TITANIC kaggle.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Dense, Dropout\n",
    "from keras.models import Sequential \n",
    "\n",
    "from keras.utils import np_utils\n",
    "\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     PassengerId  Pclass                                               Name  \\\n",
      "0            892       3                                   Kelly, Mr. James   \n",
      "1            893       3                   Wilkes, Mrs. James (Ellen Needs)   \n",
      "2            894       2                          Myles, Mr. Thomas Francis   \n",
      "3            895       3                                   Wirz, Mr. Albert   \n",
      "4            896       3       Hirvonen, Mrs. Alexander (Helga E Lindqvist)   \n",
      "5            897       3                         Svensson, Mr. Johan Cervin   \n",
      "6            898       3                               Connolly, Miss. Kate   \n",
      "7            899       2                       Caldwell, Mr. Albert Francis   \n",
      "8            900       3          Abrahim, Mrs. Joseph (Sophie Halaut Easu)   \n",
      "9            901       3                            Davies, Mr. John Samuel   \n",
      "10           902       3                                   Ilieff, Mr. Ylio   \n",
      "11           903       1                         Jones, Mr. Charles Cresson   \n",
      "12           904       1      Snyder, Mrs. John Pillsbury (Nelle Stevenson)   \n",
      "13           905       2                               Howard, Mr. Benjamin   \n",
      "14           906       1  Chaffee, Mrs. Herbert Fuller (Carrie Constance...   \n",
      "15           907       2      del Carlo, Mrs. Sebastiano (Argenia Genovesi)   \n",
      "16           908       2                                  Keane, Mr. Daniel   \n",
      "17           909       3                                  Assaf, Mr. Gerios   \n",
      "18           910       3                       Ilmakangas, Miss. Ida Livija   \n",
      "19           911       3              Assaf Khalil, Mrs. Mariana (Miriam\")\"   \n",
      "20           912       1                             Rothschild, Mr. Martin   \n",
      "21           913       3                          Olsen, Master. Artur Karl   \n",
      "22           914       1               Flegenheim, Mrs. Alfred (Antoinette)   \n",
      "23           915       1                    Williams, Mr. Richard Norris II   \n",
      "24           916       1    Ryerson, Mrs. Arthur Larned (Emily Maria Borie)   \n",
      "25           917       3                            Robins, Mr. Alexander A   \n",
      "26           918       1                       Ostby, Miss. Helene Ragnhild   \n",
      "27           919       3                                  Daher, Mr. Shedid   \n",
      "28           920       1                            Brady, Mr. John Bertram   \n",
      "29           921       3                                  Samaan, Mr. Elias   \n",
      "..           ...     ...                                                ...   \n",
      "388         1280       3                               Canavan, Mr. Patrick   \n",
      "389         1281       3                        Palsson, Master. Paul Folke   \n",
      "390         1282       1                         Payne, Mr. Vivian Ponsonby   \n",
      "391         1283       1     Lines, Mrs. Ernest H (Elizabeth Lindsey James)   \n",
      "392         1284       3                      Abbott, Master. Eugene Joseph   \n",
      "393         1285       2                               Gilbert, Mr. William   \n",
      "394         1286       3                           Kink-Heilmann, Mr. Anton   \n",
      "395         1287       1     Smith, Mrs. Lucien Philip (Mary Eloise Hughes)   \n",
      "396         1288       3                               Colbert, Mr. Patrick   \n",
      "397         1289       1  Frolicher-Stehli, Mrs. Maxmillian (Margaretha ...   \n",
      "398         1290       3                     Larsson-Rondberg, Mr. Edvard A   \n",
      "399         1291       3                           Conlon, Mr. Thomas Henry   \n",
      "400         1292       1                            Bonnell, Miss. Caroline   \n",
      "401         1293       2                                    Gale, Mr. Harry   \n",
      "402         1294       1                     Gibson, Miss. Dorothy Winifred   \n",
      "403         1295       1                             Carrau, Mr. Jose Pedro   \n",
      "404         1296       1                       Frauenthal, Mr. Isaac Gerald   \n",
      "405         1297       2       Nourney, Mr. Alfred (Baron von Drachstedt\")\"   \n",
      "406         1298       2                          Ware, Mr. William Jeffery   \n",
      "407         1299       1                         Widener, Mr. George Dunton   \n",
      "408         1300       3                    Riordan, Miss. Johanna Hannah\"\"   \n",
      "409         1301       3                          Peacock, Miss. Treasteall   \n",
      "410         1302       3                             Naughton, Miss. Hannah   \n",
      "411         1303       1    Minahan, Mrs. William Edward (Lillian E Thorpe)   \n",
      "412         1304       3                     Henriksson, Miss. Jenny Lovisa   \n",
      "413         1305       3                                 Spector, Mr. Woolf   \n",
      "414         1306       1                       Oliva y Ocana, Dona. Fermina   \n",
      "415         1307       3                       Saether, Mr. Simon Sivertsen   \n",
      "416         1308       3                                Ware, Mr. Frederick   \n",
      "417         1309       3                           Peter, Master. Michael J   \n",
      "\n",
      "        Sex   Age  SibSp  Parch              Ticket      Fare  \\\n",
      "0      male  34.5      0      0              330911    7.8292   \n",
      "1    female  47.0      1      0              363272    7.0000   \n",
      "2      male  62.0      0      0              240276    9.6875   \n",
      "3      male  27.0      0      0              315154    8.6625   \n",
      "4    female  22.0      1      1             3101298   12.2875   \n",
      "5      male  14.0      0      0                7538    9.2250   \n",
      "6    female  30.0      0      0              330972    7.6292   \n",
      "7      male  26.0      1      1              248738   29.0000   \n",
      "8    female  18.0      0      0                2657    7.2292   \n",
      "9      male  21.0      2      0           A/4 48871   24.1500   \n",
      "10     male   NaN      0      0              349220    7.8958   \n",
      "11     male  46.0      0      0                 694   26.0000   \n",
      "12   female  23.0      1      0               21228   82.2667   \n",
      "13     male  63.0      1      0               24065   26.0000   \n",
      "14   female  47.0      1      0         W.E.P. 5734   61.1750   \n",
      "15   female  24.0      1      0       SC/PARIS 2167   27.7208   \n",
      "16     male  35.0      0      0              233734   12.3500   \n",
      "17     male  21.0      0      0                2692    7.2250   \n",
      "18   female  27.0      1      0    STON/O2. 3101270    7.9250   \n",
      "19   female  45.0      0      0                2696    7.2250   \n",
      "20     male  55.0      1      0            PC 17603   59.4000   \n",
      "21     male   9.0      0      1             C 17368    3.1708   \n",
      "22   female   NaN      0      0            PC 17598   31.6833   \n",
      "23     male  21.0      0      1            PC 17597   61.3792   \n",
      "24   female  48.0      1      3            PC 17608  262.3750   \n",
      "25     male  50.0      1      0           A/5. 3337   14.5000   \n",
      "26   female  22.0      0      1              113509   61.9792   \n",
      "27     male  22.5      0      0                2698    7.2250   \n",
      "28     male  41.0      0      0              113054   30.5000   \n",
      "29     male   NaN      2      0                2662   21.6792   \n",
      "..      ...   ...    ...    ...                 ...       ...   \n",
      "388    male  21.0      0      0              364858    7.7500   \n",
      "389    male   6.0      3      1              349909   21.0750   \n",
      "390    male  23.0      0      0               12749   93.5000   \n",
      "391  female  51.0      0      1            PC 17592   39.4000   \n",
      "392    male  13.0      0      2           C.A. 2673   20.2500   \n",
      "393    male  47.0      0      0          C.A. 30769   10.5000   \n",
      "394    male  29.0      3      1              315153   22.0250   \n",
      "395  female  18.0      1      0               13695   60.0000   \n",
      "396    male  24.0      0      0              371109    7.2500   \n",
      "397  female  48.0      1      1               13567   79.2000   \n",
      "398    male  22.0      0      0              347065    7.7750   \n",
      "399    male  31.0      0      0               21332    7.7333   \n",
      "400  female  30.0      0      0               36928  164.8667   \n",
      "401    male  38.0      1      0               28664   21.0000   \n",
      "402  female  22.0      0      1              112378   59.4000   \n",
      "403    male  17.0      0      0              113059   47.1000   \n",
      "404    male  43.0      1      0               17765   27.7208   \n",
      "405    male  20.0      0      0       SC/PARIS 2166   13.8625   \n",
      "406    male  23.0      1      0               28666   10.5000   \n",
      "407    male  50.0      1      1              113503  211.5000   \n",
      "408  female   NaN      0      0              334915    7.7208   \n",
      "409  female   3.0      1      1  SOTON/O.Q. 3101315   13.7750   \n",
      "410  female   NaN      0      0              365237    7.7500   \n",
      "411  female  37.0      1      0               19928   90.0000   \n",
      "412  female  28.0      0      0              347086    7.7750   \n",
      "413    male   NaN      0      0           A.5. 3236    8.0500   \n",
      "414  female  39.0      0      0            PC 17758  108.9000   \n",
      "415    male  38.5      0      0  SOTON/O.Q. 3101262    7.2500   \n",
      "416    male   NaN      0      0              359309    8.0500   \n",
      "417    male   NaN      1      1                2668   22.3583   \n",
      "\n",
      "               Cabin Embarked  \n",
      "0                NaN        Q  \n",
      "1                NaN        S  \n",
      "2                NaN        Q  \n",
      "3                NaN        S  \n",
      "4                NaN        S  \n",
      "5                NaN        S  \n",
      "6                NaN        Q  \n",
      "7                NaN        S  \n",
      "8                NaN        C  \n",
      "9                NaN        S  \n",
      "10               NaN        S  \n",
      "11               NaN        S  \n",
      "12               B45        S  \n",
      "13               NaN        S  \n",
      "14               E31        S  \n",
      "15               NaN        C  \n",
      "16               NaN        Q  \n",
      "17               NaN        C  \n",
      "18               NaN        S  \n",
      "19               NaN        C  \n",
      "20               NaN        C  \n",
      "21               NaN        S  \n",
      "22               NaN        S  \n",
      "23               NaN        C  \n",
      "24   B57 B59 B63 B66        C  \n",
      "25               NaN        S  \n",
      "26               B36        C  \n",
      "27               NaN        C  \n",
      "28               A21        S  \n",
      "29               NaN        C  \n",
      "..               ...      ...  \n",
      "388              NaN        Q  \n",
      "389              NaN        S  \n",
      "390              B24        S  \n",
      "391              D28        S  \n",
      "392              NaN        S  \n",
      "393              NaN        S  \n",
      "394              NaN        S  \n",
      "395              C31        S  \n",
      "396              NaN        Q  \n",
      "397              B41        C  \n",
      "398              NaN        S  \n",
      "399              NaN        Q  \n",
      "400               C7        S  \n",
      "401              NaN        S  \n",
      "402              NaN        C  \n",
      "403              NaN        S  \n",
      "404              D40        C  \n",
      "405              D38        C  \n",
      "406              NaN        S  \n",
      "407              C80        C  \n",
      "408              NaN        Q  \n",
      "409              NaN        S  \n",
      "410              NaN        Q  \n",
      "411              C78        Q  \n",
      "412              NaN        S  \n",
      "413              NaN        S  \n",
      "414             C105        C  \n",
      "415              NaN        S  \n",
      "416              NaN        S  \n",
      "417              NaN        C  \n",
      "\n",
      "[418 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "train = pd.read_csv('./titanic/train.csv')\n",
    "test = pd.read_csv('titanic/test.csv', sep=',')\n",
    "gender_submission = pd.read_csv('titanic/gender_submission.csv', sep=',')\n",
    "print(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train[['PassengerId', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Cabin', 'Embarked']].copy()\n",
    "Y_train = train[['Survived']].copy()\n",
    "\n",
    "#X_test = test[['PassengerId', 'Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Cabin', 'Embarked']].copy()\n",
    "#Y_test = test[['Survived']].copy()\n",
    "\n",
    "Y_train = np_utils.to_categorical(Y_train, 2)\n",
    "#Y_test = np_utils.to_categorical(Y_test, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\users\\professional\\appdata\\local\\programs\\python\\python36\\envs\\keras-jupyter\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(30, activation='relu'))\n",
    "model.add(Dense(2, activation='softmax'))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Please provide as model inputs either a single array or a list of arrays. You passed: x=     PassengerId  Pclass     Sex   Age  SibSp  Parch      Fare        Cabin  \\\n0              1       3    male  22.0      1      0    7.2500          NaN   \n1              2       1  female  38.0      1      0   71.2833          C85   \n2              3       3  female  26.0      0      0    7.9250          NaN   \n3              4       1  female  35.0      1      0   53.1000         C123   \n4              5       3    male  35.0      0      0    8.0500          NaN   \n5              6       3    male   NaN      0      0    8.4583          NaN   \n6              7       1    male  54.0      0      0   51.8625          E46   \n7              8       3    male   2.0      3      1   21.0750          NaN   \n8              9       3  female  27.0      0      2   11.1333          NaN   \n9             10       2  female  14.0      1      0   30.0708          NaN   \n10            11       3  female   4.0      1      1   16.7000           G6   \n11            12       1  female  58.0      0      0   26.5500         C103   \n12            13       3    male  20.0      0      0    8.0500          NaN   \n13            14       3    male  39.0      1      5   31.2750          NaN   \n14            15       3  female  14.0      0      0    7.8542          NaN   \n15            16       2  female  55.0      0      0   16.0000          NaN   \n16            17       3    male   2.0      4      1   29.1250          NaN   \n17            18       2    male   NaN      0      0   13.0000          NaN   \n18            19       3  female  31.0      1      0   18.0000          NaN   \n19            20       3  female   NaN      0      0    7.2250          NaN   \n20            21       2    male  35.0      0      0   26.0000          NaN   \n21            22       2    male  34.0      0      0   13.0000          D56   \n22            23       3  female  15.0      0      0    8.0292          NaN   \n23            24       1    male  28.0      0      0   35.5000           A6   \n24            25       3  female   8.0      3      1   21.0750          NaN   \n25            26       3  female  38.0      1      5   31.3875          NaN   \n26            27       3    male   NaN      0      0    7.2250          NaN   \n27            28       1    male  19.0      3      2  263.0000  C23 C25 C27   \n28            29       3  female   NaN      0      0    7.8792          NaN   \n29            30       3    male   NaN      0      0    7.8958          NaN   \n..           ...     ...     ...   ...    ...    ...       ...          ...   \n861          862       2    male  21.0      1      0   11.5000          NaN   \n862          863       1  female  48.0      0      0   25.9292          D17   \n863          864       3  female   NaN      8      2   69.5500          NaN   \n864          865       2    male  24.0      0      0   13.0000          NaN   \n865          866       2  female  42.0      0      0   13.0000          NaN   \n866          867       2  female  27.0      1      0   13.8583          NaN   \n867          868       1    male  31.0      0      0   50.4958          A24   \n868          869       3    male   NaN      0      0    9.5000          NaN   \n869          870       3    male   4.0      1      1   11.1333          NaN   \n870          871       3    male  26.0      0      0    7.8958          NaN   \n871          872       1  female  47.0      1      1   52.5542          D35   \n872          873       1    male  33.0      0      0    5.0000  B51 B53 B55   \n873          874       3    male  47.0      0      0    9.0000          NaN   \n874          875       2  female  28.0      1      0   24.0000          NaN   \n875          876       3  female  15.0      0      0    7.2250          NaN   \n876          877       3    male  20.0      0      0    9.8458          NaN   \n877          878       3    male  19.0      0      0    7.8958          NaN   \n878          879       3    male   NaN      0      0    7.8958          NaN   \n879          880       1  female  56.0      0      1   83.1583          C50   \n880          881       2  female  25.0      0      1   26.0000          NaN   \n881          882       3    male  33.0      0      0    7.8958          NaN   \n882          883       3  female  22.0      0      0   10.5167          NaN   \n883          884       2    male  28.0      0      0   10.5000          NaN   \n884          885       3    male  25.0      0      0    7.0500          NaN   \n885          886       3  female  39.0      0      5   29.1250          NaN   \n886          887       2    male  27.0      0      0   13.0000          NaN   \n887          888       1  female  19.0      0      0   30.0000          B42   \n888          889       3  female   NaN      1      2   23.4500          NaN   \n889          890       1    male  26.0      0      0   30.0000         C148   \n890          891       3    male  32.0      0      0    7.7500          NaN   \n\n    Embarked  \n0          S  \n1          C  \n2          S  \n3          S  \n4          S  \n5          Q  \n6          S  \n7          S  \n8          S  \n9          C  \n10         S  \n11         S  \n12         S  \n13         S  \n14         S  \n15         S  \n16         Q  \n17         S  \n18         S  \n19         C  \n20         S  \n21         S  \n22         Q  \n23         S  \n24         S  \n25         S  \n26         C  \n27         S  \n28         Q  \n29         S  \n..       ...  \n861        S  \n862        S  \n863        S  \n864        S  \n865        S  \n866        C  \n867        S  \n868        S  \n869        S  \n870        S  \n871        S  \n872        S  \n873        S  \n874        C  \n875        C  \n876        S  \n877        S  \n878        S  \n879        C  \n880        S  \n881        S  \n882        S  \n883        S  \n884        S  \n885        Q  \n886        S  \n887        S  \n888        S  \n889        C  \n890        Q  \n\n[891 rows x 9 columns]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-30-a9ee1bc22ec9>\u001b[0m in \u001b[0;36masync-def-wrapper\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mc:\\users\\professional\\appdata\\local\\programs\\python\\python36\\envs\\keras-jupyter\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[0;32m    950\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    951\u001b[0m             \u001b[0mclass_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 952\u001b[1;33m             batch_size=batch_size)\n\u001b[0m\u001b[0;32m    953\u001b[0m         \u001b[1;31m# Prepare validation data.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    954\u001b[0m         \u001b[0mdo_validation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\professional\\appdata\\local\\programs\\python\\python36\\envs\\keras-jupyter\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[1;34m(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)\u001b[0m\n\u001b[0;32m    668\u001b[0m                                      \u001b[1;34m'either a single '\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    669\u001b[0m                                      \u001b[1;34m'array or a list of arrays. '\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 670\u001b[1;33m                                      'You passed: x=' + str(x))\n\u001b[0m\u001b[0;32m    671\u001b[0m                 \u001b[0mall_inputs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    672\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Please provide as model inputs either a single array or a list of arrays. You passed: x=     PassengerId  Pclass     Sex   Age  SibSp  Parch      Fare        Cabin  \\\n0              1       3    male  22.0      1      0    7.2500          NaN   \n1              2       1  female  38.0      1      0   71.2833          C85   \n2              3       3  female  26.0      0      0    7.9250          NaN   \n3              4       1  female  35.0      1      0   53.1000         C123   \n4              5       3    male  35.0      0      0    8.0500          NaN   \n5              6       3    male   NaN      0      0    8.4583          NaN   \n6              7       1    male  54.0      0      0   51.8625          E46   \n7              8       3    male   2.0      3      1   21.0750          NaN   \n8              9       3  female  27.0      0      2   11.1333          NaN   \n9             10       2  female  14.0      1      0   30.0708          NaN   \n10            11       3  female   4.0      1      1   16.7000           G6   \n11            12       1  female  58.0      0      0   26.5500         C103   \n12            13       3    male  20.0      0      0    8.0500          NaN   \n13            14       3    male  39.0      1      5   31.2750          NaN   \n14            15       3  female  14.0      0      0    7.8542          NaN   \n15            16       2  female  55.0      0      0   16.0000          NaN   \n16            17       3    male   2.0      4      1   29.1250          NaN   \n17            18       2    male   NaN      0      0   13.0000          NaN   \n18            19       3  female  31.0      1      0   18.0000          NaN   \n19            20       3  female   NaN      0      0    7.2250          NaN   \n20            21       2    male  35.0      0      0   26.0000          NaN   \n21            22       2    male  34.0      0      0   13.0000          D56   \n22            23       3  female  15.0      0      0    8.0292          NaN   \n23            24       1    male  28.0      0      0   35.5000           A6   \n24            25       3  female   8.0      3      1   21.0750          NaN   \n25            26       3  female  38.0      1      5   31.3875          NaN   \n26            27       3    male   NaN      0      0    7.2250          NaN   \n27            28       1    male  19.0      3      2  263.0000  C23 C25 C27   \n28            29       3  female   NaN      0      0    7.8792          NaN   \n29            30       3    male   NaN      0      0    7.8958          NaN   \n..           ...     ...     ...   ...    ...    ...       ...          ...   \n861          862       2    male  21.0      1      0   11.5000          NaN   \n862          863       1  female  48.0      0      0   25.9292          D17   \n863          864       3  female   NaN      8      2   69.5500          NaN   \n864          865       2    male  24.0      0      0   13.0000          NaN   \n865          866       2  female  42.0      0      0   13.0000          NaN   \n866          867       2  female  27.0      1      0   13.8583          NaN   \n867          868       1    male  31.0      0      0   50.4958          A24   \n868          869       3    male   NaN      0      0    9.5000          NaN   \n869          870       3    male   4.0      1      1   11.1333          NaN   \n870          871       3    male  26.0      0      0    7.8958          NaN   \n871          872       1  female  47.0      1      1   52.5542          D35   \n872          873       1    male  33.0      0      0    5.0000  B51 B53 B55   \n873          874       3    male  47.0      0      0    9.0000          NaN   \n874          875       2  female  28.0      1      0   24.0000          NaN   \n875          876       3  female  15.0      0      0    7.2250          NaN   \n876          877       3    male  20.0      0      0    9.8458          NaN   \n877          878       3    male  19.0      0      0    7.8958          NaN   \n878          879       3    male   NaN      0      0    7.8958          NaN   \n879          880       1  female  56.0      0      1   83.1583          C50   \n880          881       2  female  25.0      0      1   26.0000          NaN   \n881          882       3    male  33.0      0      0    7.8958          NaN   \n882          883       3  female  22.0      0      0   10.5167          NaN   \n883          884       2    male  28.0      0      0   10.5000          NaN   \n884          885       3    male  25.0      0      0    7.0500          NaN   \n885          886       3  female  39.0      0      5   29.1250          NaN   \n886          887       2    male  27.0      0      0   13.0000          NaN   \n887          888       1  female  19.0      0      0   30.0000          B42   \n888          889       3  female   NaN      1      2   23.4500          NaN   \n889          890       1    male  26.0      0      0   30.0000         C148   \n890          891       3    male  32.0      0      0    7.7500          NaN   \n\n    Embarked  \n0          S  \n1          C  \n2          S  \n3          S  \n4          S  \n5          Q  \n6          S  \n7          S  \n8          S  \n9          C  \n10         S  \n11         S  \n12         S  \n13         S  \n14         S  \n15         S  \n16         Q  \n17         S  \n18         S  \n19         C  \n20         S  \n21         S  \n22         Q  \n23         S  \n24         S  \n25         S  \n26         C  \n27         S  \n28         Q  \n29         S  \n..       ...  \n861        S  \n862        S  \n863        S  \n864        S  \n865        S  \n866        C  \n867        S  \n868        S  \n869        S  \n870        S  \n871        S  \n872        S  \n873        S  \n874        C  \n875        C  \n876        S  \n877        S  \n878        S  \n879        C  \n880        S  \n881        S  \n882        S  \n883        S  \n884        S  \n885        Q  \n886        S  \n887        S  \n888        S  \n889        C  \n890        Q  \n\n[891 rows x 9 columns]"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    " model.fit(X_train, Y_train, batch_size=128, epochs=10, validation_split=0.2, verbose=1)"
   ]
  }
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