diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
deleted file mode 100644
index 98a9a96..0000000
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ /dev/null
@@ -1,10 +0,0 @@
-### :warning: This is a Pull Request Template :warning: 
-##### This will help you complete a pull request. Please follow the instructions below. And also,make sure that you're bound to [code of conduct](#)
-
-## Submission Checklist :heavy_check_mark:
-
-- [] I read the instructions to submit this assignment.<br>
-- [] I created the submission files inside my folder, ```MY-USERNAME```.<br>
-- [] I applied the ```week-number``` and ```submission``` labels. <br>
-- [] I've read the Community Guidelines.
-- [] I've Updated .gitignore file to ignore unnecessary files.
diff --git a/.github/settings.yml b/.github/settings.yml
deleted file mode 100644
index 7ca90fa..0000000
--- a/.github/settings.yml
+++ /dev/null
@@ -1,67 +0,0 @@
-# https://probot.github.io/apps/settings/
-
-repository:
-  # See https://developer.github.com/v3/repos/#edit for all available settings.
-
-  # The name of the repository. Changing this will rename the repository
-  name: Member-Sumbissions
-
-  # A short description of the repository that will show up on GitHub
-  description: The first official batch of GVP-Ai-Club Student Chapter :computer:
-
-  # A URL with more information about the repository
-  homepage: #
-
-  # Either `true` to make the repository private, or `false` to make it public.
-  private: false
-
-  # Either `true` to enable issues for this repository, `false` to disable them.
-  has_issues: true
-
-  # Either `true` to enable the wiki for this repository, `false` to disable it.
-  has_wiki: false
-
-  # Either `true` to enable downloads for this repository, `false` to disable them.
-  has_downloads: true
-
-  # Updates the default branch for this repository.
-  default_branch: master
-
-  # Either `true` to allow squash-merging pull requests, or `false` to prevent
-  # squash-merging.
-  allow_squash_merge: true
-
-  # Either `true` to allow merging pull requests with a merge commit, or `false`
-  # to prevent merging pull requests with merge commits.
-  allow_merge_commit: true
-
-  # Either `true` to allow rebase-merging pull requests, or `false` to prevent
-  # rebase-merging.
-  allow_rebase_merge: true
-
-# Labels: define labels for Issues and Pull Requests
-labels:
-  - name: help
-    color: fbca04
-  - name: week-1
-    color: D71E3C
-  - name: week-2
-    color: D71E3C
-  - name: bug
-    color: 0000CC
-  - name: feature-request
-    color: 00CC00
-  - name: discussion
-    color: CC00CC
-  - name: submission
-    color: 00CCCC
-  - name: announcement
-    color: fbca04
-  - name: subscribe-to-me
-    color: fbca04
-  - name: question
-    color: fbca04
-  - name: exempt
-    color: fbca04
-  - name: rude
-    color: fbca04
diff --git a/.gitignore b/.gitignore
deleted file mode 100644
index 34bfbc9..0000000
--- a/.gitignore
+++ /dev/null
@@ -1,2 +0,0 @@
-.vscode/
-.ipynb*
\ No newline at end of file
diff --git a/KaggleResult.png b/KaggleResult.png
new file mode 100644
index 0000000..6027c85
Binary files /dev/null and b/KaggleResult.png differ
diff --git a/LICENSE b/LICENSE
deleted file mode 100644
index 480ce23..0000000
--- a/LICENSE
+++ /dev/null
@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2018 GVP Artificial Intelligence Club
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
diff --git a/README.md b/README.md
deleted file mode 100644
index 2851a54..0000000
--- a/README.md
+++ /dev/null
@@ -1,7 +0,0 @@
-# Member-Submissions
-
-###  As a part of the AI Club events, this repository holds all the submissions of the students attending
-
-## **Structure of Repo**
- - ### Different Branch for every user
- - ### Each Contain Work done by every member of GVP-AI Club
diff --git a/Titanic.ipynb b/Titanic.ipynb
new file mode 100644
index 0000000..a34636d
--- /dev/null
+++ b/Titanic.ipynb
@@ -0,0 +1,4824 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 259,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <center><h1>Titanic Kaggle</h1></center>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 259,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.core.display import HTML\n",
+    "HTML(\"\"\"\n",
+    "        <center><h1>Titanic Kaggle</h1></center>\n",
+    "    \"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 260,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import seaborn as sbn\n",
+    "%matplotlib inline\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 261,
+   "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>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>892</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Kelly, Mr. James</td>\n",
+       "      <td>male</td>\n",
+       "      <td>34.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>330911</td>\n",
+       "      <td>7.8292</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>893</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>363272</td>\n",
+       "      <td>7.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>894</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Myles, Mr. Thomas Francis</td>\n",
+       "      <td>male</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>240276</td>\n",
+       "      <td>9.6875</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>895</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Wirz, Mr. Albert</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315154</td>\n",
+       "      <td>8.6625</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>896</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3101298</td>\n",
+       "      <td>12.2875</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Pclass                                          Name     Sex  \\\n",
+       "0          892       3                              Kelly, Mr. James    male   \n",
+       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
+       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
+       "3          895       3                              Wirz, Mr. Albert    male   \n",
+       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
+       "\n",
+       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
+       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
+       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
+       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
+       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
+       "4  22.0      1      1  3101298  12.2875   NaN        S  "
+      ]
+     },
+     "execution_count": 261,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train = pd.read_csv('D:\\\\titanic_testdata\\\\train.csv')\n",
+    "train.head()\n",
+    "\n",
+    "train.shape\n",
+    "test = pd.read_csv('D:\\\\titanic_testdata\\\\test.csv')\n",
+    "\n",
+    "test.shape\n",
+    "#shape of both data sets\n",
+    "test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 262,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\kk\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\pandas\\core\\frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
+      "of pandas will change to not sort by default.\n",
+      "\n",
+      "To accept the future behavior, pass 'sort=False'.\n",
+      "\n",
+      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
+      "\n",
+      "  sort=sort)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0    892\n",
+       "1    893\n",
+       "2    894\n",
+       "3    895\n",
+       "4    896\n",
+       "5    897\n",
+       "6    898\n",
+       "7    899\n",
+       "8    900\n",
+       "9    901\n",
+       "Name: PassengerId, dtype: int64"
+      ]
+     },
+     "execution_count": 262,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#merge both the data sets\n",
+    "data=train.append(test)\n",
+    "data.shape\n",
+    "\n",
+    "data.head()\n",
+    "\n",
+    "#store the passenger id of the test data set\n",
+    "passenger_id = test['PassengerId']\n",
+    "passenger_id.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 263,
+   "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>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>892</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Kelly, Mr. James</td>\n",
+       "      <td>male</td>\n",
+       "      <td>34.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>330911</td>\n",
+       "      <td>7.8292</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>893</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>363272</td>\n",
+       "      <td>7.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>894</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Myles, Mr. Thomas Francis</td>\n",
+       "      <td>male</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>240276</td>\n",
+       "      <td>9.6875</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>895</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Wirz, Mr. Albert</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315154</td>\n",
+       "      <td>8.6625</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>896</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3101298</td>\n",
+       "      <td>12.2875</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Pclass                                          Name     Sex  \\\n",
+       "0          892       3                              Kelly, Mr. James    male   \n",
+       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
+       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
+       "3          895       3                              Wirz, Mr. Albert    male   \n",
+       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
+       "\n",
+       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
+       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
+       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
+       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
+       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
+       "4  22.0      1      1  3101298  12.2875   NaN        S  "
+      ]
+     },
+     "execution_count": 263,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 264,
+   "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": 264,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 265,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId      0\n",
+       "Survived         0\n",
+       "Pclass           0\n",
+       "Name             0\n",
+       "Sex              0\n",
+       "Age            177\n",
+       "SibSp            0\n",
+       "Parch            0\n",
+       "Ticket           0\n",
+       "Fare             0\n",
+       "Cabin          687\n",
+       "Embarked         2\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 265,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 266,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId      0\n",
+       "Pclass           0\n",
+       "Name             0\n",
+       "Sex              0\n",
+       "Age             86\n",
+       "SibSp            0\n",
+       "Parch            0\n",
+       "Ticket           0\n",
+       "Fare             1\n",
+       "Cabin          327\n",
+       "Embarked         0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 266,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.isnull().sum()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 267,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <center><h3>Cleaning the Data</h3></center>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 267,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\"\"\"\n",
+    "        <center><h3>Cleaning the Data</h3></center>\n",
+    "    \"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 268,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1296e870>"
+      ]
+     },
+     "execution_count": 268,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import missingno as mn\n",
+    "mn.matrix(train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 269,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\kk\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\utils\\deprecation.py:58: DeprecationWarning: Class Imputer is deprecated; Imputer was deprecated in version 0.20 and will be removed in 0.22. Import impute.SimpleImputer from sklearn instead.\n",
+      "  warnings.warn(msg, category=DeprecationWarning)\n"
+     ]
+    },
+    {
+     "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>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",
+       "      <th>Age_new</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>892</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Kelly, Mr. James</td>\n",
+       "      <td>male</td>\n",
+       "      <td>34.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>330911</td>\n",
+       "      <td>7.8292</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>34.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>893</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>363272</td>\n",
+       "      <td>7.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>47.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>894</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Myles, Mr. Thomas Francis</td>\n",
+       "      <td>male</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>240276</td>\n",
+       "      <td>9.6875</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>62.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>895</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Wirz, Mr. Albert</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315154</td>\n",
+       "      <td>8.6625</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>896</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3101298</td>\n",
+       "      <td>12.2875</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>22.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Pclass                                          Name     Sex  \\\n",
+       "0          892       3                              Kelly, Mr. James    male   \n",
+       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
+       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
+       "3          895       3                              Wirz, Mr. Albert    male   \n",
+       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
+       "\n",
+       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  Age_new  \n",
+       "0  34.5      0      0   330911   7.8292   NaN        Q     34.5  \n",
+       "1  47.0      1      0   363272   7.0000   NaN        S     47.0  \n",
+       "2  62.0      0      0   240276   9.6875   NaN        Q     62.0  \n",
+       "3  27.0      0      0   315154   8.6625   NaN        S     27.0  \n",
+       "4  22.0      1      1  3101298  12.2875   NaN        S     22.0  "
+      ]
+     },
+     "execution_count": 269,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#imputer class from scikit to fill the missing values\n",
+    "from sklearn.preprocessing import Imputer\n",
+    "#filling up those missing values with the median of all the values in a particular column,axis=1 column wise\n",
+    "imp = Imputer(missing_values='NaN', strategy='median', axis=1)\n",
+    "age_missing = imp.fit_transform(train['Age'].values.reshape(1,-1))\n",
+    "#T == transpose\n",
+    "age_missing = age_missing.T\n",
+    "#creating a new column in the train set \n",
+    "train['Age_new'] = age_missing\n",
+    "train.head()\n",
+    "\n",
+    "#similarly for test data set\n",
+    "age_test = imp.fit_transform(test['Age'].values.reshape(1,-1))\n",
+    "age_test = age_test.T\n",
+    "test['Age_new'] = age_test\n",
+    "test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 270,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId      0\n",
+       "Survived         0\n",
+       "Pclass           0\n",
+       "Name             0\n",
+       "Sex              0\n",
+       "Age            177\n",
+       "SibSp            0\n",
+       "Parch            0\n",
+       "Ticket           0\n",
+       "Fare             0\n",
+       "Cabin          687\n",
+       "Embarked         2\n",
+       "Age_new          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 270,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 271,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId      0\n",
+       "Survived         0\n",
+       "Pclass           0\n",
+       "Name             0\n",
+       "Sex              0\n",
+       "Age            177\n",
+       "SibSp            0\n",
+       "Parch            0\n",
+       "Ticket           0\n",
+       "Fare             0\n",
+       "Cabin          687\n",
+       "Embarked         0\n",
+       "Age_new          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 271,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.Embarked.value_counts()\n",
+    "#using fillna for embarked values\n",
+    "train.Embarked.fillna('S', inplace=True)\n",
+    "train.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 272,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId      0\n",
+       "Pclass           0\n",
+       "Name             0\n",
+       "Sex              0\n",
+       "Age             86\n",
+       "SibSp            0\n",
+       "Parch            0\n",
+       "Ticket           0\n",
+       "Fare             1\n",
+       "Cabin          327\n",
+       "Embarked         0\n",
+       "Age_new          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 272,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 273,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId      0\n",
+       "Pclass           0\n",
+       "Name             0\n",
+       "Sex              0\n",
+       "Age             86\n",
+       "SibSp            0\n",
+       "Parch            0\n",
+       "Ticket           0\n",
+       "Fare             0\n",
+       "Cabin          327\n",
+       "Embarked         0\n",
+       "Age_new          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 273,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#filling fare\n",
+    "test.Fare.fillna(test.Fare.mean(), inplace=True)\n",
+    "test.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 274,
+   "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>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>Age_new</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>S</td>\n",
+       "      <td>22.0</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C</td>\n",
+       "      <td>38.0</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>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>S</td>\n",
+       "      <td>26.0</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>S</td>\n",
+       "      <td>35.0</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>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>S</td>\n",
+       "      <td>35.0</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  SibSp  Parch  \\\n",
+       "0                            Braund, Mr. Owen Harris    male      1      0   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female      1      0   \n",
+       "2                             Heikkinen, Miss. Laina  female      0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female      1      0   \n",
+       "4                           Allen, Mr. William Henry    male      0      0   \n",
+       "\n",
+       "             Ticket     Fare Embarked  Age_new  \n",
+       "0         A/5 21171   7.2500        S     22.0  \n",
+       "1          PC 17599  71.2833        C     38.0  \n",
+       "2  STON/O2. 3101282   7.9250        S     26.0  \n",
+       "3            113803  53.1000        S     35.0  \n",
+       "4            373450   8.0500        S     35.0  "
+      ]
+     },
+     "execution_count": 274,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#cabin - approximately 80 % of values are missing, so we drop the cabin attribute.\n",
+    "#age - column which contains NaN is also dropped.\n",
+    "\n",
+    "train.drop(['Age','Cabin'], axis = 1, inplace = True)\n",
+    "test.drop(['Age','Cabin'], axis = 1, inplace = True)\n",
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 275,
+   "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>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>Age_new</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>892</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Kelly, Mr. James</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>330911</td>\n",
+       "      <td>7.8292</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>34.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>893</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>363272</td>\n",
+       "      <td>7.0000</td>\n",
+       "      <td>S</td>\n",
+       "      <td>47.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>894</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Myles, Mr. Thomas Francis</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>240276</td>\n",
+       "      <td>9.6875</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>62.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>895</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Wirz, Mr. Albert</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315154</td>\n",
+       "      <td>8.6625</td>\n",
+       "      <td>S</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>896</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3101298</td>\n",
+       "      <td>12.2875</td>\n",
+       "      <td>S</td>\n",
+       "      <td>22.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Pclass                                          Name     Sex  \\\n",
+       "0          892       3                              Kelly, Mr. James    male   \n",
+       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
+       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
+       "3          895       3                              Wirz, Mr. Albert    male   \n",
+       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
+       "\n",
+       "   SibSp  Parch   Ticket     Fare Embarked  Age_new  \n",
+       "0      0      0   330911   7.8292        Q     34.5  \n",
+       "1      1      0   363272   7.0000        S     47.0  \n",
+       "2      0      0   240276   9.6875        Q     62.0  \n",
+       "3      0      0   315154   8.6625        S     27.0  \n",
+       "4      1      1  3101298  12.2875        S     22.0  "
+      ]
+     },
+     "execution_count": 275,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 276,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#now there are no missing values in the train and test data sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 277,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('O')"
+      ]
+     },
+     "execution_count": 277,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['Sex'].dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 278,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1\n",
+       "1    0\n",
+       "2    0\n",
+       "3    0\n",
+       "4    1\n",
+       "Name: Sex, dtype: int64"
+      ]
+     },
+     "execution_count": 278,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#changing the sex from object to int64\n",
+    "train['Sex'] = train.Sex.apply(lambda x:0 if x == 'female' else 1)\n",
+    "test['Sex'] = test.Sex.apply(lambda x:0 if x == 'female' else 1)\n",
+    "test.Sex.head()\n",
+    "train.Sex.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 279,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <h5>Detecting Outliers</h5>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 279,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\"\"\"\n",
+    "        <h5>Detecting Outliers</h5>\n",
+    "    \"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 280,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x127e7e30>"
+      ]
+     },
+     "execution_count": 280,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sbn.boxplot(x='Survived', y='Fare', data=train)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 281,
+   "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>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>Age_new</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>892</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Kelly, Mr. James</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>330911</td>\n",
+       "      <td>7.8292</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>34.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>893</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>363272</td>\n",
+       "      <td>7.0000</td>\n",
+       "      <td>S</td>\n",
+       "      <td>47.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>894</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Myles, Mr. Thomas Francis</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>240276</td>\n",
+       "      <td>9.6875</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>62.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>895</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Wirz, Mr. Albert</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315154</td>\n",
+       "      <td>8.6625</td>\n",
+       "      <td>S</td>\n",
+       "      <td>27.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>896</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3101298</td>\n",
+       "      <td>12.2875</td>\n",
+       "      <td>S</td>\n",
+       "      <td>22.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Pclass                                          Name  Sex  \\\n",
+       "0          892       3                              Kelly, Mr. James    1   \n",
+       "1          893       3              Wilkes, Mrs. James (Ellen Needs)    0   \n",
+       "2          894       2                     Myles, Mr. Thomas Francis    1   \n",
+       "3          895       3                              Wirz, Mr. Albert    1   \n",
+       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)    0   \n",
+       "\n",
+       "   SibSp  Parch   Ticket     Fare Embarked  Age_new  \n",
+       "0      0      0   330911   7.8292        Q     34.5  \n",
+       "1      1      0   363272   7.0000        S     47.0  \n",
+       "2      0      0   240276   9.6875        Q     62.0  \n",
+       "3      0      0   315154   8.6625        S     27.0  \n",
+       "4      1      1  3101298  12.2875        S     22.0  "
+      ]
+     },
+     "execution_count": 281,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train['Fare'] = train[train['Fare'] <=400]\n",
+    "#test['Fare'] = test[test['Fare'] <=400]\n",
+    "test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 282,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1283a070>"
+      ]
+     },
+     "execution_count": 282,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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fTPKxJMtJnjvnvG+MsSXJGXPOh9cd57WL4W5NsnPO+egTnHN3PAIFAAAAAICnaKOPQLkuySeTvDzJvjHGR7L2Eszzs/Z4lJHkrXPO+xb7n5Fk/xjjE0kOxeuXJNmZ5D+TvPpI8XuM8cHDfr5gsXzvGOOhxfc/n3P+4wZnBgAAAADgFLahO8CTZIzxDUl+IclPJbkga5H7/iR7k7x/znnjun3/JMn3ZO0FmMla+L4uye+vvzP8sP99vWF+7kgPPwcAAAAAgPU2HMABAAAAAOBkstGXYAIAAAAAwElFAAcAAAAAoJIADgAAAABAJQEcAAAAAIBKAjgAAAAAAJUEcAAAAAAAKgngAAAAAABUEsABAAAAAKgkgAMAAAAAUEkABwAAAACg0v8B+rFfZ2fsHisAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1800x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mn.matrix(train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 283,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <center><h3>Checking Correlation</h3></center>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 283,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\"\"\"\n",
+    "        <center><h3>Checking Correlation</h3></center>\n",
+    "    \"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 284,
+   "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>Sex</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Age_new</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.383838</td>\n",
+       "      <td>2.308642</td>\n",
+       "      <td>0.647587</td>\n",
+       "      <td>0.523008</td>\n",
+       "      <td>0.381594</td>\n",
+       "      <td>29.361582</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>257.353842</td>\n",
+       "      <td>0.486592</td>\n",
+       "      <td>0.836071</td>\n",
+       "      <td>0.477990</td>\n",
+       "      <td>1.102743</td>\n",
+       "      <td>0.806057</td>\n",
+       "      <td>13.019697</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.420000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>223.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>22.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>28.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>668.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>35.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>80.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       PassengerId    Survived      Pclass         Sex       SibSp  \\\n",
+       "count   891.000000  891.000000  891.000000  891.000000  891.000000   \n",
+       "mean    446.000000    0.383838    2.308642    0.647587    0.523008   \n",
+       "std     257.353842    0.486592    0.836071    0.477990    1.102743   \n",
+       "min       1.000000    0.000000    1.000000    0.000000    0.000000   \n",
+       "25%     223.500000    0.000000    2.000000    0.000000    0.000000   \n",
+       "50%     446.000000    0.000000    3.000000    1.000000    0.000000   \n",
+       "75%     668.500000    1.000000    3.000000    1.000000    1.000000   \n",
+       "max     891.000000    1.000000    3.000000    1.000000    8.000000   \n",
+       "\n",
+       "            Parch     Age_new  \n",
+       "count  891.000000  891.000000  \n",
+       "mean     0.381594   29.361582  \n",
+       "std      0.806057   13.019697  \n",
+       "min      0.000000    0.420000  \n",
+       "25%      0.000000   22.000000  \n",
+       "50%      0.000000   28.000000  \n",
+       "75%      0.000000   35.000000  \n",
+       "max      6.000000   80.000000  "
+      ]
+     },
+     "execution_count": 284,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 285,
+   "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>Pclass</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Age_new</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Survived</th>\n",
+       "      <th></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>0</th>\n",
+       "      <td>447.016393</td>\n",
+       "      <td>2.531876</td>\n",
+       "      <td>0.852459</td>\n",
+       "      <td>0.553734</td>\n",
+       "      <td>0.329690</td>\n",
+       "      <td>30.028233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>444.368421</td>\n",
+       "      <td>1.950292</td>\n",
+       "      <td>0.318713</td>\n",
+       "      <td>0.473684</td>\n",
+       "      <td>0.464912</td>\n",
+       "      <td>28.291433</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          PassengerId    Pclass       Sex     SibSp     Parch    Age_new\n",
+       "Survived                                                                \n",
+       "0          447.016393  2.531876  0.852459  0.553734  0.329690  30.028233\n",
+       "1          444.368421  1.950292  0.318713  0.473684  0.464912  28.291433"
+      ]
+     },
+     "execution_count": 285,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#groupby survived\n",
+    "train.groupby('Survived').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 286,
+   "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>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Age_new</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sex</th>\n",
+       "      <th></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>0</th>\n",
+       "      <td>431.028662</td>\n",
+       "      <td>0.742038</td>\n",
+       "      <td>2.159236</td>\n",
+       "      <td>0.694268</td>\n",
+       "      <td>0.649682</td>\n",
+       "      <td>27.929936</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>454.147314</td>\n",
+       "      <td>0.188908</td>\n",
+       "      <td>2.389948</td>\n",
+       "      <td>0.429809</td>\n",
+       "      <td>0.235702</td>\n",
+       "      <td>30.140676</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     PassengerId  Survived    Pclass     SibSp     Parch    Age_new\n",
+       "Sex                                                                \n",
+       "0     431.028662  0.742038  2.159236  0.694268  0.649682  27.929936\n",
+       "1     454.147314  0.188908  2.389948  0.429809  0.235702  30.140676"
+      ]
+     },
+     "execution_count": 286,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#groupby sex\n",
+    "train.groupby('Sex').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 287,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#implies survived female = 74% , male = 18% "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 288,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <h5>Correlation Matrix</h5>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 288,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\"\"\"\n",
+    "        <h5>Correlation Matrix</h5>\n",
+    "    \"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 289,
+   "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>Sex</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Age_new</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>PassengerId</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.005007</td>\n",
+       "      <td>-0.035144</td>\n",
+       "      <td>0.042939</td>\n",
+       "      <td>-0.057527</td>\n",
+       "      <td>-0.001652</td>\n",
+       "      <td>0.034212</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Survived</th>\n",
+       "      <td>-0.005007</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.338481</td>\n",
+       "      <td>-0.543351</td>\n",
+       "      <td>-0.035322</td>\n",
+       "      <td>0.081629</td>\n",
+       "      <td>-0.064910</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pclass</th>\n",
+       "      <td>-0.035144</td>\n",
+       "      <td>-0.338481</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.131900</td>\n",
+       "      <td>0.083081</td>\n",
+       "      <td>0.018443</td>\n",
+       "      <td>-0.339898</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sex</th>\n",
+       "      <td>0.042939</td>\n",
+       "      <td>-0.543351</td>\n",
+       "      <td>0.131900</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.114631</td>\n",
+       "      <td>-0.245489</td>\n",
+       "      <td>0.081163</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SibSp</th>\n",
+       "      <td>-0.057527</td>\n",
+       "      <td>-0.035322</td>\n",
+       "      <td>0.083081</td>\n",
+       "      <td>-0.114631</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.414838</td>\n",
+       "      <td>-0.233296</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Parch</th>\n",
+       "      <td>-0.001652</td>\n",
+       "      <td>0.081629</td>\n",
+       "      <td>0.018443</td>\n",
+       "      <td>-0.245489</td>\n",
+       "      <td>0.414838</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.172482</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Age_new</th>\n",
+       "      <td>0.034212</td>\n",
+       "      <td>-0.064910</td>\n",
+       "      <td>-0.339898</td>\n",
+       "      <td>0.081163</td>\n",
+       "      <td>-0.233296</td>\n",
+       "      <td>-0.172482</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             PassengerId  Survived    Pclass       Sex     SibSp     Parch  \\\n",
+       "PassengerId     1.000000 -0.005007 -0.035144  0.042939 -0.057527 -0.001652   \n",
+       "Survived       -0.005007  1.000000 -0.338481 -0.543351 -0.035322  0.081629   \n",
+       "Pclass         -0.035144 -0.338481  1.000000  0.131900  0.083081  0.018443   \n",
+       "Sex             0.042939 -0.543351  0.131900  1.000000 -0.114631 -0.245489   \n",
+       "SibSp          -0.057527 -0.035322  0.083081 -0.114631  1.000000  0.414838   \n",
+       "Parch          -0.001652  0.081629  0.018443 -0.245489  0.414838  1.000000   \n",
+       "Age_new         0.034212 -0.064910 -0.339898  0.081163 -0.233296 -0.172482   \n",
+       "\n",
+       "              Age_new  \n",
+       "PassengerId  0.034212  \n",
+       "Survived    -0.064910  \n",
+       "Pclass      -0.339898  \n",
+       "Sex          0.081163  \n",
+       "SibSp       -0.233296  \n",
+       "Parch       -0.172482  \n",
+       "Age_new      1.000000  "
+      ]
+     },
+     "execution_count": 289,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 290,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <h4>HeatMap</h4>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 290,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\"\"\"\n",
+    "        <h4>HeatMap</h4>\n",
+    "    \"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 291,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1289b0b0>"
+      ]
+     },
+     "execution_count": 291,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplots(figsize=(15,8))\n",
+    "\n",
+    "sbn.heatmap(train.corr(), annot = True, cmap = 'RdGy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 292,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <h4>Variable relation and Visualization</h4>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 292,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\"\"\"\n",
+    "    <h4>Variable relation and Visualization</h4>\n",
+    "\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 293,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x12911f70>"
+      ]
+     },
+     "execution_count": 293,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# sex vs survived\n",
+    "sbn.barplot(x='Sex', y='Survived', data=train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 294,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#female survival rate is more when compared to male ."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 295,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x12b576f0>"
+      ]
+     },
+     "execution_count": 295,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Pclass and Survived\n",
+    "sbn.barplot(x=\"Pclass\", y=\"Survived\", data=train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 296,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# survival rate from above plot\n",
+    "# class1 > class2 > class3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 297,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <h4>Density Plot</h4>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 297,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    \"\"\"\n",
+    "        <h4>Density Plot</h4>\n",
+    "    \"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 298,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x12bab4b0>"
+      ]
+     },
+     "execution_count": 298,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Age and Survived\n",
+    "plt.subplots(figsize=(15,8))\n",
+    "\n",
+    "sbn.kdeplot(train.loc[(train['Survived']==0),'Age_new'], color='r', shade=True, Label=\"Not Survived\")\n",
+    "sbn.kdeplot(train.loc[(train['Survived']==1),'Age_new'], color='b', shade=True, Label=\"Survived\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 299,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <center><h2>Feature Engineering</h2></center>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 299,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\"\"\"\n",
+    "    <center><h2>Feature Engineering</h2></center>\n",
+    "\"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 300,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1     537\n",
+       "2     161\n",
+       "3     102\n",
+       "4      29\n",
+       "6      22\n",
+       "5      15\n",
+       "7      12\n",
+       "11      7\n",
+       "8       6\n",
+       "Name: family_size, dtype: int64"
+      ]
+     },
+     "execution_count": 300,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Since there is a SibSp and Parch, we can create a family_size attribute\n",
+    "train['family_size'] = train['SibSp'] + train['Parch'] + 1\n",
+    "test['family_size'] = test['SibSp'] + test['Parch'] + 1\n",
+    "train.family_size.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 301,
+   "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>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>Age_new</th>\n",
+       "      <th>family_size</th>\n",
+       "      <th>family_group</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>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>1</td>\n",
+       "      <td>S</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>small</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>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>2</td>\n",
+       "      <td>C</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>small</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>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>3</td>\n",
+       "      <td>S</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>alone</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>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>4</td>\n",
+       "      <td>S</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>small</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>alone</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  SibSp  Parch  \\\n",
+       "0                            Braund, Mr. Owen Harris    1      1      0   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0      1      0   \n",
+       "2                             Heikkinen, Miss. Laina    0      0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0      1      0   \n",
+       "4                           Allen, Mr. William Henry    1      0      0   \n",
+       "\n",
+       "             Ticket Fare Embarked  Age_new  family_size family_group  \n",
+       "0         A/5 21171    1        S     22.0            2        small  \n",
+       "1          PC 17599    2        C     38.0            2        small  \n",
+       "2  STON/O2. 3101282    3        S     26.0            1        alone  \n",
+       "3            113803    4        S     35.0            2        small  \n",
+       "4            373450    5        S     35.0            1        alone  "
+      ]
+     },
+     "execution_count": 301,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#creating familyGroups with the known family_size attribute\n",
+    "def familyGroup(size):\n",
+    "    a=''\n",
+    "    if(size==1):\n",
+    "        a='alone'\n",
+    "    elif(size<=4):\n",
+    "        a='small'\n",
+    "    elif(size>4):\n",
+    "        a='large'\n",
+    "    return a\n",
+    "\n",
+    "train['family_group'] = train.family_size.map(familyGroup)\n",
+    "test['family_group'] = test.family_size.map(familyGroup)\n",
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 302,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      34\n",
+       "1      47\n",
+       "2      62\n",
+       "3      27\n",
+       "4      22\n",
+       "5      14\n",
+       "6      30\n",
+       "7      26\n",
+       "8      18\n",
+       "9      21\n",
+       "10     27\n",
+       "11     46\n",
+       "12     23\n",
+       "13     63\n",
+       "14     47\n",
+       "15     24\n",
+       "16     35\n",
+       "17     21\n",
+       "18     27\n",
+       "19     45\n",
+       "20     55\n",
+       "21      9\n",
+       "22     27\n",
+       "23     21\n",
+       "24     48\n",
+       "25     50\n",
+       "26     22\n",
+       "27     22\n",
+       "28     41\n",
+       "29     27\n",
+       "       ..\n",
+       "388    21\n",
+       "389     6\n",
+       "390    23\n",
+       "391    51\n",
+       "392    13\n",
+       "393    47\n",
+       "394    29\n",
+       "395    18\n",
+       "396    24\n",
+       "397    48\n",
+       "398    22\n",
+       "399    31\n",
+       "400    30\n",
+       "401    38\n",
+       "402    22\n",
+       "403    17\n",
+       "404    43\n",
+       "405    20\n",
+       "406    23\n",
+       "407    50\n",
+       "408    27\n",
+       "409     3\n",
+       "410    27\n",
+       "411    37\n",
+       "412    28\n",
+       "413    27\n",
+       "414    39\n",
+       "415    38\n",
+       "416    27\n",
+       "417    27\n",
+       "Name: Age_new, Length: 418, dtype: int64"
+      ]
+     },
+     "execution_count": 302,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#categorising the age\n",
+    "train['Age_new'].astype('int64')\n",
+    "test['Age_new'].astype('int64')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 303,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "young adult    375\n",
+       "adult          202\n",
+       "middle_age     122\n",
+       "teenager        95\n",
+       "child           29\n",
+       "old             28\n",
+       "baby            26\n",
+       "infant          14\n",
+       "Name: age_group, dtype: int64"
+      ]
+     },
+     "execution_count": 303,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def ageGroup(age):\n",
+    "    a=''\n",
+    "    if(age<=1):\n",
+    "        a='infant'\n",
+    "    elif(age<=4):\n",
+    "        a='baby'\n",
+    "    elif(age<=12):\n",
+    "        a='child'\n",
+    "    elif(age<=19):\n",
+    "        a='teenager'\n",
+    "    elif(age<=28):\n",
+    "        a='young adult'\n",
+    "    elif(age<=40):\n",
+    "        a='adult'\n",
+    "    elif(age<=58):\n",
+    "        a='middle_age'\n",
+    "    else:\n",
+    "        a='old'\n",
+    "    return a\n",
+    "\n",
+    "train['age_group'] = train['Age_new'].map(ageGroup)\n",
+    "test['age_group'] = test['Age_new'].map(ageGroup)\n",
+    "train['age_group'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 304,
+   "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>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>Age_new</th>\n",
+       "      <th>family_size</th>\n",
+       "      <th>family_group</th>\n",
+       "      <th>age_group</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>892</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Kelly, Mr. James</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>330911</td>\n",
+       "      <td>7.8292</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>34.5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>alone</td>\n",
+       "      <td>adult</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>893</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>363272</td>\n",
+       "      <td>7.0000</td>\n",
+       "      <td>S</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>small</td>\n",
+       "      <td>middle_age</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>894</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Myles, Mr. Thomas Francis</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>240276</td>\n",
+       "      <td>9.6875</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>alone</td>\n",
+       "      <td>old</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>895</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Wirz, Mr. Albert</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315154</td>\n",
+       "      <td>8.6625</td>\n",
+       "      <td>S</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>alone</td>\n",
+       "      <td>young adult</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>896</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3101298</td>\n",
+       "      <td>12.2875</td>\n",
+       "      <td>S</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>small</td>\n",
+       "      <td>young adult</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Pclass                                          Name  Sex  \\\n",
+       "0          892       3                              Kelly, Mr. James    1   \n",
+       "1          893       3              Wilkes, Mrs. James (Ellen Needs)    0   \n",
+       "2          894       2                     Myles, Mr. Thomas Francis    1   \n",
+       "3          895       3                              Wirz, Mr. Albert    1   \n",
+       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)    0   \n",
+       "\n",
+       "   SibSp  Parch   Ticket     Fare Embarked  Age_new  family_size family_group  \\\n",
+       "0      0      0   330911   7.8292        Q     34.5            1        alone   \n",
+       "1      1      0   363272   7.0000        S     47.0            2        small   \n",
+       "2      0      0   240276   9.6875        Q     62.0            1        alone   \n",
+       "3      0      0   315154   8.6625        S     27.0            1        alone   \n",
+       "4      1      1  3101298  12.2875        S     22.0            3        small   \n",
+       "\n",
+       "     age_group  \n",
+       "0        adult  \n",
+       "1   middle_age  \n",
+       "2          old  \n",
+       "3  young adult  \n",
+       "4  young adult  "
+      ]
+     },
+     "execution_count": 304,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.head()\n",
+    "test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 305,
+   "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>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>Age_new</th>\n",
+       "      <th>family_size</th>\n",
+       "      <th>family_group</th>\n",
+       "      <th>age_group</th>\n",
+       "      <th>fare_per_person</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>892</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Kelly, Mr. James</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>330911</td>\n",
+       "      <td>7.8292</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>34.5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>alone</td>\n",
+       "      <td>adult</td>\n",
+       "      <td>7.829200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>893</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>363272</td>\n",
+       "      <td>7.0000</td>\n",
+       "      <td>S</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>small</td>\n",
+       "      <td>middle_age</td>\n",
+       "      <td>3.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>894</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Myles, Mr. Thomas Francis</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>240276</td>\n",
+       "      <td>9.6875</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>62.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>alone</td>\n",
+       "      <td>old</td>\n",
+       "      <td>9.687500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>895</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Wirz, Mr. Albert</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315154</td>\n",
+       "      <td>8.6625</td>\n",
+       "      <td>S</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>alone</td>\n",
+       "      <td>young adult</td>\n",
+       "      <td>8.662500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>896</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3101298</td>\n",
+       "      <td>12.2875</td>\n",
+       "      <td>S</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>small</td>\n",
+       "      <td>young adult</td>\n",
+       "      <td>4.095833</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Pclass                                          Name  Sex  \\\n",
+       "0          892       3                              Kelly, Mr. James    1   \n",
+       "1          893       3              Wilkes, Mrs. James (Ellen Needs)    0   \n",
+       "2          894       2                     Myles, Mr. Thomas Francis    1   \n",
+       "3          895       3                              Wirz, Mr. Albert    1   \n",
+       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)    0   \n",
+       "\n",
+       "   SibSp  Parch   Ticket     Fare Embarked  Age_new  family_size family_group  \\\n",
+       "0      0      0   330911   7.8292        Q     34.5            1        alone   \n",
+       "1      1      0   363272   7.0000        S     47.0            2        small   \n",
+       "2      0      0   240276   9.6875        Q     62.0            1        alone   \n",
+       "3      0      0   315154   8.6625        S     27.0            1        alone   \n",
+       "4      1      1  3101298  12.2875        S     22.0            3        small   \n",
+       "\n",
+       "     age_group  fare_per_person  \n",
+       "0        adult         7.829200  \n",
+       "1   middle_age         3.500000  \n",
+       "2          old         9.687500  \n",
+       "3  young adult         8.662500  \n",
+       "4  young adult         4.095833  "
+      ]
+     },
+     "execution_count": 305,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#categorising the fare\n",
+    "train['fare_per_person'] = train['Fare'] / train['family_size']\n",
+    "test['fare_per_person'] = test['Fare'] / test['family_size']\n",
+    "train.head()\n",
+    "test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 306,
+   "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>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>Age_new</th>\n",
+       "      <th>family_size</th>\n",
+       "      <th>family_group</th>\n",
+       "      <th>age_group</th>\n",
+       "      <th>fare_per_person</th>\n",
+       "      <th>fare_group</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>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>1</td>\n",
+       "      <td>S</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>small</td>\n",
+       "      <td>young adult</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>VeryLow</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>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>2</td>\n",
+       "      <td>C</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>small</td>\n",
+       "      <td>adult</td>\n",
+       "      <td>1</td>\n",
+       "      <td>VeryLow</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>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>3</td>\n",
+       "      <td>S</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>alone</td>\n",
+       "      <td>young adult</td>\n",
+       "      <td>3</td>\n",
+       "      <td>VeryLow</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>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>4</td>\n",
+       "      <td>S</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>small</td>\n",
+       "      <td>adult</td>\n",
+       "      <td>2</td>\n",
+       "      <td>VeryLow</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>alone</td>\n",
+       "      <td>adult</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Low</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  SibSp  Parch  \\\n",
+       "0                            Braund, Mr. Owen Harris    1      1      0   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0      1      0   \n",
+       "2                             Heikkinen, Miss. Laina    0      0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0      1      0   \n",
+       "4                           Allen, Mr. William Henry    1      0      0   \n",
+       "\n",
+       "             Ticket Fare Embarked  Age_new  family_size family_group  \\\n",
+       "0         A/5 21171    1        S     22.0            2        small   \n",
+       "1          PC 17599    2        C     38.0            2        small   \n",
+       "2  STON/O2. 3101282    3        S     26.0            1        alone   \n",
+       "3            113803    4        S     35.0            2        small   \n",
+       "4            373450    5        S     35.0            1        alone   \n",
+       "\n",
+       "     age_group fare_per_person fare_group  \n",
+       "0  young adult             0.5    VeryLow  \n",
+       "1        adult               1    VeryLow  \n",
+       "2  young adult               3    VeryLow  \n",
+       "3        adult               2    VeryLow  \n",
+       "4        adult               5        Low  "
+      ]
+     },
+     "execution_count": 306,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#categorising fare into groups\n",
+    "def fareGroup(fare):\n",
+    "    a=''\n",
+    "    if(fare<=4):\n",
+    "        a='VeryLow'\n",
+    "    elif(fare<=10):\n",
+    "        a='Low'\n",
+    "    elif(fare<=20):\n",
+    "        a='Medium'\n",
+    "    elif(fare<=40):\n",
+    "        a='High'\n",
+    "    else:\n",
+    "        a='VeryHigh'\n",
+    "        \n",
+    "    return a\n",
+    "\n",
+    "train['fare_group'] = train.fare_per_person.map(fareGroup)\n",
+    "test['fare_group'] = test.fare_per_person.map(fareGroup)\n",
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 307,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "        <h3>Creating Dummy Variables</h3>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 307,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    \"\"\"\n",
+    "        <h3>Creating Dummy Variables</h3>\n",
+    "    \"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 308,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#creating dummies for Embarked,family_group,fare_group,age_group\n",
+    "train = pd.get_dummies(train, columns = ['Embarked', 'family_group', 'age_group', 'fare_group'], drop_first = True)\n",
+    "test = pd.get_dummies(test, columns = ['Embarked', 'family_group', 'age_group', 'fare_group'], drop_first = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 309,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((891, 27), (418, 26))"
+      ]
+     },
+     "execution_count": 309,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.shape , test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 310,
+   "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>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Age_new</th>\n",
+       "      <th>...</th>\n",
+       "      <th>age_group_child</th>\n",
+       "      <th>age_group_infant</th>\n",
+       "      <th>age_group_middle_age</th>\n",
+       "      <th>age_group_old</th>\n",
+       "      <th>age_group_teenager</th>\n",
+       "      <th>age_group_young adult</th>\n",
+       "      <th>fare_group_Low</th>\n",
+       "      <th>fare_group_Medium</th>\n",
+       "      <th>fare_group_VeryHigh</th>\n",
+       "      <th>fare_group_VeryLow</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>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>1</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</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>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>2</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</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>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>3</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</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>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>4</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</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>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>5</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 27 columns</p>\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  SibSp  Parch  \\\n",
+       "0                            Braund, Mr. Owen Harris    1      1      0   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0      1      0   \n",
+       "2                             Heikkinen, Miss. Laina    0      0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0      1      0   \n",
+       "4                           Allen, Mr. William Henry    1      0      0   \n",
+       "\n",
+       "             Ticket Fare  Age_new  ...  age_group_child age_group_infant  \\\n",
+       "0         A/5 21171    1     22.0  ...                0                0   \n",
+       "1          PC 17599    2     38.0  ...                0                0   \n",
+       "2  STON/O2. 3101282    3     26.0  ...                0                0   \n",
+       "3            113803    4     35.0  ...                0                0   \n",
+       "4            373450    5     35.0  ...                0                0   \n",
+       "\n",
+       "   age_group_middle_age  age_group_old  age_group_teenager  \\\n",
+       "0                     0              0                   0   \n",
+       "1                     0              0                   0   \n",
+       "2                     0              0                   0   \n",
+       "3                     0              0                   0   \n",
+       "4                     0              0                   0   \n",
+       "\n",
+       "   age_group_young adult  fare_group_Low  fare_group_Medium  \\\n",
+       "0                      1               0                  0   \n",
+       "1                      0               0                  0   \n",
+       "2                      1               0                  0   \n",
+       "3                      0               0                  0   \n",
+       "4                      0               1                  0   \n",
+       "\n",
+       "   fare_group_VeryHigh  fare_group_VeryLow  \n",
+       "0                    0                   1  \n",
+       "1                    0                   1  \n",
+       "2                    0                   1  \n",
+       "3                    0                   1  \n",
+       "4                    0                   0  \n",
+       "\n",
+       "[5 rows x 27 columns]"
+      ]
+     },
+     "execution_count": 310,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 311,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#dropping unnecessary columns like: Name, Ticket, Fare, Age_new, fare_per_person, family_size\n",
+    "train.drop(['Name', 'Ticket', 'Fare', 'Age_new', 'fare_per_person', 'family_size'], axis = 1, inplace=True)\n",
+    "test.drop(['Name', 'Ticket', 'Fare', 'Age_new', 'fare_per_person', 'family_size'], axis = 1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 312,
+   "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>Pclass</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Embarked_Q</th>\n",
+       "      <th>Embarked_S</th>\n",
+       "      <th>family_group_large</th>\n",
+       "      <th>family_group_small</th>\n",
+       "      <th>age_group_baby</th>\n",
+       "      <th>age_group_child</th>\n",
+       "      <th>age_group_infant</th>\n",
+       "      <th>age_group_middle_age</th>\n",
+       "      <th>age_group_old</th>\n",
+       "      <th>age_group_teenager</th>\n",
+       "      <th>age_group_young adult</th>\n",
+       "      <th>fare_group_Low</th>\n",
+       "      <th>fare_group_Medium</th>\n",
+       "      <th>fare_group_VeryHigh</th>\n",
+       "      <th>fare_group_VeryLow</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>892</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>893</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>894</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>895</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>896</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Pclass  Sex  SibSp  Parch  Embarked_Q  Embarked_S  \\\n",
+       "0          892       3    1      0      0           1           0   \n",
+       "1          893       3    0      1      0           0           1   \n",
+       "2          894       2    1      0      0           1           0   \n",
+       "3          895       3    1      0      0           0           1   \n",
+       "4          896       3    0      1      1           0           1   \n",
+       "\n",
+       "   family_group_large  family_group_small  age_group_baby  age_group_child  \\\n",
+       "0                   0                   0               0                0   \n",
+       "1                   0                   1               0                0   \n",
+       "2                   0                   0               0                0   \n",
+       "3                   0                   0               0                0   \n",
+       "4                   0                   1               0                0   \n",
+       "\n",
+       "   age_group_infant  age_group_middle_age  age_group_old  age_group_teenager  \\\n",
+       "0                 0                     0              0                   0   \n",
+       "1                 0                     1              0                   0   \n",
+       "2                 0                     0              1                   0   \n",
+       "3                 0                     0              0                   0   \n",
+       "4                 0                     0              0                   0   \n",
+       "\n",
+       "   age_group_young adult  fare_group_Low  fare_group_Medium  \\\n",
+       "0                      0               1                  0   \n",
+       "1                      0               0                  0   \n",
+       "2                      0               1                  0   \n",
+       "3                      1               1                  0   \n",
+       "4                      1               1                  0   \n",
+       "\n",
+       "   fare_group_VeryHigh  fare_group_VeryLow  \n",
+       "0                    0                   0  \n",
+       "1                    0                   1  \n",
+       "2                    0                   0  \n",
+       "3                    0                   0  \n",
+       "4                    0                   0  "
+      ]
+     },
+     "execution_count": 312,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train.head()\n",
+    "test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 313,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "       <center><h1> DATA MODELLING </h1></center>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 313,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    \"\"\"\n",
+    "       <center><h1> DATA MODELLING </h1></center>\n",
+    "    \"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 314,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 891 entries, 0 to 890\n",
+      "Data columns (total 21 columns):\n",
+      "PassengerId              891 non-null int64\n",
+      "Survived                 891 non-null int64\n",
+      "Pclass                   891 non-null int64\n",
+      "Sex                      891 non-null int64\n",
+      "SibSp                    891 non-null int64\n",
+      "Parch                    891 non-null int64\n",
+      "Embarked_Q               891 non-null uint8\n",
+      "Embarked_S               891 non-null uint8\n",
+      "family_group_large       891 non-null uint8\n",
+      "family_group_small       891 non-null uint8\n",
+      "age_group_baby           891 non-null uint8\n",
+      "age_group_child          891 non-null uint8\n",
+      "age_group_infant         891 non-null uint8\n",
+      "age_group_middle_age     891 non-null uint8\n",
+      "age_group_old            891 non-null uint8\n",
+      "age_group_teenager       891 non-null uint8\n",
+      "age_group_young adult    891 non-null uint8\n",
+      "fare_group_Low           891 non-null uint8\n",
+      "fare_group_Medium        891 non-null uint8\n",
+      "fare_group_VeryHigh      891 non-null uint8\n",
+      "fare_group_VeryLow       891 non-null uint8\n",
+      "dtypes: int64(6), uint8(15)\n",
+      "memory usage: 54.9 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "train.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 315,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 418 entries, 0 to 417\n",
+      "Data columns (total 20 columns):\n",
+      "PassengerId              418 non-null int64\n",
+      "Pclass                   418 non-null int64\n",
+      "Sex                      418 non-null int64\n",
+      "SibSp                    418 non-null int64\n",
+      "Parch                    418 non-null int64\n",
+      "Embarked_Q               418 non-null uint8\n",
+      "Embarked_S               418 non-null uint8\n",
+      "family_group_large       418 non-null uint8\n",
+      "family_group_small       418 non-null uint8\n",
+      "age_group_baby           418 non-null uint8\n",
+      "age_group_child          418 non-null uint8\n",
+      "age_group_infant         418 non-null uint8\n",
+      "age_group_middle_age     418 non-null uint8\n",
+      "age_group_old            418 non-null uint8\n",
+      "age_group_teenager       418 non-null uint8\n",
+      "age_group_young adult    418 non-null uint8\n",
+      "fare_group_Low           418 non-null uint8\n",
+      "fare_group_Medium        418 non-null uint8\n",
+      "fare_group_VeryHigh      418 non-null uint8\n",
+      "fare_group_VeryLow       418 non-null uint8\n",
+      "dtypes: int64(5), uint8(15)\n",
+      "memory usage: 22.5 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "test.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 316,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data = train.drop('Survived', axis=1)\n",
+    "target = train['Survived']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 317,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((891, 20), (891,), (418, 20))"
+      ]
+     },
+     "execution_count": 317,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_data.shape , target.shape , test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 318,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    0\n",
+       "1    1\n",
+       "2    1\n",
+       "3    1\n",
+       "4    0\n",
+       "Name: Survived, dtype: int64"
+      ]
+     },
+     "execution_count": 318,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 319,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing Classifier Modules\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier,ExtraTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier,ExtraTreesClassifier,BaggingClassifier,AdaBoostClassifier,GradientBoostingClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 320,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <h3>Cross Validation(K-Fold)</h3>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 320,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    \"\"\"\n",
+    "    <h3>Cross Validation(K-Fold)</h3>\n",
+    "    \"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 321,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "k_fold = KFold(n_splits=10, shuffle=True, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 322,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <h3>1.kNN</h3>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 322,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    \"\"\"\n",
+    "    <h3>1.kNN</h3>\n",
+    "    \"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 323,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.5        0.61797753 0.52808989 0.58426966 0.60674157 0.57303371\n",
+      " 0.49438202 0.56179775 0.56179775 0.46067416]\n"
+     ]
+    }
+   ],
+   "source": [
+    "clf = KNeighborsClassifier(n_neighbors = 13)\n",
+    "scoring = 'accuracy'\n",
+    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
+    "print(score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 324,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "54.89"
+      ]
+     },
+     "execution_count": 324,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# kNN Score\n",
+    "round(np.mean(score)*100, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 325,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <h3>2.Decision Tree</h3>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 325,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    \"\"\"\n",
+    "    <h3>2.Decision Tree</h3>\n",
+    "    \"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 326,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.82222222 0.76404494 0.76404494 0.76404494 0.68539326 0.75280899\n",
+      " 0.71910112 0.73033708 0.74157303 0.69662921]\n"
+     ]
+    }
+   ],
+   "source": [
+    "clf = DecisionTreeClassifier()\n",
+    "scoring = 'accuracy'\n",
+    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
+    "print(score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 327,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "74.4"
+      ]
+     },
+     "execution_count": 327,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# decision tree Score\n",
+    "round(np.mean(score)*100, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 328,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <h3>3.Random Forest</h3>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 328,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    \"\"\"\n",
+    "    <h3>3.Random Forest</h3>\n",
+    "    \"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 329,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.86666667 0.78651685 0.70786517 0.71910112 0.74157303 0.82022472\n",
+      " 0.78651685 0.71910112 0.83146067 0.7752809 ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "clf = RandomForestClassifier(n_estimators=13)\n",
+    "scoring = 'accuracy'\n",
+    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
+    "print(score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 330,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "77.54"
+      ]
+     },
+     "execution_count": 330,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Random Forest Score\n",
+    "round(np.mean(score)*100, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 331,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <h3>4.Naive Bayes</h3>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 331,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    \"\"\"\n",
+    "    <h3>4.Naive Bayes</h3>\n",
+    "    \"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 332,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.81111111 0.78651685 0.74157303 0.71910112 0.84269663 0.80898876\n",
+      " 0.75280899 0.82022472 0.79775281 0.80898876]\n"
+     ]
+    }
+   ],
+   "source": [
+    "clf = GaussianNB()\n",
+    "scoring = 'accuracy'\n",
+    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
+    "print(score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 333,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "78.9"
+      ]
+     },
+     "execution_count": 333,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Naive Bayes Score\n",
+    "round(np.mean(score)*100, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 334,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <h3>5.SVM</h3>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 334,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    \"\"\"\n",
+    "    <h3>5.SVM</h3>\n",
+    "    \"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 335,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\kk\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+      "  \"avoid this warning.\", FutureWarning)\n",
+      "c:\\users\\kk\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+      "  \"avoid this warning.\", FutureWarning)\n",
+      "c:\\users\\kk\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+      "  \"avoid this warning.\", FutureWarning)\n",
+      "c:\\users\\kk\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+      "  \"avoid this warning.\", FutureWarning)\n",
+      "c:\\users\\kk\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+      "  \"avoid this warning.\", FutureWarning)\n",
+      "c:\\users\\kk\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+      "  \"avoid this warning.\", FutureWarning)\n",
+      "c:\\users\\kk\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+      "  \"avoid this warning.\", FutureWarning)\n",
+      "c:\\users\\kk\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+      "  \"avoid this warning.\", FutureWarning)\n",
+      "c:\\users\\kk\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+      "  \"avoid this warning.\", FutureWarning)\n",
+      "c:\\users\\kk\\appdata\\local\\programs\\python\\python37-32\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+      "  \"avoid this warning.\", FutureWarning)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.5        0.68539326 0.52808989 0.58426966 0.64044944 0.66292135\n",
+      " 0.53932584 0.62921348 0.59550562 0.51685393]\n"
+     ]
+    }
+   ],
+   "source": [
+    "clf = SVC()\n",
+    "scoring = 'accuracy'\n",
+    "score = cross_val_score(clf, train_data, target, cv=k_fold, n_jobs=1, scoring=scoring)\n",
+    "print(score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 336,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "58.82"
+      ]
+     },
+     "execution_count": 336,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#SVM Score\n",
+    "round(np.mean(score)*100,2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 337,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <center><h1>Testing</h1></center>\n",
+       "    "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 337,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "HTML(\n",
+    "    \"\"\"\n",
+    "    <center><h1>Testing</h1></center>\n",
+    "    \"\"\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 369,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf = DecisionTreeClassifier()\n",
+    "clf.fit(train_data, target)\n",
+    "\n",
+    "test_data = test.copy()\n",
+    "prediction = clf.predict(test_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 370,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(891, 20)"
+      ]
+     },
+     "execution_count": 370,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_data.shape\n",
+    "train_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 371,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kaggle = pd.DataFrame({'PassengerId': passenger_id, 'Survived': prediction})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 372,
+   "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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>892</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>893</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>894</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>895</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>896</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>897</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>898</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>899</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>900</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>901</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>902</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>903</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>904</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>905</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>906</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>907</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>908</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>909</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>910</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>911</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>912</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>913</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>914</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>915</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>916</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>917</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>918</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>919</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>920</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>921</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>1280</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>389</th>\n",
+       "      <td>1281</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>390</th>\n",
+       "      <td>1282</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>391</th>\n",
+       "      <td>1283</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>392</th>\n",
+       "      <td>1284</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>393</th>\n",
+       "      <td>1285</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>394</th>\n",
+       "      <td>1286</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>395</th>\n",
+       "      <td>1287</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>396</th>\n",
+       "      <td>1288</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>397</th>\n",
+       "      <td>1289</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>398</th>\n",
+       "      <td>1290</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>1291</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>400</th>\n",
+       "      <td>1292</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>401</th>\n",
+       "      <td>1293</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>402</th>\n",
+       "      <td>1294</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>403</th>\n",
+       "      <td>1295</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>404</th>\n",
+       "      <td>1296</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>405</th>\n",
+       "      <td>1297</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>406</th>\n",
+       "      <td>1298</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>407</th>\n",
+       "      <td>1299</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>408</th>\n",
+       "      <td>1300</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>409</th>\n",
+       "      <td>1301</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>410</th>\n",
+       "      <td>1302</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>411</th>\n",
+       "      <td>1303</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>412</th>\n",
+       "      <td>1304</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>413</th>\n",
+       "      <td>1305</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>414</th>\n",
+       "      <td>1306</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>415</th>\n",
+       "      <td>1307</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>416</th>\n",
+       "      <td>1308</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>417</th>\n",
+       "      <td>1309</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>418 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     PassengerId  Survived\n",
+       "0            892         0\n",
+       "1            893         0\n",
+       "2            894         0\n",
+       "3            895         0\n",
+       "4            896         0\n",
+       "5            897         0\n",
+       "6            898         1\n",
+       "7            899         0\n",
+       "8            900         1\n",
+       "9            901         0\n",
+       "10           902         0\n",
+       "11           903         0\n",
+       "12           904         1\n",
+       "13           905         0\n",
+       "14           906         1\n",
+       "15           907         1\n",
+       "16           908         0\n",
+       "17           909         0\n",
+       "18           910         0\n",
+       "19           911         1\n",
+       "20           912         1\n",
+       "21           913         1\n",
+       "22           914         1\n",
+       "23           915         1\n",
+       "24           916         1\n",
+       "25           917         0\n",
+       "26           918         1\n",
+       "27           919         0\n",
+       "28           920         0\n",
+       "29           921         0\n",
+       "..           ...       ...\n",
+       "388         1280         0\n",
+       "389         1281         0\n",
+       "390         1282         0\n",
+       "391         1283         1\n",
+       "392         1284         0\n",
+       "393         1285         0\n",
+       "394         1286         0\n",
+       "395         1287         1\n",
+       "396         1288         0\n",
+       "397         1289         1\n",
+       "398         1290         0\n",
+       "399         1291         0\n",
+       "400         1292         1\n",
+       "401         1293         0\n",
+       "402         1294         1\n",
+       "403         1295         0\n",
+       "404         1296         1\n",
+       "405         1297         0\n",
+       "406         1298         0\n",
+       "407         1299         1\n",
+       "408         1300         1\n",
+       "409         1301         0\n",
+       "410         1302         1\n",
+       "411         1303         1\n",
+       "412         1304         0\n",
+       "413         1305         0\n",
+       "414         1306         1\n",
+       "415         1307         0\n",
+       "416         1308         0\n",
+       "417         1309         0\n",
+       "\n",
+       "[418 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 372,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kaggle\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 373,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kaggle.to_csv('D:\\\\Submission3.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.7.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}