From 0f3daa74e6b4fcb6459bfc079ef513886bfee9e5 Mon Sep 17 00:00:00 2001
From: NoidFrancis <noidfrancis122@gmail.com>
Date: Mon, 1 Sep 2025 01:43:01 +0200
Subject: [PATCH] Week2 Lab3 done

---
 lab-dw-data-structuring-and-combining.ipynb | 403 +++++++++++++++++++-
 1 file changed, 389 insertions(+), 14 deletions(-)

diff --git a/lab-dw-data-structuring-and-combining.ipynb b/lab-dw-data-structuring-and-combining.ipynb
index ec4e3f9..0746da8 100644
--- a/lab-dw-data-structuring-and-combining.ipynb
+++ b/lab-dw-data-structuring-and-combining.ipynb
@@ -36,14 +36,105 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "492d06e3-92c7-4105-ac72-536db98d3244",
    "metadata": {
     "id": "492d06e3-92c7-4105-ac72-536db98d3244"
    },
    "outputs": [],
    "source": [
-    "# Your code goes here"
+    "import pandas as pd\n",
+    "\n",
+    "df1 = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file1.csv\")\n",
+    "df2 = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file2.csv\")\n",
+    "df3 = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file3.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e6299e3e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def clean_data(df):\n",
+    "    # Example cleaning steps (adapt based on your last lab)\n",
+    "    df.columns = df.columns.str.lower().str.strip().str.replace(\" \", \"_\")\n",
+    "    df = df.drop_duplicates()\n",
+    "    df = df.dropna(how=\"all\")\n",
+    "    return df\n",
+    "\n",
+    "df1_clean = clean_data(df1)\n",
+    "df2_clean = clean_data(df2)\n",
+    "df3_clean = clean_data(df3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "2ee519f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "combined_df = pd.concat([df1_clean, df2_clean, df3_clean], ignore_index=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0611c2c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 9137 entries, 0 to 9136\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column                     Non-Null Count  Dtype  \n",
+      "---  ------                     --------------  -----  \n",
+      " 0   customer                   9137 non-null   object \n",
+      " 1   st                         2067 non-null   object \n",
+      " 2   gender                     9015 non-null   object \n",
+      " 3   education                  9137 non-null   object \n",
+      " 4   customer_lifetime_value    9130 non-null   object \n",
+      " 5   income                     9137 non-null   float64\n",
+      " 6   monthly_premium_auto       9137 non-null   float64\n",
+      " 7   number_of_open_complaints  9137 non-null   object \n",
+      " 8   policy_type                9137 non-null   object \n",
+      " 9   vehicle_class              9137 non-null   object \n",
+      " 10  total_claim_amount         9137 non-null   float64\n",
+      " 11  state                      7070 non-null   object \n",
+      "dtypes: float64(3), object(9)\n",
+      "memory usage: 856.7+ KB\n",
+      "None\n",
+      "  customer          st gender             education customer_lifetime_value  \\\n",
+      "0  RB50392  Washington    NaN                Master                     NaN   \n",
+      "1  QZ44356     Arizona      F              Bachelor              697953.59%   \n",
+      "2  AI49188      Nevada      F              Bachelor             1288743.17%   \n",
+      "3  WW63253  California      M              Bachelor              764586.18%   \n",
+      "4  GA49547  Washington      M  High School or Below              536307.65%   \n",
+      "\n",
+      "    income  monthly_premium_auto number_of_open_complaints     policy_type  \\\n",
+      "0      0.0                1000.0                    1/0/00   Personal Auto   \n",
+      "1      0.0                  94.0                    1/0/00   Personal Auto   \n",
+      "2  48767.0                 108.0                    1/0/00   Personal Auto   \n",
+      "3      0.0                 106.0                    1/0/00  Corporate Auto   \n",
+      "4  36357.0                  68.0                    1/0/00   Personal Auto   \n",
+      "\n",
+      "   vehicle_class  total_claim_amount state  \n",
+      "0  Four-Door Car            2.704934   NaN  \n",
+      "1  Four-Door Car         1131.464935   NaN  \n",
+      "2   Two-Door Car          566.472247   NaN  \n",
+      "3            SUV          529.881344   NaN  \n",
+      "4  Four-Door Car           17.269323   NaN  \n"
+     ]
+    }
+   ],
+   "source": [
+    "print(combined_df.info())\n",
+    "print(combined_df.head())\n"
    ]
   },
   {
@@ -72,14 +163,107 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "aa10d9b0-1c27-4d3f-a8e4-db6ab73bfd26",
    "metadata": {
     "id": "aa10d9b0-1c27-4d3f-a8e4-db6ab73bfd26"
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Unnamed: 0 Customer       State  Customer Lifetime Value Response  \\\n",
+      "0           0  DK49336     Arizona              4809.216960       No   \n",
+      "1           1  KX64629  California              2228.525238       No   \n",
+      "2           2  LZ68649  Washington             14947.917300       No   \n",
