TitanicExample.ipynb

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   "source": [
    "Data found at https://www.kaggle.com/c/titanic/data\n",
    "Based on solution https://www.kaggle.com/startupsci/titanic-data-science-solutions?scriptVersionId=1145136\n",
    "\n",
    "## Question and problem definition\n",
    "\n",
    "Competition sites like Kaggle define the problem to solve or questions to ask while providing the datasets for training your data science model and testing the model results against a test dataset. The question or problem definition for Titanic Survival competition is [described here at Kaggle](https://www.kaggle.com/c/titanic).\n",
    "\n",
    "> Knowing from a training set of samples listing passengers who survived or did not survive the Titanic disaster, can our model determine based on a given test dataset not containing the survival information, if these passengers in the test dataset survived or not.\n",
    "\n",
    "They have provided us with 2 separate datasets, training and test. This is only since this is a competition, often you randomly split the data in to a test and a training part.\n",
    "\n",
    "We may also want to develop some early understanding about the domain of our problem. This is described on the [Kaggle competition description page here](https://www.kaggle.com/c/titanic). Here are the highlights to note.\n",
    "\n",
    "- On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. Translated 32% survival rate.\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.\n",
    "- 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"
   ]
  },
  {
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   "source": [
    "# data analysis and wrangling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "\n",
    "# visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# machine learning\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
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    "_uuid": "bb08a570873b4a5fdb2d2485a8621c516dbef992",
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('train.csv')\n",
    "test_df = pd.read_csv('test.csv')\n",
    "combine = [train_df, test_df]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "** Data Dictionary **\n",
    "\n",
    "Variable\tDefinition\tKey\n",
    "survival\tSurvival\t0 = No, 1 = Yes\n",
    "pclass\tTicket class\t1 = 1st, 2 = 2nd, 3 = 3rd\n",
    "sex\tSex\t\n",
    "Age\tAge in years\t\n",
    "sibsp\t# of siblings / spouses aboard the Titanic\t\n",
    "parch\t# of parents / children aboard the Titanic\t\n",
    "ticket\tTicket number\t\n",
    "fare\tPassenger fare\t\n",
    "cabin\tCabin number\t\n",
    "embarked\tPort of Embarkation\tC = Cherbourg, Q = Queenstown, S = Southampton\n",
    "Variable Notes\n",
    "\n",
    "pclass: A proxy for socio-economic status (SES)\n",
    "1st = Upper\n",
    "2nd = Middle\n",
    "3rd = Lower\n",
    "\n",
    "age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5\n",
    "\n",
    "sibsp: The dataset defines family relations in this way...\n",
    "Sibling = brother, sister, stepbrother, stepsister\n",
    "Spouse = husband, wife (mistresses and fiancés were ignored)\n",
    "\n",
    "parch: The dataset defines family relations in this way...\n",
    "Parent = mother, father\n",
    "Child = daughter, son, stepdaughter, stepson\n",
    "Some children travelled only with a nanny, therefore parch=0 for them.\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch'\n",
      " 'Ticket' 'Fare' 'Cabin' 'Embarked']\n"
     ]
    }
   ],
   "source": [
    "print(train_df.columns.values)"
   ]
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   "source": [
    "**Which features are categorical?**\n",
    "\n",
    "These values classify the samples into sets of similar samples. Within categorical features are the values nominal, ordinal, ratio, or interval based? Among other things this helps us select the appropriate plots for visualization.\n",
    "\n",
