAAAAAElFTkSuQmCC",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Plot 2: Min\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "sns.barplot(data=data, x='Education', y='min', hue='Gender')\n",
+        "plt.title('Minimum Customer Lifetime Value by Education and Gender')\n",
+        "plt.xticks(rotation=45)\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "# Min CLV is consistantly low (around 1900 - 2400) across all groups\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "a03bdaea",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Plot 3: Median\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "sns.barplot(data=data, x='Education', y='median', hue='Gender')\n",
+        "plt.title('Median Customer Lifetime Value by Education and Gender')\n",
+        "plt.xticks(rotation=45)\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n",
+        "\n",
+        "# the highest median CLV is found in Male with high school or below education \n",
+        "# the lowest median CLV appears in Female doctor and male bachlor "
+      ]
+    },
     {
       "cell_type": "markdown",
       "id": "b42999f9-311f-481e-ae63-40a5577072c5",
@@ -88,6 +833,98 @@
         "5. The marketing team wants to analyze the number of policies sold by state and month. Present the data in a table where the months are arranged as columns and the states are arranged as rows."
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": 83,
+      "id": "449513f4-0459-46a0-a18d-9398d974c9ad",
+      "metadata": {
+        "id": "449513f4-0459-46a0-a18d-9398d974c9ad"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>Month</th>\n",
+              "      <th>February</th>\n",
+              "      <th>January</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>State</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Arizona</th>\n",
+              "      <td>2864</td>\n",
+              "      <td>3052</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>California</th>\n",
+              "      <td>4929</td>\n",
+              "      <td>5673</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Nevada</th>\n",
+              "      <td>1278</td>\n",
+              "      <td>1493</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Oregon</th>\n",
+              "      <td>3969</td>\n",
+              "      <td>4697</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Washington</th>\n",
+              "      <td>1225</td>\n",
+              "      <td>1358</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "Month       February  January\n",
+              "State                        \n",
+              "Arizona         2864     3052\n",
+              "California      4929     5673\n",
+              "Nevada          1278     1493\n",
+              "Oregon          3969     4697\n",
+              "Washington      1225     1358"
+            ]
+          },
+          "execution_count": 83,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df['Effective To Date'] = pd.to_datetime(df['Effective To Date'],format = '%m%d%y')\n",
+        "df['Month'] = df['Effective To Date'].dt.month_name()\n",
+        "df['Month']\n",
+        "\n",
+        "policy_df = df.pivot_table('Number of Policies',index='State',columns='Month',aggfunc='sum')\n",
+        "policy_df"
+      ]
+    },
     {
       "cell_type": "markdown",
       "id": "b6aec097-c633-4017-a125-e77a97259cda",
@@ -103,6 +940,86 @@
         "- *Finally, you will create a new DataFrame that contains the number of policies sold by month for each of the top 3 states.*"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": 91,
+      "id": "b69cbeb0",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th>Month</th>\n",
+              "      <th>February</th>\n",
+              "      <th>January</th>\n",
+              "      <th>Total</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>State</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>California</th>\n",
+              "      <td>4929</td>\n",
+              "      <td>5673</td>\n",
+              "      <td>10602</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Oregon</th>\n",
+              "      <td>3969</td>\n",
+              "      <td>4697</td>\n",
+              "      <td>8666</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Arizona</th>\n",
+              "      <td>2864</td>\n",
+              "      <td>3052</td>\n",
+              "      <td>5916</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "Month       February  January  Total\n",
+              "State                               \n",
+              "California      4929     5673  10602\n",
+              "Oregon          3969     4697   8666\n",
+              "Arizona         2864     3052   5916"
+            ]
+          },
+          "execution_count": 91,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "policy_df['Total'] = policy_df['February'] + policy_df['January']\n",
+        "policy_df.sort_values(by=['Total'],ascending=False,inplace=True)\n",
+        "policy_df.head(3)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "id": "ba975b8a-a2cf-4fbf-9f59-ebc381767009",
@@ -127,14 +1044,163 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
-      "id": "449513f4-0459-46a0-a18d-9398d974c9ad",
-      "metadata": {
-        "id": "449513f4-0459-46a0-a18d-9398d974c9ad"
