titanic-survived-prediction-model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# Import Necessary Packages and Models for ML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style('dark')\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# ignore warning\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# 1. Data Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <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",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read Train.csv File \n",
    "trainDF = pd.read_csv('./../input/titanic/train.csv')\n",
    "\n",
    "# show first five rows from training dataset\n",
    "trainDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
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       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
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       "      <td>330911</td>\n",
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       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
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       "      <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",
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       "      <td>240276</td>\n",
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       "      <td>Q</td>\n",
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       "    <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",
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      "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read Test.csv File \n",
    "testDF = pd.read_csv('./../input/titanic/test.csv')\n",
    "\n",
    "#  show 5 rows\n",
    "testDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "  </thead>\n",
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       "      <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",
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       "      <td>330911</td>\n",
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       "      <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",
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       "      <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",
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       "      <td>240276</td>\n",
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       "      <td>Q</td>\n",
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       "      <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",
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       "  </tbody>\n",
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      ],
      "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print 5 Rows\n",
    "testDF.head() # train() for last 5 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_shape(train, test):\n",
    "    \"\"\" \n",
    "    display the shape of train and test DF \n",
    "    \n",
    "    \"\"\"   \n",
    "    print(\" Shape of Training DF\", train.shape)\n",
    "    print(\"\")\n",
    "    print(\" Shape of Testing DF\", test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Shape of Training DF (891, 12)\n",
      "\n",
      " Shape of Testing DF (418, 11)\n"
     ]
    }
   ],
   "source": [
    "#  to know shape of the training and testing data\n",
    "show_shape(trainDF, testDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_info(train, test):\n",
    "    \"\"\" \n",
    "    display the Information of train and test DF \n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    print(\"Information of Training DF\"+ \"-\"*10)\n",
    "    print(train.info())\n",
    "    print(\"\")\n",
    "    print(\"\")\n",
    "    print(\"\")\n",
    "    print(\"Information of Testing DF\"+ \"-\"*10)\n",
    "    print(test.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Information of Training DF----------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n",
      "None\n",
      "\n",
      "\n",
      "\n",
      "Information of Testing DF----------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "show_info(trainDF, testDF)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# 2. Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "* Remvoe unnecessary features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "</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>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>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>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>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>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>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     Sex   Age  SibSp  Parch  \\\n",
       "0            1         0       3    male  22.0      1      0   \n",
       "1            2         1       1  female  38.0      1      0   \n",
       "2            3         1       3  female  26.0      0      0   \n",
       "3            4         1       1  female  35.0      1      0   \n",
       "4            5         0       3    male  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare Cabin Embarked  \n",
       "0         A/5 21171   7.2500   NaN        S  \n",
       "1          PC 17599  71.2833   C85        C  \n",
       "2  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3            113803  53.1000  C123        S  \n",
       "4            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainDF = trainDF.drop(['Name'], axis=1)\n",
    "trainDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remve Cabin\n",
    "\n",
    "trainDF = trainDF.drop(['Cabin'], axis=1) # remove from train DF\n",
    "testDF = testDF.drop(['Cabin'], axis=1) # remove from test DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove Ticket\n",
    "\n",
    "trainDF = trainDF.drop(['Ticket'], axis = 1)\n",
    "testDF = testDF.drop(['Ticket'], axis = 1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Age Feature\n",
    "\n",
    "trainDF['Age'] = trainDF['Age'].fillna(trainDF['Age'].median()) # fill for train DF\n",
    "testDF['Age'] = testDF['Age'].fillna(testDF['Age'].median()) # fill for test DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill to Embarked column NA with S\n",
    " \n",
    "trainDF['Embarked'] = trainDF['Embarked'].fillna('S') # for train DF only\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fill na with median for Fare feature\n",
    "\n",
    "testDF[\"Fare\"] = testDF[\"Fare\"].fillna(testDF[\"Fare\"].median()) # for test DF only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Information of Training DF----------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Sex          891 non-null    object \n",
      " 4   Age          891 non-null    float64\n",
      " 5   SibSp        891 non-null    int64  \n",
      " 6   Parch        891 non-null    int64  \n",
      " 7   Fare         891 non-null    float64\n",
      " 8   Embarked     891 non-null    object \n",
      "dtypes: float64(2), int64(5), object(2)\n",
      "memory usage: 62.8+ KB\n",
      "None\n",
      "\n",
      "\n",
      "\n",
      "Information of Testing DF----------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          418 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Fare         418 non-null    float64\n",
      " 8   Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(3)\n",
      "memory usage: 29.5+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# show info of train and test data set by calling function\n",
    "\n",
    "show_info(trainDF, testDF)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# 3. Visualization\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show servived graph\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "sns.countplot(x ='Survived', data=trainDF, )\n",
    "plt.title(\"Survived Counting\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x504 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "sns.catplot(x=\"Pclass\", col=\"Survived\", data=trainDF, kind=\"count\");\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#  Pclass wise survived graph \n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "sns.barplot(x= 'Pclass', y='Survived', data=trainDF)\n",
    "plt.title(\"Pclass wise survived \")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gender wise Survived graph\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "\n",
    "sns.barplot(x='Sex', y = 'Survived', data= trainDF)\n",
    "plt.title(\"Sex and Survived Graph\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Parch and Survived Bar graph\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "sns.barplot(x = 'Parch', y= 'Survived', data= trainDF)\n",
    "plt.title(\"Parch and Survived Graph\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Embarked and Survived bar Graph\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "sns.barplot(x= 'Embarked', y = 'Survived', data= trainDF)\n",
    "plt.title(\"Embarked and Survived Graph\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap show\n",
    "plt.figure(figsize=(10, 7))\n",
    "sns.heatmap(trainDF.corr(), cmap='Greens', linewidths=1, annot=True, fmt='.1f')\n",
    "\n",
    "fig=plt.gcf()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Information of Training DF----------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Sex          891 non-null    object \n",
      " 4   Age          891 non-null    float64\n",
      " 5   SibSp        891 non-null    int64  \n",
      " 6   Parch        891 non-null    int64  \n",
      " 7   Fare         891 non-null    float64\n",
      " 8   Embarked     891 non-null    object \n",
      "dtypes: float64(2), int64(5), object(2)\n",
      "memory usage: 62.8+ KB\n",
      "None\n",
      "\n",
      "\n",
      "\n",
      "Information of Testing DF----------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 9 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          418 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Fare         418 non-null    float64\n",
      " 8   Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(3)\n",
      "memory usage: 29.5+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# show the info\n",
    "show_info(trainDF, testDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert sex object values to numeric male=1 and female=0, for both train and test DF\n",
    "\n",
    "trainDF['Sex'] = trainDF['Sex'].replace({'male': 1, 'female': 0})\n",
    "testDF['Sex'] = testDF['Sex'].replace({'male': 1, 'female': 0})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S    270\n",
      "C    102\n",
      "Q     46\n",
      "Name: Embarked, dtype: int64\n",
      "S    646\n",
      "C    168\n",
      "Q     77\n",
      "Name: Embarked, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# count values for Embarked\n",
    "print(testDF['Embarked'].value_counts())\n",
    "print(trainDF['Embarked'].value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Now, Replace with alphabets to Numbers, for both train and test DF\n",
    "\n",
    "trainDF['Embarked'] = trainDF['Embarked'].replace({'C': 0, 'S': 1, 'Q': 2})\n",
    "testDF['Embarked'] = testDF['Embarked'].replace({'C': 0, 'S': 1, 'Q': 2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   PassengerId  Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
      "0            1         0       3    1  22.0      1      0   7.2500         1\n",
      "1            2         1       1    0  38.0      1      0  71.2833         0\n",
      "2            3         1       3    0  26.0      0      0   7.9250         1\n",
      "3            4         1       1    0  35.0      1      0  53.1000         1\n",
      "4            5         0       3    1  35.0      0      0   8.0500         1\n",
      "   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",
      "    Age  SibSp  Parch     Fare  Embarked  \n",
      "0  34.5      0      0   7.8292         2  \n",
      "1  47.0      1      0   7.0000         1  \n",
      "2  62.0      0      0   9.6875         2  \n",
      "3  27.0      0      0   8.6625         1  \n",
      "4  22.0      1      1  12.2875         1  \n"
     ]
    }
   ],
   "source": [
    "print(trainDF.head())\n",
    "print(testDF.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# 4. Model Prediction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set Prediction value\n",
    "\n",
    "X_train = trainDF.drop(['PassengerId', 'Survived'], axis=1)\n",
    "y_train = trainDF['Survived']\n",
    "X_test = testDF.drop(['PassengerId', 'Name'], axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 7)\n",
      "(891,)\n",
      "(418, 7)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load Model\n",
    "from sklearn.tree import DecisionTreeClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "model = DecisionTreeClassifier()\n",
    "model.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418,)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To predict our model\n",
    "\n",
    "pred = model.predict(X_test)\n",
    "pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Prediction Score 97.98\n"
     ]
    }
   ],
   "source": [
    "# show prediction\n",
    "\n",
    "accu = model.score(X_train, y_train) # model accuracy\n",
    "print( \"Model Prediction Score\", (accu * 100).round(2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# 5. Model Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 2)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict = {\n",
    "    'PassengerId' : testDF['PassengerId'],\n",
    "    'Survived' : pred\n",
    "}\n",
    "\n",
    "new_submission = pd.DataFrame(dict, )\n",
    "new_submission.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate Submission File\n",
    "# new_submission.to_csv('./my_new_submission.csv', index=False)\n",
    "# print(\"Submission Successfully Saved...\")"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "- \n",
    "Other Machine learning scores calculating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import other Models Classes\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_wise_predict(models):\n",
    "    \"\"\" \n",
    "    Model Predictions\n",
    "    \n",
    "    \"\"\"\n",
    "    ans_score = []\n",
    "    for mdl, filename in models:\n",
    "        m = mdl\n",
    "        m.fit(X_train, y_train)\n",
    "        pred = m.predict(X_test)\n",
    "        m_accuracy = m.score(X_train, y_train)\n",
    "        ans_score.append((m_accuracy*100).round(2))\n",
    "        \n",
    "        dict = {\n",
    "            'PassengerId' : testDF['PassengerId'],\n",
    "            'Survived' : pred\n",
    "        }\n",
    "        new_submission = pd.DataFrame(dict, )\n",
    "        new_submission.to_csv(filename, index=False)\n",
    "    return ans_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores are [97.98, 97.98, 79.69, 76.99, 79.12, 73.4, 80.7]\n"
     ]
    }
   ],
   "source": [
    "#  Using DecisionTreeClassifier Model\n",
    "\n",
    "#  make list of Models\n",
    "models = [\n",
    "    (DecisionTreeClassifier(), 'DTC_submission.csv'),\n",
    "    (RandomForestClassifier(), 'RFC_submission.csv'),\n",
    "    (LogisticRegression(), 'LR_submission.csv'),\n",
    "    (LinearSVC(), 'SVC_submission.csv'),\n",
    "    (GaussianNB(), 'GNB_submission.csv'),\n",
    "    (SGDClassifier(), 'SGD_submission.csv'),\n",
    "    (KNeighborsClassifier(), 'KNC_submission.csv')\n",
    "]\n",
    "\n",
    "data = model_wise_predict(models)\n",
    "print(\"scores are\", data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "list_model_name = [\n",
    "    'DecisionTreeClassifier',\n",
    "    'RandomForestClassifier',\n",
    "    'LogisticRegression', \n",
    "    'LinearSVC',\n",
    "    'GaussianNB',\n",
    "    'SGDClassifier', \n",
    "    'KNeighborsClassifier'\n",
    "]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "# Prediction Dashboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Model_Name</th>\n",
       "      <th>Pred_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DecisionTreeClassifier</td>\n",
       "      <td>97.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>97.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>79.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LinearSVC</td>\n",
       "      <td>76.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GaussianNB</td>\n",
       "      <td>79.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SGDClassifier</td>\n",
       "      <td>73.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>80.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Model_Name  Pred_Score\n",
       "0  DecisionTreeClassifier       97.98\n",
       "1  RandomForestClassifier       97.98\n",
       "2      LogisticRegression       79.69\n",
       "3               LinearSVC       76.99\n",
       "4              GaussianNB       79.12\n",
       "5           SGDClassifier       73.40\n",
       "6    KNeighborsClassifier       80.70"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "modelDF = pd.DataFrame({\"Model_Name\" : list_model_name, \"Pred_Score\": data})\n",
    "modelDF.sort_values(by='Pred_Score', ascending=False)\n",
    "modelDF"
   ]
  }
 ],
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