diff --git a/Titanic_Missing_Data_Handling.ipynb b/Titanic_Missing_Data_Handling.ipynb
new file mode 100644
index 0000000..823c727
--- /dev/null
+++ b/Titanic_Missing_Data_Handling.ipynb
@@ -0,0 +1,703 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np       ### importing numpy and pandas\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "0            1         0       3   \n",
+       "1            2         1       1   \n",
+       "2            3         1       3   \n",
+       "3            4         1       1   \n",
+       "4            5         0       3   \n",
+       "\n",
+       "                                                Name     Sex   Age  SibSp  \\\n",
+       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+       "\n",
+       "   Parch            Ticket     Fare Cabin Embarked  \n",
+       "0      0         A/5 21171   7.2500   NaN        S  \n",
+       "1      0          PC 17599  71.2833   C85        C  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+       "3      0            113803  53.1000  C123        S  \n",
+       "4      0            373450   8.0500   NaN        S  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset = pd.read_csv(\"E:\\MAYUKH\\\\naive bayes\\\\titanic\\\\titanic.csv\")  ### import the csv dataset\n",
+    "dataset.head()          ### looking at the head of the csv dataset..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>7.2500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>71.2833</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>7.9250</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>53.1000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>8.0500</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Survived  Pclass     Sex   Age     Fare\n",
+       "0         0       3    male  22.0   7.2500\n",
+       "1         1       1  female  38.0  71.2833\n",
+       "2         1       3  female  26.0   7.9250\n",
+       "3         1       1  female  35.0  53.1000\n",
+       "4         0       3    male  35.0   8.0500"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.drop(['PassengerId','Name','SibSp','Parch','Ticket','Cabin','Embarked'],axis='columns',inplace=True) ###Dropping the unwanted columns...\n",
+    "dataset.head()  ### After dropping the unwanted columns..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target=dataset.Survived                 ### Fixing Survived columns as target to predict the survivals...\n",
+    "inputs=dataset.drop('Survived',axis='columns') ### Columns other than \"Survived\" are taken as input..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>female</th>\n",
+       "      <th>male</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   female  male\n",
+       "0       0     1\n",
+       "1       1     0\n",
+       "2       1     0\n",
+       "3       1     0\n",
+       "4       0     1"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dummies=pd.get_dummies(inputs.Sex) ### Converting the Sex column to integer type from text...\n",
+    "dummies.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>Pclass</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>female</th>\n",
+       "      <th>male</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Pclass     Sex   Age     Fare  female  male\n",
+       "0       3    male  22.0   7.2500       0     1\n",
+       "1       1  female  38.0  71.2833       1     0\n",
+       "2       3  female  26.0   7.9250       1     0\n",
+       "3       1  female  35.0  53.1000       1     0\n",
+       "4       3    male  35.0   8.0500       0     1"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs = pd.concat([inputs,dummies],axis='columns') ### Appending the dummy columns replacing Sex columns in inputs...\n",
+    "inputs.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>female</th>\n",
+       "      <th>male</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Pclass   Age     Fare  female  male\n",
+       "0       3  22.0   7.2500       0     1\n",
+       "1       1  38.0  71.2833       1     0\n",
+       "2       3  26.0   7.9250       1     0\n",
+       "3       1  35.0  53.1000       1     0\n",
+       "4       3  35.0   8.0500       0     1"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs.drop('Sex',axis='columns',inplace=True) ### Dropping the Sex column...\n",
+    "inputs.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Looking for missing data in any of the attributes in the data..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Age'], dtype='object')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs.columns[inputs.isna().any()] ### Searching for any NaN value in any column...found in Age column..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    22.0\n",
+       "1    38.0\n",
+       "2    26.0\n",
+       "3    35.0\n",
+       "4    35.0\n",
+       "5     NaN\n",
+       "6    54.0\n",
+       "7     2.0\n",
+       "8    27.0\n",
+       "9    14.0\n",
+       "Name: Age, dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs.Age[:10] ###Looking for the NaN values in Age column..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    22.000000\n",
+       "1    38.000000\n",
+       "2    26.000000\n",
+       "3    35.000000\n",
+       "4    35.000000\n",
+       "5    29.699118\n",
+       "6    54.000000\n",
+       "7     2.000000\n",
+       "8    27.000000\n",
+       "9    14.000000\n",
+       "Name: Age, dtype: float64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs.Age = inputs.Age.fillna(inputs.Age.mean()) ### Filling the NaN values with the mean of the Age column...  \n",
+    "inputs.Age[:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### After replacing the missing values with the mean value...\n",
+    "### Again checking for any missing data in any of the attributes..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index([], dtype='object')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs.columns[inputs.isna().any()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### So there are no more missing values ini any of the attrinutes in the data..."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}