+      "3           3  XL78013      Oregon             22332.439460      Yes   \n",
+      "4           4  QA50777      Oregon              9025.067525       No   \n",
+      "\n",
+      "   Coverage Education Effective To Date EmploymentStatus Gender  ...  \\\n",
+      "0     Basic   College           2/18/11         Employed      M  ...   \n",
+      "1     Basic   College           1/18/11       Unemployed      F  ...   \n",
+      "2     Basic  Bachelor           2/10/11         Employed      M  ...   \n",
+      "3  Extended   College           1/11/11         Employed      M  ...   \n",
+      "4   Premium  Bachelor           1/17/11    Medical Leave      F  ...   \n",
+      "\n",
+      "   Number of Open Complaints Number of Policies     Policy Type        Policy  \\\n",
+      "0                        0.0                  9  Corporate Auto  Corporate L3   \n",
+      "1                        0.0                  1   Personal Auto   Personal L3   \n",
+      "2                        0.0                  2   Personal Auto   Personal L3   \n",
+      "3                        0.0                  2  Corporate Auto  Corporate L3   \n",
+      "4                        NaN                  7   Personal Auto   Personal L2   \n",
+      "\n",
+      "   Renew Offer Type  Sales Channel  Total Claim Amount  Vehicle Class  \\\n",
+      "0            Offer3          Agent          292.800000  Four-Door Car   \n",
+      "1            Offer4    Call Center          744.924331  Four-Door Car   \n",
+      "2            Offer3    Call Center          480.000000            SUV   \n",
+      "3            Offer2         Branch          484.013411  Four-Door Car   \n",
+      "4            Offer1         Branch          707.925645  Four-Door Car   \n",
+      "\n",
+      "  Vehicle Size Vehicle Type  \n",
+      "0      Medsize          NaN  \n",
+      "1      Medsize          NaN  \n",
+      "2      Medsize            A  \n",
+      "3      Medsize            A  \n",
+      "4      Medsize          NaN  \n",
+      "\n",
+      "[5 rows x 26 columns]\n",
+      "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')\n",
+      "               Total Claim Amount\n",
+      "Sales Channel                    \n",
+      "Agent                  1810226.82\n",
+      "Branch                 1301204.00\n",
+      "Call Center             926600.82\n",
+      "Web                     706600.04\n",
+      "Top sales channel: Agent\n",
+      "Education  Bachelor  College   Doctor  High School or Below   Master\n",
+      "Gender                                                              \n",
+      "F           7874.27  7748.82  7328.51               8675.22  8157.05\n",
+      "M           7703.60  8052.46  7415.33               8149.69  8168.83\n"
+     ]
+    }
+   ],
    "source": [
-    "# Your code goes here"
+    "# load dataset\n",
+    "url = \"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv\"\n",
+    "df = pd.read_csv(url)\n",
+    "\n",
+    "print(df.head())   # quick preview\n",
+    "print(df.columns)  # check column names\n",
+    "\n",
+    "# total revenue by sales channel\n",
+    "sales_summary = pd.pivot_table(\n",
+    "    df,\n",
+    "    values=\"Total Claim Amount\",   # revenue column\n",
+    "    index=\"Sales Channel\",\n",
+    "    aggfunc=\"sum\"\n",
+    ").round(2)\n",
+    "\n",
+    "print(sales_summary)\n",
+    "\n",
+    "# top revenue channel\n",
+    "top_channel = sales_summary[\"Total Claim Amount\"].idxmax()\n",
+    "print(\"Top sales channel:\", top_channel)\n",
+    "\n",
+    "# average CLV by gender + education\n",
+    "clv_summary = pd.pivot_table(\n",
+    "    df,\n",
+    "    values=\"Customer Lifetime Value\",\n",
+    "    index=\"Gender\",\n",
+    "    columns=\"Education\",\n",
+    "    aggfunc=\"mean\"\n",
+    ").round(2)\n",
+    "\n",
+    "print(clv_summary)\n"
    ]
   },
   {
@@ -103,6 +287,147 @@
     "2. Create a pivot table that shows the average customer lifetime value per gender and education level. Analyze the resulting table to draw insights."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4528fd91",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Unnamed: 0 Customer       State  Customer Lifetime Value Response  \\\n",
+      "0           0  DK49336     Arizona              4809.216960       No   \n",
+      "1           1  KX64629  California              2228.525238       No   \n",
+      "2           2  LZ68649  Washington             14947.917300       No   \n",
+      "3           3  XL78013      Oregon             22332.439460      Yes   \n",
+      "4           4  QA50777      Oregon              9025.067525       No   \n",
+      "\n",
+      "   Coverage Education Effective To Date EmploymentStatus Gender  ...  \\\n",
+      "0     Basic   College           2/18/11         Employed      M  ...   \n",
+      "1     Basic   College           1/18/11       Unemployed      F  ...   \n",
+      "2     Basic  Bachelor           2/10/11         Employed      M  ...   \n",
+      "3  Extended   College           1/11/11         Employed      M  ...   \n",
