Titanic Competition.ipynb

{
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    "_uuid": "213592d544cbb34b454281467efafbad48736d31"
   },
   "source": [
    "# Titanic: Machine Learning from Disaster\n",
    "\n",
    "***By Joe Corliss***\n",
    "\n",
    "**March 27, 2018**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "37f53c0e-7466-4ce4-b54e-4819e7f2f66a",
    "_uuid": "784c07fe0f703e35ebec603f1a4f3800c552966a"
   },
   "source": [
    "## Contents"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "5077cc0e-50e3-4554-ae19-238a34576115",
    "_uuid": "9f1df6a4940e81711ac57e29cc078378bc871e13"
   },
   "source": [
    "* [1 Introduction](#1)\n",
    "* [2 Getting Started](#2)\n",
    "    * [2.1 Imports](#2.1)\n",
    "    * [2.2 Read In the Data](#2.2)\n",
    "* [3 Pre-processing](#3)\n",
    "    * [3.1 Feature Engineering](#3.1)\n",
    "    * [3.2 One-Hot Encoding](#3.2)\n",
    "    * [3.3 Imputation with Mean Substitution](#3.3)\n",
    "    * [3.4 Standardization](#3.4)\n",
    "    * [3.5 Recursive Feature Elimination](#3.5)\n",
    "* [4 Predictive Modeling](#4)\n",
    "    * [4.1 Random Forest](#4.1)\n",
    "    * [4.2 Gradient Boosting](#4.2)\n",
    "    * [4.3 Logistic Regression](#4.3)\n",
    "    * [4.4 Gaussian Naive Bayes](#4.4)\n",
    "    * [4.5 Support Vector Classifier](#4.5)\n",
    "    * [4.6 k-Nearest Neighbors](#4.6)\n",
    "* [5 Conclusion](#5)\n",
    "    * [5.1 Results Summary](#5.1)\n",
    "    * [5.2 Test Set Predictions](#5.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 Introduction\n",
    "<a id='1'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "90a1c88a-d4a7-48ca-b4b3-74c250e27fd5",
    "_uuid": "213592d544cbb34b454281467efafbad48736d31"
   },
   "source": [
    "[Kaggle competition](https://www.kaggle.com/c/titanic) - The competition page on Kaggle\n",
    "\n",
    "[Kaggle notebook](https://www.kaggle.com/pileatedperch/titanic-predicting-survival) - This notebook hosted on Kaggle\n",
    "\n",
    "[GitHub repository](https://github.com/jgcorliss/titanic-competition) - GitHub repo for this project\n",
    "\n",
    "\"The sinking of the RMS Titanic is one of the most infamous shipwrecks in history.  On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.\n",
    "\n",
    "One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class.\n",
    "\n",
    "In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy.\n",
    "\n",
    "Your score is the percentage of passengers you correctly predict. This is known simply as 'accuracy.'\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "4206781a-4d77-4f05-8c80-118aa3435b97",
    "_uuid": "59bcf9970c0270cdbdbee5bef3e6e75769fe0fdb"
   },
   "source": [
    "# 2 Getting Started\n",
    "<a id='2'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "4854dd55-5520-43e2-aca6-bb8b6e03da7b",
    "_uuid": "4b5e6c4c800de224b736daa05dfcdf67148b113b"
   },
   "source": [
    "## 2.1 Imports\n",
    "<a id='2.1'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "8420f9fa-de58-4f21-a90a-115d1c4a2011",
    "_uuid": "ffb7d06a14aec97edaec81d0cb54908da444edf3"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Pandas options\n",
    "pd.set_option('display.max_colwidth', 1000, 'display.max_rows', None, 'display.max_columns', None)\n",
    "\n",
    "# Plotting options\n",
    "%matplotlib inline\n",
    "mpl.style.use('ggplot')\n",
    "sns.set(style='whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2e5433d5-fe95-4ebb-bce1-414436082bb4",
    "_uuid": "478b69fc0bd0274d36af44ffe2c4bd8463ea05ca"
   },
   "source": [
    "## 2.2 Read In the Data\n",
    "<a id='2.2'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "02e20ec7-d0c6-4c6e-ae31-cff681a87829",
    "_uuid": "43d7e435bffdc4679e32cf849d56a36ce6c7ecff"
   },
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('train.csv')\n",
    "df_test = pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the test set passenders IDs for later:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "436a57e4-3c43-43ac-b771-af61e202c793",
    "_uuid": "84c91238e4e9988d53f00f94a64f8c25296c9e08"
   },
   "outputs": [],
   "source": [
    "TestPassengerId = df_test.loc[:,'PassengerId']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e4602e11-2fe6-4b73-88a0-e130a92d1d97",
    "_uuid": "eaf68050fc43e3cc63237e77b0831bf3ff74ee20"
   },
   "source": [
    "Concatenate the train and test sets together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "9dfd7979-5352-43a2-8099-ce51f930ca61",
    "_uuid": "5f387da6c09e77e5739b2e28a2261dcf46359c61"
   },
   "outputs": [],
   "source": [
    "df = df_train.append(df_test, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "66719bbe-55f3-4df6-8bfb-ab7ac27ea1c5",
    "_uuid": "094479ac9967a7c553b7ae55a75c6aea946c84e9"
   },
   "source": [
    "Basic metadata:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "17164ee6-9f01-4451-89f6-d53a2b580c78",
    "_uuid": "48cf30c3c5603242160472529dba8e78b85a3a46"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 12)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "1ef85023-dde2-4b9c-9ffb-85c64f067f43",
    "_uuid": "38249892b22613a75abec38c216bc1e7075abb9e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 12 columns):\n",
      "Age            1046 non-null float64\n",
      "Cabin          295 non-null object\n",
      "Embarked       1307 non-null object\n",
      "Fare           1308 non-null float64\n",
      "Name           1309 non-null object\n",
      "Parch          1309 non-null int64\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Sex            1309 non-null object\n",
      "SibSp          1309 non-null int64\n",
      "Survived       891 non-null float64\n",
      "Ticket         1309 non-null object\n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 122.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0e30a35b-ddc7-45b5-81c4-825be2016750",
    "_uuid": "026d5ff3c9addc88fdb630e4f87552e0907f0f9c"
   },
   "source": [
    "Find incomplete columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "869570d5-6b7c-4d46-ab2b-a9588d524c47",
    "_uuid": "641cc600a50db00a26ead9b74a3847fa68a2bbad"
   },
   "outputs": [],
   "source": [
    "def incomplete_cols(df):\n",
    "    \"\"\"\n",
    "    Returns a list of incomplete columns in df and their fraction of non-null values.\n",
    "    \n",
    "    Input: pandas DataFrame\n",
    "    Returns: pandas Series\n",
    "    \"\"\"\n",
    "    cmp = df.notnull().mean().sort_values()\n",
    "    return cmp.loc[cmp<1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "096a9550-6c1c-4e0e-9591-d00f759e9c63",
    "_uuid": "c8153c310f14522980df6f513e9375a05584ec0d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cabin       0.225363\n",
       "Survived    0.680672\n",
       "Age         0.799083\n",
       "Embarked    0.998472\n",
       "Fare        0.999236\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incomplete_cols(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "500d81e7-0f3a-4cf3-98fe-7b2eaa5c3e72",
    "_uuid": "76f67ea9eeadfc8a09a36bc6c0e9cd99069b3b81"
   },
   "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>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>23.0000</td>\n",
       "      <td>Doling, Miss. Elsie</td>\n",
       "      <td>1</td>\n",
       "      <td>652</td>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>231919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>752</th>\n",
       "      <td>33.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>9.5000</td>\n",
       "      <td>Vande Velde, Mr. Johannes Joseph</td>\n",
       "      <td>0</td>\n",
       "      <td>753</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>345780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1211</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>Andersson, Mr. Johan Samuel</td>\n",
       "      <td>0</td>\n",
       "      <td>1212</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>347075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>10.7083</td>\n",
       "      <td>Lamb, Mr. John Joseph</td>\n",
       "      <td>0</td>\n",
       "      <td>976</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>240261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>15.8500</td>\n",
       "      <td>Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)</td>\n",
       "      <td>0</td>\n",
       "      <td>143</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>STON/O2. 3101279</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age Cabin Embarked     Fare  \\\n",
       "651   18.0   NaN        S  23.0000   \n",
       "752   33.0   NaN        S   9.5000   \n",
       "1211  26.0   NaN        S   7.7750   \n",
       "975    NaN   NaN        Q  10.7083   \n",
       "142   24.0   NaN        S  15.8500   \n",
       "\n",
       "                                                      Name  Parch  \\\n",
       "651                                    Doling, Miss. Elsie      1   \n",
       "752                       Vande Velde, Mr. Johannes Joseph      0   \n",
       "1211                           Andersson, Mr. Johan Samuel      0   \n",
       "975                                  Lamb, Mr. John Joseph      0   \n",
       "142   Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)      0   \n",
       "\n",
       "      PassengerId  Pclass     Sex  SibSp  Survived            Ticket  \n",
       "651           652       2  female      0       1.0            231919  \n",
       "752           753       3    male      0       0.0            345780  \n",
       "1211         1212       3    male      0       NaN            347075  \n",
       "975           976       2    male      0       NaN            240261  \n",
       "142           143       3  female      1       1.0  STON/O2. 3101279  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5) # Display some random rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b4ff88b1-e15b-4d4f-b542-314fd09c7ece",
    "_uuid": "e99ff4d2c81864021628789da6d58fea21176579"
   },
   "source": [
    "# 3 Pre-processing\n",
    "<a id='3'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "5432957e-5cd7-4bff-bb0c-61dd54c54f3f",
    "_uuid": "99db4fdd03df26b6c96a721455b4018054affc16"
   },
   "source": [
    "## 3.1 Feature Engineering\n",
    "<a id='3.1'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "cf786f16-547a-41ed-89f4-6bb1fa1b606e",
    "_uuid": "127f53b6a9cea43ac45edf419b497a273241a10d"
   },
   "source": [
    "### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "7439ca28-afb1-446b-b3c5-5b62d312b1f2",
    "_uuid": "7dfb8a654c2fb09cca5b8d15f2ac392058ee7c97"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.799083269671505"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].notnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "1b89659a-c518-417c-9ccd-478ce19ab16d",
    "_uuid": "52de823ce8baf7e3245e0056dfadb9b72bca5d45"
   },
   "outputs": [],
   "source": [
    "df['Age_NA'] = df['Age'].isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "ad4b22fb-a623-4d1b-9075-4acf60655632",
    "_uuid": "cf85e2f3cca48bd7468029dfddea6542280929bd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Histogram of Passenger Age')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ptdde07Zt2+R0OrVo0SI5HA4lJyfrq6++0pdffqmKigplZGSoZ8+eNdrjBwAA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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df145f3198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4), dpi=90)\n",
    "sns.distplot(df.loc[df['Age'].notnull(), 'Age'], bins=range(0, 90, 2), kde=False)\n",
    "plt.ylabel('Count')\n",
    "plt.title('Histogram of Passenger Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "bf995e49-d3b8-47eb-b946-2d385e41463b",
    "_uuid": "5af969adff301f75bdcf39a0f3c83c44f2885822"
   },
   "source": [
    "### Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "c8406943-e749-4834-89d4-0fc1640aeaa4",
    "_uuid": "af0f901dca9b72dd90deab05e6ffc735c190a066"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22536287242169595"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cabin'].notnull().mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ca11a261-b3bc-404b-9c0f-86605a1a1601",
    "_uuid": "02a8a2bc7c6c5d7cc1408092ecda5a779c199ad8"
   },
   "source": [
    "Extract the cabin letter (A, B, C, etc), or NA if the cabin data is missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_cabin(s):\n",
    "    try:\n",
    "        return s[0]\n",
    "    except:\n",
    "        return 'NA'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:,'Cabin'] = df['Cabin'].apply(find_cabin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NA    1014\n",
       "C       94\n",
       "B       65\n",
       "D       46\n",
       "E       41\n",
       "A       22\n",
       "F       21\n",
       "G        5\n",
       "T        1\n",
       "Name: Cabin, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cabin'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "4da3d77e-dbec-4998-a9ac-86bdf76a1499",
    "_uuid": "4c37e1be45712c93214324f5673cff0c09dd699b"
   },
   "source": [
    "### Embarked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "a33fa003-9c34-4b9f-b843-642655355aaa",
    "_uuid": "2330d2a4ed35739e0368119e61a15e11e4ef8987"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S      914\n",
       "C      270\n",
       "Q      123\n",
       "NaN      2\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Embarked'].value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only 2 passengers do not have a port of embarkation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_cell_guid": "4c71aec8-0182-4205-b53d-8b3f11a95d44",
    "_uuid": "7ee52adc60281680b9adbe2d9fbe84b5a10beb7f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Passenger Ports of Embarkation')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OZWVlKC8vh6Ojo+l5t8ednJzqnH5OTo5W0ameuC6IHm2aFYGjoyPc3NxgY2MDNzc3NG3a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\nEZHkWARERJJjERARSe7/AOp3ZehPgasaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df145f30f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Embarked', data=df)\n",
    "plt.title('Passenger Ports of Embarkation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "56a2c730-63ba-4184-823e-b0f215ca9b4e",
    "_uuid": "c143233a6e8911e69d6a23692c162abe23f46064"
   },
   "source": [
    "### Fare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "0ec02c10-a92a-4778-a22e-69c015d64e5a",
    "_uuid": "d4b1943c2e2d2c3788377fc336910527b2b5566a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fare'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only one fare is missing. Who is that?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Age_NA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1043</th>\n",
       "      <td>60.5</td>\n",
       "      <td>NA</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Storey, Mr. Thomas</td>\n",
       "      <td>0</td>\n",
       "      <td>1044</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3701</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age Cabin Embarked  Fare                Name  Parch  PassengerId  \\\n",
       "1043  60.5    NA        S   NaN  Storey, Mr. Thomas      0         1044   \n",
       "\n",
       "      Pclass   Sex  SibSp  Survived Ticket  Age_NA  \n",
       "1043       3  male      0       NaN   3701   False  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Fare'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not sure why his fare is missing. Let's plot the fares:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "527dc1b1-ce23-4b7f-8ccf-2cec94dc5798",
    "_uuid": "ccbdc89f8372b3b7e801569e2f7351751c3170ae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Histogram of Passenger Fares')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rr3XrrbfqmmuukfTbxU1iY2P1yiuvqE+fPh6uEHxGAIByHCkEABjOYrHo1VdfVffu3fX0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\n9tlnuvXWW2W1WtW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JEzVixAidO3fusj3DvhCXY7fbNX78eE2cOLFCj7jqGfoJrpw5c0Yx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\nE/v/k9twzC0WCbwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df14782240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4), dpi=90)\n",
    "sns.distplot(df.loc[df['Fare'].notnull(), 'Fare'], kde=False)\n",
    "plt.ylabel('Count')\n",
    "plt.title('Histogram of Passenger Fares')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.367709134122922"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Fare'].skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fares are right-skewed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "90626cbc-77af-4836-97fc-1b476e2b260e",
    "_uuid": "f3b9e805fcc0282eeab21783440f4df35e115806"
   },
   "source": [
    "### Name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_cell_guid": "a7f75ed8-4c51-43f8-8dec-d07c24870c13",
    "_uuid": "736dbfa5f1fd5338e864122131f2f19c5f41c38f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Name'].notnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_cell_guid": "31159b84-819f-4471-a8c3-13e53481edb3",
    "_uuid": "f76a55b063fc6257429fda90c44ee8901b27571b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1002                 Shine, Miss. Ellen Natalia\n",
       "1083          van Billiard, Master. Walter John\n",
       "930                               Hee, Mr. Ling\n",
       "1259    Gibson, Mrs. Leonard (Pauline C Boeson)\n",
       "1067                       Sincock, Miss. Maude\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Name'].sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c22fb2bf-7dbd-4b51-8a29-e4175c948b58",
    "_uuid": "7b9c32bfbf46a8742a8e4ef818e8ff640c03303e"
   },
   "source": [
    "Let's extract everyone's titles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_cell_guid": "455ec35c-054a-4119-842c-64aed32c402d",
    "_uuid": "1c8ab237976b35bafb1a61ceb1488e3c4fc320ca"
   },
   "outputs": [],
   "source": [
    "df['Title'] = df['Name'].apply(lambda s: s.split(', ')[1].split(' ')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_cell_guid": "5e4effc8-6680-474b-b87a-8eec472d7c80",
    "_uuid": "3fe5985090680e30cb6031f1f0b71f3694192cf6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Title'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "46429572-770a-4870-af77-f66d5d96d4d4",
    "_uuid": "1e5c5985e825d45f5864d77e0ada1145c6b1f780"
   },
   "source": [
    "Did it work?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_cell_guid": "f4144167-b1fc-43ee-8f45-9d5837065be3",
    "_uuid": "fa2cd3eb696113290200b96b8d399b10d567f52f"
   },
   "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>Name</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1210</th>\n",
       "      <td>Jefferys, Mr. Ernest Wilfred</td>\n",
       "      <td>Mr.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>Widener, Mr. Harry Elkins</td>\n",
       "      <td>Mr.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>Hosono, Mr. Masabumi</td>\n",
       "      <td>Mr.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1227</th>\n",
       "      <td>de Brito, Mr. Jose Joaquim</td>\n",
       "      <td>Mr.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>921</th>\n",
       "      <td>Louch, Mr. Charles Alexander</td>\n",
       "      <td>Mr.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>Hagland, Mr. Konrad Mathias Reiersen</td>\n",
       "      <td>Mr.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1209</th>\n",
       "      <td>Jonsson, Mr. Nils Hilding</td>\n",
       "      <td>Mr.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Giglio, Mr. Victor</td>\n",
       "      <td>Mr.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>Razi, Mr. Raihed</td>\n",
       "      <td>Mr.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>723</th>\n",
       "      <td>Hodges, Mr. Henry Price</td>\n",
       "      <td>Mr.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Name Title\n",
       "1210          Jefferys, Mr. Ernest Wilfred   Mr.\n",
       "377              Widener, Mr. Harry Elkins   Mr.\n",
       "288                   Hosono, Mr. Masabumi   Mr.\n",
       "1227            de Brito, Mr. Jose Joaquim   Mr.\n",
       "921           Louch, Mr. Charles Alexander   Mr.\n",
       "490   Hagland, Mr. Konrad Mathias Reiersen   Mr.\n",
       "1209             Jonsson, Mr. Nils Hilding   Mr.\n",
       "139                     Giglio, Mr. Victor   Mr.\n",
       "859                       Razi, Mr. Raihed   Mr.\n",
       "723                Hodges, Mr. Henry Price   Mr."
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Name', 'Title']].sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "89683a49-5d01-49d2-8ee5-6ae3f5edf932",
    "_uuid": "b3da83f2cc54037709422c2571d6c383afd4177d"
   },
   "source": [
    "Seems good. Title value counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mr.          757\n",
       "Miss.        260\n",
       "Mrs.         197\n",
       "Master.       61\n",
       "Rev.           8\n",
       "Dr.            8\n",
       "Col.           4\n",
       "Major.         2\n",
       "Ms.            2\n",
       "Mlle.          2\n",
       "Lady.          1\n",
       "the            1\n",
       "Dona.          1\n",
       "Sir.           1\n",
       "Mme.           1\n",
       "Capt.          1\n",
       "Don.           1\n",
       "Jonkheer.      1\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Title'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "dc432258-bd12-4462-b766-1ae892b9526f",
    "_uuid": "a3359b575f1eb9422ffe97fbdcf769d918ee45ed"
   },
   "source": [
    "The only odd-looking value is \"the\". Let's investigate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_cell_guid": "5e3541e2-fe64-4f64-9f50-cbfb8537c7c2",
    "_uuid": "9014ac33d59502c6d0f56aca411dceb179674ab5"
   },
   "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>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Age_NA</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>759</th>\n",
       "      <td>33.0</td>\n",
       "      <td>B</td>\n",
       "      <td>S</td>\n",
       "      <td>86.5</td>\n",
       "      <td>Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)</td>\n",
       "      <td>0</td>\n",
       "      <td>760</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>110152</td>\n",
       "      <td>False</td>\n",
       "      <td>the</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Cabin Embarked  Fare  \\\n",
       "759  33.0     B        S  86.5   \n",
       "\n",
       "                                                         Name  Parch  \\\n",
       "759  Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards)      0   \n",
       "\n",
       "     PassengerId  Pclass     Sex  SibSp  Survived  Ticket  Age_NA Title  \n",
       "759          760       1  female      0       1.0  110152   False   the  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Title']=='the']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b43f197c-e02c-4cd8-8e7c-449d8d333347",
    "_uuid": "0349f64c2e64078f9ca01cce91089e21663f1f85"
   },
   "source": [
    "Her title is actually \"Countess.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There doesn't seem to be any new information in `Title`. We already know the passenger age, sex, marital status (`SibSp`), and economic class (`Pclass`). Furthermore, many of the titles have too few data points to be useful. So we're going to drop the `Name` and `Title` columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_cell_guid": "0439a7ba-caae-4d9b-a6aa-cc811da29ac4",
    "_uuid": "217fc0b3b27cbeeec459220a8018b478553c9f39"
   },
   "outputs": [],
   "source": [
    "df.drop(labels=['Name','Title'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1dd6e81d-1e33-46f5-b17f-ea603d3ff361",
    "_uuid": "9d680ff0c687a4ecfc6e36b812b97ebb2bcbdb35"
   },
   "source": [
    "### Parch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_cell_guid": "88fa44d2-fdbb-4a32-bc2d-a1d45b28dddf",
    "_uuid": "f712d82526ef7869415b7b003644c6cdc9586f3b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Parch'].notnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_cell_guid": "c329a9de-4687-4940-8b7e-1571dd17e142",
    "_uuid": "8cd5b962de5d848b5e5171c52c5ee2e08bc2d2f8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1002\n",
       "1     170\n",
       "2     113\n",
       "3       8\n",
       "5       6\n",
       "4       6\n",
       "9       2\n",
       "6       2\n",
       "Name: Parch, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Parch'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_cell_guid": "47300d9d-3914-454b-b04b-12b21d701ce1",
    "_uuid": "0f3d62ceefc2e5db1de466cbefaa127146b146b7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Number of Parents/Children')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1ONWN+U96xOgIIiJu60b786vpSiEiImIKKjQRETEFFZqIiJiCCk1ERExBhSYiIqagQhMR\nEVNQoYmIiCmo0ERExBRUaCIiYgoqNBERMQUVmoiImIIKTURETEGFJiIipqBCExERU1ChiYiIKajQ\nRETEFFRoIiJiCio0ERExBRWaiIiYggpNRERMQYUmIiKmoEITERFTUKGJiIgpqNBERMQUVGgiImIK\nKjQRETEFFZqIiJiCCk1ERExBhSYiIqagQhMREVNQoYmIiCmo0ERExBRUaCIiYgoqNBERMQUVmoiI\nmIIKTURETEGFJiIipmB4oW3dupXBgwczePBgFi5cCEBeXh6xsbFERkYyZ84cysvLAcjPzyc+Pp6o\nqCgSEhIoLi42MrqIiLgRQwvtp59+Yt68ebz++uts3ryZ7OxssrKymDlzJklJSWRkZACQlpYGQEpK\nCqNGjWLHjh0EBQWxdOlSI+OLiIgbsRq5cpvNhs1m46effsLX1xe73Y7VaqW0tJSQkBAAYmNjeeWV\nVxg9ejQHDx50llhsbCxxcXEkJia6tC673V5r21FT6kNGERGjVLWPNLTQfH19mTp1KgMHDqRhw4Z0\n6dIFLy8vAgICnMv4+/tTUFDAuXPn8PX1xcvLq9J8V+Xm5jq/b1Nzm1CjcnJyjI4gIlJvGVpoX331\nFenp6WRmZtK4cWOmT5/Ovn37sFgszmUcDgcWi8X59WrXTv+SoKAgPD09ASj8OK9mNqCGBQcHGx1B\nRMRt2e32SoOTaxlaaB9++CFhYWE0a9YM+Pkw4muvvVZp5HXmzBkCAgLw8/Pj4sWL2Gw2rFYrBQUF\nlUZyVfH09HQWmrty93wiIu7M0JNCOnTowN69eykuLsbhcJCZmUloaCg+Pj5kZ2cDkJ6eTkREBF5e\nXoSGhrJt27ZK80VERMDgEVqPHj0YOnQow4YNw9vbm3vvvZcJEyYQGRlJUlISJSUldOzYkXHjxgGQ\nnJxMYmIiK1asoGXLlixatMjI+CIi4kYsDofDYXSI2mS328nJySE4ONh5SK9g+VqDU92Y/6RHjI4g\nIuK2brQ/v5rhH6wWERGpCSo0ERExBRWaiIiYggpNRERMQYUmIiKmoEITERFTUKGJiIgpqNBERMQU\nVGgiImIKKjQRETEFFZqIiJiCCk1ERExBhSYiIqagQhMREVNQoYmIiCmo0ERExBRUaCIiYgoqNBER\nMQUVmoiImIIKTURETEGFJiIipqBCExERU1ChiYiIKajQRETEFFRoIiJiCio0ERExBRWaiIiYggpN\nRERMQYUmIiKmoEITERFTUKGJiIgpqNBERMQUVGgiImIKKjQRETEFFZqIiJiCCk1EREzB8ELbtWsX\nsbGxDBw4kPnz5wOQlZVFdHQ0AwYMYPHixTgcDgDy8vKIjY0lMjKSOXPmUF5ebmR0ERFxI4YW2rFj\nx0hOTubVV19ly5Yt5OXlsXv3bmbPns2yZcvYvn07hw8fJjMzE4CZM2eSlJRERkYGAGlpaUbGFxER\nN2JooX3wwQcMGjSIwMBArFYrixYtolGjRtx11120adMGq9VKdHQ0GRkZnDhxgtLSUkJCQgCIjY11\nFpuIiIjVyJUfPXoUb29vnnrqKY4fP06fPn245557CAgIcC4TEBBAQUEBp0+frjTf39+fgoICl9dl\nt9trNHttqA8ZRUSMUtU+0tBCs9vtfPjhh7z55pvcdtttTJo0iQYNGmCxWJzLOBwOLBYLFRUVN5zv\nqtzcXOf3bWomfo3LyckxOoKISL1laKE1b96c8PBwmjVrBkC/fv3YsWMHnp6ezmUKCgoICAggMDCw\n0ojszJkzlUZsVQkKCnK+buHHeTW0BTUrODjY6AgiIm7LbrdXGpxcy9BC69OnDzNnzqSoqAhfX1/2\n7t1LZGQkK1eu5MiRI7Rp04YtW7YwfPhwWrdujY+PD9nZ2YSGhpKenk5ERITL6/L09KxUlO7I3fOJ\niLgzQwutU6dOTJgwgfj4eMrLywkPD2fMmDG0a9eOZ555hsuXL9OrVy+ioqIASE1NJSkpiZKSEjp2\n7Mi4ceOMjC8iIm7E4rjyIa8qvPzyy0yfPv26+SkpKSQnJ9d4sJpit9vJyckhODjYOQIqWL7W4FQ3\n5j/pEaMjiIi4rRvtz6/2iyO0kydP8u677wKwZs0abrvttkqPl5SUsG3bNrcuNBER+b/hFwstMDCQ\noqIizp8/T0VFBUePHq30uJeXF6mpqbUaUERExBVVvoc2a9YsAEJCQhg+fHitBxIREbkVLp8UMnTo\nULZu3cqxY8eu+3Db5MmTazyYiIhIdbhcaMnJyRw8eJAuXbro9HIREXE7Lhfarl272LFjB02bNq3N\nPCIiIrfE5YsTBwYGcunSpdrMIiIicstcHqGFh4cTFxdHVFTUdaO0hISEGg8mIiJSHS4XWlFREd27\nd+fChQtcuHChNjOJiIhUm8uFtmDBgtrMISIi8qu4XGhjx4696e1aXn/99RoLJCIicitcLrQRI0ZU\nmr5w4QJpaWkMHTq0xkOJiIhUV7U+WH2tQYMGERcXx4QJE2o0lIiISHW5fNr+jZSUlHDu3LmayiIi\nInLLXB6hjR8/vtJ7aDabjc8//5yRI0fWSjAREZHquOVDjhaLhalTp3L//ffXeCgREZHqcvmQ48MP\nP0y/fv3w8fHh/PnzWCwW2rZtW4vRREREXOdyoeXm5jJgwADeeOMNcnNzWbduHVFRURw6dKg284mI\niLjE5UOOL774IrNnz6506HHz5s3Mnz+fjRs31ko4ERERV7k8Qvv2228ZMmRIpXlDhgzh+++/r+lM\nIiIi1eZyobVt25YPPvig0rx//OMfeh9NRETcgsuHHGfPns3kyZNZu3YtrVq1Ij8/n//85z8sX768\nNvOJiIi4xOVCa9myJR07dqRTp074+PiQnZ3N/fffz1133VWb+URERFzi8iHHxMRE7rnnHh577DGe\nfPJJNm3aRLt27Zg9e3Zt5hMREXGJyyO0w4cPs3r1aufVQpo0acK0adPo1q1brYUTERFxlcsjtNat\nW/Ovf/2r0rwDBw7QsmXLGg8lIiJSXS6P0GbNmsWUKVMICgoiMDCQU6dO8fnnn/OXv/ylNvOJiIi4\nxOVCe+CBB8jIyGDPnj2cPXuWsLAwUlNTadasWW3mExERcYnLhQbQvHlzhg0bVltZREREbtmvuh+a\niIiIu1ChiYiIKajQRETEFFRoIiJiCio0ERExBRWaiIiYggpNRERMQYUmIiKm4DaFtnDhQhITEwHI\ny8sjNjaWyMhI5syZQ3l5OQD5+fnEx8cTFRVFQkICxcXFRkYWERE34haF9tFHH7Fp0ybn9MyZM0lK\nSiIjIwOAtLQ0AFJSUhg1ahQ7duwgKCiIpUuXGpJXRETcj+GFdv78eRYvXkxCQgIAJ06coLS0lJCQ\nEABiY2PJyMigvLycgwcPMnDgwErzRUREoJrXcqwNzz//PNOmTePHH38E4PTp0wQEBDgf9/f3p6Cg\ngHPnzuHr64uXl1el+a6y2+01G7wW1IeMIiJGqWofaWihvfPOO7Rs2ZLw8HDS09MBqKiocN5EFMDh\ncGCxWJxfr3bt9C/Jzc11ft/mV+auLTk5OUZHEBGptwwttO3bt1NQUEBMTAxFRUVcunQJi8VSaeR1\n5swZAgIC8PPz4+LFi9hsNqxWKwUFBZVGclUJCgrC09MTgMKP82p8W2pCcHCw0RFERNyW3W6vNDi5\nlqGFtmrVKuf36enpfPzxxyxYsIAhQ4aQnZ1NaGgo6enpRERE4OXlRWhoKNu2bSMmJsY531Wenp7O\nQnNX7p5PRMSdGX5SyI2kpqayYMECoqKiuHz5MuPGjQMgOTmZDRs2MGjQID755BOmTp1qcFIREXEX\nFofD4TA6RG2y2+3k5OQQHBzsHAEVLF9rcKob85/0iNERRETc1o3251dzyxGaiIhIdanQRETEFFRo\nIiJiCio0ERExBRWaiIiYggpNRERMQYUmIiKmoEITERFTUKGJiIgpqNBERMQUVGgiImIKKjQRETEF\nFZqIiJiCCk1ERExBhSYiIqagQhMREVNQoYmIiCmo0ERExBRUaCIiYgoqNBERMQUVmoiImIIKTURE\nTEGFJiIipqBCExERU1ChiYiIKajQRETEFFRoIiJiCio0ERExBRWaiIiYggpNRERMQYUmIiKmoEIT\nERFTUKGJiIgpqNBERMQUVGgiImIKKjQRETEFFZqIiJiC4YW2atUqBg8eTHR0NLNnz6asrIy8vDxi\nY2OJjIxkzpw5lJeXA5Cfn098fDxRUVEkJCRQXFxscHoREXEXhhbaZ599Rnp6Ou+88w7vvfceNpuN\nt956i5kzZ5KUlERGRgYAaWlpAKSkpDBq1Ch27NhBUFAQS5cuNTK+iIi4EUMLrUmTJsydO5dGjRph\nsVjo0KEDX331FaWlpYSEhAAQGxtLRkYG5eXlHDx4kIEDB1aaLyIiAmA1cuVt27albdu2AJw9e5Y3\n33yTUaNG8f333zuX8ff3p6CggHPnzuHr64uXl1el+a6y2+01Gb1W1IeMIiJGqWofaWihXXH8+HEm\nTpzIiBEjCA0NZc+ePc7HHA4HFovF+fVq107/ktzcXOf3bX595FqRk5NjdAQRkXrL8ELLy8tjwoQJ\nTJgwgbFjx3LixIlKI68zZ84QEBCAn58fFy9exGazYbVaKSgoICAgwOX1BAUF4enpCUDhx3k1vh01\nITg42OgIIiJuy263VxqcXMvQQissLOSJJ54gOTmZAQMGANC6dWt8fHzIzs4mNDSU9PR0IiIi8PLy\nIjQ0lG3bthETE+Oc7ypPT09nobkrd88nIuLODC20NWvWUFxczLJly1i2bBkAvXv3JjU1laSkJEpK\nSujYsSPjxo0DIDk5mcTERFasWEHLli1ZtGiRkfFFRMSNWBwOh8PoELXJbreTk5NDcHCwcwRUsHyt\nwaluzH/SI0ZHEBFxWzfan1/N8A9Wi4iI1AQVmoiImILhZzlK9RxcEW10hOt0mbjF6AgiIhqhiYiI\nOajQRETEFFRoIiJiCio0ERExBRWaiIiYggpNRERMQYUmIiKmoEITERFTUKGJiIgpqNBERMQUVGgi\nImIKKjQRETEFXZxY6sSKNyKNjnBDE8dmGB1BRGqIRmgiImIKKjQRETEFFZqIiJiCCk1ERExBhSYi\nIqagQhMREVNQoYmIiCmo0ERExBRUaCIiYgoqNBERMQUVmoiImIIKTURETEGFJiIipqBCExERU1Ch\niYiIKajQRETEFFRoIiJiCio0ERExBRWaiIiYggpNRERMQYUmIiKmUO8KbcuWLQwaNIgBAwawdu1a\no+OIiIibsBodoDpOnTrFokWLSE9Px8fHh9GjR9OlSxfat29vdDQRETFYvSq0rKwsunXrRtOmTQGI\njIwkIyNDhSa1atC7042OcJ3tD71c5TJDNrxZB0mqb+vw+CqXGb7xkzpIUj0bht1vdASpQr0qtNOn\nTxMQEOCcDggI4LPPPvvF5zgcDgDKysrw9PQEoMLDPY+0lpWVVb2Qh0/tB6kmV3J7uGFucC27j8X9\n/jdxLXf9/T33tjjqIEn1uJJ717vn6yBJ9fV96I4qlznz6rd1kKR6mj/VrtK03W4H/v9+/VoWx80e\ncUPLly/n8uXLTJ06FYD169dz+PBhXnjhhZs+p6ysjNzc3LqKKCIitSwoKAhvb+/r5rvfn56/IDAw\nkOzsbOd0QUFBpRHbjVitVoKCgvDw8MBisdR2RBERqSUOh4OKigqs1htXV70qtO7du7NkyRLOnj1L\nw4YN2bFjBy+++OIvPsfDw+OGTS4iIuZSrwqtRYsWTJs2jXHjxmGz2Rg+fDj33Xef0bFERMQN1Kv3\n0ERERG7GPU+DEhERqSYVmoiImIIKTURETEGFdovq+zUli4uLiY6O5vjx40ZHcdmqVasYPHgw0dHR\nzJ4927UPoruJl19+mYEDBzJo0CBWrVpldJxqW7hwIYmJiUbHqJY//vGPREZGEhMTQ0xMDB988IHR\nkVyya9cuYmNjGThwIPPnzzc6TrWsXLmSyMhIoqOjWb58ed0HcEi1nTx50tG7d29HYWGho6SkxBEd\nHe348ssvjY7lspycHMfQoUMdHTt2dBw7dszoOC759NNPHUOGDHGUlJQ4KioqHDNmzHCsWrXK6Fgu\n2b17tyMuLs5RXl7uuHTpkqNPnz6Ob7/91uhYLsvKynKEhYU5Zs2aZXSUaunfv7/j3LlzRseolh9+\n+MHRo0cPx48//ugoLy93xMUOHz6HAAAGUUlEQVTFOTIzM42O5ZKsrCzH4MGDHRcuXHDYbDbHxIkT\nHRkZGXWaQSO0W3D1NSUbNWrkvKZkfZGWlsbcuXOr/FC6O2nSpAlz586lUaNGWCwWOnToQH5+vtGx\nXNKrVy9Wr16N1WqlsLCQiooKGjVqZHQsl5w/f57FixeTkJBgdJRqOX/+PIWFhUyfPp3o6GiWLl16\n08sluZMPPviAQYMGERgYiNVqZdGiRXTq1MnoWC75/PPP6dmzJ40bN8bT05OePXuyc+fOOs2gQrsF\nN7qmZEFBgYGJqmfBggWEhoYaHaNa2rZtS9euXQE4e/Ysb775Jn379jU4leu8vLxYvHgxgwYNolu3\nbrRo0cLoSC55/vnnmTZtGk2aNDE6SrWcOXOG8PBw/vSnP7F+/Xqys7PZuHGj0bGqdPToUSoqKnjq\nqacYOnQob731FnfcUfV1GN1Bx44d2bt3L+fPn+fy5cvs2rWrzveLKrRbUFFRUekyWg6HQ5fVqiPH\njx9n3LhxjBgxgm7duhkdp1qmTZvG/v37OXnyJOvXrzc6TpXeeecdWrZsSXh4uNFRqu3uu+9myZIl\n+Pv707BhQx555BF2795tdKwq2e12PvzwQ+bNm8f69ev57LPP2LRpk9GxXBIeHk5sbCxjx47liSee\nICQkBC8vrzrNoEK7BYGBgZX+8nDlmpLy6+Xl5TFmzBhGjx7NpEmTjI7jsq+//pqvvvoKgIYNG9K/\nf3/ntDvbvn07+/btIyYmhr/+9a/s2rWr3pykkJubW+lwl91ud95tw501b96c8PBwmjVrRoMGDejX\nr1+VdxRxF8XFxfTv358tW7bwxhtvYLVaufPOO+s0gwrtFnTv3p2PPvqIs2fPcunSJXbs2EFERITR\nsUytsLCQJ554grlz5zJ27Fij41TLt99+S3JyMmVlZZSVlfGPf/yjXhzyXbVqFVu3bmXz5s1MmTKF\nvn378txzzxkdyyUVFRW8+OKLXLx4kfLyctLS0ujfv7/RsarUp08f9u3bR1FREXa7nb1799KxY0ej\nY7nkxIkTTJo0ifLycoqKiti4cSNRUVF1mqFeXcvRXeiaknVvzZo1FBcXs2zZMpYtWwZA7969mTZt\nmsHJqhYVFcUXX3zBQw89hKenJ1FRUQwaNMjoWKbWqVMnxo0bx8iRI7Hb7QwYMIAhQ4YYHatKnTp1\nYsKECcTHx1NeXu48jFcftG/fniFDhhATE4PdbufRRx+t8z/cdC1HERExBR1yFBERU1ChiYiIKajQ\nRETEFFRoIiJiCio0ERExBZ22L+JG2rdvT8OGDZ1XnvHy8iIqKornnnsOb2/vGlvHnj17CAwMrJHX\nE3EXGqGJuJkdO3Zw6NAhDh065Pz+lVdeMTqWiNtToYm4MT8/P3r27Mk333wDwKeffsqYMWMICwsj\nLCyMxYsXAz9f4zIiIoJJkyYRFhbG119/zeeff87IkSPp3Lkzw4YN48svv3S+7rp16+jbty9hYWG8\n9tprhmybSE1ToYm4KYfDwXfffcfOnTsJDQ3F4XA4r1Bz4MABXn/9dVatWsV3330HwKlTp5y37Ljr\nrrtISEjgoYceIjs7m4ceeoiZM2c6X/uHH37g/fff59VXXyU1NbVe3S1C5Gb0HpqImxk8eDAWiwWH\nw8Htt99Ov379GD9+PACrV6+mTZs2FBcXc/HiRXx9fTlz5gytWrUCYODAgfj6+vLRRx/h7e1NXFwc\nAGPGjKl0ebZJkybh4+NDSEgIfn5+5Ofn4+/vX/cbK1KDVGgibmbbtm03PWHj4MGDPProo1gsFoKC\ngrDb7c4bV3p6etK0aVPg53vGXf0aVqu10o0ir76/mbe3N+Xl5bWxKSJ1SoUmUk/8+OOPzJs3j/T0\ndH73u98B3PSecP7+/pw+fdo5XVFRwcsvv8yUKVPqJKuIEfQemkg9UVJSgsVicY6oVqxYwblz57DZ\nbNctGxwcTFlZGRs3bsRut7Nu3Tr27t2Lj4+PAclF6oYKTaSeuPvuuxk7diwPP/wwERERfPXVV3Tv\n3p0jR45ct6yPjw+vvvoqb7/9Nl27dmXr1q0sWrTIgNQidUe3jxEREVPQCE1ERExBhSYiIqagQhMR\nEVNQoYmIiCmo0ERExBRUaCIiYgoqNBERMQUVmoiImIIKTURETEGFJiIipvD/AKxTa8U5vGMaAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df14afa9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=80)\n",
    "sns.countplot(x='Parch', data=df)\n",
    "plt.title('Number of Parents/Children')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "188f7903-7bd9-4649-87c2-da9da4a402ce",
    "_uuid": "46616c0bead2f168033219dd195e7ed9028298e1"
   },
   "source": [
    "### PassengerId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1309"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1309"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['PassengerId'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1f6b401d-300c-43fb-b809-1ceeb9258c42",
    "_uuid": "683879abe6127babafe36fd800862ebbf8ec18db"
   },
   "source": [
    "All passenger IDs are unique. The IDs will (probably) be removed in automatic variable selection."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "302b2845-bb3e-49d8-969d-18f5628a668b",
    "_uuid": "fa336232d0ac2f784fddf0f128007693fc77def6"
   },
   "source": [
    "### Pclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_cell_guid": "f19fb071-c415-4f84-a971-309a286ffbcc",
    "_uuid": "e5a63e6d67f0719d77f1324bcd8b93a21b24b69e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Pclass'].notnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_cell_guid": "7a0422c3-0dd9-40cc-a328-a887056bf6f1",
    "_uuid": "2acde18cea3ab3d4f22a5e112436b6764bb30bbb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    709\n",
       "1    323\n",
       "2    277\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Pclass'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2539c2e5-a7ab-47eb-a0c0-45086e9732de",
    "_uuid": "4438a75d251e871eec96ccd8c665e505c671194f"
   },
   "source": [
    "A majority of the passengers are Lower Class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_cell_guid": "043a8ee1-136c-4217-b4e0-74662fc9b499",
    "_uuid": "71a68671e84509617af199778ab3c71897489a68"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Passenger Ticket Class')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Bw5WISIIGDddPP/0U/v7+1R4+Pj7Yt28fLl68iIiICPj5+SEyMrLaX/2srUZEZIuserPs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\nYLgSEUnAcCUikuD/Aew3Z4v/7aR7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df14af2860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,4), dpi=90)\n",
    "sns.countplot(x='Pclass', data=df)\n",
    "plt.title('Passenger Ticket Class')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "df301dfc-0e82-436d-8e7b-90518b5c3827",
    "_uuid": "ee4124fdb82b539cb913da4406d9845a3795473c"
   },
   "source": [
    "### Sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sex'].notnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      0.644003\n",
       "female    0.355997\n",
       "Name: Sex, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sex'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The passengers are 64.4% male."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "796daaab-780a-4bbb-9315-e527d6f9a302",
    "_uuid": "3d8aa7ef91ef5adebd69e7a0bba971127ea36a14"
   },
   "source": [
    "### SibSp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "_cell_guid": "c116eb26-2672-46a4-92c9-edd86ca7b3a8",
    "_uuid": "d688e889914b1ca5bff03bd88406589b288c6c93"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SibSp'].notnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "_cell_guid": "ccea83a5-7186-499a-9df4-27208bbb9efd",
    "_uuid": "da6c4b99a95313303f66b7bde710ef38a5a3e938"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    891\n",
       "1    319\n",
       "2     42\n",
       "4     22\n",
       "3     20\n",
       "8      9\n",
       "5      6\n",
       "Name: SibSp, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SibSp'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "_cell_guid": "b96506aa-d516-429a-846f-70dd60bb4cf7",
    "_uuid": "acc446036daee67234c2f64ed551a7c00b3eb929"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Number of Siblings/Spouses')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kZKSaNGmihQsXFnmZukmTJvrggw9066236qWXXlK7du30wgsvKDs7W++//75q1qxZ6L79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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df14d576d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=90)\n",
    "sns.countplot(x='SibSp', data=df)\n",
    "plt.title('Number of Siblings/Spouses')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "9245c8b6-25f3-4612-a3a2-1ea05bbd29d5",
    "_uuid": "23386a78707653d63f6257942e6442eac642c0c6"
   },
   "source": [
    "### Survived"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c7c14b92-7c42-4945-bc03-afa656d4aeeb",
    "_uuid": "203409f74da58477f510b2227d7e8beff9432d34"
   },
   "source": [
    "This is the target variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "_cell_guid": "58b7fe62-69bc-4722-98ae-f33b9af90402",
    "_uuid": "eea2a7033eb3058c35d2508bb9350946f7b5ec8e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    0.616162\n",
       "1.0    0.383838\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Survived'].notnull(), 'Survived'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2a7d4d53-d2be-4520-b126-d68143380362",
    "_uuid": "1836750cbfc317d17fbe8020cf8ddee2c5111530"
   },
   "source": [
    "So there's a 38.4% survival rate in the training set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1bac0b33-a5d8-4e40-9de2-dcc0be955ff9",
    "_uuid": "b7fb4b0d13ef8d3247f660599255156a8a8d3c50"
   },
   "source": [
    "### Ticket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "_cell_guid": "fd354bf4-ba65-4797-a312-c43bb222c3d8",
    "_uuid": "6b2c19282a755630e4c22b90b7e8764c719e47a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Ticket'].notnull().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(929, 1309)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df['Ticket'].nunique(), df.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "8d0204df-7fd5-42d0-8a46-492157343935",
    "_uuid": "a9c0248813bad9874f740006c0a772acaf6378cf"
   },
   "source": [
    "Apparently there are duplicate tickets...? That's weird."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_cell_guid": "7d5d7f95-c4eb-4373-97e1-ab3e8c087490",
    "_uuid": "2944fcf09f9c7dec8d7b638689ff43cc98d7c64b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "730           24160\n",
       "793        PC 17600\n",
       "454        A/5 2817\n",
       "335          349225\n",
       "510          382651\n",
       "150     S.O.P. 1166\n",
       "823          392096\n",
       "1010    SC/AH 29037\n",
       "444           65306\n",
       "957          330963\n",
       "Name: Ticket, dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Ticket'].sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "7e46cac0-63c0-4860-826b-1ceca8bced14",
    "_uuid": "4b78129f816ebc8d3b72114bd3d90e21a077959b"
   },
   "source": [
    "Maybe the prefix on some of the tickets is important? Let's grab it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_cell_guid": "e1fd4540-bd71-461a-8fc2-8e4a3a9933ef",
    "_uuid": "2afb4f12537db853fea4ea3cc6c5743ed7529ea3"
   },
   "outputs": [],
   "source": [
    "def ticket_prefix(s):\n",
    "    'Find the content of the ticket before the ticket number'\n",
    "    temp = s.split(' ')\n",
    "    if len(temp) > 1:\n",
    "        return ' '.join(temp[:-1])\n",
    "    else:\n",
    "        return 'NONE'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_cell_guid": "39b6358e-02f4-49b7-b059-ea815ed18511",
    "_uuid": "ce2c9ad8596cd6c113d9ddb56febc2a8cbce3532"
   },
   "outputs": [],
   "source": [
    "df.loc[:,'Ticket Prefix'] = df['Ticket'].apply(ticket_prefix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "_cell_guid": "41fb25fe-d40a-4583-8900-08e9c36c6b76",
    "_uuid": "1d94bd26180680d130a307b816c994c66e537e14"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Ticket Prefix'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "_cell_guid": "7677b83c-795d-437c-a939-8908b9044fe4",
    "_uuid": "1dc0d42227134cfe3248cf3e41a353ddc9cfc939",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NONE           961\n",
       "PC              92\n",
       "C.A.            46\n",
       "SOTON/O.Q.      16\n",
       "W./C.           14\n",
       "STON/O 2.       14\n",
       "A/5             12\n",
       "CA.             12\n",
       "SC/PARIS        11\n",
       "CA              10\n",
       "A/5.            10\n",
       "F.C.C.           9\n",
       "C                8\n",
       "SOTON/OQ         8\n",
       "S.O.C.           7\n",
       "S.O./P.P.        7\n",
       "STON/O2.         7\n",
       "A/4              6\n",
       "SC/Paris         5\n",
       "SC/AH            4\n",
       "PP               4\n",
       "A/4.             3\n",
       "A.5.             3\n",
       "A./5.            3\n",
       "SOTON/O2         3\n",
       "S.C./PARIS       3\n",
       "F.C.             3\n",
       "P/PP             2\n",
       "W.E.P.           2\n",
       "WE/P             2\n",
       "SC               2\n",
       "STON/OQ.         1\n",
       "A/S              1\n",
       "A4.              1\n",
       "SCO/W            1\n",
       "LP               1\n",
       "SW/PP            1\n",
       "Fa               1\n",
       "SO/C             1\n",
       "S.P.             1\n",
       "SC/AH Basle      1\n",
       "C.A./SOTON       1\n",
       "S.O.P.           1\n",
       "SC/A.3           1\n",
       "S.C./A.4.        1\n",
       "AQ/3.            1\n",
       "A. 2.            1\n",
       "W/C              1\n",
       "SC/A4            1\n",
       "S.W./PP          1\n",
       "AQ/4             1\n",
       "Name: Ticket Prefix, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Ticket Prefix'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "fcf109b0-7078-4024-90b9-285c9fbc6d19",
    "_uuid": "950de308f2150a4aeaab0baeffa2101fdad1c333"
   },
   "source": [
    "Some of the prefixes are very similar. Let's assume that the characters `.`, `/`, and whitespace and not significant, and that `SC/PARIS` is the same as `SC/Paris`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "_cell_guid": "1c7783df-7dd0-43c7-aecc-2a7a5827e9ba",
    "_uuid": "6a9dd140ec438cd3453af93a3f77d23c298d63d6"
   },
   "outputs": [],
   "source": [
    "df.loc[:,'Ticket Prefix'] = df['Ticket Prefix'].apply(lambda s: s.replace('.','').replace('/','').replace(' ','').upper())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "_cell_guid": "31fbdcc5-38f3-49bb-908b-924fb93c4708",
    "_uuid": "077353cab5f6a90ce5000d2bdc1bd8b6b67370cd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Ticket Prefix'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "_cell_guid": "1a245941-aaa1-43bd-939f-ef43252ca6c6",
    "_uuid": "cc7a3ff6dec657071c36739979ae774dc106fcf5",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NONE         961\n",
       "PC            92\n",
       "CA            68\n",
       "A5            28\n",
       "SOTONOQ       24\n",
       "STONO2        21\n",
       "SCPARIS       19\n",
       "WC            15\n",
       "A4            10\n",
       "FCC            9\n",
       "C              8\n",
       "SOC            8\n",
       "SOPP           7\n",
       "PP             4\n",
       "SCAH           4\n",
       "WEP            4\n",
       "FC             3\n",
       "SOTONO2        3\n",
       "SWPP           2\n",
       "PPP            2\n",
       "SCA4           2\n",
       "SC             2\n",
       "FA             1\n",
       "AQ4            1\n",
       "SCOW           1\n",
       "SP             1\n",
       "CASOTON        1\n",
       "AS             1\n",
       "STONOQ         1\n",
       "LP             1\n",
       "SCA3           1\n",
       "A2             1\n",
       "SOP            1\n",
       "AQ3            1\n",
       "SCAHBASLE      1\n",
       "Name: Ticket Prefix, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Ticket Prefix'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If there are fewer than 10 occurences of a particular prefix, we'll reclassify it as \"other.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "vals = df['Ticket Prefix'].value_counts()\n",
    "\n",
    "def other_prefix(prefix):\n",
    "    if vals[prefix] < 10:\n",
    "        return \"OTHER\"\n",
    "    else:\n",
    "        return prefix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:,'Ticket Prefix'] = df['Ticket Prefix'].apply(other_prefix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NONE       961\n",
       "PC          92\n",
       "OTHER       71\n",
       "CA          68\n",
       "A5          28\n",
       "SOTONOQ     24\n",
       "STONO2      21\n",
       "SCPARIS     19\n",
       "WC          15\n",
       "A4          10\n",
       "Name: Ticket Prefix, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Ticket Prefix'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f270c4fa-ec2f-4c94-b21e-d8e9ca067564",
    "_uuid": "5cf6dc7724e19d85a0919e64f7b0009062f3c7c2"
   },
   "source": [
    "Now let's extract the ticket number. As it turns out, a very small number of tickets have no ticket number, so we'll assign NaN for those tickets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "_cell_guid": "9524e3e3-85cd-4d53-9ac5-e0b4e58e95a6",
    "_uuid": "d4aec663fcec3fcaa9a690bd31db1484ab0d5f68"
   },
   "outputs": [],
   "source": [
    "def ticket_number(s):\n",
    "    'Find the ticket number on a ticket'\n",
    "    try:\n",
    "        return np.int64(s.split(' ')[-1])\n",
    "    except:\n",
    "        return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "_cell_guid": "37775ec0-55d3-4803-970d-7f35c5f6e796",
    "_uuid": "f7b79a8a1c1f7deef946ccda9f52710df22020f4"
   },
   "outputs": [],
   "source": [
    "df['Ticket Number'] = df['Ticket'].apply(ticket_number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "_cell_guid": "a9c92891-798c-474f-97d9-b8e8c0884b30",
    "_uuid": "abe42ff17b85776f0bfcf6c8d6ba1a275a90de7a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Ticket Number'].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 4 tickets with no ticket number. Let's look at those:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Age_NA</th>\n",
       "      <th>Ticket Prefix</th>\n",
       "      <th>Ticket Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>36.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>S</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>LINE</td>\n",
       "      <td>False</td>\n",
       "      <td>NONE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>25.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>S</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>272</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>LINE</td>\n",
       "      <td>False</td>\n",
       "      <td>NONE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>19.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>S</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>303</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>LINE</td>\n",
       "      <td>False</td>\n",
       "      <td>NONE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>49.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>S</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>598</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>LINE</td>\n",
       "      <td>False</td>\n",
       "      <td>NONE</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Cabin Embarked  Fare  Parch  PassengerId  Pclass   Sex  SibSp  \\\n",
       "179  36.0    NA        S   0.0      0          180       3  male      0   \n",
       "271  25.0    NA        S   0.0      0          272       3  male      0   \n",
       "302  19.0    NA        S   0.0      0          303       3  male      0   \n",
       "597  49.0    NA        S   0.0      0          598       3  male      0   \n",
       "\n",
       "     Survived Ticket  Age_NA Ticket Prefix  Ticket Number  \n",
       "179       0.0   LINE   False          NONE            NaN  \n",
       "271       1.0   LINE   False          NONE            NaN  \n",
       "302       0.0   LINE   False          NONE            NaN  \n",
       "597       0.0   LINE   False          NONE            NaN  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['Ticket Number'].isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Their ticket just says \"LINE.\" What does that mean?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the ticket numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "_cell_guid": "2e548fea-98fd-46e8-96d1-dbdda2c2582c",
    "_uuid": "b8ab1de1317b4c18341105f4393a3315100d5784"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Histogram of Ticket Number')"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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39eCDD7qWMzMzrZYtW1rZ2dkeH8PFGzdunPXggw9aEyZMcAUFX+ot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\nhQAAAABgsP8PxcG0H6uuW7cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df14df27b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4), dpi=90)\n",
    "sns.distplot(df.loc[df['Ticket Number'].notnull(), 'Ticket Number'], bins=400, kde=False)\n",
    "plt.ylabel('Count')\n",
    "plt.title('Histogram of Ticket Number')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "_cell_guid": "2e548fea-98fd-46e8-96d1-dbdda2c2582c",
    "_uuid": "b8ab1de1317b4c18341105f4393a3315100d5784"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Histogram of Ticket Number')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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688476U24nZ+fn+bPn6/du3erqKhIq1atks1mU2xsbMV6X3f5FkgV\nxHfffWf07t3bCAsLMwYMGGBkZma6uyRUEi+99JL9rrOGYRjbtm0z7rrrLiM8PNwYOXKkcfbsWbeN\noWqaM2eO0bRpUyMsLMzhv127dtGfcLtdu3YZ99xzj3HzzTcbgwYNMtLT0w3DKP19urzHgN/vOmsY\n9CY8w9q1a40uXboYbdq0Me677z7jwIEDhmFUrM+dXoZhGGWbyQEAAAAAVUmlnDoLAAAAAHAfgiYA\nAAAAwFQETQAAAACAqQiaAAAAAABTETQBAAAAAKYiaAIAAAAATEXQBADARWfOnNGFCxfcXQYAAB6L\noAkAqDCeeuophYeHKzw8XK1atVKzZs3sy+Hh4crKytKIESO0ePHiK+4rJiZGa9ascbmGM2fOqGvX\nrsrOzi5xPCUlRS1bttS33357yVjz5s21c+dOl495JYmJiUpMTDR9vwAAXC0fdxcAAICzkpKSlJSU\nJElav369XnzxRX322WcO27z66qtlWkN+fr7y8vJK3aawsFAJCQlat26datWqVab1AADgibiiCQCo\nVAYNGqSUlBRJ0oULF/TMM88oKipK7dq10+jRo/Xzzz9f8pgtW7bo5ptv1ubNmyVJp0+fVkJCgjp0\n6KBOnTpp5syZys/PV3FxsXr06CFJ6tGjhz755JMSawgPD9ff/vY3TZs2zak6JWn37t1q2rSpJOnE\niRNq3ry53nnnHd1+++0KDw/XtGnTtHv3bvXs2VPh4eEaMmSIw1XV7OxsjR49Wu3atdO9996rL774\nwj52ufORpDVr1qh3794aMmSIIiIi9PHHHzv1PAMAUBqCJgCg0nr66ae1d+9erV27Vtu3b5fFYtHk\nyZMdttm8ebMmTZqkhQsXqnPnzrp48aLGjBkjX19fffTRR1q7dq0OHz6sWbNmydvbWxs3bpQkbdy4\nUV26dCnxuL6+vnr++ee1detWvfXWW1dVe3FxsT799FNt2rRJb731lt555x3NnDlTr776qj799FNl\nZWXpzTfftG+/fft29enTR19++aUGDx6sUaNG6cSJE6Wez+++//579e3bVzt27FCnTp2uql4AAP6I\noAkAqJRsNpvef/99Pfroo/r73/8uf39/Pfnkk3rsscfs22zdulXx8fGaPXu2oqKiJEn79u3T/v37\n9dRTT6lmzZqqU6eOEhIStHr1atlsNqeP36hRI02fPl2zZs3SoUOHruocxowZo4CAAIWGhqpu3bq6\n5557VL9+fV133XUKCwvTiRMn7Nt26dJFXbp0kY+Pj+Li4tS8eXNt3LjRqfPx9/fX3XffLX9/f/n7\n+19VrQAA/BHf0QQAVEq//PKLbDabrr/+evu62rVrq3bt2vblr776SjfddJPWrl2rmJgYSb9NWy0u\nLtZtt93msD9vb2/9+OOP8vX1dbqGuLg47dixQxMmTNDq1atdPodrr73W4fjXXHONfblatWoyDMO+\nfMMNNzg89vrrr9epU6eueD6SVKdOHXl5eblcHwAAl0PQBABUSnXq1JGfn59++uknhYSESJJ9uun4\n8eMlSQkJCerYsaN69Oih9evX268YBgQEaOfOnfL29pYkWa1WZWVlKSgoSCdPnnSpjhkzZujee+/V\nzJkzHdZXq1ZNRUVF9uXz589f8lhXwt+pU6cclk+cOKEWLVpc8XxSU1MJmQAA0zF1FgBQKVWrVk33\n3HOPUlJSdPr0aVmtVj3//PPav3+/PXD5+vqqQYMGevzxx/Xss8/q1KlTat26tRo0aKD58+fLarUq\nLy9PM2bM0NixYyVJfn5+kqTc3Fyn6rBYLHr++ee1du1aFRcX29cHBwfrs88+U3Z2trKzs7Vy5cq/\ndL6ffPKJtm3bpsLCQr399tvKyMhQz549r3g+AACUBYImAKDSmjx5spo3b67evXvrtttuU2FhoebN\nm3fJdgMGDFCzZs00depU+fr6asmSJcrKytIdd9yhzp076/z583r11Vfl7e2tunXrKiYmRvfdd5/e\neecdp+po2bKlJk6c6LBu1KhRqlu3ru68804NGDBA3bt3/0vn2qVLFy1evFiRkZFavXq1li9frvr1\n61/xfAAAKAtexh+/4AEAAAAAwF/EFU0AAAAAgKkImgAAAAAAUxE0AQAAAACmImgCAAAAAExF0AQA\nAAAAmIqgCQAAAAAwFUETAAAAAGAqgiYAAAAAwFQETQAAAACAqQiaAAAAAABTETQBAAAAAKb6/wBJ\nEGec762ZbAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df14fdd400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4), dpi=90)\n",
    "sns.distplot(df.loc[df['Ticket Number'].notnull(), 'Ticket Number'], bins=500, kde=False)\n",
    "plt.xlim([0, 500000])\n",
    "plt.ylabel('Count')\n",
    "plt.title('Histogram of Ticket Number')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 5 \"classes\" of ticket numbers. Let's convert the ticket numbers into these categorical classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tick_num_class(tick_num):\n",
    "    if tick_num < 100000:\n",
    "        return 'A'\n",
    "    elif tick_num < 200000:\n",
    "        return 'B'\n",
    "    elif tick_num < 300000:\n",
    "        return 'C'\n",
    "    elif tick_num < 400000:\n",
    "        return 'D'\n",
    "    elif tick_num >= 400000:\n",
    "        return 'E'\n",
    "    else:\n",
    "        return 'NA'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:,'Ticket Number'] = df['Ticket Number'].apply(tick_num_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A     639\n",
       "D     380\n",
       "C     125\n",
       "B     101\n",
       "E      60\n",
       "NA      4\n",
       "Name: Ticket Number, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Ticket Number'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "_cell_guid": "62e3582f-4feb-4602-a8a6-a9892037669c",
    "_uuid": "b58fb7a5d07e46bb2f2ca3cc229d66ebc378bf3d"
   },
   "outputs": [],
   "source": [
    "df.drop(labels='Ticket', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "27db2ffd-5a2b-4cac-83d7-9b7b29688eb1",
    "_uuid": "adadf704ee1a1e511bedeb65a57b666ab7718018"
   },
   "source": [
    "## 3.2 One-Hot Encoding\n",
    "<a id='3.2'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e1987db1-f4dd-41b0-a008-13c9edfc2e31",
    "_uuid": "6662cb60eb7b5980342dc4516b2732402b6ae755"
   },
   "source": [
    "Convert categorical features to binary features via one-hot encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "_cell_guid": "f33798e9-2dcb-40da-8c93-678c761cfe66",
    "_uuid": "3574f532b5eec8c77ff13546a8730b9b9bd30d60"
   },
   "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>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age_NA</th>\n",
       "      <th>Ticket Prefix</th>\n",
       "      <th>Ticket Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>23.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>S</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>659</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>NONE</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>C</td>\n",
       "      <td>13.4167</td>\n",
       "      <td>1</td>\n",
       "      <td>692</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>NONE</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130</th>\n",
       "      <td>48.0</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>106.4250</td>\n",
       "      <td>0</td>\n",
       "      <td>1131</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>PC</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>54.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>S</td>\n",
       "      <td>14.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>318</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>NONE</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>S</td>\n",
       "      <td>7.7750</td>\n",
       "      <td>0</td>\n",
       "      <td>163</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>NONE</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age Cabin Embarked      Fare  Parch  PassengerId  Pclass     Sex  \\\n",
       "658   23.0    NA        S   13.0000      0          659       2    male   \n",
       "691    4.0    NA        C   13.4167      1          692       3  female   \n",
       "1130  48.0     C        C  106.4250      0         1131       1  female   \n",
       "317   54.0    NA        S   14.0000      0          318       2    male   \n",
       "162   26.0    NA        S    7.7750      0          163       3    male   \n",
       "\n",
       "      SibSp  Survived  Age_NA Ticket Prefix Ticket Number  \n",
       "658       0       0.0   False          NONE             A  \n",
       "691       0       1.0   False          NONE             D  \n",
       "1130      1       NaN   False            PC             A  \n",
       "317       0       0.0   False          NONE             A  \n",
       "162       0       0.0   False          NONE             D  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "_cell_guid": "005fed63-ef26-4f59-ae77-039d5c73c2b5",
    "_uuid": "5e5a9c13b73ff08d9b5b63aad6d8c65f6afc154a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1309 entries, 0 to 1308\n",
      "Data columns (total 13 columns):\n",
      "Age              1046 non-null float64\n",
      "Cabin            1309 non-null object\n",
      "Embarked         1307 non-null object\n",
      "Fare             1308 non-null float64\n",
      "Parch            1309 non-null int64\n",
      "PassengerId      1309 non-null int64\n",
      "Pclass           1309 non-null int64\n",
      "Sex              1309 non-null object\n",
      "SibSp            1309 non-null int64\n",
      "Survived         891 non-null float64\n",
      "Age_NA           1309 non-null bool\n",
      "Ticket Prefix    1309 non-null object\n",
      "Ticket Number    1309 non-null object\n",
      "dtypes: bool(1), float64(3), int64(4), object(5)\n",
      "memory usage: 124.1+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Five columns will be one-hot encoded: `Cabin`, `Embarked`, `Sex`, `Ticket Prefix`, and `Ticket Number`. Are any of these columns incomplete?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "_cell_guid": "404916c5-a47b-4de6-9d7a-2be03c2a5d22",
    "_uuid": "db92fb31b67c3aa4ad4872079c35aa5d60a23809"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0.680672\n",
       "Age         0.799083\n",
       "Embarked    0.998472\n",
       "Fare        0.999236\n",
       "dtype: float64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incomplete_cols(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only `Embarked` is. But only 2 values were missing in that column, so we won't create columns for NA values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "_cell_guid": "e193dcf9-7493-4fd2-a111-dd1ea860294e",
    "_uuid": "d5c74d93edf546975096132943910c8813fb6a4d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 13)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "_cell_guid": "50f084b2-2231-403a-ac99-c4ed1ffa239f",
    "_uuid": "a667e750dab3e8e9b372c54836fb936aa744d2ad"
   },
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "_cell_guid": "7baf9304-ffc5-4c57-9820-129cd1b606d6",
    "_uuid": "fdb9d46c24872f75ce22fd52382131bd9d19b820"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1309, 33)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "_cell_guid": "208265fa-8de0-4ecc-8845-37f7e16eed08",
    "_uuid": "94cd7b9edb2e3a5e6a6068103cf40543e0ce59ed"
   },
   "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>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Age_NA</th>\n",
       "      <th>Cabin_B</th>\n",
       "      <th>Cabin_C</th>\n",
       "      <th>Cabin_D</th>\n",
       "      <th>Cabin_E</th>\n",
       "      <th>Cabin_F</th>\n",
       "      <th>Cabin_G</th>\n",
       "      <th>Cabin_NA</th>\n",
       "      <th>Cabin_T</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Ticket Prefix_A5</th>\n",
       "      <th>Ticket Prefix_CA</th>\n",
       "      <th>Ticket Prefix_NONE</th>\n",
       "      <th>Ticket Prefix_OTHER</th>\n",
       "      <th>Ticket Prefix_PC</th>\n",
       "      <th>Ticket Prefix_SCPARIS</th>\n",
       "      <th>Ticket Prefix_SOTONOQ</th>\n",
       "      <th>Ticket Prefix_STONO2</th>\n",
       "      <th>Ticket Prefix_WC</th>\n",
       "      <th>Ticket Number_B</th>\n",
       "      <th>Ticket Number_C</th>\n",
       "      <th>Ticket Number_D</th>\n",
       "      <th>Ticket Number_E</th>\n",
       "      <th>Ticket Number_NA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>619</th>\n",
       "      <td>26.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0</td>\n",
       "      <td>620</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>40.0</td>\n",
       "      <td>27.9</td>\n",
       "      <td>4</td>\n",
       "      <td>361</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>29.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>48.0</td>\n",
       "      <td>39.6</td>\n",
       "      <td>0</td>\n",
       "      <td>557</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1094</th>\n",
       "      <td>8.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1095</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age  Fare  Parch  PassengerId  Pclass  SibSp  Survived  Age_NA  \\\n",
       "619   26.0  10.5      0          620       2      0       0.0   False   \n",
       "360   40.0  27.9      4          361       3      1       0.0   False   \n",
       "53    29.0  26.0      0           54       2      1       1.0   False   \n",
       "556   48.0  39.6      0          557       1      1       1.0   False   \n",
       "1094   8.0  26.0      1         1095       2      1       NaN   False   \n",
       "\n",
       "      Cabin_B  Cabin_C  Cabin_D  Cabin_E  Cabin_F  Cabin_G  Cabin_NA  Cabin_T  \\\n",
       "619         0        0        0        0        0        0         1        0   \n",
       "360         0        0        0        0        0        0         1        0   \n",
       "53          0        0        0        0        0        0         1        0   \n",
       "556         0        0        0        0        0        0         0        0   \n",
       "1094        0        0        0        0        0        0         1        0   \n",
       "\n",
       "      Embarked_Q  Embarked_S  Sex_male  Ticket Prefix_A5  Ticket Prefix_CA  \\\n",
       "619            0           1         1                 0                 0   \n",
       "360            0           1         1                 0                 0   \n",
       "53             0           1         0                 0                 0   \n",
       "556            0           0         0                 0                 0   \n",
       "1094           0           1         0                 0                 0   \n",
       "\n",
       "      Ticket Prefix_NONE  Ticket Prefix_OTHER  Ticket Prefix_PC  \\\n",
       "619                    1                    0                 0   \n",
       "360                    1                    0                 0   \n",
       "53                     1                    0                 0   \n",
       "556                    1                    0                 0   \n",
       "1094                   1                    0                 0   \n",
       "\n",
       "      Ticket Prefix_SCPARIS  Ticket Prefix_SOTONOQ  Ticket Prefix_STONO2  \\\n",
       "619                       0                      0                     0   \n",
       "360                       0                      0                     0   \n",
       "53                        0                      0                     0   \n",
       "556                       0                      0                     0   \n",
       "1094                      0                      0                     0   \n",
       "\n",
       "      Ticket Prefix_WC  Ticket Number_B  Ticket Number_C  Ticket Number_D  \\\n",
       "619                  0                0                0                0   \n",
       "360                  0                0                0                1   \n",
       "53                   0                0                0                0   \n",
       "556                  0                0                0                0   \n",
       "1094                 0                0                0                0   \n",
       "\n",
       "      Ticket Number_E  Ticket Number_NA  \n",
       "619                 0                 0  \n",
       "360                 0                 0  \n",
       "53                  0                 0  \n",
       "556                 0                 0  \n",
       "1094                0                 0  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "328608c6-a46f-4082-812e-5807d272ac9c",
    "_uuid": "59636167d171b7d420ca92d0a0860f58763382f5"
   },
   "source": [
    "## 3.3 Imputation with Mean Substitution\n",
    "<a id='3.3'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "_cell_guid": "7ee9e5a3-307f-4672-a5d7-348fa0ae6c0c",
    "_uuid": "bfad0c22c5489c28c764afd0d0fd6c62b403890e"
   },
   "outputs": [],
   "source": [
    "X = df.drop(labels='Survived', axis=1)\n",
    "y = df.loc[:,'Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "_cell_guid": "fb94f616-479c-4ff6-9b9e-e1e30d26cf01",
    "_uuid": "34c0873a4871cb5ccd68c14f5b7c544224e45423"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age     0.799083\n",
       "Fare    0.999236\n",
       "dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incomplete_cols(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "_cell_guid": "75e07649-decd-4c1f-b095-91a7688b83c6",
    "_uuid": "c4df02bc854a239cdb7a07fce746c7a4c4d8ca34"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Imputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "_cell_guid": "88279fa3-319a-4e23-a4bf-36f18752ad57",
    "_uuid": "c0fdf37f598ae888bc8231a8e2e3b9844ab3e044"
   },
   "outputs": [],
   "source": [
    "imputer = Imputer().fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "_cell_guid": "6e5786cb-b5c4-401e-8cfd-c8d885704b09",
    "_uuid": "8fe218bc5a71cba64edd273f0df8112ce1a89930"
   },
   "outputs": [],
   "source": [
    "X = pd.DataFrame(imputer.transform(X), columns=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "_cell_guid": "56b861aa-a6dc-4b53-83a5-3b039b9cad50",
    "_uuid": "01565b48cb3407bb402ba8131cb2a630c65976eb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], dtype: float64)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incomplete_cols(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6ad43292-73fa-4d4a-bd8d-7a59574d9f7f",
    "_uuid": "c39223e61a6d27f5f3f9268e182f66a3ec7430b0"
   },
   "source": [
    "## 3.4 Standardization\n",
    "<a id='3.4'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "286b44e8-cfc5-4bcb-96be-665341374849",
    "_uuid": "884e82b20fc0cb02ff276df8528f74df11d3534d"
   },
   "source": [
    "Transform all the features to zero mean and unit variance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "_cell_guid": "a02a0639-449d-4610-912b-f4168148f1af",
    "_uuid": "57776747d47bf523af89fd258768aaa807142c8a"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "_cell_guid": "f86eebed-353b-47ad-910b-2078e39c811e",
    "_uuid": "cafffdf1b618ab2ce8f92966d9f4808ecf77e5f4"
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler().fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "_cell_guid": "b83467c8-4541-4f5f-ae45-e81c44fcbfca",
    "_uuid": "c2c211a54f89a076da28415d2f36e2e8a730dd8e"
   },
   "outputs": [],
   "source": [
    "X = pd.DataFrame(scaler.transform(X), columns=X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Age_NA</th>\n",
       "      <th>Cabin_B</th>\n",
       "      <th>Cabin_C</th>\n",
       "      <th>Cabin_D</th>\n",
       "      <th>Cabin_E</th>\n",
       "      <th>Cabin_F</th>\n",
       "      <th>Cabin_G</th>\n",
       "      <th>Cabin_NA</th>\n",
       "      <th>Cabin_T</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Ticket Prefix_A5</th>\n",
       "      <th>Ticket Prefix_CA</th>\n",
       "      <th>Ticket Prefix_NONE</th>\n",
       "      <th>Ticket Prefix_OTHER</th>\n",
       "      <th>Ticket Prefix_PC</th>\n",
       "      <th>Ticket Prefix_SCPARIS</th>\n",
       "      <th>Ticket Prefix_SOTONOQ</th>\n",
       "      <th>Ticket Prefix_STONO2</th>\n",
       "      <th>Ticket Prefix_WC</th>\n",
       "      <th>Ticket Number_B</th>\n",
       "      <th>Ticket Number_C</th>\n",
       "      <th>Ticket Number_D</th>\n",
       "      <th>Ticket Number_E</th>\n",
       "      <th>Ticket Number_NA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>0.164530</td>\n",
       "      <td>-0.490544</td>\n",
       "      <td>-0.445000</td>\n",
       "      <td>-0.719814</td>\n",
       "      <td>0.841916</td>\n",
       "      <td>-0.479087</td>\n",
       "      <td>-0.501432</td>\n",
       "      <td>-0.228584</td>\n",
       "      <td>-0.278148</td>\n",
       "      <td>-0.190843</td>\n",
       "      <td>-0.179818</td>\n",
       "      <td>-0.127688</td>\n",
       "      <td>-0.061922</td>\n",
       "      <td>0.539377</td>\n",
       "      <td>-0.02765</td>\n",
       "      <td>-0.32204</td>\n",
       "      <td>0.657394</td>\n",
       "      <td>0.743497</td>\n",
       "      <td>-0.147844</td>\n",
       "      <td>-0.234082</td>\n",
       "      <td>-1.661774</td>\n",
       "      <td>-0.23948</td>\n",
       "      <td>-0.274947</td>\n",
       "      <td>-0.121362</td>\n",
       "      <td>-0.136664</td>\n",
       "      <td>7.831560</td>\n",
       "      <td>-0.107666</td>\n",
       "      <td>-0.289153</td>\n",
       "      <td>-0.324922</td>\n",
       "      <td>-0.639564</td>\n",
       "      <td>4.562529</td>\n",
       "      <td>-0.055364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>1.329283</td>\n",
       "      <td>0.361656</td>\n",
       "      <td>-0.445000</td>\n",
       "      <td>-1.439627</td>\n",
       "      <td>-1.546098</td>\n",
       "      <td>-0.479087</td>\n",
       "      <td>-0.501432</td>\n",
       "      <td>-0.228584</td>\n",
       "      <td>3.595210</td>\n",
       "      <td>-0.190843</td>\n",
       "      <td>-0.179818</td>\n",
       "      <td>-0.127688</td>\n",
       "      <td>-0.061922</td>\n",
       "      <td>-1.853992</td>\n",
       "      <td>-0.02765</td>\n",
       "      <td>-0.32204</td>\n",
       "      <td>0.657394</td>\n",
       "      <td>0.743497</td>\n",
       "      <td>-0.147844</td>\n",
       "      <td>-0.234082</td>\n",
       "      <td>0.601766</td>\n",
       "      <td>-0.23948</td>\n",
       "      <td>-0.274947</td>\n",
       "      <td>-0.121362</td>\n",
       "      <td>-0.136664</td>\n",
       "      <td>-0.127688</td>\n",
       "      <td>-0.107666</td>\n",
       "      <td>3.458381</td>\n",
       "      <td>-0.324922</td>\n",
       "      <td>-0.639564</td>\n",
       "      <td>-0.219177</td>\n",
       "      <td>-0.055364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>-0.223721</td>\n",
       "      <td>-0.237736</td>\n",
       "      <td>-0.445000</td>\n",
       "      <td>-1.622227</td>\n",
       "      <td>-0.352091</td>\n",
       "      <td>0.481288</td>\n",
       "      <td>-0.501432</td>\n",
       "      <td>-0.228584</td>\n",
       "      <td>-0.278148</td>\n",
       "      <td>-0.190843</td>\n",
       "      <td>-0.179818</td>\n",
       "      <td>-0.127688</td>\n",
       "      <td>-0.061922</td>\n",
       "      <td>0.539377</td>\n",
       "      <td>-0.02765</td>\n",
       "      <td>-0.32204</td>\n",
       "      <td>0.657394</td>\n",
       "      <td>-1.344995</td>\n",
       "      <td>-0.147844</td>\n",
       "      <td>-0.234082</td>\n",
       "      <td>0.601766</td>\n",
       "      <td>-0.23948</td>\n",
       "      <td>-0.274947</td>\n",
       "      <td>-0.121362</td>\n",
       "      <td>-0.136664</td>\n",
       "      <td>-0.127688</td>\n",
       "      <td>-0.107666</td>\n",
       "      <td>-0.289153</td>\n",
       "      <td>-0.324922</td>\n",
       "      <td>-0.639564</td>\n",
       "      <td>-0.219177</td>\n",
       "      <td>-0.055364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779</th>\n",
       "      <td>1.018682</td>\n",
       "      <td>3.442480</td>\n",
       "      <td>0.710763</td>\n",
       "      <td>0.330797</td>\n",
       "      <td>-1.546098</td>\n",
       "      <td>-0.479087</td>\n",
       "      <td>-0.501432</td>\n",
       "      <td>4.374753</td>\n",
       "      <td>-0.278148</td>\n",
       "      <td>-0.190843</td>\n",
       "      <td>-0.179818</td>\n",
       "      <td>-0.127688</td>\n",
       "      <td>-0.061922</td>\n",
       "      <td>-1.853992</td>\n",
       "      <td>-0.02765</td>\n",
       "      <td>-0.32204</td>\n",
       "      <td>0.657394</td>\n",
       "      <td>-1.344995</td>\n",
       "      <td>-0.147844</td>\n",
       "      <td>-0.234082</td>\n",
       "      <td>0.601766</td>\n",
       "      <td>-0.23948</td>\n",
       "      <td>-0.274947</td>\n",
       "      <td>-0.121362</td>\n",
       "      <td>-0.136664</td>\n",
       "      <td>-0.127688</td>\n",
       "      <td>-0.107666</td>\n",
       "      <td>-0.289153</td>\n",
       "      <td>-0.324922</td>\n",
       "      <td>-0.639564</td>\n",
       "      <td>-0.219177</td>\n",
       "      <td>-0.055364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>1.639883</td>\n",
       "      <td>-0.507381</td>\n",
       "      <td>-0.445000</td>\n",
       "      <td>-0.060867</td>\n",
       "      <td>0.841916</td>\n",
       "      <td>-0.479087</td>\n",
       "      <td>-0.501432</td>\n",
       "      <td>-0.228584</td>\n",
       "      <td>-0.278148</td>\n",
       "      <td>-0.190843</td>\n",
       "      <td>-0.179818</td>\n",
       "      <td>-0.127688</td>\n",
       "      <td>-0.061922</td>\n",
       "      <td>0.539377</td>\n",
       "      <td>-0.02765</td>\n",
       "      <td>-0.32204</td>\n",
       "      <td>0.657394</td>\n",
       "      <td>0.743497</td>\n",
       "      <td>-0.147844</td>\n",
       "      <td>-0.234082</td>\n",
       "      <td>0.601766</td>\n",
       "      <td>-0.23948</td>\n",
       "      <td>-0.274947</td>\n",
       "      <td>-0.121362</td>\n",
       "      <td>-0.136664</td>\n",
       "      <td>-0.127688</td>\n",
       "      <td>-0.107666</td>\n",
       "      <td>-0.289153</td>\n",
       "      <td>-0.324922</td>\n",
       "      <td>1.563565</td>\n",
       "      <td>-0.219177</td>\n",
       "      <td>-0.055364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Age      Fare     Parch  PassengerId    Pclass     SibSp    Age_NA  \\\n",
       "382  0.164530 -0.490544 -0.445000    -0.719814  0.841916 -0.479087 -0.501432   \n",
       "110  1.329283  0.361656 -0.445000    -1.439627 -1.546098 -0.479087 -0.501432   \n",
       "41  -0.223721 -0.237736 -0.445000    -1.622227 -0.352091  0.481288 -0.501432   \n",
       "779  1.018682  3.442480  0.710763     0.330797 -1.546098 -0.479087 -0.501432   \n",
       "631  1.639883 -0.507381 -0.445000    -0.060867  0.841916 -0.479087 -0.501432   \n",
       "\n",
       "      Cabin_B   Cabin_C   Cabin_D   Cabin_E   Cabin_F   Cabin_G  Cabin_NA  \\\n",
       "382 -0.228584 -0.278148 -0.190843 -0.179818 -0.127688 -0.061922  0.539377   \n",
       "110 -0.228584  3.595210 -0.190843 -0.179818 -0.127688 -0.061922 -1.853992   \n",
       "41  -0.228584 -0.278148 -0.190843 -0.179818 -0.127688 -0.061922  0.539377   \n",
       "779  4.374753 -0.278148 -0.190843 -0.179818 -0.127688 -0.061922 -1.853992   \n",
       "631 -0.228584 -0.278148 -0.190843 -0.179818 -0.127688 -0.061922  0.539377   \n",
       "\n",
       "     Cabin_T  Embarked_Q  Embarked_S  Sex_male  Ticket Prefix_A5  \\\n",
       "382 -0.02765    -0.32204    0.657394  0.743497         -0.147844   \n",
       "110 -0.02765    -0.32204    0.657394  0.743497         -0.147844   \n",
       "41  -0.02765    -0.32204    0.657394 -1.344995         -0.147844   \n",
       "779 -0.02765    -0.32204    0.657394 -1.344995         -0.147844   \n",
       "631 -0.02765    -0.32204    0.657394  0.743497         -0.147844   \n",
       "\n",
       "     Ticket Prefix_CA  Ticket Prefix_NONE  Ticket Prefix_OTHER  \\\n",
       "382         -0.234082           -1.661774             -0.23948   \n",
       "110         -0.234082            0.601766             -0.23948   \n",
       "41          -0.234082            0.601766             -0.23948   \n",
       "779         -0.234082            0.601766             -0.23948   \n",
       "631         -0.234082            0.601766             -0.23948   \n",
       "\n",
       "     Ticket Prefix_PC  Ticket Prefix_SCPARIS  Ticket Prefix_SOTONOQ  \\\n",
       "382         -0.274947              -0.121362              -0.136664   \n",
       "110         -0.274947              -0.121362              -0.136664   \n",
       "41          -0.274947              -0.121362              -0.136664   \n",
       "779         -0.274947              -0.121362              -0.136664   \n",
       "631         -0.274947              -0.121362              -0.136664   \n",
       "\n",
       "     Ticket Prefix_STONO2  Ticket Prefix_WC  Ticket Number_B  Ticket Number_C  \\\n",
       "382              7.831560         -0.107666        -0.289153        -0.324922   \n",
       "110             -0.127688         -0.107666         3.458381        -0.324922   \n",
       "41              -0.127688         -0.107666        -0.289153        -0.324922   \n",
       "779             -0.127688         -0.107666        -0.289153        -0.324922   \n",
       "631             -0.127688         -0.107666        -0.289153        -0.324922   \n",
       "\n",
       "     Ticket Number_D  Ticket Number_E  Ticket Number_NA  \n",
       "382        -0.639564         4.562529         -0.055364  \n",
       "110        -0.639564        -0.219177         -0.055364  \n",
       "41         -0.639564        -0.219177         -0.055364  \n",
       "779        -0.639564        -0.219177         -0.055364  \n",
       "631         1.563565        -0.219177         -0.055364  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.5 Recursive Feature Elimination\n",
    "<a id='3.5'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "63072724-81a6-4851-83c7-4a04ddd18906",
    "_uuid": "5dffaa7b8f12364a780764c1a46936e67b45ca14"
   },
   "source": [
    "Train/test split:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "_cell_guid": "fe647b6d-766a-4ea4-a11f-4c15f677f8c7",
    "_uuid": "fd43bf048e54213491f43de1fb6f626bf1d6c6cd"
   },
   "outputs": [],
   "source": [
    "X_train = X.loc[y.notnull()]\n",
    "X_test = X.loc[y.isnull()]\n",
    "y_train = y[y.notnull()].apply(np.bool)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recursive feature elimination using a Linear Support Vector Classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.feature_selection import RFECV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "selector = RFECV(estimator=LinearSVC(), scoring='accuracy', verbose=0, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RFECV(cv=None,\n",
       "   estimator=LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0),\n",
       "   n_jobs=-1, scoring='accuracy', step=1, verbose=0)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selector.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many features were retained?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selector.n_features_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which features were retained?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Age', 'Pclass', 'SibSp', 'Cabin_C', 'Cabin_E', 'Cabin_NA',\n",
       "       'Embarked_S', 'Sex_male', 'Ticket Prefix_CA', 'Ticket Prefix_NONE',\n",
       "       'Ticket Prefix_STONO2', 'Ticket Prefix_WC', 'Ticket Number_B',\n",
       "       'Ticket Number_D'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns[selector.support_]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Score of the underlying LinearSVC on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8181818181818182"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selector.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reduce our data to the retained features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.loc[:,selector.support_]\n",
    "X_test = X_test.loc[:,selector.support_]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6875765d-a8ef-48c1-af88-1b97a7201611",
    "_uuid": "b322d535ee67d7d99f6fa262a0294b897324b74e"
   },
   "source": [
    "# 4 Predictive Modeling\n",
    "<a id='4'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "_cell_guid": "9db82d71-360d-4fe0-a5c7-edefc7b7e272",
    "_uuid": "062e18739e985c454d32a809c76631b1bfacc8a7"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import accuracy_score, make_scorer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a179f547-9c41-41ce-929a-bb739b7601c9",
    "_uuid": "251ce8ed2a832097d2802a011567d1c8e83157bd"
   },
   "source": [
    "## 4.1 Random Forest\n",
    "<a id='4.1'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "_cell_guid": "752720d7-9ace-42dc-959a-436bdbd7a67f",
    "_uuid": "cca1de5abc1cdb68e9270bea170d1ad41d7ac1e7"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "_cell_guid": "a3a92229-13e8-4f1d-9098-3281e189c2f2",
    "_uuid": "6bf6fb3898b571b1cbbef90f0bea8a269e7997a3"
   },
   "outputs": [],
   "source": [
    "param_grid = {'n_estimators': [10, 55, 100],\n",
    "              'max_features': ['log2', 'sqrt', None],\n",
    "              'max_depth': [5, 15, 30, None],\n",
    "              'min_samples_split': [2, 10, 50],\n",
    "              'min_samples_leaf': [1, 5, 10]\n",
    "             }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "_cell_guid": "481e6cb2-1c13-41f9-bfcc-ebca1207601d",
    "_uuid": "8384a29b2dff77157c4684d99af0c4edbb6eb273"
   },
   "outputs": [],
   "source": [
    "model_rfc = GridSearchCV(estimator=RandomForestClassifier(n_jobs=-1), param_grid=param_grid, scoring=make_scorer(accuracy_score), n_jobs=-1, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "_cell_guid": "b601067c-098b-4995-be03-a246c450051e",
    "_uuid": "a3a005f8f16125d56b36d0742be29b0f2cef6261"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 324 candidates, totalling 972 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    8.0s\n",
      "[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:   22.3s\n",
      "[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed:   46.1s\n",
      "[Parallel(n_jobs=-1)]: Done 792 tasks      | elapsed:  1.4min\n",
      "[Parallel(n_jobs=-1)]: Done 972 out of 972 | elapsed:  1.7min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=-1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'n_estimators': [10, 55, 100], 'max_features': ['log2', 'sqrt', None], 'max_depth': [5, 15, 30, None], 'min_samples_split': [2, 10, 50], 'min_samples_leaf': [1, 5, 10]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=make_scorer(accuracy_score), verbose=1)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "_cell_guid": "ef44e665-38fe-4770-a6b8-4b0f425b6b66",
    "_uuid": "e83ea2ceafc9cc711e2d64228558179c269cafcc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 5,\n",
       " 'max_features': None,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'n_estimators': 100}"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rfc.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "_cell_guid": "f9a8dbb3-69ce-417d-962d-8894645fd352",
    "_uuid": "4842cda3b0dd6fafe4af8e187ce136b3e8544e34"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8260381593714927"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_rfc.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Feature importances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Random Forest Feature Importances')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jo/niiy+oWrUqXbt2xdDQkEGDBvHNN9+wc+dOrly5wg8//MCvv/7KoEGDnlifkZERAFFR\nUaSnp2NqasqJEyc4f/48V65cYfHixYSHhxf63wHAkCFDWLFiBdu3b+fatWt89913/PHHH7Rv3x5P\nT09SU1OZMGECFy5cICoqinHjxnHlyhUaNGiAsbExa9euZf78+Vy7do1z586xZ8+efEOtJUmGRkWZ\n0KhRI4YPH05wcDD//e9/+fjjj4mJiWHs2LHk5ubSqFEjAgICmDhxIlFRUYU6YnvvvfeYOXMm3333\nHfPmzaN169b06dOHmJgY4OGQ5LJly5g1axb9+/dHX18fR0dH1q5dS+XKlUu6yyo9PT0WL17MzJkz\nGT58OLm5uVhbW7Nu3To1qRXks88+Q6PRMGfOHG7fvk29evWYNm0aHh4eAHz55ZfMnj0bX19fUlJS\nqF+/PjNnzsTOzg54eN7Sz8+P999/n4iIiH+UMNq1a8ecOXP44YcfWLRoEaampvTo0YOxY8cCFOpz\n/Hscn3zyCdeuXWPMmDFotVp69er1zP+YmJub8/3337Nw4ULWrVuHsbExzs7O6q0IJalPnz5MmzaN\nW7duYWdnx8qVK6lYsSKAeg7366+/5s6dOzRq1Ij58+fTtWvXJ9Znb2+PnZ0dH330Eb6+vkyZMoUv\nv/ySvn37UqFCBSwsLJg2bRr+/v7ExcUVKsaPP/6YBw8eMHfuXJKTk2nSpAkhISE0adIEgOXLlzNv\n3jz69OmDoaEh9vb2LFy4EK1WS4MGDfj2229ZtGgRq1atQqPR4OTk9MSnDpUEPaWwx79CCCFemNjY\nWDp06MDatWuxtbUt7XBeazI0KoQQokyTRCiEEKJMk6FRIYQQZZocEQohhCjTJBEKIYQo0+T2CVHq\n8vLySE9PR6PRFMtN2EIIAQ8fqpCdnY2RkdFTn9YkiVCUuvT0dJ1nbwohRHEyMzNTn49aEEmEotQ9\nelZhw4YN1adevKqysrKIjo7GzMzsic+CfFVIX15O0pei1/+s56FKIhSl7tFwaJs2/yYhIaOUo3m1\nJSfPKPY6tVptkWaUeJlJX15OJd2XZ51ykYtlhBBClGmSCIUQQpRpkgiFEEKUaZIIxRP17dsXFxcX\n8vLySjsUIYQoMZIIRYEuX75McnIyNWvWZO/evaUdjhBClBi5alQUaOPGjTg7O9OgQQM2bNiAs7Mz\nAPHx8UyYMIGoqCiaNWtG/fr1qV27Nt7e3qSnpzNr1ix2796NVqulf//+DBkypJR7IoQQTydHhCKf\n3NxctmzZQs+ePenWrRtHjx5VZ/f29fXF3NycI0eO8Omnn7JlyxZ1v1mzZpGcnMyuXbtYvXo1Gzdu\nJDw8vLS6IYQQhSKJUOSzb98+ateuTdOmTTE2Nua9997j559/Ji4ujj///JNx48ah1WpxdHSkU6dO\nwMNHGW3ZsoXPP/+cSpUqUbduXQYOHMimTZtKuTdCCPF0MjQq8tm4cSOXLl2iTZs2ANy/f58KFSrg\n5OSEiYkJhoaG6rZ16tQBIDk5mczMTHr16qWW5eXlUbdu3RcbvBBCFJEkQqEjOTmZffv2sWnTJqpU\nqaKu79OnD9evX+fOnTs8ePBATYY3b96kfv36mJiYoNFo2LVrF9WqVQPgzp07ZGVllUo/hBCisGRo\nVOjYsmUL1tbWNG7cmBo1aqh/3bp1Y9OmTVhbW7Nw4UKysrI4fvw4v/76KwD6+vp07dqVefPmkZGR\nwd27dxkzZgwhISGl3CMhhHg6SYRCx6ZNm+jatWu+9e7u7hw9epSpU6fyxx9/YG9vz8KFC3FwcFAf\naOvv7w9Ax44d6dSpE//617/44osvXmj8QghRVDI0KnRs3bq1wPWNGzfm/PnzHDlyhDVr1qhze40d\nO1YdQjU2NmbmzJkvLFYhhCgOckQoCk1PT48pU6aot0ycPn2aAwcOYG9vX8qRCSHEPyeJUBTJvHnz\nWL16NdbW1vj6+vLVV1/x1ltvlXZYQgjxj8nQqCgSKyurErs38OjRMU+dRfpVkJmZyenTp2nRosVr\nM1ecEK87OSIUQghRpkkiFEIIUabJ0Kh4adjZLSIhIaO0wygmxT98nJw8o9jrFELIEaEQQogyThKh\nEEKIMk0SoRBCiDJNzhG+pszNzalQoQJ6enoAaDQanJyc+Oqrr6hcufIT95swYQL/+te/8Pb2flGh\nCiFEqZIjwtfYtm3bOHXqFKdOnWL37t0kJiYyderU0g5LCCFeKpIIy4hKlSrRuXNnoqOjgYdTJPn4\n+GBjY0P79u1Zt25dvn2uXLnCkCFDaNu2LdbW1owcOZJ79+4BsHPnTjp16oSdnR19+/YlKioKgIsX\nL9KnTx9sbW1xdXUlNDT0xXVSCCH+AUmEZcSNGzfYunUrdnZ2AEyZMgUDAwP279/P8uXLWbhwIWfO\nnNHZZ8qUKdjY2LBv3z4iIiK4evUq27ZtIzc3l0mTJrFo0SIiIyNp164d8+fPB2Du3Ll069aN48eP\nM3XqVAIDA7l///4L768QQhSWnCN8jbm5uVGuXDkURaFSpUq0bduWcePGkZmZye+//86OHTuoWLEi\nb731FqtWreKNN97Q2X/27NlUr16dBw8ekJCQgImJCbdv30ZfXx8jIyN++uknevbsyfDhwxk1ahTw\ncAaK3bt306BBAxwcHDh27Jg6U4UQQryMJBG+xsLCwqhbt26+9bdu3SI7O5tatWqp68zNzfNtd/Hi\nRYYNG0ZKSgpvv/02aWlpKIoCwNKlSwkODmbgwIEYGRnh6+tLjx498Pf3Z/78+UyePJl79+7Rt29f\n/Pz80NfXL7mOCiHEc5D/qpdB1apVQ6PRkJCQoK7buHEjJ0+eVJezsrL47LPPmDBhAgcOHGDJkiXU\nq1cPgIyMDFJSUli8eDFHjhxh/PjxTJo0idTUVKKjo/niiy/Yv38/K1euJCwsjL17977wPgohRGFJ\nIiyD9PX16dy5M8HBwTx48ICYmBiCgoKoUKGCuk1WVhaZmZlUrFgRRVHYvXs3+/fvJycnh9zcXIYP\nH87+/fvRarVUrVoVQ0NDypcvT1BQEEuWLCE3N5caNWoAqBP3CiHEy0iGRsuoKVOmEBgYSPv27TEy\nMuKLL77g7bffVssrVarE5MmTGT16NDk5OZibm9OzZ09iYmIwNjYmKCiIr7/+mps3b1K7dm2++eYb\nypcvz4wZM5gyZQqtWrWiYsWKDBgwABsbm1LsqRBCPJ2e8uikjxCl5NEcfp6eO1+jh24Xvxf90O3X\naW5F6cvLqaT7Utj6ZWhUCCFEmSaJUAghRJkm5wjFS+Po0TEYGxuXdhjP5XUathKirJAjQiGEEGWa\nJEIhhBBlmiRCIYQQZZqcIxQvDTu7RS/k9okXfRuCEOLlJkeEQgghyjRJhEIIIco0SYRCCCHKNEmE\nZcSlS5cYMmQINjY2tGzZEk9PT44fPw6Aq6ur+trc3JzY2NgC60hISMDHxwc7Ozusra3x8PAgIiLi\nhfVBCCFKgiTCMiA3N5dhw4bRrl07jhw5wtGjR3F3d2fo0KEkJiayfft2bG1tn1nP2LFj+de//sXe\nvXs5fvw43t7e+Pn5cfbs2RfQCyGEKBmSCMuAlJQUbty4gaurKxqNBgMDA3r37k3fvn1JSkrCxcWF\nyMhIdfuff/6Zdu3a0bFjR0JDQ9X1Z86coUuXLlSoUAF9fX2cnZ0ZNWoUqampAHh5eTF//nxcXFyw\ntbVl5syZ5ObmvvD+CiFEUcjtE2VAtWrVsLGxoX///vTs2RN7e3uaN2/O+PHjC9z+0qVL7Nixg0uX\nLjFw4ECaN29OkyZN6NixI97e3nh4eODg4IClpSXDhg3T2Xfbtm0sX76cChUqMGjQIOrWrYuXl9eL\n6KYQQvwjckRYRixbtgwPDw9+++03+vXrh6OjIwsWLKCgWbjGjh2LkZERlpaWdOzYkV9//RWAWbNm\nMXz4cI4dO8bgwYOxt7fH39+fzMxMdd+PP/6Y+vXrU7NmTQYOHMjOnTtfWB+FEOKfkCPCMqJChQoM\nGzaMYcOGce/ePfbu3cu0adOoXbt2vm3feOMN9XWtWrVITEwEwMDAgH79+tGvXz8ePHjAkSNHmD59\nOsHBwfj5+QFQt25ddd/atWtz+/btEu6ZEEI8HzkiLAO2b99O9+7d1eVKlSrh6upK165diY6Ozrf9\no8QHEBcXR506dTh58iSOjo5kZWUBYGhoyLvvvsuHH36oU8fj+8bHx1OrVq2S6JIQQhQbSYRlgKOj\nI7du3SI4OJi0tDRyc3OJiopi7969tG/fPt/2CxcuJCMjg2PHjvH777/TtWtXWrRogbGxMYGBgdy+\nfRtFUbh06RLh4eE6daxevZqEhAQSEhJYsWKFTgIWQoiXkQyNlgGmpqasWbOGoKAg3nvvPTIzM6lX\nrx4+Pj68++67TJs2TWf7t956i/bt22NqasrcuXOpV68eACtXrmTevHm4ubmRkZFBzZo1+eijj+jf\nv7+6b4sWLfDy8uLu3bt4enrSq1evF9pXIYQoKkmEZUSTJk0ICQkpsGz37t3q6wsXLgAwZsyYfNu9\n8cYbBAUFPbUdBwcH5syZ8xyRCiHEiyVDo0IIIco0OSIUL42jR8dgbGxc2mEIIcoYSYSi2Kxevbq0\nQxBCiCKToVEhhBBlmiRCIYQQZZokQvHSsLNbVNohCCHKIEmEQgghyjRJhEIIIco0SYRCCCHKNEmE\nQgghyjRJhK+QiIgI+vbti62tLa1bt8bPz4+EhISn7hMbG4u5uXmBZXFxcVhbWxdLbL/88gs9e/ak\nZcuWtG3bloCAAO7du1csdQshREmSRPiKWLNmDVOnTsXb25sjR46wa9cujIyM+Pjjj9WpkYqqTp06\nnDp16rljmz17NsuXLycgIIDjx48TGhrKrVu3GDly5HPXLYQQJU0S4SsgPT2doKAgpk+fTps2bTAw\nMMDY2Bh/f39atGjBtWvX2Lx5M25ubuoR2apVq3TqCA4OxtHREVdXV/bv3w/oHi1GRkbSp08fvvzy\nS1q2bEnnzp05ePDgM2O7fv06y5cvZ+HChVhYWFCuXDmqV6/OrFmzMDExkYl5hRAvPUmEr4BTp06R\nl5dH27Ztddbr6+sTFBSEVqtlxowZBAUFcfLkSQIDA5kzZ47O0GRSUhJ79+7F19eXMWPGkJycnK+d\nP/74g6ZNmxIZGYm7uzsBAQHPjO3QoUPUr1+fJk2a6Kw3NjYmODiY6tWr/8NeCyHEiyGJ8BWQkpJC\n5cqVMTAo+NGwtWvXJiwsjCZNmpCYmIi+vj7Z2dncvXsXAD09Pfz8/NBqtbi4uNCsWTP27duXr56K\nFSvi6emJRqPB1dWVa9euFSq2qlWrPl8HhRCiFMlDt18B1apV4+7du+Tk5ORLhsnJyZiYmLB69WpC\nQ0MxNTXFwsICAEVRAKhSpQqVKlVS96lVqxaJiYn52nk8oenr66v7Pyu2go4uH8Vmamr67A4KIUQp\nkiPCV4C1tTX6+vr5ztnl5ubSq1cvtmzZwoEDB9ixYwfbt29n4sSJOtulpaVx//59dTkuLo46deoU\nS2yOjo5cv36dixcv6qy/d+8eHTp04OjRo8XSjhBClBRJhK8AQ0NDfHx8mDJlCocPHyYvL4+kpCQm\nTJiAoaEhGRkZaDQaNBoN6enpzJ07F4CcnBzgYcL85ptvyMzMJDw8nCtXrtC+fftiie1f//oX/fv3\n57PPPiMqKgpFUYiNjcXHx4cWLVrQqlWrYmlHCCFKigyNviIGDhyIkZERs2bNIjY2FkNDQ9q2bcvy\n5csxNjbmwIEDODk5UbFiRbp27Urjxo2JiYnB3NwcExMTFEXB0dGRevXqERISQqVKlUhJSSmW2CZN\nmsTSpUv5/PPPSUhIwNjYmE6dOuHj44Oenl6xtCGEECVFTynMiSAhSlBmZianT5/G03Mn585NLu1w\nnsujvrRo0YLy5cuXdjjPRfrycpK+FH/9MjQqhBCiTJOhUfFUe/bsYdy4cQWWabVaIiMji62to0fH\nFFtdQghRWJIIxVM5OzsXy2PYhBDiZSVDo0IIIco0SYRCCCHKNBkaFS8NO7tFJCRkqMvJyTNKMRoh\nRFkhR4RCCCHKNEmEQgghyjRJhEIIIco0SYSvmIiICPr27YutrS2tW7fGz8+PhISEp+7z+AS8fxcX\nF4e1tfVzxfSofmtr63x/BU33JIQQLxO5WOYVsmbNGkJCQpg9ezb29vbcv3+fefPm8fHHH7N161a0\nWm2R66xTp06x3Sco9xsKIV6VX9nCAAAgAElEQVRFckT4ikhPTycoKIjp06fTpk0bDAwMMDY2xt/f\nnxYtWnDt2jU2b96Mm5sbLVu2pG3btqxatUqnjuDgYBwdHXF1dWX//v2A7tFiZGQkffr04csvv6Rl\ny5Z07tw539RPQgjxupFE+Io4deoUeXl5tG3bVme9vr4+QUFBaLVaZsyYQVBQECdPniQwMJA5c+Zw\n7949ddukpCT27t2Lr68vY8aMKXBC3T/++IOmTZsSGRmJu7s7AQEBJd43IYQoTTI0+opISUmhcuXK\n+Waof6R27dqEhYVRp04dEhMT0dfXJzs7m7t37wKgp6eHn58fWq0WFxcXmjVrxr59+7C1tdWpp2LF\ninh6egLg6urKokWLCh1jQXXJOUIhxMtOEuErolq1aty9e5ecnJx8yTA5ORkTExNWr15NaGgopqam\nWFhYAPBolq0qVapQqVIldZ9atWqRmJiYr52qVauqr/X19SnKLF3Hjx8vUp+EEOJlIEOjrwhra2v0\n9fXznbPLzc2lV69ebNmyhQMHDrBjxw62b9/OxIkTdbZLS0vj/v376nJcXBx16tR5IbELIcTLTBLh\nK8LQ0BAfHx+mTJnC4cOHycvLIykpiQkTJmBoaEhGRgYajQaNRkN6ejpz584FICcnB3iYML/55hsy\nMzMJDw/nypUrtG/fvjS7JIQQLwUZGn2FDBw4ECMjI2bNmkVsbCyGhoa0bduW5cuXY2xszIEDB3By\ncqJixYp07dqVxo0bExMTg7m5OSYmJiiKgqOjI/Xq1SMkJIRKlSqRkpJSbPEVdD/i8OHDGTFiRLG1\nIYQQxU0S4Sumd+/e9O7du8Cyf//730/c79EEupMmTdJZX7duXS5cuACAvb09u3fvLrDsaQq7nRBC\nvIxkaFQIIUSZJkeE4pn27NnDuHHjCizTarXq0ebzOnp0DMbGxsVSlxBCFJYkQvFMzs7O8vg0IcRr\nS4ZGhRBClGmSCIUQQpRpMjQqXhp2dotISMgo8XaSk2eUeBtCiFeHHBEKIYQo0yQRCiGEKNMkEQoh\nhCjTJBEKIYQo0yQRvkQiIiLo27cvtra2tG7dGj8/PxISEp66z+MzzP9dXFxcgc//LIpH9a9fv15n\nfWRkJC4uLjrr8vLyePfdd+nXr99ztSmEEC+SJMKXxJo1a5g6dSre3t4cOXKEXbt2YWRkxMcff0xW\nVtY/qrNOnTrFdiP83LlziYuLe+o2Bw8epEGDBsTGxhIdHV0s7QohREmTRPgSSE9PJygoiOnTp9Om\nTRsMDAwwNjbG39+fFi1acO3aNTZv3oybmxstW7akbdu2rFq1SqeO4OBgHB0dcXV1Zf/+/YDu0WJk\nZCR9+vThyy+/pGXLlnTu3Dnf3IZP07lzZ7788sunbrNx40ZcXFxwc3Njw4YNRXwXhBCidEgifAmc\nOnWKvLw82rZtq7NeX1+foKAgtFotM2bMICgoiJMnTxIYGMicOXO4d++eum1SUhJ79+7F19eXMWPG\nkJycnK+dP/74g6ZNmxIZGYm7uzsBAQGFjnHixIlcvHiRX375pcDylJQU9u3bR7du3fjggw8ICwsj\nI6Pk7wkUQojnJYnwJZCSkkLlypUxMCj4+Qa1a9cmLCyMJk2akJiYiL6+PtnZ2dy9excAPT09/Pz8\n0Gq1uLi40KxZM/bt25evnooVK+Lp6YlGo8HV1ZVr164VOsbKlSszbdo0Zs+ezc2bN/OVb9u2DScn\nJ0xNTWnYsCFmZmZs27at0PULIURpkSfLvASqVavG3bt3ycnJyZcMk5OTMTExYfXq1YSGhmJqaoqF\nhQUAiqIAUKVKFSpVqqTuU6tWLRITE/O1U7VqVfW1vr6+un9hOTs74+zszJQpUxgyZIhO2caNG7l6\n9Spt2rQBHg733r9/nz59+hSpDSGEeNEkEb4ErK2t0dfX5+DBg7Rv315dn5ubS69evfD29ubAgQPs\n2LEDExMTUlNT2bRpk7pdWloa9+/fp0KFCsDDq0U7dOhQIrFOnjwZV1dXNm/erK47f/48N27cYMeO\nHZQr93CQITMzk27duvHnn3+qiVsIIV5GMjT6EjA0NMTHx4cpU6Zw+PBh8vLySEpKYsKECRgaGpKR\nkYFGo0Gj0ZCens7cuXMByMnJAR4mzG+++YbMzEzCw8O5cuWKTkItTiYmJkydOlUnEf7nP//hvffe\no1atWtSoUYMaNWpQt25dXFxc8t12IYQQLxtJhC+JgQMH4u3tzaxZs2jVqhVubm6UK1eO5cuX07Nn\nT2rVqoWTkxOdO3fG0NCQxo0bExMTAzxMToqi4OjoyPfff09ISIjOUGlx69ixI++//z4AWVlZbN26\nla5du+bbzt3dnfDwcPVcphBCvIz0lKKeKBKimGVmZnL69Gk8PXe+8rNPPOpLixYtKF++fIm18yJI\nX15O0pfir1+OCIUQQpRpcrFMGbdnzx7GjRtXYJlWqyUyMvKFxXL06BiMjY1fWHtCCAH/IBGGh4ez\ndu1aEhISWLNmDYsXL8bf3x+tVlsS8YkS5uzsXGyPYRNCiFdRkYZGf/rpJ+bNm0eHDh1ISUlBq9Vy\n+vRp5syZU1LxCSGEECWqSEeEK1as4N///jfm5uaEhIRgamrKv//9b3r37v3M51AK8Sx2dov+0cUy\nJXnxixDi9VekI8KkpCSaNGkC/P+nmtSqVesfz44ghBBClLYiJUJzc3N+/vln4OHzLQF27txJ48aN\niz8yIYQQ4gUo0tDo559/zqBBgwgNDSUjI4Nhw4Zx4sQJvv/++5KKTwghhChRRUqEFhYWhIeHs3Xr\nVt5++21q1arFlClTqFevXknFJ4QQQpSoIg2N9u3bFyMjI4YMGcJXX33FiBEjJAn+H3Nzc6ysrLC2\nttb5++mnn4pUz6ZNm/Dy8nrueCIjI3FxcXnuelxcXAp1L2FCQgI+Pj7Y2dlhbW2Nh4cHERERz92+\nEEKUtCIdEV6/fl2dXUDkt23bNurWrVvaYZSKsWPHYmVlxd69e9Fqtezbt4+xY8eybt06mjVrVtrh\nCSHEExUpq3Xo0IGBAwfyww8/sHnzZkJDQ9U/8XQuLi6sWLECZ2dnbGxs+OGHH1i7di2tW7embdu2\n7Ny5U902IyNDPbr6+OOP1Ql0c3NzmT17Np06dcLKyoquXbuqR2vBwcGMHDmSDh060KNHD522ExIS\n6NChA6tXrwYeXv3r4+ODvb09nTp10vn8/ve//+Hm5oa1tTX+/v7k5uYWqn9nzpyhS5cuVKhQAX19\nfZydnRk1ahSpqanP9b4JIURJK9IR4YEDBwDYsGGDzno9Pb18P74iv3379rFt2zb+/PNPPvnkEzw8\nPPj999/ZuHEjs2fPpkuXLgCcPn2aAQMGMHfuXIKDg/H19eWXX35hy5YtREZG8ssvv1CpUiXmz5/P\n/Pnz1eHXY8eOsXnzZqpWrcqZM2cAuHv3LkOGDOHDDz9Uh1y/+OIL3nzzTfbv38/ly5cZMmQIDRs2\nxNzcnE8//ZQxY8bg4eHB8uXLC5yNviAdO3bE29sbDw8PHBwcsLS0ZNiwYSXwLgohRPEqUiLcvXt3\nScXxWng0ddLjIiIiMDExAeDDDz/EyMgIOzs78vLy8PLyQqvV0rZtWwICAtR9WrRogZubGwCjR49m\n+fLlxMfH06lTJ9q3b0+lSpWIi4vDyMhIZyZ6S0tLnXO2WVlZjBgxAnNzczUp3bp1iyNHjrB48WK0\nWi1NmzbFw8ODTZs20aVLF8qXL8+HH34IwJAhQ/jxxx8L1fdZs2bx888/Ex4ezpIlS9DX18fd3Z3J\nkye/8k/IF0K83oqUCI8dO/bEslatWj13MK+6sLCwp54jrFKlCgD6+voA6pyBenp6PD4bVp06ddTX\n5cuXx8TEhMTERMqXL4+/vz/Hjh3jzTffpGbNmjr1V69eXWc5MTGRxo0bc+jQIe7evUuVKlW4efMm\nubm5ODk5qdvl5uZib2/P7du3deosV64ctWvXLlTfDQwM6NevH/369ePBgwccOXKE6dOnExwcjJ+f\nX6HqEEKI0lCkRDhw4ECd5dzcXMqVK0e9evXYtWtXccb1Wnr0EIJnefwo7/79+9y5c4c33niDBQsW\nYGxszMGDB9FoNERERPD1118/sf6aNWuyfPlyRo4cybx58wgMDKR69epUqFCBo0ePqkeviYmJ6Ovr\nc+HCBeLj41EURU3Ot2/ffma8J0+e5NNPP1UvlDE0NOTdd9/l4sWLT/3PkxBCvAyKdLHMmTNndP6O\nHj2Kl5cXH3zwQUnFVyb98ccf7Nixg6ysLIKCgrC3t6dGjRqkpqZSvnx59PX1iY+P5/vvvyc7O/uJ\n9Wg0GvT09Jg8eTJhYWGcPHmSOnXq0KxZMxYtWkRWVhYJCQkMHDiQTZs2YWNjg56eHitXriQ7O5tV\nq1bpJOUnadGiBcbGxgQGBnL79m0UReHSpUuEh4fTvn374nxrhBCi2D3XvRCVK1fG19dXvRqxrOvW\nrVu++wj9/f2LXI+9vT0///wzDg4OXLt2jdmzZwPg7e1NVFQUNjY2fPzxx3Tq1Ik7d+5w586dp9ZX\nr149Bg8ezFdffUV2djbz588nJiYGJycnevTogZOTE4MGDUKr1RISEkJYWBitWrXif//7H+bm5s+M\nV6vVsnLlSjIyMtQrTkeNGoWbmxv9+/cvcv+FEOJF0lMePzn1D5w7d45PPvmEw4cPF1dMoozJzMzk\n9OnTeHrufOVnn3jUlxYtWrzyFwlJX15O0pfir79I5wi9vLx0zkPl5uZy9uxZunfv/s8jFUIIIUpR\nkRKhvb29znK5cuXw9PSkU6dOxRqUeLmkpaXRrl27J5avXLkSCwuL527n6NExGBsbP3c9QghRFEVK\nhG+//TYdOnTIt37z5s307Nmz2IISLxdjY2NOnTpV2mEIIUSJeGYifHT5PoCfnx/h4eE697ylpaUx\nbdo0SYRCCCFeSc9MhBkZGXTv3p2MjIcXMTw+o8Gj+82cnZ1LLkIhhBCiBBXqqtGkpCTu379P9+7d\n2bZtm06ZVqulRo0aJRageP0971WjQoiyoahXiBfrVaPVqlUD4MSJEwVOw5Sbm6s+NkwIIYR4lRTp\nYpn4+HiCg4O5efMmeXl5AOTk5HD16lUOHjxYIgEKIYQQJalIT5aZMmUKsbGxGBsbk52djZmZGdHR\n0epsBUIIIcSrpkhHhKdOneL3338nPj6eoKAgvvzyS9q1a8cPP/xQUvEJIYQQJapIR4QVKlSgSpUq\nNGjQgEuXLgHQrl07YmJiSiS4subSpUsMGTIEGxsbWrZsiaenJ8ePHy/tsJ4oMjJS5ypiIYR4FRUp\nET6a1dzQ0BA9PT1iY2NJSEggJyenpOIrM3Jzcxk2bBjt2rXjyJEjHD16FHd3d4YOHVqoGSCEEEL8\nM0VKhMOHD8fb25vr16/Tu3dv+vTpQ+/evQt82owompSUFG7cuIGrqysajQYDAwN69+5N3759SUpK\nIikpCR8fH+zt7enUqROhoaEAnD9/nnfeeYeoqCgA1q5dS+fOnbl///5T25swYQLz5s2jZ8+eWFlZ\nMWbMGI4cOUKXLl2wsbFh7ty56rabN2/Gzc2Nli1b0rZtW1atWlVgnVu3bqVLly7Y2dkxevRokpKS\niundEUKIklOkc4TOzs7s2rWLatWqMXLkSOrXr09aWpo8VaYYVKtWDRsbG/r370/Pnj2xt7enefPm\njB8/HoDBgwerR+SXL19myJAhNGzYEEtLS4YMGcJXX33FwoULWbBgAT/88AMVKlR4Zptbtmxh3bp1\nGBkZ0bVrV27cuMG6detITEzkgw8+oG/fviiKwowZM1i/fj1NmjRhz549eHt74+HhoVPXyZMn+frr\nr1m2bBmNGjUiKCiI8ePHs3Tp0hJ5v4QQorgUeT7CGjVqcPz4cf7zn//QoUMHWrdujVarLYnYypxl\ny5bh4eHBb7/9Rr9+/XB0dGTBggXcunWLI0eO8MUXX6DVamnatCkeHh5s2rQJgJEjR5KVlUW/fv3o\n06cPLVu2LFR7nTp1ol69epiamtKoUSPc3NwwNTXF3Nyc6tWrc/PmTWrXrk1YWBhNmjRRZ7LPzs7m\n7t27OnWFhobSp08fmjVrRvny5Rk7diyHDh0q1Az3QghRmop0RHjz5k2GDh1KfHw8ubm5WFlZ4eHh\nwQ8//ICDg0NJxVhmVKhQgWHDhjFs2DDu3bvH3r17mTZtGhqNhtzcXJycnNRtc3Nz1dlAtFot3bt3\nZ/78+UWaEqtKlSrqa319fZ2ZH8qVK0deXh4GBgasXr2a0NBQTE1N1Vkm/v5Aovj4eLZs2cLatWvV\ndQYGBsTFxVG9evWivRFCCPECFSkRTp8+HWdnZ3x8fHBwcKBx48aMHz+eoKAgfvnll5KKsUzYvn07\nISEhbN26FYBKlSrh6urKsWPHuHHjBhUqVODo0aPqk30eHZ0B3Lp1ix9//JFu3boREBDAunXrCnwC\n0N89Prfk0+I6cOAAO3bswMTEhNTUVPVI9HHVq1dn9OjRDB06VF0XExND/fr1C9V/IYQoLUUaGj1x\n4gSjR49GX19f/RH96KOP+Ouvv0okuLLE0dGRW7duERwcTFpaGrm5uURFRbF37146d+5Ms2bNWLRo\nEVlZWSQkJDBw4EA1IQUEBNC5c2dmzZpFWloaa9asKba4UlNT0Wg0aDQa0tPT1Yto/n6lsJubG+vX\nrycmJoa8vDxWrlxJ//79yc7OLrZYhBCiJBTpiLBixYokJSXxxhtvqOtu375N5cqViz2wssbU1JQ1\na9YQFBTEe++9R2ZmJvXq1cPHx4d3332Xt99+m+nTp+Pk5ES5cuVwd3dn0KBB7Nixg1OnTrFjxw40\nGg1fffUVw4cPx8XFhbp16z53XD179uTAgQM4OTlRsWJFunbtSuPGjYmJiaFSpUrqdo6Ojnz66aeM\nGjWKxMREGjVqxPfff1+oi3aEEKI0FWr2iUcWLFjA/v378fX1xcfHhx9//JH58+fTrFkzvvjii5KM\nU7zGZPYJIURhlOrsE498+umnZGZmMnr0aO7fv4+Xlxc9evTAx8enSMEJUZCjR8foXLDzKirsP7xX\ngfTl5SR9KX6FSoSfffYZ33zzDVqtlgkTJjBhwgSSk5OpWrVqoS64EC/euHHj2LNnT4FlvXr1YvLk\nyS84IiGEeDkVKhHu27dPZ9nR0ZHDhw+XSECieMyfP7+0QxBCiFdCkW+oh/xXDAohhBCvqn+UCGU4\nVAghxOviHyVCIYQQ4nVRqHOEOTk56mwHANnZ2TrLAD169CjeyIQQQogXoFCJsHr16ixatEhdrlq1\nqs6ynp6eJMJCyMjI4MGDB5iampZ2KEIIIf5PoRLh7t27SzqOF2bIkCGcOHECgAcPHmBgYICBwcO3\nISAggLi4OOLi4pg2bdoT69i0aRObN29m9erVRWrb09OT8ePHqw/LfpyLiwu3b99Wnx9arlw5WrVq\nhb+/P3Xq1ClSO+np6QwbNoyzZ88yfPhwvv/+e7Zv317keh53/vx5goODOXHiBHl5eTRt2pRx48Zh\nZWWls92jaaB27NhBgwYN/nF7QgjxohTphvrXwePz43l5edGzZ898c+uVlJSUlKeWL1myRE2SmZmZ\nTJ48mbFjx/LTTz8VqZ3z589z8eJFIiMj0Wq1jBgx4h/HDBAVFcWAAQPw9fVl3rx5GBgYEBoayuDB\ng9mwYQNNmjQBIC8vj9DQULp3785PP/2kzqUohBAvM7lY5m+Cg4OZMGEC8PCI0d/fn1atWtG6dWud\n4eBHLl68SOvWrfntt98AuH79OoMHD6ZVq1Z0796d/fv3A/D5558TFxfH0KFDCQ8Pf2Yc5cuXp1u3\nbkRHRwMPk/b48eNxcHBg+vTpAKxYsYIOHTrg6OjIpEmTSE9P588//2TQoEGkpqZib2/P9evXMTc3\nJzY2lnXr1tGuXTvu3bsHgI+PD35+fs+MZc6cOXz44Yf079+fChUqoNFo6N27NwMGDODy5cvqdvv3\n78fU1JThw4ezadMmMjMzn1m3EEKUNkmET7FgwQL++usvfv31V0JDQwkLC1MTHsCNGzcYOnQoEydO\npGPHjuTk5DBixAjs7e05dOgQkydPxtfXl7i4OObOnUudOnVYsmQJ77///jPbTkpK4ueff8bOzk5d\nd/XqVXbv3s1nn33G1q1bWb9+PcuXLyciIoL09HTmzJmDhYUFS5YsoU6dOpw6dYp69eqp+3/00UfU\nr1+fRYsWsWvXLk6cOMGUKVOeGkdmZibHjh2jY8eO+crGjBlD586d1eWNGzfSs2dPGjVqRMOGDdmx\nY8cz+ymEEKVNEuFT7Ny5k1GjRlG1alVq1qzJkiVLsLa2Bh4Oc37yySe4u7urk+FGRUWRlpbGsGHD\n0Gg0ODg44OTkxLZt2wrV3ogRI7C1tcXW1hZ3d3c0Gg2BgYFqubOzMxUrVqRSpUrq0OSbb76JkZER\nn332GaGhofkmzH2cnp4e06dPZ+PGjQQEBDBjxgydyXkLcvfuXRRFeeYFPikpKRw8eBA3NzcAPvjg\nAzZs2FCofgshRGkqc+cIiyIpKYlatWqpyw0bNlRfR0dH4+joSEREBKNHj0aj0RAfH8/t27extbVV\nt8vNzcXExKRQ7YWEhBR4Ic0jj8/0Hh8fz8yZM5kzZ466Li8vj6SkpKe2Ub9+faysrDh//jwODg7P\njMnExAQDAwOSkpLyXfySmpqqDpWGhYXx4MEDXF1dgYe33KSkpHD+/HmaNm36zHaEEKK0SCJ8ilq1\nanHr1i3eeustACIiItQrTK2trVm2bBkeHh4sW7aMESNGUKNGDRo0aKBzDjA+Ph4jI6NiiefxJ/o8\nmhH+0TBrVlYWN27coFq1asTExDyxjr1793LhwgUaN27M4sWL8fX1fWqbWq2WVq1a8d///hcbGxud\nssDAQAwMDJg5cyabNm0iMDAQJycntXzmzJls2LCBqVOn/oPeCiHEiyFDo0/h6upKSEgIqampJCQk\nMHfuXPLy8gDQaDTo6+vj7+9PSEgI169fx9LSkuzsbH766Sdyc3OJiYmhV69eHDx4UN3n0YUqz6t7\n9+4sXbqU+Ph4srOzWbBgAd7e3k/d5969e3z11VdMnDiRwMBA1qxZw5kzZ57Z1tixY1m/fj0bNmwg\nMzOTBw8esGLFCv773/8ycOBAzpw5w9WrV3n//fepUaOG+tezZ0/CwsJIT08vlj4LIURJkET4FKNG\njaJhw4Z07dqVDz74gF69euHi4qKzjY2NDZ07d2bq1KlotVpCQkLYuXMnDg4ODBo0iAEDBtC1a1cA\n3N3d8fX1LfLtEAXp1asXXbp0oX///jg4OHD27FmCg4Of+hzYoKAg3nrrLVxdXXnzzTcZPHgwkydP\nfuZD1C0tLVmyZAk7duzAyckJJycn9u7dy4oVKzA3N2fTpk04OztjaGios1/r1q2pWLEiW7dufe7+\nCiFESSnSDPVClIRHk3OamZnJxLwvEenLy0n6Uvz1yxGhEEKIMk0ulhG0a9eOtLS0AstmzZqlc6+g\nEEK8biQRCvbt21faIQghRKmRoVEhhBBlmiRCIYQQZZokQiGEEGWaJELx0rCzyz+7hxBClDRJhEII\nIco0SYRCCCHKNEmEz5CRkUFycnJphyGEEKKEvBKJcMiQIVhbW2Ntbc3bb7/NO++8oy6HhYUREhKC\nv7//U+vYtGkTXl5eRW7b09OTixcvFljm4uKChYWFGouNjQ0jRowgLi6uyO2kp6fTv39/rK2tCQkJ\nwdra+h/V84iXlxd9+/ZVHxL+eMyRkZHqckxMDN7e3tjZ2WFjY0OfPn10Zs8AMDc3Z968eTrrYmNj\nMTc319nGyspKfS8e/RXHc1WFEKIkvRI31C9dulR97eXlRc+ePfHw8HghbaekpDy1fMmSJeocgpmZ\nmUyePJmxY8cWOQGcP3+eixcvEhkZiVarZcSIEf845kdOnTrFqlWrGDhwYIHl0dHR9O/fH09PTwIC\nAqhSpQqHDh1i0qRJxMbGMmzYMHXb5cuX06lTJywsLJ7Y3rZt26hbt+5zxy2EEC/SK3FE+CzBwcFM\nmDABgAcPHuDv70+rVq1o3bo1ixblvxLx4sWLtG7dmt9++w2A69evM3jwYFq1akX37t3Zv38/AJ9/\n/jlxcXEMHTo031FSQcqXL0+3bt2Ijo4GHibt8ePH4+DgwPTp0wFYsWIFHTp0wNHRkUmTJpGens6f\nf/7JoEGDSE1Nxd7enuvXr2Nubk5sbCzr1q2jXbt26vRNPj4++Pn5Fep96dmzJwsXLuTq1asFls+e\nPZsePXrg4+ODqakp+vr6ODk5sWDBAhYtWkRiYqK6rZubG5MmTSIrK6tQbQshxKvitUiEj1uwYAF/\n/fUXv/76K6GhoYSFhakJD+DGjRsMHTqUiRMn0rFjR3JychgxYgT29vYcOnSIyZMn4+vrS1xcHHPn\nzqVOnTosWbJEnQD3aZKSkvj555+xs7NT1129epXdu3fz2WefsXXrVtavX8/y5cuJiIggPT2dOXPm\nYGFhwZIlS6hTpw6nTp2iXr166v4fffQR9evXZ9GiRezatYsTJ04wZcqUQr0XdnZ29OjRg0mTJvH3\nSUYyMzM5cuQIXbp0ybefra0tNWvW1Hn02vDhw9HX1+fbb78tVNtCCPGqeO0S4c6dOxk1ahRVq1al\nZs2aLFmyBGtra+DhMOcnn3yCu7s73bt3ByAqKoq0tDSGDRuGRqPBwcEBJycntm3bVqj2RowYga2t\nLba2tri7u6PRaAgMDFTLnZ2dqVixIpUqVSI0NJTBgwfz5ptvYmRkxGeffUZoaGi+JPU4PT09pk+f\nzsaNGwkICGDGjBlUqVKl0O+Hn58f8fHxrFmzRmd9SkoKOTk5VKtWrcD9qlevTlJSkrr8aCb6FStW\nPHEyXzc3N/W9ePT3rKFlIYQoba/EOcKiSEpKolatWupyw4YN1dfR0dE4OjoSERHB6NGj0Wg0xMfH\nc/v2bWxtbdXtcnNzMWgDgfkAACAASURBVDExKVR7ISEh6jnCglSvXl19HR8fz8yZM5kzZ466Li8v\nTyfhFKR+/fpYWVlx/vx5HBwcChXXI0ZGRkyfPp1PP/2Ud999V11ftWpV9PX1uXnzJg0aNMi3X3x8\nPKampjrrmjVrxqBBg5g4cWKBQ85hYWFyjlAI8cp57Y4Ia9Wqxa1bt9TliIgIfv/9dwCsra1ZtmwZ\nBgYGLFu2DIAaNWrQoEEDjh8/rv6Fh4fj4+NTLPE8PmN89erVmTFjhtrOoUOHCAsLe+JR2SN79+7l\nwoULNG7cmMWLFxc5htatW9OtWzedIVKtVkubNm0KnD3+8OHDJCcn065du3xlo0aNIi8vj5CQkCLH\nIYQQL6PXLhG6uroSEhJCamoqCQkJzJ07V72FQKPRoK+vj7+/PyEhIVy/fh1LS0uys7P56aefyM3N\nJSYmhl69enHw4EF1n0cXqjyv7t27s3TpUuLj48nOzmbBggV4e3s/dZ979+7x1VdfMXHiRAIDA1mz\nZs0ThyafZvz48Vy/fl3nlowJ/6+9u4/L+d4fOP5SrkJp7nPaGTK/WWOpdXeVZOVYqETyOzPMTbJ0\n5N7UIlHmprO1n5iLNH7bfqwdpsXDCc2DoY0xdpiZGFZKCFO6u6rr94dH34drpa7cVFvv5+Phset7\n9/m831fs3ed79wkPZ+/evXzwwQfcunWLsrIyDhw4wPz585k+fTpdunSp1o6JiQnvvvsuqamp9Y5B\nCCGaoj9dIQwNDcXa2pqhQ4cyatQoAgMD8fLy0tvHwcEBb29voqOjMTExQaPRkJaWhlqtZtKkSUyY\nMIGhQ4cC4O/vz9y5c5/I83CBgYEMGTKEsWPHolarOXv2LAkJCXqjxt9777336NmzJz4+PnTr1o2g\noCAiIyMpLy+vV9/m5uYsXbpUb93zzz9PcnIyly9fZtiwYajVatauXcuCBQuYNm3aQ9uytbVl8uTJ\n1db7+vpWe46wruc7hRCisbXQ1XanhhANoLS0lDNnzjBuXBo//RTZ2OE8lqpc+vbti6mpaWOH81gk\nl6ZJcnny7f/pRoRCCCFEffzp7hptLjw8PCgoKKhx24oVK/D29m7giB7fsWMzGjsEIUQzJIXwD+rB\nh92FEEI8Ojk1KoQQolmTQiiEEKJZk1Ojoslwdl5NXl6Rsnzr1rJGjEYI0VzIiFAIIUSzJoVQCCFE\ns9YsCmFRURG3bt1q7DCEEEI0QQ1WCKdMmaK8dsvGxoaXX35ZWU5NTUWj0dT5Oq4vvviC8ePH17vv\ncePGkZmZWeM2Ly8vbG1tlVgcHBwICQnReyenoe7du8fYsWOxt7dHo9Fgb2//SO1UKSgoIDIyEldX\nV+zt7fH19eVf//oXAMePH1ditrW15cUXX1SWfXx8lDZOnTpFUFAQr7zyCs7Ozrz55ptkZGQo27Oz\ns+nduzdbt27V6/vo0aPVXk2XkpLCyJEjsbOzw83Njblz5+pN+qvVaomNjaV///64uLgwf/78hz7r\nKIQQTUWD3SyzceNG5fP48eMZOXIkAQEBDdJ3XXPiJSYmKlMplZaWEhkZyezZs+v9ftFz586RmZnJ\n0aNHMTExISQk5JFjBli6dCk6nY49e/bQtm1bTp06RUhICB06dGDQoEGcPHkSuF+0IiIi2L9/v97x\nGRkZhIWFMXPmTD744ANatWrFnj17mDVrFlFRUfj6+ir7xsXFMXDgQKysrGqMZc2aNWzbto3o6Gjc\n3NwoKSlh06ZNjB49muTkZKytrUlKSuLs2bPs3LkTExMTFixYwKpVq/TmZxRCiKamyZwaTUhIIDw8\nHICSkhKioqJwcnLCzc2txrnvMjMzcXNzU2afz8rKIigoCCcnJ/z8/Dh06BAA8+fPJycnh+DgYHbv\n3l1nHKampvj6+nL+/HngftFesGABarWa2NhYADZv3sygQYNwdXXlnXfe4d69e/znP/9h0qRJ3L17\nFxcXF7KysujduzfZ2dls2bIFDw8PZRaLmTNnMm/evDpj+fHHH/Hy8sLCwoIWLVpgb2/PvHnzKCsr\nM+AbvV9Iw8LCePPNN2nbti0qlQpfX18iIyOJiYnRa8fb25uFCxfW2E5ubi4ajYYPP/yQV199FRMT\nEywsLJg5cyaDBw9m5cqVABQXFzNt2jQ6dOiAubk5o0aN4tSpUwbFKoQQjaXJFMIHxcfHc+nSJfbu\n3UtKSgqpqalKwQO4evUqwcHBREREMHjwYMrLywkJCcHFxYWMjAwiIyOZO3cuOTk5xMXFYWVlRWJi\nIsOGDauz7/z8fD7//HOcnZ2VdVeuXGH//v3MmjWLnTt3snXrVjZt2kR6ejr37t1j1apV2NrakpiY\niJWVFSdPnuS5555Tjh8zZgzdu3dn9erV7NmzhxMnTrBo0aI6Yxk8eDBLlixh5cqVfP311xQWFjJ6\n9GhlZoza/Prrr1y6dKnGfYcOHUpRURHff/+9si4iIoLMzEzl1OuDDh8+jJWVFS+99FK1bcOHD+fQ\noUNotVpmz57NgAEDlG0HDhzghRdeqDNWIYRoTE2yEKalpREaGkr79u3p0qULiYmJ2NvbA/dPc06e\nPBl/f3/8/PwAOH36NAUFBUydOhWVSoVarWbAgAHs2rXLoP5CQkJwdHTE0dERf39/VCqV3uk8T09P\n2rRpg7m5OSkpKQQFBdGtWzfMzMyYNWsWKSkp1DaJR4sWLYiNjWX79u0sWbKEZcuW8cwzz9QZ16xZ\ns1i4cCHnz58nLCwMtVrNrFmz6jzVC3Dz5k2AarPMA8qILj8/X1lnYWHB0qVLWblyJdeuXdPbPz8/\nv8Z24P5kw+Xl5dVi2rJlC2lpacyYIe8PFUI0bU3ygfr8/HwsLS2VZWtra+Xz+fPncXV1JT09nenT\np6NSqcjNzeXmzZs4Ojoq+1VUVNCuXTuD+tNoNMo1wpp06tRJ+Zybm8vy5ctZtWqVsq6yslKvqNSk\ne/fu2NnZce7cOdRqtUFxtWjRAj8/P/z8/NBqtZw8eZIVK1awZMkS4uPjaz22KuZr167pjU4BysrK\nuHXrVrXi5unpiaenJ4sWLWLKlCnK+o4dO5KXl1djP9euXcPY2Fjvu05ISGDLli1s3LiR7t27G5Sr\nEEI0liY5IrS0tOT69evKcnp6OgcOHADA3t6epKQkWrZsSVJSEgCdO3emR48eHD9+XPmze/duZs6c\n+UTieXDi3E6dOrFs2TKln4yMDFJTU+nYsWOtbRw8eJCff/6ZXr16sWbNmjr7vHbtGv369ePGjRsA\nqFQqnJ2dmTJlinL9sjbdunWjV69e7Ny5s9q21NRULCwscHBwqLYtMjKSs2fPsmPHDmWdh4cHN27c\n4Pjx49X2//LLL3F3d0elUqHT6YiMjGTXrl189tln2Nra1hmnEEI0tiZZCH18fNBoNNy9e5e8vDzi\n4uKorKwE7hcEY2NjoqKi0Gg0ZGVl0a9fP7RaLcnJyVRUVHDx4kUCAwM5cuSIckzVjSqPy8/Pj40b\nN5Kbm4tWqyU+Pp6wsLBajyksLGTx4sVEREQQExPDp59+yo8//ljrMV27duXll19m8eLFZGdnA/cf\nddi+fTseHh4GxRoVFcXGjRv5+OOPKSgooKioiC+//JKVK1cSERGBiYlJtWPatWtHdHS0XiG0tLRk\n+vTpzJkzh4MHDyojyvj4eL766isWLFgAwPr16zl27Bhbt26VkaAQ4g+jSRbC0NBQrK2tGTp0KKNG\njSIwMLDaM20ODg54e3sTHR2NiYkJGo2GtLQ01Go1kyZNYsKECcqNIv7+/sydO7fej0PUJDAwkCFD\nhjB27FjUajVnz54lISFBb9T4e++99x49e/bEx8eHbt26ERQURGRkJOXl5bX2tWbNGjp37swbb7xB\nv379GDduHH379mXOnDkGxeri4sLmzZs5fPgwgwYNwsPDg23btvH+++8zYsSIhx43ePDgajcWhYSE\nMH/+fBISElCr1QwbNowrV66wbds2nn/+eQA2bdpEbm4ugwYNqvGZRiGEaIpa6Gq7y0OIBlBaWsqZ\nM2cYNy7tD//S7apc+vbti6mpaWOH81gkl6ZJcnny7TfJEaEQQgjRUJrkXaPNhYeHx0NfQbZixQq8\nvb0bOCIhhGh+pBA2oq+//rqxQ2hSjh2bQdu2bRs7DCFEMyOnRoUQQjRrUgiFEEI0a1IIhRBCNGtS\nCEWT4excfZYRIYR42qQQCiGEaNakED5FRUVF3Lp1q7HDEEIIUYs/fSGcMmWK8rovGxsbXn75ZWU5\nNTUVjUZDVFRUrW188cUXjB8/vt59jxs3jszMzBq3eXl5YWtrq8Ti4OBASEgIOTk59e7n3r17jB07\nFnt7ezQaDfb29o/UDsCGDRuYOHGi3roZM2bQt29fvfe1bt26lTFjxijL6enpvP766zg6OuLm5sa8\nefMeOmOFEEI0JX/65wg3btyofB4/fjwjR44kICCgQfqua97AxMREZfqn0tJSIiMjmT17dr3fiXru\n3DkyMzM5evQoJiYmhISEPHLMbm5urFu3joqKCoyNjamoqODo0aP813/9F99++y1/+9vfADh+/Dj9\n+/cH4NNPP0Wj0bBy5UpcXFwoLi7mn//8J2+++SY7d+6s8eXeQgjRVPzpR4R1SUhIIDw8HICSkhKi\noqJwcnLCzc2N1aur37yRmZmJm5sb+/btAyArK4ugoCCcnJzw8/Pj0KFDAMyfP5+cnByCg4PZvXt3\nnXGYmpri6+urTLE0fvx4FixYgFqtJjY2FoDNmzczaNAgXF1deeedd7h37x7/+c9/mDRpEnfv3sXF\nxYWsrCx69+5NdnY2W7ZswcPDQxnJzZw5k3nz5tUax0svvYSJiQnnzp0D4OTJkzz33HO89tprei8A\nqCqE9+7d47333iM2Npb+/fvTsmVL2rZtS1RUFH379uXXX3+tM3chhGhMzb4QPig+Pp5Lly6xd+9e\nUlJSSE1NVQoewNWrVwkODiYiIoLBgwdTXl5OSEgILi4uZGRkEBkZydy5c8nJySEuLg4rKysSExOr\nzeRQk/z8fD7//HOcnZ2VdVeuXGH//v3MmjWLnTt3snXrVjZt2kR6ejr37t1j1apV2NrakpiYiJWV\nlVK0qowZM4bu3buzevVq9uzZw4kTJ1i0aFGtcRgZGeHi4sL3338PwOHDh3F3d6d///4cPnwYuF/8\ni4qKsLW15eTJk1RWVuLu7q7XjrGxMe+99x69evWq+4sXQohGJIXwAWlpaYSGhtK+fXu6dOlCYmIi\n9vb2wP3TnJMnT8bf3x8/Pz8ATp8+TUFBAVOnTkWlUqFWqxkwYAC7du0yqL+QkBAcHR1xdHTE398f\nlUpFTEyMst3T05M2bdpgbm5OSkoKQUFBdOvWDTMzM2bNmkVKSgq1TR7SokULYmNj2b59O0uWLGHZ\nsmU888wzdcbl6uqqFMJDhw7h7u5O3759uXfvHr/88gvHjh1DrVZjbGzMnTt3sLCwoGXLP/1ZdiHE\nn5T83+sB+fn5WFpaKsvW1tbK5/Pnz+Pq6kp6ejrTp09HpVKRm5vLzZs3cXR0VParqKigXbt2BvWn\n0WiUa4Q16dSpk/I5NzeX5cuXs2rVKmVdZWUl+fn5tfbRvXt37OzsOHfuHGq12qC4XF1d0Wg03Lp1\ni6ysLOzs7DAyMsLNzY1vv/2W06dP4+bmBkDHjh357bffKC8vr1YMb926RYcOHQzqUwghGouMCB9g\naWnJ9evXleX09HQOHDgAgL29PUlJSbRs2ZKkpCQAOnfuTI8ePTh+/LjyZ/fu3cycOfOJxPPgZL+d\nOnVi2bJlSj8ZGRmkpqbSsWPHWts4ePAgP//8M7169WLNmjUG9dujRw+MjIz48ssvcXFxUQqcu7s7\nJ06c4MSJE8qpUHt7e4yNjTly5IheGxUVFQQGBurNdC+EEE2RFMIH+Pj4oNFouHv3Lnl5ecTFxVFZ\nWQmASqXC2NiYqKgoNBoNWVlZ9OvXD61WS3JyMhUVFVy8eJHAwEClKKhUKr1HDh6Hn58fGzduJDc3\nF61WS3x8PGFhYbUeU1hYyOLFi4mIiCAmJoZPP/2UH3/80aD+1Go1H3/8sd61P3d3d7777jsA5Vpk\nq1atmDlzJosWLeKbb75RRqnh4eG0atWKIUOGPGLGQgjRMKQQPiA0NBRra2uGDh3KqFGjCAwMxMvL\nS28fBwcHvL29iY6OxsTEBI1GQ1paGmq1mkmTJjFhwgSGDh0KgL+/P3Pnzq334xA1CQwMZMiQIYwd\nOxa1Ws3Zs2dJSEjQGzX+3nvvvUfPnj3x8fGhW7duBAUFERkZSXl5eZ39ubm5kZOTo1cILS0teeaZ\nZ5TTolUmTpxIWFgYK1aswMnJieHDh2NkZMSmTZto3br1oycthBANoIWutrsthGgApaWlnDlzhnHj\n0vjpp8jGDuexVOXSt29fTE1NGzucxyK5NE2Sy5NvX0aEQgghmjW5a7QZ8vDwoKCgoMZtK1aswNvb\nu4EjEkKIxiOFsBl68A0xTcmxYzMaOwQhRDMkp0aFEEI0a1IIhRBCNGtSCIUQQjRrco1QNBnOzqvJ\nyyvSW3fr1rJGikYI0VzIiFAIIUSzJoVQCCFEsyaF8CkrKiri1q1bjR2GEEKIh2gWhXDKlCnY29tj\nb2+PjY0NL7/8srKcmpqKRqMhKiqq1ja++OILxo8fX+++x40bR2ZmZo3bvLy8mD17drX1VTPMPynh\n4eEkJCQ8sfZqkpCQQJ8+fbC3t8fOzg5nZ2fefvttioqK6j5YCCEaUbO4WWbjxo3K5/HjxzNy5EgC\nAgIapO87d+7Uun337t0MGzaMwYMHN0g8T5Ofnx8rVqwAoKCggJCQED788EPmzZvXyJEJIcTDNYsR\nYV0SEhIIDw8HoKSkhKioKJycnHBzc2P16tXV9s/MzMTNzY19+/YBkJWVRVBQEE5OTvj5+XHo0CEA\n5s+fT05ODsHBwezevbvGvgMCAoiOjq6xYGZnZ9O7d2+9dVWjxezsbDw8PFizZg3Ozs54eHhw8OBB\nFi5cyCuvvIKvry8XLlxQjrt8+TKBgYGo1WoiIiKUkZpWq+X9999n4MCBuLu7s2rVKrRaLXD/l4YF\nCxagVquJjY2t13fatm1bBg8ezLlz5+p1nBBCNDQphL8THx/PpUuX2Lt3LykpKaSmpioFD+Dq1asE\nBwcTERHB4MGDKS8vJyQkBBcXFzIyMoiMjGTu3Lnk5OQQFxeHlZUViYmJDBs2rMb+RowYQZ8+fepd\naADy8vLQarV88803BAYGMm3aNOzs7Pj222+xsbEhMTFR2ffw4cPExMSQnp5OTk4Oa9euBSApKYlj\nx46xfft2UlNT+eGHH9i0aZNy3JUrV9i/fz+zZs2qV2y3b98mPT0dDw+PeuclhBANSQrh76SlpREa\nGkr79u3p0qULiYmJ2NvbA/dPc06ePBl/f3/8/PwAOH36NAUFBUydOhWVSoVarWbAgAHs2rXL4D5j\nYmI4cOAA+/fvr3e8EydOxNjYGCcnJ8zMzAgMDMTExAQXFxdyc3OV/QICArCxscHc3JyQkBD27NkD\nwI4dOwgLC6NTp0506NCBadOm6c0q7+npSZs2bTA3N68zll27duHo6IiDgwOurq5cvXoVT0/Peuck\nhBANqVlcI6yP/Px8LC0tlWVra2vl8/nz53F1dSU9PZ3p06ejUqnIzc3l5s2bODo6KvtVVFTQrl07\ng/u0tLRkwYIFLF68WK8dQ1T1Y2RkRNu2bZX1RkZGPDjVpJWVlV5/N27cAODatWuEhYVhZHT/dyKd\nTqc32W+nTp0MjsXX11e5RlhaWsr69esZM2YMe/fupU2bNvXKSwghGooUwt+xtLTk+vXr9OzZE4D0\n9HRatrz/Ndnb25OUlERAQABJSUmEhITQuXNnevTooXcNMDc3FzMzs3r1O3r0aNLS0nj33XeVdVXF\nqby8nJYtW3L79u1qx9U2Q/2DqgofQE5ODn/5y1+A+4UuPj4eW1tbAAoLC/X6MbT93zM1NSUoKIi1\na9eSmZlJv379HqkdIYR42uTU6O/4+Pig0Wi4e/cueXl5xMXFUVlZCYBKpcLY2JioqCg0Gg1ZWVn0\n69cPrVZLcnIyFRUVXLx4kcDAQI4cOaIcU1hYaFDfMTEx7N27V1nu2LEjZmZm7Ny5E61Wy9q1ax+5\nMG3bto3z589z584d1q5dy4gRI4D7d3omJCRw+/ZtiouLiYqKIiYm5pH6eFB5eTlbt27FwsJC+aVC\nCCGaIimEvxMaGoq1tTVDhw5l1KhRBAYG4uXlpbePg4MD3t7eREdHY2JigkajIS0tDbVazaRJk5gw\nYQJDhw4FwN/fn7lz55KcnFxn31ZWVsyfP19ZNjU1ZenSpSQkJODu7o6FhYXeKc768PT0ZMaMGXh7\ne9OnTx8mT56s5NurVy/8/f0ZMGAAJSUlLF++/JH62Llzp/J8prOzM/v27ePDDz/UO2UrhBBNTQvd\ngxeShGgEpaWlnDlzhnHj0v7wL92uyqVv376Ympo2djiPRXJpmiSXJ9++jAiFEEI0a3KzjDBIXFwc\nW7ZsqXGbm5ub8lyiEEL80UghFAaZP3++3vXLp+HYsRlyPVEI0eDk1KgQQohmTQqhEEKIZk0KoRBC\niGZNCqEQQohmTQqhEEKIZk0K4VNUVFTErVu3GjsMIYQQtfjTF8IpU6Yor/2ysbHh5ZdfVpZTU1PR\naDRERUXV2sYXX3zB+PHj6933uHHjyMzMrHGbl5cXs2fPrra+auLdJyU8PJyEhIQn1l5NvvjiC2xs\nbJTv1d7entdff52MjIyn2q8QQjwJf/rnCDdu3Kh8Hj9+PCNHjiQgIKBB+q5p1vkH7d69m2HDhjF4\n8OAGiedpcnR05JNPPgGgrKyM1NRUpk2bxieffKLMbCGEEE3Rn35EWJeEhATCw8MBKCkpISoqCicn\nJ9zc3Fi9enW1/TMzM3Fzc1Nmrc/KyiIoKAgnJyf8/Pw4dOgQcP8B9JycHIKDg/WmaHpQQEAA0dHR\nNRbM7OxsevfurbeuarSYnZ2Nh4cHa9aswdnZGQ8PDw4ePMjChQt55ZVX8PX15cKFC8pxly9fJjAw\nELVaTUREBEVF99/nqdVqef/99xk4cCDu7u6sWrUKrVYL3P+lYcGCBajVamJjY+v1nZqYmBAYGIiv\nry+JiYn1OlYIIRpasy+ED4qPj+fSpUvs3buXlJQUUlNTlYIHcPXqVYKDg4mIiGDw4MGUl5cTEhKC\ni4sLGRkZREZGMnfuXHJycoiLi8PKyorExESGDRtWY38jRoygT58+9S40AHl5eWi1Wr755hsCAwOZ\nNm0adnZ2fPvtt9jY2OgVoMOHDxMTE0N6ejo5OTnK69CSkpI4duwY27dvJzU1lR9++IFNmzYpx125\ncoX9+/cza9asescH0L9/f06cOPFIxwohREORQviAtLQ0QkNDad++PV26dCExMRF7e3vg/mnOyZMn\n4+/vj5+fHwCnT5+moKCAqVOnolKpUKvVDBgwgF27dhncZ0xMDAcOHGD//v31jnfixIkYGxvj5OSE\nmZkZgYGBmJiY4OLiQm5urrJfQEAANjY2mJubExISwp49ewDYsWMHYWFhdOrUiQ4dOjBt2jR27Nih\nHOfp6UmbNm0wNzevd2wA7dq1o6Cg4JGOFUKIhvKnv0ZYH/n5+VhaWirL1tbWyufz58/j6upKeno6\n06dPR6VSkZuby82bN3F0dFT2q6iooF27dgb3aWlpyYIFC1i8eLFeO4ao6sfIyEjvHZ1GRkY8OLvW\ng3MYWlpaKrPVX7t2jbCwMIyM7v8+pNPp9Cb+7dSpU73i+b3bt2/TtWvXx2pDCCGeNimED7C0tOT6\n9evKjOrp6em0bHn/K7K3tycpKYmAgACSkpIICQmhc+fO9OjRQ+8aYG5uLmZmZvXqd/To0aSlpfHu\nu+8q66qKU3l5OS1btuT27dvVjjN0tvqqwgeQk5PDX/7yF+B+oYuPj1duZiksLNTrx9D2HyYjIwMb\nG5vHakMIIZ42OTX6AB8fHzQaDXfv3iUvL4+4uDgqKysBUKlUGBsbExUVhUajISsri379+qHVaklO\nTqaiooKLFy8SGBjIkSNHlGMKCwsN6jsmJoa9e/cqyx07dsTMzIydO3ei1WpZu3btIxembdu2cf78\nee7cucPatWsZMWIEAH5+fiQkJHD79m2Ki4uJiooiJibmkfp4UFlZGZ999hm7d+/mrbfeeuz2hBDi\naZJC+IDQ0FCsra0ZOnQoo0aNIjAwEC8vL719HBwc8Pb2Jjo6GhMTEzQaDWlpaajVaiZNmsSECRMY\nOnQoAP7+/sydO5fk5OQ6+7aystKb5sjU1JSlS5eSkJCAu7s7FhYWeqc468PT05MZM2bg7e1Nnz59\nmDx5spJvr1698Pf3Z8CAAZSUlLB8+fJH6uP48ePKM4QeHh78+9//JjExkT59+jxSe0II0VBa6B68\nmCREIygtLeXMmTO88MILf/j5CKty6du3L6ampo0dzmORXJomyeXJty8jQiGEEM2a3Cwj6hQXF8eW\nLVtq3Obm5qY8lyiEEH9EUghFnebPn693/VIIIf5MpBCKRld1mVqr1VJaWtrI0TyesrIyvf/+kUku\nTZPkUv/267oVRm6WEY2uoKCA8+fPN3YYQog/qbpuxJNCKBpdZWUl9+7dQ6VSPfZD/EIIUUWn06HV\najEzM1NeUlITKYRCCCGaNXl8QgghRLMmhVAIIUSzJoVQCCFEsyaFUAghRLMmhVAIIUSzJoVQCCFE\nsyaFUDSYH374gREjRmBnZ8cbb7zBr7/+Wm2fyspKli1bhouLC66urmzYsKERIq2bIblUuXPnDl5e\nXmRnZzdghIYzJJeCggLmz5+PWq2mf//+xMbGNsk3mxiay+zZs3F2dkatVvPuu+9SXl7eCNHWrj5/\nx+D+qxDDw8MbKLr6MSSXwsJCbGxslOnc7O3t2bRpU8MEqBOiAZSUlOj69++vS01N1ZWWluoSEhJ0\nY8aMqbbf5s2b7on39QAADaBJREFUdaNHj9bdvn1bd/nyZZ2np6cuIyOjESJ+OENz0el0uh9++EHn\n6+ure+GFF3RZWVkNHGndDM1l4cKFurCwMF1hYaEuPz9f99///d+6devWNULED2doLu+8845uzpw5\nuuLiYt3t27d1w4cP133++eeNEPHD1efvmE6n0+3bt0/34osv6hYsWNCAURrG0FyOHz+u8/Pza4QI\ndToZEYoG8e2339KuXTv8/PwwMTFh2rRpZGZmcvHiRb39du3axeTJk2nXrh3du3dn3LhxfP75540U\ndc0MzeXChQuEhIQoEyE3RYbmotPpCA0NxczMjA4dOuDr68upU6caKeqaGZpLdHQ0y5cvx8TEhJs3\nb1JaWkq7du0aKeqaGZoLwK1bt4iLiyMgIKARIq2bobmcO3eOF198sVFilEIoGsSlS5fo2bOnsmxs\nbMxzzz1X7R/DL7/8oreftbU1Fy5caLA4DWFoLl27dmXfvn2MHDmyoUM0mKG5xMbG6v1P6uDBg7zw\nwgsNFqchDM1FpVJhYmLCrFmz8PHx4dlnn8XT07Ohw62VobkALFmyhClTptC1a9eGDNFghuby888/\nc/nyZby9vRkwYAArVqxosNPvUghFgygqKqJVq1Z661q3bk1xcbHeuuLiYlq3bq0st2rVipKSkgaJ\n0VCG5mJubo6ZmVlDhlZvhubyoLi4OH755RcmTZr0tMOrl/rmEhcXx+HDh7lz5w7/8z//0xAhGszQ\nXHbv3k1hYSGjR49uyPDqxdBc2rRpg7OzM9u2bSM5OZnvvvuO9evXN0iMUghFg2jdunW1glZcXFyt\nUPy+8JWUlNCmTZsGidFQhubyR1CfXMrLy4mMjGTPnj1s3ryZ9u3bN1SYBqnvz8XU1JTOnTszdepU\nDhw40AARGs6QXG7evMn7779PbGxsQ4dXL4b+XMLDw5k3bx5t27bFysqKqVOnsn///gaJUQqhaBA9\ne/bk8uXLynJFRQW//vor1tbWte536dKlavs0NkNz+SMwNJeysjKmTZvG+fPnSU5Oplu3bg0cad0M\nzeWtt97SK3xlZWW1TtHTGAzJ5ciRI9y8eRM/Pz8cHR3ZsGEDu3btws/PrxEifjhDfy6rV68mKytL\nWS4rK8PU1LRBYpRCKBqEi4sL+fn5pKSkUFZWxrp16+jWrRvPP/+83n4+Pj5s2LCB/Px8fv31Vz79\n9NMm9w/b0Fz+CAzNJSYmhrt37/Lxxx/TsWPHRoq2dobm8tJLL6HRaPjtt9/Izc1lw4YN+Pv7N1LU\nNTMkF39/f06dOsXx48c5fvw4U6dOxdfXl507dzZi5NUZ+nM5e/Ys77//PsXFxVy9epUNGzYwfPjw\nhgmyUe5VFc3S6dOndQEBATo7OzvdmDFjdFeuXNHpdDrdsGHDdF9++aVOp9PptFqtbsWKFTo3Nzed\nq6urbv369Y0Z8kMZksuDmurjEzpd3bncvXtX9+KLL+r69u2rs7OzU/4EBQU1cuTVGfJzKS0t1S1Z\nskSnVqt1AwcO1K1fv15XWVnZmGHXqL5/x1avXt0kH5/Q6QzL5caNG7rp06frnJycdGq1WvfBBx80\n2M9F5iMUQgjRrMmpUSGEEM2aFEIhhBDNmhRCIYQQzZoUQiGEEM2aFEIhhBDNmhRCIYQQzZoUQiHE\nE1VWVkZeXl5jh9Eobt68SVFRUWOHIeqpZWMHIISomZeXFzdu3KBly/v/THU6HS1btqRfv34sXrxY\nec3Z7/erMmDAAFavXl2t3fDwcHbu3ImJiYne+jZt2nDkyJHHjnvs2LGMGTOmQaYFys7OZtCgQXz1\n1Vf89a9/fer91ebmzZu89tprpKamNrn344raSSEUoglbsmSJXkHJzs5m9uzZvP3223z22WcP3a8u\nfn5+rFix4onGWiU/P/+ptNvUlZSUcO/evcYOQzwCOTUqxB/IX//6V/z9/fn555+faj+pqan4+vri\n4ODA6NGjOX78uLLt4sWLvPXWW7z66qvY2tri5+fHoUOHAJg8eTI5OTksXryYFStWcPToUXr37q3X\n9sSJE0lISADuj05nzJiBt7c3rq6u5OXlcePGDebMmYObmxseHh4sW7bM4Km4xo8fz5o1a3j99dex\ns7Nj5MiRnD59mtmzZ2Nvb4+Xl5fywu2jR4/i4eHBmjVrcHFxwcXFhZUrV6LVagGorKxk/fr1DBo0\nCEdHR8aMGcP333+v9NW7d2+WLl2Ki4sLM2bMwNfXFwBfX1/S09PRarWsWrWKoUOH8sorr9C/f39W\nrlypF+s///lP3njjDezt7fH19WXPnj3K9qysLEJCQnBwcMDNzY1ly5ZRXl4OwOXLl5k6dSouLi4M\nGjSIDz/8kIqKCoO+I1GdFEIh/kAuXLjA559/zoABA55aH/v372fp0qVER0dz7NgxgoODCQ4OVmYG\nmDFjBi+++CL79u3j+PHjuLq6smjRIgA++ugjrKysWLJkCeHh4Qb1d+TIEdatW8eePXvo3Lkz06ZN\nQ6VSsXfvXnbs2MGFCxd49913DY7/k08+YenSpRw7dgwzMzPGjBmDr68v3333Hd7e3nrTFuXl5fHL\nL7/w1Vdf8dlnn7Fv3z7WrVsHwNq1a0lOTubDDz/km2++Yfjw4QQFBZGdna0cf+3aNQ4ePEhsbCy7\ndu0CYNeuXfztb3/jf//3fzl8+DCffPIJ33//PWvWrOHjjz/mm2++UY7/17/+RWRkJMeOHeO1114j\nKiqKsrIytFotkydPpnPnzhw6dIiUlBSOHDnCRx99RFFREZMmTaJ3794cOnSIzZs3s2fPHpKSkgz+\njoQ+KYRCNGFLlizB0dEROzs7XnrpJWbMmMGgQYOqndas2q/qj4uLS63t7tq1S29/R0dHTp06BcDW\nrVsZM2YMjo6OGBsb89prr+Hs7Mz27dsB0Gg0/OMf/6BFixbk5uZiYWHBtWvXHjlHOzs7evbsiYWF\nBWfOnOGnn34iKioKc3NzOnbsyJw5c9i2bZvBs5X7+PjwwgsvYGJigqOjI88//zyDBg2iZcuWeHh4\n6BUyIyMjoqOjMTc3x9ramilTppCSkgLA9u3bCQ4Opnfv3qhUKsaMGUPv3r2VggcwbNgwWrVqhYWF\nRbU4Ro8ezUcffUSnTp3Iz8+npKQEMzMzvRuJhgwZQp8+fVCpVAwfPpw7d+6Qn5/PyZMnycnJ4Z13\n3qFNmzZ06dIFjUaDj48PBw4cQKvVMmfOHExMTHjuueeYNm0a//d///eoP4JmT64RCtGELV68mICA\nAEpLS9mwYQPJycm89tpr1W7GqNrPUL6+vg+9Rnj16lWOHTvG1q1blXUVFRU888wzAJw7d47p06dz\n/fp1evToQceOHXmcd/d36tRJ+ZydnU1FRQUDBw7U28fY2JirV68aNOdju3bt9I57sEgZGRnpxdqh\nQwe97VZWVly/fh24f/PLs88+q9f2X//6V3JycmqM/feKi4uJjY3lu+++o0uXLtjY2KDT6aisrKzx\n+KqbnSoqKrhx4wbt27endevWyvaqm6N2797NrVu3cHJyUrbpdDrKy8spLS1tsDn8/kykEArxB2Bq\nakpYWBjXrl0jODiYbdu20bVr16fSl6WlJSNGjGDq1KnKuqysLMzNzbl+/TozZ85kzZo1eHl5AbBn\nzx727dtXY1vGxsbA/dntq/5Hf/v2bb19WrRoodd369atOXr0qHJscXExOTk5Bk8G/GB7dblz5w4l\nJSW0atUKuF+Iq4rfs88+qzd6hPvfg7u7u0F9LVq0CAsLC77++mtMTU2prKzUK1616dq1K7dv39aL\nLSMjg6ysLCwtLenevTv//ve/lf1/++03fvvtNymCj0hOjQrxB7Jw4ULMzc15++239UYWT9KoUaP4\n9NNPOXPmDAAnTpxgxIgRHDx4kMLCQioqKpQR6YULF1i7di2AcpOJSqWioKAAuD+KMTY2ZseOHVRU\nVLB7924yMzMf2retrS3PPvsscXFxFBcXc+/ePaKjowkNDX0quZaXl7Ny5UpKS0u5ePEiSUlJ/P3v\nf1e+h40bN/Lzzz+j1WrZunUrZ8+eZdiwYTW2VfU4SmFhIQB3796lVatWGBkZUVhYyKpVqygsLFRu\neKmNra0t3bt3Jy4ujpKSEvLy8li+fDm//fYbr776Knfv3mXTpk2UlZVx584dZs+erVynFfUnhVCI\nP5DWrVuzatUqTpw4wUcfffRU+vD19eUf//gHb7/9Nvb29oSHhzNz5kxGjBhBz549efvtt5k3bx4O\nDg7MnDmTUaNG0bJlSy5cuADcLyDx8fG88847dOnShYiICNatW4eTkxP79+9nyJAhD+1bpVKxfv16\ncnJyGDRoEJ6enty5c4eNGzcqI8QnrW3btnh5eTFhwgRGjRrFxIkTgft3wI4ePZrQ0FCcnZ3ZsWMH\nGzdurDazepXOnTvj5eXF3//+d7Zv386iRYv46aefcHZ2ZsiQIRQWFuLu7l7rLwJVqr6Hq1evMnDg\nQEaNGsWrr77KlClTsLCw4KOPPuLw4cMMGDAAb29v2rdvT3x8/JP8WpoVmZhXCNEsHT16lDfffPOp\nP4oimj4ZEQohhGjWpBAKIYRo1uTUqBBCiGZNRoRCCCGaNSmEQgghmjUphEIIIZo1KYRCCCGaNSmE\nQgghmjUphEIIIZo1KYRCCCGaNSmEQgghmrX/B0DwD+MAJpd4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df14df6e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,4), dpi=90)\n",
    "sns.barplot(y=X_train.columns, x=model_rfc.best_estimator_.feature_importances_, color='darkblue', orient='h')\n",
    "plt.xlabel('RF Feature Importance')\n",
    "plt.ylabel('Feature')\n",
    "plt.title('Random Forest Feature Importances')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "64bee6ad-bed2-4f18-9904-a8517b1ae6b5",
    "_uuid": "a674b7d7985cf8b6bd71abfff08f574a18541b4e"
   },
   "source": [
    "## 4.2 Gradient Boosting\n",
    "<a id='4.2'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "_cell_guid": "eef1124b-3fd7-4f67-b23f-2af728779915",
    "_uuid": "cc6c5a454a7f1b0af474b2d7c56d33ed067809ea"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "_cell_guid": "3e3c85c1-324a-4d24-9e97-5ee04f292cee",
    "_uuid": "e775717f496cb38df95c432237e995aa3c4137d8"
   },
   "outputs": [],
   "source": [
    "param_grid = {'max_depth': [3, 12, 25],\n",
    "              'subsample': [0.6, 0.8, 1.0],\n",
    "              'max_features': [None, 'sqrt', 'log2']\n",
    "             }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "_cell_guid": "442cc37a-f443-4e42-a603-0719d5d0ab88",
    "_uuid": "cdd7484f620bbee294d9b5036c3ea499e60bb586"
   },
   "outputs": [],
   "source": [
    "model_gb = GridSearchCV(estimator=GradientBoostingClassifier(), param_grid=param_grid, scoring=make_scorer(accuracy_score), n_jobs=-1, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "_cell_guid": "be3f204b-6935-47e4-a269-c861726c3501",
    "_uuid": "5a6bc45e1d92d106a36409eb952dd6c82f600fda"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 27 candidates, totalling 81 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:    8.2s\n",
      "[Parallel(n_jobs=-1)]: Done  81 out of  81 | elapsed:   22.3s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "              presort='auto', random_state=None, subsample=1.0, verbose=0,\n",
       "              warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'max_depth': [3, 12, 25], 'subsample': [0.6, 0.8, 1.0], 'max_features': [None, 'sqrt', 'log2']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=make_scorer(accuracy_score), verbose=1)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_gb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "_cell_guid": "d329193c-3d54-4efd-8245-f8046e5db321",
    "_uuid": "c690cab3cd8d93ea526745b59ebcaf4c64cb4b9c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 3, 'max_features': 'log2', 'subsample': 0.6}"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_gb.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "_cell_guid": "edccb66d-793d-4a7b-8aaa-c310e0fe6779",
    "_uuid": "90afd19f2b74734fdba4f620bac5e5c258186155"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8226711560044894"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_gb.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "3a3dcfd8-a3c7-4ee7-9376-e8b4f93bdb65",
    "_uuid": "4648f866a7604dfc064938747464720765b4f649"
   },
   "source": [
    "## 4.3 Logistic Regression\n",
    "<a id='4.3'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "_cell_guid": "7aac652d-820a-4243-a370-f0d90bb0aa81",
    "_uuid": "d00cb8e4a3edff746bc74a9ea2b699e6bbe991ff"
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "_cell_guid": "8bc0a44b-58c1-4476-b06a-fdcdba0b7687",
    "_uuid": "40aac63531e463a2bb68a804cc29646d2658705b"
   },
   "outputs": [],
   "source": [
    "param_grid = {'penalty': ['l1', 'l2'],\n",
    "              'C': [10**k for k in range(-3,3)],\n",
    "              'class_weight': [None, 'balanced'],\n",
    "              'warm_start': [True]\n",
    "             }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "_cell_guid": "2d7e0c9f-0352-46d2-bd0d-e5d9a4cadc67",
    "_uuid": "7bf9f7039fdf478a4a619f5ce652fe352aff84fd"
   },
   "outputs": [],
   "source": [
    "model_logreg = GridSearchCV(estimator=LogisticRegression(), param_grid=param_grid, scoring=make_scorer(accuracy_score), n_jobs=-1, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "_cell_guid": "86bcafe3-3a88-48b3-a278-dd0672640d29",
    "_uuid": "d303f752b83aeaaf3faf9d4ce1d795486a66040b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 24 candidates, totalling 72 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  72 out of  72 | elapsed:    3.8s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100], 'class_weight': [None, 'balanced'], 'warm_start': [True]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=make_scorer(accuracy_score), verbose=1)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "_cell_guid": "3c816c8b-4e63-4a26-aa53-21269d5f79ab",
    "_uuid": "5dc012df5faff20ba31f3c6c77bb17d8c4f9c76f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 0.01, 'class_weight': None, 'penalty': 'l2', 'warm_start': True}"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_logreg.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "_cell_guid": "1ea29b5e-1011-4381-b4d2-c43c6b633917",
    "_uuid": "f55919ca7e72f5c3022aef31dd481b389da37c71"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8047138047138047"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_logreg.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "9e5a4526-fb07-426d-b477-f287011d1fdb",
    "_uuid": "5ebe1afad97d4333b697e21582a32c2630d5ccc8"
   },
   "source": [
    "## 4.4 Gaussian Naive Bayes\n",
    "<a id='4.4'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "_cell_guid": "aa8aef86-ef17-4d38-b122-a3f98e7d871e",
    "_uuid": "b39aefd475e1bfd98e199570943e669464a95efd"
   },
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "_cell_guid": "318f1bd3-d0b8-4744-b853-c8b9492a96dc",
    "_uuid": "2284e8d9ab1ab6ca04fe839bfac784301367d49d"
   },
   "outputs": [],
   "source": [
    "model_gnb = GaussianNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "_cell_guid": "98753cad-ed66-44d4-aa34-6a3a3ceb8ca9",
    "_uuid": "2d965fa5bd8b364b6aee796309bb318aa10b97a9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_gnb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "_cell_guid": "27349fad-35a3-4434-aac2-a2805da748ed",
    "_uuid": "c6c01e0bf2dac73eedf49f695de17e035c3ddcce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7631874298540965"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_train, model_gnb.predict(X_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d9519f5a-5cc4-425e-a1bf-e603950e6523",
    "_uuid": "12fd3e802f3dc4b27019a8095b7566d81172da1a"
   },
   "source": [
    "## 4.5 Support Vector Classifier\n",
    "<a id='4.5'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "_cell_guid": "f0e812a1-ddac-4331-8047-f0acd4fe2b34",
    "_uuid": "9a5d1cad16bd3b6bc90f779a5acb49ab150ca7e5"
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "_cell_guid": "921be59f-ae57-49b3-96d9-6b54e8cc1bea",
    "_uuid": "07b5bd55e71e409f7133290d91eafa91b2b4a644"
   },
   "outputs": [],
   "source": [
    "param_grid = {'C': [10**k for k in range(-3,4)],\n",
    "              'class_weight': [None, 'balanced'],\n",
    "              'shrinking': [True, False]\n",
    "             }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "_cell_guid": "6e10f8ac-6ad8-42d9-b03f-83f20aa36172",
    "_uuid": "4564db4cda9209e76d3b2cd0ee5d95c34aa4eaec"
   },
   "outputs": [],
   "source": [
    "model_svc = GridSearchCV(estimator=SVC(), param_grid=param_grid, scoring=make_scorer(accuracy_score), n_jobs=-1, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "_cell_guid": "02d64840-953a-4702-9cb5-78b14c9c7b20",
    "_uuid": "04431df8508da0702c9490f59bc3bc79d2ddcabf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 28 candidates, totalling 84 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  84 out of  84 | elapsed:    4.7s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000], 'class_weight': [None, 'balanced'], 'shrinking': [True, False]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=make_scorer(accuracy_score), verbose=1)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "_cell_guid": "097a7c5a-aa30-4b25-a0e0-2320250e5c15",
    "_uuid": "396992fc127cc82cd996b893c0687344ba1454cd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 10, 'class_weight': None, 'shrinking': True}"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svc.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "_cell_guid": "f6835b71-6cb8-490f-b15a-649772afc17a",
    "_uuid": "da0c96b2f1797ac386177f6889f5675f5b227dae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.797979797979798"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_svc.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "20a1b934-1f7e-444f-8c41-591e1e249cd5",
    "_uuid": "7bf6462534b8edbbb2f9157215597a1d42a66c32"
   },
   "source": [
    "## 4.6 k-Nearest Neighbors\n",
    "<a id='4.6'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "_cell_guid": "afddb9c2-b8bf-496a-a954-c6907f067d97",
    "_uuid": "13238a65541195d5bcc36af5bcdd42d5d84dff46"
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "_cell_guid": "ee763ea0-b83d-456d-9977-cdd8afa727bc",
    "_uuid": "b34530581f817152cae8565b58cf24ab213385d4"
   },
   "outputs": [],
   "source": [
    "param_grid = {'n_neighbors': [1, 2, 4, 8, 16, 32, 64, 128, 256],\n",
    "              'weights': ['uniform', 'distance'],\n",
    "              'p': [1, 2, 3, 4, 5]\n",
    "             }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "_cell_guid": "6e3ae28d-4f81-461f-b8e9-6bc95985acd5",
    "_uuid": "4a3e862739ced66586a59106725078a45a38e10e"
   },
   "outputs": [],
   "source": [
    "model_knn = GridSearchCV(estimator=KNeighborsClassifier(), param_grid=param_grid, scoring=make_scorer(accuracy_score), n_jobs=-1, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "_cell_guid": "76644168-db2e-460d-9366-40ec4a5760f3",
    "_uuid": "014986b616e087a356bf21805cb8df888021c1c5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 90 candidates, totalling 270 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  43 tasks      | elapsed:    4.3s\n",
      "[Parallel(n_jobs=-1)]: Done 263 out of 270 | elapsed:   14.6s remaining:    0.3s\n",
      "[Parallel(n_jobs=-1)]: Done 270 out of 270 | elapsed:   15.0s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform'),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'n_neighbors': [1, 2, 4, 8, 16, 32, 64, 128, 256], 'weights': ['uniform', 'distance'], 'p': [1, 2, 3, 4, 5]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=make_scorer(accuracy_score), verbose=1)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "_cell_guid": "ad3e78ef-3fdb-475c-b635-f6c7f1bd45e6",
    "_uuid": "3d72711097beae24c68633c61164ceb4b6786215"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 8, 'p': 1, 'weights': 'distance'}"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_knn.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "_cell_guid": "d1de56a5-4e49-402e-9269-66f225309df6",
    "_uuid": "b087dc1aef4a70d6a1aeab28aad96d35e9f9c780"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7890011223344556"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_knn.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "4571b3d2-7aaf-4195-ab7d-c2322143192b",
    "_uuid": "db4534bae04478139856c67fb979245435a37665"
   },
   "source": [
    "# 5 Conclusion\n",
    "<a id='5'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "4b22bd3f-2663-4954-979d-b77f77c00814",
    "_uuid": "8e436ffe56a1139142ff0b25a051485ca71a2786"
   },
   "source": [
    "## 5.1 Results Summary\n",
    "<a id='5.1'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "_cell_guid": "f029e839-5cfa-4c9e-a151-60799ee5e32a",
    "_uuid": "dbbad8f6d3ffbcc4b323e2047ded29b761c50c94"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy Scores\n",
      "Random Forest:  0.8260381593714927\n",
      "Gradient Boosting:  0.8226711560044894\n",
      "Logistic Regression:  0.8047138047138047\n",
      "Gaussian Naive Bayes:  0.7631874298540965\n",
      "Support Vector Classifier:  0.797979797979798\n",
      "k-Nearest Neighbors:  0.7890011223344556\n"
     ]
    }
   ],
   "source": [
    "print('Training Accuracy Scores')\n",
    "print('Random Forest: ', model_rfc.best_score_)\n",
    "print('Gradient Boosting: ', model_gb.best_score_)\n",
    "print('Logistic Regression: ', model_logreg.best_score_)\n",
    "print('Gaussian Naive Bayes: ', accuracy_score(y_train, model_gnb.predict(X_train)))\n",
    "print('Support Vector Classifier: ', model_svc.best_score_)\n",
    "print('k-Nearest Neighbors: ', model_knn.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "feb2a13b-b0e7-4125-b3d7-ce3701b11401",
    "_uuid": "297ce8fcdf1a4cefe7aca44e13b6ea1ab94756a5"
   },
   "source": [
    "## 5.2 Test Set Predictions\n",
    "<a id='5.2'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "_cell_guid": "b6bdc424-e3bf-4f30-93f8-ee382b787c69",
    "_uuid": "43132aba2a1e0b6e2e4099d58ed302f14665b3a7"
   },
   "outputs": [],
   "source": [
    "y_preds = model_rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "_cell_guid": "4d132704-a097-4414-8f58-08ccd5a00a01",
    "_uuid": "90cea7aba198e4511c0e0b216239fb0ab72f0e26"
   },
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({'PassengerId':TestPassengerId, 'Survived':np.uint8(y_preds)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "_cell_guid": "b6a0a118-cf83-4ec8-8cde-83f2b719d2c3",
    "_uuid": "db11bcc992164a59ff5a0cc111a2ed061c26789e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(418, 2)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "_cell_guid": "225a7755-6b45-4142-98f2-fc7f6dffd39e",
    "_uuid": "9afa72772db377e682b9eb85eb2a8f5f49041eaa"
   },
   "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>42</th>\n",
       "      <td>934</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>1196</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>79</th>\n",
       "      <td>971</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>963</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived\n",
       "42           934         0\n",
       "304         1196         1\n",
       "2            894         0\n",
       "79           971         1\n",
       "71           963         0"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "_cell_guid": "11c5c376-2d0b-4a4d-9fb9-38dedc57f5fb",
    "_uuid": "f95f4ca7852b1de1b9730da2cbe668203e6709f7"
   },
   "outputs": [],
   "source": [
    "submission.to_csv('my_submission.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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