Titanic_Machine Learning from Disaster.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data analysis and wrangling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "\n",
    "#visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# machine learning\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Acquire data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Python Pandas packages helps u work with our datasets. We start by acquiring the training and testing datasets into Pandas DataFrames. We also combine these datasets to run certain operations on both datasets\n",
    " together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df=pd.read_csv('train.csv')\n",
    "test_df=pd.read_csv('test.csv')\n",
    "combine=[train_df,test_df]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze by describing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch'\n",
      " 'Ticket' 'Fare' 'Cabin' 'Embarked']\n"
     ]
    }
   ],
   "source": [
    "print(train_df.columns.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Which features are categorical?**\n",
    "\n",
    "These values classify the samples into sets of similar samples. Within categorical features are the values nominal, ordinal, ratio, or interval based? Among other things this helps us select the appropriate plots for visualization.\n",
    "\n",
    "- categorical:Survived, Sex,  and Embarked, Ordinal: Pclass.\n",
    "\n",
    "**which features are numerical**\n",
    "\n",
    "Which features are numerical? These values change from sample to sample. Within numerical features are the values discrete, continuous, or timeseries based? Among other things this helps us select the appropriate plots for visualization.\n",
    "\n",
    "- Continous: Age, Fare. Discrete: SibSp, Parch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\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",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#preview the data\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <th>886</th>\n",
       "      <td>887</td>\n",
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       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
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       "      <td>female</td>\n",
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       "      <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>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>889</th>\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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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: 83.6+ KB\n",
      "________________________________________\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()\n",
    "print('_'*40)\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>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": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>681</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Svensson, Mr. Johan</td>\n",
       "      <td>male</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Name   Sex    Ticket Cabin Embarked\n",
       "count                   891   891       891   204      889\n",
       "unique                  891     2       681   147        3\n",
       "top     Svensson, Mr. Johan  male  CA. 2343    G6        S\n",
       "freq                      1   577         7     4      644"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze by pivoting features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Pclass','Survived']].groupby(['Pclass'],as_index=False).mean().sort_values(by='Survived',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"Sex\", \"Survived\"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp  Survived\n",
       "1      1  0.535885\n",
       "2      2  0.464286\n",
       "0      0  0.345395\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
       "5      5  0.000000\n",
       "6      8  0.000000"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"SibSp\", \"Survived\"]].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>Parch</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parch  Survived\n",
       "3      3  0.600000\n",
       "1      1  0.550847\n",
       "2      2  0.500000\n",
       "0      0  0.343658\n",
       "5      5  0.200000\n",
       "4      4  0.000000\n",
       "6      6  0.000000"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"Parch\", \"Survived\"]].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze by visualizing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlating numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x18cd13c8>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18cd1c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(train_df, col='Survived')\n",
    "g.map(plt.hist, 'Age', bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlating numerical and ordinal features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18d73da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Pclass', hue='Survived')\n",
    "grid = sns.FacetGrid(train_df, col='Survived', row='Pclass', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
    "grid.add_legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlating categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x15bdaeb8>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18fe98d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Embarked')\n",
    "grid = sns.FacetGrid(train_df, row='Embarked', size=2.2, aspect=1.6)\n",
    "grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Correlating categorical and numerical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x18dd40b8>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19272e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Embarked', hue='Survived', palette={0: 'k', 1: 'w'})\n",
    "grid = sns.FacetGrid(train_df, row='Embarked', col='Survived', size=2.2, aspect=1.6)\n",
    "grid.map(sns.barplot, 'Sex', 'Fare', alpha=.5, ci=None)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correcting by dropping features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before (891, 12) (418, 11) (891, 12) (418, 11)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('After', (891, 10), (418, 9), (891, 10), (418, 9))"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Before\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)\n",
    "\n",
    "train_df = train_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "test_df = test_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "\n",
    "\"After\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating new feature extracting from existing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capt</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Countess</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonkheer</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lady</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Title                 \n",
       "Capt           0     1\n",
       "Col            0     2\n",
       "Countess       1     0\n",
       "Don            0     1\n",
       "Dr             1     6\n",
       "Jonkheer       0     1\n",
       "Lady           1     0\n",
       "Major          0     2\n",
       "Master         0    40\n",
       "Miss         182     0\n",
       "Mlle           2     0\n",
       "Mme            1     0\n",
       "Mr             0   517\n",
       "Mrs          125     0\n",
       "Ms             1     0\n",
       "Rev            0     6\n",
       "Sir            0     1"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "\n",
    "pd.crosstab(train_df['Title'], train_df['Sex'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can replace many titles with a more common name or classify them as Rare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rare</td>\n",
       "      <td>0.347826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title  Survived\n",
       "0  Master  0.575000\n",
       "1    Miss  0.702703\n",
       "2      Mr  0.156673\n",
       "3     Mrs  0.793651\n",
       "4    Rare  0.347826"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\\\n",
    " \t'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "\n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
    "    \n",
    "train_df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can convert the categorical titles to ordinal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</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     Fare Embarked  Title  \n",
       "0      0   7.2500        S      1  \n",
       "1      0  71.2833        C      3  \n",
       "2      0   7.9250        S      2  \n",
       "3      0  53.1000        S      3  \n",
       "4      0   8.0500        S      1  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 9), (418, 9))"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['Name', 'PassengerId'], axis=1)\n",
    "test_df = test_df.drop(['Name'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting a categorical feature\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0  22.0      1      0   7.2500        S      1\n",
       "1         1       1    1  38.0      1      0  71.2833        C      3\n",
       "2         1       3    1  26.0      0      0   7.9250        S      2\n",
       "3         1       1    1  35.0      1      0  53.1000        S      3\n",
       "4         0       3    0  35.0      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Completing a numerical continuous feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x194a9eb8>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x194a9898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Pclass', hue='Gender')\n",
    "grid = sns.FacetGrid(train_df, row='Pclass', col='Sex', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us start by preparing an empty array to contain guessed Age values based on Pclass x Gender combinations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guess_ages = np.zeros((2,3))\n",
    "guess_ages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we iterate over Sex (0 or 1) and Pclass (1, 2, 3) to calculate guessed values of Age for the six combinations.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0   22      1      0   7.2500        S      1\n",
       "1         1       1    1   38      1      0  71.2833        C      3\n",
       "2         1       3    1   26      0      0   7.9250        S      2\n",
       "3         1       1    1   35      1      0  53.1000        S      3\n",
       "4         0       3    0   35      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            guess_df = dataset[(dataset['Sex'] == i) & \\\n",
    "                                  (dataset['Pclass'] == j+1)]['Age'].dropna()\n",
    "\n",
    "            # age_mean = guess_df.mean()\n",
    "            # age_std = guess_df.std()\n",
    "            # age_guess = rnd.uniform(age_mean - age_std, age_mean + age_std)\n",
    "\n",
    "            age_guess = guess_df.median()\n",
    "\n",
    "            # Convert random age float to nearest .5 age\n",
    "            guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5\n",
    "            \n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            dataset.loc[ (dataset.Age.isnull()) & (dataset.Sex == i) & (dataset.Pclass == j+1),\\\n",
    "                    'Age'] = guess_ages[i,j]\n",
    "\n",
    "    dataset['Age'] = dataset['Age'].astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AgeBand</th>\n",
       "      <th>Survived</th>\n",
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       "      <td>(48.0, 64.0]</td>\n",
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       "      <td>(64.0, 80.0]</td>\n",
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      ],
      "text/plain": [
       "         AgeBand  Survived\n",
       "0  (-0.08, 16.0]  0.550000\n",
       "1   (16.0, 32.0]  0.337374\n",
       "2   (32.0, 48.0]  0.412037\n",
       "3   (48.0, 64.0]  0.434783\n",
       "4   (64.0, 80.0]  0.090909"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['AgeBand'] = pd.cut(train_df['Age'], 5)\n",
    "train_df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <th>3</th>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title  \\\n",
       "0         0       3    0    1      1      0   7.2500        S      1   \n",
       "1         1       1    1    2      1      0  71.2833        C      3   \n",
       "2         1       3    1    1      0      0   7.9250        S      2   \n",
       "3         1       1    1    2      1      0  53.1000        S      3   \n",
       "4         0       3    0    2      0      0   8.0500        S      1   \n",
       "\n",
       "        AgeBand  \n",
       "0  (16.0, 32.0]  \n",
       "1  (32.0, 48.0]  \n",
       "2  (16.0, 32.0]  \n",
       "3  (32.0, 48.0]  \n",
       "4  (32.0, 48.0]  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:    \n",
    "    dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n",
    "    dataset.loc[ dataset['Age'] > 64, 'Age']\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can not remove the AgeBand feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0    1      1      0   7.2500        S      1\n",
       "1         1       1    1    2      1      0  71.2833        C      3\n",
       "2         1       3    1    1      0      0   7.9250        S      2\n",
       "3         1       1    1    2      1      0  53.1000        S      3\n",
       "4         0       3    0    2      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['AgeBand'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create new feature combining existing features\n",
    "\n",
    "We can create a new feature for FamilySize which combines Parch and SibSp. This will enable us to drop Parch and SibSp from our datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>3</th>\n",
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       "      <td>0.333333</td>\n",
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       "      <th>8</th>\n",
       "      <td>11</td>\n",
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       "</div>"
      ],
      "text/plain": [
       "   FamilySize  Survived\n",
       "3           4  0.724138\n",
       "2           3  0.578431\n",
       "1           2  0.552795\n",
       "6           7  0.333333\n",
       "0           1  0.303538\n",
       "4           5  0.200000\n",
       "5           6  0.136364\n",
       "7           8  0.000000\n",
       "8          11  0.000000"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\n",
    "\n",
    "train_df[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create another feature called IsAlone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Survived</th>\n",
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      ],
      "text/plain": [
       "   IsAlone  Survived\n",
       "0        0  0.505650\n",
       "1        1  0.303538"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['IsAlone'] = 0\n",
    "    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1\n",
    "\n",
    "train_df[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us drop Parch, SibSp, and FamilySize features in favor of IsAlone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age     Fare Embarked  Title  IsAlone\n",
       "0         0       3    0    1   7.2500        S      1        0\n",
       "1         1       1    1    2  71.2833        C      3        0\n",
       "2         1       3    1    1   7.9250        S      2        1\n",
       "3         1       1    1    2  53.1000        S      3        0\n",
       "4         0       3    0    2   8.0500        S      1        1"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\n",
    "test_df = test_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also create an artificial feature combining Pclass and Age."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age*Class</th>\n",
       "      <th>Age</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
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       "      <td>3</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>2</td>\n",
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       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age*Class  Age  Pclass\n",
       "0          3    1       3\n",
       "1          2    2       1\n",
       "2          3    1       3\n",
       "3          2    2       1\n",
       "4          6    2       3\n",
       "5          3    1       3\n",
       "6          3    3       1\n",
       "7          0    0       3\n",
       "8          3    1       3\n",
       "9          0    0       2"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Age*Class'] = dataset.Age * dataset.Pclass\n",
    "\n",
    "train_df.loc[:, ['Age*Class', 'Age', 'Pclass']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Completing a categorical feature\n",
    "\n",
    "Embarked feature takes S, Q, C values based on port of embarkation. Our training dataset has two missing values. We simply fill these with the most common occurance.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'S'"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_port = train_df.Embarked.dropna().mode()[0]\n",
    "freq_port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q</td>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S</td>\n",
       "      <td>0.339009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Embarked  Survived\n",
       "0        C  0.553571\n",
       "1        Q  0.389610\n",
       "2        S  0.339009"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)\n",
    "    \n",
    "train_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting categorical feature to numeric\n",
    "\n",
    "We can now convert the EmbarkedFill feature by creating a new numeric Port feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
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       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age     Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0         0       3    0    1   7.2500         0      1        0          3\n",
       "1         1       1    1    2  71.2833         1      3        0          2\n",
       "2         1       3    1    1   7.9250         0      2        1          3\n",
       "3         1       1    1    2  53.1000         0      3        0          2\n",
       "4         0       3    0    2   8.0500         0      1        1          6"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### completing and converting a numeric feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
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       "  <tbody>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>894</td>\n",
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       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age     Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0          892       3    0    2   7.8292         2      1        1          6\n",
       "1          893       3    1    2   7.0000         0      3        0          6\n",
       "2          894       2    0    3   9.6875         2      1        1          6\n",
       "3          895       3    0    1   8.6625         0      1        1          3\n",
       "4          896       3    1    1  12.2875         0      3        0          3"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['Fare'].fillna(test_df['Fare'].dropna().median(), inplace=True)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can not create FareBand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FareBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.001, 7.91]</td>\n",
       "      <td>0.197309</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(7.91, 14.454]</td>\n",
       "      <td>0.303571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(14.454, 31.0]</td>\n",
       "      <td>0.454955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(31.0, 512.329]</td>\n",
       "      <td>0.581081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          FareBand  Survived\n",
       "0   (-0.001, 7.91]  0.197309\n",
       "1   (7.91, 14.454]  0.303571\n",
       "2   (14.454, 31.0]  0.454955\n",
       "3  (31.0, 512.329]  0.581081"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['FareBand'] = pd.qcut(train_df['Fare'], 4)\n",
    "train_df[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the Fare feature to ordinal values based on the FareBand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0         0       3    0    1     0         0      1        0          3\n",
       "1         1       1    1    2     3         1      3        0          2\n",
       "2         1       3    1    1     1         0      2        1          3\n",
       "3         1       1    1    2     3         0      3        0          2\n",
       "4         0       3    0    2     1         0      1        1          6\n",
       "5         0       3    0    1     1         2      1        1          3\n",
       "6         0       1    0    3     3         0      1        1          3\n",
       "7         0       3    0    0     2         0      4        0          0\n",
       "8         1       3    1    1     1         0      3        0          3\n",
       "9         1       2    1    0     2         1      3        0          0"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0\n",
    "    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n",
    "    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare']   = 2\n",
    "    dataset.loc[ dataset['Fare'] > 31, 'Fare'] = 3\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)\n",
    "\n",
    "train_df = train_df.drop(['FareBand'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "    \n",
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Age*Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</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>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>897</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone  Age*Class\n",
       "0          892       3    0    2     0         2      1        1          6\n",
       "1          893       3    1    2     0         0      3        0          6\n",
       "2          894       2    0    3     1         2      1        1          6\n",
       "3          895       3    0    1     1         0      1        1          3\n",
       "4          896       3    1    1     1         0      3        0          3\n",
       "5          897       3    0    0     1         0      1        1          0\n",
       "6          898       3    1    1     0         2      2        1          3\n",
       "7          899       2    0    1     2         0      1        0          2\n",
       "8          900       3    1    1     0         1      3        1          3\n",
       "9          901       3    0    1     2         0      1        0          3"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model, predict and solve\n",
    "\n",
    "Now we are ready to train a model and predict the required solution. \n",
    "\n",
    "\n",
    "- Logistic Regression\n",
    "- KNN or k-Nearest Neighbors\n",
    "- Support Vector Machines\n",
    "- Naive Bayes classifier\n",
    "- Decision Tree\n",
    "- Random Forrest\n",
    "- Perceptron\n",
    "- Artificial neural network\n",
    "- RVM or Relevance Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 8), (891,), (418, 8))"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = train_df.drop(\"Survived\", axis=1)\n",
    "Y_train = train_df[\"Survived\"]\n",
    "X_test  = test_df.drop(\"PassengerId\", axis=1).copy()\n",
    "X_train.shape, Y_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.36"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, Y_train)\n",
    "Y_pred = logreg.predict(X_test)\n",
    "acc_log = round(logreg.score(X_train, Y_train) * 100, 2)\n",
    "acc_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>Feature</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sex</td>\n",
       "      <td>2.201527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Title</td>\n",
       "      <td>0.398234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age</td>\n",
       "      <td>0.287163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Embarked</td>\n",
       "      <td>0.261762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>IsAlone</td>\n",
       "      <td>0.129140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fare</td>\n",
       "      <td>-0.085150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Age*Class</td>\n",
       "      <td>-0.311200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pclass</td>\n",
       "      <td>-0.749007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Feature  Correlation\n",
       "1        Sex     2.201527\n",
       "5      Title     0.398234\n",
       "2        Age     0.287163\n",
       "4   Embarked     0.261762\n",
       "6    IsAlone     0.129140\n",
       "3       Fare    -0.085150\n",
       "7  Age*Class    -0.311200\n",
       "0     Pclass    -0.749007"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame(train_df.columns.delete(0))\n",
    "coeff_df.columns = ['Feature']\n",
    "coeff_df[\"Correlation\"] = pd.Series(logreg.coef_[0])\n",
    "\n",
    "coeff_df.sort_values(by='Correlation', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.84"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Support Vector Machines\n",
    "\n",
    "svc = SVC()\n",
    "svc.fit(X_train, Y_train)\n",
    "Y_pred = svc.predict(X_test)\n",
    "acc_svc = round(svc.score(X_train, Y_train) * 100, 2)\n",
    "acc_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.74"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X_train, Y_train)\n",
    "Y_pred = knn.predict(X_test)\n",
    "acc_knn = round(knn.score(X_train, Y_train) * 100, 2)\n",
    "acc_knn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72.28"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gaussian Naive Bayes\n",
    "\n",
    "gaussian = GaussianNB()\n",
    "gaussian.fit(X_train, Y_train)\n",
    "Y_pred = gaussian.predict(X_test)\n",
    "acc_gaussian = round(gaussian.score(X_train, Y_train) * 100, 2)\n",
    "acc_gaussian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.0"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perceptron\n",
    "\n",
    "perceptron = Perceptron()\n",
    "perceptron.fit(X_train, Y_train)\n",
    "Y_pred = perceptron.predict(X_test)\n",
    "acc_perceptron = round(perceptron.score(X_train, Y_train) * 100, 2)\n",
    "acc_perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.12"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear SVC\n",
    "\n",
    "linear_svc = LinearSVC()\n",
    "linear_svc.fit(X_train, Y_train)\n",
    "Y_pred = linear_svc.predict(X_test)\n",
    "acc_linear_svc = round(linear_svc.score(X_train, Y_train) * 100, 2)\n",
    "acc_linear_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.22"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Stochastic Gradient Descent\n",
    "\n",
    "sgd = SGDClassifier()\n",
    "sgd.fit(X_train, Y_train)\n",
    "Y_pred = sgd.predict(X_test)\n",
    "acc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)\n",
    "acc_sgd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.76"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree\n",
    "\n",
    "decision_tree = DecisionTreeClassifier()\n",
    "decision_tree.fit(X_train, Y_train)\n",
    "Y_pred = decision_tree.predict(X_test)\n",
    "acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)\n",
    "acc_decision_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.76"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest\n",
    "\n",
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "Y_pred = random_forest.predict(X_test)\n",
    "random_forest.score(X_train, Y_train)\n",
    "acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\n",
    "acc_random_forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model evaluation\n",
    "\n",
    "We can now rank our evaluation of all the models to choose the best one for our problem. While both Decision Tree and Random Forest score the same, we choose to use Random Forest as they correct for decision trees' habit of overfitting to their training set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>3</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>86.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>86.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>84.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>83.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>80.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>79.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Perceptron</td>\n",
       "      <td>78.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Stochastic Gradient Decent</td>\n",
       "      <td>77.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>72.28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model  Score\n",
       "3               Random Forest  86.76\n",
       "8               Decision Tree  86.76\n",
       "1                         KNN  84.74\n",
       "0     Support Vector Machines  83.84\n",
       "2         Logistic Regression  80.36\n",
       "7                  Linear SVC  79.12\n",
       "5                  Perceptron  78.00\n",
       "6  Stochastic Gradient Decent  77.22\n",
       "4                 Naive Bayes  72.28"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', \n",
    "              'Random Forest', 'Naive Bayes', 'Perceptron', \n",
    "              'Stochastic Gradient Decent', 'Linear SVC', \n",
    "              'Decision Tree'],\n",
    "    'Score': [acc_svc, acc_knn, acc_log, \n",
    "              acc_random_forest, acc_gaussian, acc_perceptron, \n",
    "              acc_sgd, acc_linear_svc, acc_decision_tree]})\n",
    "models.sort_values(by='Score', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": test_df[\"PassengerId\"],\n",
    "        \"Survived\": Y_pred\n",
    "    })\n",
    "# submission.to_csv('../output/submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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