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+{
+ "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": "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": "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": "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": "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": "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": "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": 1,
+   "id": "449513f4-0459-46a0-a18d-9398d974c9ad",
+   "metadata": {
+    "id": "449513f4-0459-46a0-a18d-9398d974c9ad"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></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",
+    "# Load the raw dataset\n",
+    "df = pd.read_csv(\"marketing_customer_analysis.csv\")\n",
+    "\n",
+    "# Standardize column names\n",
+    "df.columns = df.columns.str.lower().str.replace(\" \", \"_\")\n",
+    "df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "309965c1-700c-4685-aeb9-cb8c5f03486b",
+   "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",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>unnamed:_0</th>\n",
+       "      <th>customer</th>\n",
+       "      <th>state</th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <th>response</th>\n",
+       "      <th>coverage</th>\n",
+       "      <th>education</th>\n",
+       "      <th>effective_to_date</th>\n",
+       "      <th>employmentstatus</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>...</th>\n",
+       "      <th>number_of_open_complaints</th>\n",
+       "      <th>number_of_policies</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>policy</th>\n",
+       "      <th>renew_offer_type</th>\n",
+       "      <th>sales_channel</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "      <th>vehicle_size</th>\n",
+       "      <th>vehicle_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>XL78013</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>22332.439460</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>College</td>\n",
+       "      <td>1/11/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L3</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>484.013411</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>FM55990</td>\n",
+       "      <td>California</td>\n",
+       "      <td>5989.773931</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Premium</td>\n",
+       "      <td>College</td>\n",
+       "      <td>1/19/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L1</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>739.200000</td>\n",
+       "      <td>Sports Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>15</td>\n",
+       "      <td>CW49887</td>\n",
+       "      <td>California</td>\n",
+       "      <td>4626.801093</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1/16/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>Special L1</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>547.200000</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>19</td>\n",
+       "      <td>NJ54277</td>\n",
+       "      <td>California</td>\n",
+       "      <td>3746.751625</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2/26/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L2</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>19.575683</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>Large</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>27</td>\n",
+       "      <td>MQ68407</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>4376.363592</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Premium</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>2/28/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L3</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Agent</td>\n",
+       "      <td>60.036683</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </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          484.013411   \n",
+       "8    Personal L1            Offer2         Branch          739.200000   \n",
+       "15    Special L1            Offer2         Branch          547.200000   \n",
+       "19   Personal L2            Offer2    Call Center           19.575683   \n",
+       "27   Personal L3            Offer2          Agent           60.036683   \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": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Customers with total claim < $1,000 and response = 'Yes'\n",
+    "low_claim_yes_response = df[(df['total_claim_amount'] < 1000) & (df['response'] == 'Yes')]\n",
+    "low_claim_yes_response.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "00fd4d8e-aeae-4f5b-96e7-c2b68c4eec3d",
+   "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>monthly_premium_auto</th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>gender</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Corporate Auto</th>\n",
+       "      <th>F</th>\n",
+       "      <td>94.30</td>\n",
+       "      <td>7712.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>92.19</td>\n",
+       "      <td>7944.47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Personal Auto</th>\n",
+       "      <th>F</th>\n",
+       "      <td>99.00</td>\n",
+       "      <td>8339.79</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>91.09</td>\n",
