diff --git a/lab-dw-aggregating.ipynb b/lab-dw-aggregating.ipynb
index fadd718..8b1254b 100644
--- a/lab-dw-aggregating.ipynb
+++ b/lab-dw-aggregating.ipynb
@@ -1,165 +1,1908 @@
 {
-  "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": "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": 2,
+   "id": "449513f4-0459-46a0-a18d-9398d974c9ad",
+   "metadata": {
+    "id": "449513f4-0459-46a0-a18d-9398d974c9ad"
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "31969215-2a90-4d8b-ac36-646a7ae13744",
-      "metadata": {
-        "id": "31969215-2a90-4d8b-ac36-646a7ae13744"
-      },
-      "source": [
-        "# Lab | Data Aggregation and Filtering"
+     "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",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10905</th>\n",
+       "      <td>10905</td>\n",
+       "      <td>FE99816</td>\n",
+       "      <td>Nevada</td>\n",
+       "      <td>15563.369440</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Premium</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>1/19/11</td>\n",
+       "      <td>Unemployed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L1</td>\n",
+       "      <td>Offer3</td>\n",
+       "      <td>Web</td>\n",
+       "      <td>1214.400000</td>\n",
+       "      <td>Luxury Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10906</th>\n",
+       "      <td>10906</td>\n",
+       "      <td>KX53892</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>5259.444853</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>College</td>\n",
+       "      <td>1/6/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L3</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>273.018929</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10907</th>\n",
+       "      <td>10907</td>\n",
+       "      <td>TL39050</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>23893.304100</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>2/6/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</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>Offer1</td>\n",
+       "      <td>Web</td>\n",
+       "      <td>381.306996</td>\n",
+       "      <td>Luxury SUV</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10908</th>\n",
+       "      <td>10908</td>\n",
+       "      <td>WA60547</td>\n",
+       "      <td>California</td>\n",
+       "      <td>11971.977650</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Premium</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2/13/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L1</td>\n",
+       "      <td>Offer1</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>618.288849</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10909</th>\n",
+       "      <td>10909</td>\n",
+       "      <td>IV32877</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6857.519928</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>1/8/11</td>\n",
+       "      <td>Unemployed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L1</td>\n",
+       "      <td>Offer4</td>\n",
+       "      <td>Web</td>\n",
+       "      <td>1021.719397</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10910 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",
+       "10905       10905  FE99816      Nevada             15563.369440       No   \n",
+       "10906       10906  KX53892      Oregon              5259.444853       No   \n",
+       "10907       10907  TL39050     Arizona             23893.304100       No   \n",
+       "10908       10908  WA60547  California             11971.977650       No   \n",
+       "10909       10909  IV32877         NaN              6857.519928      NaN   \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",
+       "10905   Premium  Bachelor           1/19/11       Unemployed      F  ...   \n",
+       "10906     Basic   College            1/6/11         Employed      F  ...   \n",
+       "10907  Extended  Bachelor            2/6/11         Employed      F  ...   \n",
+       "10908   Premium   College           2/13/11         Employed      F  ...   \n",
+       "10909     Basic  Bachelor            1/8/11       Unemployed      M  ...   \n",
+       "\n",
+       "       Number of Open Complaints Number of Policies     Policy Type  \\\n",
+       "0                            0.0                  9  Corporate Auto   \n",
+       "1                            0.0                  1   Personal Auto   \n",
+       "2                            0.0                  2   Personal Auto   \n",
+       "3                            0.0                  2  Corporate Auto   \n",
+       "4                            NaN                  7   Personal Auto   \n",
+       "...                          ...                ...             ...   \n",
+       "10905                        NaN                  7   Personal Auto   \n",
+       "10906                        0.0                  6   Personal Auto   \n",
+       "10907                        0.0                  2  Corporate Auto   \n",
