diff --git a/[lab_customer_analysis_round5] leonardo_pagliacci.ipynb b/[lab_customer_analysis_round5] leonardo_pagliacci.ipynb
new file mode 100644
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bc9713bb",
+   "metadata": {},
+   "source": [
+    "# Lab | Customer Analysis Round 5\n",
+    "\n",
+    "For this lab, we still keep using the `marketing_customer_analysis.csv` file that you can find in the `files_for_lab` folder.\n",
+    "\n",
+    "### Get the data\n",
+    "\n",
+    "We are using the `marketing_customer_analysis.csv` file.\n",
+    "\n",
+    "### Dealing with the data\n",
+    "\n",
+    "Already done in the round 2.\n",
+    "\n",
+    "### Explore the data\n",
+    "\n",
+    "Done in the round 3.\n",
+    "\n",
+    "### Processing Data\n",
+    "\n",
+    "(_Further processing..._)\n",
+    "\n",
+    "- X-y split.\n",
+    "- Normalize (numerical).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "332f3c58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing the libraries\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "0112f86c",
+   "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>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>income</th>\n",
+       "      <th>...</th>\n",
+       "      <th>months_since_policy_inception</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\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>48029</td>\n",
+       "      <td>...</td>\n",
+       "      <td>52</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\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>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\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>22139</td>\n",
+       "      <td>...</td>\n",
+       "      <td>31</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\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>49078</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\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>23675</td>\n",
+       "      <td>...</td>\n",
+       "      <td>31</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",
+       "    </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>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>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>40</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10906</th>\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>61146</td>\n",
+       "      <td>...</td>\n",
+       "      <td>68</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10907</th>\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>39837</td>\n",
+       "      <td>...</td>\n",
+       "      <td>63</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10908</th>\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>64195</td>\n",
+       "      <td>...</td>\n",
+       "      <td>27</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10909</th>\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>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</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",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10910 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      customer       state  customer_lifetime_value response  coverage  \\\n",
+       "0      DK49336     Arizona              4809.216960       No     Basic   \n",
+       "1      KX64629  California              2228.525238       No     Basic   \n",
+       "2      LZ68649  Washington             14947.917300       No     Basic   \n",
+       "3      XL78013      Oregon             22332.439460      Yes  Extended   \n",
+       "4      QA50777      Oregon              9025.067525       No   Premium   \n",
+       "...        ...         ...                      ...      ...       ...   \n",
+       "10905  FE99816      Nevada             15563.369440       No   Premium   \n",
+       "10906  KX53892      Oregon              5259.444853       No     Basic   \n",
+       "10907  TL39050     Arizona             23893.304100       No  Extended   \n",
+       "10908  WA60547  California             11971.977650       No   Premium   \n",
+       "10909  IV32877         NaN              6857.519928      NaN     Basic   \n",
+       "\n",
+       "      education effective_to_date employmentstatus gender  income  ...  \\\n",
+       "0       College           2/18/11         Employed      M   48029  ...   \n",
+       "1       College           1/18/11       Unemployed      F       0  ...   \n",
+       "2      Bachelor           2/10/11         Employed      M   22139  ...   \n",
+       "3       College           1/11/11         Employed      M   49078  ...   \n",
+       "4      Bachelor           1/17/11    Medical Leave      F   23675  ...   \n",
+       "...         ...               ...              ...    ...     ...  ...   \n",
+       "10905  Bachelor           1/19/11       Unemployed      F       0  ...   \n",
+       "10906   College            1/6/11         Employed      F   61146  ...   \n",
+       "10907  Bachelor            2/6/11         Employed      F   39837  ...   \n",
+       "10908   College           2/13/11         Employed      F   64195  ...   \n",
+       "10909  Bachelor            1/8/11       Unemployed      M       0  ...   \n",
+       "\n",
+       "      months_since_policy_inception number_of_open_complaints  \\\n",
+       "0                                52                       0.0   \n",
+       "1                                26                       0.0   \n",
+       "2                                31                       0.0   \n",
+       "3                                 3                       0.0   \n",
+       "4                                31                       NaN   \n",
+       "...                             ...                       ...   \n",
+       "10905                            40                       NaN   \n",
+       "10906                            68                       0.0   \n",
+       "10907                            63                       0.0   \n",
+       "10908                            27                       4.0   \n",
+       "10909                             1                       0.0   \n",
+       "\n",
+       "       number_of_policies     policy_type        policy  renew_offer_type  \\\n",
+       "0                       9  Corporate Auto  Corporate L3            Offer3   \n",
+       "1                       1   Personal Auto   Personal L3            Offer4   \n",
+       "2                       2   Personal Auto   Personal L3            Offer3   \n",
+       "3                       2  Corporate Auto  Corporate L3            Offer2   \n",
+       "4                       7   Personal Auto   Personal L2            Offer1   \n",
+       "...                   ...             ...           ...               ...   \n",
+       "10905                   7   Personal Auto   Personal L1            Offer3   \n",
+       "10906                   6   Personal Auto   Personal L3            Offer2   \n",
+       "10907                   2  Corporate Auto  Corporate L3            Offer1   \n",
+       "10908                   6   Personal Auto   Personal L1            Offer1   \n",
+       "10909                   3   Personal Auto   Personal L1            Offer4   \n",
+       "\n",
+       "       sales_channel total_claim_amount  vehicle_class vehicle_size  \n",
+       "0              Agent         292.800000  Four-Door Car      Medsize  \n",
+       "1        Call Center         744.924331  Four-Door Car      Medsize  \n",
+       "2        Call Center         480.000000            SUV      Medsize  \n",
+       "3             Branch         484.013411  Four-Door Car      Medsize  \n",
+       "4             Branch         707.925645  Four-Door Car      Medsize  \n",
+       "...              ...                ...            ...          ...  \n",
+       "10905            Web        1214.400000     Luxury Car      Medsize  \n",
+       "10906         Branch         273.018929  Four-Door Car      Medsize  \n",
+       "10907            Web         381.306996     Luxury SUV      Medsize  \n",
+       "10908         Branch         618.288849            SUV      Medsize  \n",
+       "10909            Web        1021.719397            SUV      Medsize  \n",
+       "\n",
+       "[10910 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Importing the dataframe\n",
+    "\n",
+    "customer_df = pd.read_csv(\"/Users/leozinho.air/Desktop/ironhack_da/class_04/lab-customer-analysis-round-2/files_for_lab/csv_files/marketing_customer_analysis.csv\")\n",
+    "\n",
+    "# Cleaning operations\n",
+    "\n",
+    "columns = []\n",
+    "\n",
+    "for i in range(len(customer_df.columns)):\n",
+    "    columns.append(customer_df.columns[i].lower().replace(' ', '_'))\n",
+    "\n",
+    "customer_df.columns = columns\n",
+    "\n",
+    "customer_df\n",
+    "\n",
+    "# Dropping the column 'unnamed:_0'\n",
+    "\n",
+    "customer_df = customer_df.drop(['unnamed:_0'], axis = 1)\n",
+    "\n",
+    "# The only values are Nan or vehicle class 'A', let's create a new df without the column.\n",
+    "\n",
+    "customer_df = customer_df.drop(['vehicle_type'], axis = 1)\n",
+    "\n",
+    "customer_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "958cdaf7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This is for predictions\n",
+    "from sklearn import linear_model\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "\n",
+    "# These Libs are for stats -> this ones are for description\n",
+    "import statsmodels.api as sm\n",
+    "from statsmodels.formula.api import ols\n",
+    "\n",
+    "# Creating a dataframe with only numerical values\n",
+    "\n",
+    "num_cust = customer_df.select_dtypes(include = np.number)\n",
+    "\n",
+    "# Checking for null values\n",
+    "\n",
+    "num_cust.isnull().any() # Two columns have nan values\n",
+    "\n",
+    "# I chose to fill the nan valus with the means of the two columns\n",
+    "\n",
+    "mean_months = num_cust['months_since_last_claim'].mean()\n",
+    "\n",
+    "num_cust['months_since_last_claim'].fillna(value = mean_months, inplace = True)\n",
+    "\n",
+    "\n",
+    "mean_complaints = num_cust['number_of_open_complaints'].mean()\n",
+    "num_cust['number_of_open_complaints'].fillna(value = mean_complaints, inplace = True)\n",
+    "\n",
+    "num_cust.isnull().sum().sum() # There are no nan values\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "95c51d22",
+   "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>customer_lifetime_value</th>\n",
+       "      <th>income</th>\n",
+       "      <th>monthly_premium_auto</th>\n",
+       "      <th>months_since_last_claim</th>\n",
+       "      <th>months_since_policy_inception</th>\n",
+       "      <th>number_of_open_complaints</th>\n",
+       "      <th>number_of_policies</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.035752</td>\n",
+       "      <td>0.480381</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>0.525253</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.101171</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.004059</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.012658</td>\n",
+       "      <td>0.085714</td>\n",
+       "      <td>0.262626</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>0.257445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.160264</td>\n",
+       "      <td>0.221432</td>\n",
+       "      <td>0.164557</td>\n",
+       "      <td>0.971429</td>\n",
+       "      <td>0.313131</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.165875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.250953</td>\n",
+       "      <td>0.490873</td>\n",
+       "      <td>0.151899</td>\n",
+       "      <td>0.285714</td>\n",
+       "      <td>0.030303</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.167263</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.087527</td>\n",
+       "      <td>0.236795</td>\n",
+       "      <td>0.236287</td>\n",
+       "      <td>0.432831</td>\n",
+       "      <td>0.313131</td>\n",
+       "      <td>0.076851</td>\n",
+       "      <td>0.750</td>\n",
+       "      <td>0.244657</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10905</th>\n",
+       "      <td>0.167823</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.810127</td>\n",
+       "      <td>0.432831</td>\n",
+       "      <td>0.404040</td>\n",
+       "      <td>0.076851</td>\n",
+       "      <td>0.750</td>\n",
+       "      <td>0.419717</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10906</th>\n",
+       "      <td>0.041281</td>\n",
+       "      <td>0.611576</td>\n",
+       "      <td>0.016878</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>0.686869</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.625</td>\n",
+       "      <td>0.094333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10907</th>\n",
+       "      <td>0.270122</td>\n",
+       "      <td>0.398446</td>\n",
+       "      <td>0.590717</td>\n",
+       "      <td>0.314286</td>\n",
+       "      <td>0.636364</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.125</td>\n",
+       "      <td>0.131763</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10908</th>\n",
+       "      <td>0.123717</td>\n",
+       "      <td>0.642072</td>\n",
+       "      <td>0.409283</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.272727</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>0.625</td>\n",
+       "      <td>0.213674</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10909</th>\n",
+       "      <td>0.060907</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.168776</td>\n",
+       "      <td>0.885714</td>\n",
+       "      <td>0.010101</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.250</td>\n",
+       "      <td>0.353118</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10910 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       customer_lifetime_value    income  monthly_premium_auto  \\\n",
+       "0                     0.035752  0.480381              0.000000   \n",
+       "1                     0.004059  0.000000              0.012658   \n",
+       "2                     0.160264  0.221432              0.164557   \n",
+       "3                     0.250953  0.490873              0.151899   \n",
+       "4                     0.087527  0.236795              0.236287   \n",
+       "...                        ...       ...                   ...   \n",
+       "10905                 0.167823  0.000000              0.810127   \n",
+       "10906                 0.041281  0.611576              0.016878   \n",
+       "10907                 0.270122  0.398446              0.590717   \n",
+       "10908                 0.123717  0.642072              0.409283   \n",
+       "10909                 0.060907  0.000000              0.168776   \n",
+       "\n",
+       "       months_since_last_claim  months_since_policy_inception  \\\n",
+       "0                     0.200000                       0.525253   \n",
+       "1                     0.085714                       0.262626   \n",
+       "2                     0.971429                       0.313131   \n",
+       "3                     0.285714                       0.030303   \n",
+       "4                     0.432831                       0.313131   \n",
+       "...                        ...                            ...   \n",
+       "10905                 0.432831                       0.404040   \n",
+       "10906                 0.200000                       0.686869   \n",
+       "10907                 0.314286                       0.636364   \n",
+       "10908                 0.000000                       0.272727   \n",
+       "10909                 0.885714                       0.010101   \n",
+       "\n",
+       "       number_of_open_complaints  number_of_policies  total_claim_amount  \n",
+       "0                       0.000000               1.000            0.101171  \n",
+       "1                       0.000000               0.000            0.257445  \n",
+       "2                       0.000000               0.125            0.165875  \n",
+       "3                       0.000000               0.125            0.167263  \n",
+       "4                       0.076851               0.750            0.244657  \n",
+       "...                          ...                 ...                 ...  \n",
+       "10905                   0.076851               0.750            0.419717  \n",
+       "10906                   0.000000               0.625            0.094333  \n",
+       "10907                   0.000000               0.125            0.131763  \n",
+       "10908                   0.800000               0.625            0.213674  \n",
+       "10909                   0.000000               0.250            0.353118  \n",
+       "\n",
+       "[10910 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# X-y split.\n",
+    "\n",
+    "Y = customer_df['total_claim_amount']\n",
+    "X = customer_df.drop(['total_claim_amount'], axis = 1)\n",
+    "\n",
+    "# Normalizing the DataFrame\n",
+    "\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "\n",
+    "scaler = MinMaxScaler() # This is the normalization process\n",
+    "\n",
+    "normalized_data = scaler.fit_transform(num_cust) # Fit it to the data\n",
+    "normalized_data = pd.DataFrame(normalized_data, columns = num_cust.columns)\n",
+    "\n",
+    "normalized_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ccdae9d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}