+       "      <td>7448.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Special Auto</th>\n",
+       "      <th>F</th>\n",
+       "      <td>92.31</td>\n",
+       "      <td>7691.58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>86.34</td>\n",
+       "      <td>8247.09</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       monthly_premium_auto  customer_lifetime_value\n",
+       "policy_type    gender                                               \n",
+       "Corporate Auto F                      94.30                  7712.63\n",
+       "               M                      92.19                  7944.47\n",
+       "Personal Auto  F                      99.00                  8339.79\n",
+       "               M                      91.09                  7448.38\n",
+       "Special Auto   F                      92.31                  7691.58\n",
+       "               M                      86.34                  8247.09"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Filter for only \"Yes\" responders\n",
+    "yes_responders = df[df['response'] == 'Yes']\n",
+    "\n",
+    "# Group by policy_type and gender\n",
+    "insights = yes_responders.groupby(['policy_type', 'gender'])[['monthly_premium_auto', 'customer_lifetime_value']].mean().round(2)\n",
+    "insights\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5a4aa49d-99d2-41ef-8890-cdc5c25f8d0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "policy_type     gender\n",
+       "Corporate Auto  F         433.74\n",
+       "                M         408.58\n",
+       "Personal Auto   F         452.97\n",
+       "                M         457.01\n",
+       "Special Auto    F         453.28\n",
+       "                M         429.53\n",
+       "Name: total_claim_amount, dtype: float64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Compare with total claim amount in the same groups\n",
+    "claim_patterns = yes_responders.groupby(['policy_type', 'gender'])['total_claim_amount'].mean().round(2)\n",
+    "claim_patterns\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ecfc0712-01a0-40f9-9079-654bc52b8a5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "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": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Total number of customers per state\n",
+    "customers_by_state = df['state'].value_counts()\n",
+    "\n",
+    "# Filter states with more than 500 customers\n",
+    "states_over_500 = customers_by_state[customers_by_state > 500]\n",
+    "states_over_500\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "81f131d1-24ac-4e29-a655-7d984f83162f",
+   "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>median</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>education</th>\n",
+       "      <th>gender</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Bachelor</th>\n",
+       "      <th>F</th>\n",
+       "      <td>73225.96</td>\n",
+       "      <td>1904.00</td>\n",
+       "      <td>5640.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>67907.27</td>\n",
+       "      <td>1898.01</td>\n",
+       "      <td>5548.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">College</th>\n",
+       "      <th>F</th>\n",
+       "      <td>61850.19</td>\n",
+       "      <td>1898.68</td>\n",
+       "      <td>5623.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>61134.68</td>\n",
+       "      <td>1918.12</td>\n",
+       "      <td>6005.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Doctor</th>\n",
+       "      <th>F</th>\n",
+       "      <td>44856.11</td>\n",
+       "      <td>2395.57</td>\n",
+       "      <td>5332.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>32677.34</td>\n",
+       "      <td>2267.60</td>\n",
+       "      <td>5577.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">High School or Below</th>\n",
+       "      <th>F</th>\n",
+       "      <td>55277.45</td>\n",
+       "      <td>2144.92</td>\n",
+       "      <td>6039.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>83325.38</td>\n",
+       "      <td>1940.98</td>\n",
+       "      <td>6286.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Master</th>\n",
+       "      <th>F</th>\n",
+       "      <td>51016.07</td>\n",
+       "      <td>2417.78</td>\n",
+       "      <td>5729.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>50568.26</td>\n",
+       "      <td>2272.31</td>\n",
+       "      <td>5579.10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  max      min   median\n",
+       "education            gender                            \n",
+       "Bachelor             F       73225.96  1904.00  5640.51\n",
+       "                     M       67907.27  1898.01  5548.03\n",
+       "College              F       61850.19  1898.68  5623.61\n",
+       "                     M       61134.68  1918.12  6005.85\n",
+       "Doctor               F       44856.11  2395.57  5332.46\n",
+       "                     M       32677.34  2267.60  5577.67\n",
+       "High School or Below F       55277.45  2144.92  6039.55\n",
+       "                     M       83325.38  1940.98  6286.73\n",
+       "Master               F       51016.07  2417.78  5729.86\n",
