diff --git a/lab-dw-aggregating.ipynb b/lab-dw-aggregating.ipynb
index fadd718..bfe6bab 100644
--- a/lab-dw-aggregating.ipynb
+++ b/lab-dw-aggregating.ipynb
@@ -1,165 +1,2769 @@
 {
-  "cells": [
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "31969215-2a90-4d8b-ac36-646a7ae13744",
+   "metadata": {
+    "id": "31969215-2a90-4d8b-ac36-646a7ae13744"
+   },
+   "source": [
+    "# Lab | Data Aggregation and Filtering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8f08a52-bec0-439b-99cc-11d3809d8b5d",
+   "metadata": {
+    "id": "a8f08a52-bec0-439b-99cc-11d3809d8b5d"
+   },
+   "source": [
+    "In this challenge, we will continue to work with customer data from an insurance company. We will use the dataset called marketing_customer_analysis.csv, which can be found at the following link:\n",
+    "\n",
+    "https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv\n",
+    "\n",
+    "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": "markdown",
+   "id": "9c98ddc5-b041-4c94-ada1-4dfee5c98e50",
+   "metadata": {
+    "id": "9c98ddc5-b041-4c94-ada1-4dfee5c98e50"
+   },
+   "source": [
+    "1. Create a new DataFrame that only includes customers who:\n",
+    "   - have a **low total_claim_amount** (e.g., below $1,000),\n",
+    "   - have a response \"Yes\" to the last marketing campaign."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8f1f3dea",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "31969215-2a90-4d8b-ac36-646a7ae13744",
-      "metadata": {
-        "id": "31969215-2a90-4d8b-ac36-646a7ae13744"
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Unnamed: 0",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Customer",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "State",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Customer Lifetime Value",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Response",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Coverage",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Education",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Effective To Date",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "EmploymentStatus",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Gender",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Income",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Location Code",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Marital Status",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Monthly Premium Auto",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Months Since Last Claim",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Months Since Policy Inception",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Number of Open Complaints",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Number of Policies",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Policy Type",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Policy",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Renew Offer Type",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Sales Channel",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Total Claim Amount",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Vehicle Class",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Vehicle Size",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Vehicle Type",
+         "rawType": "object",
+         "type": "unknown"
+        }
+       ],
+       "ref": "0183b8b4-b53c-4959-93ef-3679d8daee96",
+       "rows": [
+        [
+         "0",
+         "0",
+         "DK49336",
+         "Arizona",
+         "4809.21696",
+         "No",
+         "Basic",
+         "College",
+         "2/18/11",
+         "Employed",
+         "M",
+         "48029",
+         "Suburban",
+         "Married",
+         "61",
+         "7.0",
+         "52",
+         "0.0",
+         "9",
+         "Corporate Auto",
+         "Corporate L3",
+         "Offer3",
+         "Agent",
+         "292.8",
+         "Four-Door Car",
+         "Medsize",
+         null
+        ],
+        [
+         "1",
+         "1",
+         "KX64629",
+         "California",
+         "2228.525238",
+         "No",
+         "Basic",
+         "College",
+         "1/18/11",
+         "Unemployed",
+         "F",
+         "0",
+         "Suburban",
+         "Single",
+         "64",
+         "3.0",
+         "26",
+         "0.0",
+         "1",
+         "Personal Auto",
+         "Personal L3",
+         "Offer4",
+         "Call Center",
+         "744.924331",
+         "Four-Door Car",
+         "Medsize",
+         null
+        ],
+        [
+         "2",
+         "2",
+         "LZ68649",
+         "Washington",
+         "14947.9173",
+         "No",
+         "Basic",
+         "Bachelor",
+         "2/10/11",
+         "Employed",
+         "M",
+         "22139",
+         "Suburban",
+         "Single",
+         "100",
+         "34.0",
+         "31",
+         "0.0",
+         "2",
+         "Personal Auto",
+         "Personal L3",
+         "Offer3",
+         "Call Center",
+         "480.0",
+         "SUV",
+         "Medsize",
+         "A"
+        ],
+        [
+         "3",
+         "3",
+         "XL78013",
+         "Oregon",
+         "22332.43946",
+         "Yes",
+         "Extended",
+         "College",
+         "1/11/11",
+         "Employed",
+         "M",
+         "49078",
+         "Suburban",
+         "Single",
+         "97",
+         "10.0",
+         "3",
+         "0.0",
+         "2",
+         "Corporate Auto",
+         "Corporate L3",
+         "Offer2",
+         "Branch",
+         "484.013411",
+         "Four-Door Car",
+         "Medsize",
+         "A"
+        ],
+        [
+         "4",
+         "4",
+         "QA50777",
+         "Oregon",
+         "9025.067525",
+         "No",
+         "Premium",
+         "Bachelor",
+         "1/17/11",
+         "Medical Leave",
+         "F",
+         "23675",
+         "Suburban",
+         "Married",
+         "117",
+         null,
+         "31",
+         null,
+         "7",
+         "Personal Auto",
+         "Personal L2",
+         "Offer1",
+         "Branch",
+         "707.925645",
+         "Four-Door Car",
+         "Medsize",
+         null
+        ]
+       ],
+       "shape": {
+        "columns": 26,
+        "rows": 5
+       }
       },
-      "source": [
-        "# Lab | Data Aggregation and Filtering"
+      "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": 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",
+    "df = pd.read_csv(url)\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ec7b25d6",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "a8f08a52-bec0-439b-99cc-11d3809d8b5d",
-      "metadata": {
-        "id": "a8f08a52-bec0-439b-99cc-11d3809d8b5d"
-      },
-      "source": [
-        "In this challenge, we will continue to work with customer data from an insurance company. We will use the dataset called marketing_customer_analysis.csv, which can be found at the following link:\n",
-        "\n",
-        "https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv\n",
-        "\n",
-        "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."
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0         292.800000\n",
+      "1         744.924331\n",
+      "2         480.000000\n",
+      "3         484.013411\n",
+      "4         707.925645\n",
+      "            ...     \n",
+      "10905    1214.400000\n",
+      "10906     273.018929\n",
+      "10907     381.306996\n",
+      "10908     618.288849\n",
+      "10909    1021.719397\n",
+      "Name: Total Claim Amount, Length: 10910, dtype: float64\n",
+      "0         292\n",
+      "1         744\n",
+      "2         480\n",
+      "3         484\n",
+      "4         707\n",
+      "         ... \n",
+      "10905    1214\n",
+      "10906     273\n",
+      "10907     381\n",
+      "10908     618\n",
+      "10909    1021\n",
+      "Name: Total Claim Amount, Length: 10910, dtype: int64\n",
+      "Los nulos son: 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df['Total Claim Amount'])\n",
+    "\n",
+    "#Convert 'Total Claim Amount' to interger\n",
+    "df['Total Claim Amount'] = df['Total Claim Amount'].astype(int)\n",
+    "\n",
+    "print(df['Total Claim Amount'])\n",
+    "\n",
+    "print('Los nulos son:', df['Total Claim Amount'].isnull().sum()) # Check for null values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c2f8ba03",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "9c98ddc5-b041-4c94-ada1-4dfee5c98e50",
-      "metadata": {
-        "id": "9c98ddc5-b041-4c94-ada1-4dfee5c98e50"
-      },
-      "source": [
-        "1. Create a new DataFrame that only includes customers who:\n",
-        "   - have a **low total_claim_amount** (e.g., below $1,000),\n",
-        "   - have a response \"Yes\" to the last marketing campaign."
+     "data": {
+      "text/plain": [
+       "array(['No', 'Yes', nan], dtype=object)"
       ]
-    },
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Response'].unique() # Check unique values in 'Response' column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "831526e7",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "b9be383e-5165-436e-80c8-57d4c757c8c3",
-      "metadata": {
-        "id": "b9be383e-5165-436e-80c8-57d4c757c8c3"
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+         "type": "float"
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+         "type": "string"
+        },
+        {
+         "name": "Marital Status",
+         "rawType": "object",
+         "type": "string"
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+         "rawType": "int64",
+         "type": "integer"
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+         "rawType": "float64",
+         "type": "float"
+        },
+        {
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+         "rawType": "int64",
+         "type": "integer"
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+        {
+         "name": "Number of Open Complaints",
+         "rawType": "float64",
+         "type": "float"
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+         "rawType": "int64",
+         "type": "integer"
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+         "XL78013",
+         "Oregon",
+         "22332.43946",
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+         "3",
+         "0.0",
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+         "Corporate L3",
+         "Offer2",
+         "Branch",
+         "484",
+         "Four-Door Car",
+         "Medsize",
+         "A"
+        ],
+        [
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+         "8",
+         "FM55990",
+         "California",
+         "5989.773931",
+         "Yes",
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+         "Offer2",
+         "Branch",
+         "739",
+         "Sports Car",
+         "Medsize",
+         null
+        ],
+        [
+         "15",
+         "15",
+         "CW49887",
+         "California",
+         "4626.801093",
+         "Yes",
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+         "Employed",
+         "F",
+         "79487",
+         "Suburban",
+         "Divorced",
+         "114",
+         "20.0",
+         "87",
+         "0.0",
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+         "Special L1",
+         "Offer2",
+         "Branch",
+         "547",
+         "SUV",
+         "Medsize",
+         null
+        ],
+        [
+         "19",
+         "19",
+         "NJ54277",
+         "California",
+         "3746.751625",
+         "Yes",
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+         "2/26/11",
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+         "Personal L2",
+         "Offer2",
+         "Call Center",
+         "19",
+         "Two-Door Car",
+         "Large",
+         "A"
+        ],
+        [
+         "27",
+         "27",
+         "MQ68407",
+         "Oregon",
+         "4376.363592",
+         "Yes",
+         "Premium",
+         "Bachelor",
+         "2/28/11",
+         "Employed",
+         "F",
+         "63774",
+         "Rural",
+         "Divorced",
+         "111",
+         "18.0",
+         "63",
+         "0.0",
+         "1",
+         "Personal Auto",
+         "Personal L3",
+         "Offer2",
+         "Agent",
+         "60",
+         "Four-Door Car",
+         "Medsize",
+         null
+        ]
+       ],
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+        "columns": 26,
+        "rows": 5
+       }
       },
-      "source": [
-        "2. Using the original Dataframe, analyze:\n",
-        "   - the average `monthly_premium` and/or customer lifetime value by `policy_type` and `gender` for customers who responded \"Yes\", and\n",
-        "   - compare these insights to `total_claim_amount` patterns, and discuss which segments appear most profitable or low-risk for the company."
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+       "      <th></th>\n",
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+       "      <th>Customer</th>\n",
+       "      <th>State</th>\n",
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+       "      <th>Response</th>\n",
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+       "      <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",
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+       "      <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</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</td>\n",
+       "      <td>Sports Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>15</td>\n",
+       "      <td>CW49887</td>\n",
+       "      <td>California</td>\n",
+       "      <td>4626.801093</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1/16/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>Special L1</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>547</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>19</td>\n",
+       "      <td>NJ54277</td>\n",
+       "      <td>California</td>\n",
+       "      <td>3746.751625</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2/26/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L2</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>19</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</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",
+       "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",
+       "    Coverage Education Effective To Date EmploymentStatus Gender  ...  \\\n",
+       "3   Extended   College           1/11/11         Employed      M  ...   \n",
+       "8    Premium   College           1/19/11         Employed      M  ...   \n",
+       "15     Basic    Master           1/16/11         Employed      F  ...   \n",
+       "19  Extended   College           2/26/11         Employed      F  ...   \n",
+       "27   Premium  Bachelor           2/28/11         Employed      F  ...   \n",
+       "\n",
+       "    Number of Open Complaints Number of Policies     Policy Type  \\\n",
+       "3                         0.0                  2  Corporate Auto   \n",
+       "8                         0.0                  1   Personal Auto   \n",
+       "15                        0.0                  1    Special Auto   \n",
+       "19                        1.0                  1   Personal Auto   \n",
+       "27                        0.0                  1   Personal Auto   \n",
+       "\n",
+       "          Policy  Renew Offer Type  Sales Channel  Total Claim Amount  \\\n",
+       "3   Corporate L3            Offer2         Branch                 484   \n",
+       "8    Personal L1            Offer2         Branch                 739   \n",
+       "15    Special L1            Offer2         Branch                 547   \n",
+       "19   Personal L2            Offer2    Call Center                  19   \n",
+       "27   Personal L3            Offer2          Agent                  60   \n",
+       "\n",
+       "    Vehicle Class Vehicle Size Vehicle Type  \n",
+       "3   Four-Door Car      Medsize            A  \n",
+       "8      Sports Car      Medsize          NaN  \n",
+       "15            SUV      Medsize          NaN  \n",
+       "19   Two-Door Car        Large            A  \n",
+       "27  Four-Door Car      Medsize          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
       ]
-    },
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#1. Create a new DataFrame that only includes customers who:\n",
+    "   #- have a **low total_claim_amount** (e.g., below $1,000),\n",
+    "   #- have a response \"Yes\" to the last marketing campaign.\n",
+    "\n",
+    "low_claim_df = df[(df['Total Claim Amount'] < 1000) & (df['Response'] == 'Yes')]\n",
+    "\n",
+    "low_claim_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9be383e-5165-436e-80c8-57d4c757c8c3",
+   "metadata": {
+    "id": "b9be383e-5165-436e-80c8-57d4c757c8c3"
+   },
+   "source": [
+    "2. Using the original Dataframe, analyze:\n",
+    "   - the average `monthly_premium` and/or customer lifetime value by `policy_type` and `gender` for customers who responded \"Yes\", and\n",
+    "   - compare these insights to `total_claim_amount` patterns, and discuss which segments appear most profitable or low-risk for the company."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "09db4b9e",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "7050f4ac-53c5-4193-a3c0-8699b87196f0",
-      "metadata": {
-        "id": "7050f4ac-53c5-4193-a3c0-8699b87196f0"
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Policy Type",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Gender",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Monthly Premium Auto",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Customer Lifetime Value",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "Total Claim Amount",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "ref": "bdec9b37-0675-418e-a28a-f8d72f9acb89",
+       "rows": [
+        [
+         "0",
+         "Corporate Auto",
+         "F",
+         "94.30177514792899",
+         "7712.62873610651",
+         "433.29585798816566"
+        ],
+        [
+         "1",
+         "Corporate Auto",
+         "M",
+         "92.18831168831169",
+         "7944.465413844156",
+         "408.1233766233766"
+        ],
+        [
+         "2",
+         "Personal Auto",
+         "F",
+         "98.99814814814815",
+         "8339.791842237037",
+         "452.4981481481482"
+        ],
+        [
+         "3",
+         "Personal Auto",
+         "M",
+         "91.08582089552239",
+         "7448.383280707089",
+         "456.5764925373134"
+        ],
+        [
+         "4",
+         "Special Auto",
+         "F",
+         "92.31428571428572",
+         "7691.584111285713",
+         "452.85714285714283"
+        ],
+        [
+         "5",
+         "Special Auto",
+         "M",
+         "86.34375",
+         "8247.08870234375",
+         "429.125"
+        ]
+       ],
+       "shape": {
+        "columns": 5,
+        "rows": 6
+       }
       },
-      "source": [
-        "3. Analyze the total number of customers who have policies in each state, and then filter the results to only include states where there are more than 500 customers."
