titanic_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic: Machine Learning from Disaster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial, I would explain how to build a basic ML model to predict whether a particular passenger in Titanic ship would survive or not based on their profile.\n",
    "We will go through how to load data sets, visualise to have a better understanding of our data to choose relevant features for the model, and build a complete ML model (Random forest) to predict the label of passengers (survived or not survived).\n",
    "\n",
    "Let's get started!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all, we need to import proper libraries. As the data we have are stored in the CSV files, **pandas** library is the most powerful library supporting data manipulating in the data frame type.\n",
    "\n",
    "We also use **matplotlib** package for visualisation.\n",
    "\n",
    "We also need packages and libraries to build machine learning models and evaluate models as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic background in building ML models##\n",
    "Normally, based on the learning method, there are four types of ML algorithms, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning. This link [https://towardsdatascience.com/types-of-machine-learning-algorithms-you-should-know-953a08248861] contains very useful information about the types of machine learning algorithms.\n",
    "\n",
    "The Titanic problem is a supervised problem. We already know the (**input** (information of each passenger), **label** (survived/ not survived)) in training data, which is explained later below. What we need to do is to predict the **label** (survived/ not survived) of the passengers based their data in test data. There are only 2 categories in predictions, hence it is a classification problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Training data in supervised learning is the data set contains **known** (**input**, **label**), or the sample of data used to fit the model. We use training data to train the model, which means letting the machine \"learn\".\n",
    "\n",
    "We use testing data to evaluate the complete model. So they are the sample of data used to provide an unbiased evaluation of a final model fit on the training dataset. \n",
    "\n",
    "The good practice is to introduce the validation data, which is used to evaluate a given model, but this is for frequent evaluation. General speaking, this data is to tune the parameters and weights to have the best fit model, then we evaluate the final model with test data.\n",
    "\n",
    "For more information, please have a look at this link: https://towardsdatascience.com/train-validation-and-test-sets-72cb40cba9e7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As such, building a supervised ML model means we apply ML algorithm that allows the machine to learn how to fit the training data, and give the best predictions. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualisation and Manipulation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we want to look at the train data to know which information and data types we have."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read train data \n",
    "train_data = pd.read_csv(\"train.csv\")\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this table, information about each passenger is stored in each row.\n",
    "\n",
    "The label of data is stored in *Survived* column, in which, 0 presents non-survived when 1 presents survived.\n",
    "\n",
    "We also have information about their seat classes, names, genders, ages, sibsps, parches, ticket numbers, fares, cabins, embarked. These information are known as the input. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some of these inputs are relevant to the passengers' survivals, some are not. We all know that when the ship sank, the children, the older, and women were prioritised to settle in the rescue boats before men. So the features of ages, genders should be included in the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will have a look at the feature of genders first, we want to know the percentage of women/ men that survived compared to the total number of women/ men in the ship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of women who survived: 74.20382165605095\n"
     ]
    }
   ],
   "source": [
    "female_data = train_data.loc[train_data[\"Sex\"] == \"female\"][\"Survived\"]\n",
    "female_survived_rate = sum(female_data) / len(female_data) \n",
    "\n",
    "print(\"% of women who survived:\", female_survived_rate * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "% of men who survived: 18.890814558058924\n"
     ]
    }
   ],
   "source": [
    "male_data = train_data.loc[train_data[\"Sex\"] == \"male\"][\"Survived\"]\n",
    "male_survived_rate = sum(male_data) / len(male_data)\n",
    "\n",
    "print(\"% of men who survived:\", male_survived_rate * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the % of survived women is 74%, while the figure of men is just 18.8%. We decide to take into account of gender as one of the features in our model.\n",
    "\n",
    "For better understanding of the data, the good practice is to visualise the relationship among the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualisation(df, feature_col, survived_col_name=\"Survived\"):\n",
    "\n",
    "    index_value = df[feature_col].unique().tolist()\n",
    "\n",
    "    survived = []\n",
    "    total = []\n",
    "\n",
    "    for value in index_value:\n",
    "        col_data = df.loc[df[feature_col] == value][survived_col_name]\n",
    "\n",
    "        survived.append(sum(col_data))\n",
    "        total.append(len(col_data))\n",
    "\n",
    "    new_df = pd.DataFrame({\"survived\": survived, \"total\": total}, index=index_value)\n",
    "    \n",
    "    new_df.plot.bar(rot=0)\n",
    "    \n",
    "    plt.title(\"Survived and total number of passengers by \" + feature_col)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that, the Passenger ID, name, and tickets are not so relevant to the survival of passenger. The fare of tickets is corresponding with the their seat classes. We will visualise the features of *Pclass*, *Sex*, *Age*, *SibSP*, *Parch*, *Embarked*. \n",
    "\n",
    "We want to group the age data into groups: children (0 - 15), adult (15 - 60), old people (> 60).\n",
    "We also note that the age data contains NaN value, which can not be inputed in the model, hence we replace all NaN value with the mean value of age, which is 30."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "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",
       "      <th>age_group</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>22.000000</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",
       "      <td>2</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>38.000000</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",
       "      <td>2</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>26.000000</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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.000000</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",
       "      <td>2</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>35.000000</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",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 13 columns</p>\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",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex        Age  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.000000   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.000000   \n",
       "2                               Heikkinen, Miss. Laina  female  26.000000   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.000000   \n",
       "4                             Allen, Mr. William Henry    male  35.000000   \n",
       "..                                                 ...     ...        ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.000000   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.000000   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female  29.699118   \n",
       "889                              Behr, Mr. Karl Howell    male  26.000000   \n",
       "890                                Dooley, Mr. Patrick    male  32.000000   \n",
       "\n",
       "     SibSp  Parch            Ticket     Fare Cabin Embarked  age_group  \n",
       "0        1      0         A/5 21171   7.2500   NaN        S          2  \n",
       "1        1      0          PC 17599  71.2833   C85        C          2  \n",
       "2        0      0  STON/O2. 3101282   7.9250   NaN        S          2  \n",
       "3        1      0            113803  53.1000  C123        S          2  \n",
       "4        0      0            373450   8.0500   NaN        S          2  \n",
       "..     ...    ...               ...      ...   ...      ...        ...  \n",
       "886      0      0            211536  13.0000   NaN        S          2  \n",
       "887      0      0            112053  30.0000   B42        S          2  \n",
       "888      1      2        W./C. 6607  23.4500   NaN        S          2  \n",
       "889      0      0            111369  30.0000  C148        C          2  \n",
       "890      0      0            370376   7.7500   NaN        Q          2  \n",
       "\n",
       "[891 rows x 13 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace empty cells in \"Age\" column \n",
    "train_data[\"Age\"] = train_data[\"Age\"].fillna(train_data[\"Age\"].mean())\n",
    "\n",
    "# Make age_group\n",
    "train_data[\"age_group\"] = pd.cut(train_data[\"Age\"], bins=[-1, 0, 15, 60, 1000], labels=False)\n",
    "labels = np.array('Children Adult Old'.split())\n",
    "\n",
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faac0b46cf8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faab59da390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XucVfV97vHPwyXi/YIcAgxxaIOJiIEYJKaooZpUjBY8JypoJZqSknhpaE9qglZb08ZziPXEeNIY60la8QpEm0g1NUETo8ZUBQPKRSPqGAdREIWIRgP4PX/8fuBinMveM7PZzOJ5v17zmnVf37X22s9e+7fW3lsRgZmZlVevehdgZma15aA3Mys5B72ZWck56M3MSs5Bb2ZWcg56M7OSc9BXSdI1ki6pwXIvlXRjdy+3jXU1SfrEjlhXK+s+W9ID9Vh3izoaJYWkPnVa/zhJT0naKOnketTQ00m6V9LndsB6tj3nJY2X1FzrdXa3UgS9pKMkPShpg6RXJP1C0hG1WFdEfCEi/rEWy94ZSLpO0tdqNb1t8w/AP0fEXhHxw3oXs6uTNE3SE5Jek/SSpB9J2huqe85LapB0m6SXcx4tlXR2TYuvQF3OZrqTpH2AO4BzgHnAe4Cjgbc6sSwBioi3u7VIKzVJfSJic5WzHQQsq0U9PVUn92N3rPfjwP8CJkTEryQdAPxpJxd3A7CE9Pi+BRwGvLdbCu2KiOjRf8AYYH074y8Fbiz0NwIB9Mn99wKXAb8Afgd8BVjYYhl/DczP3dcBX8vdK4CTCtP1AdYCh+f+I4EHgfWkB398YdphwM+B14AFwD8X62yx/v1JL2ZrgVdzd0Nh/L3AP+ZteA34CXBgYfxU4DlgHfC3QBPwiVbWMx3YBPwe2Aj8Rx5+SF7HelI4Texg+pnA07mW5cB/L6zjbOCBNrZz62NzFvAb4GXgbwvjt+373D8eaC70NwEXAI8BrwPfAwYC/5lruRvYv8W6pgMvAKuBvyksq1dhO9aRTiIOaDHvtFznfW1sz18AK4FXgPnA4Dz8aeBt0vG2EditlXmbgAvz/nsV+DegX4XHw9nAM3mbnwX+LA9/P+mY25D37dzCPB8kHYevAE8Cp7XY798G7szLfAj4w8L4P8nzbACuzuv4XGH8n5OeK68CPwYOKowL4DzgqVyrgCuBNcBvgceBkW3s33uB/w08nKe9vfAY3Qn8ZYvpH6NwLBaG/w3ww3Yy5Dreec6PB5qBi/I+bNq6f/P4jcDoDo7vVo+5Wv7VPai7vAGwD+mJOBs4gfxELoy/lI6D/jfAoaSg3jcfzMML8zwCTGnlQf874KbCdCcCK3L3kFzXp0ih8cncPyCP/yXwDWA34Ji8zraCvj/waWAPYG/g+8UDM2/D08DBwO65f1YeNyIffMfkdX0D2EwrQd9y+3J/X1JYXUR6t3RsrvUDrU2fh50KDM7bPZkUuoPyuLPpOOj/X96OUaSzokPaqG087w76/yKF+xBSWDwKfBjoB/wU+PsW67oF2JN05rV2634BZuRlNeT99i/ALS3mvT7Pu3sr23IsKQgOz/N/i8ILAm282LYYvxQYChxAehHfety1eTzken5beHwGAYfm7ltIL/S98v44qjDP88BnSc+BD+faRxT2+zpgbB5/EzAnjzswr+9/5HEzSC/+n8vjJ5GOn0Py+IuBBwvbGaQXmAPyY348sAjYjxT6h5CPnVb20b3AKmBk3obbyM8h4DTgocK0o/I2vKeV5RxNetH9KjCOFi+8vDvoN/POc/fjpON76/6+Oz9WU4D3tXF8t3rM1TQna72CHfGXD4brSK+0m0lnTwPzuEvpOOj/ocXybgT+LncPJwXbHq086O9vMe6mwnxfAW5osdwfk85W35fr3LMw7mbaCPpWtnc08GqLA/7iQv+5wF25++/IT8rcvyfpDLzSoD8aeBHoVRh2C3Bpa9O3sczFwKTcfTYdB33x7PRhWnmRzf3jeXfQF8+ubgO+U+j/S94JxK3r+mBh/OXA93L3CuC4wrhBpADrU5j3D9rZ5u8Blxf698rzNxZq7Sjov1Do/xTwdEfHQ35815NeCHZvMd31wLXF/ZuHTwbubzHsX3jnRfE64Lstankid38G+GVhnEgvGluD/j+BaYXxvYA3yGf1eT8eWxh/LPBr0rvhXq1tb4vjflahfwTp2O5NeiF7lXzCBlwBXN3Osk4A/iPvu42kIO/dynN+PO9+7s4DLsnd+wOzSO98t5CO/SMqOeZq+VeKi7ERsSIizo6IBtKr+2Dgm1Us4vkW/TcDp+fuM0jh8EYr611JCoQ/lbQHMDHPC6mN7lRJ67f+AUeRAmMw6Yn5emFxz7VVnKQ9JP2LpOck/Ra4D9hPUu/CZC8Wut8gBQt5Xdu2L69zXVvrasVg4PnY/rrFc6Qz5rbq/YykxYXtHkk686tUW9tSiZcK3b9rpb/lsoqP/XOk7YX0+P2gsA0rSE/cgW3M29JgCo9pRGwk7fc291srWq2tveMhP76TgS8AqyXdKemDeRlfJgXxw5KWSfrzwrZ+tMWx+mds37Zc6fEVpBOurQ4Crios95VcQ3E/FOf/KakZ89vAGknX5utwle6jvqRmyzeBucCZknqRns83tLWQiPjPiPhT0juLSaQTkrbu6GntuTs4L+fViJgZEYeSjpXFwA/z9b+2ah5MjZUi6Isi4gnSK/DIPOh10lvcrVq7MBIt+hcAAySNJh0gN797lm1uydNMApbn8If0YN4QEfsV/vaMiFmktrn9Je1ZWM772lnHl4APAB+NiH1IzTCQnjAdWU16+59mSC9I/duZvuW+eAEYmp8sxVpXtTa9pINITS/nA/0jYj9SE0QltXakkseyWkML3e8jbS+kx++EFo9fv4hYVZi+5b4qeoEUcgDkx7o/7+y3rtTW7vEQET+OiE+STiqeID0eRMSLEfEXETEY+DxwtaT35239eYtt3SsizqmgxtWk5q2t26lif17251sse/eIeLAwzXb7MSL+b0R8hHSGfjDpuktbWu6jTaRmJ0jNuX8GHAe8ERG/7GhjIuLtiLiH1Mw3so3JWnvuvtByooh4mfROYjDpBaStmt81b3fr8UEv6YOSviSpIfcPJQXvf+VJFgPHSHqfpH1JF7jaFRGbSO2e/0R6gBa0M/kc0sWoc9j+BeFG0pn+8ZJ6S+qX78FtiIjngIXAVyW9R9JRtH+Vf2/S2ej6fEfA33e0DQW3AiflW1DfQ7qtr73H/SXgDwr9D5HO4L4sqa+k8bnWOW1MvyfpibsWQNJnafsJU63FwKckHSDpvcBfdcMyL8lnyIeS2qjn5uHXAJflFy4kDZA0qYrl3gJ8VtJoSbuR7up4KCKaqljGefl2vQNIbetba2vzeJA0UNKkHERvkZoh3s7jTt36PCE1a0QedwdwsKSp+THuK+kISYdUUOOdwGGSTlb6TMJ5bP8CfA1wYd6/SNpX0qltLSyv96OS+pJe2N/cWn8bzpQ0Ip/A/ANwa0RsAcjB/jbwf2jnbD7vrymS9lcyltT2/l9tzcM7z92jgZNIeYGkr0saKalPvj3zHGBlRBTfRbd1zNVMjw96Uhv5R4GHJL1OenCWks56iIgFpB35GOkizx0VLvdm4BPA96OdW74iYjXpwuofUXjAIuJ50ln+RaTQe550ZrJ1n5+R636F9ES9vp1avkm6UPVy3r67KtwGImIZ6cl3M+ns61W2f2vd0veAEfmt9g8j4vekYD8hr/9q4DP5nVNr0y8nPbF+SXoROIx0cao7bL11rYl0Z1F3PEF+TrpYeA9wRUT8JA+/inSt5yeSXiPt949WutCIuBu4hHSdYDXwh6QLdNW4mbSdz5Autm/9vEJ7x0Mv4H+SzhJfIQXW1jPzI0jPk41522ZExDMR8RrpZGVKnu9F4Ouki40dbefLpIvvl5OapkaQTmLeyuN/kJc1JzczLSUdS23Zh/QO5FXeuVPsn9qZ/gbSO/gXSe3yX2wx/nrSMdjehxFfJd0h9RTpwvKNwD9FxE1tTP9inucF0nW5LxSeD3sAPyC19T9Delc3scX8bR1zNaN8QcDMdiKSmkgXNO+udy3VyE18zaSL4j/bCer5DDA9Io7aCWppJN1C2re9k8daKMMZvZnVUW6e3C83UV1EulbQXrPHDpGbc84l3Wm0S3PQm1lXfYzUtPQyqZnv5Ij4XT0LknQ8qcn0Jdq/mWKX4KYbM7OS8xm9mVnJ7RRfanbggQdGY2NjvcswM+tRFi1a9HJEDOhoup0i6BsbG1m4cGG9yzAz61EktfmJ+iI33ZiZlZyD3sys5Bz0ZmYlt1O00ZvZrmHTpk00Nzfz5ptv1ruUHqVfv340NDTQt2/fTs3voDezHaa5uZm9996bxsZGtv/mXmtLRLBu3Tqam5sZNmxYp5bhphsz22HefPNN+vfv75CvgiT69+/fpXdBDnoz26Ec8tXr6j5z0JuZlVxFbfSS9gO+S/oBiSD9qvuTpO8DbyR9P/hpEfFqnv5CYBrpp9e+GBE/7u7Czazna5x5Z7cur2nWid26vErNnz+f5cuXM3PmzC4va6+99mLjxo3dUNU7Kr0YexXpx6ZPyb9StAfp60jviYhZkmYCM4GvSBpB+gGDQ0k/oXW3pIO3/uqLdcKl+1Y5/Yba1GG2C9u8eTN9+rQemRMnTmTixJa/L7Lz6LDpJv/83jGkXxIiIn4fEetJv540O082Gzg5d08C5kTEWxHxLOmXVMZ2d+FmZp3x+uuvc+KJJzJq1ChGjhzJ3LlzaWxs5OWX00/NLly4kPHjxwNw6aWXMnXqVMaNG8fUqVM58sgjWbZs2bZljR8/noULF3Lddddx/vnns2HDBg466CDefvvtbesaOnQomzZt4umnn2bChAl85CMf4eijj+aJJ9KPUj377LN87GMf47DDDuPiiy+uyTZX0kY/jPS9zv8m6VeSvpt/j3Jg/hk9SD+tNTB3D2H7XzlvZvtffAdA0nRJCyUtXLt2bee3wMysCnfddReDBw9myZIlLF26lAkTJrQ7/fLly7n77ru55ZZbmDx5MvPmzQNg9erVrF69mjFjxmybdt9992X06NH8/Oc/B+COO+7g+OOPp2/fvkyfPp1vfetbLFq0iCuuuIJzzz0XgBkzZnDOOefw+OOPM2jQoJpscyVB3wc4HPhORHyY9IO92zVERfpS+6q+2D4iro2IMRExZsCADr98zcysWxx22GEsWLCAr3zlK9x///3su2/7TaMTJ05k9913B+C0007j1ltvBWDevHmccsop75p+8uTJzJ2bfs54zpw5TJ48mY0bN/Lggw9y6qmnMnr0aD7/+c+zenU6T/7FL37B6aefDsDUqVO7bTuLKmmjbwaaI+Kh3H8rKehfkjQoIlZLGgSsyeNXAUML8zfkYWZmdXfwwQfz6KOP8qMf/YiLL76Y4447jj59+mxrbml5v/qee+65rXvIkCH079+fxx57jLlz53LNNde8a/kTJ07koosu4pVXXmHRokUce+yxvP766+y3334sXry41Zpqfctph2f0EfEi8LykD+RBxwHLSb8if1YedhZwe+6eD0yRtJukYcBw4OFurdrMrJNeeOEF9thjD84880wuuOACHn30URobG1m0aBEAt912W7vzT548mcsvv5wNGzbwoQ996F3j99prL4444ghmzJjBSSedRO/evdlnn30YNmwY3//+94H0adclS5YAMG7cOObMmQPATTfd1J2buk2ld938JXBTvuPmGeCzpBeJeZKmAc8BpwFExDJJ80gvBpuB83zHjZm1ph63Qz7++ONccMEF9OrVi759+/Kd73yH3/3ud0ybNo1LLrlk24XYtpxyyinMmDGDSy65pM1pJk+ezKmnnsq99967bdhNN93EOeecw9e+9jU2bdrElClTGDVqFFdddRVnnHEGX//615k0aVI3beX2dorfjB0zZkz4h0fa4dsrrSRWrFjBIYccUu8yeqTW9p2kRRExpo1ZtvEnY83MSs5Bb2ZWcg56M7OSc9CbmZWcg97MrOQc9GZmJeefEjSz+qn21uEOl9f+rcXr16/n5ptv3vY9M61pamriwQcf5Iwzzmh3WU1NTZx00kksXbq0U6XuSD6jN7Ndxvr167n66qvbnaapqYmbb755B1W0YzjozWyXMXPmTJ5++mlGjx7NBRdcwAUXXMDIkSM57LDDtn0R2cyZM7n//vsZPXo0V155JU1NTRx99NEcfvjhHH744Tz44IN13orquenGzHYZs2bNYunSpSxevJjbbruNa665hiVLlvDyyy9zxBFHcMwxxzBr1iyuuOIK7rjjDgDeeOMNFixYQL9+/Xjqqac4/fTT6Wmf5HfQm9ku6YEHHuD000+nd+/eDBw4kI9//OM88sgj7LPPPttNt2nTJs4//3wWL15M7969+fWvf12nijvPQW9m1o4rr7ySgQMHsmTJEt5++2369etX75Kq5jZ6M9tl7L333rz22msAHH300cydO5ctW7awdu1a7rvvPsaOHbvdNAAbNmxg0KBB9OrVixtuuIEtW3rel/H6jN7M6mcHf9Nq//79GTduHCNHjuSEE07gQx/6EKNGjUISl19+Oe9973vp378/vXv3ZtSoUZx99tmce+65fPrTn+b6669nwoQJ2/0QSU/hrynuCfw1xVYS/prizvPXFJuZWZsc9GZmJeegN7MdamdoLu5purrPHPRmtsP069ePdevWOeyrEBGsW7euS7d1+q4bM9thGhoaaG5uZu3atfUupUfp168fDQ0NnZ7fQW9mO0zfvn0ZNmxYvcvY5bjpxsys5Bz0ZmYl56A3Mys5B72ZWclVFPSSmiQ9LmmxpIV52AGSFkh6Kv/fvzD9hZJWSnpS0vG1Kt7MzDpWzRn9H0fE6ML3KswE7omI4cA9uR9JI4ApwKHABOBqSb27sWYzM6tCV5puJgGzc/ds4OTC8DkR8VZEPAusBMZ2YT1mZtYFlQZ9AHdLWiRpeh42MCJW5+4XgYG5ewjwfGHe5jxsO5KmS1ooaaE/PGFmVjuVfmDqqIhYJem/AQskPVEcGREhqarPNEfEtcC1kL6muJp5zcyschWd0UfEqvx/DfADUlPMS5IGAeT/a/Lkq4Chhdkb8jAzM6uDDoNe0p6S9t7aDfwJsBSYD5yVJzsLuD13zwemSNpN0jBgOPBwdxduZmaVqaTpZiDwA0lbp785Iu6S9AgwT9I04DngNICIWCZpHrAc2AycFxE970cWzcxKosOgj4hngFGtDF8HHNfGPJcBl3W5OjMz6zJ/MtbMrOQc9GZmJeegNzMrOQe9mVnJOejNzErOQW9mVnIOejOzknPQm5mVnIPezKzkHPRmZiXnoDczKzkHvZlZyTnozcxKzkFvZlZyDnozs5Jz0JuZlZyD3sys5Bz0ZmYl56A3Mys5B72ZWck56M3MSs5Bb2ZWcg56M7OSc9CbmZWcg97MrOQqDnpJvSX9StIduf8ASQskPZX/71+Y9kJJKyU9Ken4WhRuZmaVqeaMfgawotA/E7gnIoYD9+R+JI0ApgCHAhOAqyX17p5yzcysWhUFvaQG4ETgu4XBk4DZuXs2cHJh+JyIeCsingVWAmO7p1wzM6tWpWf03wS+DLxdGDYwIlbn7heBgbl7CPB8YbrmPMzMzOqgw6CXdBKwJiIWtTVNRAQQ1axY0nRJCyUtXLt2bTWzmplZFSo5ox8HTJTUBMwBjpV0I/CSpEEA+f+aPP0qYGhh/oY8bDsRcW1EjImIMQMGDOjCJpiZWXs6DPqIuDAiGiKikXSR9acRcSYwHzgrT3YWcHvung9MkbSbpGHAcODhbq/czMwq0qcL884C5kmaBjwHnAYQEcskzQOWA5uB8yJiS5crNTOzTqkq6CPiXuDe3L0OOK6N6S4DLutibWZm1g38yVgzs5Jz0JuZlZyD3sys5Bz0ZmYl56A3Mys5B72ZWck56M3MSs5Bb2ZWcg56M7OSc9CbmZWcg97MrOQc9GZmJeegNzMrOQe9mVnJOejNzErOQW9mVnIOejOzknPQm5mVnIPezKzkHPRmZiXnoDczKzkHvZlZyTnozcxKzkFvZlZyDnozs5LrMOgl9ZP0sKQlkpZJ+moefoCkBZKeyv/3L8xzoaSVkp6UdHwtN8DMzNpXyRn9W8CxETEKGA1MkHQkMBO4JyKGA/fkfiSNAKYAhwITgKsl9a5F8WZm1rEOgz6Sjbm3b/4LYBIwOw+fDZycuycBcyLirYh4FlgJjO3Wqs3MrGIVtdFL6i1pMbAGWBARDwEDI2J1nuRFYGDuHgI8X5i9OQ8zM7M6qCjoI2JLRIwGGoCxkka2GB+ks/yKSZouaaGkhWvXrq1mVjMzq0JVd91ExHrgZ6S295ckDQLI/9fkyVYBQwuzNeRhLZd1bUSMiYgxAwYM6EztZmZWgUruuhkgab/cvTvwSeAJYD5wVp7sLOD23D0fmCJpN0nDgOHAw91duJmZVaZPBdMMAmbnO2d6AfMi4g5JvwTmSZoGPAecBhARyyTNA5YDm4HzImJLbco3M7OOdBj0EfEY8OFWhq8DjmtjnsuAy7pcnZmZdZk/GWtmVnIOejOzknPQm5mVnIPezKzkHPRmZiXnoDczKzkHvZlZyTnozcxKzkFvZlZyDnozs5Jz0JuZlZyD3sys5Bz0ZmYl56A3Mys5B72ZWck56M3MSs5Bb2ZWcg56M7OSc9CbmZWcg97MrOQc9GZmJeegNzMrOQe9mVnJOejNzErOQW9mVnJ96l3Arqhx5p1VTd/Ur0aFmNkuocOglzQUuB4YCARwbURcJekAYC7QCDQBp0XEq3meC4FpwBbgixHx45pUX6lL961y+g21qcPMrA4qabrZDHwpIkYARwLnSRoBzATuiYjhwD25nzxuCnAoMAG4WlLvWhRvZmYd6zDoI2J1RDyau18DVgBDgEnA7DzZbODk3D0JmBMRb0XEs8BKYGx3F25mZpWp6mKspEbgw8BDwMCIWJ1HvUhq2oH0IvB8YbbmPKzlsqZLWihp4dq1a6ss28zMKlVx0EvaC7gN+KuI+G1xXEQEqf2+YhFxbUSMiYgxAwYMqGZWMzOrQkVBL6kvKeRvioh/z4NfkjQojx8ErMnDVwFDC7M35GFmZlYHHQa9JAHfA1ZExDcKo+YDZ+Xus4DbC8OnSNpN0jBgOPBw95VsZmbVqOQ++nHAVOBxSYvzsIuAWcA8SdOA54DTACJimaR5wHLSHTvnRcSWbq/czMwq0mHQR8QDgNoYfVwb81wGXNaFuszMrJv4KxDMzErOQW9mVnIOejOzknPQm5mVnIPezKzkHPRmZiXnoDczKzkHvZlZyTnozcxKzkFvZlZyDnozs5Jz0JuZlZyD3sys5Bz0ZmYl56A3Mys5B72ZWck56M3MSs5Bb2ZWcg56M7OSc9CbmZWcg97MrOQc9GZmJeegNzMrOQe9mVnJ9al3AZ3ROPPOqqZv6lejQszMeoAOz+gl/aukNZKWFoYdIGmBpKfy//0L4y6UtFLSk5KOr1XhZmZWmUqabq4DJrQYNhO4JyKGA/fkfiSNAKYAh+Z5rpbUu9uqNTOzqnUY9BFxH/BKi8GTgNm5ezZwcmH4nIh4KyKeBVYCY7upVjMz64TOXowdGBGrc/eLwMDcPQR4vjBdcx72LpKmS1ooaeHatWs7WYaZmXWky3fdREQA0Yn5ro2IMRExZsCAAV0tw8zM2tDZoH9J0iCA/H9NHr4KGFqYriEPMzOzOuls0M8HzsrdZwG3F4ZPkbSbpGHAcODhrpVoZmZd0eF99JJuAcYDB0pqBv4emAXMkzQNeA44DSAilkmaBywHNgPnRcSWGtVuZmYV6DDoI+L0NkYd18b0lwGXdaUoMzPrPv4KBDOzknPQm5mVnIPezKzkHPRmZiXnoDczKzkHvZlZyTnozcxKzkFvZlZyDnozs5Jz0JuZlZyD3sys5Bz0ZmYl56A3Mys5B72ZWck56M3MSs5Bb2ZWcg56M7OSc9CbmZVchz8laGY93KX7Vjn9htrUYXXjM3ozs5Jz0JuZlZyD3sys5NxGb7XX09uIe3r9tstz0Jv1MI0z76xq+qZ+NSrEegw33ZiZlVzNzuglTQCuAnoD342IWbVal5nZTmknafarSdBL6g18G/gk0Aw8Iml+RCyvxfpsx3LTge0wO0lQ9nS1OqMfC6yMiGcAJM0BJgEOeqs7v1DZrkYR0f0LlU4BJkTE53L/VOCjEXF+YZrpwPTc+wHgyW4v5B0HAi/XcPm15vrry/XXT0+uHWpf/0ERMaCjiep2101EXAtcuyPWJWlhRIzZEeuqBddfX66/fnpy7bDz1F+ru25WAUML/Q15mJmZ7WC1CvpHgOGShkl6DzAFmF+jdZmZWTtq0nQTEZslnQ/8mHR75b9GxLJarKtCO6SJqIZcf325/vrpybXDTlJ/TS7GmpnZzsOfjDUzKzkHvZlZyZU66CX9q6Q1kpbWu5bOkjRB0pOSVkqaWe96KiWpn6SHJS2RtEzSV+tdU2dI6i3pV5LuqHct1ZA0VNLPJC3P+39GvWuqlqQmSY9LWixpYb3rqZakv877fqmkWyTV7aN3pQ564DpgQr2L6KzCV0mcAIwATpc0or5VVewt4NiIGAWMBiZIOrLONXXGDGBFvYvohM3AlyJiBHAkcF4POnaK/jgiRu8M96JXQ9IQ4IvAmIgYSbopZUq96il10EfEfcAr9a6jC7Z9lURE/B7Y+lUSO71INubevvmvR135l9QAnAh8t961VCsiVkfEo7n7NdKL1ZD6VrXL6QPsLqkPsAfwQr0KKXXQl8AQ4PlCfzM96Mmamz0WA2uABRHxUL1rqtI3gS8Db9e7kK6Q1Ah8GOhp+z+AuyUtyl+Z0mNExCrgCuA3wGpgQ0T8pF71OOitZiJiS0SMJn0yeqykkfWuqVKSTgLWRMSietfSFZL2Am4D/ioiflvveqp0VD5+TiA1PR1T74IqJWl/0rvvYcBgYE9JZ9arHgf9zq0UXyUREeuBn9GzrpeMAyZKaiI1mR0r6cb6llQdSX1JIX9TRPx7veupVj4rJiLWAD8gNWX2FJ8Ano2ItRGxCfh34I/qVYyDfufWY79KQtIASfvl7t3NK+Z6AAAA00lEQVRJv03wRH2rqlxEXBgRDRHRSNrvP42Iup2RVUuSgO8BKyLiG/Wup1qS9pS099Zu4E+AnnT33G+AIyXtkR+L46jjRf1SB72kW4BfAh+Q1CxpWr1rqkZEbAa2fpXECmBenb9KohqDgJ9Jeoz0grUgInrULYo93DhgKumdyOL896l6F1WFgcADkpYADwN3RsRdda6pYvl61K3Ao8DjpKyt29ch+CsQzMxKrtRn9GZm5qA3Mys9B72ZWck56M3MSs5Bb2ZWcg56M7OSc9CbmZXc/wcdj0rOMMUWogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faab58bbb38>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faab57b84a8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faab57a0358>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faab56475c0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features_vis = [\"Pclass\", \"Sex\", \"SibSp\", \"Parch\", \"Embarked\", \"age_group\"]\n",
    "\n",
    "for feature in features_vis:\n",
    "    visualisation(train_data, feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graphs, we can see those features have impacts to the probability of passenger surviving or not.\n",
    "Hence, we decide to take these features into account to build our model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we discussed above, we need 3 types of datasets to build a complete model, which are training , validation, and test data sets. \n",
    "\n",
    "We will split the original train_data into training and validation data sets. We build the model, fit this model with train data, then evaluate this model with validation data. By evaluating the model with validation data, we are trying to produce the most accurate predictions we can.\n",
    "\n",
    "Finally, when we have the complete model, we will test the model with test data and submit our answer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split ratio train_data : val_data\n",
    "split_ratio = 0.8\n",
    "\n",
    "split_data = np.split(train_data, [int(train_data.shape[0] * 0.8)], axis=0)\n",
    "\n",
    "train_data_n = split_data[0]\n",
    "val_data = split_data[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this tutorial, we use Random Forest Algorithm to predict label or classify them as survived or not survived.\n",
    "\n",
    "Random Forest is a common used algorithm with pros: fast, accurate, and non-overfitting.\n",
    "https://towardsdatascience.com/understanding-random-forest-58381e0602d2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".... Finish training model.\n"
     ]
    }
   ],
   "source": [
    "# Import library\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Build model\n",
    "\n",
    "features = [\"Pclass\", \"Sex\", \"SibSp\", \"Parch\", \"Embarked\", \"Age\"]\n",
    "\n",
    "X = pd.get_dummies(train_data_n[features])\n",
    "y = train_data_n[\"Survived\"]\n",
    "\n",
    "model = RandomForestClassifier(n_estimators=400, max_depth=5, random_state=1)\n",
    "model.fit(X, y)\n",
    "\n",
    "print(\".... Finish training model.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have finished training our model. We now need to evaluate this model by validation data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_val = pd.get_dummies(val_data[features])\n",
    "y_val = val_data[\"Survived\"].tolist()\n",
    "\n",
    "predictions = model.predict(X_val)\n",
    "pred_list = predictions.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will need to know how well our model predicts, the easiest way is to calculate the percentage of accurate predictions.\n",
    "\n",
    "There are many metrics to evaluate a model, please have a look at: https://towardsdatascience.com/the-5-classification-evaluation-metrics-you-must-know-aa97784ff226"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percentage of correct predictions is:  87.70949720670392\n"
     ]
    }
   ],
   "source": [
    "y_true = []\n",
    "y_pred = []\n",
    "\n",
    "accurate_pred = 0\n",
    "total_pred = 0\n",
    "\n",
    "for i in range(val_data.shape[0]):\n",
    "    if pred_list[i] == y_val[i]:\n",
    "        accurate_pred += 1\n",
    "    \n",
    "    total_pred += 1\n",
    "    \n",
    "    y_true.append(y_val[i])\n",
    "    y_pred.append(pred_list[i])\n",
    "\n",
    "print(\"The percentage of correct predictions is: \", accurate_pred / total_pred * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to visualise these predictions by confusion matrix. (https://machinelearningmastery.com/confusion-matrix-machine-learning/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7faab4b28828>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7faab5543b70>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import confusion_matrix lib\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Build confusion matrix\n",
    "cf_matrix = confusion_matrix(y_true, y_pred)\n",
    "\n",
    "# Visualise confusion matrix\n",
    "df_cm = pd.DataFrame(cf_matrix, index = [i for i in range(2)], columns = [i for i in range(2)])\n",
    "plt.figure(figsize = (10,7))\n",
    "sn.heatmap(df_cm, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will need to do feature engineering as well as other techniques and tune parameters to have a better prediction.\n",
    "These will be found in **models** folder in this repo (under development).\n",
    "We will accept this result of ~ 87% to move on the next step with testing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "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>34.50000</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>47.00000</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>62.00000</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>27.00000</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>22.00000</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>3</td>\n",
       "      <td>Spector, Mr. Woolf</td>\n",
       "      <td>male</td>\n",
       "      <td>30.27259</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>A.5. 3236</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "      <td>Oliva y Ocana, Dona. Fermina</td>\n",
       "      <td>female</td>\n",
       "      <td>39.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17758</td>\n",
       "      <td>108.9000</td>\n",
       "      <td>C105</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>3</td>\n",
       "      <td>Saether, Mr. Simon Sivertsen</td>\n",
       "      <td>male</td>\n",
       "      <td>38.50000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/O.Q. 3101262</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>3</td>\n",
       "      <td>Ware, Mr. Frederick</td>\n",
       "      <td>male</td>\n",
       "      <td>30.27259</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>359309</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>3</td>\n",
       "      <td>Peter, Master. Michael J</td>\n",
       "      <td>male</td>\n",
       "      <td>30.27259</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2668</td>\n",
       "      <td>22.3583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     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",
       "..           ...     ...                                           ...   \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 Cabin  \\\n",
       "0      male  34.50000      0      0              330911    7.8292   NaN   \n",
       "1    female  47.00000      1      0              363272    7.0000   NaN   \n",
       "2      male  62.00000      0      0              240276    9.6875   NaN   \n",
       "3      male  27.00000      0      0              315154    8.6625   NaN   \n",
       "4    female  22.00000      1      1             3101298   12.2875   NaN   \n",
       "..      ...       ...    ...    ...                 ...       ...   ...   \n",
       "413    male  30.27259      0      0           A.5. 3236    8.0500   NaN   \n",
       "414  female  39.00000      0      0            PC 17758  108.9000  C105   \n",
       "415    male  38.50000      0      0  SOTON/O.Q. 3101262    7.2500   NaN   \n",
       "416    male  30.27259      0      0              359309    8.0500   NaN   \n",
       "417    male  30.27259      1      1                2668   22.3583   NaN   \n",
       "\n",
       "    Embarked  \n",
       "0          Q  \n",
       "1          S  \n",
       "2          Q  \n",
       "3          S  \n",
       "4          S  \n",
       "..       ...  \n",
       "413        S  \n",
       "414        C  \n",
       "415        S  \n",
       "416        S  \n",
       "417        C  \n",
       "\n",
       "[418 rows x 11 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = pd.read_csv(\"test.csv\")\n",
    "test_data[\"Age\"] = test_data[\"Age\"].fillna(test_data[\"Age\"].mean())\n",
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your file was successfully saved\n"
     ]
    },
    {
     "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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = pd.get_dummies(test_data[features])\n",
    "y_test = model.predict(X_test)\n",
    "\n",
    "predict_data = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': y_test})\n",
    "predict_data.to_csv('predict.csv', index=False)\n",
    "print(\"Your file was successfully saved\")\n",
    "predict_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have successfully built the model that can predict the probability of survival of passengers in Titanic based on their profile using Machine Learning algorithm: Random Forest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To have a better model, we need to try different algorithms as well as tune parameters for the model. Please visit the **models** folder for more information.\n"
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}