+       "10908                        4.0                  6   Personal Auto   \n",
+       "10909                        0.0                  3   Personal Auto   \n",
+       "\n",
+       "             Policy  Renew Offer Type  Sales Channel  Total Claim Amount  \\\n",
+       "0      Corporate L3            Offer3          Agent          292.800000   \n",
+       "1       Personal L3            Offer4    Call Center          744.924331   \n",
+       "2       Personal L3            Offer3    Call Center          480.000000   \n",
+       "3      Corporate L3            Offer2         Branch          484.013411   \n",
+       "4       Personal L2            Offer1         Branch          707.925645   \n",
+       "...             ...               ...            ...                 ...   \n",
+       "10905   Personal L1            Offer3            Web         1214.400000   \n",
+       "10906   Personal L3            Offer2         Branch          273.018929   \n",
+       "10907  Corporate L3            Offer1            Web          381.306996   \n",
+       "10908   Personal L1            Offer1         Branch          618.288849   \n",
+       "10909   Personal L1            Offer4            Web         1021.719397   \n",
+       "\n",
+       "       Vehicle Class Vehicle Size Vehicle Type  \n",
+       "0      Four-Door Car      Medsize          NaN  \n",
+       "1      Four-Door Car      Medsize          NaN  \n",
+       "2                SUV      Medsize            A  \n",
+       "3      Four-Door Car      Medsize            A  \n",
+       "4      Four-Door Car      Medsize          NaN  \n",
+       "...              ...          ...          ...  \n",
+       "10905     Luxury Car      Medsize            A  \n",
+       "10906  Four-Door Car      Medsize            A  \n",
+       "10907     Luxury SUV      Medsize          NaN  \n",
+       "10908            SUV      Medsize            A  \n",
+       "10909            SUV      Medsize          NaN  \n",
+       "\n",
+       "[10910 rows x 26 columns]"
       ]
-    },
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code goes here\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "file_path = \"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv\"\n",
+    "\n",
+    "df = pd.read_csv(file_path)\n",
+    "df\n",
+    "# df.info()\n",
+    "# df.dtypes\n",
+    "# df.nunique()\n",
+    "# df.describe()\n",
+    "# display(df.isna().sum())\n",
+    "# display((df.isna().sum()/df.shape[0])*100)\n",
+    "# df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b340f67e-2d77-4f09-b134-66f3d05c5ed0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 10910 entries, 0 to 10909\n",
+      "Data columns (total 26 columns):\n",
+      " #   Column                         Non-Null Count  Dtype  \n",
+      "---  ------                         --------------  -----  \n",
+      " 0   Unnamed: 0                     10910 non-null  int64  \n",
+      " 1   Customer                       10910 non-null  object \n",
+      " 2   State                          10279 non-null  object \n",
+      " 3   Customer Lifetime Value        10910 non-null  float64\n",
+      " 4   Response                       10279 non-null  object \n",
+      " 5   Coverage                       10910 non-null  object \n",
+      " 6   Education                      10910 non-null  object \n",
+      " 7   Effective To Date              10910 non-null  object \n",
+      " 8   EmploymentStatus               10910 non-null  object \n",
+      " 9   Gender                         10910 non-null  object \n",
+      " 10  Income                         10910 non-null  int64  \n",
+      " 11  Location Code                  10910 non-null  object \n",
+      " 12  Marital Status                 10910 non-null  object \n",
+      " 13  Monthly Premium Auto           10910 non-null  int64  \n",
+      " 14  Months Since Last Claim        10277 non-null  float64\n",
+      " 15  Months Since Policy Inception  10910 non-null  int64  \n",
+      " 16  Number of Open Complaints      10277 non-null  float64\n",
+      " 17  Number of Policies             10910 non-null  int64  \n",
+      " 18  Policy Type                    10910 non-null  object \n",
+      " 19  Policy                         10910 non-null  object \n",
+      " 20  Renew Offer Type               10910 non-null  object \n",
+      " 21  Sales Channel                  10910 non-null  object \n",
+      " 22  Total Claim Amount             10910 non-null  float64\n",
+      " 23  Vehicle Class                  10288 non-null  object \n",
+      " 24  Vehicle Size                   10288 non-null  object \n",
+      " 25  Vehicle Type                   5428 non-null   object \n",
+      "dtypes: float64(4), int64(5), object(17)\n",
+      "memory usage: 2.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "f3b778ba-e6ca-4004-b4de-bf783e193bb5",
+   "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."
+     "data": {
+      "text/plain": [
+       "Unnamed: 0                       10910\n",
+       "Customer                          9134\n",
+       "State                                5\n",
+       "Customer Lifetime Value           8041\n",
+       "Response                             2\n",
+       "Coverage                             3\n",
+       "Education                            5\n",
+       "Effective To Date                   59\n",
+       "EmploymentStatus                     5\n",
+       "Gender                               2\n",
+       "Income                            5694\n",
+       "Location Code                        3\n",
+       "Marital Status                       3\n",
+       "Monthly Premium Auto               202\n",
+       "Months Since Last Claim             36\n",
+       "Months Since Policy Inception      100\n",
+       "Number of Open Complaints            6\n",
+       "Number of Policies                   9\n",
+       "Policy Type                          3\n",
+       "Policy                               9\n",
+       "Renew Offer Type                     4\n",
+       "Sales Channel                        4\n",
+       "Total Claim Amount                5106\n",
+       "Vehicle Class                        6\n",
+       "Vehicle Size                         3\n",
+       "Vehicle Type                         1\n",
+       "Month                                2\n",
+       "dtype: int64"
       ]
-    },
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d4566702-f6a9-414a-a72a-48b880a215f3",
+   "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": [
+       "Unnamed: 0                         int64\n",
+       "Customer                          object\n",
+       "State                             object\n",
+       "Customer Lifetime Value          float64\n",
+       "Response                          object\n",
+       "Coverage                          object\n",
+       "Education                         object\n",
+       "Effective To Date                 object\n",
+       "EmploymentStatus                  object\n",
+       "Gender                            object\n",
+       "Income                             int64\n",
+       "Location Code                     object\n",
+       "Marital Status                    object\n",
+       "Monthly Premium Auto               int64\n",
+       "Months Since Last Claim          float64\n",
+       "Months Since Policy Inception      int64\n",
+       "Number of Open Complaints        float64\n",
+       "Number of Policies                 int64\n",
+       "Policy Type                       object\n",
+       "Policy                            object\n",
+       "Renew Offer Type                  object\n",
+       "Sales Channel                     object\n",
+       "Total Claim Amount               float64\n",
+       "Vehicle Class                     object\n",
+       "Vehicle Size                      object\n",
+       "Vehicle Type                      object\n",
+       "dtype: object"
       ]
-    },
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e5712570-100d-482f-b987-5527707dd9e2",
+   "metadata": {},
+   "outputs": [
     {
-      "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."
+     "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 Lifetime Value</th>\n",
+       "      <th>Income</th>\n",
+       "      <th>Monthly Premium Auto</th>\n",
+       "      <th>Months Since Last Claim</th>\n",
+       "      <th>Months Since Policy Inception</th>\n",
+       "      <th>Number of Open Complaints</th>\n",
+       "      <th>Number of Policies</th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>10910.000000</td>\n",
+       "      <td>10910.000000</td>\n",
+       "      <td>10910.000000</td>\n",
+       "      <td>10910.000000</td>\n",
+       "      <td>10277.000000</td>\n",
+       "      <td>10910.000000</td>\n",
+       "      <td>10277.000000</td>\n",
+       "      <td>10910.000000</td>\n",
+       "      <td>10910.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>5454.500000</td>\n",
+       "      <td>8018.241094</td>\n",
+       "      <td>37536.284785</td>\n",
+       "      <td>93.196059</td>\n",
+       "      <td>15.149071</td>\n",
+       "      <td>48.091934</td>\n",
+       "      <td>0.384256</td>\n",
+       "      <td>2.979193</td>\n",
+       "      <td>434.888330</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3149.590053</td>\n",
+       "      <td>6885.081434</td>\n",
+       "      <td>30359.195670</td>\n",
+       "      <td>34.442532</td>\n",
+       "      <td>10.080349</td>\n",
+       "      <td>27.940675</td>\n",
+       "      <td>0.912457</td>\n",
+       "      <td>2.399359</td>\n",
+       "      <td>292.180556</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1898.007675</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>61.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.099007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2727.250000</td>\n",
+       "      <td>4014.453113</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>68.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>24.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>271.082527</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>5454.500000</td>\n",
+       "      <td>5771.147235</td>\n",
+       "      <td>33813.500000</td>\n",
+       "      <td>83.000000</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>48.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>382.564630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>8181.750000</td>\n",
+       "      <td>8992.779137</td>\n",
+       "      <td>62250.750000</td>\n",
+       "      <td>109.000000</td>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>71.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>547.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>10909.000000</td>\n",
+       "      <td>83325.381190</td>\n",
+       "      <td>99981.000000</td>\n",
+       "      <td>298.000000</td>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>2893.239678</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Unnamed: 0  Customer Lifetime Value        Income  \\\n",
+       "count  10910.000000             10910.000000  10910.000000   \n",
+       "mean    5454.500000              8018.241094  37536.284785   \n",
+       "std     3149.590053              6885.081434  30359.195670   \n",
+       "min        0.000000              1898.007675      0.000000   \n",
+       "25%     2727.250000              4014.453113      0.000000   \n",
+       "50%     5454.500000              5771.147235  33813.500000   \n",
+       "75%     8181.750000              8992.779137  62250.750000   \n",
+       "max    10909.000000             83325.381190  99981.000000   \n",
+       "\n",
+       "       Monthly Premium Auto  Months Since Last Claim  \\\n",
+       "count          10910.000000             10277.000000   \n",
+       "mean              93.196059                15.149071   \n",
+       "std               34.442532                10.080349   \n",
+       "min               61.000000                 0.000000   \n",
+       "25%               68.000000                 6.000000   \n",
+       "50%               83.000000                14.000000   \n",
+       "75%              109.000000                23.000000   \n",
+       "max              298.000000                35.000000   \n",
+       "\n",
+       "       Months Since Policy Inception  Number of Open Complaints  \\\n",
+       "count                   10910.000000               10277.000000   \n",
+       "mean                       48.091934                   0.384256   \n",
+       "std                        27.940675                   0.912457   \n",
+       "min                         0.000000                   0.000000   \n",
+       "25%                        24.000000                   0.000000   \n",
+       "50%                        48.000000                   0.000000   \n",
+       "75%                        71.000000                   0.000000   \n",
+       "max                        99.000000                   5.000000   \n",
+       "\n",
+       "       Number of Policies  Total Claim Amount  \n",
+       "count        10910.000000        10910.000000  \n",
+       "mean             2.979193          434.888330  \n",
+       "std              2.399359          292.180556  \n",
+       "min              1.000000            0.099007  \n",
+       "25%              1.000000          271.082527  \n",
+       "50%              2.000000          382.564630  \n",
+       "75%              4.000000          547.200000  \n",
+       "max              9.000000         2893.239678  "
       ]
-    },
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d5e8b4c8-0f90-4ee7-b19e-02c484101bc6",
+   "metadata": {},
+   "outputs": [
     {
-      "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."
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
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+       "        vertical-align: top;\n",
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+       "</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 Lifetime Value</th>\n",
+       "      <th>Income</th>\n",
+       "      <th>Monthly Premium Auto</th>\n",
+       "      <th>Months Since Last Claim</th>\n",
+       "      <th>Months Since Policy Inception</th>\n",
+       "      <th>Number of Open Complaints</th>\n",
+       "      <th>Number of Policies</th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1399.000000</td>\n",
+       "      <td>1399.000000</td>\n",
+       "      <td>1399.000000</td>\n",
+       "      <td>1399.000000</td>\n",
+       "      <td>1324.000000</td>\n",
+       "      <td>1399.000000</td>\n",
+       "      <td>1324.000000</td>\n",
+       "      <td>1399.000000</td>\n",
+       "      <td>1399.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>5369.293066</td>\n",
+       "      <td>7709.166530</td>\n",
+       "      <td>38483.350250</td>\n",
+       "      <td>88.720515</td>\n",
+       "      <td>14.640483</td>\n",
+       "      <td>47.423159</td>\n",
+       "      <td>0.364048</td>\n",
+       "      <td>2.894210</td>\n",
+       "      <td>412.134087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>3138.693825</td>\n",
+       "      <td>6261.525485</td>\n",
+       "      <td>28191.664861</td>\n",
+       "      <td>22.642518</td>\n",
+       "      <td>9.781315</td>\n",
+       "      <td>26.797704</td>\n",
+       "      <td>0.927667</td>\n",
+       "      <td>2.424591</td>\n",
+       "      <td>180.460971</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2004.350666</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>61.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>7.345946</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2627.000000</td>\n",
+       "      <td>3936.410182</td>\n",
+       "      <td>18495.000000</td>\n",
+       "      <td>67.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>312.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>5423.000000</td>\n",
+       "      <td>5548.031892</td>\n",
+       "      <td>33190.000000</td>\n",
+       "      <td>84.000000</td>\n",
+       "      <td>14.000000</td>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>398.502948</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>7933.500000</td>\n",
+       "      <td>9031.214859</td>\n",
+       "      <td>60920.000000</td>\n",
+       "      <td>107.000000</td>\n",
+       "      <td>22.000000</td>\n",
+       "      <td>68.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>528.200860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>10897.000000</td>\n",
+       "      <td>41787.903430</td>\n",
+       "      <td>99845.000000</td>\n",
+       "      <td>154.000000</td>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>99.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>960.115399</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Unnamed: 0  Customer Lifetime Value        Income  \\\n",
+       "count   1399.000000              1399.000000   1399.000000   \n",
+       "mean    5369.293066              7709.166530  38483.350250   \n",
+       "std     3138.693825              6261.525485  28191.664861   \n",
+       "min        3.000000              2004.350666      0.000000   \n",
+       "25%     2627.000000              3936.410182  18495.000000   \n",
+       "50%     5423.000000              5548.031892  33190.000000   \n",
+       "75%     7933.500000              9031.214859  60920.000000   \n",
+       "max    10897.000000             41787.903430  99845.000000   \n",
+       "\n",
+       "       Monthly Premium Auto  Months Since Last Claim  \\\n",
+       "count           1399.000000              1324.000000   \n",
+       "mean              88.720515                14.640483   \n",
+       "std               22.642518                 9.781315   \n",
+       "min               61.000000                 0.000000   \n",
+       "25%               67.000000                 6.000000   \n",
+       "50%               84.000000                14.000000   \n",
+       "75%              107.000000                22.000000   \n",
+       "max              154.000000                35.000000   \n",
+       "\n",
+       "       Months Since Policy Inception  Number of Open Complaints  \\\n",
+       "count                    1399.000000                1324.000000   \n",
+       "mean                       47.423159                   0.364048   \n",
+       "std                        26.797704                   0.927667   \n",
+       "min                         0.000000                   0.000000   \n",
+       "25%                        25.000000                   0.000000   \n",
+       "50%                        50.000000                   0.000000   \n",
+       "75%                        68.000000                   0.000000   \n",
+       "max                        99.000000                   5.000000   \n",
+       "\n",
+       "       Number of Policies  Total Claim Amount  \n",
+       "count         1399.000000         1399.000000  \n",
+       "mean             2.894210          412.134087  \n",
+       "std              2.424591          180.460971  \n",
+       "min              1.000000            7.345946  \n",
+       "25%              1.000000          312.000000  \n",
+       "50%              2.000000          398.502948  \n",
+       "75%              3.000000          528.200860  \n",
+       "max              9.000000          960.115399  "
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# DataFrame for customers with low total claim and \"Yes\" response\n",
+    "# Filter for total claim amount < 1000 and Response == \"Yes\"\n",
+    "filtered_df = df[(df[\"Total Claim Amount\"] < 1000) & (df[\"Response\"] == \"Yes\")]\n",
+    "display(filtered_df.describe())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a1124b90-411d-4d64-a067-c2f1f3b9f49e",
+   "metadata": {},
+   "outputs": [
     {
-      "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."
+     "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>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>433.738499</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>408.582459</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>452.965929</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>457.010178</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>453.280164</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>429.527942</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          433.738499  \n",
+       "1          408.582459  \n",
+       "2          452.965929  \n",
+       "3          457.010178  \n",
+       "4          453.280164  \n",
+       "5          429.527942  "
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "cell_type": "markdown",
-      "id": "b42999f9-311f-481e-ae63-40a5577072c5",
-      "metadata": {
-        "id": "b42999f9-311f-481e-ae63-40a5577072c5"
-      },
-      "source": [
-        "## Bonus"
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>median</th>\n",
+       "      <th>min</th>\n",
+       "      <th>max</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Policy Type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th></th>\n",
+       "      <th></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>433.738499</td>\n",
+       "      <td>420.041981</td>\n",
+       "      <td>7.345946</td>\n",
+       "      <td>1358.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>408.582459</td>\n",
+       "      <td>364.800000</td>\n",
+       "      <td>57.712985</td>\n",
+       "      <td>1324.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Personal Auto</th>\n",
+       "      <th>F</th>\n",
+       "      <td>452.965929</td>\n",
+       "      <td>424.330166</td>\n",
+       "      <td>7.345946</td>\n",
+       "      <td>1358.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>457.010178</td>\n",
+       "      <td>412.800000</td>\n",
+       "      <td>57.712985</td>\n",
+       "      <td>1324.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Special Auto</th>\n",
+       "      <th>F</th>\n",
+       "      <td>453.280164</td>\n",
+       "      <td>420.041981</td>\n",
+       "      <td>56.603330</td>\n",
+       "      <td>1358.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>429.527942</td>\n",
+       "      <td>345.600000</td>\n",
+       "      <td>86.461582</td>\n",
+       "      <td>1027.000029</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                    mean             median  \\\n",
+       "                      Total Claim Amount Total Claim Amount   \n",
+       "Policy Type    Gender                                         \n",
+       "Corporate Auto F              433.738499         420.041981   \n",
+       "               M              408.582459         364.800000   \n",
+       "Personal Auto  F              452.965929         424.330166   \n",
+       "               M              457.010178         412.800000   \n",
+       "Special Auto   F              453.280164         420.041981   \n",
+       "               M              429.527942         345.600000   \n",
+       "\n",
+       "                                     min                max  \n",
+       "                      Total Claim Amount Total Claim Amount  \n",
+       "Policy Type    Gender                                        \n",
+       "Corporate Auto F                7.345946        1358.400000  \n",
+       "               M               57.712985        1324.800000  \n",
+       "Personal Auto  F                7.345946        1358.400000  \n",
+       "               M               57.712985        1324.800000  \n",
+       "Special Auto   F               56.603330        1358.400000  \n",
+       "               M               86.461582        1027.000029  "
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Subset for customers who responded \"Yes\"\n",
+    "yes_df = df[df[\"Response\"] == \"Yes\"]\n",
+    "\n",
+    "# Group by Policy Type and Gender, calculate means\n",
+    "agg_results = yes_df.groupby([\"Policy Type\", \"Gender\"]).agg({\n",
+    "    \"Monthly Premium Auto\": \"mean\",\n",
+    "    \"Customer Lifetime Value\": \"mean\",\n",
+    "    \"Total Claim Amount\": \"mean\"\n",
+    "}).reset_index()\n",
+    "\n",
+    "display(agg_results)\n",
+    "\n",
+    "# Optional: Compare claim amount patterns\n",
+    "pivot_claims = yes_df.pivot_table(index=[\"Policy Type\", \"Gender\"], values=\"Total Claim Amount\", aggfunc=[\"mean\", \"median\", \"min\", \"max\"])\n",
+    "display(pivot_claims)\n",
+    "\n",
+    "# Discussion:\n",
+    "# - Segments with higher average lifetime value and lower claim amounts are most profitable/low-risk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9fa29403-98ba-4cb4-835a-325b2006ef24",
+   "metadata": {},
+   "outputs": [
     {
-      "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."
+     "data": {
+      "text/plain": [
+       "State\n",
+       "California    3552\n",
+       "Oregon        2909\n",
+       "Arizona       1937\n",
+       "Nevada         993\n",
+       "Washington     888\n",
+       "Name: count, dtype: int64"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "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.*"
-      ]
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---------------------------\n"
+     ]
     },
     {
-      "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."
+     "data": {
+      "text/plain": [
+       "State\n",
+       "California    3552\n",
+       "Oregon        2909\n",
+       "Arizona       1937\n",
+       "Nevada         993\n",
+       "Washington     888\n",
+       "Name: count, dtype: int64"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
-      "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"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Number of policies per state, only states with >500 customers\n",
+    "# Count policies by state\n",
+    "state_counts = df[\"State\"].value_counts()\n",
+    "display(state_counts)\n",
+    "print('---------------------------')\n",
+    "states_over_500 = state_counts[state_counts > 500]\n",
+    "display(states_over_500)\n",
+    "print('---------------------------')\n",
+    "# Optionally, show filtered DataFrame\n",
+    "# df_states_500 = df[df[\"State\"].isin(states_over_500.index)]\n",
+    "# display(df_states_500[\"State\"].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "caecdc93-d1e3-4592-b913-be416156f7a5",
+   "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>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"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "clv_stats = df.groupby([\"Education\", \"Gender\"])[\"Customer Lifetime Value\"].agg([\"max\", \"min\", \"median\"]).reset_index()\n",
+    "display(clv_stats)\n",
+    "\n",
+    "# Conclusion:\n",
+    "# - \"Bachelor\" educated females have the highest max CLV.\n",
+    "# - The median CLV is generally higher for females across most education levels, indicating better long-term value.\n",
+    "# - Segments with high median and low minimum CLV can be targeted for premium policies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2804c565-f758-4225-a5eb-3273afae83d1",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "449513f4-0459-46a0-a18d-9398d974c9ad",
-      "metadata": {
-        "id": "449513f4-0459-46a0-a18d-9398d974c9ad"
-      },
-      "outputs": [],
-      "source": [
-        "# your code goes here"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
-  ],
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "sns.barplot(x=\"Education\", y=\"median\", hue=\"Gender\", data=clv_stats)\n",
+    "plt.title('Median Customer Lifetime Value by Education Level and Gender')\n",
+    "plt.ylabel('Median CLV')\n",
+    "plt.xlabel('Education Level')\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "06b4a600-4199-47c7-aa52-af4b5a5154a9",
+   "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>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"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Policies Sold by State and Month (Pivot Table)\n",
+    "# Ensure Effective To Date is datetime type\n",
+    "df[\"Effective To Date\"] = pd.to_datetime(df[\"Effective To Date\"], format=\"%d %m %Y\", errors='coerce')\n",
+    "df[\"Month\"] = df[\"Effective To Date\"].dt.month_name()\n",
+    "\n",
+    "# Group by State and Month, count number of policies sold\n",
+    "policies_by_state_month = df.groupby([\"State\", \"Month\"])[\"Policy\"].count().reset_index()\n",
+    "# display(policies_by_state_month)\n",
+    "\n",
+    "# Pivot: States as rows, Months as columns, values = number of policies\n",
+    "pivot_table = policies_by_state_month.pivot(index=\"State\", columns=\"Month\", values=\"Policy\").fillna(0)\n",
+    "display(pivot_table)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "5694b98c-189d-486e-84b5-1f3aa91c5b13",
+   "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"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Policies Sold by Month for Top 3 States\n",
+    "# Total policies sold by state\n",
+    "state_totals = df.groupby(\"State\")[\"Policy\"].count().sort_values(ascending=False)\n",
+    "top_3_states = state_totals.head(3).index\n",
+    "# display(top_3_states)\n",
+    "\n",
+    "# Filter original group for only top 3 states\n",
+    "top3_policies = policies_by_state_month[policies_by_state_month[\"State\"].isin(top_3_states)]\n",
+    "# display(top3_policies )\n",
+    "\n",
+    "# Pivot for top 3 states only\n",
+    "top3_pivot = top3_policies.pivot(index=\"State\", columns=\"Month\", values=\"Policy\").fillna(0)\n",
+    "display(top3_pivot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "cdaf043f-4b67-4726-9926-09ba10d32631",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sales Channel</th>\n",
+       "      <th>Response Rate Yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Agent</td>\n",
+       "      <td>0.190746</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Branch</td>\n",
+       "      <td>0.113787</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>0.109786</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Web</td>\n",
+       "      <td>0.117141</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Sales Channel  Response Rate Yes\n",
+       "0         Agent           0.190746\n",
+       "1        Branch           0.113787\n",
+       "2   Call Center           0.109786\n",
+       "3           Web           0.117141"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Customer Response Rate by Marketing Channel\n",
+    "# Calculate response rate (Yes) by Sales Channel\n",
+    "response_rate = df.groupby(\"Sales Channel\")[\"Response\"].value_counts(normalize=True).unstack().fillna(0)\n",
+    "response_rate_yes = response_rate.get(\"Yes\", pd.Series())\n",
+    "response_rate_yes = response_rate_yes.reset_index().rename(columns={ \"Yes\": \"Response Rate Yes\" })\n",
+    "\n",
+    "display(response_rate_yes)\n",
+    "\n",
+    "# Or, melt for visualization\n",
+    "# melted = df[df[\"Response\"] == \"Yes\"].groupby(\"Sales Channel\").size().reset_index(name=\"Yes Responses\")\n",
+    "# total_by_channel = df.groupby(\"Sales Channel\").size().reset_index(name=\"Total\")\n",
+    "# merged = pd.merge(melted, total_by_channel, on=\"Sales Channel\")\n",
+    "# merged[\"Response Rate Yes\"] = merged[\"Yes Responses\"] / merged[\"Total\"]\n",
+    "\n",
+    "# display(merged[[\"Sales Channel\", \"Response Rate Yes\"]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "34ed0be6-edb0-4845-9bd1-a54e4e821b96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.13"
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,6))\n",
+    "sns.heatmap(top3_pivot, annot=True, fmt=\".0f\", cmap=\"Blues\")\n",
+    "plt.title(\"Policies Sold by Month for Top 3 States\")\n",
+    "plt.ylabel(\"State\")\n",
+    "plt.xlabel(\"Month\")\n",
+    "plt.show()\n",
+    "\n",
+    "plt.figure(figsize=(8,5))\n",
+    "sns.barplot(x=\"Sales Channel\", y=\"Response Rate Yes\", data=merged)\n",
+    "plt.title(\"Customer Response Rate by Marketing Channel\")\n",
+    "plt.ylabel(\"Response Rate (Yes)\")\n",
+    "plt.xlabel(\"Sales Channel\")\n",
+    "plt.ylim(0,1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c68df5c1-7e3d-4c2d-b4b6-a113f43abfd2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }