diff --git a/lab-dw-aggregating.ipynb b/lab-dw-aggregating.ipynb
index fadd718..93fcb36 100644
--- a/lab-dw-aggregating.ipynb
+++ b/lab-dw-aggregating.ipynb
@@ -1,165 +1,2491 @@
 {
-  "cells": [
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "31969215-2a90-4d8b-ac36-646a7ae13744",
+   "metadata": {
+    "id": "31969215-2a90-4d8b-ac36-646a7ae13744"
+   },
+   "source": [
+    "# Lab | Data Aggregation and Filtering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8f08a52-bec0-439b-99cc-11d3809d8b5d",
+   "metadata": {
+    "id": "a8f08a52-bec0-439b-99cc-11d3809d8b5d"
+   },
+   "source": [
+    "In this challenge, we will continue to work with customer data from an insurance company. We will use the dataset called marketing_customer_analysis.csv, which can be found at the following link:\n",
+    "\n",
+    "https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv\n",
+    "\n",
+    "This dataset contains information such as customer demographics, policy details, vehicle information, and the customer's response to the last marketing campaign. Our goal is to explore and analyze this data by first performing data cleaning, formatting, and structuring."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c98ddc5-b041-4c94-ada1-4dfee5c98e50",
+   "metadata": {
+    "id": "9c98ddc5-b041-4c94-ada1-4dfee5c98e50"
+   },
+   "source": [
+    "1. Create a new DataFrame that only includes customers who:\n",
+    "   - have a **low total_claim_amount** (e.g., below $1,000),\n",
+    "   - have a response \"Yes\" to the last marketing campaign."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9be383e-5165-436e-80c8-57d4c757c8c3",
+   "metadata": {
+    "id": "b9be383e-5165-436e-80c8-57d4c757c8c3"
+   },
+   "source": [
+    "2. Using the original Dataframe, analyze:\n",
+    "   - the average `monthly_premium` and/or customer lifetime value by `policy_type` and `gender` for customers who responded \"Yes\", and\n",
+    "   - compare these insights to `total_claim_amount` patterns, and discuss which segments appear most profitable or low-risk for the company."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7050f4ac-53c5-4193-a3c0-8699b87196f0",
+   "metadata": {
+    "id": "7050f4ac-53c5-4193-a3c0-8699b87196f0"
+   },
+   "source": [
+    "3. Analyze the total number of customers who have policies in each state, and then filter the results to only include states where there are more than 500 customers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b60a4443-a1a7-4bbf-b78e-9ccdf9895e0d",
+   "metadata": {
+    "id": "b60a4443-a1a7-4bbf-b78e-9ccdf9895e0d"
+   },
+   "source": [
+    "4. Find the maximum, minimum, and median customer lifetime value by education level and gender. Write your conclusions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b42999f9-311f-481e-ae63-40a5577072c5",
+   "metadata": {
+    "id": "b42999f9-311f-481e-ae63-40a5577072c5"
+   },
+   "source": [
+    "## Bonus"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81ff02c5-6584-4f21-a358-b918697c6432",
+   "metadata": {
+    "id": "81ff02c5-6584-4f21-a358-b918697c6432"
+   },
+   "source": [
+    "5. The marketing team wants to analyze the number of policies sold by state and month. Present the data in a table where the months are arranged as columns and the states are arranged as rows."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6aec097-c633-4017-a125-e77a97259cda",
+   "metadata": {
+    "id": "b6aec097-c633-4017-a125-e77a97259cda"
+   },
+   "source": [
+    "6.  Display a new DataFrame that contains the number of policies sold by month, by state, for the top 3 states with the highest number of policies sold.\n",
+    "\n",
+    "*Hint:*\n",
+    "- *To accomplish this, you will first need to group the data by state and month, then count the number of policies sold for each group. Afterwards, you will need to sort the data by the count of policies sold in descending order.*\n",
+    "- *Next, you will select the top 3 states with the highest number of policies sold.*\n",
+    "- *Finally, you will create a new DataFrame that contains the number of policies sold by month for each of the top 3 states.*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba975b8a-a2cf-4fbf-9f59-ebc381767009",
+   "metadata": {
+    "id": "ba975b8a-a2cf-4fbf-9f59-ebc381767009"
+   },
+   "source": [
+    "7. The marketing team wants to analyze the effect of different marketing channels on the customer response rate.\n",
+    "\n",
+    "Hint: You can use melt to unpivot the data and create a table that shows the customer response rate (those who responded \"Yes\") by marketing channel."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4378d94-48fb-4850-a802-b1bc8f427b2d",
+   "metadata": {
+    "id": "e4378d94-48fb-4850-a802-b1bc8f427b2d"
+   },
+   "source": [
+    "External Resources for Data Filtering: https://towardsdatascience.com/filtering-data-frames-in-pandas-b570b1f834b9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "449513f4-0459-46a0-a18d-9398d974c9ad",
+   "metadata": {
+    "id": "449513f4-0459-46a0-a18d-9398d974c9ad"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "453bb8b0-5abf-4cbd-a9a3-80449cbd69ca",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "31969215-2a90-4d8b-ac36-646a7ae13744",
-      "metadata": {
-        "id": "31969215-2a90-4d8b-ac36-646a7ae13744"
-      },
-      "source": [
-        "# Lab | Data Aggregation and Filtering"
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>Customer</th>\n",
+       "      <th>State</th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "      <th>Response</th>\n",
+       "      <th>Coverage</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>Effective To Date</th>\n",
+       "      <th>EmploymentStatus</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Number of Open Complaints</th>\n",
+       "      <th>Number of Policies</th>\n",
+       "      <th>Policy Type</th>\n",
+       "      <th>Policy</th>\n",
+       "      <th>Renew Offer Type</th>\n",
+       "      <th>Sales Channel</th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "      <th>Vehicle Class</th>\n",
+       "      <th>Vehicle Size</th>\n",
+       "      <th>Vehicle Type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>DK49336</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>4809.216960</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2/18/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L3</td>\n",
+       "      <td>Offer3</td>\n",
+       "      <td>Agent</td>\n",
+       "      <td>292.800000</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>KX64629</td>\n",
+       "      <td>California</td>\n",
+       "      <td>2228.525238</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>College</td>\n",
+       "      <td>1/18/11</td>\n",
+       "      <td>Unemployed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L3</td>\n",
+       "      <td>Offer4</td>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>744.924331</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>LZ68649</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>14947.917300</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>2/10/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L3</td>\n",
+       "      <td>Offer3</td>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>480.000000</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>XL78013</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>22332.439460</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>College</td>\n",
+       "      <td>1/11/11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L3</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>484.013411</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>QA50777</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>9025.067525</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Premium</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>1/17/11</td>\n",
+       "      <td>Medical Leave</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L2</td>\n",
+       "      <td>Offer1</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>707.925645</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 26 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0 Customer       State  Customer Lifetime Value Response  \\\n",
+       "0           0  DK49336     Arizona              4809.216960       No   \n",
+       "1           1  KX64629  California              2228.525238       No   \n",
+       "2           2  LZ68649  Washington             14947.917300       No   \n",
+       "3           3  XL78013      Oregon             22332.439460      Yes   \n",
+       "4           4  QA50777      Oregon              9025.067525       No   \n",
+       "\n",
+       "   Coverage Education Effective To Date EmploymentStatus Gender  ...  \\\n",
+       "0     Basic   College           2/18/11         Employed      M  ...   \n",
+       "1     Basic   College           1/18/11       Unemployed      F  ...   \n",
+       "2     Basic  Bachelor           2/10/11         Employed      M  ...   \n",
+       "3  Extended   College           1/11/11         Employed      M  ...   \n",
+       "4   Premium  Bachelor           1/17/11    Medical Leave      F  ...   \n",
+       "\n",
+       "   Number of Open Complaints Number of Policies     Policy Type        Policy  \\\n",
+       "0                        0.0                  9  Corporate Auto  Corporate L3   \n",
+       "1                        0.0                  1   Personal Auto   Personal L3   \n",
+       "2                        0.0                  2   Personal Auto   Personal L3   \n",
+       "3                        0.0                  2  Corporate Auto  Corporate L3   \n",
+       "4                        NaN                  7   Personal Auto   Personal L2   \n",
+       "\n",
+       "   Renew Offer Type  Sales Channel  Total Claim Amount  Vehicle Class  \\\n",
+       "0            Offer3          Agent          292.800000  Four-Door Car   \n",
+       "1            Offer4    Call Center          744.924331  Four-Door Car   \n",
+       "2            Offer3    Call Center          480.000000            SUV   \n",
+       "3            Offer2         Branch          484.013411  Four-Door Car   \n",
+       "4            Offer1         Branch          707.925645  Four-Door Car   \n",
+       "\n",
+       "  Vehicle Size Vehicle Type  \n",
+       "0      Medsize          NaN  \n",
+       "1      Medsize          NaN  \n",
+       "2      Medsize            A  \n",
+       "3      Medsize            A  \n",
+       "4      Medsize          NaN  \n",
+       "\n",
+       "[5 rows x 26 columns]"
       ]
-    },
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "url = \"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv\"\n",
+    "df = pd.read_csv(url)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "d394381a-9844-464e-90ea-c59fbd8e43f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1. Create a new DataFrame that only includes customers who:\n",
+    "#have a low total_claim_amount (e.g., below $1,000),\n",
+    "#have a response \"Yes\" to the last marketing campaign."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "7d086474-204e-495c-8e2f-18c098150c0f",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "a8f08a52-bec0-439b-99cc-11d3809d8b5d",
-      "metadata": {
-        "id": "a8f08a52-bec0-439b-99cc-11d3809d8b5d"
-      },
-      "source": [
-        "In this challenge, we will continue to work with customer data from an insurance company. We will use the dataset called marketing_customer_analysis.csv, which can be found at the following link:\n",
-        "\n",
-        "https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis.csv\n",
-        "\n",
-        "This dataset contains information such as customer demographics, policy details, vehicle information, and the customer's response to the last marketing campaign. Our goal is to explore and analyze this data by first performing data cleaning, formatting, and structuring."
+     "data": {
+      "text/plain": [
+       "Index(['Unnamed: 0', 'Customer', 'State', 'Customer Lifetime Value',\n",
+       "       'Response', 'Coverage', 'Education', 'Effective To Date',\n",
+       "       'EmploymentStatus', 'Gender', 'Income', 'Location Code',\n",
+       "       'Marital Status', 'Monthly Premium Auto', 'Months Since Last Claim',\n",
+       "       'Months Since Policy Inception', 'Number of Open Complaints',\n",
+       "       'Number of Policies', 'Policy Type', 'Policy', 'Renew Offer Type',\n",
+       "       'Sales Channel', 'Total Claim Amount', 'Vehicle Class', 'Vehicle Size',\n",
+       "       'Vehicle Type'],\n",
+       "      dtype='object')"
       ]
-    },
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "52527928-33a1-4ce7-b3a2-e86b2eadd2c2",
+   "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>Response</th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>No</td>\n",
+       "      <td>292.800000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>No</td>\n",
+       "      <td>744.924331</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>No</td>\n",
+       "      <td>480.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>484.013411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>No</td>\n",
+       "      <td>707.925645</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10905</th>\n",
+       "      <td>No</td>\n",
+       "      <td>1214.400000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10906</th>\n",
+       "      <td>No</td>\n",
+       "      <td>273.018929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10907</th>\n",
+       "      <td>No</td>\n",
+       "      <td>381.306996</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10908</th>\n",
+       "      <td>No</td>\n",
+       "      <td>618.288849</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10909</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1021.719397</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10910 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Response  Total Claim Amount\n",
+       "0           No          292.800000\n",
+       "1           No          744.924331\n",
+       "2           No          480.000000\n",
+       "3          Yes          484.013411\n",
+       "4           No          707.925645\n",
+       "...        ...                 ...\n",
+       "10905       No         1214.400000\n",
+       "10906       No          273.018929\n",
+       "10907       No          381.306996\n",
+       "10908       No          618.288849\n",
+       "10909      NaN         1021.719397\n",
+       "\n",
+       "[10910 rows x 2 columns]"
       ]
-    },
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_reponse_complaints = df[[\"Response\",\"Total Claim Amount\"]].copy()\n",
+    "df_reponse_complaints"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "e00af658-7e73-40d2-a44e-fdbb92f94bc4",
+   "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."
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 10910 entries, 0 to 10909\n",
+      "Data columns (total 2 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   Response            10279 non-null  object \n",
+      " 1   Total Claim Amount  10910 non-null  float64\n",
+      "dtypes: float64(1), object(1)\n",
+      "memory usage: 170.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_reponse_complaints.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "07355d6d-1182-48b5-ae35-d1fe3486e3a1",
+   "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>Response</th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>484.013411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>739.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>547.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>19.575683</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>60.036683</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10844</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>547.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10852</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>791.878042</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10872</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>547.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10887</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>528.200860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10897</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>158.077504</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1466 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Response  Total Claim Amount\n",
+       "3          Yes          484.013411\n",
+       "8          Yes          739.200000\n",
+       "15         Yes          547.200000\n",
+       "19         Yes           19.575683\n",
+       "27         Yes           60.036683\n",
+       "...        ...                 ...\n",
+       "10844      Yes          547.200000\n",
+       "10852      Yes          791.878042\n",
+       "10872      Yes          547.200000\n",
+       "10887      Yes          528.200860\n",
+       "10897      Yes          158.077504\n",
+       "\n",
+       "[1466 rows x 2 columns]"
       ]
-    },
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_reponse_complaintsd[df_reponse_complaints.Response == 'Yes']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "e915dc34-f3aa-4fcf-9432-c2563adef407",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_filter = df_reponse_complaints[(df_reponse_complaints[\"Total Claim Amount\"] < 1000) & (df_reponse_complaints.Response == 'Yes')].reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "289b2490-5ffb-49b2-896e-86ba3aea4021",
+   "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>Response</th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>484.013411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>739.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>547.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>19.575683</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>60.036683</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1394</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>547.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1395</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>791.878042</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1396</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>547.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1397</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>528.200860</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1398</th>\n",
+       "      <td>Yes</td>\n",
+       "      <td>158.077504</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1399 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Response  Total Claim Amount\n",
+       "0         Yes          484.013411\n",
+       "1         Yes          739.200000\n",
+       "2         Yes          547.200000\n",
+       "3         Yes           19.575683\n",
+       "4         Yes           60.036683\n",
+       "...       ...                 ...\n",
+       "1394      Yes          547.200000\n",
+       "1395      Yes          791.878042\n",
+       "1396      Yes          547.200000\n",
+       "1397      Yes          528.200860\n",
+       "1398      Yes          158.077504\n",
+       "\n",
+       "[1399 rows x 2 columns]"
       ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_filter"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "856907ed-c837-46f2-ba81-288c468b616a",
+   "metadata": {},
+   "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.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c520800-e806-4b1c-931c-bb7ab0086a72",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "verage monthly_premium. customer lifetime value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "69a37d12-f2eb-4b30-92b3-bf97238ccf7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/f1/77pps9rs2l9ffb8_p5tj2yrr0000gp/T/ipykernel_58644/3304530285.py:2: FutureWarning: The provided callable <function mean at 0x1110da020> is currently using DataFrameGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"mean\" instead.\n",
+      "  pd.pivot_table(data =  df[df.Response == 'Yes'],\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "id": "b60a4443-a1a7-4bbf-b78e-9ccdf9895e0d",
-      "metadata": {
-        "id": "b60a4443-a1a7-4bbf-b78e-9ccdf9895e0d"
-      },
-      "source": [
-        "4. Find the maximum, minimum, and median customer lifetime value by education level and gender. Write your conclusions."
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "      <th>Monthly Premium Auto</th>\n",
+       "      <th>Total Claim Amount</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Policy Type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Corporate Auto</th>\n",
+       "      <th>F</th>\n",
+       "      <td>7712.628736</td>\n",
+       "      <td>94.301775</td>\n",
+       "      <td>433.738499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>7944.465414</td>\n",
+       "      <td>92.188312</td>\n",
+       "      <td>408.582459</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Personal Auto</th>\n",
+       "      <th>F</th>\n",
+       "      <td>8339.791842</td>\n",
+       "      <td>98.998148</td>\n",
+       "      <td>452.965929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>7448.383281</td>\n",
+       "      <td>91.085821</td>\n",
+       "      <td>457.010178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Special Auto</th>\n",
+       "      <th>F</th>\n",
+       "      <td>7691.584111</td>\n",
+       "      <td>92.314286</td>\n",
+       "      <td>453.280164</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>8247.088702</td>\n",
+       "      <td>86.343750</td>\n",
+       "      <td>429.527942</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       Customer Lifetime Value  Monthly Premium Auto  \\\n",
+       "Policy Type    Gender                                                  \n",
+       "Corporate Auto F                   7712.628736             94.301775   \n",
+       "               M                   7944.465414             92.188312   \n",
+       "Personal Auto  F                   8339.791842             98.998148   \n",
+       "               M                   7448.383281             91.085821   \n",
+       "Special Auto   F                   7691.584111             92.314286   \n",
+       "               M                   8247.088702             86.343750   \n",
+       "\n",
+       "                       Total Claim Amount  \n",
+       "Policy Type    Gender                      \n",
+       "Corporate Auto F               433.738499  \n",
+       "               M               408.582459  \n",
+       "Personal Auto  F               452.965929  \n",
+       "               M               457.010178  \n",
+       "Special Auto   F               453.280164  \n",
+       "               M               429.527942  "
       ]
-    },
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "pd.pivot_table(data =  df[df.Response == 'Yes'],\n",
+    "               values = [\"Monthly Premium Auto\", 'Customer Lifetime Value', 'Total Claim Amount'],\n",
+    "               index = [\"Policy Type\",\"Gender\"],\n",
+    "               aggfunc = np.mean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "781b466a-49bb-47d1-9e69-0be6862382fb",
+   "metadata": {},
+   "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",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></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>Policy Type</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\">Corporate Auto</th>\n",
+       "      <th>F</th>\n",
+       "      <td>94.301775</td>\n",
+       "      <td>7712.628736</td>\n",
+       "      <td>433.738499</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>92.188312</td>\n",
+       "      <td>7944.465414</td>\n",
+       "      <td>408.582459</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Personal Auto</th>\n",
+       "      <th>F</th>\n",
+       "      <td>98.998148</td>\n",
+       "      <td>8339.791842</td>\n",
+       "      <td>452.965929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>91.085821</td>\n",
+       "      <td>7448.383281</td>\n",
+       "      <td>457.010178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Special Auto</th>\n",
+       "      <th>F</th>\n",
+       "      <td>92.314286</td>\n",
+       "      <td>7691.584111</td>\n",
+       "      <td>453.280164</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</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",
+       "Policy Type    Gender                                                  \n",
+       "Corporate Auto F                  94.301775              7712.628736   \n",
+       "               M                  92.188312              7944.465414   \n",
+       "Personal Auto  F                  98.998148              8339.791842   \n",
+       "               M                  91.085821              7448.383281   \n",
+       "Special Auto   F                  92.314286              7691.584111   \n",
+       "               M                  86.343750              8247.088702   \n",
+       "\n",
+       "                       Total Claim Amount  \n",
+       "Policy Type    Gender                      \n",
+       "Corporate Auto F               433.738499  \n",
+       "               M               408.582459  \n",
+       "Personal Auto  F               452.965929  \n",
+       "               M               457.010178  \n",
+       "Special Auto   F               453.280164  \n",
+       "               M               429.527942  "
       ]
-    },
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#df_filter2 = df[df.Response == 'Yes']\n",
+    "df[df.Response == 'Yes'].groupby([\"Policy Type\", \"Gender\"])[['Monthly Premium Auto', 'Customer Lifetime Value','Total Claim Amount']].mean()\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "b772abb7-c832-4b5f-872e-61d0e17f11a6",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "id": "81ff02c5-6584-4f21-a358-b918697c6432",
-      "metadata": {
-        "id": "81ff02c5-6584-4f21-a358-b918697c6432"
-      },
-      "source": [
-        "5. The marketing team wants to analyze the number of policies sold by state and month. Present the data in a table where the months are arranged as columns and the states are arranged as rows."
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Monthly Premium Auto</th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Policy Type</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Corporate Auto</th>\n",
+       "      <th>F</th>\n",
+       "      <td>91.384615</td>\n",
+       "      <td>7980.306825</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>94.764249</td>\n",
+       "      <td>7750.741082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Personal Auto</th>\n",
+       "      <th>F</th>\n",
+       "      <td>93.153179</td>\n",
+       "      <td>8074.660516</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>93.301056</td>\n",
+       "      <td>7971.386285</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Special Auto</th>\n",
+       "      <th>F</th>\n",
+       "      <td>93.563025</td>\n",
+       "      <td>8460.398042</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>93.197044</td>\n",
+       "      <td>9010.601583</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       Monthly Premium Auto  Customer Lifetime Value\n",
+       "Policy Type    Gender                                               \n",
+       "Corporate Auto F                  91.384615              7980.306825\n",
+       "               M                  94.764249              7750.741082\n",
+       "Personal Auto  F                  93.153179              8074.660516\n",
+       "               M                  93.301056              7971.386285\n",
+       "Special Auto   F                  93.563025              8460.398042\n",
+       "               M                  93.197044              9010.601583"
       ]
-    },
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "df.groupby([\"Policy Type\", \"Gender\"]).agg({'Monthly Premium Auto': 'mean', 'Customer Lifetime Value': 'mean'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "02c26780-688b-467d-ba55-000220619902",
+   "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/plain": [
+       "Index(['Unnamed: 0', 'Customer', 'State', 'Customer Lifetime Value',\n",
+       "       'Response', 'Coverage', 'Education', 'Effective To Date',\n",
+       "       'EmploymentStatus', 'Gender', 'Income', 'Location Code',\n",
+       "       'Marital Status', 'Monthly Premium Auto', 'Months Since Last Claim',\n",
+       "       'Months Since Policy Inception', 'Number of Open Complaints',\n",
+       "       'Number of Policies', 'Policy Type', 'Policy', 'Renew Offer Type',\n",
+       "       'Sales Channel', 'Total Claim Amount', 'Vehicle Class', 'Vehicle Size',\n",
+       "       'Vehicle Type'],\n",
+       "      dtype='object')"
       ]
-    },
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns\n"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "df46c1be-5324-4a10-8afb-0d3d4184bcd5",
+   "metadata": {},
+   "source": [
+    "Female Personal Auto customers are highly valuable.\n",
+    "They show the highest Customer Lifetime Value (~8340) and the highest monthly premium (~99), suggesting strong long-term profitability despite moderate claim amounts.\n",
+    "\n",
+    "Male Corporate Auto customers are low-risk.\n",
+    "Their claim amounts (~409) are the lowest across all groups, making them a stable and less costly segment for the company.\n",
+    "\n",
+    "Male Personal Auto customers appear riskier.\n",
+    "They combine the lowest Customer Lifetime Value (~7448) with the highest claim amounts (~457), signaling a segment that may be less profitable.\n",
+    "\n",
+    "Special Auto customers show mixed patterns.\n",
+    "Male Special Auto customers have a high Customer Lifetime Value (~8247) with relatively low premiums (~86), suggesting loyalty and potential for upselling, while female Special Auto customers are more average in both value and claims."
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "3544dd01-58f6-4f8a-86ff-057c920486e5",
+   "metadata": {},
+   "source": [
+    "3. Analyze the total number of customers who have policies in each state, and then filter the results to only include states where there are more than 500 customers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "02ac3184-cc66-4b24-8eb9-87c6b499f410",
+   "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(['Unnamed: 0', 'Customer', 'State', 'Customer Lifetime Value',\n",
+       "       'Response', 'Coverage', 'Education', 'Effective To Date',\n",
+       "       'EmploymentStatus', 'Gender', 'Income', 'Location Code',\n",
+       "       'Marital Status', 'Monthly Premium Auto', 'Months Since Last Claim',\n",
+       "       'Months Since Policy Inception', 'Number of Open Complaints',\n",
+       "       'Number of Policies', 'Policy Type', 'Policy', 'Renew Offer Type',\n",
+       "       'Sales Channel', 'Total Claim Amount', 'Vehicle Class', 'Vehicle Size',\n",
+       "       'Vehicle Type'],\n",
+       "      dtype='object')"
       ]
-    },
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "bf8daf47-0734-411e-a14a-87b9815cd17d",
+   "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/plain": [
+       "array(['Arizona', 'California', 'Washington', 'Oregon', nan, 'Nevada'],\n",
+       "      dtype=object)"
       ]
-    },
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.State.unique()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "d324e72e-93eb-4679-a2c4-39ad0220aafa",
+   "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"
-      ]
-    }
-  ],
-  "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
+     "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>Customer</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>1937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>3552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Nevada</th>\n",
+       "      <td>993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Oregon</th>\n",
+       "      <td>2909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Washington</th>\n",
+       "      <td>888</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Customer\n",
+       "State               \n",
+       "Arizona         1937\n",
+       "California      3552\n",
+       "Nevada           993\n",
+       "Oregon          2909\n",
+       "Washington       888"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.pivot_table(data =  df,\n",
+    "               values = 'Customer',\n",
+    "               index = [\"State\"],\n",
+    "               aggfunc = 'count')"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "6622b8b9-2732-46ef-a197-0208747b1f4d",
+   "metadata": {},
+   "source": [
+    "5. The marketing team wants to analyze the number of policies sold by state and month. Present the data in a table where the months are arranged as columns and the states are arranged as rows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "id": "961cdaf8-31dc-4416-aec1-e7a33011034d",
+   "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>Gender</th>\n",
+       "      <th>F</th>\n",
+       "      <th>M</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>3046</td>\n",
+       "      <td>2870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>California</th>\n",
+       "      <td>5236</td>\n",
+       "      <td>5366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Nevada</th>\n",
+       "      <td>1318</td>\n",
+       "      <td>1453</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Oregon</th>\n",
+       "      <td>4441</td>\n",
+       "      <td>4225</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Washington</th>\n",
+       "      <td>1255</td>\n",
+       "      <td>1328</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Gender         F     M\n",
+       "State                 \n",
+       "Arizona     3046  2870\n",
+       "California  5236  5366\n",
+       "Nevada      1318  1453\n",
+       "Oregon      4441  4225\n",
+       "Washington  1255  1328"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "pd.pivot_table(data =  df,\n",
+    "               columns = 'Gender',\n",
+    "               values = 'Number of Policies',\n",
+    "               index = [\"State\"],\n",
+    "               aggfunc = 'sum')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "id": "77673282-6273-4184-a685-f8df4619b8c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head().to_clipboard()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "a1e38843-e1a5-479c-8fe2-53a63c80fb67",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_count = df.groupby([\"State\"]).agg({'Customer': 'count'}).reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "f7236266-d725-4035-887c-55e6ae45ade1",
+   "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>State</th>\n",
+       "      <th>Customer</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>1937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>California</td>\n",
+       "      <td>3552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Nevada</td>\n",
+       "      <td>993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>2909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Washington</td>\n",
+       "      <td>888</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        State  Customer\n",
+       "0     Arizona      1937\n",
+       "1  California      3552\n",
+       "2      Nevada       993\n",
+       "3      Oregon      2909\n",
+       "4  Washington       888"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_count "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "7239a447-56d4-4830-a8cb-2ba727131012",
+   "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>State</th>\n",
+       "      <th>Customer</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>1937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>California</td>\n",
+       "      <td>3552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Nevada</td>\n",
+       "      <td>993</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>2909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Washington</td>\n",
+       "      <td>888</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        State  Customer\n",
+       "0     Arizona      1937\n",
+       "1  California      3552\n",
+       "2      Nevada       993\n",
+       "3      Oregon      2909\n",
+       "4  Washington       888"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# FILTROOOOOO : df[df.Response == 'Yes]\n",
+    "df_count[df_count.Customer >500]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "e5fc2852-7306-401b-a33f-fa59c93a72af",
+   "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>State</th>\n",
+       "      <th>Customer</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>1937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>California</td>\n",
+       "      <td>3552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>2909</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        State  Customer\n",
+       "0     Arizona      1937\n",
+       "1  California      3552\n",
+       "3      Oregon      2909"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_count[df_count.Customer >1000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "7b90628d-75b2-41fa-b167-51bca180b823",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_count = df.value_counts(['State'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "acd8b34e-a9bd-4dd5-b5a5-d390146dc7ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "State     \n",
+       "California    3552\n",
+       "Oregon        2909\n",
+       "Arizona       1937\n",
+       "Nevada         993\n",
+       "Washington     888\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_count"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "b37ec6ff-2d9d-45c4-bef5-2c17fe49e83b",
+   "metadata": {},
+   "source": [
+    "4. Find the maximum, minimum, and median customer lifetime value by education level and gender. Write your conclusions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "4bb78fa7-9e03-4638-a579-51990a7b11c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 10910 entries, 0 to 10909\n",
+      "Data columns (total 26 columns):\n",
+      " #   Column                         Non-Null Count  Dtype  \n",
+      "---  ------                         --------------  -----  \n",
+      " 0   Unnamed: 0                     10910 non-null  int64  \n",
+      " 1   Customer                       10910 non-null  object \n",
+      " 2   State                          10279 non-null  object \n",
+      " 3   Customer Lifetime Value        10910 non-null  float64\n",
+      " 4   Response                       10279 non-null  object \n",
+      " 5   Coverage                       10910 non-null  object \n",
+      " 6   Education                      10910 non-null  object \n",
+      " 7   Effective To Date              10910 non-null  object \n",
+      " 8   EmploymentStatus               10910 non-null  object \n",
+      " 9   Gender                         10910 non-null  object \n",
+      " 10  Income                         10910 non-null  int64  \n",
+      " 11  Location Code                  10910 non-null  object \n",
+      " 12  Marital Status                 10910 non-null  object \n",
+      " 13  Monthly Premium Auto           10910 non-null  int64  \n",
+      " 14  Months Since Last Claim        10277 non-null  float64\n",
+      " 15  Months Since Policy Inception  10910 non-null  int64  \n",
+      " 16  Number of Open Complaints      10277 non-null  float64\n",
+      " 17  Number of Policies             10910 non-null  int64  \n",
+      " 18  Policy Type                    10910 non-null  object \n",
+      " 19  Policy                         10910 non-null  object \n",
+      " 20  Renew Offer Type               10910 non-null  object \n",
+      " 21  Sales Channel                  10910 non-null  object \n",
+      " 22  Total Claim Amount             10910 non-null  float64\n",
+      " 23  Vehicle Class                  10288 non-null  object \n",
+      " 24  Vehicle Size                   10288 non-null  object \n",
+      " 25  Vehicle Type                   5428 non-null   object \n",
+      "dtypes: float64(4), int64(5), object(17)\n",
+      "memory usage: 2.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "b5f2d049-9d0b-4d34-9129-9ecb37e4c5b1",
+   "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 tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"3\" halign=\"left\">Customer Lifetime Value</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>maximum</th>\n",
+       "      <th>minimum</th>\n",
+       "      <th>median</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Education</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Bachelor</th>\n",
+       "      <th>F</th>\n",
+       "      <td>73225.96</td>\n",
+       "      <td>1904.00</td>\n",
+       "      <td>5640.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>67907.27</td>\n",
+       "      <td>1898.01</td>\n",
+       "      <td>5548.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">College</th>\n",
+       "      <th>F</th>\n",
+       "      <td>61850.19</td>\n",
+       "      <td>1898.68</td>\n",
+       "      <td>5623.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>61134.68</td>\n",
+       "      <td>1918.12</td>\n",
+       "      <td>6005.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Doctor</th>\n",
+       "      <th>F</th>\n",
+       "      <td>44856.11</td>\n",
+       "      <td>2395.57</td>\n",
+       "      <td>5332.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>32677.34</td>\n",
+       "      <td>2267.60</td>\n",
+       "      <td>5577.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">High School or Below</th>\n",
+       "      <th>F</th>\n",
+       "      <td>55277.45</td>\n",
+       "      <td>2144.92</td>\n",
+       "      <td>6039.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>83325.38</td>\n",
+       "      <td>1940.98</td>\n",
+       "      <td>6286.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Master</th>\n",
+       "      <th>F</th>\n",
+       "      <td>51016.07</td>\n",
+       "      <td>2417.78</td>\n",
+       "      <td>5729.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>50568.26</td>\n",
+       "      <td>2272.31</td>\n",
+       "      <td>5579.10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            Customer Lifetime Value                  \n",
+       "                                            maximum  minimum   median\n",
+       "Education            Gender                                          \n",
+       "Bachelor             F                     73225.96  1904.00  5640.51\n",
+       "                     M                     67907.27  1898.01  5548.03\n",
+       "College              F                     61850.19  1898.68  5623.61\n",
+       "                     M                     61134.68  1918.12  6005.85\n",
+       "Doctor               F                     44856.11  2395.57  5332.46\n",
+       "                     M                     32677.34  2267.60  5577.67\n",
+       "High School or Below F                     55277.45  2144.92  6039.55\n",
+       "                     M                     83325.38  1940.98  6286.73\n",
+       "Master               F                     51016.07  2417.78  5729.86\n",
+       "                     M                     50568.26  2272.31  5579.10"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby(['Education', 'Gender']).agg({\n",
+    "    'Customer Lifetime Value':[('maximum', 'max'),\n",
+    "                               ('minimum', 'min'),\n",
+    "                               ('median', 'median')]}).round (2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "id": "9cbd18b8-c4ec-496b-ac6b-4d37dd19b0ad",
+   "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 tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>Education</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>max</th>\n",
+       "      <th>min</th>\n",
+       "      <th>median</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>F</td>\n",
+       "      <td>73225.96</td>\n",
+       "      <td>1904.00</td>\n",
+       "      <td>5640.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>M</td>\n",
+       "      <td>67907.27</td>\n",
+       "      <td>1898.01</td>\n",
+       "      <td>5548.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>College</td>\n",
+       "      <td>F</td>\n",
+       "      <td>61850.19</td>\n",
+       "      <td>1898.68</td>\n",
+       "      <td>5623.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>College</td>\n",
+       "      <td>M</td>\n",
+       "      <td>61134.68</td>\n",
+       "      <td>1918.12</td>\n",
+       "      <td>6005.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Doctor</td>\n",
+       "      <td>F</td>\n",
+       "      <td>44856.11</td>\n",
+       "      <td>2395.57</td>\n",
+       "      <td>5332.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Doctor</td>\n",
+       "      <td>M</td>\n",
+       "      <td>32677.34</td>\n",
+       "      <td>2267.60</td>\n",
+       "      <td>5577.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>F</td>\n",
+       "      <td>55277.45</td>\n",
+       "      <td>2144.92</td>\n",
+       "      <td>6039.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>M</td>\n",
+       "      <td>83325.38</td>\n",
+       "      <td>1940.98</td>\n",
+       "      <td>6286.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Master</td>\n",
+       "      <td>F</td>\n",
+       "      <td>51016.07</td>\n",
+       "      <td>2417.78</td>\n",
+       "      <td>5729.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Master</td>\n",
+       "      <td>M</td>\n",
+       "      <td>50568.26</td>\n",
+       "      <td>2272.31</td>\n",
+       "      <td>5579.10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Education Gender                     max  \\\n",
+       "                               Customer Lifetime Value   \n",
+       "0              Bachelor      F                73225.96   \n",
+       "1              Bachelor      M                67907.27   \n",
+       "2               College      F                61850.19   \n",
+       "3               College      M                61134.68   \n",
+       "4                Doctor      F                44856.11   \n",
+       "5                Doctor      M                32677.34   \n",
+       "6  High School or Below      F                55277.45   \n",
+       "7  High School or Below      M                83325.38   \n",
+       "8                Master      F                51016.07   \n",
+       "9                Master      M                50568.26   \n",
+       "\n",
+       "                      min                  median  \n",
+       "  Customer Lifetime Value Customer Lifetime Value  \n",
+       "0                 1904.00                 5640.51  \n",
+       "1                 1898.01                 5548.03  \n",
+       "2                 1898.68                 5623.61  \n",
+       "3                 1918.12                 6005.85  \n",
+       "4                 2395.57                 5332.46  \n",
+       "5                 2267.60                 5577.67  \n",
+       "6                 2144.92                 6039.55  \n",
+       "7                 1940.98                 6286.73  \n",
+       "8                 2417.78                 5729.86  \n",
+       "9                 2272.31                 5579.10  "
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.pivot_table(data=df,\n",
+    "                values = ['Customer Lifetime Value'],\n",
+    "               index = [\"Education\",\"Gender\"],\n",
+    "               aggfunc = ['max','min', 'median'] ).round(2).reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "id": "3aae0c7b-dd04-4d12-86fe-f759ff46617c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
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+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>max</th>\n",
+       "      <th>min</th>\n",
+       "      <th>median</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Education</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Bachelor</th>\n",
+       "      <th>F</th>\n",
+       "      <td>73225.96</td>\n",
+       "      <td>1904.00</td>\n",
+       "      <td>5640.51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>67907.27</td>\n",
+       "      <td>1898.01</td>\n",
+       "      <td>5548.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">College</th>\n",
+       "      <th>F</th>\n",
+       "      <td>61850.19</td>\n",
+       "      <td>1898.68</td>\n",
+       "      <td>5623.61</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>61134.68</td>\n",
+       "      <td>1918.12</td>\n",
+       "      <td>6005.85</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Doctor</th>\n",
+       "      <th>F</th>\n",
+       "      <td>44856.11</td>\n",
+       "      <td>2395.57</td>\n",
+       "      <td>5332.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>32677.34</td>\n",
+       "      <td>2267.60</td>\n",
+       "      <td>5577.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">High School or Below</th>\n",
+       "      <th>F</th>\n",
+       "      <td>55277.45</td>\n",
+       "      <td>2144.92</td>\n",
+       "      <td>6039.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>83325.38</td>\n",
+       "      <td>1940.98</td>\n",
+       "      <td>6286.73</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Master</th>\n",
+       "      <th>F</th>\n",
+       "      <td>51016.07</td>\n",
+       "      <td>2417.78</td>\n",
+       "      <td>5729.86</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>50568.26</td>\n",
+       "      <td>2272.31</td>\n",
+       "      <td>5579.10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                max                     min  \\\n",
+       "                            Customer Lifetime Value Customer Lifetime Value   \n",
+       "Education            Gender                                                   \n",
+       "Bachelor             F                     73225.96                 1904.00   \n",
+       "                     M                     67907.27                 1898.01   \n",
+       "College              F                     61850.19                 1898.68   \n",
+       "                     M                     61134.68                 1918.12   \n",
+       "Doctor               F                     44856.11                 2395.57   \n",
+       "                     M                     32677.34                 2267.60   \n",
+       "High School or Below F                     55277.45                 2144.92   \n",
+       "                     M                     83325.38                 1940.98   \n",
+       "Master               F                     51016.07                 2417.78   \n",
+       "                     M                     50568.26                 2272.31   \n",
+       "\n",
+       "                                             median  \n",
+       "                            Customer Lifetime Value  \n",
+       "Education            Gender                          \n",
+       "Bachelor             F                      5640.51  \n",
+       "                     M                      5548.03  \n",
+       "College              F                      5623.61  \n",
+       "                     M                      6005.85  \n",
+       "Doctor               F                      5332.46  \n",
+       "                     M                      5577.67  \n",
+       "High School or Below F                      6039.55  \n",
+       "                     M                      6286.73  \n",
+       "Master               F                      5729.86  \n",
+       "                     M                      5579.10  "
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.pivot_table(data=df,\n",
+    "                values = ['Customer Lifetime Value'],\n",
+    "               index = [\"Education\",\"Gender\"],\n",
+    "               aggfunc = ['max','min', 'median'] ).round(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "id": "533f02c8-89e3-47ee-ac89-5834ffe9d207",
+   "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 tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>Education</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Bachelor</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">College</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Doctor</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">High School or Below</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">Master</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>F</th>\n",
+       "      <th>M</th>\n",
+       "      <th>F</th>\n",
+       "      <th>M</th>\n",
+       "      <th>F</th>\n",
+       "      <th>M</th>\n",
+       "      <th>F</th>\n",
+       "      <th>M</th>\n",
+       "      <th>F</th>\n",
+       "      <th>M</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "      <td>73225.96</td>\n",
+       "      <td>67907.27</td>\n",
+       "      <td>61850.19</td>\n",
+       "      <td>61134.68</td>\n",
+       "      <td>44856.11</td>\n",
+       "      <td>32677.34</td>\n",
+       "      <td>55277.45</td>\n",
+       "      <td>83325.38</td>\n",
+       "      <td>51016.07</td>\n",
+       "      <td>50568.26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "      <td>1904.00</td>\n",
+       "      <td>1898.01</td>\n",
+       "      <td>1898.68</td>\n",
+       "      <td>1918.12</td>\n",
+       "      <td>2395.57</td>\n",
+       "      <td>2267.60</td>\n",
+       "      <td>2144.92</td>\n",
+       "      <td>1940.98</td>\n",
+       "      <td>2417.78</td>\n",
+       "      <td>2272.31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>median</th>\n",
+       "      <th>Customer Lifetime Value</th>\n",
+       "      <td>5640.51</td>\n",
+       "      <td>5548.03</td>\n",
+       "      <td>5623.61</td>\n",
+       "      <td>6005.85</td>\n",
+       "      <td>5332.46</td>\n",
+       "      <td>5577.67</td>\n",
+       "      <td>6039.55</td>\n",
+       "      <td>6286.73</td>\n",
+       "      <td>5729.86</td>\n",
+       "      <td>5579.10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Education                       Bachelor             College            \\\n",
+       "Gender                                 F         M         F         M   \n",
+       "max    Customer Lifetime Value  73225.96  67907.27  61850.19  61134.68   \n",
+       "min    Customer Lifetime Value   1904.00   1898.01   1898.68   1918.12   \n",
+       "median Customer Lifetime Value   5640.51   5548.03   5623.61   6005.85   \n",
+       "\n",
+       "Education                         Doctor           High School or Below  \\\n",
+       "Gender                                 F         M                    F   \n",
+       "max    Customer Lifetime Value  44856.11  32677.34             55277.45   \n",
+       "min    Customer Lifetime Value   2395.57   2267.60              2144.92   \n",
+       "median Customer Lifetime Value   5332.46   5577.67              6039.55   \n",
+       "\n",
+       "Education                                   Master            \n",
+       "Gender                                 M         F         M  \n",
+       "max    Customer Lifetime Value  83325.38  51016.07  50568.26  \n",
+       "min    Customer Lifetime Value   1940.98   2417.78   2272.31  \n",
+       "median Customer Lifetime Value   6286.73   5729.86   5579.10  "
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.pivot_table(data=df,\n",
+    "                values = ['Customer Lifetime Value'],\n",
+    "               index = [\"Education\",\"Gender\"],\n",
+    "               aggfunc = ['max','min', 'median'] ).round(2).T"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "50f41e93-9875-4786-9944-e57140942f0e",
+   "metadata": {},
+   "source": [
+    "The highest CLV overall is for High School or Below (Male) customers at $83,325.38, which is higher than even Doctorates or Masters.This suggests that having less formal education doesn’t necessarily limit long-term customer value; other factors might drive loyalty or spending.\n",
+    "\n",
+    "The lowest CLVs are fairly consistent across education and gender, all hovering just under $2,000.This indicates that at the low end, education level and gender do not make a large difference.\n",
+    "\n",
+    "Medians are much closer to each other than maximums.\n",
+    "Most groups sit between $5,300 and $6,300.\n",
+    "This suggests that, for the average customer, education and gender have only modest impact on CLV.\n",
+    "The highest median is for High School or Below (Male) at $6,286.73, while the lowest median is for Doctorate (Female) at $5,332.46.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "id": "1f478a09-ca30-45fd-9ef0-fd249dc47a76",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Unnamed: 0', 'Customer', 'State', 'Customer Lifetime Value',\n",
+       "       'Response', 'Coverage', 'Education', 'Effective To Date',\n",
+       "       'EmploymentStatus', 'Gender', 'Income', 'Location Code',\n",
+       "       'Marital Status', 'Monthly Premium Auto', 'Months Since Last Claim',\n",
+       "       'Months Since Policy Inception', 'Number of Open Complaints',\n",
+       "       'Number of Policies', 'Policy Type', 'Policy', 'Renew Offer Type',\n",
+       "       'Sales Channel', 'Total Claim Amount', 'Vehicle Class', 'Vehicle Size',\n",
+       "       'Vehicle Type'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "id": "9ffba624-394f-4cbc-bc14-bd2334747db6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([52, 26, 31,  3, 73, 99, 45, 24,  8, 29, 32, 25, 28, 87, 10, 74,  1,\n",
+       "       38, 58, 37,  7, 80, 95, 78, 63, 27, 97, 39, 11, 59, 46, 62, 13, 54,\n",
+       "       51, 22, 82, 91, 44, 43, 76, 48, 84,  6, 92, 12, 61,  4, 18, 66, 70,\n",
+       "       16, 75, 34, 35, 64,  9, 89,  0, 60, 71, 23, 55, 93,  2, 67, 81, 40,\n",
+       "       57, 86, 19, 72, 69, 33, 47, 42, 17, 49, 21, 83, 94, 30, 15, 50, 53,\n",
+       "       77, 41, 90,  5, 79, 56, 98, 20, 88, 65, 14, 85, 96, 36, 68])"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Months Since Policy Inception'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "affcbaed-7609-421e-b9e0-90f0b35ace18",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }