Redes Neurais.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Rede Neural - Titanic Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from time import time\n",
    "import matplotlib as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "testDataSet = pd.read_csv('titanic\\\\test.csv')\n",
    "trainDataSet = pd.read_csv('titanic\\\\train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>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>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330911</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>363272</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240276</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>315154</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3101298</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testDataSet.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainDataSet.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting more information about data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainDataSet.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we can see that some of columns has missing values. Since we don't have a huge data set, we cannot affort to discard that amount of data, so we are gone dig in those fields where we have missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.00    30\n",
       "22.00    27\n",
       "18.00    26\n",
       "19.00    25\n",
       "30.00    25\n",
       "         ..\n",
       "55.50     1\n",
       "70.50     1\n",
       "66.00     1\n",
       "23.50     1\n",
       "0.42      1\n",
       "Name: Age, Length: 88, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainDataSet['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'count'), Text(0.5, 0, 'ages')]"
      ]
     },
     "execution_count": 8,
     "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": [
    "ax = sns.scatterplot(x=trainDataSet['Age'].value_counts().index, y=trainDataSet['Age'].value_counts())\n",
    "ax.set(ylabel='count', xlabel='ages')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the plot and the numbers of value_counts, we can see that we don't have any outlier that will probably mess up our values. But if we consider only the mean value, since we have 177 missing data (which represents almost 20% of our data set), we'll turn the mean value in a outlier. So, the approach we are gona follow is to generate random numbers for each missing value based on the values that we have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainDataSet[\"Age\"] = trainDataSet[\"Age\"].apply(lambda x: np.random.choice(trainDataSet[\"Age\"].dropna().values) if np.isnan(x) else x)\n",
    "testDataSet[\"Age\"] = testDataSet[\"Age\"].apply(lambda x: np.random.choice(testDataSet[\"Age\"].dropna().values) if np.isnan(x) else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age              0\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainDataSet.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'count'), Text(0.5, 0, 'ages')]"
      ]
     },
     "execution_count": 11,
     "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": [
    "ax = sns.scatterplot(x=trainDataSet['Age'].value_counts().index, y=trainDataSet['Age'].value_counts())\n",
    "ax.set(ylabel='count', xlabel='ages')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.00    38\n",
       "24.00    35\n",
       "22.00    34\n",
       "28.00    32\n",
       "29.00    30\n",
       "         ..\n",
       "36.50     1\n",
       "12.00     1\n",
       "14.50     1\n",
       "0.92      1\n",
       "0.42      1\n",
       "Name: Age, Length: 88, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainDataSet['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have all Age column filled and we were able to maintain the distribuition of the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Cabin column represents the number of each cabin on the ship. Since we have almost 80% of data missing, i don't think, as a first approach, that generate 80% of data in a column will bring better results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainDataSet.drop(['Cabin', 'Ticket'], axis=1, inplace=True)\n",
    "testDataSet.drop(['Cabin', 'Ticket'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As for the Embarked column, that represents the port where passangers get in, since we only have 2 missing data, we can fill them with the most commun port."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainDataSet[\"Embarked\"].fillna(trainDataSet[\"Embarked\"].value_counts().index.max(), inplace=True)\n",
    "testDataSet[\"Embarked\"].fillna(testDataSet[\"Embarked\"].value_counts().index.max(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    646\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainDataSet[\"Embarked\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Fare           0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainDataSet.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replacing string values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "trainDataSet.replace({'Sex': {'male': 0, 'female': 1}}, inplace=True)\n",
    "testDataSet.replace({'Sex': {'male': 0, 'female': 1}}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainDataSet.replace({'Embarked': {'S': 0, 'C': 1, 'Q':2}}, inplace=True)\n",
    "testDataSet.replace({'Embarked': {'S': 0, 'C': 1, 'Q':2}}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()\n",
    "features = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']\n",
    "\n",
    "X_train = scaler.fit_transform(trainDataSet[features].values) #variables that we use to predict\n",
    "y_train = trainDataSet['Survived'].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_onehot = pd.get_dummies(trainDataSet['Survived']).values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = scaler.fit_transform(testDataSet[features].values) #variables that we use to predict\n",
    "#y_test = trainDataSet[600:]['Survived'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28/28 [==============================] - 0s 3ms/step - loss: 0.5349 - accuracy: 0.7755\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x204759c4908>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(input_dim=7, units=1000))\n",
    "\n",
    "model.add(Dense(units=500))\n",
    "\n",
    "model.add(Dense(units=300))\n",
    "\n",
    "model.add(Dense(units=2))\n",
    "\n",
    "model.add(Activation(\"softmax\"))\n",
    "\n",
    "model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n",
    "\n",
    "model.fit(X_train, y_train_onehot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-24-7a393326e738>:1: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
      "Instructions for updating:\n",
      "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n"
     ]
    }
   ],
   "source": [
    "y_prediction = model.predict_classes(X_test)\n",
    "#print(\"\\nAcuracia: \", np.sum(y_prediction == y_test) / float(len(y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "418"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.read_csv('titanic\\\\gender_submission.csv')\n"
   ]
  },
  {
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived\n",
       "0            892         0\n",
       "1            893         1\n",
       "2            894         0\n",
       "3            895         0\n",
       "4            896         1\n",
       "..           ...       ...\n",
       "413         1305         0\n",
       "414         1306         1\n",
       "415         1307         0\n",
       "416         1308         0\n",
       "417         1309         0\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = y_prediction.astype(np.int16)\n",
    "submission['Survived'] = preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived\n",
       "0            892         0\n",
       "1            893         0\n",
       "2            894         0\n",
       "3            895         0\n",
       "4            896         1\n",
       "..           ...       ...\n",
       "413         1305         0\n",
       "414         1306         1\n",
       "415         1307         0\n",
       "416         1308         0\n",
       "417         1309         0\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}