-      },
-      "outputs": [],
+      "execution_count": 229,
+      "id": "c2f19f06",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Sales Channel</th>\n",
+              "      <th>Measures</th>\n",
+              "      <th>value</th>\n",
+              "      <th>value_formatted</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Agent</td>\n",
+              "      <td>count_of_response</td>\n",
+              "      <td>742.000000</td>\n",
+              "      <td>742</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Branch</td>\n",
+              "      <td>count_of_response</td>\n",
+              "      <td>326.000000</td>\n",
+              "      <td>326</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Call Center</td>\n",
+              "      <td>count_of_response</td>\n",
+              "      <td>221.000000</td>\n",
+              "      <td>221</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Web</td>\n",
+              "      <td>count_of_response</td>\n",
+              "      <td>177.000000</td>\n",
+              "      <td>177</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Agent</td>\n",
+              "      <td>count_of_total</td>\n",
+              "      <td>4121.000000</td>\n",
+              "      <td>4121</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Branch</td>\n",
+              "      <td>count_of_total</td>\n",
+              "      <td>3022.000000</td>\n",
+              "      <td>3022</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Call Center</td>\n",
+              "      <td>count_of_total</td>\n",
+              "      <td>2141.000000</td>\n",
+              "      <td>2141</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>Web</td>\n",
+              "      <td>count_of_total</td>\n",
+              "      <td>1626.000000</td>\n",
+              "      <td>1626</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>Agent</td>\n",
+              "      <td>response_ratio</td>\n",
+              "      <td>18.005339</td>\n",
+              "      <td>18%</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>Branch</td>\n",
+              "      <td>response_ratio</td>\n",
+              "      <td>10.787558</td>\n",
+              "      <td>11%</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10</th>\n",
+              "      <td>Call Center</td>\n",
+              "      <td>response_ratio</td>\n",
+              "      <td>10.322279</td>\n",
+              "      <td>10%</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>11</th>\n",
+              "      <td>Web</td>\n",
+              "      <td>response_ratio</td>\n",
+              "      <td>10.885609</td>\n",
+              "      <td>11%</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Sales Channel           Measures        value value_formatted\n",
+              "0          Agent  count_of_response   742.000000             742\n",
+              "1         Branch  count_of_response   326.000000             326\n",
+              "2    Call Center  count_of_response   221.000000             221\n",
+              "3            Web  count_of_response   177.000000             177\n",
+              "4          Agent     count_of_total  4121.000000            4121\n",
+              "5         Branch     count_of_total  3022.000000            3022\n",
+              "6    Call Center     count_of_total  2141.000000            2141\n",
+              "7            Web     count_of_total  1626.000000            1626\n",
+              "8          Agent     response_ratio    18.005339             18%\n",
+              "9         Branch     response_ratio    10.787558             11%\n",
+              "10   Call Center     response_ratio    10.322279             10%\n",
+              "11           Web     response_ratio    10.885609             11%"
+            ]
+          },
+          "execution_count": 229,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
       "source": [
-        "# your code goes here"
+        "s1 = df[df['Response'] == 'Yes'].groupby('Sales Channel').size()\n",
+        "s2 = df.groupby('Sales Channel').size()\n",
+        "\n",
+        "df_response = pd.concat([s1,s2],axis=1).reset_index()\n",
+        "df_response.rename(columns={0:'count_of_response',1:'count_of_total'},inplace=True)\n",
+        "df_response['response_ratio'] = (df_response['count_of_response']/df_response['count_of_total'])*100\n",
+        "\n",
+        "df_response_long = pd.melt(df_response,id_vars='Sales Channel',value_vars=['count_of_response','count_of_total','response_ratio'],var_name='Measures')\n",
+        "df_response_long['value_formatted'] = df_response_long.apply(\n",
+        "    lambda row: f\"{int(row['value'])}\" if row['Measures'] in ['count_of_response', 'count_of_total']\n",
+        "    else f\"{round(row['value'])}%\",\n",
+        "    axis=1\n",
+        ")\n",
+        "\n",
+        "df_response_long\n"
       ]
     }
   ],
@@ -143,7 +1209,7 @@
       "provenance": []
     },
     "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
+      "display_name": "3.10.10",
       "language": "python",
       "name": "python3"
     },
@@ -157,7 +1223,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.13"
+      "version": "3.10.10"
     }
   },
   "nbformat": 4,