+      "4   Premium  Bachelor           1/17/11    Medical Leave      F  ...   \n",
+      "\n",
+      "   Number of Open Complaints Number of Policies     Policy Type        Policy  \\\n",
+      "0                        0.0                  9  Corporate Auto  Corporate L3   \n",
+      "1                        0.0                  1   Personal Auto   Personal L3   \n",
+      "2                        0.0                  2   Personal Auto   Personal L3   \n",
+      "3                        0.0                  2  Corporate Auto  Corporate L3   \n",
+      "4                        NaN                  7   Personal Auto   Personal L2   \n",
+      "\n",
+      "   Renew Offer Type  Sales Channel  Total Claim Amount  Vehicle Class  \\\n",
+      "0            Offer3          Agent          292.800000  Four-Door Car   \n",
+      "1            Offer4    Call Center          744.924331  Four-Door Car   \n",
+      "2            Offer3    Call Center          480.000000            SUV   \n",
+      "3            Offer2         Branch          484.013411  Four-Door Car   \n",
+      "4            Offer1         Branch          707.925645  Four-Door Car   \n",
+      "\n",
+      "  Vehicle Size Vehicle Type  \n",
+      "0      Medsize          NaN  \n",
+      "1      Medsize          NaN  \n",
+      "2      Medsize            A  \n",
+      "3      Medsize            A  \n",
+      "4      Medsize          NaN  \n",
+      "\n",
+      "[5 rows x 26 columns]\n",
+      "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')\n",
+      "               Total Claim Amount\n",
+      "Sales Channel                    \n",
+      "Agent                  1810226.82\n",
+      "Branch                 1301204.00\n",
+      "Call Center             926600.82\n",
+      "Web                     706600.04\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkAAAAIGCAYAAAC8mzBHAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjUsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvWftoOwAAAAlwSFlzAAAPYQAAD2EBqD+naQAAR2JJREFUeJzt3Qm8jdUe//GfeajM09EVSRGFUqQoopCUbknDzVCp3NyUJqcbEqWJKG6aZGgwNOiKNJAkJENJt4TIPGWKQtj/13f9/3v/9z5nn+PQcZ599vq8X6+n7Gc/Z5/n2fucs797rd9aK08oFAoZAACAR/IGfQIAAAA5jQAEAAC8QwACAADeIQABAADvEIAAAIB3CEAAAMA7BCAAAOAdAhAAAPAOAQgAAHiHAATksBkzZliePHnc/5H9mjRpYmeccYblNiNHjnQ/F6tWrbJEpPPS+T3zzDOWDMLXo+cdfiIAwQv6Q5eVLSuh5PHHH7eJEyfm2BtieMufP7+deOKJ1qlTJ1u3bt0x//7I3O7du61Pnz4ubB133HFWunRpq1u3rnXv3t3Wr19vuck333xj//jHP6xSpUpWqFAhK1WqlDVv3txee+01O3jwYNCnBxwT+Y/NwwKJZcyYMTG3R48ebZ988km6/aeffnqWAtA111xjbdu2tZzw6KOP2sknn2x79+61uXPnumA0a9YsW7JkiRUuXDhHzgGx/vzzT7vwwgvtxx9/tI4dO9q//vUvF4i+//57e/PNN+2qq66yihUrWm7wyiuv2B133GHly5e3m266yU499VT77bffbNq0aXbLLbfYhg0b7KGHHgr6NIFsRwCCF/TpNpqChAJQ2v2JqFWrVnbOOee4f996661WpkwZe/LJJ+2///2vXXvttUGfnpfUArho0SJ744037IYbboi5T0F1//79lhvo90Dhp2HDhjZlyhQ74YQTIvfdfffdNn/+fBe0gWREFxjw/+zZs8fuvffeSDdA9erVXb1DKBSKHKOuKB03atSoSNeUuqTkl19+sX/+85/u64oUKeK6RNq1a5ftNR2NGzd2/1+xYkXMfrVGqGVK3RdqGVJoUkgK05uZzlfnntZHH33k7vvggw8i+9TNdvPNN7uWAT0ftWrVshEjRsStZxo/frw99thj9re//c1972bNmtny5ctjjq1SpUrkuUpbs6Mt2r59+1z3UrVq1dz31mvywAMPuP1ZtWDBAjv//PPda6EWtOHDh0fuU2uNuq3UXZXW2rVrLV++fDZgwIAMHzv83F9wwQXp7tP1FytWLHJ78eLF7rqrVq3q7qtQoYJ7Xn/99dcsXceHH37oXnOdrwJK69atXUtTtI0bN1rnzp3d86/nKyUlxa688srD/uz17dvXvX4KctHhJ0w/Q/Fes5deeslOOeUU973OPfdc+/rrr2Puz+o1P/LII+7762dFx5coUcKKFy/uruX333+POVbHdevWzYVPdTuGfyanTp2a7vyy8rML0AIEmLmQc8UVV9hnn33mmv1Vy6FQcP/997s/ps8++6w7Tl1maoWpX7++3XbbbW6f3ghEbwKzZ8+26667zr0R6c3nhRdecG/u//vf/6xo0aLZcq7hN7WSJUtG9ukNUW/GqhHq2bOne7NUKFE33TvvvOO6ZPRmpjck7Ve3TbRx48a5x2vRooW7vWnTJjvvvPMibzply5Z1b8R6bnbt2uVaB6I98cQTljdvXrvvvvts586d9tRTT9mNN95oX3311RFf36FDh9xroW4+Pcfqlvzuu+/ca/DTTz9lqf5q+/btdtlll7kWsuuvv95dc9euXa1gwYLujfH44493z4mue9CgQS7whL311lvu50Hnn5HKlStHulIffvhh9zxlRC2NP//8s3tTVxDQa6UAof+rBSazr9XPm14rvS5q9VMo0M9Uo0aNXAuUQqVcffXV7vHUFad9mzdvdt939erVkWPS0mOpm0tdeSeddJJllbr41EV2++23u3PXa/33v//dXWOBAgWO6pr1OimkKnQuXLjQdcuVK1fOXXM0/Uy8++677oOGAttzzz3nrl3XqQ8cR/OzC4+FAA/deeedataJ3J44caK73b9//5jjrrnmmlCePHlCy5cvj+w77rjjQh07dkz3mL///nu6fXPmzHGPO3r06Mi+zz77zO3T/zPz2muvueM+/fTT0JYtW0Jr1qwJvf3226GyZcuGChUq5G6HNWvWLHTmmWeG9u7dG9l36NCh0Pnnnx869dRTI/tSU1NDBQoUCG3bti2yb9++faESJUqEbr755si+W265JZSSkhLaunVrzDldd911oeLFi0euNXwtp59+unucsCFDhrj93333XWRf5cqV4z5vF110kdvCxowZE8qbN2/oiy++iDlu+PDh7jG//PLLTJ83PZaOGzhwYMw11q1bN1SuXLnQ/v373b6PPvrIHffhhx/GfH3t2rVjziceXX/16tXd1+u6OnXqFHr11VdDmzZtintsWm+99Zb72pkzZ6Z7vVeuXOlu//bbb+516dKlS8zXbty40b0G4f3bt293X/f000+HjsS3337rvq579+5ZOl7npeNLly4d8/Pz/vvvu/2TJk064mvu06eP2xf9sydXXXWV+z7RdFzBggVjfhfD1/D8888f8c9u+Hr0vMNPdIEBZq7+Qa0Ad911V8x+dYnpb68+QR6Oulqii2TV3K8uHDXr61Pt0dJoHH2KVTeQurjUuqOuLbUyybZt22z69OnuU7Q+mW/dutVt+v5qOVi2bFlk1Fj79u3duelTdNjHH39sO3bscPeJrletRm3atHH/Dj+eNj2eWnjSXo8+6at1JW03nVoBjtSECRNcq0+NGjVivvfFF1/s7lcr3eFoxJxaKMJ0brqtlhF1jYWfVxUqq/snTPUu6r45XG2YXmu1bqmFUFSYrhYGdT2pFSa6qy7650L1QboWtVBIZj8XakXR66IWrOjnQT+nDRo0iDwPenxdn7oj1fKVVWoNkXhdX5nRz0l062O81/pIr1l1SNH0mPr5DZ9jmF6zcIur1K5d23U3hr/30fzswl8EoMOYOXOm+2XSH0o1qR7N8Gf9IqqW5LTTTnP90eqmUL0EEofqd/Qap30zCI8K0/2H88cff1jv3r0jNUQqVlZw0ZuY/vAerWHDhrk3w7ffftt16+iPuR4/TPUT+hnr1auX+37Rm+poRG/8UqdOHRcs1PUTpn/rXMMBY8uWLe6c1