diff --git a/03_05/03_05 Missing Values [Begin].ipynb b/03_05/03_05 Missing Values [Begin].ipynb
index 4097606..92fbeb8 100644
--- a/03_05/03_05 Missing Values [Begin].ipynb	
+++ b/03_05/03_05 Missing Values [Begin].ipynb	
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -70,23 +70,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10127, 23)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 5,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10127"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "To fill in the missing values, we will use the following code:"
+    "data[\"CLIENTNUM\"].nunique()"
    ]
   },
   {
@@ -96,6 +115,26 @@
    "outputs": [],
    "source": []
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10127, 23)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -105,18 +144,88 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['CLIENTNUM', 'Attrition_Flag', 'Customer_Age', 'Gender',\n",
+       "       'Dependent_count', 'Education_Level', 'Marital_Status',\n",
+       "       'Income_Category', 'Card_Category', 'Months_on_book',\n",
+       "       'Total_Relationship_Count', 'Months_Inactive_12_mon',\n",
+       "       'Contacts_Count_12_mon', 'Credit_Limit', 'Total_Revolving_Bal',\n",
+       "       'Avg_Open_To_Buy', 'Total_Amt_Chng_Q4_Q1', 'Total_Trans_Amt',\n",
+       "       'Total_Trans_Ct', 'Total_Ct_Chng_Q4_Q1', 'Avg_Utilization_Ratio',\n",
+       "       'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_1',\n",
+       "       'Naive_Bayes_Classifier_Attrition_Flag_Card_Category_Contacts_Count_12_mon_Dependent_count_Education_Level_Months_Inactive_12_mon_2'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": []
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Datatypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CLIENTNUM                     int64\n",
+       "Attrition_Flag               object\n",
+       "Customer_Age                  int64\n",
+       "Gender                       object\n",
+       "Dependent_count               int64\n",
+       "Education_Level              object\n",
+       "Marital_Status               object\n",
+       "Income_Category              object\n",
+       "Card_Category                object\n",
+       "Months_on_book                int64\n",
+       "Total_Relationship_Count      int64\n",
+       "Months_Inactive_12_mon        int64\n",
+       "Contacts_Count_12_mon         int64\n",
+       "Credit_Limit                float64\n",
+       "Total_Revolving_Bal           int64\n",
+       "Avg_Open_To_Buy             float64\n",
+       "Total_Amt_Chng_Q4_Q1        float64\n",
+       "Total_Trans_Amt               int64\n",
+       "Total_Trans_Ct                int64\n",
+       "Total_Ct_Chng_Q4_Q1         float64\n",
+       "Avg_Utilization_Ratio       float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.dtypes"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -141,7 +250,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.4 (main, Jan 18 2023, 00:26:41) [GCC 9.4.0]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "3ad933181bd8a04b432d3370b9dc3b0662ad032c4dfaa4e4f1596c548f763858"
+   }
   }
  },
  "nbformat": 4,