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Policy Type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Monthly Premium Auto</th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>F</td>\n",
+       "      <td>94.301775</td>\n",
+       "      <td>7712.628736</td>\n",
+       "      <td>433.295858</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>M</td>\n",
+       "      <td>92.188312</td>\n",
+       "      <td>7944.465414</td>\n",
+       "      <td>408.123377</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>F</td>\n",
+       "      <td>98.998148</td>\n",
+       "      <td>8339.791842</td>\n",
+       "      <td>452.498148</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>M</td>\n",
+       "      <td>91.085821</td>\n",
+       "      <td>7448.383281</td>\n",
+       "      <td>456.576493</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>F</td>\n",
+       "      <td>92.314286</td>\n",
+       "      <td>7691.584111</td>\n",
+       "      <td>452.857143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>M</td>\n",
+       "      <td>86.343750</td>\n",
+       "      <td>8247.088702</td>\n",
+       "      <td>429.125000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Policy Type Gender  Monthly Premium Auto  Customer Lifetime Value  \\\n",
+       "0  Corporate Auto      F             94.301775              7712.628736   \n",
+       "1  Corporate Auto      M             92.188312              7944.465414   \n",
+       "2   Personal Auto      F             98.998148              8339.791842   \n",
+       "3   Personal Auto      M             91.085821              7448.383281   \n",
+       "4    Special Auto      F             92.314286              7691.584111   \n",
+       "5    Special Auto      M             86.343750              8247.088702   \n",
+       "\n",
+       "   Total Claim Amount  \n",
+       "0          433.295858  \n",
+       "1          408.123377  \n",
+       "2          452.498148  \n",
+       "3          456.576493  \n",
+       "4          452.857143  \n",
+       "5          429.125000  "
       ]
-    },
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#the average `monthly_premium` and/or customer lifetime value by `policy_type` and `gender` for customers who responded \"Yes\"\n",
+    "\n",
+    "subset = ['Policy Type','Gender']\n",
+    "\n",
+    "avg_values = df[df['Response'] == 'Yes'].groupby(subset)[['Monthly Premium Auto', 'Customer Lifetime Value', 'Total Claim Amount']].mean().reset_index()\n",
+    "\n",
+    "avg_values\n",
+    "\n",
+    "\n",
+    "#INSIGHTS:\n",
+    "\n",
+    "# 1. Policy Type: \n",
+    "# Customers with 'Personal Auto' policies tend to have lower average total claim amounts compared to those with 'Corporate Auto' policies. \n",
+    "# This suggests that 'Personal Auto' customers may represent a lower-risk segment for the company.\n",
+    "\n",
+    "# 2. Gender: \n",
+    "# Female customers generally have slightly lower average total claim amounts than male customers, indicating that female customers may be a more profitable segment for the company.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7050f4ac-53c5-4193-a3c0-8699b87196f0",
+   "metadata": {
+    "id": "7050f4ac-53c5-4193-a3c0-8699b87196f0"
+   },
+   "source": [
+    "3. Analyze the total number of customers who have policies in each state, and then filter the results to only include states where there are more than 500 customers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "8b2e63ef",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "b60a4443-a1a7-4bbf-b78e-9ccdf9895e0d",
-      "metadata": {
-        "id": "b60a4443-a1a7-4bbf-b78e-9ccdf9895e0d"
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "State",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "count",
+         "rawType": "int64",
+         "type": "integer"
+        }
+       ],
+       "ref": "64818959-add1-4e88-9b03-7c17a0a23527",
+       "rows": [
+        [
+         "California",
+         "3552"
+        ],
+        [
+         "Oregon",
+         "2909"
+        ],
+        [
+         "Arizona",
+         "1937"
+        ],
+        [
+         "Nevada",
+         "993"
+        ],
+        [
+         "Washington",
+         "888"
+        ]
+       ],
+       "shape": {
+        "columns": 1,
+        "rows": 5
+       }
       },
-      "source": [
-        "4. Find the maximum, minimum, and median customer lifetime value by education level and gender. Write your conclusions."
+      "text/plain": [
+       "State\n",
+       "California    3552\n",
+       "Oregon        2909\n",
+       "Arizona       1937\n",
+       "Nevada         993\n",
+       "Washington     888\n",
+       "Name: count, dtype: int64"
       ]
-    },
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#3. Analyze the total number of customers who have policies in each state, and then filter the results to only include states where there are more than 500 customers.\n",
+    "\n",
+    "state_counts = df['State'].value_counts()\n",
+    "\n",
+    "filtered_states = state_counts[state_counts > 500]\n",
+    "\n",
+    "filtered_states"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b60a4443-a1a7-4bbf-b78e-9ccdf9895e0d",
+   "metadata": {
+    "id": "b60a4443-a1a7-4bbf-b78e-9ccdf9895e0d"
+   },
+   "source": [
+    "4. Find the maximum, minimum, and median customer lifetime value by education level and gender. Write your conclusions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f41ffb00",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "b42999f9-311f-481e-ae63-40a5577072c5",
-      "metadata": {
-        "id": "b42999f9-311f-481e-ae63-40a5577072c5"
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Education",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Gender",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "max",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "min",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "median",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "ref": "0350ed8e-3045-4142-aa03-aa3441820144",
+       "rows": [
+        [
+         "0",
+         "Bachelor",
+         "F",
+         "73225.95652",
+         "1904.000852",
+         "5640.505303"
+        ],
+        [
+         "1",
+         "Bachelor",
+         "M",
+         "67907.2705",
+         "1898.007675",
+         "5548.031892"
+        ],
+        [
+         "2",
+         "College",
+         "F",
+         "61850.18803",
+         "1898.683686",
+         "5623.611187"
+        ],
+        [
+         "3",
+         "College",
+         "M",
+         "61134.68307",
+         "1918.1197",
+         "6005.847375"
+        ],
+        [
+         "4",
+         "Doctor",
+         "F",
+         "44856.11397",
+         "2395.57",
+         "5332.462694"
+        ],
+        [
+         "5",
+         "Doctor",
+         "M",
+         "32677.34284",
+         "2267.604038",
+         "5577.669457"
+        ],
+        [
+         "6",
+         "High School or Below",
+         "F",
+         "55277.44589",
+         "2144.921535",
+         "6039.5531869999995"
+        ],
+        [
+         "7",
+         "High School or Below",
+         "M",
+         "83325.38119",
+         "1940.981221",
+         "6286.731006"
+        ],
+        [
+         "8",
+         "Master",
+         "F",
+         "51016.06704",
+         "2417.777032",
+         "5729.855012"
+        ],
+        [
+         "9",
+         "Master",
+         "M",
+         "50568.25912",
+         "2272.30731",
+         "5579.0992074999995"
+        ]
+       ],
+       "shape": {
+        "columns": 5,
+        "rows": 10
+       }
       },
-      "source": [
-        "## Bonus"
+      "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>Education</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>max</th>\n",
+       "      <th>min</th>\n",
+       "      <th>median</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>F</td>\n",
+       "      <td>73225.95652</td>\n",
+       "      <td>1904.000852</td>\n",
+       "      <td>5640.505303</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>M</td>\n",
+       "      <td>67907.27050</td>\n",
+       "      <td>1898.007675</td>\n",
+       "      <td>5548.031892</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>College</td>\n",
+       "      <td>F</td>\n",
+       "      <td>61850.18803</td>\n",
+       "      <td>1898.683686</td>\n",
+       "      <td>5623.611187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>College</td>\n",
+       "      <td>M</td>\n",
+       "      <td>61134.68307</td>\n",
+       "      <td>1918.119700</td>\n",
+       "      <td>6005.847375</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Doctor</td>\n",
+       "      <td>F</td>\n",
+       "      <td>44856.11397</td>\n",
+       "      <td>2395.570000</td>\n",
+       "      <td>5332.462694</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Doctor</td>\n",
+       "      <td>M</td>\n",
+       "      <td>32677.34284</td>\n",
+       "      <td>2267.604038</td>\n",
+       "      <td>5577.669457</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>F</td>\n",
+       "      <td>55277.44589</td>\n",
+       "      <td>2144.921535</td>\n",
+       "      <td>6039.553187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>M</td>\n",
+       "      <td>83325.38119</td>\n",
+       "      <td>1940.981221</td>\n",
+       "      <td>6286.731006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Master</td>\n",
+       "      <td>F</td>\n",
+       "      <td>51016.06704</td>\n",
+       "      <td>2417.777032</td>\n",
+       "      <td>5729.855012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Master</td>\n",
+       "      <td>M</td>\n",
+       "      <td>50568.25912</td>\n",
+       "      <td>2272.307310</td>\n",
+       "      <td>5579.099207</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Education Gender          max          min       median\n",
+       "0              Bachelor      F  73225.95652  1904.000852  5640.505303\n",
+       "1              Bachelor      M  67907.27050  1898.007675  5548.031892\n",
+       "2               College      F  61850.18803  1898.683686  5623.611187\n",
+       "3               College      M  61134.68307  1918.119700  6005.847375\n",
+       "4                Doctor      F  44856.11397  2395.570000  5332.462694\n",
+       "5                Doctor      M  32677.34284  2267.604038  5577.669457\n",
+       "6  High School or Below      F  55277.44589  2144.921535  6039.553187\n",
+       "7  High School or Below      M  83325.38119  1940.981221  6286.731006\n",
+       "8                Master      F  51016.06704  2417.777032  5729.855012\n",
+       "9                Master      M  50568.25912  2272.307310  5579.099207"
       ]
-    },
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#4. Find the maximum, minimum, and median customer lifetime value by education level and gender. Write your conclusions.\n",
+    "\n",
+    "CLV_analysis = df.groupby(['Education','Gender'])['Customer Lifetime Value'].agg(['max','min','median']).reset_index()\n",
+    "\n",
+    "CLV_analysis\n",
+    "\n",
+    "#INSIGHTS:\n",
+    "# 1. Education Level:\n",
+    "#Customers with Bachelor or College education levels show the highest customer lifetime value, while Doctor and Master levels show lower averages.\n",
+    "\n",
+    "# 2. Gender:\n",
+    "#Gender differences vary by education level. \n",
+    "#Male customers show higher CLV in some segments (High School or Below College), while females lead in others (Bachelor, Doctor).\n",
+    "#There is no consistent gender advantage across all levels."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b42999f9-311f-481e-ae63-40a5577072c5",
+   "metadata": {
+    "id": "b42999f9-311f-481e-ae63-40a5577072c5"
+   },
+   "source": [
+    "## Bonus"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81ff02c5-6584-4f21-a358-b918697c6432",
+   "metadata": {
+    "id": "81ff02c5-6584-4f21-a358-b918697c6432"
+   },
+   "source": [
+    "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": 15,
+   "id": "de566c50",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "81ff02c5-6584-4f21-a358-b918697c6432",
-      "metadata": {
-        "id": "81ff02c5-6584-4f21-a358-b918697c6432"
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "State",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "January",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "February",
+         "rawType": "int64",
+         "type": "integer"
+        }
+       ],
+       "ref": "112468aa-ec6b-45a9-8394-c7fdbb9a3621",
+       "rows": [
+        [
+         "Arizona",
+         "3052",
+         "2864"
+        ],
+        [
+         "California",
+         "5673",
+         "4929"
+        ],
+        [
+         "Nevada",
+         "1493",
+         "1278"
+        ],
+        [
+         "Oregon",
+         "4697",
+         "3969"
+        ],
+        [
+         "Washington",
+         "1358",
+         "1225"
+        ]
+       ],
+       "shape": {
+        "columns": 2,
+        "rows": 5
+       }
       },
-      "source": [
-        "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."
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Month</th>\n",
+       "      <th>January</th>\n",
+       "      <th>February</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>State</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>3052</td>\n",
+       "      <td>2864</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>5673</td>\n",
+       "      <td>4929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Nevada</th>\n",
+       "      <td>1493</td>\n",
+       "      <td>1278</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Oregon</th>\n",
+       "      <td>4697</td>\n",
+       "      <td>3969</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Washington</th>\n",
+       "      <td>1358</td>\n",
+       "      <td>1225</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Month       January  February\n",
+       "State                        \n",
+       "Arizona        3052      2864\n",
+       "California     5673      4929\n",
+       "Nevada         1493      1278\n",
+       "Oregon         4697      3969\n",
+       "Washington     1358      1225"
       ]
-    },
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#5. The marketing team wants to analyze the number of policies sold by state and month.\n",
+    "#Present the data in a table where the months are arranged as columns and the states are arranged as rows.\n",
+    "\n",
+    "\n",
+    "# First, ensure that the 'Effective To Date' column is in datetime format\n",
+    "df['Effective To Date'] = pd.to_datetime(df['Effective To Date'])\n",
+    "\n",
+    "# Extract month from 'Effective To Date'\n",
+    "df['Month'] = df['Effective To Date'].dt.month_name()\n",
+    "\n",
+    "# Create a pivot table with states as rows and months as columns\n",
+    "# The dataset column is named 'Number of Policies' — sum it to get total policies sold per state/month\n",
+    "\n",
+    "policy_pivot = pd.pivot_table(\n",
+    "    df,\n",
+    "    index='State',\n",
+    "    columns='Month',\n",
+    "    values='Number of Policies',\n",
+    "    aggfunc='sum',\n",
+    "    fill_value=0\n",
+    ")\n",
+    "\n",
+    "policy_pivot = policy_pivot.reindex(columns=[\n",
+    "    'January', 'February'\n",
+    "])\n",
+    "    \n",
+    "policy_pivot\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6aec097-c633-4017-a125-e77a97259cda",
+   "metadata": {
+    "id": "b6aec097-c633-4017-a125-e77a97259cda"
+   },
+   "source": [
+    "6.  Display a new DataFrame that contains the number of policies sold by month, by state, for the top 3 states with the highest number of policies sold.\n",
+    "\n",
+    "*Hint:*\n",
+    "- *To accomplish this, you will first need to group the data by state and month, then count the number of policies sold for each group. Afterwards, you will need to sort the data by the count of policies sold in descending order.*\n",
+    "- *Next, you will select the top 3 states with the highest number of policies sold.*\n",
+    "- *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": null,
+   "id": "de5ac07e",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "b6aec097-c633-4017-a125-e77a97259cda",
-      "metadata": {
-        "id": "b6aec097-c633-4017-a125-e77a97259cda"
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "State",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Month",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Number of Policies",
+         "rawType": "int64",
+         "type": "integer"
+        }
+       ],
+       "ref": "79f202be-b39a-445e-ac9f-84ff2347af07",
+       "rows": [
+        [
+         "3",
+         "California",
+         "January",
+         "5673"
+        ],
+        [
+         "2",
+         "California",
+         "February",
+         "4929"
+        ],
+        [
+         "7",
+         "Oregon",
+         "January",
+         "4697"
+        ],
+        [
+         "6",
+         "Oregon",
+         "February",
+         "3969"
+        ],
+        [
+         "1",
+         "Arizona",
+         "January",
+         "3052"
+        ],
+        [
+         "0",
+         "Arizona",
+         "February",
+         "2864"
+        ],
+        [
+         "5",
+         "Nevada",
+         "January",
+         "1493"
+        ],
+        [
+         "9",
+         "Washington",
+         "January",
+         "1358"
+        ],
+        [
+         "4",
+         "Nevada",
+         "February",
+         "1278"
+        ],
+        [
+         "8",
+         "Washington",
+         "February",
+         "1225"
+        ]
+       ],
+       "shape": {
+        "columns": 3,
+        "rows": 10
+       }
       },
-      "source": [
-        "6.  Display a new DataFrame that contains the number of policies sold by month, by state, for the top 3 states with the highest number of policies sold.\n",
-        "\n",
-        "*Hint:*\n",
-        "- *To accomplish this, you will first need to group the data by state and month, then count the number of policies sold for each group. Afterwards, you will need to sort the data by the count of policies sold in descending order.*\n",
-        "- *Next, you will select the top 3 states with the highest number of policies sold.*\n",
-        "- *Finally, you will create a new DataFrame that contains the number of policies sold by month for each of the top 3 states.*"
+      "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>State</th>\n",
+       "      <th>Month</th>\n",
+       "      <th>Number of Policies</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>California</td>\n",
+       "      <td>January</td>\n",
+       "      <td>5673</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>California</td>\n",
+       "      <td>February</td>\n",
+       "      <td>4929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>January</td>\n",
+       "      <td>4697</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>February</td>\n",
+       "      <td>3969</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>January</td>\n",
+       "      <td>3052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>February</td>\n",
+       "      <td>2864</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Nevada</td>\n",
+       "      <td>January</td>\n",
+       "      <td>1493</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Washington</td>\n",
+       "      <td>January</td>\n",
+       "      <td>1358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Nevada</td>\n",
+       "      <td>February</td>\n",
+       "      <td>1278</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Washington</td>\n",
+       "      <td>February</td>\n",
+       "      <td>1225</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        State     Month  Number of Policies\n",
+       "3  California   January                5673\n",
+       "2  California  February                4929\n",
+       "7      Oregon   January                4697\n",
+       "6      Oregon  February                3969\n",
+       "1     Arizona   January                3052\n",
+       "0     Arizona  February                2864\n",
+       "5      Nevada   January                1493\n",
+       "9  Washington   January                1358\n",
+       "4      Nevada  February                1278\n",
+       "8  Washington  February                1225"
       ]
-    },
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#1. Group the data by state and month, then count the number of policies sold for each group.\n",
+    "# Afterwards, you will need to sort the data by the count of policies sold in descending order.\n",
+    "\n",
+    "policy_counts = df.groupby(['State', 'Month'])['Number of Policies'].sum().reset_index()\n",
+    "policy_counts = policy_counts.sort_values(by='Number of Policies', ascending=False)\n",
+    "policy_counts\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d404675",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "ba975b8a-a2cf-4fbf-9f59-ebc381767009",
-      "metadata": {
-        "id": "ba975b8a-a2cf-4fbf-9f59-ebc381767009"
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "State",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Number of Policies",
+         "rawType": "int64",
+         "type": "integer"
+        }
+       ],
+       "ref": "4e617224-6ea1-47da-9f8b-69dbbb133bd6",
+       "rows": [
+        [
+         "0",
+         "California",
+         "10602"
+        ],
+        [
+         "1",
+         "Oregon",
+         "8666"
+        ],
+        [
+         "2",
+         "Arizona",
+         "5916"
+        ]
+       ],
+       "shape": {
+        "columns": 2,
+        "rows": 3
+       }
       },
-      "source": [
-        "7. The marketing team wants to analyze the effect of different marketing channels on the customer response rate.\n",
-        "\n",
-        "Hint: You can use melt to unpivot the data and create a table that shows the customer response rate (those who responded \"Yes\") by marketing channel."
+      "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>State</th>\n",
+       "      <th>Number of Policies</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>California</td>\n",
+       "      <td>10602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>8666</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>5916</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        State  Number of Policies\n",
+       "0  California               10602\n",
+       "1      Oregon                8666\n",
+       "2     Arizona                5916"
       ]
-    },
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#2. Select the top 3 states with the highest number of policies sold.\n",
+    "\n",
+    "top_states = policy_counts.groupby('State')['Number of Policies'].sum().nlargest(3).reset_index()\n",
+    "top_states"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0a037d7",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "e4378d94-48fb-4850-a802-b1bc8f427b2d",
-      "metadata": {
-        "id": "e4378d94-48fb-4850-a802-b1bc8f427b2d"
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "State",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Month",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Number of Policies",
+         "rawType": "int64",
+         "type": "integer"
+        }
+       ],
+       "ref": "306f6a29-68a0-437a-88ce-ddab0bc88542",
+       "rows": [
+        [
+         "3",
+         "California",
+         "January",
+         "5673"
+        ],
+        [
+         "2",
+         "California",
+         "February",
+         "4929"
+        ],
+        [
+         "7",
+         "Oregon",
+         "January",
+         "4697"
+        ],
+        [
+         "6",
+         "Oregon",
+         "February",
+         "3969"
+        ],
+        [
+         "1",
+         "Arizona",
+         "January",
+         "3052"
+        ],
+        [
+         "0",
+         "Arizona",
+         "February",
+         "2864"
+        ]
+       ],
+       "shape": {
+        "columns": 3,
+        "rows": 6
+       }
       },
-      "source": [
-        "External Resources for Data Filtering: https://towardsdatascience.com/filtering-data-frames-in-pandas-b570b1f834b9"
+      "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>State</th>\n",
+       "      <th>Month</th>\n",
+       "      <th>Number of Policies</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>California</td>\n",
+       "      <td>January</td>\n",
+       "      <td>5673</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>California</td>\n",
+       "      <td>February</td>\n",
+       "      <td>4929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>January</td>\n",
+       "      <td>4697</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>February</td>\n",
+       "      <td>3969</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>January</td>\n",
+       "      <td>3052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>February</td>\n",
+       "      <td>2864</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        State     Month  Number of Policies\n",
+       "3  California   January                5673\n",
+       "2  California  February                4929\n",
+       "7      Oregon   January                4697\n",
+       "6      Oregon  February                3969\n",
+       "1     Arizona   January                3052\n",
+       "0     Arizona  February                2864"
       ]
-    },
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#3. Create a new DataFrame that contains the number of policies sold by month for each of the top 3 states.\n",
+    "\n",
+    "state_list = top_states['State'].tolist()\n",
+    "top_state_policies = policy_counts[policy_counts['State'].isin(state_list)]\n",
+    "top_state_policies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba975b8a-a2cf-4fbf-9f59-ebc381767009",
+   "metadata": {
+    "id": "ba975b8a-a2cf-4fbf-9f59-ebc381767009"
+   },
+   "source": [
+    "7. The marketing team wants to analyze the effect of different marketing channels on the customer response rate.\n",
+    "\n",
+    "Hint: You can use melt to unpivot the data and create a table that shows the customer response rate (those who responded \"Yes\") by marketing channel."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4378d94-48fb-4850-a802-b1bc8f427b2d",
+   "metadata": {
+    "id": "e4378d94-48fb-4850-a802-b1bc8f427b2d"
+   },
+   "source": [
+    "External Resources for Data Filtering: https://towardsdatascience.com/filtering-data-frames-in-pandas-b570b1f834b9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e48c7914",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "449513f4-0459-46a0-a18d-9398d974c9ad",
-      "metadata": {
-        "id": "449513f4-0459-46a0-a18d-9398d974c9ad"
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Response",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Marketing Channel",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Channel Type",
+         "rawType": "object",
+         "type": "string"
+        }
+       ],
+       "ref": "15ee26d9-704a-48a9-be56-394beffb5a1f",
+       "rows": [
+        [
+         "0",
+         "No",
+         "Sales Channel",
+         "Agent"
+        ],
+        [
+         "1",
+         "No",
+         "Sales Channel",
+         "Call Center"
+        ],
+        [
+         "2",
+         "No",
+         "Sales Channel",
+         "Call Center"
+        ],
+        [
+         "3",
+         "Yes",
+         "Sales Channel",
+         "Branch"
+        ],
+        [
+         "4",
+         "No",
+         "Sales Channel",
+         "Branch"
+        ]
+       ],
+       "shape": {
+        "columns": 3,
+        "rows": 5
+       }
       },
-      "outputs": [],
-      "source": [
-        "# your code goes here"
+      "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>Response</th>\n",
+       "      <th>Marketing Channel</th>\n",
+       "      <th>Channel Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>No</td>\n",
+       "      <td>Sales Channel</td>\n",
+       "      <td>Agent</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>No</td>\n",
+       "      <td>Sales Channel</td>\n",
+       "      <td>Call Center</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>No</td>\n",
+       "      <td>Sales Channel</td>\n",
+       "      <td>Call Center</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Sales Channel</td>\n",
+       "      <td>Branch</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>No</td>\n",
+       "      <td>Sales Channel</td>\n",
+       "      <td>Branch</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Response Marketing Channel Channel Type\n",
+       "0       No     Sales Channel        Agent\n",
+       "1       No     Sales Channel  Call Center\n",
+       "2       No     Sales Channel  Call Center\n",
+       "3      Yes     Sales Channel       Branch\n",
+       "4       No     Sales Channel       Branch"
       ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
     }
-  ],
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
+   ],
+   "source": [
+    "#7. The marketing team wants to analyze the effect of different marketing channels on the customer response rate.\n",
+    "\n",
+    "#Hint: You can use melt to unpivot the data and create a table that shows the customer response rate (those who responded \"Yes\") by marketing channel.\n",
+    "\n",
+    "melted_df = pd.melt(\n",
+    "    df,\n",
+    "    id_vars=['Response'],\n",
+    "    value_vars=['Sales Channel'], #derretimos las columnas de canales de marketing\n",
+    "    var_name='Marketing Channel', #nombre de variable de la nueva columna\n",
+    "    value_name='Channel Type' #nombre de la nueva columna con los valores de Sales Channel\n",
+    ")\n",
+    "\n",
+    "melted_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd5bae2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Channel Type",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Response Rate",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "ref": "64ba04f7-e048-4372-8e39-177970379a6f",
+       "rows": [
+        [
+         "0",
+         "Agent",
+         "0.1800533851007037"
+        ],
+        [
+         "1",
+         "Branch",
+         "0.10787557908669755"
+        ],
+        [
+         "2",
+         "Call Center",
+         "0.10322279308734236"
+        ],
+        [
+         "3",
+         "Web",
+         "0.1088560885608856"
+        ]
+       ],
+       "shape": {
+        "columns": 2,
+        "rows": 4
+       }
       },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.13"
+      "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>Channel Type</th>\n",
+       "      <th>Response Rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Agent</td>\n",
+       "      <td>0.180053</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Branch</td>\n",
+       "      <td>0.107876</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>0.103223</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Web</td>\n",
+       "      <td>0.108856</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Channel Type  Response Rate\n",
+       "0        Agent       0.180053\n",
+       "1       Branch       0.107876\n",
+       "2  Call Center       0.103223\n",
+       "3          Web       0.108856"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
     }
+   ],
+   "source": [
+    "# Calcular el % de respuestas \"Yes\" por tipo de canal\n",
+    "response_rate = (\n",
+    "    melted_df.groupby('Channel Type')['Response'] #agrupa todas las filas por cada tipo de canal y dentro de cada grupo, nos quedamos con la serie de respuestas\n",
+    "    .apply(lambda x: (x == 'Yes').mean()) #aplica una funcion lambda que calcula el porcentaje de respuestas \"Yes\" en cada grupo\n",
+    "    .reset_index(name='Response Rate') #crea un nuevo DataFrame con el resultado\n",
+    ")\n",
+    "\n",
+    "response_rate"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 5
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }