diff --git a/LabsDataStructuringAndCombing.ipynb b/LabsDataStructuringAndCombing.ipynb
new file mode 100644
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+++ b/LabsDataStructuringAndCombing.ipynb
@@ -0,0 +1,2387 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "149fd7de-5571-4a43-831b-bf404fde8451",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "1cebe2cb-7eb3-47fb-b91e-03d7e43e6035",
+   "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>gender</th>\n",
+       "      <th>education</th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <th>income</th>\n",
+       "      <th>monthly_premium_auto</th>\n",
+       "      <th>number_of_open_complaints</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>RB50392</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>588174</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>QZ44356</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>697953</td>\n",
+       "      <td>0</td>\n",
+       "      <td>94</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>1131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AI49188</td>\n",
+       "      <td>Nevada</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>1288743</td>\n",
+       "      <td>48767</td>\n",
+       "      <td>108</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>566</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>WW63253</td>\n",
+       "      <td>California</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>764586</td>\n",
+       "      <td>0</td>\n",
+       "      <td>106</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>GA49547</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>M</td>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>536307</td>\n",
+       "      <td>36357</td>\n",
+       "      <td>68</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>17</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1067</th>\n",
+       "      <td>VJ51327</td>\n",
+       "      <td>California</td>\n",
+       "      <td>F</td>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>2031499</td>\n",
+       "      <td>63209</td>\n",
+       "      <td>102</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>207</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1068</th>\n",
+       "      <td>GS98873</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>323912</td>\n",
+       "      <td>16061</td>\n",
+       "      <td>88</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1069</th>\n",
+       "      <td>CW49887</td>\n",
+       "      <td>California</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>462680</td>\n",
+       "      <td>79487</td>\n",
+       "      <td>114</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>547</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1070</th>\n",
+       "      <td>MY31220</td>\n",
+       "      <td>California</td>\n",
+       "      <td>F</td>\n",
+       "      <td>College</td>\n",
+       "      <td>899704</td>\n",
+       "      <td>54230</td>\n",
+       "      <td>112</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1071</th>\n",
+       "      <td>AA71604</td>\n",
+       "      <td>California</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>588174</td>\n",
+       "      <td>36234</td>\n",
+       "      <td>83</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>354</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1072 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     customer       state gender             education  \\\n",
+       "0     RB50392  Washington    NaN                Master   \n",
+       "1     QZ44356     Arizona      F              Bachelor   \n",
+       "2     AI49188      Nevada      F              Bachelor   \n",
+       "3     WW63253  California      M              Bachelor   \n",
+       "4     GA49547  Washington      M  High School or Below   \n",
+       "...       ...         ...    ...                   ...   \n",
+       "1067  VJ51327  California      F  High School or Below   \n",
+       "1068  GS98873     Arizona      F              Bachelor   \n",
+       "1069  CW49887  California      F                Master   \n",
+       "1070  MY31220  California      F               College   \n",
+       "1071  AA71604  California    NaN              Bachelor   \n",
+       "\n",
+       "      customer_lifetime_value  income  monthly_premium_auto  \\\n",
+       "0                      588174       0                  1000   \n",
+       "1                      697953       0                    94   \n",
+       "2                     1288743   48767                   108   \n",
+       "3                      764586       0                   106   \n",
+       "4                      536307   36357                    68   \n",
+       "...                       ...     ...                   ...   \n",
+       "1067                  2031499   63209                   102   \n",
+       "1068                   323912   16061                    88   \n",
+       "1069                   462680   79487                   114   \n",
+       "1070                   899704   54230                   112   \n",
+       "1071                   588174   36234                    83   \n",
+       "\n",
+       "      number_of_open_complaints     policy_type  vehicle_class  \\\n",
+       "0                             0   Personal Auto  Four-Door Car   \n",
+       "1                             0   Personal Auto  Four-Door Car   \n",
+       "2                             0   Personal Auto   Two-Door Car   \n",
+       "3                             0  Corporate Auto            SUV   \n",
+       "4                             0   Personal Auto  Four-Door Car   \n",
+       "...                         ...             ...            ...   \n",
+       "1067                          2   Personal Auto            SUV   \n",
+       "1068                          0   Personal Auto  Four-Door Car   \n",
+       "1069                          0    Special Auto            SUV   \n",
+       "1070                          0   Personal Auto   Two-Door Car   \n",
+       "1071                          0   Personal Auto  Four-Door Car   \n",
+       "\n",
+       "      total_claim_amount  \n",
+       "0                      2  \n",
+       "1                   1131  \n",
+       "2                    566  \n",
+       "3                    529  \n",
+       "4                     17  \n",
+       "...                  ...  \n",
+       "1067                 207  \n",
+       "1068                 633  \n",
+       "1069                 547  \n",
+       "1070                 537  \n",
+       "1071                 354  \n",
+       "\n",
+       "[1072 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cleaned_file1 = pd.read_csv('cleaned_data.csv') ### cleaned file from previous LABS\n",
+    "cleaned_file1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "b6333d2f-77c9-477b-a375-78cb469459fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "customer                     object\n",
+      "state                        object\n",
+      "gender                       object\n",
+      "education                    object\n",
+      "customer_lifetime_value       int64\n",
+      "income                        int64\n",
+      "monthly_premium_auto          int64\n",
+      "number_of_open_complaints     int64\n",
+      "policy_type                  object\n",
+      "vehicle_class                object\n",
+      "total_claim_amount            int64\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cleaned_file1.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "98615fba-e2a8-4a98-aae6-3166b0fb4be4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url_2 = \"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file2.csv\"\n",
+    "df_2 = pd.read_csv(url_2) ### reading second file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "1f0e2dba-c82a-4f43-8024-d82ce7868528",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'Customer Lifetime Value'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.13/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'Customer Lifetime Value'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[25], line 8\u001b[0m\n\u001b[1;32m      1\u001b[0m df_2\u001b[38;5;241m.\u001b[39mrename(columns\u001b[38;5;241m=\u001b[39m{\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mState\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mST\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m      3\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGender\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGENDER\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m      4\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTotal Claim Amount\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTotal Claim Amount\u001b[39m\u001b[38;5;124m'\u001b[39m,   \u001b[38;5;66;03m### ensuring naming of columns is the same\u001b[39;00m\n\u001b[1;32m      5\u001b[0m }, inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m----> 8\u001b[0m df_2[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCustomer Lifetime Value\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df_2[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCustomer Lifetime Value\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mstr\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mfloat\u001b[39m) \u001b[38;5;66;03m### removing %\u001b[39;00m\n\u001b[1;32m     11\u001b[0m df_2[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNumber of Open Complaints\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mto_numeric(df_2[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNumber of Open Complaints\u001b[39m\u001b[38;5;124m'\u001b[39m], errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcoerce\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39mfillna(\u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mint\u001b[39m)\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.13/site-packages/pandas/core/frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[1;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
+      "File \u001b[0;32m/opt/anaconda3/lib/python3.13/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m   3810\u001b[0m     ):\n\u001b[1;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
+      "\u001b[0;31mKeyError\u001b[0m: 'Customer Lifetime Value'"
+     ]
+    }
+   ],
+   "source": [
+    "df_2.rename(columns={\n",
+    "    'State': 'ST',\n",
+    "    'Gender': 'GENDER',\n",
+    "    'Total Claim Amount': 'Total Claim Amount',   ### ensuring naming of columns is the same\n",
+    "}, inplace=True)\n",
+    "\n",
+    "\n",
+    "df_2['Customer Lifetime Value'] = df_2['Customer Lifetime Value'].str.replace('%', '').astype(float) ### removing %\n",
+    "\n",
+    "\n",
+    "df_2['Number of Open Complaints'] = pd.to_numeric(df_2['Number of Open Complaints'], errors='coerce').fillna(0).astype(int)\n",
+    "### converts Num of Open Complaints to an integer. Replacing missing or invalid with the integer 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "9ddf0082-6deb-49ea-a6fe-a97e7cecedd9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_2.rename(columns={\n",
+    "    'Customer': 'customer',\n",
+    "    'ST': 'state',\n",
+    "    'GENDER': 'gender',\n",
+    "    'Education': 'education',\n",
+    "    'Customer Lifetime Value': 'customer_lifetime_value', ### ensuring all column names are the same\n",
+    "    'Income': 'income',\n",
+    "    'Monthly Premium Auto': 'monthly_premium_auto',\n",
+    "    'Number of Open Complaints': 'number_of_open_complaints',\n",
+    "    'Policy Type': 'policy_type',\n",
+    "    'Vehicle Class': 'vehicle_class',\n",
+    "    'Total Claim Amount': 'total_claim_amount'\n",
+    "}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "c6bc08af-bf69-43ad-8840-f92c7996ed2c",
+   "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>gender</th>\n",
+       "      <th>education</th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <th>income</th>\n",
+       "      <th>monthly_premium_auto</th>\n",
+       "      <th>number_of_open_complaints</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>RB50392</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>588174</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>QZ44356</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>697953</td>\n",
+       "      <td>0</td>\n",
+       "      <td>94</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>1131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AI49188</td>\n",
+       "      <td>Nevada</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>1288743</td>\n",
+       "      <td>48767</td>\n",
+       "      <td>108</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>566</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>WW63253</td>\n",
+       "      <td>California</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>764586</td>\n",
+       "      <td>0</td>\n",
+       "      <td>106</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>GA49547</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>M</td>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>536307</td>\n",
+       "      <td>36357</td>\n",
+       "      <td>68</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  customer       state gender             education  customer_lifetime_value  \\\n",
+       "0  RB50392  Washington    NaN                Master                   588174   \n",
+       "1  QZ44356     Arizona      F              Bachelor                   697953   \n",
+       "2  AI49188      Nevada      F              Bachelor                  1288743   \n",
+       "3  WW63253  California      M              Bachelor                   764586   \n",
+       "4  GA49547  Washington      M  High School or Below                   536307   \n",
+       "\n",
+       "   income  monthly_premium_auto  number_of_open_complaints     policy_type  \\\n",
+       "0       0                  1000                          0   Personal Auto   \n",
+       "1       0                    94                          0   Personal Auto   \n",
+       "2   48767                   108                          0   Personal Auto   \n",
+       "3       0                   106                          0  Corporate Auto   \n",
+       "4   36357                    68                          0   Personal Auto   \n",
+       "\n",
+       "   vehicle_class  total_claim_amount  \n",
+       "0  Four-Door Car                   2  \n",
+       "1  Four-Door Car                1131  \n",
+       "2   Two-Door Car                 566  \n",
+       "3            SUV                 529  \n",
+       "4  Four-Door Car                  17  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cleaned_file1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "0dc342ac-4d6a-4ab1-a80d-6bfd0fedd15b",
+   "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>gender</th>\n",
+       "      <th>education</th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <th>income</th>\n",
+       "      <th>monthly_premium_auto</th>\n",
+       "      <th>number_of_open_complaints</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>GS98873</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>323912.47</td>\n",
+       "      <td>16061</td>\n",
+       "      <td>88</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>633.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CW49887</td>\n",
+       "      <td>California</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>462680.11</td>\n",
+       "      <td>79487</td>\n",
+       "      <td>114</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>547.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>MY31220</td>\n",
+       "      <td>California</td>\n",
+       "      <td>F</td>\n",
+       "      <td>College</td>\n",
+       "      <td>899704.02</td>\n",
+       "      <td>54230</td>\n",
+       "      <td>112</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>537.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>UH35128</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>F</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2580706.30</td>\n",
+       "      <td>71210</td>\n",
+       "      <td>214</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Luxury Car</td>\n",
+       "      <td>1027.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>WH52799</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>F</td>\n",
+       "      <td>College</td>\n",
+       "      <td>380812.21</td>\n",
+       "      <td>94903</td>\n",
+       "      <td>94</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>451.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  customer       state gender education  customer_lifetime_value  income  \\\n",
+       "0  GS98873     Arizona      F  Bachelor                323912.47   16061   \n",
+       "1  CW49887  California      F    Master                462680.11   79487   \n",
+       "2  MY31220  California      F   College                899704.02   54230   \n",
+       "3  UH35128      Oregon      F   College               2580706.30   71210   \n",
+       "4  WH52799     Arizona      F   College                380812.21   94903   \n",
+       "\n",
+       "   monthly_premium_auto  number_of_open_complaints     policy_type  \\\n",
+       "0                    88                          0   Personal Auto   \n",
+       "1                   114                          0    Special Auto   \n",
+       "2                   112                          0   Personal Auto   \n",
+       "3                   214                          0   Personal Auto   \n",
+       "4                    94                          0  Corporate Auto   \n",
+       "\n",
+       "   vehicle_class  total_claim_amount  \n",
+       "0  Four-Door Car               633.6  \n",
+       "1            SUV               547.2  \n",
+       "2   Two-Door Car               537.6  \n",
+       "3     Luxury Car              1027.2  \n",
+       "4   Two-Door Car               451.2  "
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "764af229-0101-4a49-8410-e0d28f31b89d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "customer                      object\n",
+      "state                         object\n",
+      "gender                        object\n",
+      "education                     object\n",
+      "customer_lifetime_value      float64\n",
+      "income                         int64\n",
+      "monthly_premium_auto           int64\n",
+      "number_of_open_complaints      int64\n",
+      "policy_type                   object\n",
+      "vehicle_class                 object\n",
+      "total_claim_amount           float64\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df_2.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "f11a00ce-4278-445d-aa46-d1bba15b5bb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_2 = df_2[cleaned_file1.columns] ### matching the names to cleaned_file1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "c51c7301-092e-43ac-a5fb-ffd0c1ce97e2",
+   "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>gender</th>\n",
+       "      <th>education</th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <th>income</th>\n",
+       "      <th>monthly_premium_auto</th>\n",
+       "      <th>number_of_open_complaints</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>GS98873</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>323912.47</td>\n",
+       "      <td>16061</td>\n",
+       "      <td>88</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>633.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CW49887</td>\n",
+       "      <td>California</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>462680.11</td>\n",
+       "      <td>79487</td>\n",
+       "      <td>114</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>547.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>MY31220</td>\n",
+       "      <td>California</td>\n",
+       "      <td>F</td>\n",
+       "      <td>College</td>\n",
+       "      <td>899704.02</td>\n",
+       "      <td>54230</td>\n",
+       "      <td>112</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>537.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>UH35128</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>F</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2580706.30</td>\n",
+       "      <td>71210</td>\n",
+       "      <td>214</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Luxury Car</td>\n",
+       "      <td>1027.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>WH52799</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>F</td>\n",
+       "      <td>College</td>\n",
+       "      <td>380812.21</td>\n",
+       "      <td>94903</td>\n",
+       "      <td>94</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>451.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  customer       state gender education  customer_lifetime_value  income  \\\n",
+       "0  GS98873     Arizona      F  Bachelor                323912.47   16061   \n",
+       "1  CW49887  California      F    Master                462680.11   79487   \n",
+       "2  MY31220  California      F   College                899704.02   54230   \n",
+       "3  UH35128      Oregon      F   College               2580706.30   71210   \n",
+       "4  WH52799     Arizona      F   College                380812.21   94903   \n",
+       "\n",
+       "   monthly_premium_auto  number_of_open_complaints     policy_type  \\\n",
+       "0                    88                          0   Personal Auto   \n",
+       "1                   114                          0    Special Auto   \n",
+       "2                   112                          0   Personal Auto   \n",
+       "3                   214                          0   Personal Auto   \n",
+       "4                    94                          0  Corporate Auto   \n",
+       "\n",
+       "   vehicle_class  total_claim_amount  \n",
+       "0  Four-Door Car               633.6  \n",
+       "1            SUV               547.2  \n",
+       "2   Two-Door Car               537.6  \n",
+       "3     Luxury Car              1027.2  \n",
+       "4   Two-Door Car               451.2  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "7ff2eeff-0d53-4090-92bc-dd146adc5993",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url_3 = \"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file3.csv\"\n",
+    "df_3 = pd.read_csv(url_3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "8ac860ef-7307-4263-bc38-bc7a971fbc88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_3.rename(columns={\n",
+    "    'Customer': 'customer',\n",
+    "    'State': 'state',\n",
+    "    'Gender': 'gender',\n",
+    "    'Education': 'education',\n",
+    "    'Customer Lifetime Value': 'customer_lifetime_value', ### changing the column names again\n",
+    "    'Income': 'income',\n",
+    "    'Monthly Premium Auto': 'monthly_premium_auto',\n",
+    "    'Number of Open Complaints': 'number_of_open_complaints',\n",
+    "    'Policy Type': 'policy_type',\n",
+    "    'Vehicle Class': 'vehicle_class',\n",
+    "    'Total Claim Amount': 'total_claim_amount'\n",
+    "}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "5fb4c370-dfdc-49e6-8d12-290543964d52",
+   "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>education</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>income</th>\n",
+       "      <th>monthly_premium_auto</th>\n",
+       "      <th>number_of_open_complaints</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>SA25987</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>3479.137523</td>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>M</td>\n",
+       "      <td>0</td>\n",
+       "      <td>104</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>499.200000</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>TB86706</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>2502.637401</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>M</td>\n",
+       "      <td>0</td>\n",
+       "      <td>66</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>3.468912</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>ZL73902</td>\n",
+       "      <td>Nevada</td>\n",
+       "      <td>3265.156348</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>F</td>\n",
+       "      <td>25820</td>\n",
+       "      <td>82</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>393.600000</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>KX23516</td>\n",
+       "      <td>California</td>\n",
+       "      <td>4455.843406</td>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>F</td>\n",
+       "      <td>0</td>\n",
+       "      <td>121</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>699.615192</td>\n",
+       "      <td>SUV</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>FN77294</td>\n",
+       "      <td>California</td>\n",
+       "      <td>7704.958480</td>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>M</td>\n",
+       "      <td>30366</td>\n",
+       "      <td>101</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>484.800000</td>\n",
+       "      <td>SUV</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  customer       state  customer_lifetime_value             education gender  \\\n",
+       "0  SA25987  Washington              3479.137523  High School or Below      M   \n",
+       "1  TB86706     Arizona              2502.637401                Master      M   \n",
+       "2  ZL73902      Nevada              3265.156348              Bachelor      F   \n",
+       "3  KX23516  California              4455.843406  High School or Below      F   \n",
+       "4  FN77294  California              7704.958480  High School or Below      M   \n",
+       "\n",
+       "   income  monthly_premium_auto  number_of_open_complaints    policy_type  \\\n",
+       "0       0                   104                          0  Personal Auto   \n",
+       "1       0                    66                          0  Personal Auto   \n",
+       "2   25820                    82                          0  Personal Auto   \n",
+       "3       0                   121                          0  Personal Auto   \n",
+       "4   30366                   101                          2  Personal Auto   \n",
+       "\n",
+       "   total_claim_amount  vehicle_class  \n",
+       "0          499.200000   Two-Door Car  \n",
+       "1            3.468912   Two-Door Car  \n",
+       "2          393.600000  Four-Door Car  \n",
+       "3          699.615192            SUV  \n",
+       "4          484.800000            SUV  "
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_3.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "29bdd186-b520-47bc-8fb9-31f6ff74ead5",
+   "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>gender</th>\n",
+       "      <th>education</th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <th>income</th>\n",
+       "      <th>monthly_premium_auto</th>\n",
+       "      <th>number_of_open_complaints</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>RB50392</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>588174</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>QZ44356</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>697953</td>\n",
+       "      <td>0</td>\n",
+       "      <td>94</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>1131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AI49188</td>\n",
+       "      <td>Nevada</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>1288743</td>\n",
+       "      <td>48767</td>\n",
+       "      <td>108</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>566</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>WW63253</td>\n",
+       "      <td>California</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>764586</td>\n",
+       "      <td>0</td>\n",
+       "      <td>106</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>GA49547</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>M</td>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>536307</td>\n",
+       "      <td>36357</td>\n",
+       "      <td>68</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  customer       state gender             education  customer_lifetime_value  \\\n",
+       "0  RB50392  Washington    NaN                Master                   588174   \n",
+       "1  QZ44356     Arizona      F              Bachelor                   697953   \n",
+       "2  AI49188      Nevada      F              Bachelor                  1288743   \n",
+       "3  WW63253  California      M              Bachelor                   764586   \n",
+       "4  GA49547  Washington      M  High School or Below                   536307   \n",
+       "\n",
+       "   income  monthly_premium_auto  number_of_open_complaints     policy_type  \\\n",
+       "0       0                  1000                          0   Personal Auto   \n",
+       "1       0                    94                          0   Personal Auto   \n",
+       "2   48767                   108                          0   Personal Auto   \n",
+       "3       0                   106                          0  Corporate Auto   \n",
+       "4   36357                    68                          0   Personal Auto   \n",
+       "\n",
+       "   vehicle_class  total_claim_amount  \n",
+       "0  Four-Door Car                   2  \n",
+       "1  Four-Door Car                1131  \n",
+       "2   Two-Door Car                 566  \n",
+       "3            SUV                 529  \n",
+       "4  Four-Door Car                  17  "
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cleaned_file1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "75dc2923-0757-42a0-aa0b-5f7508ad5853",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_3 = df_3[cleaned_file1.columns] ### putting df_3 in the same order"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "2ec69f90-2598-458f-8692-f692dbb0df65",
+   "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>gender</th>\n",
+       "      <th>education</th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <th>income</th>\n",
+       "      <th>monthly_premium_auto</th>\n",
+       "      <th>number_of_open_complaints</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>SA25987</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>M</td>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>3479.137523</td>\n",
+       "      <td>0</td>\n",
+       "      <td>104</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>499.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  customer       state gender             education  customer_lifetime_value  \\\n",
+       "0  SA25987  Washington      M  High School or Below              3479.137523   \n",
+       "\n",
+       "   income  monthly_premium_auto  number_of_open_complaints    policy_type  \\\n",
+       "0       0                   104                          0  Personal Auto   \n",
+       "\n",
+       "  vehicle_class  total_claim_amount  \n",
+       "0  Two-Door Car               499.2  "
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_3.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "b1fa2ffc-e6cc-42ad-aba4-d839b9299dbe",
+   "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>gender</th>\n",
+       "      <th>education</th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <th>income</th>\n",
+       "      <th>monthly_premium_auto</th>\n",
+       "      <th>number_of_open_complaints</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>RB50392</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>588174</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  customer       state gender education  customer_lifetime_value  income  \\\n",
+       "0  RB50392  Washington    NaN    Master                   588174       0   \n",
+       "\n",
+       "   monthly_premium_auto  number_of_open_complaints    policy_type  \\\n",
+       "0                  1000                          0  Personal Auto   \n",
+       "\n",
+       "   vehicle_class  total_claim_amount  \n",
+       "0  Four-Door Car                   2  "
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cleaned_file1.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "ab3ea6f7-3bc2-4c91-b754-a7c05f9e748a",
+   "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>gender</th>\n",
+       "      <th>education</th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <th>income</th>\n",
+       "      <th>monthly_premium_auto</th>\n",
+       "      <th>number_of_open_complaints</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>GS98873</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>323912.47</td>\n",
+       "      <td>16061</td>\n",
+       "      <td>88</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>633.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  customer    state gender education  customer_lifetime_value  income  \\\n",
+       "0  GS98873  Arizona      F  Bachelor                323912.47   16061   \n",
+       "\n",
+       "   monthly_premium_auto  number_of_open_complaints    policy_type  \\\n",
+       "0                    88                          0  Personal Auto   \n",
+       "\n",
+       "   vehicle_class  total_claim_amount  \n",
+       "0  Four-Door Car               633.6  "
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_2.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "22d9cdf1-90fb-4b75-b610-f7589d82621d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def strip_whitespace(df):\n",
+    "    str_cols = df.select_dtypes(include='object').columns\n",
+    "    for col in str_cols:\n",
+    "        df[col] = df[col].str.strip() ### cleaning the whitespace in the columns in all datasets\n",
+    "    return df\n",
+    "\n",
+    "cleaned_file1 = strip_whitespace(cleaned_file1)\n",
+    "df_2 = strip_whitespace(df_2)\n",
+    "df_3 = strip_whitespace(df_3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "0fe732b1-99a6-49b0-a264-b74b0b53d40b",
+   "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>gender</th>\n",
+       "      <th>education</th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <th>income</th>\n",
+       "      <th>monthly_premium_auto</th>\n",
+       "      <th>number_of_open_complaints</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>RB50392</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>588174</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  customer       state gender education  customer_lifetime_value  income  \\\n",
+       "0  RB50392  Washington    NaN    Master                   588174       0   \n",
+       "\n",
+       "   monthly_premium_auto  number_of_open_complaints    policy_type  \\\n",
+       "0                  1000                          0  Personal Auto   \n",
+       "\n",
+       "   vehicle_class  total_claim_amount  \n",
+       "0  Four-Door Car                   2  "
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cleaned_file1.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "7e36043e-63be-4c62-9acb-ce2daab72251",
+   "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>gender</th>\n",
+       "      <th>education</th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <th>income</th>\n",
+       "      <th>monthly_premium_auto</th>\n",
+       "      <th>number_of_open_complaints</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>GS98873</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>323912.47</td>\n",
+       "      <td>16061</td>\n",
+       "      <td>88</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>633.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  customer    state gender education  customer_lifetime_value  income  \\\n",
+       "0  GS98873  Arizona      F  Bachelor                323912.47   16061   \n",
+       "\n",
+       "   monthly_premium_auto  number_of_open_complaints    policy_type  \\\n",
+       "0                    88                          0  Personal Auto   \n",
+       "\n",
+       "   vehicle_class  total_claim_amount  \n",
+       "0  Four-Door Car               633.6  "
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_2.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "ddd6619a-4369-4833-8520-378e8f237ede",
+   "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>gender</th>\n",
+       "      <th>education</th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <th>income</th>\n",
+       "      <th>monthly_premium_auto</th>\n",
+       "      <th>number_of_open_complaints</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>SA25987</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>M</td>\n",
+       "      <td>High School or Below</td>\n",
+       "      <td>3479.137523</td>\n",
+       "      <td>0</td>\n",
+       "      <td>104</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>499.2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  customer       state gender             education  customer_lifetime_value  \\\n",
+       "0  SA25987  Washington      M  High School or Below              3479.137523   \n",
+       "\n",
+       "   income  monthly_premium_auto  number_of_open_complaints    policy_type  \\\n",
+       "0       0                   104                          0  Personal Auto   \n",
+       "\n",
+       "  vehicle_class  total_claim_amount  \n",
+       "0  Two-Door Car               499.2  "
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_3.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "5932abd2-fb75-42dd-a44b-128f07f66076",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "customer                     object\n",
+      "state                        object\n",
+      "gender                       object\n",
+      "education                    object\n",
+      "customer_lifetime_value       int64\n",
+      "income                        int64\n",
+      "monthly_premium_auto          int64\n",
+      "number_of_open_complaints     int64\n",
+      "policy_type                  object\n",
+      "vehicle_class                object\n",
+      "total_claim_amount            int64\n",
+      "dtype: object\n",
+      "customer                      object\n",
+      "state                         object\n",
+      "gender                        object\n",
+      "education                     object\n",
+      "customer_lifetime_value      float64\n",
+      "income                         int64\n",
+      "monthly_premium_auto           int64\n",
+      "number_of_open_complaints      int64\n",
+      "policy_type                   object\n",
+      "vehicle_class                 object\n",
+      "total_claim_amount           float64\n",
+      "dtype: object\n",
+      "customer                      object\n",
+      "state                         object\n",
+      "gender                        object\n",
+      "education                     object\n",
+      "customer_lifetime_value      float64\n",
+      "income                         int64\n",
+      "monthly_premium_auto           int64\n",
+      "number_of_open_complaints      int64\n",
+      "policy_type                   object\n",
+      "vehicle_class                 object\n",
+      "total_claim_amount           float64\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cleaned_file1.dtypes)\n",
+    "print(df_2.dtypes)\n",
+    "print(df_3.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "c798e3f2-e36d-4ce4-b103-3fe9dc6c859e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1072, 11)"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cleaned_file1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "692a322b-d178-413c-b188-bb252e2dd7dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(996, 11)"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "a58c29f5-abaa-4915-8845-22b64221b1f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7070, 11)"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_3.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "cfc5f58c-9859-4ced-8c54-3a7f28089f9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for df in [df_2, df_3]:\n",
+    "    df['customer_lifetime_value'] = df['customer_lifetime_value'].fillna(0).astype(int) ### converting to all the same data type\n",
+    "    df['total_claim_amount'] = df['total_claim_amount'].fillna(0).astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "897a2b9c-1b0a-4dc8-aa0f-4b44993076a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "customer                     object\n",
+      "state                        object\n",
+      "gender                       object\n",
+      "education                    object\n",
+      "customer_lifetime_value       int64\n",
+      "income                        int64\n",
+      "monthly_premium_auto          int64\n",
+      "number_of_open_complaints     int64\n",
+      "policy_type                  object\n",
+      "vehicle_class                object\n",
+      "total_claim_amount            int64\n",
+      "dtype: object\n",
+      "customer                     object\n",
+      "state                        object\n",
+      "gender                       object\n",
+      "education                    object\n",
+      "customer_lifetime_value       int64\n",
+      "income                        int64\n",
+      "monthly_premium_auto          int64\n",
+      "number_of_open_complaints     int64\n",
+      "policy_type                  object\n",
+      "vehicle_class                object\n",
+      "total_claim_amount            int64\n",
+      "dtype: object\n",
+      "customer                     object\n",
+      "state                        object\n",
+      "gender                       object\n",
+      "education                    object\n",
+      "customer_lifetime_value       int64\n",
+      "income                        int64\n",
+      "monthly_premium_auto          int64\n",
+      "number_of_open_complaints     int64\n",
+      "policy_type                  object\n",
+      "vehicle_class                object\n",
+      "total_claim_amount            int64\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cleaned_file1.dtypes)\n",
+    "print(df_2.dtypes)\n",
+    "print(df_3.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "e3eb3e74-7a7a-4a75-8511-a54288499885",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_df = pd.concat([cleaned_file1, df_2, df_3], ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "95cd7cb1-cc14-4b01-ad8f-f38b59d465bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(9138, 11)"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "26ebcac5-cd9d-44e4-97ee-05fafbc5029f",
+   "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>gender</th>\n",
+       "      <th>education</th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <th>income</th>\n",
+       "      <th>monthly_premium_auto</th>\n",
+       "      <th>number_of_open_complaints</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>RB50392</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>588174</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>QZ44356</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>697953</td>\n",
+       "      <td>0</td>\n",
+       "      <td>94</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>1131</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>AI49188</td>\n",
+       "      <td>Nevada</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>1288743</td>\n",
+       "      <td>48767</td>\n",
+       "      <td>108</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>566</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  customer       state gender education  customer_lifetime_value  income  \\\n",
+       "0  RB50392  Washington    NaN    Master                   588174       0   \n",
+       "1  QZ44356     Arizona      F  Bachelor                   697953       0   \n",
+       "2  AI49188      Nevada      F  Bachelor                  1288743   48767   \n",
+       "\n",
+       "   monthly_premium_auto  number_of_open_complaints    policy_type  \\\n",
+       "0                  1000                          0  Personal Auto   \n",
+       "1                    94                          0  Personal Auto   \n",
+       "2                   108                          0  Personal Auto   \n",
+       "\n",
+       "   vehicle_class  total_claim_amount  \n",
+       "0  Four-Door Car                   2  \n",
+       "1  Four-Door Car                1131  \n",
+       "2   Two-Door Car                 566  "
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "cd507cc8-1198-467e-944d-6c249b93da9a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "final_df.to_csv('final_cleaned_data.csv', index=False) ### saving the damn thing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "407f8813-eeb1-4205-9311-cdd567eb551a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### next challenge ###"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "888cc347-2789-4646-a443-1bebf6ed6125",
+   "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>unnamed:_0</th>\n",
+       "      <th>customer</th>\n",
+       "      <th>state</th>\n",
+       "      <th>customer_lifetime_value</th>\n",
+       "      <th>response</th>\n",
+       "      <th>coverage</th>\n",
+       "      <th>education</th>\n",
+       "      <th>effective_to_date</th>\n",
+       "      <th>employmentstatus</th>\n",
+       "      <th>gender</th>\n",
+       "      <th>...</th>\n",
+       "      <th>number_of_policies</th>\n",
+       "      <th>policy_type</th>\n",
+       "      <th>policy</th>\n",
+       "      <th>renew_offer_type</th>\n",
+       "      <th>sales_channel</th>\n",
+       "      <th>total_claim_amount</th>\n",
+       "      <th>vehicle_class</th>\n",
+       "      <th>vehicle_size</th>\n",
+       "      <th>vehicle_type</th>\n",
+       "      <th>month</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>DK49336</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>4809.216960</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2011-02-18</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>9</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L3</td>\n",
+       "      <td>Offer3</td>\n",
+       "      <td>Agent</td>\n",
+       "      <td>292.800000</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>KX64629</td>\n",
+       "      <td>California</td>\n",
+       "      <td>2228.525238</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Basic</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2011-01-18</td>\n",
+       "      <td>Unemployed</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L3</td>\n",
+       "      <td>Offer4</td>\n",
+       "      <td>Call Center</td>\n",
+       "      <td>744.924331</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</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>2011-02-10</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</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",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>XL78013</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>22332.439460</td>\n",
+       "      <td>Yes</td>\n",
+       "      <td>Extended</td>\n",
+       "      <td>College</td>\n",
+       "      <td>2011-01-11</td>\n",
+       "      <td>Employed</td>\n",
+       "      <td>M</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>Corporate L3</td>\n",
+       "      <td>Offer2</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>484.013411</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>QA50777</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>9025.067525</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Premium</td>\n",
+       "      <td>Bachelor</td>\n",
+       "      <td>2011-01-17</td>\n",
+       "      <td>Medical Leave</td>\n",
+       "      <td>F</td>\n",
+       "      <td>...</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Personal L2</td>\n",
+       "      <td>Offer1</td>\n",
+       "      <td>Branch</td>\n",
+       "      <td>707.925645</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>Medsize</td>\n",
+       "      <td>A</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 27 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   unnamed:_0 customer       state  customer_lifetime_value response  \\\n",
+       "0           0  DK49336     Arizona              4809.216960       No   \n",
+       "1           1  KX64629  California              2228.525238       No   \n",
+       "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        2011-02-18         Employed      M  ...   \n",
+       "1     Basic   College        2011-01-18       Unemployed      F  ...   \n",
+       "2     Basic  Bachelor        2011-02-10         Employed      M  ...   \n",
+       "3  Extended   College        2011-01-11         Employed      M  ...   \n",
+       "4   Premium  Bachelor        2011-01-17    Medical Leave      F  ...   \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",
+       "   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",
+       "  vehicle_type month  \n",
+       "0            A     2  \n",
+       "1            A     1  \n",
+       "2            A     2  \n",
+       "3            A     1  \n",
+       "4            A     1  \n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "marketing_data = \"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis_clean.csv\"\n",
+    "marketing_data = pd.read_csv(marketing_data) ### reading data\n",
+    "marketing_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "2e3dd394-bee5-459e-9daa-1d58c61ca3fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "               total_claim_amount\n",
+      "sales_channel                    \n",
+      "Agent                  1810226.82\n",
+      "Branch                 1301204.00\n",
+      "Call Center             926600.82\n",
+      "Web                     706600.04\n"
+     ]
+    }
+   ],
+   "source": [
+    "pivot_table = marketing_data.pivot_table(\n",
+    "    index='sales_channel',\n",
+    "    values='total_claim_amount',  \n",
+    "    aggfunc='sum'\n",
+    ").round(2)\n",
+    "\n",
+    "print(pivot_table)\n",
+    "\n",
+    "### Create a pivot table of total revenue by sales channel, \n",
+    "### rounded to 2 decimal places. Then, briefly analyze the results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "b1074acf-3d14-42a2-9be0-dc30a269f0a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Agent and Branch channels generated the highest revenue\n",
+    "### shows that people still like using personal interactions\n",
+    "\n",
+    "### web underperforming. Could this be lack of interaction? Performance of website?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "873ba78f-0393-49e3-a94d-312712975e60",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "education  Bachelor  College   Doctor  High School or Below   Master\n",
+      "gender                                                              \n",
+      "F           8032.68  7671.33  7070.40               8746.25  8535.33\n",
+      "M           7734.13  8057.15  7848.96               7908.65  8112.83\n"
+     ]
+    }
+   ],
+   "source": [
+    "### Create a pivot table that shows the average customer lifetime value per gender and education level. \n",
+    "###Analyze the resulting table to draw insights.\n",
+    "\n",
+    "pivot_table = df.pivot_table(\n",
+    "    values='customer_lifetime_value',\n",
+    "    index='gender',\n",
+    "    columns='education',\n",
+    "    aggfunc='mean'\n",
+    ").round(2)\n",
+    "\n",
+    "print(pivot_table)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "e8d8f2aa-5ece-40db-8838-09586476f1aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Femalees with High School education have the highest average lifetime value.\n",
+    "### Females tend to have higher lifetime values (Bachelor, High School, Master, 3 out of 5)\n",
+    "### Doesn't seem to be any particular trend?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "860d2c14-a997-4995-9bf3-0fea1fc82efe",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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
+}