diff --git a/lab-dw-aggregating.ipynb b/lab-dw-aggregating.ipynb
index fadd718..b291e02 100644
--- a/lab-dw-aggregating.ipynb
+++ b/lab-dw-aggregating.ipynb
@@ -24,6 +24,1092 @@
         "This dataset contains information such as customer demographics, policy details, vehicle information, and the customer's response to the last marketing campaign. Our goal is to explore and analyze this data by first performing data cleaning, formatting, and structuring."
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "3c03c548",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "(10910, 26)"
+            ]
+          },
+          "execution_count": 1,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "url = \"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv\"\n",
+        "\n",
+        "df = pd.read_csv(url)\n",
+        "\n",
+        "\n",
+        "df.shape"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "b07e1b84",
+      "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>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>DK49336</td>\n",
+              "      <td>Arizona</td>\n",
+              "      <td>4809.216960</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Basic</td>\n",
+              "      <td>College</td>\n",
+              "      <td>2/18/11</td>\n",
+              "      <td>Employed</td>\n",
+              "      <td>M</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>9</td>\n",
+              "      <td>Corporate Auto</td>\n",
+              "      <td>Corporate L3</td>\n",
+              "      <td>Offer3</td>\n",
+              "      <td>Agent</td>\n",
+              "      <td>292.800000</td>\n",
+              "      <td>Four-Door Car</td>\n",
+              "      <td>Medsize</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>KX64629</td>\n",
+              "      <td>California</td>\n",
+              "      <td>2228.525238</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Basic</td>\n",
+              "      <td>College</td>\n",
+              "      <td>1/18/11</td>\n",
+              "      <td>Unemployed</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>Offer4</td>\n",
+              "      <td>Call Center</td>\n",
+              "      <td>744.924331</td>\n",
+              "      <td>Four-Door Car</td>\n",
+              "      <td>Medsize</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2</td>\n",
+              "      <td>LZ68649</td>\n",
+              "      <td>Washington</td>\n",
+              "      <td>14947.917300</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Basic</td>\n",
+              "      <td>Bachelor</td>\n",
+              "      <td>2/10/11</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 L3</td>\n",
+              "      <td>Offer3</td>\n",
+              "      <td>Call Center</td>\n",
+              "      <td>480.000000</td>\n",
+              "      <td>SUV</td>\n",
+              "      <td>Medsize</td>\n",
+              "      <td>A</td>\n",
+              "    </tr>\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>4</th>\n",
+              "      <td>4</td>\n",
+              "      <td>QA50777</td>\n",
+              "      <td>Oregon</td>\n",
+              "      <td>9025.067525</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Premium</td>\n",
+              "      <td>Bachelor</td>\n",
+              "      <td>1/17/11</td>\n",
+              "      <td>Medical Leave</td>\n",
+              "      <td>F</td>\n",
+              "      <td>...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>7</td>\n",
+              "      <td>Personal Auto</td>\n",
+              "      <td>Personal L2</td>\n",
+              "      <td>Offer1</td>\n",
+              "      <td>Branch</td>\n",
+              "      <td>707.925645</td>\n",
+              "      <td>Four-Door Car</td>\n",
+              "      <td>Medsize</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 26 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   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]"
+            ]
+          },
+          "execution_count": 2,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "9456bb7a",
+      "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": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.columns = df.columns.str.replace(\"ST\", \"state\")\n",
+        "\n",
+        "df.columns = (\n",
+        "    df.columns\n",
+        "      .str.lower()\n",
+        "      .str.replace(\" \", \"_\")\n",
+        ")\n",
+        "\n",
+        "df.columns"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "e55fd1db",
+      "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>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>DK49336</td>\n",
+              "      <td>Arizona</td>\n",
+              "      <td>4809.216960</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Basic</td>\n",
+              "      <td>College</td>\n",
+              "      <td>2/18/11</td>\n",
+              "      <td>Employed</td>\n",
+              "      <td>M</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>9</td>\n",
+              "      <td>Corporate Auto</td>\n",
+              "      <td>Corporate L3</td>\n",
+              "      <td>Offer3</td>\n",
+              "      <td>Agent</td>\n",
+              "      <td>292.800000</td>\n",
+              "      <td>Four-Door Car</td>\n",
+              "      <td>Medsize</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>KX64629</td>\n",
+              "      <td>California</td>\n",
+              "      <td>2228.525238</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Basic</td>\n",
+              "      <td>College</td>\n",
+              "      <td>1/18/11</td>\n",
