titanic_data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic: Machine Learning from Disaster\n",
    "### Predict survival on the Titanic\n",
    "- Defining the problem statement\n",
    "- Collecting the data\n",
    "- Exploratory data analysis\n",
    "- Feature engineering\n",
    "- Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Defining the problem statement\n",
    "The sinking of the Titanic is one of the most infamous shipwrecks in history.\n",
    "\n",
    "On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew.\n",
    "\n",
    "While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others.\n",
    "\n",
    "In this challenge, we ask you to build a predictive model that answers the question: “which people are more likely to survive” using passenger data (ie name, age, gender, socio-economic class, etc)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Collecting the data\n",
    "\n",
    "training data set and testing data set are given by you can download from kaggle directly [kaggle](https://www.kaggle.com/c/titanic/data)  \n",
    "\n",
    "Let's import the necesssary header files needed for this problem\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully imported libraries\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import style\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import cross_val_score, KFold\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "print('Successfully imported libraries')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully loaded data\n"
     ]
    }
   ],
   "source": [
    "train_df = pd.read_csv('train.csv')\n",
    "#test_df = pd.read_csv('processed_test.csv')\n",
    "print('Successfully loaded data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you are done processing the test data you can uncomment the line, as test data processing is not done in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Dictionary\n",
    "- Survived: \t0 = No, 1 = Yes  \n",
    "- pclass: \tTicket class\t1 = 1st, 2 = 2nd, 3 = 3rd  \t\n",
    "- sibsp:\t# of siblings / spouses aboard Titanic  \t\n",
    "- parch:\t# of parents / children aboard Titanic  \t\n",
    "- ticket:\tTicket number\t\n",
    "- cabin:\tCabin number\t\n",
    "- embarked:\tPort of Embarkation\tC = Cherbourg, Q = Queenstown, S = Southampton  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Total rows and columns**\n",
    "\n",
    "We can see that there are 891 rows and 12 columns in our training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>Ticket</th>\n",
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       "      <td>0</td>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
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       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>STON/O2. 3101282</td>\n",
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       "      <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",
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       "      <td>53.1000</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <td>35.0</td>\n",
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       "      <td>8.0500</td>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <td>886</td>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <td>887</td>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <td>888</td>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>889</td>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
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       "      <td>891</td>\n",
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       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 12 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",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         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",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      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  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ramakrishnan\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:3: 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",
      "  This is separate from the ipykernel package so we can avoid doing imports until\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>Count</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Age</td>\n",
       "      <td>177</td>\n",
       "      <td>0.198653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Cabin</td>\n",
       "      <td>687</td>\n",
       "      <td>0.771044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Embarked</td>\n",
       "      <td>2</td>\n",
       "      <td>0.002245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Fare</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Name</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Parch</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>PassengerId</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Pclass</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Sex</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SibSp</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Survived</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Ticket</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Count         %\n",
       "Age            177  0.198653\n",
       "Cabin          687  0.771044\n",
       "Embarked         2  0.002245\n",
       "Fare             0  0.000000\n",
       "Name             0  0.000000\n",
       "Parch            0  0.000000\n",
       "PassengerId      0  0.000000\n",
       "Pclass           0  0.000000\n",
       "Sex              0  0.000000\n",
       "SibSp            0  0.000000\n",
       "Survived         0  0.000000\n",
       "Ticket           0  0.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = train_df.isnull().sum().sort_values()\n",
    "percent = total / train_df.isnull().count().sort_values()\n",
    "pd.concat([total,percent],axis = 1,keys = ['Count','%'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that *Age* value is missing for many rows. \n",
    "\n",
    "Out of 891 rows, the *Age* value is present only in 714 rows.\n",
    "\n",
    "Similarly, *Cabin* values are also missing in many rows. Only 204 out of 891 rows have *Cabin* values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Univariate Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  - Bar Chart for Categorical Features\n",
    "- Sex\n",
    "- Pclass\n",
    "- SibSp ( # of siblings and spouse)\n",
    "- Parch ( # of parents and children)\n",
    "- Embarked\n",
    "- Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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": [
    "sns.countplot(x = 'Sex',data = train_df,hue = 'Survived')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Chart confirms **Women** more likely survivied than **Men**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.countplot(x = 'Pclass',data = train_df,hue = 'Survived')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Chart confirms **1st class** more likely survivied than **other classes**  \n",
    "The Chart confirms **3rd class** more likely dead than **other classes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "sns.countplot(x = 'SibSp',data = train_df,hue = 'Survived')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Chart confirms that **a person aboarded without siblings or spouse** is more likely dead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.countplot(x = 'Parch',data = train_df,hue = 'Survived')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  \n",
    "The Chart confirms that **a person aboarded alone** is more likely dead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.countplot(x = 'Embarked',data = train_df,hue = 'Survived')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Chart confirms that **a person aboarded from C** is slightly more likely survived  \n",
    "The Chart confirms that **a person aboarded from Q** is more likely dead  \n",
    "The Chart confirms that **a person aboarded from S** is more likely dead"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Bivariate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ramakrishnan\\Anaconda\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:447: RuntimeWarning: invalid value encountered in greater\n",
      "  X = X[np.logical_and(X > clip[0], X < clip[1])] # won't work for two columns.\n",
      "C:\\Users\\Ramakrishnan\\Anaconda\\lib\\site-packages\\statsmodels\\nonparametric\\kde.py:447: RuntimeWarning: invalid value encountered in less\n",
      "  X = X[np.logical_and(X > clip[0], X < clip[1])] # won't work for two columns.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1cd9de57fc8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 591.875x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data = train_df, hue = 'Survived', vars = ['Age','Pclass','SibSp'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "People having **more siblings** and also belong to **3rd class,** have good chances of survival\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Studying Correlation using correlation matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cd9dbbc208>"
      ]
     },
     "execution_count": 16,
     "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": [
    "cor = train_df.corr()\n",
    "sns.heatmap(cor, cbar = False, annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions\n",
    "- Age has a **negative** correlation with **Pclass** \n",
    "- Age has a <b>negative</b> correlation with <b>SibSp</b> and <b>Parch</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cd9dbe48c8>"
      ]
     },
     "execution_count": 17,
     "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": [
    "sns.scatterplot(x = 'Pclass', y = 'Fare', data = train_df )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Pclass 1** corresponds to **higher** fares while **Pclass 2 and 3** coressponds to **lower** fares"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting the passenger titles\n",
    "\n",
    "If you closely look at the name of the passengers: \n",
    "\n",
    "- Braund, <b> Mr.</b> Owen Harris\t\n",
    "- Heikkinen, <b>Miss.</b> Laina\n",
    "- Oliva y Ocana, <b>Dona.</b> Fermina\n",
    "- Peter, <b>Master.</b> Michael J\n",
    "\n",
    "You will notice that each name has a title in it.  This can be a simple Miss. or Mrs. but it can be sometimes something more sophisticated like Master, Sir or Dona. In that case, we might introduce an additional information about the social status by simply parsing the name and extracting the title and converting to a binary variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Miss', 'Don', 'Col', 'the Countess', 'Sir', 'Major', 'Mrs', 'Mr', 'Lady', 'Mlle', 'Jonkheer', 'Ms', 'Mme', 'Dr', 'Capt', 'Master', 'Rev'}\n"
     ]
    }
   ],
   "source": [
    "titles = set()\n",
    "for name in train_df['Name']:\n",
    "    titles.add(name.split(',')[1].split('.')[0].strip())\n",
    "print(titles)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "Title_Dictionary = {\n",
    "    \"Capt\": \"Officer\",\n",
    "    \"Col\": \"Officer\",\n",
    "    \"Major\": \"Officer\",\n",
    "    \"Jonkheer\": \"Royalty\",\n",
    "    \"Don\": \"Royalty\",\n",
    "    \"Sir\" : \"Royalty\",\n",
    "    \"Dr\": \"Officer\",\n",
    "    \"Rev\": \"Officer\",\n",
    "    \"the Countess\":\"Royalty\",\n",
    "    \"Mme\": \"Mrs\",\n",
    "    \"Mlle\": \"Miss\",\n",
    "    \"Ms\": \"Mrs\",\n",
    "    \"Mr\" : \"Mr\",\n",
    "    \"Mrs\" : \"Mrs\",\n",
    "    \"Miss\" : \"Miss\",\n",
    "    \"Master\" : \"Master\",\n",
    "    \"Lady\" : \"Royalty\"\n",
    "}\n",
    " \n",
    "def get_titles(train_df):\n",
    "    train_df['Title'] = train_df['Name'].map(lambda name:name.split(',')[1].split('.')[0].strip())\n",
    "    train_df['Title'] = train_df.Title.map(Title_Dictionary)\n",
    "    return train_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function parses the names and extract the titles. Then, it maps the titles to categories of titles. \n",
    "We selected : \n",
    "\n",
    "- Officer\n",
    "- Royalty \n",
    "- Mr\n",
    "- Mrs\n",
    "- Miss\n",
    "- Master"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = get_titles(train_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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",
       "      <td>Mr</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 Title  \n",
       "0      0         A/5 21171   7.2500   NaN        S    Mr  \n",
       "1      0          PC 17599  71.2833   C85        C   Mrs  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  Miss  \n",
       "3      0            113803  53.1000  C123        S   Mrs  \n",
       "4      0            373450   8.0500   NaN        S    Mr  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check if the titles have been filled correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked, Title]\n",
       "Index: []"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[train_df['Title'].isnull()]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing the ages\n",
    "\n",
    "We have seen in the first part that the Age variable was missing 177 values. This is a large number ( ~ 13% of the dataset). Simply replacing them with the mean or the median age might not be the best solution since the age may differ by groups and categories of passengers. \n",
    "\n",
    "Let's group our dataset by sex, Title and passenger class and visualize the relationships between them.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cd9dbab288>"
      ]
     },
     "execution_count": 23,
     "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": [
    "sns.pointplot(x = 'Sex', y = 'Age', data = train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cd9f29d208>"
      ]
     },
     "execution_count": 24,
     "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": [
    "sns.pointplot(x = 'Pclass', y = 'Age', data = train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cd9f297808>"
      ]
     },
     "execution_count": 25,
     "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": [
    "sns.pointplot(x = 'Title', y = 'Age', data = train_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let us find the median by groupping Pclass,title and sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Title</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Master</td>\n",
       "      <td>male</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Miss</td>\n",
       "      <td>female</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Mr</td>\n",
       "      <td>male</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>female</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Officer</td>\n",
       "      <td>female</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Officer</td>\n",
       "      <td>male</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Royalty</td>\n",
       "      <td>female</td>\n",
       "      <td>40.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Royalty</td>\n",
       "      <td>male</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>Master</td>\n",
       "      <td>male</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>Miss</td>\n",
       "      <td>female</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>Mr</td>\n",
       "      <td>male</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>female</td>\n",
       "      <td>31.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>Officer</td>\n",
       "      <td>male</td>\n",
       "      <td>46.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>Master</td>\n",
       "      <td>male</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>Miss</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>Mr</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>female</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pclass    Title     Sex   Age\n",
       "0        1   Master    male   4.0\n",
       "1        1     Miss  female  30.0\n",
       "2        1       Mr    male  40.0\n",
       "3        1      Mrs  female  40.0\n",
       "4        1  Officer  female  49.0\n",
       "5        1  Officer    male  51.0\n",
       "6        1  Royalty  female  40.5\n",
       "7        1  Royalty    male  40.0\n",
       "8        2   Master    male   1.0\n",
       "9        2     Miss  female  24.0\n",
       "10       2       Mr    male  31.0\n",
       "11       2      Mrs  female  31.5\n",
       "12       2  Officer    male  46.5\n",
       "13       3   Master    male   4.0\n",
       "14       3     Miss  female  18.0\n",
       "15       3       Mr    male  26.0\n",
       "16       3      Mrs  female  31.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = train_df.groupby(['Pclass','Title','Sex']).median()\n",
    "grouped_median_train = x['Age'].reset_index()\n",
    "grouped_median_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataframe will help us impute missing age values based on different criteria.\n",
    "\n",
    "For example: \n",
    "\n",
    "- If the passenger is female, from Pclass 1, and from royalty the median age is 40.5.\n",
    "- If the passenger is male, from Pclass 3, with a Mr title, the median age is 26.\n",
    "\n",
    "The function defined below will complete imputing of age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_age(row):\n",
    "    condition = (\n",
    "        (grouped_median_train['Sex'] == row['Sex']) & \n",
    "        (grouped_median_train['Title'] == row['Title']) & \n",
    "        (grouped_median_train['Pclass'] == row['Pclass'])\n",
    "    ) \n",
    "    return grouped_median_train[condition]['Age'].values[0]\n",
    "\n",
    "\n",
    "def process_age():\n",
    "    global train_df\n",
    "    train_df['Age'] = train_df.apply(lambda row: fill_age(row) if np.isnan(row['Age']) else row['Age'], axis=1)\n",
    "    return train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = process_age()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 13 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            891 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "Title          891 non-null object\n",
      "dtypes: float64(2), int64(5), object(6)\n",
      "memory usage: 90.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    " def process_cabin():\n",
    "    global train_df    \n",
    "    train_df.Cabin.fillna('U', inplace=True)\n",
    "    train_df['Cabin'] = train_df['Cabin'].map(lambda c: c[0])\n",
    "    cabin_dummies = pd.get_dummies(train_df['Cabin'], prefix='Cabin')    \n",
    "    train_df = pd.concat([train_df, cabin_dummies], axis=1)\n",
    "    train_df.drop('Cabin', axis=1, inplace=True)\n",
    "    return train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = process_cabin()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>...</th>\n",
       "      <th>Title</th>\n",
       "      <th>Cabin_A</th>\n",
       "      <th>Cabin_B</th>\n",
       "      <th>Cabin_C</th>\n",
       "      <th>Cabin_D</th>\n",
       "      <th>Cabin_E</th>\n",
       "      <th>Cabin_F</th>\n",
       "      <th>Cabin_G</th>\n",
       "      <th>Cabin_T</th>\n",
       "      <th>Cabin_U</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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>...</td>\n",
       "      <td>Mr</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",
       "      <td>1</td>\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>...</td>\n",
       "      <td>Mrs</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>...</td>\n",
       "      <td>Miss</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",
       "      <td>3</td>\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>...</td>\n",
       "      <td>Mrs</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>...</td>\n",
       "      <td>Mr</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 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   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  ... Title Cabin_A  Cabin_B  Cabin_C  \\\n",
       "0      0         A/5 21171   7.2500  ...    Mr       0        0        0   \n",
       "1      0          PC 17599  71.2833  ...   Mrs       0        0        1   \n",
       "2      0  STON/O2. 3101282   7.9250  ...  Miss       0        0        0   \n",
       "3      0            113803  53.1000  ...   Mrs       0        0        1   \n",
       "4      0            373450   8.0500  ...    Mr       0        0        0   \n",
       "\n",
       "   Cabin_D  Cabin_E  Cabin_F  Cabin_G  Cabin_T  Cabin_U  \n",
       "0        0        0        0        0        0        1  \n",
       "1        0        0        0        0        0        0  \n",
       "2        0        0        0        0        0        1  \n",
       "3        0        0        0        0        0        0  \n",
       "4        0        0        0        0        0        1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing Family"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This part includes creating new variables based on the size of the family (the size is a new feature we develop from the existing features).\n",
    "\n",
    "This creation of new variables is done under a assumption that large families are grouped together, hence they are more likely to get rescued than people traveling alone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['Family Size'] = train_df['Parch'] + train_df['SibSp'] + 1\n",
    "train_df['Alone'] = train_df['Family Size'].map(lambda c : 1 if c==1 else 0 )\n",
    "train_df['Not_Alone'] = train_df['Family Size'].map(lambda c : 1 if c > 1 else 0 ) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<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",
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       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>...</th>\n",
       "      <th>Cabin_C</th>\n",
       "      <th>Cabin_D</th>\n",
       "      <th>Cabin_E</th>\n",
       "      <th>Cabin_F</th>\n",
       "      <th>Cabin_G</th>\n",
       "      <th>Cabin_T</th>\n",
       "      <th>Cabin_U</th>\n",
       "      <th>Family Size</th>\n",
       "      <th>Alone</th>\n",
       "      <th>Not_Alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</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>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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>...</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>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\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>...</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\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>...</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>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>...</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 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   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_C Cabin_D  Cabin_E  Cabin_F  \\\n",
       "0      0         A/5 21171   7.2500  ...       0       0        0        0   \n",
       "1      0          PC 17599  71.2833  ...       1       0        0        0   \n",
       "2      0  STON/O2. 3101282   7.9250  ...       0       0        0        0   \n",
       "3      0            113803  53.1000  ...       1       0        0        0   \n",
       "4      0            373450   8.0500  ...       0       0        0        0   \n",
       "\n",
       "   Cabin_G  Cabin_T  Cabin_U  Family Size  Alone  Not_Alone  \n",
       "0        0        0        1            2      0          1  \n",
       "1        0        0        0            2      0          1  \n",
       "2        0        0        1            1      1          0  \n",
       "3        0        0        0            2      0          1  \n",
       "4        0        0        1            1      1          0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binning Age\n",
    "Now we need to convert the **age** feature. First we will convert it from float into integer. Then we will create the new **Age** variable, by categorizing every age into a group. Note that it is important on how you form these groups, since you don’t want, for example, that 80% of your data falls into group 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [train_df]\n",
    "for dataset in data:\n",
    "    dataset['Age'] = dataset['Age'].astype(int)\n",
    "    dataset.loc[ dataset['Age'] <= 11, 'Age'] = 0\n",
    "    dataset.loc[(dataset['Age'] > 11) & (dataset['Age'] <= 18), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 18) & (dataset['Age'] <= 22), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 22) & (dataset['Age'] <= 27), 'Age'] = 3\n",
    "    dataset.loc[(dataset['Age'] > 27) & (dataset['Age'] <= 33), 'Age'] = 4\n",
    "    dataset.loc[(dataset['Age'] > 33) & (dataset['Age'] <= 40), 'Age'] = 5\n",
    "    dataset.loc[(dataset['Age'] > 40) & (dataset['Age'] <= 66), 'Age'] = 6\n",
    "    dataset.loc[ dataset['Age'] > 66, 'Age'] = 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cd9f297ac8>"
      ]
     },
     "execution_count": 36,
     "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": [
    "train_df['Age'].value_counts().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing Fare"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's change the dataype of fare into integer and apply binning here also"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['Fare'].astype(int)\n",
    "data = [train_df]\n",
    "for dataset in data:\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)\n",
    "    dataset.loc[ dataset['Fare'] == 6, 'Fare'] = 0\n",
    "    dataset.loc[ dataset['Fare'] == 7, 'Fare'] = 0\n",
    "    dataset.loc[ dataset['Fare'] == 5, 'Fare'] = 0\n",
    "    dataset.loc[(dataset['Fare'] > 7) & (dataset['Fare'] <= 10), 'Fare'] = 1\n",
    "    dataset.loc[(dataset['Fare'] > 10) & (dataset['Fare'] <= 20), 'Fare'] = 2\n",
    "    dataset.loc[(dataset['Fare'] > 20) & (dataset['Fare'] <= 30), 'Fare'] = 3\n",
    "    dataset.loc[(dataset['Fare'] > 30) & (dataset['Fare'] <= 70), 'Fare'] = 4\n",
    "    dataset.loc[(dataset['Fare'] > 70), 'Fare'] = 5\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the above results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1cd9f561688>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD3CAYAAADmBxSSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAANwUlEQVR4nO3dXYxc9X2H8ecbnKAmRAnIC3VsJ4tS94WoraErh8q9IKIKb1VNpBJBJUCI1rkAFaSoqkMvyA0SF02iRmqRnEICVQKlSRCugpJQN21EW17WFPHmUNzgwMbGbBoUoESkhl8v5lidLLPe9c7ODv77+UirmfnPmTm/wfKzx2dnh1QVkqS2vG3cA0iSlp9xl6QGGXdJapBxl6QGGXdJapBxl6QGLRj3JOuTfCfJ7iRPJLmmW/90kh8meaT7Or/vMZ9KsifJU0nOGeULkCS9WRZ6n3uSNcCaqno4ybuBXcCFwMeBV6rqL+ZsfxpwO7AJeB/wj8AvV9XrI5hfkjTAqoU2qKr9wP7u+stJdgNrD/OQLcAdVfUa8EySPfRC/+/zPWD16tU1OTl5JHNL0jFv165dP6qqiUH3LRj3fkkmgdOBB4DNwNVJLgOmgU9W1Yv0wn9/38NmOPw3AyYnJ5menj6SUSTpmJfkB/Pdt+gfqCY5AfgacG1VvQTcBHwQ2EjvyP4zhzYd8PA3nftJsjXJdJLp2dnZxY4hSVqERcU9ydvphf3LVfV1gKo6UFWvV9UbwBfonXqB3pH6+r6HrwP2zX3OqtpeVVNVNTUxMfBfFZKkJVrMu2UC3AzsrqrP9q2v6dvsY8Dj3fUdwMVJjk9yKrABeHD5RpYkLWQx59w3A5cCjyV5pFu7DrgkyUZ6p1z2Ap8AqKonktwJPAkcBK7ynTKStLIW826Z+xh8Hv2ewzzmBuCGIeaSJA3B31CVpAYZd0lqkHGXpAYd0S8xvdVMbvvGiu5v740XrOj+JGmpPHKXpAYZd0lqkHGXpAYZd0lqkHGXpAYZd0lqkHGXpAYZd0lqkHGXpAYZd0lqkHGXpAYZd0lqkHGXpAYZd0lqkHGXpAYZd0lqkHGXpAYZd0lqkHGXpAYZd0lqkHGXpAYZd0lqkHGXpAYZd0lqkHGXpAYZd0lqkHGXpAYZd0lqkHGXpAYZd0lq0IJxT7I+yXeS7E7yRJJruvWTktyb5Onu8sRuPUk+n2RPkkeTnDHqFyFJ+nmLOXI/CHyyqn4NOBO4KslpwDZgZ1VtAHZ2twHOAzZ0X1uBm5Z9aknSYS0Y96raX1UPd9dfBnYDa4EtwK3dZrcCF3bXtwC3Vc/9wHuTrFn2ySVJ8zqic+5JJoHTgQeAU6pqP/S+AQAnd5utBZ7re9hMtyZJWiGLjnuSE4CvAddW1UuH23TAWg14vq1JppNMz87OLnYMSdIiLCruSd5OL+xfrqqvd8sHDp1u6S5f6NZngPV9D18H7Jv7nFW1vaqmqmpqYmJiqfNLkgZYzLtlAtwM7K6qz/bdtQO4vLt+OXB33/pl3btmzgR+cuj0jSRpZaxaxDabgUuBx5I80q1dB9wI3JnkSuBZ4KLuvnuA84E9wKvAFcs6sSRpQQvGvaruY/B5dICzB2xfwFVDziVJGoK/oSpJDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDTLuktQg4y5JDVo17gE0v8lt31jR/e298YIV3Z+k0fHIXZIaZNwlqUHGXZIaZNwlqUELxj3JLUleSPJ439qnk/wwySPd1/l9930qyZ4kTyU5Z1SDS5Lmt5gj9y8B5w5Y/1xVbey+7gFIchpwMfCh7jF/neS45RpWkrQ4C8a9qr4L/HiRz7cFuKOqXquqZ4A9wKYh5pMkLcEw59yvTvJod9rmxG5tLfBc3zYz3ZokaQUtNe43AR8ENgL7gc906xmwbQ16giRbk0wnmZ6dnV3iGJKkQZYU96o6UFWvV9UbwBf4/1MvM8D6vk3XAfvmeY7tVTVVVVMTExNLGUOSNI8lxT3Jmr6bHwMOvZNmB3BxkuOTnApsAB4cbkRJ0pFa8LNlktwOnAWsTjIDXA+clWQjvVMue4FPAFTVE0nuBJ4EDgJXVdXroxldkjSfBeNeVZcMWL75MNvfANwwzFCSpOH4G6qS1CDjLkkN8vPcNTZ+Xr00Oh65S1KDjLskNci4S1KDjLskNci4S1KDjLskNci4S1KDjLskNci4S1KDjLskNci4S1KDjLskNci4S1KDjLskNci4S1KD/Dx3aURW8vPq/ax6zeWRuyQ1yCN3SUfM/4vWW59H7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUIOMuSQ0y7pLUID8VUpLmaOFTLxc8ck9yS5IXkjzet3ZSknuTPN1dntitJ8nnk+xJ8miSM5Z9YknSghZzWuZLwLlz1rYBO6tqA7Czuw1wHrCh+9oK3LQ8Y0qSjsSCca+q7wI/nrO8Bbi1u34rcGHf+m3Vcz/w3iRrlmtYSdLiLPUHqqdU1X6A7vLkbn0t8FzfdjPd2psk2ZpkOsn07OzsEseQJA2y3O+WyYC1GrRhVW2vqqmqmpqYmFjmMSTp2LbUuB84dLqlu3yhW58B1vdttw7Yt/TxJElLsdS47wAu765fDtzdt35Z966ZM4GfHDp9I0laOQu+zz3J7cBZwOokM8D1wI3AnUmuBJ4FLuo2vwc4H9gDvApcMYKZJUkLWDDuVXXJPHedPWDbAq4adihJ0nD8+AFJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGGXdJapBxl6QGrRrmwUn2Ai8DrwMHq2oqyUnA3wGTwF7g41X14nBjSpKOxHIcuX+kqjZW1VR3exuws6o2ADu725KkFTSK0zJbgFu767cCF45gH5Kkwxg27gV8O8muJFu7tVOqaj9Ad3nyoAcm2ZpkOsn07OzskGNIkvoNdc4d2FxV+5KcDNyb5HuLfWBVbQe2A0xNTdWQc0iS+gx15F5V+7rLF4C7gE3AgSRrALrLF4YdUpJ0ZJYc9yTvSvLuQ9eBjwKPAzuAy7vNLgfuHnZISdKRGea0zCnAXUkOPc9XquqbSR4C7kxyJfAscNHwY0qSjsSS415V3wd+c8D6fwNnDzOUJGk4/oaqJDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg4y7JDXIuEtSg0YW9yTnJnkqyZ4k20a1H0nSm40k7kmOA/4KOA84DbgkyWmj2Jck6c1GdeS+CdhTVd+vqp8BdwBbRrQvSdIcqarlf9LkD4Bzq+qPutuXAh+uqqv7ttkKbO1u/grw1LIPMr/VwI9WcH8rzdd39Gr5tYGvb7l9oKomBt2xakQ7zIC1n/suUlXbge0j2v9hJZmuqqlx7Hsl+PqOXi2/NvD1raRRnZaZAdb33V4H7BvRviRJc4wq7g8BG5KcmuQdwMXAjhHtS5I0x0hOy1TVwSRXA98CjgNuqaonRrGvJRrL6aAV5Os7erX82sDXt2JG8gNVSdJ4+RuqktQg4y5JDTLuktSgUb3P/S0lya/S+w3ZtfTeb78P2FFVu8c62DLpXt9a4IGqeqVv/dyq+ub4Jhtekk1AVdVD3UdYnAt8r6ruGfNoWoIkt1XVZeOeYxSS/A69385/vKq+PfZ5Wv+BapI/Ay6h9xEIM93yOnpvz7yjqm4c12zLIcmfAFcBu4GNwDVVdXd338NVdcY45xtGkuvpfT7RKuBe4MPAPwO/C3yrqm4Y33Sjl+SKqvriuOdYqiRz3/4c4CPAPwFU1e+v+FDLKMmDVbWpu/7H9P4e3gV8FPiHcbflWIj7fwIfqqr/nbP+DuCJqtownsmWR5LHgN+uqleSTAJfBf62qv4yyX9U1eljHXAI3WvbCBwPPA+sq6qXkvwCvX+l/MZYBxyxJM9W1fvHPcdSJXkYeBL4G3r/Yg5wO70DK6rqX8Y33fD6/34leQg4v6pmk7wLuL+qfn2c8x0Lp2XeAN4H/GDO+pruvqPdcYdOxVTV3iRnAV9N8gEGfwzE0eRgVb0OvJrkv6rqJYCq+mmSFv7sSPLofHcBp6zkLCMwBVwD/Dnwp1X1SJKfHu1R7/O2JCfS+9llqmoWoKr+J8nB8Y52bMT9WmBnkqeB57q19wO/BFw976OOHs8n2VhVjwB0R/C/B9wCjPXIYRn8LMk7q+pV4LcOLSZ5D218Y4ZewM8BXpyzHuDfVn6c5VNVbwCfS/L33eUB2mrOe4Bd9P6sKskvVtXzSU7gLXBg1fxpGYAkb6P3g4619P6jzwAPdUeFR7Uk6+gd4T4/4L7NVfWvYxhrWSQ5vqpeG7C+GlhTVY+NYaxlleRm4ItVdd+A+75SVX84hrFGIskFwOaqum7cs4xSkncCp1TVM2Od41iIuyQda3yfuyQ1yLhLUoOMuyQ1yLhLUoOMuyQ16P8AVEWLQoMCIscAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df['Fare'].value_counts().plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dropping the ticket column since it is not used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.drop(['Ticket'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Embarked** has **two** missing values. Let's fill those with modal value which is **Southampton (S)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df['Embarked'] = train_df['Embarked'].fillna('S')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encoding categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pd.get_dummies(train_df['Sex'],drop_first = True)\n",
    "train_df = pd.concat([train_df,x],axis = 1)\n",
    "train_df = pd.concat([train_df, pd.get_dummies(train_df['Title'], prefix = 'Title')],axis = 1)\n",
    "train_df = pd.concat([train_df, pd.get_dummies(train_df['Embarked'], prefix = 'Embarked')],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>...</th>\n",
       "      <th>male</th>\n",
       "      <th>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Officer</th>\n",
       "      <th>Title_Royalty</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\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>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>...</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>...</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>...</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",
       "      <td>3</td>\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>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>S</td>\n",
       "      <td>...</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>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>...</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>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 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  Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male    2      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female    5      1   \n",
       "2                             Heikkinen, Miss. Laina  female    3      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female    5      1   \n",
       "4                           Allen, Mr. William Henry    male    5      0   \n",
       "\n",
       "   Parch  Fare Embarked  ... male  Title_Master  Title_Miss  Title_Mr  \\\n",
       "0      0     0        S  ...    1             0           0         1   \n",
       "1      0     5        C  ...    0             0           0         0   \n",
       "2      0     0        S  ...    0             0           1         0   \n",
       "3      0     4        S  ...    0             0           0         0   \n",
       "4      0     1        S  ...    1             0           0         1   \n",
       "\n",
       "   Title_Mrs  Title_Officer  Title_Royalty  Embarked_C  Embarked_Q  Embarked_S  \n",
       "0          0              0              0           0           0           1  \n",
       "1          1              0              0           1           0           0  \n",
       "2          0              0              0           0           0           1  \n",
       "3          1              0              0           0           0           1  \n",
       "4          0              0              0           0           0           1  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Dropping useless features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df.drop(['Embarked_C', 'Title_Royalty', 'Cabin_T'], axis = 1, inplace = True)\n",
    "train_df.drop(['Sex'], axis = 1,inplace = True)\n",
    "train_df.drop(['Name'], axis =1, inplace = True)\n",
    "train_df.drop(['PassengerId'], axis =1, inplace = True)\n",
    "train_df.drop(['Embarked'], axis =1, inplace = True)\n",
    "train_df.drop(['Title'], axis =1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin_A</th>\n",
       "      <th>Cabin_B</th>\n",
       "      <th>Cabin_C</th>\n",
       "      <th>Cabin_D</th>\n",
       "      <th>...</th>\n",
       "      <th>Alone</th>\n",
       "      <th>Not_Alone</th>\n",
       "      <th>male</th>\n",
       "      <th>Title_Master</th>\n",
       "      <th>Title_Miss</th>\n",
       "      <th>Title_Mr</th>\n",
       "      <th>Title_Mrs</th>\n",
       "      <th>Title_Officer</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</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>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</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>...</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</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>...</td>\n",
       "      <td>1</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",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>886</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>887</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>888</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>889</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>890</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</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>...</td>\n",
       "      <td>1</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>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass  Age  SibSp  Parch  Fare  Cabin_A  Cabin_B  Cabin_C  \\\n",
       "0           0       3    2      1      0     0        0        0        0   \n",
       "1           1       1    5      1      0     5        0        0        1   \n",
       "2           1       3    3      0      0     0        0        0        0   \n",
       "3           1       1    5      1      0     4        0        0        1   \n",
       "4           0       3    5      0      0     1        0        0        0   \n",
       "..        ...     ...  ...    ...    ...   ...      ...      ...      ...   \n",
       "886         0       2    3      0      0     2        0        0        0   \n",
       "887         1       1    2      0      0     3        0        1        0   \n",
       "888         0       3    1      1      2     3        0        0        0   \n",
       "889         1       1    3      0      0     3        0        0        1   \n",
       "890         0       3    4      0      0     0        0        0        0   \n",
       "\n",
       "     Cabin_D  ...  Alone  Not_Alone  male  Title_Master  Title_Miss  Title_Mr  \\\n",
       "0          0  ...      0          1     1             0           0         1   \n",
       "1          0  ...      0          1     0             0           0         0   \n",
       "2          0  ...      1          0     0             0           1         0   \n",
       "3          0  ...      0          1     0             0           0         0   \n",
       "4          0  ...      1          0     1             0           0         1   \n",
       "..       ...  ...    ...        ...   ...           ...         ...       ...   \n",
       "886        0  ...      1          0     1             0           0         0   \n",
       "887        0  ...      1          0     0             0           1         0   \n",
       "888        0  ...      0          1     0             0           1         0   \n",
       "889        0  ...      1          0     1             0           0         1   \n",
       "890        0  ...      1          0     1             0           0         1   \n",
       "\n",
       "     Title_Mrs  Title_Officer  Embarked_Q  Embarked_S  \n",
       "0            0              0           0           1  \n",
       "1            1              0           0           0  \n",
       "2            0              0           0           1  \n",
       "3            1              0           0           1  \n",
       "4            0              0           0           1  \n",
       "..         ...            ...         ...         ...  \n",
       "886          0              1           0           1  \n",
       "887          0              0           0           1  \n",
       "888          0              0           0           1  \n",
       "889          0              0           0           0  \n",
       "890          0              0           1           0  \n",
       "\n",
       "[891 rows x 25 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Survived', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare', 'Cabin_A',\n",
       "       'Cabin_B', 'Cabin_C', 'Cabin_D', 'Cabin_E', 'Cabin_F', 'Cabin_G',\n",
       "       'Cabin_U', 'Family Size', 'Alone', 'Not_Alone', 'male', 'Title_Master',\n",
       "       'Title_Miss', 'Title_Mr', 'Title_Mrs', 'Title_Officer', 'Embarked_Q',\n",
       "       'Embarked_S'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've come up to more than 20 features so far. This number is quite large.\n",
    "\n",
    "When feature engineering is done, we usually tend to decrease the dimensionality by selecting the \"right\" features that capture the essential.\n",
    "\n",
    "In fact, feature selection comes with many benefits:\n",
    "\n",
    "**1.** It decreases redundancy among the data\n",
    "**2.** It speeds up the training process\n",
    "**3.** It reduces overfitting\n",
    "\n",
    "Tree-based estimators can be used to compute feature importance, which in turn can be used to estimate the relavance of the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_df.iloc[:,1:26]\n",
    "Y_train = train_df.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "                       n_jobs=None, oob_score=False, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier  = RandomForestClassifier()\n",
    "classifier.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the importance of each feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x269b52098d0>"
      ]
     },
     "execution_count": 58,
     "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": [
    "features = pd.DataFrame()\n",
    "features['feature'] = X_train.columns\n",
    "features['importance'] = classifier.feature_importances_\n",
    "features.sort_values(by=['importance'], ascending=True, inplace=True)\n",
    "features.set_index('feature', inplace=True)\n",
    "features.plot(kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "As you may notice, there is a great importance linked to Title_Mr, Age, Fare, and Sex(male).\n",
    "\n",
    "Let's now transform our training-set and test-set in a more compact dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 17)\n"
     ]
    }
   ],
   "source": [
    "model = SelectFromModel(classifier, prefit=True,threshold = .011)\n",
    "train_reduced = model.transform(X_train)\n",
    "print(train_reduced.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_reduced = model.transform(test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Fold Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "k_fold = KFold(n_splits=10, shuffle=True, random_state=0)\n",
    "scores = []\n",
    "model_name = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82.04244694132335\n"
     ]
    }
   ],
   "source": [
    "sc = StandardScaler()\n",
    "t = sc.fit_transform(train_reduced)\n",
    "classifier = LogisticRegression()\n",
    "score = cross_val_score(classifier, t, Y_train, cv=k_fold, n_jobs=1, scoring = 'accuracy')\n",
    "print(np.mean(score)*100)\n",
    "scores.append(np.mean(score))\n",
    "model_name.append(\"Logistic Regression\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "81.36953807740325\n"
     ]
    }
   ],
   "source": [
    "classifier = KNeighborsClassifier(n_neighbors = 10)\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(classifier, train_reduced, Y_train, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(np.mean(score) * 100)\n",
    "scores.append(np.mean(score))\n",
    "model_name.append(\"KNN\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Decision tree "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79.46192259675405\n"
     ]
    }
   ],
   "source": [
    "classifier = DecisionTreeClassifier()\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(classifier, train_reduced, Y_train, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(np.mean(score) * 100)\n",
    "scores.append(np.mean(score))\n",
    "model_name.append(\"Decision tree\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Naive Bayees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "79.45318352059925\n"
     ]
    }
   ],
   "source": [
    "classifier = GaussianNB()\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(classifier, train_reduced, Y_train, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(np.mean(score) * 100)\n",
    "scores.append(np.mean(score))\n",
    "model_name.append(\"Naive Bayees\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machines "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82.93757802746568\n"
     ]
    }
   ],
   "source": [
    "classifier = SVC()\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(classifier, train_reduced, Y_train, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(np.mean(score) * 100)\n",
    "scores.append(np.mean(score))\n",
    "model_name.append(\"SVM\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80.69538077403244\n"
     ]
    }
   ],
   "source": [
    "classifier = RandomForestClassifier()\n",
    "scoring = 'accuracy'\n",
    "score = cross_val_score(classifier, train_reduced, Y_train, cv=k_fold, n_jobs=1, scoring=scoring)\n",
    "print(np.mean(score) * 100)\n",
    "scores.append(np.mean(score))\n",
    "model_name.append(\"Random Forest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.820424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.813695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision tree</td>\n",
       "      <td>0.794619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Naive Bayees</td>\n",
       "      <td>0.794532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.829376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.806954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model     Score\n",
       "0  Logistic Regression  0.820424\n",
       "1                  KNN  0.813695\n",
       "2        Decision tree  0.794619\n",
       "3         Naive Bayees  0.794532\n",
       "4                  SVM  0.829376\n",
       "5        Random Forest  0.806954"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.DataFrame({'Model' : model_name,\n",
    "              \"Score\" : scores  \n",
    "              })\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prediction and generating csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CSV generated successfully\n"
     ]
    }
   ],
   "source": [
    "classifier = SVC() #Logistic Regression gave me a score of .78894 while svm gave me a score of .78947 on kaggle \n",
    "classifier.fit(train_reduced, Y_train)\n",
    "prediction = classifier.predict(test_reduced)\n",
    "passenger = np.array(list(range(891, 891 + 418)))\n",
    "submission = pd.DataFrame({\n",
    "    \"PassengerId\" : passenger,\n",
    "    \"Survived\" : prediction\n",
    "})\n",
    "submission.to_csv('Submission.csv', index = False)\n",
    "print(\"CSV generated successfully\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### In this article, we explored a dataset hosted by <a href=\"http://kaggle.com\">Kaggle</a>.\n",
    "\n",
    "We went through the following \n",
    "  - Defined the problem statement\n",
    "  - Loaded the data\n",
    "  - Exploratory Data Analysis\n",
    "  - Feature Engineering  \n",
    "  - Feature Selection\n",
    "  - Modelling\n",
    "\n",
    "I suggest you guys to repeat the same procedure for test set.\n",
    "\n",
    "There is definitely a room for improvement and furture works include:\n",
    "\n",
    "- Extracting more features by deeply analysing the data \n",
    "- Trying hyperparamter tuning for various models\n",
    "\n",
    "I would be  happy if you could find out a way to improve this solution. We can colaborate together to improve the work, and proper credition will be given. Any suggestion would help, fell free to comment on the the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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