diff --git a/lab-dw-data-structuring-and-combining.ipynb b/lab-dw-data-structuring-and-combining.ipynb
index ec4e3f9..9f9f179 100644
--- a/lab-dw-data-structuring-and-combining.ipynb
+++ b/lab-dw-data-structuring-and-combining.ipynb
@@ -36,14 +36,242 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "492d06e3-92c7-4105-ac72-536db98d3244",
    "metadata": {
     "id": "492d06e3-92c7-4105-ac72-536db98d3244"
    },
    "outputs": [],
    "source": [
-    "# Your code goes here"
+    "import pandas as pd\n",
+    "\n",
+    "file1 = pd.read_csv('https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file1.csv')\n",
+    "file2 = pd.read_csv('https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file2.csv')\n",
+    "file3 = pd.read_csv('https://raw.githubusercontent.com/data-bootcamp-v4/data/main/file3.csv')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6fb09589",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.concat([file1, file2, file3], ignore_index=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1a0c14dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def clean_data(df):\n",
+    "    df.columns = (df.columns.str.strip().str.lower().str.replace(' ', '_').str.replace('-', '_'))\n",
+    "\n",
+    "    df.drop_duplicates(inplace=True)\n",
+    "\n",
+    "    df.dropna(how='all', inplace=True)\n",
+    "\n",
+    "    if 'date' in df.columns:\n",
+    "        df['date'] = pd.to_datetime(df['date'], errors='coerce')\n",
+    "\n",
+    "    print(\"Missing values per column:\")\n",
+    "    print(df.isnull().sum())\n",
+    "\n",
+    "    return df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3ac200a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Missing values per column:\n",
+      "customer                        0\n",
+      "st                           7070\n",
+      "gender                       7192\n",
+      "education                       0\n",
+      "customer_lifetime_value         7\n",
+      "income                          0\n",
+      "monthly_premium_auto            0\n",
+      "number_of_open_complaints       0\n",
+      "policy_type                     0\n",
+      "vehicle_class                   0\n",
+      "total_claim_amount              0\n",
+      "state                        2064\n",
+      "gender                       2064\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "cleaned_df = clean_data(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6f6da246",
+   "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>st</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",
+       "      <th>state</th>\n",
+       "      <th>gender</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>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1000.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>2.704934</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>697953.59%</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>94.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>1131.464935</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>1288743.17%</td>\n",
+       "      <td>48767.0</td>\n",
+       "      <td>108.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Two-Door Car</td>\n",
+       "      <td>566.472247</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>764586.18%</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>106.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>SUV</td>\n",
+       "      <td>529.881344</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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>536307.65%</td>\n",
+       "      <td>36357.0</td>\n",
+       "      <td>68.0</td>\n",
+       "      <td>1/0/00</td>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>Four-Door Car</td>\n",
+       "      <td>17.269323</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  customer          st gender             education customer_lifetime_value  \\\n",
+       "0  RB50392  Washington    NaN                Master                     NaN   \n",
+       "1  QZ44356     Arizona      F              Bachelor              697953.59%   \n",
+       "2  AI49188      Nevada      F              Bachelor             1288743.17%   \n",
+       "3  WW63253  California      M              Bachelor              764586.18%   \n",
+       "4  GA49547  Washington      M  High School or Below              536307.65%   \n",
+       "\n",
+       "    income  monthly_premium_auto number_of_open_complaints     policy_type  \\\n",
+       "0      0.0                1000.0                    1/0/00   Personal Auto   \n",
+       "1      0.0                  94.0                    1/0/00   Personal Auto   \n",
+       "2  48767.0                 108.0                    1/0/00   Personal Auto   \n",
+       "3      0.0                 106.0                    1/0/00  Corporate Auto   \n",
+       "4  36357.0                  68.0                    1/0/00   Personal Auto   \n",
+       "\n",
+       "   vehicle_class  total_claim_amount state gender  \n",
+       "0  Four-Door Car            2.704934   NaN    NaN  \n",
+       "1  Four-Door Car         1131.464935   NaN    NaN  \n",
+       "2   Two-Door Car          566.472247   NaN    NaN  \n",
+       "3            SUV          529.881344   NaN    NaN  \n",
+       "4  Four-Door Car           17.269323   NaN    NaN  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cleaned_df.head()"
    ]
   },
   {
@@ -72,14 +300,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "aa10d9b0-1c27-4d3f-a8e4-db6ab73bfd26",
    "metadata": {
     "id": "aa10d9b0-1c27-4d3f-a8e4-db6ab73bfd26"
    },
    "outputs": [],
    "source": [
-    "# Your code goes here"
+    "url = \"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis_clean.csv\"\n",
+    "df = pd.read_csv(url)"
    ]
   },
   {
@@ -93,6 +322,91 @@
     "Round the total revenue to 2 decimal points.  Analyze the resulting table to draw insights."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3d92b96d",
+   "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>total_claim_amount</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sales_channel</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Agent</th>\n",
+       "      <td>1810226.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Branch</th>\n",
+       "      <td>1301204.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Call Center</th>\n",
+       "      <td>926600.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Web</th>\n",
+       "      <td>706600.04</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               total_claim_amount\n",
+       "sales_channel                    \n",
+       "Agent                  1810226.82\n",
+       "Branch                 1301204.00\n",
+       "Call Center             926600.82\n",
+       "Web                     706600.04"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "revenue_by_channel = pd.pivot_table(df, values='total_claim_amount', index='sales_channel', aggfunc='sum').round(2)\n",
+    "\n",
+    "\n",
+    "revenue_by_channel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45d04264",
+   "metadata": {},
+   "source": [
+    "Highest revenue was brought by agents.\n",
+    "Web sales are lowest. Probably because of the duration and information needed for the process.\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "640993b2-a291-436c-a34d-a551144f8196",
@@ -103,6 +417,96 @@
     "2. Create a pivot table that shows the average customer lifetime value per gender and education level. Analyze the resulting table to draw insights."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "50e659c9",
+   "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>education</th>\n",
+       "      <th>Bachelor</th>\n",
+       "      <th>College</th>\n",
+       "      <th>Doctor</th>\n",
+       "      <th>High School or Below</th>\n",
+       "      <th>Master</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gender</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>7874.27</td>\n",
+       "      <td>7748.82</td>\n",
+       "      <td>7328.51</td>\n",
+       "      <td>8675.22</td>\n",
+       "      <td>8157.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>M</th>\n",
+       "      <td>7703.60</td>\n",
+       "      <td>8052.46</td>\n",
+       "      <td>7415.33</td>\n",
+       "      <td>8149.69</td>\n",
+       "      <td>8168.83</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "education  Bachelor  College   Doctor  High School or Below   Master\n",
+       "gender                                                              \n",
+       "F           7874.27  7748.82  7328.51               8675.22  8157.05\n",
+       "M           7703.60  8052.46  7415.33               8149.69  8168.83"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clv_by_gender_education = pd.pivot_table(df, values='customer_lifetime_value', index='gender', columns='education', aggfunc='mean').round(2)\n",
+    "\n",
+    "clv_by_gender_education"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "258ecde6",
+   "metadata": {},
+   "source": [
+    "Female customers who have an education of High School or below have the highest CLV\n",
+    "Hiher education doesn't mean higher CLV"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "32c7f2e5-3d90-43e5-be33-9781b6069198",
@@ -130,14 +534,121 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "3a069e0b-b400-470e-904d-d17582191be4",
    "metadata": {
     "id": "3a069e0b-b400-470e-904d-d17582191be4"
    },
    "outputs": [],
    "source": [
-    "# Your code goes here"
+    "url = \"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/marketing_customer_analysis_clean.csv\"\n",
+    "df = pd.read_csv(url)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "2701079e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['effective_to_date'] = pd.to_datetime(df['effective_to_date'], errors='coerce')\n",
+    "df['month'] = df['effective_to_date'].dt.month_name()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "bcf6774d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "complaints_summary = df.groupby(['policy_type', 'month']).size().reset_index(name='number_of_complaints')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "701ba1f8",
+   "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>policy_type</th>\n",
+       "      <th>month</th>\n",
+       "      <th>number_of_complaints</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>February</td>\n",
+       "      <td>1089</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Corporate Auto</td>\n",
+       "      <td>January</td>\n",
+       "      <td>1252</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>February</td>\n",
+       "      <td>3799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Personal Auto</td>\n",
+       "      <td>January</td>\n",
+       "      <td>4329</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Special Auto</td>\n",
+       "      <td>February</td>\n",
+       "      <td>204</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      policy_type     month  number_of_complaints\n",
+       "0  Corporate Auto  February                  1089\n",
+       "1  Corporate Auto   January                  1252\n",
+       "2   Personal Auto  February                  3799\n",
+       "3   Personal Auto   January                  4329\n",
+       "4    Special Auto  February                   204"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "complaints_summary.head()"
    ]
   }
  ],
@@ -146,7 +657,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -160,7 +671,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.13"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,