diff --git a/Untitled.ipynb b/Untitled.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/Untitled.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/lab-dw-aggregating.ipynb b/lab-dw-aggregating.ipynb
index fadd718..87f75db 100644
--- a/lab-dw-aggregating.ipynb
+++ b/lab-dw-aggregating.ipynb
@@ -1,165 +1,2407 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "id": "31969215-2a90-4d8b-ac36-646a7ae13744",
-      "metadata": {
-        "id": "31969215-2a90-4d8b-ac36-646a7ae13744"
-      },
-      "source": [
-        "# Lab | Data Aggregation and Filtering"
+ "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": 13,
+   "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",
+       "    <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": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# your code goes here\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "url = 'https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv'\n",
+    "mkt_customer_df = pd.read_csv(url)\n",
+    "mkt_customer_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "7093dddc-b5ce-4eaf-ba23-de872734c4f5",
+   "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/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>Customer</th>\n",
+       "      <th>State</th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "      <th>Response</th>\n",
+       "      <th>Coverage</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>Effective To Date</th>\n",
+       "      <th>EmploymentStatus</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Number of Open Complaints</th>\n",
+       "      <th>Number of Policies</th>\n",
+       "      <th>Policy Type</th>\n",
+       "      <th>Policy</th>\n",
+       "      <th>Renew Offer Type</th>\n",
+       "      <th>Sales Channel</th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "      <th>Vehicle Class</th>\n",
+       "      <th>Vehicle Size</th>\n",
+       "      <th>Vehicle Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>XL78013</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>22332.439460</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>College</td>\n",
+       "      <td>1/11/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L3</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>484.013411</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8</td>\n",
+       "      <td>FM55990</td>\n",
+       "      <td>California</td>\n",
+       "      <td>5989.773931</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Premium</td>\n",
+       "      <td>College</td>\n",
+       "      <td>1/19/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L1</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>739.200000</td>\n",
+       "      <td>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",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10844</th>\n",
+       "      <td>10844</td>\n",
+       "      <td>FM31768</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>5979.724161</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>2/7/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L1</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Agent</td>\n",
+       "      <td>547.200000</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10852</th>\n",
+       "      <td>10852</td>\n",
+       "      <td>KZ80424</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>8382.478392</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>1/27/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L2</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>791.878042</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10872</th>\n",
+       "      <td>10872</td>\n",
+       "      <td>XT67997</td>\n",
+       "      <td>California</td>\n",
+       "      <td>5979.724161</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>2/7/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L3</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Agent</td>\n",
+       "      <td>547.200000</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10887</th>\n",
+       "      <td>10887</td>\n",
+       "      <td>BY78730</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>8879.790017</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>2/3/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>Special L2</td>\n",
+       "      <td>Offer1</td>\n",
+       "      <td>Agent</td>\n",
+       "      <td>528.200860</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>Small</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10897</th>\n",
+       "      <td>10897</td>\n",
+       "      <td>MM70762</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>9075.768214</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1/26/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L1</td>\n",
+       "      <td>Offer1</td>\n",
+       "      <td>Agent</td>\n",
+       "      <td>158.077504</td>\n",
+       "      <td>Sports Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1399 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Unnamed: 0 Customer       State  Customer Lifetime Value Response  \\\n",
+       "3               3  XL78013      Oregon             22332.439460      Yes   \n",
+       "8               8  FM55990  California              5989.773931      Yes   \n",
+       "15             15  CW49887  California              4626.801093      Yes   \n",
+       "19             19  NJ54277  California              3746.751625      Yes   \n",
+       "27             27  MQ68407      Oregon              4376.363592      Yes   \n",
+       "...           ...      ...         ...                      ...      ...   \n",
+       "10844       10844  FM31768     Arizona              5979.724161      Yes   \n",
+       "10852       10852  KZ80424  Washington              8382.478392      Yes   \n",
+       "10872       10872  XT67997  California              5979.724161      Yes   \n",
+       "10887       10887  BY78730      Oregon              8879.790017      Yes   \n",
+       "10897       10897  MM70762     Arizona              9075.768214      Yes   \n",
+       "\n",
+       "       Coverage             Education Effective To Date EmploymentStatus  \\\n",
+       "3      Extended               College           1/11/11         Employed   \n",
+       "8       Premium               College           1/19/11         Employed   \n",
+       "15        Basic                Master           1/16/11         Employed   \n",
+       "19     Extended               College           2/26/11         Employed   \n",
+       "27      Premium              Bachelor           2/28/11         Employed   \n",
+       "...         ...                   ...               ...              ...   \n",
+       "10844  Extended  High School or Below            2/7/11         Employed   \n",
+       "10852     Basic              Bachelor           1/27/11         Employed   \n",
+       "10872  Extended  High School or Below            2/7/11         Employed   \n",
+       "10887     Basic  High School or Below            2/3/11         Employed   \n",
+       "10897     Basic                Master           1/26/11         Employed   \n",
+       "\n",
+       "      Gender  ...  Number of Open Complaints Number of Policies  \\\n",
+       "3          M  ...                        0.0                  2   \n",
+       "8          M  ...                        0.0                  1   \n",
+       "15         F  ...                        0.0                  1   \n",
+       "19         F  ...                        1.0                  1   \n",
+       "27         F  ...                        0.0                  1   \n",
+       "...      ...  ...                        ...                ...   \n",
+       "10844      F  ...                        0.0                  3   \n",
+       "10852      M  ...                        0.0                  2   \n",
+       "10872      F  ...                        0.0                  3   \n",
+       "10887      F  ...                        0.0                  7   \n",
+       "10897      M  ...                        0.0                  8   \n",
+       "\n",
+       "          Policy Type        Policy  Renew Offer Type  Sales Channel  \\\n",
+       "3      Corporate Auto  Corporate L3            Offer2         Branch   \n",
+       "8       Personal Auto   Personal L1            Offer2         Branch   \n",
+       "15       Special Auto    Special L1            Offer2         Branch   \n",
+       "19      Personal Auto   Personal L2            Offer2    Call Center   \n",
+       "27      Personal Auto   Personal L3            Offer2          Agent   \n",
+       "...               ...           ...               ...            ...   \n",
+       "10844   Personal Auto   Personal L1            Offer2          Agent   \n",
+       "10852   Personal Auto   Personal L2            Offer2    Call Center   \n",
+       "10872   Personal Auto   Personal L3            Offer2          Agent   \n",
+       "10887    Special Auto    Special L2            Offer1          Agent   \n",
+       "10897   Personal Auto   Personal L1            Offer1          Agent   \n",
+       "\n",
+       "       Total Claim Amount  Vehicle Class Vehicle Size Vehicle Type  \n",
+       "3              484.013411  Four-Door Car      Medsize            A  \n",
+       "8              739.200000     Sports Car      Medsize          NaN  \n",
+       "15             547.200000            SUV      Medsize          NaN  \n",
+       "19              19.575683   Two-Door Car        Large            A  \n",
+       "27              60.036683  Four-Door Car      Medsize          NaN  \n",
+       "...                   ...            ...          ...          ...  \n",
+       "10844          547.200000  Four-Door Car      Medsize          NaN  \n",
+       "10852          791.878042            NaN          NaN            A  \n",
+       "10872          547.200000  Four-Door Car      Medsize          NaN  \n",
+       "10887          528.200860            SUV        Small            A  \n",
+       "10897          158.077504     Sports Car      Medsize            A  \n",
+       "\n",
+       "[1399 rows x 26 columns]"
       ]
-    },
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#1\n",
+    "mkt_customer_df_filt = mkt_customer_df[(mkt_customer_df[\"Total Claim Amount\"] < 1000) & (mkt_customer_df[\"Response\"] == \"Yes\")]\n",
+    "mkt_customer_df_filt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "84f78127-662b-43d3-8d05-56e6c1365b7b",
+   "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": [
+    "mkt_customer_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "92d5e376-9b91-40cb-b36f-0c3b366810af",
+   "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/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>mean</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Policy Type</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">F</th>\n",
+       "      <th>Corporate Auto</th>\n",
+       "      <td>94.301775</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Personal Auto</th>\n",
+       "      <td>98.998148</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Special Auto</th>\n",
+       "      <td>92.314286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">M</th>\n",
+       "      <th>Corporate Auto</th>\n",
+       "      <td>92.188312</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Personal Auto</th>\n",
+       "      <td>91.085821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Special Auto</th>\n",
+       "      <td>86.343750</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            mean\n",
+       "Gender Policy Type              \n",
+       "F      Corporate Auto  94.301775\n",
+       "       Personal Auto   98.998148\n",
+       "       Special Auto    92.314286\n",
+       "M      Corporate Auto  92.188312\n",
+       "       Personal Auto   91.085821\n",
+       "       Special Auto    86.343750"
       ]
-    },
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#2\n",
+    "mkt_customer_df_filt2 = mkt_customer_df[(mkt_customer_df[\"Response\"] == \"Yes\")]\n",
+    "mkt_customer_df_filt2.groupby(['Gender', 'Policy Type'])['Monthly Premium Auto'].agg(['mean'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "6b942d8a-bd63-4d45-89d8-448e19ac5aa0",
+   "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>mean</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>State</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>8084.373066</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>7951.655993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Nevada</th>\n",
+       "      <td>6827.612261</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Oregon</th>\n",
+       "      <td>8063.150266</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Washington</th>\n",
+       "      <td>7722.509147</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   mean\n",
+       "State                  \n",
+       "Arizona     8084.373066\n",
+       "California  7951.655993\n",
+       "Nevada      6827.612261\n",
+       "Oregon      8063.150266\n",
+       "Washington  7722.509147"
       ]
-    },
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mkt_customer_df_filt3 = mkt_customer_df[(mkt_customer_df[\"Response\"] == \"Yes\")]\n",
+    "mkt_customer_df_filt3.groupby(['State'])['Customer Lifetime Value'].agg(['mean'])\t"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "787bd73e-6f64-485f-919f-f3e6b2e515ec",
+   "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",
+       "    }\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",
+       "      <th>Total Claim Amount</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Policy Type</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">F</th>\n",
+       "      <th>Corporate Auto</th>\n",
+       "      <td>94.301775</td>\n",
+       "      <td>7712.628736</td>\n",
+       "      <td>433.738499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Personal Auto</th>\n",
+       "      <td>98.998148</td>\n",
+       "      <td>8339.791842</td>\n",
+       "      <td>452.965929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Special Auto</th>\n",
+       "      <td>92.314286</td>\n",
+       "      <td>7691.584111</td>\n",
+       "      <td>453.280164</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">M</th>\n",
+       "      <th>Corporate Auto</th>\n",
+       "      <td>92.188312</td>\n",
+       "      <td>7944.465414</td>\n",
+       "      <td>408.582459</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Personal Auto</th>\n",
+       "      <td>91.085821</td>\n",
+       "      <td>7448.383281</td>\n",
+       "      <td>457.010178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Special Auto</th>\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": [
+       "                       Monthly Premium Auto  Customer Lifetime Value  \\\n",
+       "Gender Policy Type                                                     \n",
+       "F      Corporate Auto             94.301775              7712.628736   \n",
+       "       Personal Auto              98.998148              8339.791842   \n",
+       "       Special Auto               92.314286              7691.584111   \n",
+       "M      Corporate Auto             92.188312              7944.465414   \n",
+       "       Personal Auto              91.085821              7448.383281   \n",
+       "       Special Auto               86.343750              8247.088702   \n",
+       "\n",
+       "                       Total Claim Amount  \n",
+       "Gender Policy Type                         \n",
+       "F      Corporate Auto          433.738499  \n",
+       "       Personal Auto           452.965929  \n",
+       "       Special Auto            453.280164  \n",
+       "M      Corporate Auto          408.582459  \n",
+       "       Personal Auto           457.010178  \n",
+       "       Special Auto            429.527942  "
       ]
-    },
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mkt_customer_df_filt4 = mkt_customer_df[(mkt_customer_df[\"Response\"] == \"Yes\")]\n",
+    "mkt_customer_df_filt4.groupby(['Gender', 'Policy Type'])[['Monthly Premium Auto', 'Customer Lifetime Value', 'Total Claim Amount']].agg('mean')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8de37efe-cbf1-490c-a70e-8c527c5dc51d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#The most profitable segment seems to be for women the personal auto and for men the special auto, as they are the ones with the higher customer lifetime value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "b9ddce58-7e02-44a1-b200-be0cfe49c744",
+   "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/plain": [
+       "State\n",
+       "Arizona       1937\n",
+       "California    3552\n",
+       "Nevada         993\n",
+       "Oregon        2909\n",
+       "Washington     888\n",
+       "Name: Customer, dtype: int64"
       ]
-    },
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#3\n",
+    "mkt_df_states = mkt_customer_df.groupby('State')['Customer'].count()\n",
+    "mkt_df_states = mkt_df_states[mkt_df_states > 500]\n",
+    "mkt_df_states"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "6248879f-ece2-41a6-ba85-c797e0ad521a",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
     {
-      "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 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>73225.95652</td>\n",
+       "      <td>1904.000852</td>\n",
+       "      <td>5640.505303</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>67907.27050</td>\n",
+       "      <td>1898.007675</td>\n",
+       "      <td>5548.031892</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">College</th>\n",
+       "      <th>F</th>\n",
+       "      <td>61850.18803</td>\n",
+       "      <td>1898.683686</td>\n",
+       "      <td>5623.611187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>61134.68307</td>\n",
+       "      <td>1918.119700</td>\n",
+       "      <td>6005.847375</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Doctor</th>\n",
+       "      <th>F</th>\n",
+       "      <td>44856.11397</td>\n",
+       "      <td>2395.570000</td>\n",
+       "      <td>5332.462694</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>32677.34284</td>\n",
+       "      <td>2267.604038</td>\n",
+       "      <td>5577.669457</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">High School or Below</th>\n",
+       "      <th>F</th>\n",
+       "      <td>55277.44589</td>\n",
+       "      <td>2144.921535</td>\n",
+       "      <td>6039.553187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>83325.38119</td>\n",
+       "      <td>1940.981221</td>\n",
+       "      <td>6286.731006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Master</th>\n",
+       "      <th>F</th>\n",
+       "      <td>51016.06704</td>\n",
+       "      <td>2417.777032</td>\n",
+       "      <td>5729.855012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\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": [
+       "                                     max          min       median\n",
+       "Education            Gender                                       \n",
+       "Bachelor             F       73225.95652  1904.000852  5640.505303\n",
+       "                     M       67907.27050  1898.007675  5548.031892\n",
+       "College              F       61850.18803  1898.683686  5623.611187\n",
+       "                     M       61134.68307  1918.119700  6005.847375\n",
+       "Doctor               F       44856.11397  2395.570000  5332.462694\n",
+       "                     M       32677.34284  2267.604038  5577.669457\n",
+       "High School or Below F       55277.44589  2144.921535  6039.553187\n",
+       "                     M       83325.38119  1940.981221  6286.731006\n",
+       "Master               F       51016.06704  2417.777032  5729.855012\n",
+       "                     M       50568.25912  2272.307310  5579.099207"
       ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#4\n",
+    "mkt_customer_df.groupby(['Education', 'Gender'])['Customer Lifetime Value'].agg(['max', 'min', 'median'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7ec8dafe-b585-4936-9caa-728c1a5169bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Generally, as higher the education level is, the lower the customer lifetime value is. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "f7753c61-7d04-4742-a6eb-bd7899127824",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\julia\\AppData\\Local\\Temp\\ipykernel_18512\\1644421404.py:3: 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",
+      "  mkt_customer_df['Effective To Date'] = pd.to_datetime(mkt_customer_df['Effective To Date'], errors='coerce')\n"
+     ]
     },
     {
-      "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/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 colspan=\"2\" halign=\"left\">Number of Policies</th>\n",
+       "    </tr>\n",
+       "    <tr>\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>2864</td>\n",
+       "      <td>3052</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>4929</td>\n",
+       "      <td>5673</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Nevada</th>\n",
+       "      <td>1278</td>\n",
+       "      <td>1493</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Oregon</th>\n",
+       "      <td>3969</td>\n",
+       "      <td>4697</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Washington</th>\n",
+       "      <td>1225</td>\n",
+       "      <td>1358</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Number of Policies        \n",
+       "Month                February January\n",
+       "State                                \n",
+       "Arizona                  2864    3052\n",
+       "California               4929    5673\n",
+       "Nevada                   1278    1493\n",
+       "Oregon                   3969    4697\n",
+       "Washington               1225    1358"
       ]
-    },
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#5\n",
+    "\n",
+    "mkt_customer_df['Effective To Date'] = pd.to_datetime(mkt_customer_df['Effective To Date'], errors='coerce')\n",
+    "mkt_customer_df['Month'] = mkt_customer_df['Effective To Date'].dt.month_name()\n",
+    "mkt_pivot_df = mkt_customer_df.pivot_table(index='State', columns='Month', values=['Number of Policies'], aggfunc='sum')\n",
+    "mkt_pivot_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "5713e323-bbad-4eeb-a6c0-479131bb7a3f",
+   "metadata": {},
+   "outputs": [
     {
-      "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.*"
+     "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>Number of Policies</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>State</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Arizona</th>\n",
+       "      <td>5916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>10602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Nevada</th>\n",
+       "      <td>2771</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Oregon</th>\n",
+       "      <td>8666</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Washington</th>\n",
+       "      <td>2583</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Number of Policies\n",
+       "State                         \n",
+       "Arizona                   5916\n",
+       "California               10602\n",
+       "Nevada                    2771\n",
+       "Oregon                    8666\n",
+       "Washington                2583"
       ]
-    },
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#6\n",
+    "mkt_df_st = pd.DataFrame(mkt_customer_df.groupby(['State'])['Number of Policies'].sum())\n",
+    "mkt_df_st"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "74cd214d-6396-4e0f-828a-b4aed762a63a",
+   "metadata": {},
+   "outputs": [
     {
-      "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": [
+       "Index(['California', 'Oregon', 'Arizona'], dtype='object', name='State')"
       ]
-    },
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mkt_df_st_month_filt = mkt_df_st.sort_values('Number of Policies', ascending=False).head(3).index\n",
+    "mkt_df_st_month_filt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "d59b9868-8708-472b-a8b7-34192d4350ca",
+   "metadata": {},
+   "outputs": [
     {
-      "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"
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        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 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",
+       "      <th>Month</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>2011-02-18</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</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",
+       "      <td>February</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>2011-01-18</td>\n",
+       "      <td>Unemployed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</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",
+       "      <td>January</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>2011-01-11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</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",
+       "      <td>January</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>2011-01-17</td>\n",
+       "      <td>Medical Leave</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</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",
+       "      <td>January</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6</td>\n",
+       "      <td>IW72280</td>\n",
+       "      <td>California</td>\n",
+       "      <td>5035.035257</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>Doctor</td>\n",
+       "      <td>2011-02-14</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L2</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>287.556107</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>February</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>10902</th>\n",
+       "      <td>10902</td>\n",
+       "      <td>PP30874</td>\n",
+       "      <td>California</td>\n",
+       "      <td>3579.023825</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>2011-01-24</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L2</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Agent</td>\n",
+       "      <td>655.200000</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>January</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10903</th>\n",
+       "      <td>10903</td>\n",
+       "      <td>SU71163</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>2771.663013</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2011-01-07</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L2</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>355.200000</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>January</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>2011-01-06</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</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",
+       "      <td>January</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>2011-02-06</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</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",
+       "      <td>February</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>2011-02-13</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</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",
+       "      <td>February</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8398 rows × 27 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",
+       "3               3  XL78013      Oregon             22332.439460      Yes   \n",
+       "4               4  QA50777      Oregon              9025.067525       No   \n",
+       "6               6  IW72280  California              5035.035257       No   \n",
+       "...           ...      ...         ...                      ...      ...   \n",
+       "10902       10902  PP30874  California              3579.023825       No   \n",
+       "10903       10903  SU71163     Arizona              2771.663013       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",
+       "\n",
+       "       Coverage             Education Effective To Date EmploymentStatus  \\\n",
+       "0         Basic               College        2011-02-18         Employed   \n",
+       "1         Basic               College        2011-01-18       Unemployed   \n",
+       "3      Extended               College        2011-01-11         Employed   \n",
+       "4       Premium              Bachelor        2011-01-17    Medical Leave   \n",
+       "6         Basic                Doctor        2011-02-14         Employed   \n",
+       "...         ...                   ...               ...              ...   \n",
+       "10902  Extended  High School or Below        2011-01-24         Employed   \n",
+       "10903     Basic               College        2011-01-07         Employed   \n",
+       "10906     Basic               College        2011-01-06         Employed   \n",
+       "10907  Extended              Bachelor        2011-02-06         Employed   \n",
+       "10908   Premium               College        2011-02-13         Employed   \n",
+       "\n",
+       "      Gender  ...  Number of Policies     Policy Type        Policy  \\\n",
+       "0          M  ...                   9  Corporate Auto  Corporate L3   \n",
+       "1          F  ...                   1   Personal Auto   Personal L3   \n",
+       "3          M  ...                   2  Corporate Auto  Corporate L3   \n",
+       "4          F  ...                   7   Personal Auto   Personal L2   \n",
+       "6          F  ...                   4  Corporate Auto  Corporate L2   \n",
+       "...      ...  ...                 ...             ...           ...   \n",
+       "10902      F  ...                   1   Personal Auto   Personal L2   \n",
+       "10903      M  ...                   1   Personal Auto   Personal L2   \n",
+       "10906      F  ...                   6   Personal Auto   Personal L3   \n",
+       "10907      F  ...                   2  Corporate Auto  Corporate L3   \n",
+       "10908      F  ...                   6   Personal Auto   Personal L1   \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",
+       "3                Offer2         Branch          484.013411  Four-Door Car   \n",
+       "4                Offer1         Branch          707.925645  Four-Door Car   \n",
+       "6                Offer2         Branch          287.556107  Four-Door Car   \n",
+       "...                 ...            ...                 ...            ...   \n",
+       "10902            Offer2          Agent          655.200000  Four-Door Car   \n",
+       "10903            Offer2         Branch          355.200000   Two-Door Car   \n",
+       "10906            Offer2         Branch          273.018929  Four-Door Car   \n",
+       "10907            Offer1            Web          381.306996     Luxury SUV   \n",
+       "10908            Offer1         Branch          618.288849            SUV   \n",
+       "\n",
+       "       Vehicle Size Vehicle Type     Month  \n",
+       "0           Medsize          NaN  February  \n",
+       "1           Medsize          NaN   January  \n",
+       "3           Medsize            A   January  \n",
+       "4           Medsize          NaN   January  \n",
+       "6           Medsize          NaN  February  \n",
+       "...             ...          ...       ...  \n",
+       "10902       Medsize            A   January  \n",
+       "10903       Medsize            A   January  \n",
+       "10906       Medsize            A   January  \n",
+       "10907       Medsize          NaN  February  \n",
+       "10908       Medsize            A  February  \n",
+       "\n",
+       "[8398 rows x 27 columns]"
       ]
-    },
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_df_top3 = mkt_customer_df[mkt_customer_df['State'].isin(mkt_df_st_month_filt)]\n",
+    "filtered_df_top3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "9350226e-1c85-4d90-a8b3-adc2a60e827d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Month       February  January\n",
+      "State                        \n",
+      "Arizona         2864     3052\n",
+      "California      4929     5673\n",
+      "Oregon          3969     4697\n"
+     ]
+    }
+   ],
+   "source": [
+    "mkt_df_st_month_top3 = (\n",
+    "    filtered_df_top3\n",
+    "    .groupby(['State', 'Month'])['Number of Policies']\n",
+    "    .sum()\n",
+    "    .unstack(fill_value=0)\n",
+    ")\n",
+    "\n",
+    "print(mkt_df_st_month_top3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "f6d406dd-98d1-4c8b-bf5c-4ee2bfc43826",
+   "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": {
+      "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</th>\n",
+       "      <th>Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Agent</td>\n",
+       "      <td>No</td>\n",
+       "      <td>3148</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Agent</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>742</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Branch</td>\n",
+       "      <td>No</td>\n",
+       "      <td>2539</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Branch</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>No</td>\n",
+       "      <td>1792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Web</td>\n",
+       "      <td>No</td>\n",
+       "      <td>1334</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Web</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>177</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Sales Channel Response  Count\n",
+       "0         Agent       No   3148\n",
+       "1         Agent      Yes    742\n",
+       "2        Branch       No   2539\n",
+       "3        Branch      Yes    326\n",
+       "4   Call Center       No   1792\n",
+       "5   Call Center      Yes    221\n",
+       "6           Web       No   1334\n",
+       "7           Web      Yes    177"
       ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
     }
-  ],
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.9.13"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 5
+   ],
+   "source": [
+    "#7\n",
+    "response_counts = mkt_customer_df.groupby(['Sales Channel', 'Response']).size().reset_index(name='Count')\n",
+    "response_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "06ead3e9-5a38-4c0f-8236-1e2106884224",
+   "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>Response</th>\n",
+       "      <th>Sales Channel</th>\n",
+       "      <th>No</th>\n",
+       "      <th>Yes</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Agent</td>\n",
+       "      <td>3148</td>\n",
+       "      <td>742</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Branch</td>\n",
+       "      <td>2539</td>\n",
+       "      <td>326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>1792</td>\n",
+       "      <td>221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Web</td>\n",
+       "      <td>1334</td>\n",
+       "      <td>177</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Response Sales Channel    No  Yes\n",
+       "0                Agent  3148  742\n",
+       "1               Branch  2539  326\n",
+       "2          Call Center  1792  221\n",
+       "3                  Web  1334  177"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pivot_counts = response_counts.pivot(index='Sales Channel', columns='Response', values='Count').reset_index()\n",
+    "pivot_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "b60cae33-6bd3-43e8-86a9-a7caf0fec21c",
+   "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</th>\n",
+       "      <th>Count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Agent</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>742</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Branch</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Web</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>177</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Agent</td>\n",
+       "      <td>No</td>\n",
+       "      <td>3148</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Branch</td>\n",
+       "      <td>No</td>\n",
+       "      <td>2539</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>No</td>\n",
+       "      <td>1792</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Web</td>\n",
+       "      <td>No</td>\n",
+       "      <td>1334</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Sales Channel Response  Count\n",
+       "0         Agent      Yes    742\n",
+       "1        Branch      Yes    326\n",
+       "2   Call Center      Yes    221\n",
+       "3           Web      Yes    177\n",
+       "4         Agent       No   3148\n",
+       "5        Branch       No   2539\n",
+       "6   Call Center       No   1792\n",
+       "7           Web       No   1334"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "melted_counts = pd.melt(pivot_counts, id_vars=['Sales Channel'], value_vars=['Yes', 'No'], var_name='Response', value_name='Count')\n",
+    "melted_counts"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "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
 }