Z30G0D/Titanic competition

## Titanic competition
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Titanic - Kaggle Competition\n",
    "## Hey all!\n",
    "This will be my first Python notebook depicting my code for the kaggle contest \"Titanic\".\n",
    "The competition page is __[Here](https://www.kaggle.com/c/titanic#evaluation)__\n",
    "\n",
    "I am still in my early stages of my Machine Learning journey, so this could be a nice tutorial for people who are at my stage.\n",
    "\n",
    "The target of this competition is to classify correctly the people who survived or did not survive the disaster.\n",
    "\n",
    "We will start by importing the relevent packages to our code and examine our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will first load our data, and see how it is represented. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(n=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 12), (418, 11))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape, test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see we have a training set containing 12 features and 891 samples and a test set containing 418 samples and 11 features (no \"Survived\" column).\n",
    "\n",
    "Let's visualize the data to see the features impact and choose our desired features for later on.\n",
    "From what I understood from most partcipants is that the key for this competition is to handle the features well enough in order to help the classifying algorithm to obtain decent results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Visualize_Sex(train):\n",
    "    survived = train[train['Survived'] == 1]['Sex'].value_counts()\n",
    "    not_survived = train[train['Survived'] == 0]['Sex'].value_counts()\n",
    "    df = pd.DataFrame([survived, not_survived])\n",
    "    df.index = ['Survived', 'Not survived']\n",
    "    df.plot(kind='barh',color=['r', 'b'])\n",
    "    plt.show()\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb6ef780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = Visualize_Sex(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"It is easy to notice that...\" (god, I love this phrase) the majority of male sex on the ship did not survive the disaster. This would mean that sex is a crucial feature in this data set.\n",
    "![Image of Yaktocat](https://i.pinimg.com/originals/03/29/a4/0329a484e1378fbadf0125de449dd42e.jpg)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, Lets examine the fare category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_fare(data):\n",
    "    \"\"\"plotting fare as a function of survival\"\"\"\n",
    "    plt.figure()\n",
    "    age_bins = np.arange(0, max(data['Fare']), 10)\n",
    "    plt.hist([data[data['Survived'] == 1]['Fare'], data[data['Survived'] == 0]['Fare']], stacked=True, bins=age_bins, color=['y', 'g'], label=['Survived', 'Not Survived'])\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.show()\n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb92db70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = plot_fare(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}