+       "                     M       50568.26  2272.31  5579.10"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Aggregate CLV stats\n",
+    "clv_stats = df.groupby(['education', 'gender'])['customer_lifetime_value'].agg(['max', 'min', 'median']).round(2)\n",
+    "clv_stats\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a89465e2-67c0-4c2b-8195-2d08293808af",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\maria\\AppData\\Local\\Temp\\ipykernel_36468\\3790172802.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['effective_to_date'] = pd.to_datetime(df['effective_to_date'])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>month</th>\n",
+       "      <th>February</th>\n",
+       "      <th>January</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>state</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>929</td>\n",
+       "      <td>1008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>1634</td>\n",
+       "      <td>1918</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Nevada</th>\n",
+       "      <td>442</td>\n",
+       "      <td>551</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Oregon</th>\n",
+       "      <td>1344</td>\n",
+       "      <td>1565</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Washington</th>\n",
+       "      <td>425</td>\n",
+       "      <td>463</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "month       February  January\n",
+       "state                        \n",
+       "Arizona          929     1008\n",
+       "California      1634     1918\n",
+       "Nevada           442      551\n",
+       "Oregon          1344     1565\n",
+       "Washington       425      463"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Ensure date column is datetime type\n",
+    "df['effective_to_date'] = pd.to_datetime(df['effective_to_date'])\n",
+    "\n",
+    "# Extract month name\n",
+    "df['month'] = df['effective_to_date'].dt.month_name()\n",
+    "\n",
+    "# Count policies by state and month\n",
+    "policies_state_month = df.groupby(['state', 'month']).size().unstack().fillna(0).astype(int)\n",
+    "policies_state_month\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "26f7f18f-5abc-4c11-b6fc-b139c6a1bcae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>month</th>\n",
+       "      <th>February</th>\n",
+       "      <th>January</th>\n",
+       "    </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>929</td>\n",
+       "      <td>1008</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>1634</td>\n",
+       "      <td>1918</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Oregon</th>\n",
+       "      <td>1344</td>\n",
+       "      <td>1565</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "month       February  January\n",
+       "state                        \n",
+       "Arizona          929     1008\n",
+       "California      1634     1918\n",
+       "Oregon          1344     1565"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Total policies sold per state\n",
+    "total_sales_by_state = df['state'].value_counts()\n",
+    "\n",
+    "# Get top 3 states\n",
+    "top_3_states = total_sales_by_state.head(3).index.tolist()\n",
+    "\n",
+    "# Filter and count\n",
+    "top_3_data = df[df['state'].isin(top_3_states)]\n",
+    "top_3_summary = top_3_data.groupby(['state', 'month']).size().unstack().fillna(0).astype(int)\n",
+    "top_3_summary\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "1d9f7043-8910-406c-9a5c-940134f85ad5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\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>response</th>\n",
+       "      <th>No</th>\n",
+       "      <th>Yes</th>\n",
+       "      <th>response_rate_%</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sales_channel</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Agent</th>\n",
+       "      <td>3148</td>\n",
+       "      <td>742</td>\n",
+       "      <td>19.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Branch</th>\n",
+       "      <td>2539</td>\n",
+       "      <td>326</td>\n",
+       "      <td>11.38</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Call Center</th>\n",
+       "      <td>1792</td>\n",
+       "      <td>221</td>\n",
+       "      <td>10.98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Web</th>\n",
+       "      <td>1334</td>\n",
+       "      <td>177</td>\n",
+       "      <td>11.71</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "response         No  Yes  response_rate_%\n",
+       "sales_channel                            \n",
+       "Agent          3148  742            19.07\n",
+       "Branch         2539  326            11.38\n",
+       "Call Center    1792  221            10.98\n",
+       "Web            1334  177            11.71"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Group by marketing channel and response\n",
+    "response_rate = df.groupby(['sales_channel', 'response']).size().unstack().fillna(0)\n",
+    "\n",
+    "# Calculate response percentage\n",
+    "response_rate['response_rate_%'] = (response_rate['Yes'] / response_rate.sum(axis=1) * 100).round(2)\n",
+    "response_rate\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bfe34325-0b63-4989-93b3-0df557fc29f5",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}