+              "      <td>Unemployed</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>Offer4</td>\n",
+              "      <td>Call Center</td>\n",
+              "      <td>744.924331</td>\n",
+              "      <td>Four-Door Car</td>\n",
+              "      <td>Medsize</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2</td>\n",
+              "      <td>LZ68649</td>\n",
+              "      <td>Washington</td>\n",
+              "      <td>14947.917300</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Basic</td>\n",
+              "      <td>Bachelor</td>\n",
+              "      <td>2/10/11</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 L3</td>\n",
+              "      <td>Offer3</td>\n",
+              "      <td>Call Center</td>\n",
+              "      <td>480.000000</td>\n",
+              "      <td>SUV</td>\n",
+              "      <td>Medsize</td>\n",
+              "      <td>A</td>\n",
+              "    </tr>\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>4</th>\n",
+              "      <td>4</td>\n",
+              "      <td>QA50777</td>\n",
+              "      <td>Oregon</td>\n",
+              "      <td>9025.067525</td>\n",
+              "      <td>No</td>\n",
+              "      <td>Premium</td>\n",
+              "      <td>Bachelor</td>\n",
+              "      <td>1/17/11</td>\n",
+              "      <td>Medical Leave</td>\n",
+              "      <td>F</td>\n",
+              "      <td>...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>7</td>\n",
+              "      <td>Personal Auto</td>\n",
+              "      <td>Personal L2</td>\n",
+              "      <td>Offer1</td>\n",
+              "      <td>Branch</td>\n",
+              "      <td>707.925645</td>\n",
+              "      <td>Four-Door Car</td>\n",
+              "      <td>Medsize</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>5 rows × 26 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   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]"
+            ]
+          },
+          "execution_count": 4,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "gender_map = {\n",
+        "    \"M\": \"M\",\n",
+        "    \"Male\": \"M\",\n",
+        "    \"male\": \"M\",\n",
+        "    \"F\": \"F\",\n",
+        "    \"Femal\": \"F\",\n",
+        "    \"Female\": \"F\",\n",
+        "    \"female\": \"F\"\n",
+        "}\n",
+        "\n",
+        "# Standardize gender column using map()\n",
+        "df['gender'] = df['gender'].replace(gender_map)\n",
+        "\n",
+        "state_map = {\n",
+        "    \"AZ\": \"Arizona\",\n",
+        "    \"Cali\": \"California\",\n",
+        "    \"WA\": \"Washington\",\n",
+        "}\n",
+        "\n",
+        "# Standardize gender column using map()\n",
+        "df['state'] = df['state'].replace(state_map)\n",
+        "\n",
+        "df['education'] = df['education'].replace(\"Bachelors\", \"Bachelor\")\n",
+        "\n",
+        "# Remove '%' character from customer_lifetime_value\n",
+        "#df4['customer_lifetime_value'] = df4['customer_lifetime_value'].str.replace('%', '', regex=False)\n",
+        "\n",
+        "\n",
+        "vehicle_map = {\n",
+        "    \"Sports Car\": \"Luxury\",\n",
+        "    \"Luxury SUV\": \"Luxury\",\n",
+        "    \"Luxury Car\": \"Luxury\",\n",
+        "}\n",
+        "\n",
+        "# Standardize gender column using map()\n",
+        "df['vehicle_class'] = df['vehicle_class'].replace(vehicle_map)\n",
+        "\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "id": "6585dc89",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "unnamed:_0                         int64\n",
+              "customer                          object\n",
+              "state                             object\n",
+              "customer_lifetime_value          float64\n",
+              "response                          object\n",
+              "coverage                          object\n",
+              "education                         object\n",
+              "effective_to_date                 object\n",
+              "employmentstatus                  object\n",
+              "gender                            object\n",
+              "income                             int64\n",
+              "location_code                     object\n",
+              "marital_status                    object\n",
+              "monthly_premium_auto               int64\n",
+              "months_since_last_claim          float64\n",
+              "months_since_policy_inception      int64\n",
+              "number_of_open_complaints        float64\n",
+              "number_of_policies                 int64\n",
+              "policy_type                       object\n",
+              "policy                            object\n",
+              "renew_offer_type                  object\n",
+              "sales_channel                     object\n",
+              "total_claim_amount               float64\n",
+              "vehicle_class                     object\n",
+              "vehicle_size                      object\n",
+              "vehicle_type                      object\n",
+              "dtype: object"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.dtypes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "159e55a8",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "C:\\Users\\Sofia\\AppData\\Local\\Temp\\ipykernel_23784\\2902342384.py:3: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.\n",
+            "  df = df.fillna(method=\"ffill\")\n"
+          ]
+        }
+      ],
+      "source": [
+        "df.isnull().sum()\n",
+        "\n",
+        "df = df.fillna(method=\"ffill\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "db9725ac",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "unnamed:_0                       0\n",
+              "customer                         0\n",
+              "state                            0\n",
+              "customer_lifetime_value          0\n",
+              "response                         0\n",
+              "coverage                         0\n",
+              "education                        0\n",
+              "effective_to_date                0\n",
+              "employmentstatus                 0\n",
+              "gender                           0\n",
+              "income                           0\n",
+              "location_code                    0\n",
+              "marital_status                   0\n",
+              "monthly_premium_auto             0\n",
+              "months_since_last_claim          0\n",
+              "months_since_policy_inception    0\n",
+              "number_of_open_complaints        0\n",
+              "number_of_policies               0\n",
+              "policy_type                      0\n",
+              "policy                           0\n",
+              "renew_offer_type                 0\n",
+              "sales_channel                    0\n",
+              "total_claim_amount               0\n",
+              "vehicle_class                    0\n",
+              "vehicle_size                     0\n",
+              "vehicle_type                     2\n",
+              "dtype: int64"
+            ]
+          },
+          "execution_count": 11,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.isnull().sum()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "id": "66c9ae13",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "np.int64(0)"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.duplicated().sum()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "08aa7f1a",
+      "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>Luxury</td>\n",
+              "      <td>Medsize</td>\n",
+              "      <td>A</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>A</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>A</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>A</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>Four-Door Car</td>\n",
+              "      <td>Large</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>A</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>Luxury</td>\n",
+              "      <td>Medsize</td>\n",
+              "      <td>A</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>1489 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         Luxury      Medsize            A  \n",
+              "15             547.200000            SUV      Medsize            A  \n",
+              "19              19.575683   Two-Door Car        Large            A  \n",
+              "27              60.036683  Four-Door Car      Medsize            A  \n",
+              "...                   ...            ...          ...          ...  \n",
+              "10844          547.200000  Four-Door Car      Medsize            A  \n",
+              "10852          791.878042  Four-Door Car        Large            A  \n",
+              "10872          547.200000  Four-Door Car      Medsize            A  \n",
+              "10887          528.200860            SUV        Small            A  \n",
+              "10897          158.077504         Luxury      Medsize            A  \n",
+              "\n",
+              "[1489 rows x 26 columns]"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df_low = pd.DataFrame(df[(df[\"total_claim_amount\"] <= 1000) & (df[\"response\"] == \"Yes\")])\n",
+        "\n",
+        "df_low"
+      ]
+    },
     {
       "cell_type": "markdown",
       "id": "9c98ddc5-b041-4c94-ada1-4dfee5c98e50",
@@ -36,6 +1122,63 @@
         "   - have a response \"Yes\" to the last marketing campaign."
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "7086ed52",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "gender  policy_type   \n",
+              "F       Corporate Auto    7795.525426\n",
+              "        Personal Auto     8250.423043\n",
+              "        Special Auto      7691.584111\n",
+              "M       Corporate Auto    7859.043803\n",
+              "        Personal Auto     7342.467252\n",
+              "        Special Auto      8247.088702\n",
+              "Name: customer_lifetime_value, dtype: float64"
+            ]
+          },
+          "execution_count": 15,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df_yes = pd.DataFrame(df[df[\"response\"] == \"Yes\"])\n",
+        "df_yes.groupby(['gender', \"policy_type\"])['customer_lifetime_value'].mean()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "b10eaef3",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "gender  policy_type   \n",
+              "F       Corporate Auto    436.098218\n",
+              "        Personal Auto     447.308752\n",
+              "        Special Auto      453.280164\n",
+              "M       Corporate Auto    405.145294\n",
+              "        Personal Auto     454.877533\n",
+              "        Special Auto      429.527942\n",
+              "Name: total_claim_amount, dtype: float64"
+            ]
+          },
+          "execution_count": 16,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df_yes.groupby(['gender', \"policy_type\"])['total_claim_amount'].mean()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "id": "b9be383e-5165-436e-80c8-57d4c757c8c3",
@@ -48,6 +1191,34 @@
         "   - compare these insights to `total_claim_amount` patterns, and discuss which segments appear most profitable or low-risk for the company."
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "id": "1d92a7bd",
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "state\n",
+              "Arizona       2055\n",
+              "California    3789\n",
+              "Nevada        1055\n",
+              "Oregon        3062\n",
+              "Washington     949\n",
+              "Name: customer, dtype: int64"
+            ]
+          },
+          "execution_count": 22,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.groupby(\"state\")[\"customer\"].count()\n",
+        "#df[\"state\"].nunique()"
+      ]
+    },
     {
       "cell_type": "markdown",
       "id": "7050f4ac-53c5-4193-a3c0-8699b87196f0",
@@ -58,6 +1229,137 @@
         "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."
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "id": "ecccfebe",
+      "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></th>\n",
+              "      <th>max</th>\n",
+              "      <th>min</th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>gender</th>\n",
+              "      <th>education</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"5\" valign=\"top\">F</th>\n",
+              "      <th>Bachelor</th>\n",
+              "      <td>73225.95652</td>\n",
+              "      <td>1904.000852</td>\n",
+              "      <td>1700</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>College</th>\n",
+              "      <td>61850.18803</td>\n",
+              "      <td>1898.683686</td>\n",
+              "      <td>1608</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Doctor</th>\n",
+              "      <td>44856.11397</td>\n",
+              "      <td>2395.570000</td>\n",
+              "      <td>200</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>High School or Below</th>\n",
+              "      <td>55277.44589</td>\n",
+              "      <td>2144.921535</td>\n",
+              "      <td>1590</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Master</th>\n",
+              "      <td>51016.06704</td>\n",
+              "      <td>2417.777032</td>\n",
+              "      <td>475</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"5\" valign=\"top\">M</th>\n",
+              "      <th>Bachelor</th>\n",
+              "      <td>67907.27050</td>\n",
+              "      <td>1898.007675</td>\n",
+              "      <td>1572</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>College</th>\n",
+              "      <td>61134.68307</td>\n",
+              "      <td>1918.119700</td>\n",
+              "      <td>1596</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Doctor</th>\n",
+              "      <td>32677.34284</td>\n",
+              "      <td>2267.604038</td>\n",
+              "      <td>201</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>High School or Below</th>\n",
+              "      <td>83325.38119</td>\n",
+              "      <td>1940.981221</td>\n",
+              "      <td>1556</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Master</th>\n",
+              "      <td>50568.25912</td>\n",
+              "      <td>2272.307310</td>\n",
+              "      <td>412</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                                     max          min  count\n",
+              "gender education                                            \n",
+              "F      Bachelor              73225.95652  1904.000852   1700\n",
+              "       College               61850.18803  1898.683686   1608\n",
+              "       Doctor                44856.11397  2395.570000    200\n",
+              "       High School or Below  55277.44589  2144.921535   1590\n",
+              "       Master                51016.06704  2417.777032    475\n",
+              "M      Bachelor              67907.27050  1898.007675   1572\n",
+              "       College               61134.68307  1918.119700   1596\n",
+              "       Doctor                32677.34284  2267.604038    201\n",
+              "       High School or Below  83325.38119  1940.981221   1556\n",
+              "       Master                50568.25912  2272.307310    412"
+            ]
+          },
+          "execution_count": 23,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.groupby(['gender', \"education\"])['customer_lifetime_value'].agg([\"max\", \"min\", \"count\"])"
+      ]
+    },
     {
       "cell_type": "markdown",
       "id": "b60a4443-a1a7-4bbf-b78e-9ccdf9895e0d",
@@ -78,6 +1380,49 @@
         "## Bonus"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "id": "8d2917cc",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "month       February  January\n",
+            "state                        \n",
+            "Arizona          990     1065\n",
+            "California      1758     2031\n",
+            "Nevada           477      578\n",
+            "Oregon          1413     1649\n",
+            "Washington       454      495\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "C:\\Users\\Sofia\\AppData\\Local\\Temp\\ipykernel_23784\\4207120029.py:2: 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['month'] = pd.to_datetime(df['effective_to_date']).dt.month_name()\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Count number of policies sold by state and month\n",
+        "df['month'] = pd.to_datetime(df['effective_to_date']).dt.month_name()\n",
+        "\n",
+        "policies_by_state_month = df.pivot_table(\n",
+        "    index='state',\n",
+        "    columns='month',\n",
+        "    values='policy',\n",
+        "    aggfunc='count',\n",
+        "    fill_value=0\n",
+        ")\n",
+        "\n",
+        "print(policies_by_state_month)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "id": "81ff02c5-6584-4f21-a358-b918697c6432",
@@ -88,6 +1433,41 @@
         "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": 28,
+      "id": "1c3053fc",
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "month       February  January\n",
+            "state                        \n",
+            "Arizona          990     1065\n",
+            "California      1758     2031\n",
+            "Oregon          1413     1649\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Filter DataFrame for top 3 states\n",
+        "top_states = [\"Arizona\", \"California\", \"Oregon\"]\n",
+        "\n",
+        "df_top_states = df[df['state'].isin(top_states)]\n",
+        "\n",
+        "# Pivot table: number of policies sold by month for top 3 states\n",
+        "top_states_pivot = df_top_states.pivot_table(\n",
+        "    index='state',\n",
+        "    columns='month',\n",
+        "    values='policy',\n",
+        "    aggfunc='count',\n",
+        ")\n",
+        "\n",
+        "print(top_states_pivot)"
+      ]
+    },
     {
       "cell_type": "markdown",
       "id": "b6aec097-c633-4017-a125-e77a97259cda",
@@ -103,6 +1483,140 @@
         "- *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": 33,
+      "id": "87a6ba38",
+      "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>variable</th>\n",
+              "      <th>value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Branch</td>\n",
+              "      <td>response</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>Branch</td>\n",
+              "      <td>response</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>15</th>\n",
+              "      <td>Branch</td>\n",
+              "      <td>response</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>Call Center</td>\n",
+              "      <td>response</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>27</th>\n",
+              "      <td>Agent</td>\n",
+              "      <td>response</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10844</th>\n",
+              "      <td>Agent</td>\n",
+              "      <td>response</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10852</th>\n",
+              "      <td>Call Center</td>\n",
+              "      <td>response</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10872</th>\n",
+              "      <td>Agent</td>\n",
+              "      <td>response</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10887</th>\n",
+              "      <td>Agent</td>\n",
+              "      <td>response</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>10897</th>\n",
+              "      <td>Agent</td>\n",
+              "      <td>response</td>\n",
+              "      <td>Yes</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>1558 rows × 3 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      sales_channel  variable value\n",
+              "3            Branch  response   Yes\n",
+              "8            Branch  response   Yes\n",
+              "15           Branch  response   Yes\n",
+              "19      Call Center  response   Yes\n",
+              "27            Agent  response   Yes\n",
+              "...             ...       ...   ...\n",
+              "10844         Agent  response   Yes\n",
+              "10852   Call Center  response   Yes\n",
+              "10872         Agent  response   Yes\n",
+              "10887         Agent  response   Yes\n",
+              "10897         Agent  response   Yes\n",
+              "\n",
+              "[1558 rows x 3 columns]"
+            ]
+          },
+          "execution_count": 33,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "unpivoted = pd.melt(df, id_vars=[\"sales_channel\"], value_vars=[\"response\"])\n",
+        "\n",
+        "unpivoted_yes = unpivoted[unpivoted['value'] == \"Yes\"]\n",
+        "\n",
+        "unpivoted_yes"
+      ]
+    },
     {
       "cell_type": "markdown",
       "id": "ba975b8a-a2cf-4fbf-9f59-ebc381767009",
@@ -143,7 +1657,7 @@
       "provenance": []
     },
     "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
+      "display_name": "base",
       "language": "python",
       "name": "python3"
     },
@@ -157,7 +1671,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.13"
+      "version": "3.13.5"
     }
   },
   "nbformat": 4,