AI_Class/021/Dnn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dnn 실습\n",
    "\n",
    "주어진 코드는 import lib, data와 Modeling 파트입니다.\n",
    "\n",
    "타이타닉 데이터셋을 사용했습니다.\n",
    "\n",
    "이제껏 배워왔던 내용들을 기반으로 Dnn 모델을 구축해보세요.\n",
    "\n",
    "- 목표 : 정확도 85 넘기기"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import library & data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sns.load_dataset('titanic')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA\n",
    "\n",
    "목차의 KNN내용 참고"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived         0\n",
       "pclass           0\n",
       "sex              0\n",
       "age            177\n",
       "sibsp            0\n",
       "parch            0\n",
       "fare             0\n",
       "embarked         2\n",
       "class            0\n",
       "who              0\n",
       "adult_male       0\n",
       "deck           688\n",
       "embark_town      2\n",
       "alive            0\n",
       "alone            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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>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>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>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>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>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>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>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>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": [
       "         survived      pclass         age       sibsp       parch        fare\n",
       "count  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
       "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
       "std      0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
       "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
       "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
       "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
       "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 15 columns):\n",
      " #   Column       Non-Null Count  Dtype   \n",
      "---  ------       --------------  -----   \n",
      " 0   survived     891 non-null    int64   \n",
      " 1   pclass       891 non-null    int64   \n",
      " 2   sex          891 non-null    object  \n",
      " 3   age          714 non-null    float64 \n",
      " 4   sibsp        891 non-null    int64   \n",
      " 5   parch        891 non-null    int64   \n",
      " 6   fare         891 non-null    float64 \n",
      " 7   embarked     889 non-null    object  \n",
      " 8   class        891 non-null    category\n",
      " 9   who          891 non-null    object  \n",
      " 10  adult_male   891 non-null    bool    \n",
      " 11  deck         203 non-null    category\n",
      " 12  embark_town  889 non-null    object  \n",
      " 13  alive        891 non-null    object  \n",
      " 14  alone        891 non-null    bool    \n",
      "dtypes: bool(2), category(2), float64(2), int64(4), object(5)\n",
      "memory usage: 80.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "survived\n",
      "[0 1]\n",
      "\n",
      "pclass\n",
      "[3 1 2]\n",
      "\n",
      "sex\n",
      "['male' 'female']\n",
      "\n",
      "age\n",
      "[22.   38.   26.   35.     nan 54.    2.   27.   14.    4.   58.   20.\n",
      " 39.   55.   31.   34.   15.   28.    8.   19.   40.   66.   42.   21.\n",
      " 18.    3.    7.   49.   29.   65.   28.5   5.   11.   45.   17.   32.\n",
      " 16.   25.    0.83 30.   33.   23.   24.   46.   59.   71.   37.   47.\n",
      " 14.5  70.5  32.5  12.    9.   36.5  51.   55.5  40.5  44.    1.   61.\n",
      " 56.   50.   36.   45.5  20.5  62.   41.   52.   63.   23.5   0.92 43.\n",
      " 60.   10.   64.   13.   48.    0.75 53.   57.   80.   70.   24.5   6.\n",
      "  0.67 30.5   0.42 34.5  74.  ]\n",
      "\n",
      "sibsp\n",
      "[1 0 3 4 2 5 8]\n",
      "\n",
      "parch\n",
      "[0 1 2 5 3 4 6]\n",
      "\n",
      "fare\n",
      "[  7.25    71.2833   7.925   53.1      8.05     8.4583  51.8625  21.075\n",
      "  11.1333  30.0708  16.7     26.55    31.275    7.8542  16.      29.125\n",
      "  13.      18.       7.225   26.       8.0292  35.5     31.3875 263.\n",
      "   7.8792   7.8958  27.7208 146.5208   7.75    10.5     82.1708  52.\n",
      "   7.2292  11.2417   9.475   21.      41.5792  15.5     21.6792  17.8\n",
      "  39.6875   7.8     76.7292  61.9792  27.75    46.9     80.      83.475\n",
      "  27.9     15.2458   8.1583   8.6625  73.5     14.4542  56.4958   7.65\n",
      "  29.      12.475    9.       9.5      7.7875  47.1     15.85    34.375\n",
      "  61.175   20.575   34.6542  63.3583  23.      77.2875   8.6542   7.775\n",
      "  24.15     9.825   14.4583 247.5208   7.1417  22.3583   6.975    7.05\n",
      "  14.5     15.0458  26.2833   9.2167  79.2      6.75    11.5     36.75\n",
      "   7.7958  12.525   66.6      7.3125  61.3792   7.7333  69.55    16.1\n",
      "  15.75    20.525   55.      25.925   33.5     30.6958  25.4667  28.7125\n",
      "   0.      15.05    39.      22.025   50.       8.4042   6.4958  10.4625\n",
      "  18.7875  31.     113.275   27.      76.2917  90.       9.35    13.5\n",
      "   7.55    26.25    12.275    7.125   52.5542  20.2125  86.5    512.3292\n",
      "  79.65   153.4625 135.6333  19.5     29.7     77.9583  20.25    78.85\n",
      "  91.0792  12.875    8.85   151.55    30.5     23.25    12.35   110.8833\n",
      " 108.9     24.      56.9292  83.1583 262.375   14.     164.8667 134.5\n",
      "   6.2375  57.9792  28.5    133.65    15.9      9.225   35.      75.25\n",
      "  69.3     55.4417 211.5      4.0125 227.525   15.7417   7.7292  12.\n",
      " 120.      12.65    18.75     6.8583  32.5      7.875   14.4     55.9\n",
      "   8.1125  81.8583  19.2583  19.9667  89.1042  38.5      7.725   13.7917\n",
      "   9.8375   7.0458   7.5208  12.2875   9.5875  49.5042  78.2667  15.1\n",
      "   7.6292  22.525   26.2875  59.4      7.4958  34.0208  93.5    221.7792\n",
      " 106.425   49.5     71.      13.8625   7.8292  39.6     17.4     51.4792\n",
      "  26.3875  30.      40.125    8.7125  15.      33.      42.4     15.55\n",
      "  65.      32.3208   7.0542   8.4333  25.5875   9.8417   8.1375  10.1708\n",
      " 211.3375  57.      13.4167   7.7417   9.4833   7.7375   8.3625  23.45\n",
      "  25.9292   8.6833   8.5167   7.8875  37.0042   6.45     6.95     8.3\n",
      "   6.4375  39.4     14.1083  13.8583  50.4958   5.       9.8458  10.5167]\n",
      "\n",
      "embarked\n",
      "['S' 'C' 'Q' nan]\n",
      "\n",
      "class\n",
      "['Third', 'First', 'Second']\n",
      "Categories (3, object): ['Third', 'First', 'Second']\n",
      "\n",
      "who\n",
      "['man' 'woman' 'child']\n",
      "\n",
      "adult_male\n",
      "[ True False]\n",
      "\n",
      "deck\n",
      "[NaN, 'C', 'E', 'G', 'D', 'A', 'B', 'F']\n",
      "Categories (7, object): ['C', 'E', 'G', 'D', 'A', 'B', 'F']\n",
      "\n",
      "embark_town\n",
      "['Southampton' 'Cherbourg' 'Queenstown' nan]\n",
      "\n",
      "alive\n",
      "['no' 'yes']\n",
      "\n",
      "alone\n",
      "[False  True]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(df.columns.values)):\n",
    "    print(df.columns.values[i])\n",
    "    print(df[df.columns.values[i]].unique())\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['survived' 'pclass' 'sex' 'age' 'sibsp' 'parch' 'fare' 'embarked' 'class'\n",
      " 'who' 'adult_male' 'alive' 'alone']\n"
     ]
    }
   ],
   "source": [
    "df = df.drop(['deck', 'embark_town'], axis=1)  \n",
    "print(df.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "714\n"
     ]
    }
   ],
   "source": [
    "df = df.dropna(subset=['age'], how='any', axis=0)  \n",
    "print(len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S\n"
     ]
    }
   ],
   "source": [
    "most_freq = df['embarked'].value_counts(dropna=True).idxmax()   \n",
    "print(most_freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['embarked'].fillna(most_freq, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "survived      0\n",
       "pclass        0\n",
       "sex           0\n",
       "age           0\n",
       "sibsp         0\n",
       "parch         0\n",
       "fare          0\n",
       "embarked      0\n",
       "class         0\n",
       "who           0\n",
       "adult_male    0\n",
       "alive         0\n",
       "alone         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'embarked']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "onehot_sex = pd.get_dummies(df['sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df, onehot_sex], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "onehot_embarked = pd.get_dummies(df['embarked'], prefix='town')\n",
    "df = pd.concat([df, onehot_embarked], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['sex', 'embarked'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>town_C</th>\n",
       "      <th>town_Q</th>\n",
       "      <th>town_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass   age  sibsp  parch  female  male  town_C  town_Q  town_S\n",
       "0         0       3  22.0      1      0       0     1       0       0       1\n",
       "1         1       1  38.0      1      0       1     0       1       0       0\n",
       "2         1       3  26.0      0      0       1     0       0       0       1\n",
       "3         1       1  35.0      1      0       1     0       0       0       1\n",
       "4         0       3  35.0      0      0       0     1       0       0       1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[['pclass', 'age', 'sibsp', 'parch', 'female', 'male', 'town_C', 'town_Q', 'town_S']]\n",
    "y=df['survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = StandardScaler().fit(X).transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, \n",
    "                                                    y, \n",
    "                                                    test_size=0.3, \n",
    "                                                    random_state=20) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_16 (Dense)             (None, 256)               2560      \n",
      "_________________________________________________________________\n",
      "dense_17 (Dense)             (None, 256)               65792     \n",
      "_________________________________________________________________\n",
      "dense_18 (Dense)             (None, 16)                4112      \n",
      "_________________________________________________________________\n",
      "dense_19 (Dense)             (None, 16)                272       \n",
      "_________________________________________________________________\n",
      "dense_20 (Dense)             (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 72,753\n",
      "Trainable params: 72,753\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(256, input_shape=(9,), activation='relu'))\n",
    "model.add(Dense(256, activation='relu'))\n",
    "model.add(Dense(16, activation='relu'))\n",
    "model.add(Dense(16, activation='relu'))\n",
    "model.add(Dense((1), activation='sigmoid'))\n",
    "model.compile(loss='mse', optimizer='Adam', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "16/16 [==============================] - 0s 10ms/step - loss: 0.2161 - accuracy: 0.7025 - val_loss: 0.1500 - val_accuracy: 0.8047\n",
      "Epoch 2/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1514 - accuracy: 0.7715 - val_loss: 0.1376 - val_accuracy: 0.7953\n",
      "Epoch 3/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1226 - accuracy: 0.8321 - val_loss: 0.1344 - val_accuracy: 0.8186\n",
      "Epoch 4/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1375 - accuracy: 0.8050 - val_loss: 0.1275 - val_accuracy: 0.8140\n",
      "Epoch 5/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1283 - accuracy: 0.8205 - val_loss: 0.1292 - val_accuracy: 0.8186\n",
      "Epoch 6/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1299 - accuracy: 0.8260 - val_loss: 0.1237 - val_accuracy: 0.8279\n",
      "Epoch 7/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1216 - accuracy: 0.8269 - val_loss: 0.1299 - val_accuracy: 0.8093\n",
      "Epoch 8/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1197 - accuracy: 0.8448 - val_loss: 0.1270 - val_accuracy: 0.8186\n",
      "Epoch 9/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1324 - accuracy: 0.8208 - val_loss: 0.1333 - val_accuracy: 0.8000\n",
      "Epoch 10/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1139 - accuracy: 0.8537 - val_loss: 0.1301 - val_accuracy: 0.8186\n",
      "Epoch 11/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1245 - accuracy: 0.8388 - val_loss: 0.1277 - val_accuracy: 0.8279\n",
      "Epoch 12/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1158 - accuracy: 0.8369 - val_loss: 0.1296 - val_accuracy: 0.7953\n",
      "Epoch 13/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1118 - accuracy: 0.8492 - val_loss: 0.1313 - val_accuracy: 0.8186\n",
      "Epoch 14/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1164 - accuracy: 0.8588 - val_loss: 0.1265 - val_accuracy: 0.8186\n",
      "Epoch 15/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1302 - accuracy: 0.8224 - val_loss: 0.1297 - val_accuracy: 0.8186\n",
      "Epoch 16/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1245 - accuracy: 0.8263 - val_loss: 0.1264 - val_accuracy: 0.8279\n",
      "Epoch 17/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1244 - accuracy: 0.8308 - val_loss: 0.1327 - val_accuracy: 0.8186\n",
      "Epoch 18/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1100 - accuracy: 0.8427 - val_loss: 0.1285 - val_accuracy: 0.8140\n",
      "Epoch 19/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1215 - accuracy: 0.8200 - val_loss: 0.1422 - val_accuracy: 0.8140\n",
      "Epoch 20/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1288 - accuracy: 0.8078 - val_loss: 0.1324 - val_accuracy: 0.8140\n",
      "Epoch 21/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1075 - accuracy: 0.8546 - val_loss: 0.1341 - val_accuracy: 0.8140\n",
      "Epoch 22/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1192 - accuracy: 0.8275 - val_loss: 0.1360 - val_accuracy: 0.7953\n",
      "Epoch 23/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1102 - accuracy: 0.8409 - val_loss: 0.1277 - val_accuracy: 0.8140\n",
      "Epoch 24/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1155 - accuracy: 0.8607 - val_loss: 0.1357 - val_accuracy: 0.7953\n",
      "Epoch 25/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1056 - accuracy: 0.8395 - val_loss: 0.1355 - val_accuracy: 0.8140\n",
      "Epoch 26/30\n",
      "16/16 [==============================] - 0s 5ms/step - loss: 0.1085 - accuracy: 0.8619 - val_loss: 0.1342 - val_accuracy: 0.8186\n",
      "Epoch 27/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1051 - accuracy: 0.8605 - val_loss: 0.1384 - val_accuracy: 0.7953\n",
      "Epoch 28/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1022 - accuracy: 0.8684 - val_loss: 0.1381 - val_accuracy: 0.8140\n",
      "Epoch 29/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1115 - accuracy: 0.8519 - val_loss: 0.1371 - val_accuracy: 0.8093\n",
      "Epoch 30/30\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.1072 - accuracy: 0.8344 - val_loss: 0.1401 - val_accuracy: 0.8140\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(history.history).plot(figsize=(12, 5))\n",
    "plt.grid(True)\n",
    "plt.gca().set_ylim(0, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}