Titanic_follow_by_Kannel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "titanic_raw_train = pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_raw_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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"
     ]
    }
   ],
   "source": [
    "titanic_raw_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "titanic_raw_train.hist(bins=50, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def split_train_test(data, test_ratio):\n",
    "    shuffled_indices = np.random.permutation(len(data))\n",
    "    test_set_size = int(len(data) * test_ratio)\n",
    "    test_indices = shuffled_indices[:test_set_size]\n",
    "    train_indices = shuffled_indices[test_set_size:]\n",
    "    return data.iloc[train_indices], data.iloc[test_indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set, test_set = split_train_test(titanic_raw_train, 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "713 train + 178 test\n"
     ]
    }
   ],
   "source": [
    "print(len(train_set), \"train +\", len(test_set), \"test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set, test_set = train_test_split(titanic_raw_train, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "t=titanic_raw_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22.0\n",
       "1    38.0\n",
       "2    26.0\n",
       "3    35.0\n",
       "4    35.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t[\"Age\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "t[\"Age\"].where(t[\"Age\"] < 60, 60, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t[\"Age\"].hist(bins=50, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    0\n",
       "3    1\n",
       "4    0\n",
       "Name: SibSp, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t[\"SibSp\"].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "t[\"SibSp\"].where(t[\"SibSp\"] < 4, 4, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t[\"SibSp\"].hist(bins=50, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42)\n",
    "for train_index, test_index in split.split(t, t[\"Survived\"]):\n",
    "    strat_train_set = t.loc[train_index]\n",
    "    strat_test_set = t.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for set_ in (strat_train_set, strat_test_set):\n",
    "    set_.drop(\"Survived\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#t = strat_train_set.copy()\n",
    "#t=titanic_raw_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1afcd816860>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t.plot(kind=\"scatter\", x=\"Age\", y=\"Survived\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1afcdca3898>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t.plot(kind=\"scatter\", x=\"Fare\", y=\"Survived\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1afcdcf21d0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAFX1JREFUeJzt3Xu0pXV93/H3ZwaJFlGWM6NSZnSmYaQZWSaQkxFjRCJqEAyzVmUpXlLJMk7aBmxq0izSpF5wZdWYVusFo6OxEqwiNW06cik2XMRggTmgIgwBRy5lgsgAgqJUMvDtH3vPz+Nmn9n7DOc5+zDzfq11Fvv57d+zn+/5Mc/+nOeeqkKSJIAlky5AkrR4GAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktTsN+kC5mr58uW1evXqSZchSU8o11xzzT1VtWJUvydcKKxevZrp6elJlyFJTyhJbh+nn7uPJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWdXaeQ5FPAq4G7q+rwIe8H+CBwPPAj4JSquraregBWn35+e33be0/oclF7BcdrfBs/fRWXb7uXow9dxqZTXjjpcha9i7fexZe2fpdXrnsWx6579qTLWfSmb72Xy791D0evXc7UmmWdLitdPaM5ydHAg8BfzhIKxwOn0QuFFwIfrKqRa9PU1FTtycVrM7/gdvGLbnaO1/gcq7l55Qcu4+bv/rBNH/asA7jo3xwzuYIWuTd98kr+dtu9bfolhy7j7N86as6fk+Saqpoa1a+z3UdVdTlw3266bKAXGFVVVwIHJTm4i1qGrbS7a9/XOV7j2/jpq+bUvq+7eOtdPxUIADd994dcvPWuCVW0uE3feu9PBQLAV7bdy/St984yx+M3yWMKhwB3zJje3m97jCQbk0wnmd6xY8eCFCeN4/Jtw1fO2dr3dV/a+t05te/rLv/WPXNqnw+TDIUMaRu6L6uqNlXVVFVNrVgx8n5O0oI5+tDh+3dna9/XvXLds+bUvq87eu3yObXPh0mGwnZg1YzplcCdXSxotv277vcdzvEa32wHlT3YPNyx657NYc864KfaDnvWAR5snsXUmmW8ZOAPjJccuqzTg82dHWgGSLIaOG+WA80nAKfykwPNH6qq9aM+c08PNINn08yV4zU+zz6aG88+mpv5OPto3APNXZ599DngGGA58F3gncCTAKrqY/1TUj8CHEfvlNTfrKqR3/aPJxQkaV81bih0dp1CVb1+xPsF/E5Xy5ckzZ1XNEuSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSp6TQUkhyX5KYk25KcPuT95yS5NMnXklyX5Pgu65Ek7V5noZBkKXAm8CpgHfD6JOsGuv0xcG5VHQGcDHy0q3okSaN1uaWwHthWVbdU1cPAOcCGgT4FPK3/+unAnR3WI0kaYb8OP/sQ4I4Z09uBFw70eRfwpSSnAQcAL++wHknSCF1uKWRIWw1Mvx74dFWtBI4Hzk7ymJqSbEwynWR6x44dHZQqSYJuQ2E7sGrG9Eoeu3voLcC5AFX1f4AnA8sHP6iqNlXVVFVNrVixoqNyJUldhsIWYG2SNUn2p3cgefNAn/8LHAuQ5OfohYKbApI0IZ2FQlXtBE4FLgJupHeW0Q1JzkhyYr/b7wFvTfIN4HPAKVU1uItJkrRAujzQTFVdAFww0PaOGa+3Ai/usgZJ0vi8olmS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWo6DYUkxyW5Kcm2JKfP0ue1SbYmuSHJZ7usR5K0e/t19cFJlgJnAq8AtgNbkmyuqq0z+qwF/hB4cVV9L8kzu6pHkjRal1sK64FtVXVLVT0MnANsGOjzVuDMqvoeQFXd3WE9kqQRugyFQ4A7Zkxv77fN9DzgeUmuSHJlkuM6rEeSNMJudx8l+QFQs71fVU/b3ezDZhmy/LXAMcBK4CtJDq+q+wfq2AhsBHjOc56zu5IlSY/DbkOhqg4ESHIGcBdwNr0v+zcCB4747O3AqhnTK4E7h/S5sqr+Abg1yU30QmLLQB2bgE0AU1NTs4aUJOnxGXf30a9V1Uer6gdV9f2q+nPgNSPm2QKsTbImyf7AycDmgT5/DfwqQJLl9HYn3TJ++ZKk+TRuKDyS5I1JliZZkuSNwCO7m6GqdgKnAhcBNwLnVtUNSc5IcmK/20XAvUm2ApcC/7aq7t2zX0WS9HilavTemCSrgQ8CL6Z3XOAK4Her6rYOaxtqamqqpqenF3qxkvSEluSaqpoa1W+s6xT6X/6Dp5NKkvYyY+0+SvK8JBcnub4//YIkf9xtaZKkhTbuMYVP0Lvy+B8Aquo6egeOJUl7kXFD4R9V1dUDbTvnuxhJ0mSNGwr3JPlZ+hefJTkJ+E5nVUmSJmLcG+L9Dr2Lx/5pkr8HbqV3AZskaS8ybijcXlUvT3IAsKSqftBlUZKkyRh399GtSTYBRwEPdliPJGmCxg2Fw4C/obcb6dYkH0nyK92VJUmahLFCoaoeqqpzq+qfAUcATwO+3GllkqQFN/bzFJK8NMlHgWuBJwOv7awqSdJEjHWgOcmtwNeBc+ndtO6HnVYlSZqIcc8++vmq+n6nlUiSJm7Uk9f+oKreB/xJksfcTrWq3tZZZZKkBTdqS+HG/n+9V7Uk7QNGPY7zi/2X11XV1xagHknSBI179tH7k/xdkvckeX6nFUmSJmbc6xR+FTgG2AFsSvJNn6cgSXufsa9TqKq7qupDwL+gd3rqOzqrSpI0EeM+ee3nkryr/+S1jwBfBVZ2WpkkacGNe53CfwE+B7yyqu7ssB5J0gSNDIUkS4FvV9UHF6AeSdIEjdx9VFWPAMuS7L8A9UiSJmjsh+wAVyTZDLT7HlXV+zupSpI0EeOGwp39nyXAgd2VI0mapLFCoare3XUhkqTJG/fW2ZcCw26I97J5r0iSNDHj7j76/Rmvnwy8Btg5/+VIkiZp3N1H1ww0XZHEx3FK0l5m3N1Hz5gxuQSYAp7dSUWSpIkZ995H19B7psI0vVtcvB14y6iZkhyX5KYk25Kcvpt+JyWpJFNj1iNJ6sCoJ6/9EnBHVa3pT7+Z3vGE24CtI+ZdCpwJvALYDmxJsrmqtg70OxB4G3DVHv4OkqR5MmpL4ePAwwBJjgb+A3AW8ACwacS864FtVXVLVT0MnANsGNLvPcD7gP83h7olSR0YFQpLq+q+/uvXAZuq6q+q6t8Dh46Y9xDgjhnT2/ttTZIjgFVVdd7uPijJxiTTSaZ37NgxYrGSpD01MhSS7NrFdCxwyYz3Rh2kzpC2dq1DkiXAB4DfG1VkVW2qqqmqmlqxYsWo7pKkPTTqi/1zwJeT3AM8BHwFIMmh9HYh7c52YNWM6ZX0bpWxy4HA4cBlSaB3NtPmJCdW1fTYv4Ekad7sNhSq6k+SXAwcDHypqnb9pb8EOG3EZ28B1iZZA/w9cDLwhhmf/QCwfNd0ksuA3zcQJGlyRl6nUFVXDmm7eYz5diY5FbgIWAp8qqpuSHIGMF1Vm/ekYElSd8a9zcUeqaoLgAsG2oY+27mqjumyFknSaONevCZJ2gcYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqOg2FJMcluSnJtiSnD3n/7Um2JrkuycVJnttlPZKk3essFJIsBc4EXgWsA16fZN1At68BU1X1AuALwPu6qkeSNFqXWwrrgW1VdUtVPQycA2yY2aGqLq2qH/UnrwRWdliPJGmELkPhEOCOGdPb+22zeQtw4bA3kmxMMp1keseOHfNYoiRppi5DIUPaamjH5E3AFPBnw96vqk1VNVVVUytWrJjHEiVJM+3X4WdvB1bNmF4J3DnYKcnLgT8CXlpVP+6wHknSCF1uKWwB1iZZk2R/4GRg88wOSY4APg6cWFV3d1iLJGkMnYVCVe0ETgUuAm4Ezq2qG5KckeTEfrc/A54K/LckX0+yeZaPkyQtgC53H1FVFwAXDLS9Y8brl3e5fEnS3HhFsySpMRQkSY2hIElqDAVJUmMoSJIaQ0GS1BgKkqTGUJAkNYaCJKkxFCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpMZQkCQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQkSY2hIElqDAVJUmMoSJKaTkMhyXFJbkqyLcnpQ97/mSSf779/VZLVXdYjSdq9/br64CRLgTOBVwDbgS1JNlfV1hnd3gJ8r6oOTXIy8KfA67qqafXp57fXt733hK4Ws9dwvMb3ho9fwdW338/65x7EZ3/7xZMuZ9E77TNbuOTme3jZ85bz4Tf90qTLWfSOfPeF3PfQozzjKUu49p2v6nRZXW4prAe2VdUtVfUwcA6wYaDPBuCs/usvAMcmSRfFzPyCGzatn+Z4jW/16efz1VvvZ+ej8NVb73esRlh9+vl88fq7+eHDj/LF6+92vEZYffr53PfQowDc99CjnY9Xl6FwCHDHjOnt/bahfapqJ/AAsGy+C5ltEP3HOJzjNb43fPyKObXv6077zJY5te/rjnz3hXNqnw9dhsKwv/hrD/qQZGOS6STTO3bsmJfipPlw9e33z6l9X3fJzffMqX1ft2sLYdz2+dBlKGwHVs2YXgncOVufJPsBTwfuG/ygqtpUVVNVNbVixYqOypXmbv1zD5pT+77uZc9bPqf2fd0znjL8K3q29vnQZShsAdYmWZNkf+BkYPNAn83Am/uvTwIuqarHbCk8XrMdJPXg6XCO1/hmO6jswebhZjuo7MHm4WY7qNzlwebOQqF/jOBU4CLgRuDcqrohyRlJTux3+wtgWZJtwNuBx5y2Ol8Gv9D8gts9x2t8t733BH55zUHstwR+ec1BjtUIt733BH798GdywP5L+PXDn+l4jXDbe09oWwbPeMqSzscrHfxh3qmpqamanp6edBmS9ISS5JqqmhrVzyuaJUmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDVPuOsUkuwAbn8cH7EcWIw3WrGuuVmMdS3GmsC65mpvreu5VTXyPkFPuFB4vJJMj3MBx0KzrrlZjHUtxprAuuZqX6/L3UeSpMZQkCQ1+2IobJp0AbOwrrlZjHUtxprAuuZqn65rnzumIEma3b64pSBJmsVeGwpJjktyU5JtSR7znIYkP5Pk8/33r0qyehHUdEqSHUm+3v/5ra5r6i/3U0nuTnL9LO8nyYf6dV+X5MhFUtcxSR6YMV7vWICaViW5NMmNSW5I8q+H9Fnw8RqzrkmM15OTXJ3kG/263j2kzyTWxXHqmsj62F/20iRfS3LekPe6Ha+q2ut+gKXAt4F/AuwPfANYN9DnXwEf678+Gfj8IqjpFOAjExivo4Ejgetnef944EJ6z9Q+CrhqkdR1DHDeAo/VwcCR/dcHAjcP+f+44OM1Zl2TGK8AT+2/fhJwFXDUQJ8FXRfnUNdE1sf+st8OfHbY/6+ux2tv3VJYD2yrqluq6mHgHGDDQJ8NwFn9118Ajk2SCdc0EVV1OUOejT3DBuAvq+dK4KAkBy+CuhZcVX2nqq7tv/4BvacKHjLQbcHHa8y6Flx/DB7sTz6p/zN4IHOh18Vx65qIJCuBE4BPztKl0/HaW0PhEOCOGdPbeewK0vpU79GhDwDLJlwTwGv6uxy+kGRVh/XMxbi1T8KL+rsALkzy/IVccH+z/Qh6f2XONNHx2k1dMIHx6u8K+TpwN/C/q2rW8VqgdXHcumAy6+N/Bv4AeHSW9zsdr701FIal5uBfAeP0mU/jLO+LwOqqegHwN/zkr4FJW+ixGte19C7d/3ngw8BfL9SCkzwV+Cvgd6vq+4NvD5llQcZrRF0TGa+qeqSqfgFYCaxPcvhAl4mM1xh1Lfj6mOTVwN1Vdc3uug1pm7fx2ltDYTswM9VXAnfO1ifJfsDT6XZXxciaqureqvpxf/ITwC92WM9cjDOeC66qvr9rF0BVXQA8Kcnyrpeb5En0vnj/a1X99yFdJjJeo+qa1HjNWP79wGXAcQNvLfS6OFZdE1ofXwycmOQ2eruYX5bkMwN9Oh2vvTUUtgBrk6xJsj+9gzGbB/psBt7cf30ScEn1j9xMqqaB/c4n0tsvvBhsBv55/6yao4AHquo7ky4qybN37UtNsp7ev+d7O15mgL8Abqyq98/SbcHHa5y6JjReK5Ic1H/9FODlwN8NdFvodXGsuiaxPlbVH1bVyqpaTe874pKqetNAt07Ha7/5+qDFpKp2JjkVuIjeWT+fqqobkpwBTFfVZnor0NlJttFL2ZMXQU1vS3IisLNf0yld1rRLks/ROzNleZLtwDvpHXijqj4GXEDvjJptwI+A31wkdZ0E/MskO4GHgJO7/jKh95fcbwDf7O+PBvh3wHNm1DWJ8RqnrkmM18HAWUmW0guhc6vqvEmui3OoayLr4zALOV5e0SxJavbW3UeSpD1gKEiSGkNBktQYCpKkxlCQJDWGgjSLJH/Uv4Pmdf27ZL4wySeTrOu//+As8x3Vv3vl19O7a+m7FrRw6XHYK69TkB6vJC8CXk3vzqM/7l/5u39VjXP75LOA11bVN/rnwR/WZa3SfHJLQRruYOCeXbc5qKp7qurOJJclmdrVKcl/SnJtkouTrOg3PxP4Tn++R6pqa7/vu5KcneSSJN9K8tYF/p2kkQwFabgvAauS3Jzko0leOqTPAcC1VXUk8GV6V1wDfAC4Kcn/SPLbSZ48Y54X0Lst8ouAdyT5xx3+DtKcGQrSEP0bx/0isBHYAXw+ySkD3R4FPt9//RngV/rzngFM0QuWNwD/a8Y8/7OqHqqqe4BL6T1nQ1o0PKYgzaKqHqF398zLknyTn9yEbNZZZsz7beDPk3wC2JFk2WCfWaaliXJLQRoiyWFJ1s5o+gXg9oFuS+jdZA56WwR/25/3hF13IwXWAo8A9/enN6T3fOBl9G72t6WD8qU95paCNNxTgQ/3b6+8k94dTzfSe/zhLj8Enp/kGnpPv3pdv/03gA8k+VF/3jdW1SP9nLgaOJ/e3UvfU1UTfy6FNJN3SZUWSP96hQer6j9OuhZpNu4+kiQ1bilIkhq3FCRJjaEgSWoMBUlSYyhIkhpDQZLUGAqSpOb/AzIA2VCba/6pAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t.plot(kind=\"scatter\", x=\"SibSp\", y=\"Survived\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1afcdd5fb70>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t.plot(kind=\"scatter\", x=\"Pclass\", y=\"Survived\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1afce1f3048>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t.plot(kind=\"scatter\", x=\"Fare\", y=\"Survived\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1afcd7e5cf8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "t.plot(kind=\"scatter\", x=\"Fare\", y=\"Parch\", alpha=0.4,\n",
    "    s=t[\"Age\"]/10, label=\"age\", figsize=(10,7),\n",
    "    c=\"Survived\", cmap=plt.get_cmap(\"jet\"), colorbar=True,\n",
    ")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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": "code",
   "execution_count": 27,
   "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": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "    <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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 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": 30,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>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>Goldschmidt, Mr. George B</td>\n",
       "      <td>male</td>\n",
       "      <td>1601</td>\n",
       "      <td>B96 B98</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     Goldschmidt, Mr. George B  male   1601  B96 B98        S\n",
       "freq                            1   577      7        4      644"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Sex','Survived']].groupby(['Sex'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": 33,
     "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": 34,
   "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": 34,
     "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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1afcc667668>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "g = sns.FacetGrid(train_df, col='Survived')\n",
    "g.map(plt.hist, 'Age', bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\xiexu\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\xiexu\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\Users\\xiexu\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:715: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "C:\\Users\\xiexu\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:720: UserWarning: Using the pointplot function without specifying `hue_order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "C:\\Users\\xiexu\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1afce0ab9b0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 323.94x475.2 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\xiexu\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\Users\\xiexu\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:715: UserWarning: Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1afce26eb38>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "code",
   "execution_count": 39,
   "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": 39,
     "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": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>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": 40,
     "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": "code",
   "execution_count": 41,
   "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": 41,
     "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": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>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": 42,
     "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": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 9), (418, 9))"
      ]
     },
     "execution_count": 43,
     "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": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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": 44,
     "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": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for dataset in combine:\n",
    "    \n",
    "#    dataset['Embarked'] = dataset['Embarked'].map( {'S': 1, 'C': 0,'Q':2} ).astype(int)\n",
    "\n",
    "#train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\xiexu\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1afce077550>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guess_ages = np.zeros((2,3))\n",
    "guess_ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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": 48,
     "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": 49,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AgeBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "      <td>0.337374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "      <td>0.412037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(48.0, 64.0]</td>\n",
       "      <td>0.434783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(64.0, 80.0]</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 49,
     "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": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81    67\n",
       "96    76\n",
       "Name: Age, dtype: int32"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
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       "      <td>4</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
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       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
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       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
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       "      <th>68</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>2</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
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       "      <th>69</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
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       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
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       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <th>73</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>74</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>56.4958</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
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       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.6500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
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       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>4</td>\n",
       "      <td>(-0.08, 16.0]</td>\n",
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       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.4750</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
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       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7875</td>\n",
       "      <td>Q</td>\n",
       "      <td>2</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>15.8500</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>34.3750</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(-0.08, 16.0]</td>\n",
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       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>61.1750</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>20.5750</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(48.0, 64.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.6542</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>(64.0, 80.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>63.3583</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0000</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",
       "60         0       3    0    1      0      0    7.2292        C      1   \n",
       "61         1       1    1    2      0      0   80.0000      NaN      2   \n",
       "62         0       1    0    2      1      0   83.4750        S      1   \n",
       "63         0       3    0    0      3      2   27.9000        S      4   \n",
       "64         0       1    0    2      0      0   27.7208        C      1   \n",
       "65         1       3    0    1      1      1   15.2458        C      4   \n",
       "66         1       2    1    1      0      0   10.5000        S      3   \n",
       "67         0       3    0    1      0      0    8.1583        S      1   \n",
       "68         1       3    1    1      4      2    7.9250        S      2   \n",
       "69         0       3    0    1      2      0    8.6625        S      1   \n",
       "70         0       2    0    1      0      0   10.5000        S      1   \n",
       "71         0       3    1    0      5      2   46.9000        S      2   \n",
       "72         0       2    0    1      0      0   73.5000        S      1   \n",
       "73         0       3    0    1      1      0   14.4542        C      1   \n",
       "74         1       3    0    1      0      0   56.4958        S      1   \n",
       "75         0       3    0    1      0      0    7.6500        S      1   \n",
       "76         0       3    0    1      0      0    7.8958        S      1   \n",
       "77         0       3    0    1      0      0    8.0500        S      1   \n",
       "78         1       2    0    0      0      2   29.0000        S      4   \n",
       "79         1       3    1    1      0      0   12.4750        S      2   \n",
       "80         0       3    0    1      0      0    9.0000        S      1   \n",
       "81         1       3    0    1      0      0    9.5000        S      1   \n",
       "82         1       3    1    1      0      0    7.7875        Q      2   \n",
       "83         0       1    0    1      0      0   47.1000        S      1   \n",
       "84         1       2    1    1      0      0   10.5000        S      2   \n",
       "85         1       3    1    2      3      0   15.8500        S      3   \n",
       "86         0       3    0    0      1      3   34.3750        S      1   \n",
       "87         0       3    0    1      0      0    8.0500        S      1   \n",
       "88         1       1    1    1      3      2  263.0000        S      2   \n",
       "89         0       3    0    1      0      0    8.0500        S      1   \n",
       "90         0       3    0    1      0      0    8.0500        S      1   \n",
       "91         0       3    0    1      0      0    7.8542        S      1   \n",
       "92         0       1    0    2      1      0   61.1750        S      1   \n",
       "93         0       3    0    1      1      2   20.5750        S      1   \n",
       "94         0       3    0    3      0      0    7.2500        S      1   \n",
       "95         0       3    0    1      0      0    8.0500        S      1   \n",
       "96         0       1    0   71      0      0   34.6542        C      1   \n",
       "97         1       1    0    1      0      1   63.3583        C      1   \n",
       "98         1       2    1    2      0      1   23.0000        S      3   \n",
       "99         0       2    0    2      1      0   26.0000        S      1   \n",
       "\n",
       "          AgeBand  \n",
       "60   (16.0, 32.0]  \n",
       "61   (32.0, 48.0]  \n",
       "62   (32.0, 48.0]  \n",
       "63  (-0.08, 16.0]  \n",
       "64   (32.0, 48.0]  \n",
       "65   (16.0, 32.0]  \n",
       "66   (16.0, 32.0]  \n",
       "67   (16.0, 32.0]  \n",
       "68   (16.0, 32.0]  \n",
       "69   (16.0, 32.0]  \n",
       "70   (16.0, 32.0]  \n",
       "71  (-0.08, 16.0]  \n",
       "72   (16.0, 32.0]  \n",
       "73   (16.0, 32.0]  \n",
       "74   (16.0, 32.0]  \n",
       "75   (16.0, 32.0]  \n",
       "76   (16.0, 32.0]  \n",
       "77   (16.0, 32.0]  \n",
       "78  (-0.08, 16.0]  \n",
       "79   (16.0, 32.0]  \n",
       "80   (16.0, 32.0]  \n",
       "81   (16.0, 32.0]  \n",
       "82   (16.0, 32.0]  \n",
       "83   (16.0, 32.0]  \n",
       "84   (16.0, 32.0]  \n",
       "85   (32.0, 48.0]  \n",
       "86  (-0.08, 16.0]  \n",
       "87   (16.0, 32.0]  \n",
       "88   (16.0, 32.0]  \n",
       "89   (16.0, 32.0]  \n",
       "90   (16.0, 32.0]  \n",
       "91   (16.0, 32.0]  \n",
       "92   (32.0, 48.0]  \n",
       "93   (16.0, 32.0]  \n",
       "94   (48.0, 64.0]  \n",
       "95   (16.0, 32.0]  \n",
       "96   (64.0, 80.0]  \n",
       "97   (16.0, 32.0]  \n",
       "98   (32.0, 48.0]  \n",
       "99   (32.0, 48.0]  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[60:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>AgeBand</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>(16.0, 32.0]</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</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>0</td>\n",
       "      <td>0</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(48.0, 64.0]</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>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>S</td>\n",
       "      <td>4</td>\n",
       "      <td>(-0.08, 16.0]</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>0</td>\n",
       "      <td>2</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(16.0, 32.0]</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.7000</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.5500</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>(48.0, 64.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>31.2750</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8542</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(48.0, 64.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>Q</td>\n",
       "      <td>4</td>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0292</td>\n",
       "      <td>Q</td>\n",
       "      <td>2</td>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>31.3875</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>263.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8792</td>\n",
       "      <td>Q</td>\n",
       "      <td>2</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.9292</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>69.5500</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>13.8583</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50.4958</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>S</td>\n",
       "      <td>4</td>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>52.5542</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</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>9.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.8458</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>(48.0, 64.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</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>7.8958</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>Q</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>5</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 10 columns</p>\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",
       "5           0       3    0    1      0      0    8.4583        Q      1   \n",
       "6           0       1    0    3      0      0   51.8625        S      1   \n",
       "7           0       3    0    0      3      1   21.0750        S      4   \n",
       "8           1       3    1    1      0      2   11.1333        S      3   \n",
       "9           1       2    1    0      1      0   30.0708        C      3   \n",
       "10          1       3    1    0      1      1   16.7000        S      2   \n",
       "11          1       1    1    3      0      0   26.5500        S      2   \n",
       "12          0       3    0    1      0      0    8.0500        S      1   \n",
       "13          0       3    0    2      1      5   31.2750        S      1   \n",
       "14          0       3    1    0      0      0    7.8542        S      2   \n",
       "15          1       2    1    3      0      0   16.0000        S      3   \n",
       "16          0       3    0    0      4      1   29.1250        Q      4   \n",
       "17          1       2    0    1      0      0   13.0000        S      1   \n",
       "18          0       3    1    1      1      0   18.0000        S      3   \n",
       "19          1       3    1    1      0      0    7.2250        C      3   \n",
       "20          0       2    0    2      0      0   26.0000        S      1   \n",
       "21          1       2    0    2      0      0   13.0000        S      1   \n",
       "22          1       3    1    0      0      0    8.0292        Q      2   \n",
       "23          1       1    0    1      0      0   35.5000        S      1   \n",
       "24          0       3    1    0      3      1   21.0750        S      2   \n",
       "25          1       3    1    2      1      5   31.3875        S      3   \n",
       "26          0       3    0    1      0      0    7.2250        C      1   \n",
       "27          0       1    0    1      3      2  263.0000        S      1   \n",
       "28          1       3    1    1      0      0    7.8792        Q      2   \n",
       "29          0       3    0    1      0      0    7.8958        S      1   \n",
       "..        ...     ...  ...  ...    ...    ...       ...      ...    ...   \n",
       "861         0       2    0    1      1      0   11.5000        S      1   \n",
       "862         1       1    1    2      0      0   25.9292        S      3   \n",
       "863         0       3    1    1      8      2   69.5500        S      2   \n",
       "864         0       2    0    1      0      0   13.0000        S      1   \n",
       "865         1       2    1    2      0      0   13.0000        S      3   \n",
       "866         1       2    1    1      1      0   13.8583        C      2   \n",
       "867         0       1    0    1      0      0   50.4958        S      1   \n",
       "868         0       3    0    1      0      0    9.5000        S      1   \n",
       "869         1       3    0    0      1      1   11.1333        S      4   \n",
       "870         0       3    0    1      0      0    7.8958        S      1   \n",
       "871         1       1    1    2      1      1   52.5542        S      3   \n",
       "872         0       1    0    2      0      0    5.0000        S      1   \n",
       "873         0       3    0    2      0      0    9.0000        S      1   \n",
       "874         1       2    1    1      1      0   24.0000        C      3   \n",
       "875         1       3    1    0      0      0    7.2250        C      2   \n",
       "876         0       3    0    1      0      0    9.8458        S      1   \n",
       "877         0       3    0    1      0      0    7.8958        S      1   \n",
       "878         0       3    0    1      0      0    7.8958        S      1   \n",
       "879         1       1    1    3      0      1   83.1583        C      3   \n",
       "880         1       2    1    1      0      1   26.0000        S      3   \n",
       "881         0       3    0    2      0      0    7.8958        S      1   \n",
       "882         0       3    1    1      0      0   10.5167        S      2   \n",
       "883         0       2    0    1      0      0   10.5000        S      1   \n",
       "884         0       3    0    1      0      0    7.0500        S      1   \n",
       "885         0       3    1    2      0      5   29.1250        Q      3   \n",
       "886         0       2    0    1      0      0   13.0000        S      5   \n",
       "887         1       1    1    1      0      0   30.0000        S      2   \n",
       "888         0       3    1    1      1      2   23.4500        S      2   \n",
       "889         1       1    0    1      0      0   30.0000        C      1   \n",
       "890         0       3    0    1      0      0    7.7500        Q      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]  \n",
       "5     (16.0, 32.0]  \n",
       "6     (48.0, 64.0]  \n",
       "7    (-0.08, 16.0]  \n",
       "8     (16.0, 32.0]  \n",
       "9    (-0.08, 16.0]  \n",
       "10   (-0.08, 16.0]  \n",
       "11    (48.0, 64.0]  \n",
       "12    (16.0, 32.0]  \n",
       "13    (32.0, 48.0]  \n",
       "14   (-0.08, 16.0]  \n",
       "15    (48.0, 64.0]  \n",
       "16   (-0.08, 16.0]  \n",
       "17    (16.0, 32.0]  \n",
       "18    (16.0, 32.0]  \n",
       "19    (16.0, 32.0]  \n",
       "20    (32.0, 48.0]  \n",
       "21    (32.0, 48.0]  \n",
       "22   (-0.08, 16.0]  \n",
       "23    (16.0, 32.0]  \n",
       "24   (-0.08, 16.0]  \n",
       "25    (32.0, 48.0]  \n",
       "26    (16.0, 32.0]  \n",
       "27    (16.0, 32.0]  \n",
       "28    (16.0, 32.0]  \n",
       "29    (16.0, 32.0]  \n",
       "..             ...  \n",
       "861   (16.0, 32.0]  \n",
       "862   (32.0, 48.0]  \n",
       "863   (16.0, 32.0]  \n",
       "864   (16.0, 32.0]  \n",
       "865   (32.0, 48.0]  \n",
       "866   (16.0, 32.0]  \n",
       "867   (16.0, 32.0]  \n",
       "868   (16.0, 32.0]  \n",
       "869  (-0.08, 16.0]  \n",
       "870   (16.0, 32.0]  \n",
       "871   (32.0, 48.0]  \n",
       "872   (32.0, 48.0]  \n",
       "873   (32.0, 48.0]  \n",
       "874   (16.0, 32.0]  \n",
       "875  (-0.08, 16.0]  \n",
       "876   (16.0, 32.0]  \n",
       "877   (16.0, 32.0]  \n",
       "878   (16.0, 32.0]  \n",
       "879   (48.0, 64.0]  \n",
       "880   (16.0, 32.0]  \n",
       "881   (32.0, 48.0]  \n",
       "882   (16.0, 32.0]  \n",
       "883   (16.0, 32.0]  \n",
       "884   (16.0, 32.0]  \n",
       "885   (32.0, 48.0]  \n",
       "886   (16.0, 32.0]  \n",
       "887   (16.0, 32.0]  \n",
       "888   (16.0, 32.0]  \n",
       "889   (16.0, 32.0]  \n",
       "890   (16.0, 32.0]  \n",
       "\n",
       "[891 rows x 10 columns]"
      ]
     },
     "execution_count": 50,
     "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']=4\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</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>1</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>2</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>1</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>2</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>2</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    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": 58,
     "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": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.578431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.552795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</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": 59,
     "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": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.505650</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IsAlone  Survived\n",
       "0        0  0.505650\n",
       "1        1  0.303538"
      ]
     },
     "execution_count": 60,
     "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": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "  <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",
       "    </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>1</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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>S</td>\n",
       "      <td>3</td>\n",
       "      <td>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>8.0500</td>\n",
       "      <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": 61,
     "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": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <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": 62,
     "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": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'S'"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_port = train_df.Embarked.dropna().mode()[0]\n",
    "freq_port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</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": 65,
     "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)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
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       "      <td>3</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <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": 66,
     "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": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
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       "      <th>3</th>\n",
       "      <td>895</td>\n",
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       "      <td>1</td>\n",
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       "      <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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['Fare'].fillna(test_df['Fare'].dropna().median(), inplace=True)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>FareBand</th>\n",
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      ],
      "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": 68,
     "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": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>6</th>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
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       "      <td>0</td>\n",
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       "    <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",
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       "      <td>0</td>\n",
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       "  </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": 69,
     "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": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 8), (891,), (418, 8))"
      ]
     },
     "execution_count": 72,
     "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": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\xiexu\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "80.36"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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",
    "\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": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0.287164</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>-0.311199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pclass</td>\n",
       "      <td>-0.749006</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     0.287164\n",
       "4   Embarked     0.261762\n",
       "6    IsAlone     0.129140\n",
       "3       Fare    -0.085150\n",
       "7  Age*Class    -0.311199\n",
       "0     Pclass    -0.749006"
      ]
     },
     "execution_count": 76,
     "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": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.84"
      ]
     },
     "execution_count": 77,
     "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": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.74"
      ]
     },
     "execution_count": 78,
     "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": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72.28"
      ]
     },
     "execution_count": 79,
     "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": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\xiexu\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.perceptron.Perceptron'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "78.0"
      ]
     },
     "execution_count": 80,
     "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": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.12"
      ]
     },
     "execution_count": 81,
     "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": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\xiexu\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\stochastic_gradient.py:128: FutureWarning: max_iter and tol parameters have been added in <class 'sklearn.linear_model.stochastic_gradient.SGDClassifier'> in 0.19. If both are left unset, they default to max_iter=5 and tol=None. If tol is not None, max_iter defaults to max_iter=1000. From 0.21, default max_iter will be 1000, and default tol will be 1e-3.\n",
      "  \"and default tol will be 1e-3.\" % type(self), FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "72.95"
      ]
     },
     "execution_count": 82,
     "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": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.76"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.76"
      ]
     },
     "execution_count": 84,
     "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": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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>72.95</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  72.95\n",
       "4                 Naive Bayes  72.28"
      ]
     },
     "execution_count": 85,
     "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": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": test_df[\"PassengerId\"],\n",
    "        \"Survived\": Y_pred\n",
    "    })\n",
    "submission.to_csv('./submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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