WWR9vEUdKIfLyxtF0r4DfJI3pDDFNjUVZL2e+v3J973jkevpYJitPDXh7sQ1WWnbi79TofrTRSGVLOi2q3DUa2Kun/0eNpeffVVV/81dOhQ69evX+Q4BVTVGqkeRcFA16LuHsns50LPg+h1SftcKLSGnwf9LKirSCFd30NdWjov1QVlJlynpNB8JLLyWh/pNWf15ydeV52ODR93ND+78Bc1QIehgle9aahuQP3cR0N/CPQHSyHozDPPdH8ctCG56JO/5k1RjYFG1egNUqFZNUGqazlaqjcKjwJTTY/qPzTyaOnSpa6WJfzYqr8J1/CkpZao6E/wCuAKUgp8ak1SK4NaTST8eGoFSVsrFP3JO1p0DU20tAXk8Wiemeiv1/fX74lqc+JRwMwuHTp0sKefftqFID0Hqm+5/PLL3Wt3JFQTpL8RqitSnZWCVP/+/d19aplTbZhai1RbFn7NWrZsmenPRfg+1QGpjiat8Osl+pnTBzVdh2rXFIZVT6OWwbPOOivDnwk9huqrjkRWXusjveasPGZWjjuan134iwCUhSHI2jKipu5///vfrnBSnzw0OkGfxsKjWn744QdXtKimdX06lPAnISQOvYF9+umn7tNwdCuQRlaF7z/cG7laaPRHd+DAgTHN//q5yC7h0UlNmzZ1LQ0qeNYbrqgAVV0Eh6MApNE/6irQJ3R1MyikhenTsp4DBZOsPF5W6ZN6vOdCrWvhaxB1cXz77bduJFlmBcKZ0USE+vAS3QqkAmqJLgrW76sCggKLuhRVTPv888/bX7lGnX946LhaJlRorOdbrYNpW3cyE+7qUTFwVl4HHa8uW216fAUP/Sy+/vrrcY9XUb5alxSS1qxZk23B8q9c8191rH52kZzoAvuLNMpgzpw5NnbsWFc7oKZzfcoJ/7JPmjTJ/XHX8GIFH/3x1SgiWoASi7qW9EdToSKaRh7pTTg6BOtNNd4bucJJ2k+sejPN7pl0Fa7VKjR48GAXsPQGqX0vvviim7QuLXULpO3WUwuLur60qW5F3SbR16GRNQpI8eaASft4WaU3aI0Aip4jR78XevONptYD1Sy9/PLLcbsZFWwO58CBA+75CNP31G29QdarVy/mWE3+pxZaPZ8aSZTZB54wBTS1oMULcxrxF/6wE26xSPtzoe91OGrNUzeVJt5U3VZGr4O67/RzkPa5VhA43LQB6iLVuek50NQAaaleKt60CZn5K9f8Vx2rn10kJ1qA/gJ9WlSXh/4fnvVV3RCal0L79YdLxXn6o6jCTg2Z1ZvhPffc44pZ9ckLiUHdB2pVUWue6jnU7ak3xffff991L0QXXuoNVK1F6qLR665gq6JUdZ2ou0LdJzVr1nTBWMeFh+dmJ3UtKGyr+FYFpKoTUteYgk2XLl1c6NZwYJ2D5rXRG3baViB9Ole9i4p3VQ+Tdli7imx1XXo8XY9CuwpIdU1HE+AV/NVKpg8ICjmaS0etE9HPrejNWMPWdV06Bw3v1++NWuO0X1084S7BjOh1UUusXkvV/ijoabkH1YaEh2qHqTtRcwy99957bqh82vvjUU2WwoOG66u4V108+l3XXDMKHZrfRhRgwjU5CjGq/9PP1cqVKw/7PfS1aj3W83H22We7VjoFOP29mTx5snteFNjVsqXWMj2nep3UraVr0esf3bIXj+ZJ0s+OhpWrNix6JmgVVat7NNyVl1V/5Zqzw7H42UWSCnoYWm6ip+u9996L3P7ggw/cPg2Ljt7y588fuvbaa90xGqqqY5YuXRr5ugULFrh9P/74YyDXgfTD4MPDju+5555QxYoV3VBxDR/X0GINJ4+m1+3CCy8MFSlSxD1GeGi3hiN37tw5VKZMmdDxxx8fatGihTs27fDvIx0G//XXX6e77+DBg6FTTjnFbQcOHHD7VqxYEerQoUOoQoUK7vxPPPHE0OWXX+6Gzqe1bNky99jaZs2aFff7a0i3nqdKlSq5x9Pjarj9Sy+9lO5aJkyYEPO1GQ0x1tB0nZeG8V9wwQWh+fPnpxsGLxqq/uSTT4Zq1arlji1ZsmSoXr16ob59+4Z27tyZ6fOmx9LX6bEbNmwYKly4sHsNhg4dmuHXXHbZZe58Z8+eHcqKn3/+OdS7d+/Qeeed54bW63de0xO0bt06NH369Jhj165d64Z1a0i7hmG3a9cutH79evf9NAw8o2Hw0c+xfpb0tboWveYadq/rEw331utUo0YN9/dHxzVo0CA0fvz4UFbpb9INN9wQ+dnX863XetSoUe5nLfo1jTfcPu21ZPWaw8PgNc1DtHjPhW7rOtOKN71CVn52GQaPPPpP0CEst1BXiD5ZhdeA0qdKjSLRiJW0xXn6RKjCRX1KTNuErWZ89b/rU9Ell1yS49cBIJaKl1UMnHb2agDJiy6wv0DFk2qa17DK8FwYaamZWvUIau4PN/WHizGjC2sBBEN1U+pSUvcnAH/QAnQYKgwMfypU4FHdh2pFtN6S5qTQcMsvv/zSjbbQ/Sqy0wgIDbXUmj0alqm1ctQipCJA3b7zzjtdP7lagAAEQzUp+t3VsgtaxkQfUuINNweQnBgFdhhaQFLBJjyXRo8ePdy/w8M7VeysuUQ09FQjP9Q9pj+m4Qm7VFyqkWCaaE6FgQpFGoWjUWMAgvP555+7ol8FIY10IvwAfqEFCAAAeIcWIAAA4B0CEAAA8A6jwOJQobKm0tdMqkc7FT8AAMhZqurRRJ6aDDXtBK9pEYDiUPjJzgUXAQBAztESO1rfLzMEoDjCi2HqCdRwdQAAkPi0uLMaMKIXtc4IASiOcLeXwg8BCACA3CUr5SsUQQMAAO8QgAAAgHcIQAAAwDsEIAAA4B0CEAAA8A4BCAAAeIcABAAAvEMAAgAA3iEAAQAA7xCAAACAdwhAAADAOwQgAADgHQIQAADwDgEIAAB4J3/QJ4D0qvScbD5a9UTroE8BAOAJWoAAAIB3CEAAAMA7BCAAAOAdAhAAAPAOAQgAAHiHAAQAALxDAAIAAN4JNADNnDnT2rRpYxUrVrQ8efLYxIkTMz2+U6dO7ri0W61atSLHPPLII+nur1GjRg5cDQAAyC0CDUB79uyxOnXq2LBhw7J0/JAhQ2zDhg2Rbc2aNVaqVClr165dzHEKRNHHzZo16xhdAQAAyI0CnQm6VatWbsuq4sWLuy1MLUbbt2+3zp07xxyXP39+q1ChQraeKwAASB65ugbo1VdftebNm1vlypVj9i9btsx1q1WtWtVuvPFGW716daaPs2/fPtu1a1fMBgAAkleuDUDr16+3Dz/80G699daY/Q0aNLCRI0fa1KlT7YUXXrCVK1da48aN7bfffsvwsQYMGBBpXdJWqVKlHLgCAAAQlFwbgEaNGmUlSpSwtm3bxuxXl5pqgmrXrm0tWrSwKVOm2I4dO2z8+PEZPlZqaqrt3Lkzsqm2CAAAJK9cuRp8KBSyESNG2E033WQFCxbM9FiFpNNOO82WL1+e4TGFChVyGwAA8EOubAH6/PPPXaC55ZZbDnvs7t27bcWKFZaSkpIj5wYAABJfoAFI4eSbb75xm6heR/8OFy2ra6pDhw5xi59V63PGGWeku+++++5zAWnVqlU2e/Zsu+qqqyxfvnx2/fXX58AVAQCA3CDQLrD58+db06ZNI7d79Ojh/t+xY0dXyKw5fNKO4FKNzjvvvOPmBIpn7dq1Luz8+uuvVrZsWWvUqJHNnTvX/RsAAEDyhFRQgxgaBq/RYApbxYoVy/HvX6XnZPPRqidaB30KAABP3r9zZQ0QAADAX0EAAgAA3iEAAQAA7xCAAACAdwhAAADAOwQgAADgHQIQAADwDgEIAAB4hwAEAAC8QwACAADeIQABAADvEIAAAIB3CEAAAMA7BCAAAOAdAhAAAPAOAQgAAHiHAAQAALxDAAIAAN4hAAEAAO8QgAAAgHcIQAAAwDsEIAAA4B0CEAAA8A4BCAAAeIcABAAAvEMAAgAA3iEAAQAA7xCAAACAdwhAAADAOwQgAADgHQIQAADwDgEIAAB4hwAEAAC8QwACAADeIQABAADvEIAAAIB3CEAAAMA7BCAAAOCdQAPQzJkzrU2bNlaxYkXLkyePTZw4MdPjZ8yY4Y5Lu23cuDHmuGHDhlmVKlWscOHC1qBBA5s3b94xvhIAAJCbBBqA9uzZY3Xq1HGB5UgsXbrUNmzYENnKlSsXuW/cuHHWo0cP69Onjy1cuNA9fosWLWzz5s3H4AoAAEBulD/Ib96qVSu3HSkFnhIlSsS9b9CgQdalSxfr3Lmzuz18+HCbPHmyjRgxwnr27PmXzxkAAOR+ubIGqG7dupaSkmKXXHKJffnll5H9+/fvtwULFljz5s0j+/Lmzetuz5kzJ8PH27dvn+3atStmAwAAyStXBSCFHrXovPPOO26rVKmSNWnSxHV1ydatW+3gwYNWvnz5mK/T7bR1QtEGDBhgxYsXj2x6XAAAkLwC7QI7UtWrV3db2Pnnn28rVqywZ5991saMGXPUj5uamurqhsLUAkQIAgAgeeWqABRP/fr1bdasWe7fZcqUsXz58tmmTZtijtHtChUqZPgYhQoVchsAAPBDruoCi+ebb75xXWNSsGBBq1evnk2bNi1y/6FDh9zthg0bBniWAAAgkQTaArR7925bvnx55PbKlStdoClVqpSddNJJrmtq3bp1Nnr0aHf/4MGD7eSTT7ZatWrZ3r177ZVXXrHp06fbxx9/HHkMdWV17NjRzjnnHNc6pK/RcPvwqDAAAIBAA9D8+fOtadOmkdvhOhwFmJEjR7o5flavXh0zyuvee+91oaho0aJWu3Zt+/TTT2Meo3379rZlyxbr3bu3K3zWiLGpU6emK4wGAAD+yhMKhUJBn0SiURG0RoPt3LnTihUrluPfv0rPyeajVU+0DvoUAACevH/n+hogAACAI0UAAgAA3iEAAQAA7xCAAACAdwhAAADAOwQgAADgHQIQAADwDgEIAAB4hwAEAAC8QwACAADeIQABAADvEIAAAIB3CEAAAMA7BCAAAOAdAhAAAPAOAQgAAHiHAAQAALxDAAIAAN4hAAEAAO8QgAAAgHcIQAAAwDsEIAAA4B0CEAAA8A4BCAAAeIcABAAAvEMAAgAA3iEAAQAA7xCAAACAdwhAAADAOwQgAADgHQIQAADwTv6gTwDwXZWek81Hq55oHfQpAPAYLUAAAMA7BCAAAOAdAhAAAPAOAQgAAHiHAAQAALwTaACaOXOmtWnTxipWrGh58uSxiRMnZnr8u+++a5dccomVLVvWihUrZg0bNrSPPvoo5phHHnnEPVb0VqNGjWN8JQAAIDcJNADt2bPH6tSpY8OGDctyYFIAmjJlii1YsMCaNm3qAtSiRYtijqtVq5Zt2LAhss2aNesYXQEAAMiNAp0HqFWrVm7LqsGDB8fcfvzxx+3999+3SZMm2VlnnRXZnz9/fqtQoUK2nisAAEgeuboG6NChQ/bbb79ZqVKlYvYvW7bMdatVrVrVbrzxRlu9enWmj7Nv3z7btWtXzAYAAJJXrg5AzzzzjO3evduuvfbayL4GDRrYyJEjberUqfbCCy/YypUrrXHjxi4oZWTAgAFWvHjxyFapUqUcugIAABCEXBuA3nzzTevbt6+NHz/eypUrF9mvLrV27dpZ7dq1rUWLFq5eaMeOHe64jKSmptrOnTsj25o1a3LoKgAAQBBy5VpgY8eOtVtvvdUmTJhgzZs3z/TYEiVK2GmnnWbLly/P8JhChQq5DQAA+CHXtQC99dZb1rlzZ/f/1q0Pv5iiushWrFhhKSkpOXJ+AAAg8QXaAqRwEt0yo3qdb775xhU1n3TSSa5rat26dTZ69OhIt1fHjh1tyJAhrtZn48aNbn+RIkVc7Y7cd999bmh85cqVbf369danTx/Lly+fXX/99QFdJQAASDSBtgDNnz/fDV8PD2Hv0aOH+3fv3r3dbc3hEz2C66WXXrIDBw7YnXfe6Vp0wlv37t0jx6xdu9aFnerVq7vi6NKlS9vcuXPd5IkAAACBtwA1adLEQqFQhvdrNFe0GTNmZKk+CAAAIKlqgAAAAP4qAhAAAPAOAQgAAHiHAAQAALxDAAIAAN4hAAEAAO8QgAAAgHcIQAAAwDsEIAAA4B0CEAAA8A4BCAAAeIcABAAAvEMAAgAA3iEAAQAA7xCAAACAdwhAAADAOwQgAADgHQIQAADwDgEIAAB4hwAEAAC8QwACAADeIQABAADvEIAAAIB3CEAAAMA7BCAAAOCdow5AO3bssFdeecVSU1Nt27Ztbt/ChQtt3bp12Xl+AAAA2S7/0XzR4sWLrXnz5la8eHFbtWqVdenSxUqVKmXvvvuurV692kaPHp39ZwoAABBkC1CPHj2sU6dOtmzZMitcuHBk/2WXXWYzZ87MrnMDAABInAD09ddf2+23355u/4knnmgbN27MjvMCAABIrABUqFAh27VrV7r9P/30k5UtWzY7zgsAACCxAtAVV1xhjz76qP3555/udp48eVztz4MPPmhXX311dp8jAABA8AFo4MCBtnv3bitXrpz98ccfdtFFF1m1atXshBNOsMceeyx7zxAAACARRoFp9Ncnn3xis2bNciPCFIbOPvtsNzIMAAAgKQNQWKNGjdwGAACQ9AFI9T+Z6d2799GeDwAAQGIGoPfeey/mtoqhV65cafnz57dTTjmFAAQAAJIvAC1atCjdPg2L1+SIV111VXacFwAAQOIvhlqsWDHr27ev9erVK7seEgAAIPFXg9+5c6fbskrLZrRp08YqVqzo5hKaOHHiYb9mxowZbsSZJmPU0PuRI0emO2bYsGFWpUoVt0xHgwYNbN68eUd8LQAAIHkdVRfYc889F3M7FArZhg0bbMyYMdaqVassP86ePXusTp06dvPNN9vf//73wx6vOqPWrVvbHXfcYW+88YZNmzbNbr31VktJSbEWLVq4Y8aNG+fWKhs+fLgLP4MHD3b3LV261M1bBAAAkCek9HKETj755JjbefPmdUtgXHzxxZaamuomRDziE8mTxxVXt23bNsNjNNP05MmTbcmSJZF91113ne3YscOmTp3qbiv0nHvuuTZ06FB3+9ChQ1apUiX717/+ZT179szSuaieSXMdqTVLXXs5rUrPyeajVU+0Nh/xegNA9jiS9++jagFSS0wQ5syZk26yRbXu3H333e7f+/fvtwULFrgQFh3O9DX62ozs27fPbWHx1jkDAADJI1trgI41rTRfvnz5mH26rcCiJTm2bt1qBw8ejHtMZqvUDxgwwCXG8KYWIwAAkLyOqgVItTtPPPGEq8HZvHmz62aK9vPPP1tuohYj1Q2FKVARggAASF5HFYBUePz555/bTTfd5AqQVb+TEypUqGCbNm2K2afb6ucrUqSI5cuXz23xjtHXZkQjyrQBAAA/HFUA+vDDD10x8gUXXGA5qWHDhjZlypSYfVqUVfulYMGCVq9ePdcyFS6mVuuUbnfr1i1HzxUAACRZDVDJkiWtVKlSf/mbaxX5b775xm3h4mr9e/Xq1ZGuqQ4dOkSO1/B3da898MAD9uOPP9p//vMfGz9+vN1zzz2RY9SV9fLLL9uoUaPshx9+sK5du7ouu86dO//l8wUAAB63APXr18+t96WQUbRo0aP+5vPnz7emTZtGbofrcDp27OgmONTcQuEwFB5+r5YnBZ4hQ4bY3/72N3vllVcicwBJ+/btbcuWLe78VPhct25dN0Q+bWE0AADw11HNA3TWWWfZihUr3ASImnG5QIECMfcvXLjQcjPmAQqGr/PC8HoDQC6ZByizyQoBAAAS3VEFoD59+mT/mQAAACT6RIhafkL1NypU3rZtW6Tra926ddl5fgAAAInRArR48WK3vIT62VatWmVdunRxo8LeffddV7Q8evTo7D9TAACAIFuANFqrU6dOtmzZMitcuHBk/2WXXWYzZ87MrnMDAABInAD09ddf2+23355u/4knnpjpmlsAAAC5NgBp2Yh4K6b/9NNPVrZs2ew4LwAAgMSqAbriiivs0UcfdbMwi9YCU+3Pgw8+aFdffXV2nyMAJA3mfQJycQvQwIED3TIW5cqVsz/++MMuuugiq1atmp1wwgn22GOPZf9ZAgAABN0CpNFfWoR01qxZbkSYwtDZZ5/tRoYBAAAkZQBas2aNVapUyRo1auQ2AACApO8C0/pf6vbSquvbt2/P/rMCAABItACkVdzr16/vCqFTUlLc2mBvv/227du3L/vPEAAAIBECkFaDf/rpp93Irw8//NANfb/tttusfPnydvPNN2f3OQIAACTGWmDh4e9NmzZ1XWGffvqpnXzyyTZq1KjsOzsAAIBEC0Br1661p556yurWreu6xI4//ngbNmxY9p0dAABAoowCe/HFF+3NN9+0L7/80mrUqGE33nijvf/++1a5cuXsP0MAAIBECED9+/e366+/3p577jmrU6dOdp8TAABA4gUgFT+r/gcAAMCbGiCFny+++ML+8Y9/WMOGDW3dunVu/5gxY9zs0AAAAEkXgN555x1r0aKFFSlSxBYtWhSZ/2fnzp32+OOPZ/c5AgAABB+AVAM0fPhwN/y9QIECkf0XXHCBLVy4MDvPDwAAIDEC0NKlS+3CCy+Mu0jqjh07suO8AAAAEisAVahQwZYvX55uv+p/qlatmh3nBQAAkFgBqEuXLta9e3f76quvXEH0+vXr7Y033rB7773Xunbtmv1nCQAAEPQw+J49e9qhQ4esWbNm9vvvv7vusEKFCtn9999vt956a3aeHwAAQOIMg//3v/9t27ZtsyVLltjcuXNty5YtrgZI64EBAAAkTQDScPfU1FQ755xz3IivKVOmWM2aNe3777+36tWr25AhQ+yee+45dmcLAACQ011gvXv3duuANW/e3GbPnm3t2rWzzp07uxaggQMHutv58uXLjvMCAABIjAA0YcIEGz16tF1xxRWu66t27dp24MAB+/bbb1kaAwAAJGcX2Nq1a61evXru32eccYYrfFaXF+EHAAAkbQA6ePCgFSxYMHI7f/78dvzxxx+L8wIAAEiMLrBQKGSdOnVyLT+yd+9eu+OOO+y4446LOe7dd9/N3rMEAAAIKgB17Ngx5rZWgwcAAEjqAPTaa68duzMBAABI5IkQAQAAcjMCEAAA8A4BCAAAeCchAtCwYcOsSpUqVrhwYWvQoIHNmzcvw2ObNGni5h1Ku7Vu3TpyjEaqpb2/ZcuWOXQ1AAAgKVeDz07jxo2zHj162PDhw134GTx4sLVo0cKWLl1q5cqVS3e8htjv378/cvvXX3+1OnXquGU4oinwRBdth4fuAwAABN4CNGjQIOvSpYtbU0wLqyoIFS1a1EaMGBH3+FKlSlmFChUi2yeffOKOTxuAFHiijytZsmQOXREAAEh0gQYgteQsWLDALa4aOaG8ed3tOXPmZOkxXn31VbvuuuvSTcY4Y8YM14KkVeq7du3qWooyW+V+165dMRsAAEhegQagrVu3uuU1ypcvH7Nftzdu3HjYr1etkBZlvfXWW9N1f2nR1mnTptmTTz5pn3/+ubVq1cp9r3gGDBhgxYsXj2yVKlX6i1cGAAASWeA1QH+FWn/OPPNMq1+/fsx+tQiF6X6tWn/KKae4VqFmzZqle5zU1FRXhxSmFiBCEAAAySvQFqAyZcpYvnz5bNOmTTH7dVt1O5nZs2ePjR071m655ZbDfp+qVau677V8+fK496teqFixYjEbAABIXoEGIK0sX69ePddVFXbo0CF3u2HDhpl+7YQJE1ztTlbWI1u7dq2rAUpJScmW8wYAALlb4KPA1PX08ssv26hRo+yHH35wBctq3dGoMOnQoYProorX/dW2bVsrXbp0zP7du3fb/fffb3PnzrVVq1a5MHXllVdatWrV3PB6AACAwGuA2rdvb1u2bLHevXu7wue6deva1KlTI4XRq1evdiPDommOoFmzZtnHH3+c7vHUpbZ48WIXqHbs2GEVK1a0Sy+91Pr168dcQAAAIDECkHTr1s1t8ahwOS0NbQ+FQnGPL1KkiH300UfZfo4AACB5BN4FBgAA4GULEAAAyahKz8nmo1VP/P/1ORMVLUAAAMA7BCAAAOAdAhAAAPAOAQgAAHiHAAQAALxDAAIAAN4hAAEAAO8QgAAAgHcIQAAAwDsEIAAA4B0CEAAA8A4BCAAAeIcABAAAvEMAAgAA3iEAAQAA7xCAAACAdwhAAADAOwQgAADgHQIQAADwDgEIAAB4hwAEAAC8QwACAADeIQABAADvEIAAAIB3CEAAAMA7BCAAAOAdAhAAAPAOAQgAAHiHAAQAALxDAAIAAN4hAAEAAO8QgAAAgHcIQAAAwDsEIAAA4B0CEAAA8E5CBKBhw4ZZlSpVrHDhwtagQQObN29ehseOHDnS8uTJE7Pp66KFQiHr3bu3paSkWJEiRax58+a2bNmyHLgSAACQGwQegMaNG2c9evSwPn362MKFC61OnTrWokUL27x5c4ZfU6xYMduwYUNk++WXX2Luf+qpp+y5556z4cOH21dffWXHHXece8y9e/fmwBUBAIBEF3gAGjRokHXp0sU6d+5sNWvWdKGlaNGiNmLEiAy/Rq0+FSpUiGzly5ePaf0ZPHiwPfzww3bllVda7dq1bfTo0bZ+/XqbOHFiDl0VAABIZIEGoP3799uCBQtcF1XkhPLmdbfnzJmT4dft3r3bKleubJUqVXIh5/vvv4/ct3LlStu4cWPMYxYvXtx1rWX0mPv27bNdu3bFbAAAIHkFGoC2bt1qBw8ejGnBEd1WiImnevXqrnXo/ffft9dff90OHTpk559/vq1du9bdH/66I3nMAQMGuJAU3hSsAABA8gq8C+xINWzY0Dp06GB169a1iy66yN59910rW7asvfjii0f9mKmpqbZz587ItmbNmmw9ZwAAkFgCDUBlypSxfPny2aZNm2L267Zqe7KiQIECdtZZZ9ny5cvd7fDXHcljFipUyBVWR28AACB5BRqAChYsaPXq1bNp06ZF9qlLS7fV0pMV6kL77rvv3JB3Ofnkk13QiX5M1fRoNFhWHxMAACS3/EGfgIbAd+zY0c455xyrX7++G8G1Z88eNypM1N114oknujodefTRR+28886zatWq2Y4dO+zpp592w+BvvfXWyAixu+++2/r372+nnnqqC0S9evWyihUrWtu2bQO9VgAAkBgCD0Dt27e3LVu2uIkLVaSs2p6pU6dGiphXr17tRoaFbd++3Q2b17ElS5Z0LUizZ892Q+jDHnjgAReibrvtNheSGjVq5B4z7YSJAADAT3lCmjgHMdRlptFgKogOoh6oSs/J5qNVT7Q2H/F6+4XX2y+83on7/p3rRoEBAAD8VQQgAADgHQIQAADwDgEIAAB4hwAEAAC8QwACAADeIQABAADvEIAAAIB3CEAAAMA7BCAAAOAdAhAAAPAOAQgAAHiHAAQAALxDAAIAAN4hAAEAAO8QgAAAgHcIQAAAwDsEIAAA4B0CEAAA8A4BCAAAeIcABAAAvEMAAgAA3iEAAQAA7xCAAACAdwhAAADAOwQgAADgHQIQAADwDgEIAAB4hwAEAAC8QwACAADeIQABAADvEIAAAIB3CEAAAMA7BCAAAOAdAhAAAPAOAQgAAHiHAAQAALxDAAIAAN5JiAA0bNgwq1KlihUuXNgaNGhg8+bNy/DYl19+2Ro3bmwlS5Z0W/PmzdMd36lTJ8uTJ0/M1rJlyxy4EgAAkBsEHoDGjRtnPXr0sD59+tjChQutTp061qJFC9u8eXPc42fMmGHXX3+9ffbZZzZnzhyrVKmSXXrppbZu3bqY4xR4NmzYENneeuutHLoiAACQ6AIPQIMGDbIuXbpY586drWbNmjZ8+HArWrSojRgxIu7xb7zxhv3zn/+0unXrWo0aNeyVV16xQ4cO2bRp02KOK1SokFWoUCGyqbUIAAAg8AC0f/9+W7BggevGCsubN6+7rdadrPj999/tzz//tFKlSqVrKSpXrpxVr17dunbtar/++muGj7Fv3z7btWtXzAYAAJJXoAFo69atdvDgQStfvnzMft3euHFjlh7jwQcftIoVK8aEKHV/jR492rUKPfnkk/b5559bq1at3PeKZ8CAAVa8ePHIpm41AACQvPJbLvbEE0/Y2LFjXWuPCqjDrrvuusi/zzzzTKtdu7adcsop7rhmzZqle5zU1FRXhxSmFiBCEAAAySvQFqAyZcpYvnz5bNOmTTH7dVt1O5l55plnXAD6+OOPXcDJTNWqVd33Wr58edz7VS9UrFixmA0AACSvQANQwYIFrV69ejEFzOGC5oYNG2b4dU899ZT169fPpk6dauecc85hv8/atWtdDVBKSkq2nTsAAMi9Ah8Fpq4nze0zatQo++GHH1zB8p49e9yoMOnQoYProgpTTU+vXr3cKDHNHaRaIW27d+929+v/999/v82dO9dWrVrlwtSVV15p1apVc8PrAQAAAq8Bat++vW3ZssV69+7tgoyGt6tlJ1wYvXr1ajcyLOyFF15wo8euueaamMfRPEKPPPKI61JbvHixC1Q7duxwBdKaJ0gtRurqAgAACDwASbdu3dwWjwqXo6lVJzNFihSxjz76KFvPDwAAJJfAu8AAAAByGgEIAAB4hwAEAAC8QwACAADeIQABAADvEIAAAIB3CEAAAMA7BCAAAOAdAhAAAPAOAQgAAHiHAAQAALxDAAIAAN4hAAEAAO8QgAAAgHcIQAAAwDsEIAAA4B0CEAAA8A4BCAAAeIcABAAAvEMAAgAA3iEAAQAA7xCAAACAdwhAAADAOwQgAADgHQIQAADwDgEIAAB4hwAEAAC8QwACAADeIQABAADvEIAAAIB3CEAAAMA7BCAAAOAdAhAAAPAOAQgAAHiHAAQAALxDAAIAAN4hAAEAAO8kRAAaNmyYValSxQoXLmwNGjSwefPmZXr8hAkTrEaNGu74M88806ZMmRJzfygUst69e1tKSooVKVLEmjdvbsuWLTvGVwEAAHKLwAPQuHHjrEePHtanTx9buHCh1alTx1q0aGGbN2+Oe/zs2bPt+uuvt1tuucUWLVpkbdu2dduSJUsixzz11FP23HPP2fDhw+2rr76y4447zj3m3r17c/DKAABAogo8AA0aNMi6dOlinTt3tpo1a7rQUrRoURsxYkTc44cMGWItW7a0+++/304//XTr16+fnX322TZ06NBI68/gwYPt4YcftiuvvNJq165to0ePtvXr19vEiRNz+OoAAEAiyh/kN9+/f78tWLDAUlNTI/vy5s3ruqzmzJkT92u0Xy1G0dS6Ew43K1eutI0bN7rHCCtevLjrWtPXXnfddekec9++fW4L27lzp/v/rl27LAiH9v1uPgrq+Q4ar7dfeL39wusdzPdVY0hCB6CtW7fawYMHrXz58jH7dfvHH3+M+zUKN/GO1/7w/eF9GR2T1oABA6xv377p9leqVOkIrwh/RfHBQZ8BchKvt194vf1SPODX+7fffnONHwkbgBKFWqCiW5UOHTpk27Zts9KlS1uePHnMF0rOCn1r1qyxYsWKBX06OMZ4vf3C6+0XX1/vUCjkwk/FihUPe2ygAahMmTKWL18+27RpU8x+3a5QoULcr9H+zI4P/1/7NAos+pi6devGfcxChQq5LVqJEiXMV/pl8ekXxne83n7h9faLj6938cO0/CREEXTBggWtXr16Nm3atJjWF91u2LBh3K/R/ujj5ZNPPokcf/LJJ7sQFH2MkrBGg2X0mAAAwC+Bd4Gp66ljx452zjnnWP369d0Irj179rhRYdKhQwc78cQTXZ2OdO/e3S666CIbOHCgtW7d2saOHWvz58+3l156yd2vLqu7777b+vfvb6eeeqoLRL169XLNYRouDwAAEHgAat++vW3ZssVNXKgiZXVTTZ06NVLEvHr1ajcyLOz888+3N9980w1zf+ihh1zI0QiwM844I3LMAw884ELUbbfdZjt27LBGjRq5x9TEiciYugE1H1Pa7kAkJ15vv/B6+4XX+/DyhLIyVgwAACCJBD4RIgAAQE4jAAEAAO8QgAAAgHcIQAAAwDsEIABIMlpiaObMmW4ULID4GAUGAElI03788MMPbi40AOnRAuSxiy++OO4nRM2crfsA5F6aG+3nn38O+jSQwy1/b7/9tvXr189t+veBAweCPq2ERQuQxzTBpCafLFeuXMz+zZs3u9m3//zzz8DODcBfo8lftdCz3gi15NBxxx0Xc79v60Mlu++//96uuOIK9ze9evXqbt9PP/1kZcuWtUmTJsVMFoz/iwDkocWLF7v/a9bt6dOnW6lSpWI+QegP54svvmirVq0K8CxxLGhR4Pvuu8+tlaegm/bXX68/kkP0DPpaIihMr7lu81onF611qbAzatQoK1mypNu3fft269Spk1ttYfbs2UGfYsIhAHn6hzH8BzHey1+kSBF7/vnn7eabbw7g7HAstWrVyi0v061bN0tJSYl5Y5Qrr7wysHND9vr8888zvV9rKiJ56O+21sWsVatWzP4lS5bYueeea3/88Udg55aoAl8LDDlv5cqVLvhUrVrV5s2b5z41hBUsWNB1ieXLly/Qc8SxMWvWLPviiy9c6x+SGwHHL6eddppr4U0bgNTSW61atcDOK5ERgDxUuXJl9/9Dhw4FfSrIYZUqVYrb6ofkpLCr7mwVQ0+YMMHV9o0ZM8aNDNMi0cjdNGAlbMCAAXbXXXfZI488Yuedd57bN3fuXHv00UftySefDPAsExddYJ5btmyZffbZZ+5TQtpA1Lt378DOC8fGxx9/bAMHDnRvilWqVAn6dHAMvfPOO3bTTTfZjTfe6ELP//73P9fqO3ToUJsyZYrbkDzlDBJ+O09b4kDNV3wEII+9/PLL1rVrVytTpoxVqFAh5hdJ/164cGGg54fsoYLI6Nd2z549bmhs0aJFrUCBAjHHbtu2LYAzxLFw1lln2T333GMdOnSwE044wb799lsXgBYtWuRqwTRaCMld5xWNLtH06ALzWP/+/e2xxx6zBx98MOhTwTE0ePDgoE8BAVi6dKldeOGF6fYXL16cGaKTBKHmryEAeUxDJNu1axf0aeAY69ixY9CngACoVXf58uXpujpVCK+WICQfar6ODDNBe0zhRzUh8IfqPj766KN0+/Vz8OGHHwZyTjg2unTpYt27d7evvvrKdYGuX7/e3njjDTcPlLq+kXw1Xy1atHDD4VW+sG/fPrd/586d9vjjjwd9egmJFiCPaWhkr1693EiBM888M109iEYUILn07NnTnnjiiXT7VQCv+1QbguSg11Ova7Nmzez333933WGFChVyAehf//pX0KeHY1DSMHz4cFfzNXbs2Mj+Cy64wN2H9CiC9lhmiyTqEyPrCCUffTrUAplpu0U067fmD1GBNJLL/v37XVfY7t27rWbNmnb88ccHfUo4BjSoQSP99LsdXfSuv+N63ffu3Rv0KSYcWoA8nxARflEBrP4gpg1AeoNMu1YUcjfN5D5kyBD3Zqg3wDCFXLUAjRgxItDzQ/ai5uvIUQME9wlRI0ZYNTj5aamLu+++21asWBHZpz+a9957r1tIEclDa0LFW/5A+0aPHh3IOeHYoebryNEC5DHVBeiToP5QhlcO1icF7dPoAdUQILk89dRT1rJlS6tRo4b97W9/c/vWrl1rjRs3tmeeeSbo00M2zQ6sygZtv/32mxUuXDhynybDUyG8lrtB8rTkq5yBmq8jRwDyWGpqqusnnjFjhntTDGvevLmbTp0AlJxdYFoV+pNPPnGvvWqCateuHXe+GOROJUqUcC0A2rQ+VFra37dv30DODdnvlFNOccsbNW3a1G2q8VPwpebr8CiC9ph+acaNG+fWjYkumlOXyNlnnx2zzgyA3DM7sP6sX3zxxW5odKlSpWIWO9bvfcWKFQM9R2QffYANb+r+UkmD/o7r9dfWpEkTK1++fNCnmZBoAfLYli1b4jaFq0gyeukEJJdp06a5Ld76bxTGJs/swOoa0eK3Wi8KyUsBR5topJdaeMOBSOUNf/75p+vy/v7774M+1YRDAPLYOeecY5MnT470D4dDzyuvvGINGzYM+OxwLKjrQ6tD67VPSUkh6CYxtfRoyYt58+bFDbuaLwbJRfVeavXRrM/qDtPkppoZ+scffwz61BISXWAe0/BITXz3j3/8w0aOHGm33367m0dCnyDUjF6vXr2gTxHZTKFHhdBaJRzJbdKkSW4leNWCFCtWLN1ixyx8mzzU7aUJbT/77LNIV5ha/1Tbp02tgieddFLQp5lwCECe03BozQys+h/9oVTtjxZH1czQSD6lS5d2LQIqnERyUwH0ZZdd5pZB0CR5SE5q8VHg0UgwBR2N6NT/9WEHmSMAAR5RuNWoEC2BguSmiS2/++47JsFLclrCSGGnbdu2rhZI4UcfdHB41AB5LKNRXmoe1/wRGjGC5KIiyZdeesk+/fRTN/w97fpvgwYNCuzckL20MOb8+fMJQElOdV5aBV5dX08++aRdf/31rvVPQSgciMqWLRv0aSYkWoA8ptEhmRXBaqK8Tp06WZ8+fRhJkiRUGJkR/SxMnz49R88Hx86rr77qCt47d+4cd7FjZv5OTpoDSPWd4XoglTeceuqptmTJkqBPLeEQgDym6fD//e9/u5BTv359t0/1IRo6+fDDD7th8pod+P7777eHHnoo6NMFcAQy+9CisKtZoZF8NNrv66+/dgFIm8KQWn55vdMjAHlMU6Zr5Ne1114bs3/8+PFu6KTmihkzZow99thjDKMEgAQNPOrqVGuPAs+XX37p5nLTckbh2aG1aVoExCIAeUzLICxevNg1j0ZbtmyZ1alTx60no8nUatWq5f6N5KA/lgq5q1evdsNno7377ruBnReOHbUARK8JhuShKQ4UeLQafDjsqPaHkZ6HR2GHxzRPhOoE0tI+3Se//vqrlSxZMoCzw7EwduxYO//88916Qe+9956bJVYzxKr2R+uEIXmoy6Nfv36uJUAj/37++We3XyMA4/3eI3d6+umn3e/zunXr7PXXX7dbbrmF8JNFjALzmOp72rVr52YLPffccyOtA/pl0hpCor7k9u3bB3ymyC6aE+bZZ5+1O++8063/NmTIEDd/iLpCmTckuajrWvV8mviyS5cukf1nnHGGDR482L1RIvfT7y6ODl1gnlu1apUNHz7cfvrpJ3e7evXq7hdKkyLqDyWSb24YtfhUqVLFzRWiugGNEFLo1YRqGzZsCPoUkU2qVavmavlU6xe92LHq+bTUzfbt24M+RSBQtAB5Tm+Emgk6PC/QW2+95Vp81BLEqIHko+5MDZMVdY1oaKwCkOYSoc4ruahLRCEoXtGsuj4B31EDBJs5c6Z17NjRKlasaAMHDnRFdFpXBslH6wJ98skn7t/q/uzevbvrHtHkaWopQPKoWbOmmyAvrbffftvOOuusQM4JSCS0AHlq48aNbgFUFUOq5UdD4fft22cTJ050fziRnIYOHepGBInmgNLkeFr89uqrr3ZzPyF59O7d232wUUuQWn00wm/p0qVu/q8PPvgg6NMDAkcNkIfatGnjWn1at27tVotu2bKl5cuXz70Zqk6AAJScDhw4YG+++aZbIqF8+fJBnw5ygFqANBt09GLHCkaXXnpp0KcGBI4A5KH8+fPbXXfdZV27do2ZA4gAlPy0KrgKnpkUDYDvqAHykKZGVyFsvXr1rEGDBq5bZOvWrUGfFnKAljz55ptvgj4NHEMa3fX888/HXex4586dGd4H+IYWII9p9tBx48bZiBEj3BpgGvWl1cBvvvlmN2wWyUczQKempto999zjArCGxUfTCvHI3TT5oWZ4nzBhQtz7Ve+nmd5VAwb4jAAER8WRKojW2l8aEn3JJZfYf//736BPC9mMBTKTX926dd1ozoxG9WmNv/vuu88WLVqU4+cGJBICEGLoDXDSpEmuVYgAlHx++eWXTO+nNij3U+utJrs86aST4t6vNeA0ySndYPAdw+ARQ6PB2rZt6zYkH60JpRmgZc2aNfbyyy/bH3/8YVdccYU1btw46NNDNv0Or1+/PsMApPsyawkEfMFvAeCB7777zs36Xa5cOatRo4YrhNb6b1oX7KWXXnKTX2oOKOR+muQws9dSi+AyESJAAAK88MADD7glLzT/U5MmTezyyy9380BpVJBGDWn9t/CSKMjdunXr5mqANLozuqZL/9YIsPBiuIDvqAECPFCmTBmbPn26G+WlCfGKFStmX3/9tRsJJlog87zzznMF8Mj9NMJrwIABrh5IC6DKzz//7F77+++/n7ALEIAAP6jmQ8ufqAtMolcHl02bNrm14BgFljw0tcUbb7xhy5cvN/2ZP+200+yGG25wc0EBoAga8IaGuWd2G8lFQYewA2SMAAR4olOnTlaoUCH3by2Iescdd0QmQtRCuADgE7rAAA907tw5S8e99tprx/xcACAREIAAAIB3GAYPAAC8QwACAADeoQgaAJKEZnjO6ui+hQsXHvPzARIZAQgAkgRr+AFZRxE0AADwDjVAAADAO3SBAUCSKFmyZJZrgLZt23bMzwdIZAQgAEgSgwcPDvoUgFyDGiAAAOAdWoAAIMlp7bf9+/fH7CtWrFhg5wMkAoqgASAJ7dmzx7p162blypVzi96qPih6A3xHAAKAJPTAAw/Y9OnT7YUXXrBChQrZK6+8Yn379rWKFSva6NGjgz49IHDUAAFAEjrppJNc0GnSpInr7tLMz9WqVbMxY8bYW2+9ZVOmTAn6FIFA0QIEAElIw9yrVq3q/q0AFB723qhRI5s5c2bAZwcEjwAEAElI4WflypXu3zVq1LDx48e7f0+aNMlKlCgR8NkBwaMLDACS0LPPPmv58uWzu+66yz799FNr06aN6c/9n3/+aYMGDbLu3bsHfYpAoAhAAOCBX375xRYsWODqgGrXrh306QCBIwABAADvUAMEAElEQ99r1qxpu3btSnffzp07rVatWvbFF18Ecm5AIiEAAUCSrQfWpUuXuDM9Fy9e3G6//XZXAwT4jgAEAEnk22+/tZYtW2Z4/6WXXupqgQDfEYAAIIls2rTJChQokOH9+fPnty1btuToOQGJiAAEAEnkxBNPtCVLlmR4/+LFiy0lJSVHzwlIRAQgAEgil112mfXq1cutAJ/WH3/8YX369LHLL788kHMDEgnD4AEgybrAzj77bDcJolaDr169utv/448/2rBhw+zgwYNuXbDy5csHfapAoAhAAJCEkx527drVPvroIzf7s+TJk8datGjhQtDJJ58c9CkCgSMAAUCS2r59uy1fvtyFoFNPPdVKliwZ9CkBCYMABAAAvEMRNAAA8A4BCAAAeIcABAAAvEMAAhCIkSNHWokSJYI+DWvSpIndfffdlhskynMGJAMCEIAjpqUUNMz6pJNOskKFClmFChXcEOsvv/zSEsn+/fvtqaeesjp16ljRokWtTJkydsEFF9hrr71mf/75Z9CnByBA+YP85gByp6uvvtqFi1GjRlnVqlXd5HvTpk2zX3/91RKFzk+hTIuD9uvXzwUfrZA+d+5ce+aZZ+yss86yunXrBn2aAAJCCxCAI7Jjxw774osv7Mknn7SmTZta5cqVrX79+paammpXXHFF5LhBgwbZmWeeaccdd5xVqlTJ/vnPf9ru3bszfez333/fzWJcuHBhF6z69u1rBw4ccPdpxo5HHnkk0upUsWJFu+uuuzJ8rMGDB9vMmTNdMLvzzjtd2NFj3nDDDfbVV1+5eXHCDh06ZA888ICVKlXKtWbp+0Q73LWEu6Y08eDpp59uxx9/vFuRfcOGDZFjOnXqZG3btnXhS2txlS5d2p1XdEvUvn377L777nPreel7NWjQwGbMmJHl1wZA1hGAABwRvblrmzhxonvDzkjevHntueees++//961FE2fPt2FjIwoVHXo0MG6d+9u//vf/+zFF190weKxxx5z97/zzjv27LPPuv3Lli1z31+hJCNvvPGGNW/e3LX0pKXV0hUwwnR+uq1gpC6zRx991D755JMjupbff//dhZsxY8a44LV69WoXZqJ99tlntmLFCvd/PY6uT1uYlq6YM2eOjR071i1a2q5dOxekdL0AspkmQgSAI/H222+HSpYsGSpcuHDo/PPPD6Wmpoa+/fbbTL9mwoQJodKlS0duv/baa6HixYtHbjdr1iz0+OOPx3zNmDFjQikpKe7fAwcODJ122mmh/fv3Z+kcixQpErrrrrsOe9xFF10UatSoUcy+c889N/Tggw8e0bXoz+ny5csj+4YNGxYqX7585HbHjh1DlStXDh04cCCyr127dqH27du7f//yyy+hfPnyhdatWxfzvfS86PmN95wBOHq0AAE4qhqg9evX23//+1/XQqFuGnVdRbdmfPrpp9asWTPXnXPCCSfYTTfd5GqE1FISj2p11PISbmHS1qVLF9eNpK9Ra4hWM1c3lva/9957ke6xeI5kkvvatWvH3FYX1ebNm4/oWlRkfcopp2T4GFKrVi23SGm8Y7777ju3UOlpp50W8xx8/vnnrtUIQPYiAAE4KqrTueSSS6xXr142e/ZsV+PSp08fd9+qVavs8ssvd8FCXVcLFixwi3CGi5PjUU2Nan6++eabyKZQoO4ffS/V3ixdutT+85//WJEiRVwdzoUXXpjhaC4FCa2AnhXqEoumhUNVF3Qk1xLvMdKGsMy+j65f4UiPH/0c/PDDDzZkyJAsXQeArGMUGIBsUbNmTVeXI3oT1xv7wIEDXf2MjB8/PtOvVwuSAk61atUyPEbBp02bNm5TAXGNGjVcSNLXpqVi54ceesgWLVqUrg5IoUnhJboOKCNHcy1HQ+eoFiC1CDVu3DjbHx9ALAIQgCOirh91R918882uVURdQvPnz3fFw1deeaU7RiFGIeP55593YUXzAw0fPjzTx+3du7dradEor2uuucaFDXWLLVmyxPr37++61xQQNDJK3U2vv/66C0QahRaPJjecPHmy67rSMPhGjRpFzlUj2F599dUsDYM/mms5GmqxuvHGG10huMKWApHmW9IoNj3PrVu3zvbvCfiMLjAAR0R1KQohGpGlLqgzzjjDdYOpLmfo0KHuGE08qKHjChq6XyOyBgwYkOnjas6eDz74wD7++GM799xz7bzzznPfIxxwNMz85ZdfdvP5KBCoLmfSpEluOHk8GiqvkVwaraWRY3o8Pa5Gc2n4vM4rK47mWo6WJmhUALr33nutevXqbtj8119/7UIhgOyVR5XQ2fyYAAAACY0WIAAA4B0CEAAA8A4BCAAAeIcABAAAvEMAAgAA3iEAAQAA7xCAAACAdwhAAADAOwQgAADgHQIQAADwDgEIAAB4hwAEAADMN/8H7v4cfuNMq00AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top sales channel: Agent\n",
+      "Education  Bachelor  College   Doctor  High School or Below   Master\n",
+      "Gender                                                              \n",
+      "F           7874.27  7748.82  7328.51               8675.22  8157.05\n",
+      "M           7703.60  8052.46  7415.33               8149.69  8168.83\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# load dataset\n",
+    "url = \"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv\"\n",
+    "df = pd.read_csv(url)\n",
+    "\n",
+    "print(df.head())   # preview\n",
+    "print(df.columns)  # check column names\n",
+    "\n",
+    "# total revenue by sales channel\n",
+    "sales_summary = pd.pivot_table(\n",
+    "    df,\n",
+    "    values=\"Total Claim Amount\",   # revenue column\n",
+    "    index=\"Sales Channel\",\n",
+    "    aggfunc=\"sum\"\n",
+    ").round(2)\n",
+    "\n",
+    "print(sales_summary)\n",
+    "\n",
+    "# bar chart: revenue by channel\n",
+    "sales_summary.plot(kind=\"bar\", legend=False, title=\"Total Revenue by Sales Channel\")\n",
+    "plt.ylabel(\"Revenue\")\n",
+    "plt.show()\n",
+    "\n",
+    "# top revenue channel\n",
+    "top_channel = sales_summary[\"Total Claim Amount\"].idxmax()\n",
+    "print(\"Top sales channel:\", top_channel)\n",
+    "\n",
+    "# average CLV by gender + education\n",
+    "clv_summary = pd.pivot_table(\n",
+    "    df,\n",
+    "    values=\"Customer Lifetime Value\",\n",
+    "    index=\"Gender\",\n",
+    "    columns=\"Education\",\n",
+    "    aggfunc=\"mean\"\n",
+    ").round(2)\n",
+    "\n",
+    "print(clv_summary)\n",
+    "\n",
+    "# bar chart: average CLV by gender & education\n",
+    "clv_summary.plot(kind=\"bar\", title=\"Average CLV by Gender and Education\")\n",
+    "plt.ylabel(\"CLV\")\n",
+    "plt.show()\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "32c7f2e5-3d90-43e5-be33-9781b6069198",
@@ -130,14 +455,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "3a069e0b-b400-470e-904d-d17582191be4",
-   "metadata": {
-    "id": "3a069e0b-b400-470e-904d-d17582191be4"
-   },
-   "outputs": [],
+   "execution_count": 8,
+   "id": "e05d134f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\noidf\\AppData\\Local\\Temp\\ipykernel_24564\\246590333.py:6: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+      "  df[\"Effective To Date\"] = pd.to_datetime(df[\"Effective To Date\"])\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# Your code goes here"
+    "\n",
+    "# 1. Load the dataset\n",
+    "url = \"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv\"\n",
+    "df = pd.read_csv(url)\n",
+    "\n",
+    "# 2. Convert 'Effective To Date' into datetime format\n",
+    "df[\"Effective To Date\"] = pd.to_datetime(df[\"Effective To Date\"])\n",
+    "\n",
+    "# 3. Extract the month name\n",
+    "df[\"Month\"] = df[\"Effective To Date\"].dt.month_name()\n",
+    "\n",
+    "# 4. Group by Policy Type and Month to count complaints\n",
+    "complaints_summary = df.groupby([\"Policy Type\", \"Month\"]).size().reset_index(name=\"Num_Complaints\")\n",
+    "\n",
+    "# 5. Sort the months in calendar order (Jan → Dec)\n",
+    "month_order = [\n",
+    "    \"January\", \"February\", \"March\", \"April\", \"May\", \"June\",\n",
+    "    \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"\n",
+    "]\n",
+    "complaints_summary[\"Month\"] = pd.Categorical(complaints_summary[\"Month\"], categories=month_order, ordered=True)\n",
+    "\n",
+    "# 6. Pivot the table so months are on x-axis, policy types as groups\n",
+    "pivot_table = complaints_summary.pivot(index=\"Month\", columns=\"Policy Type\", values=\"Num_Complaints\")\n",
+    "\n",
+    "# 7. Plot a bar chart\n",
+    "pivot_table.plot(kind=\"bar\", figsize=(12,6))\n",
+    "\n",
+    "plt.title(\"Number of Complaints by Policy Type and Month\")\n",
+    "plt.xlabel(\"Month\")\n",
+    "plt.ylabel(\"Number of Complaints\")\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.legend(title=\"Policy Type\")\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
    ]
   }
  ],
@@ -146,7 +521,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -160,7 +535,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,