    "- Categorical: Survived, Sex, and Embarked. Ordinal: Pclass.\n",
    "\n",
    "**Which features are numerical?**\n",
    "\n",
    "Which features are numerical? These values change from sample to sample. Within numerical features are the values discrete, continuous, or timeseries based? Among other things this helps us select the appropriate plots for visualization.\n",
    "\n",
    "- Continous: Age, Fare. Discrete: SibSp, Parch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
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    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# preview the data\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "97f4e6f8-2fea-46c4-e4e8-b69062ee3d46",
    "_uuid": "c357fbbc240cfaff20f0700861b3e5d90ce471ff"
   },
   "source": [
    "**Which features are mixed data types?**\n",
    "\n",
    "Numerical, alphanumeric data within same feature. These are candidates for correcting goal.\n",
    "\n",
    "- Ticket is a mix of numeric and alphanumeric data types. Cabin is alphanumeric.\n",
    "\n",
    "**Which features may contain errors or typos?**\n",
    "\n",
    "This is harder to review for a large dataset, however reviewing a few samples from a smaller dataset may just tell us outright, which features may require correcting.\n",
    "\n",
    "- Name feature may contain errors or typos as there are several ways used to describe a name including titles, round brackets, and quotes used for alternative or short names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
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    "_uuid": "53836a41102e1c945dc6b0007157519bc8092065"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>887</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>211536</td>\n",
       "      <td>13.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>888</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Graham, Miss. Margaret Edith</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>112053</td>\n",
       "      <td>30.00</td>\n",
       "      <td>B42</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>889</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>W./C. 6607</td>\n",
       "      <td>23.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>890</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>891</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dooley, Mr. Patrick</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "8bfe9610-689a-29b2-26ee-f67cd4719079",
    "_uuid": "50b56d72970fe20996c272f6b0002f7a0e939bba"
   },
   "source": [
    "**Which features contain blank, null or empty values?**\n",
    "\n",
    "These will require correcting.\n",
    "\n",
    "- Cabin > Age > Embarked features contain a number of null values in that order for the training dataset.\n",
    "- Cabin > Age are incomplete in case of test dataset.\n",
    "\n",
    "**What are the data types for various features?**\n",
    "\n",
    "Helping us during converting goal.\n",
    "\n",
    "- Seven features are integer or floats. Six in case of test dataset.\n",
    "- Five features are strings (object)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "_cell_guid": "9b805f69-665a-2b2e-f31d-50d87d52865d",
    "_uuid": "a4e82d951c78dfedd18119101e3c21817b490e0c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n",
      "________________________________________\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()\n",
    "print('_'*40)\n",
    "test_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "859102e1-10df-d451-2649-2d4571e5f082",
    "_uuid": "720ea30b9d3202f37634c82f033e72f74fa2e057"
   },
   "source": [
    "**What is the distribution of numerical feature values across the samples?**\n",
    "\n",
    "This helps us determine, among other early insights, how representative is the training dataset of the actual problem domain.\n",
    "\n",
    "- Total samples are 891 or 40% of the actual number of passengers on board the Titanic (2,224).\n",
    "- Survived is a categorical feature with 0 or 1 values.\n",
    "- Around 38% samples survived representative of the actual survival rate at 32%.\n",
    "- Most passengers (> 75%) did not travel with parents or children.\n",
    "- Nearly 30% of the passengers had siblings and/or spouse aboard.\n",
    "- Fares varied significantly with few passengers (<1%) paying as high as $512.\n",
    "- Few elderly passengers (<1%) within age range 65-80."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "_cell_guid": "58e387fe-86e4-e068-8307-70e37fe3f37b",
    "_uuid": "636d2697639e0e45f4a4ff91048c7fa663a9f03b"
   },
   "outputs": [
    {
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       "      <th>PassengerId</th>\n",
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       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
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       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()\n",
    "# Review survived rate using `percentiles=[.61, .62]` knowing our problem description mentions 38% survival rate.\n",
    "# Review Parch distribution using `percentiles=[.75, .8]`\n",
    "# SibSp distribution `[.68, .69]`\n",
    "# Age and Fare `[.1, .2, .3, .4, .5, .6, .7, .8, .9, .99]`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "5462bc60-258c-76bf-0a73-9adc00a2f493",
    "_uuid": "a5c14cc0293bfd37f131c5b86f21901601282204"
   },
   "source": [
    "**What is the distribution of categorical features?**\n",
    "\n",
    "- Names are unique across the dataset (count=unique=891)\n",
    "- Sex variable as two possible values with 65% male (top=male, freq=577/count=891).\n",
    "- Cabin values have several dupicates across samples. Alternatively several passengers shared a cabin.\n",
    "- Embarked takes three possible values. S port used by most passengers (top=S)\n",
    "- Ticket feature has high ratio (22%) of duplicate values (unique=681)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "_cell_guid": "8066b378-1964-92e8-1352-dcac934c6af3",
    "_uuid": "b02dcd0c4e6057e077c12e8c12155b7bbf2938aa"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
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       "      <td>3</td>\n",
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       "      <th>top</th>\n",
       "      <td>Graham, Mr. George Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "                             Name   Sex    Ticket        Cabin Embarked\n",
       "count                         891   891       891          204      889\n",
       "unique                        891     2       681          147        3\n",
       "top     Graham, Mr. George Edward  male  CA. 2343  C23 C25 C27        S\n",
       "freq                            1   577         7            4      644"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2cb22b88-937d-6f14-8b06-ea3361357889",
    "_uuid": "d367bfd0ba9f20e5a6f5f5ce0b01f1ce95a91a24"
   },
   "source": [
    "### Assumtions based on data analysis\n",
    "\n",
    "We arrive at following assumptions based on data analysis done so far. We may validate these assumptions further before taking appropriate actions.\n",
    "\n",
    "**Correlating.**\n",
    "\n",
    "We want to know how well does each feature correlate with Survival. We want to do this early in our project and match these quick correlations with modelled correlations later in the project.\n",
    "\n",
    "**Completing.**\n",
    "\n",
    "1. We may want to complete Age feature as it is definitely correlated to survival.\n",
    "2. We may want to complete the Embarked feature as it may also correlate with survival or another important feature.\n",
    "\n",
    "**Correcting.**\n",
    "\n",
    "1. Ticket feature may be dropped from our analysis as it contains high ratio of duplicates (22%) and there may not be a correlation between Ticket and survival.\n",
    "2. Cabin feature may be dropped as it is highly incomplete or contains many null values both in training and test dataset.\n",
    "3. PassengerId may be dropped from training dataset as it does not contribute to survival.\n",
    "4. Name feature is relatively non-standard, may not contribute directly to survival, so maybe dropped.\n",
    "\n",
    "**Creating.**\n",
    "\n",
    "1. We may want to create a new feature called Family based on Parch and SibSp to get total count of family members on board.\n",
    "2. We may want to engineer the Name feature to extract Title as a new feature.\n",
    "3. We may want to create new feature for Age bands. This turns a continous numerical feature into an ordinal categorical feature.\n",
    "4. We may also want to create a Fare range feature if it helps our analysis.\n",
    "\n",
    "**Classifying.**\n",
    "\n",
    "We may also add to our assumptions based on the problem description noted earlier.\n",
    "\n",
    "1. Women (Sex=female) were more likely to have survived.\n",
    "2. Children (Age<?) were more likely to have survived. \n",
    "3. The upper-class passengers (Pclass=1) were more likely to have survived."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6db63a30-1d86-266e-2799-dded03c45816",
    "_uuid": "efef959a4be3bd191caa4dad240ed9dfb8d7986a"
   },
   "source": [
    "## Analyze by pivoting features\n",
    "\n",
    "To confirm some of our observations and assumptions, we can quickly analyze our feature correlations by pivoting features against each other. We can only do so at this stage for features which do not have any empty values. It also makes sense doing so only for features which are categorical (Sex), ordinal (Pclass) or discrete (SibSp, Parch) type.\n",
    "\n",
    "- **Pclass** We observe significant correlation (>0.5) among Pclass=1 and Survived (classifying #3). We decide to include this feature in our model.\n",
    "- **Sex** We confirm the observation during problem definition that Sex=female had very high survival rate at 74% (classifying #1).\n",
    "- **SibSp and Parch** These features have zero correlation for certain values. It may be best to derive a feature or a set of features from these individual features (creating #1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "_cell_guid": "0964832a-a4be-2d6f-a89e-63526389cee9",
    "_uuid": "fac790e4ba53f30eb2e69523e015667861e4331a"
   },
   "outputs": [
    {
     "data": {
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       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
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    "train_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
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  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
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    "_uuid": "1cbbd680b5cea2362773a918fb2fc3c1e3c4ae4a"
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       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
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   "source": [
    "train_df[[\"Sex\", \"Survived\"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
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       "   SibSp  Survived\n",
       "1      1  0.535885\n",
       "2      2  0.464286\n",
       "0      0  0.345395\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
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      ]
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     "execution_count": 66,
     "metadata": {},
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    }
   ],
   "source": [
    "train_df[[\"SibSp\", \"Survived\"]].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "_cell_guid": "e686f98b-a8c9-68f8-36a4-d4598638bbd5",
    "_uuid": "31ce2d60d7376d26b7d43e7822b61fad8a3d62fe"
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   "outputs": [
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       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parch  Survived\n",
       "3      3  0.600000\n",
       "1      1  0.550847\n",
       "2      2  0.500000\n",
       "0      0  0.343658\n",
       "5      5  0.200000\n",
       "4      4  0.000000\n",
       "6      6  0.000000"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[[\"Parch\", \"Survived\"]].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0d43550e-9eff-3859-3568-8856570eff76",
    "_uuid": "4586df73138fd008bd6a53490cf66ed221723c65"
   },
   "source": [
    "## Analyze by visualizing data\n",
    "\n",
    "Now we can continue confirming some of our assumptions using visualizations for analyzing the data.\n",
    "\n",
    "### Correlating numerical features\n",
    "\n",
    "Let us start by understanding correlations between numerical features and our solution goal (Survived).\n",
    "\n",
    "A histogram chart is useful for analyzing continous numerical variables like Age where banding or ranges will help identify useful patterns. The histogram can indicate distribution of samples using automatically defined bins or equally ranged bands. This helps us answer questions relating to specific bands (Did infants have better survival rate?)\n",
    "\n",
    "Note that x-axis in historgram visualizations represents the count of samples or passengers.\n",
    "\n",
    "**Observations.**\n",
    "\n",
    "- Infants (Age <=4) had high survival rate.\n",
    "- Oldest passengers (Age = 80) survived.\n",
    "- Large number of 15-25 year olds did not survive.\n",
    "- Most passengers are in 15-35 age range.\n",
    "\n",
    "**Decisions.**\n",
    "\n",
    "This simple analysis confirms our assumptions as decisions for subsequent workflow stages.\n",
    "\n",
    "- We should consider Age (our assumption classifying #2) in our model training.\n",
    "- Complete the Age feature for null values (completing #1).\n",
    "- We should band age groups (creating #3)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "_cell_guid": "50294eac-263a-af78-cb7e-3778eb9ad41f",
    "_uuid": "9985d70220ca4f6ae6acd224471fd20d4580a04b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x112832b50>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+XnLeF1CdQ7cD+AzwWcrNexswWY9ojgZ+wWCyll4j0L46sUb6p6d1MuwGdTTws0XP74uI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\n0v8BBmEn9XSaP90AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1087bff10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(train_df, col='Survived')\n",
    "g.map(plt.hist, 'Age', bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x112c3a310>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11303a7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(train_df, col='Survived')\n",
    "g.map(plt.hist, 'Pclass', bins=20)\n",
    "\n",
    "#Plot all of the features to see what is interesting.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "87096158-4017-9213-7225-a19aea67a800",
    "_uuid": "f1ae47b12b6f26b9c805dcca5aa860d5825a4173"
   },
   "source": [
    "### Correlating numerical and ordinal features\n",
    "\n",
    "We can combine multiple features for identifying correlations using a single plot. This can be done with numerical and categorical features which have numeric values.\n",
    "\n",
    "**Observations.**\n",
    "\n",
    "- Pclass=3 had most passengers, however most did not survive. Confirms our classifying assumption #2.\n",
    "- Infant passengers in Pclass=2 and Pclass=3 mostly survived. Further qualifies our classifying assumption #2.\n",
    "- Most passengers in Pclass=1 survived. Confirms our classifying assumption #3.\n",
    "- Pclass varies in terms of Age distribution of passengers.\n",
    "\n",
    "**Decisions.**\n",
    "\n",
    "- Consider Pclass for model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "_cell_guid": "916fdc6b-0190-9267-1ea9-907a3d87330d",
    "_uuid": "f9e585a601daeb08f8780b5e76bedcb0f9cc858c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qcI8AJ7UuGBFvA84Bnl//nZGZN0XE+4FTqKr02zLz3RFxGPBnVO/bM1RnKf/Q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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112b1da90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Pclass', hue='Survived')\n",
    "grid = sns.FacetGrid(train_df, col='Survived', row='Pclass', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
    "grid.add_legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "36f5a7c0-c55c-f76f-fdf8-945a32a68cb0",
    "_uuid": "48093374829535758c7f0458492f3908ff9aa3ec"
   },
   "source": [
    "### Correlating categorical features\n",
    "\n",
    "Now we can correlate categorical features with our solution goal.\n",
    "\n",
    "**Observations.**\n",
    "\n",
    "- Female passengers had much better survival rate than males. Confirms classifying (#1).\n",
    "- Exception in Embarked=C where males had higher survival rate. This could be a correlation between Pclass and Embarked and in turn Pclass and Survived, not necessarily direct correlation between Embarked and Survived.\n",
    "- Males had better survival rate in Pclass=3 when compared with Pclass=2 for C and Q ports. Completing (#2).\n",
    "- Ports of embarkation have varying survival rates for Pclass=3 and among male passengers. Correlating (#1).\n",
    "\n",
    "**Decisions.**\n",
    "\n",
    "- Add Sex feature to model training.\n",
    "- Complete and add Embarked feature to model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "_cell_guid": "db57aabd-0e26-9ff9-9ebd-56d401cdf6e8",
    "_uuid": "efd766c8bc967a3033330f6cb05d0e2e2093bf45"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x112be9750>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xyb5s2sYmFIxpx2xCITy7LDXGpCQLbsaYlGTBzRiTkmJ2FziKivOzgB/iVJx/\nQlUfjXSOMcZEK5Yjt2uALFWdCtwPPFy/Q0TSgV8ClwMXA7eKSK+WzjHGmNZIVMX5UcA2VS0HEJEl\nwEXA1BbOaVZ+fjaJKspsjElesQxuXYDQcug+EfEEq8c33XccyItwTrPKy8Pn/Tcm1RUU5Ca6C0kr\nlpelx4DQn7w7JEg13ZcLHI1wjjHGRC2WwW0pcCVAMxXnNwPDRaSbiGTgXJIuj3COMcZELSEV51X1\njyGzpW6c2dJHmjtHVT9u6X3sCQXTkdkTCuHZ41fGtGMW3MKzRbzGmJRkwc0Yk5IsuBljUlK7v+dm\njDHNsZGbMSYlWXAzxqQkC27GmJRkwc0Yk5IsuBljUpIFN2NMSrLgZoxJSRbcjDEpyYKbMSYlWXAz\nxqQkC27GmJQUyxoKJkZEZBCwBdjUZNejqvpIlK+xCPiRqi5qYx+eBBap6pNtOHcOMENV57TxvQV4\nEBgcbFoPfEtVy9ryeiY1WXBrv/aralGiOxFvItIXWAh8Q1Xni4gL+D7wIjA9oZ0zScWCWwoSkYPA\nfJz/7AdwUrd/C+gHzFHVd4OH3ioiv8BJ6X6Pqi4SkULgcaAr0Ad4RlXvD462vgz0CL52/XtlAwuC\nxz0iIl8C7sa55bEKuFNVq0XkZuA/cYoA7QYqm/Q5LXh8U7NVVUO2bwcWqOp8AFUNiMjPgJ2RKqWZ\njsWCW/vVV0TWNmm7WVXXA72Af6rqLSKyELhWVaeLyJdxAk99cKtU1QkiMg54VUSGAV/ACVR/FpE8\nYK+IPBQ8vh8wSlW9wcvSDOAF4B/BwDYauAWYFgxoPwXuFZEngJ8DRcBh4FWaBDdV9QX3RzI+eH7T\nc5+J4lzTgVhwa78iXZa+Fvy8G1gS8nV+yDGPA6jqOhEpBUaq6kMiMlNE7gXG4ASwzsHjVzcZGf0P\n4AeuC27PBIYDK5zbYmQAq4FpwDJVPQQgIk8Dl4Z2thUjNz/OSNOYFllwS1GqWhuyGe5SLbTdBdSJ\nyMPAEOBvwEvAJzgVTKqanP8MkAP8GLgPSAPmqeq3AEQkB+d37FIaz8yf1p9WjNxWApNCG4JV0/4B\n3F4fQI2xpSAd2xcBRGQS0AXYClwGPKiqzwH9gUKcoNWctcB3gZtEpAhYBFwrIj2DN/rn4lwGLwGm\niEhhMBDNPoM+/xH4tIjU17d1AT8AelpgM6Fs5NZ+NXfP7b36UVOUckRkDeADblTVuuB9sqdE5Chw\nCGekNDh9GS3AAAAgAElEQVTcC6jqERG5H3gUmIIzinsH5w/nGuCB4P23u4C3gBOcvoQlaqp6UESu\nAB4MTiSk4Vz6XtPW1zSpyWooGGNSkl2WGmNSkgU3Y0xKsuBmjElJFtyMMSmp3c+WlpYetxkR02EV\nFOTaguYwbORmjElJFtyMMSnJgptps6oaLyer67C1kiYZtft7bia+AoEAS9cf5K1Ve9lzyEns0bNr\nJ2ZOKOTSif3wpNnfS5Mc2v0TCjahED+BQIAnX/uYxesONLv/nEH5fPuz55LusQAXLzahEJ6N3Nrg\nyLFqFq87wO6Dx0lzu5ABXZk2pg/ZWan941yx8VDYwAawaVc5ry7fxTXTh8SvU8aEEdORm4icD/xM\nVWc0aZ8F/BAn9c0TqvpoMFvE74BzgRrg66q6LdJ7xHvktmhNMX99cws+f+O3zc70cMe1YzhnULd4\nduesCAQC1Hn91Hr91Nb5qPP6qQl+rq3zOe1eP88t3EZZRXWLr9UlO52H7rzALk/jxEZu4cVsqCEi\n3wVuxskCEdqeDvwSmBzct1REXgEuALJUdaqITAEeBq6OVf/aYs3WUv7yhja772SNl18/v44ffnky\nfXt0bvaY1vAHA06jABP8XFfnoybMvvrg1GxbC8eeLcdO1nHoyEkKC3LO2msa0xaxvI7ajpOh9akm\n7aOAbapaDiAiS4CLgKnA6wCquiKYYyyi/PxsPJ5w6cbOnkAgwL+ebi5R7Cm1dX6eXbiNSycPoKbO\nR22dj5paHzV1vuC2v1FbbbC9pvbU1/X7z2bAibe8rtkUFOQmuhumg4tZcFPV54Ml6JrqAlSEbB8H\n8ppp90VT8KO8/OSZdjUqJeUn2b6vIuJx67cfZv32w3HoUWx50lxkeNJIT3eTGfxcUl5FXRRB99fP\nruHzlwxnSN8ucehpx2Z/RMJLxB3wY0Dov0gucLSZdncyVTKqrEp8VzxpbjI8bjLS3WR40shId5Pu\nSSMz+NlpD/06rcmxbjLT00j3uMlIT3NeK2RfaJvbffqtnEVri/nL681flofatq+C//eXlZw3qifX\nXzyUgq6dYvHjMKZFiQhum4HhItINpwLSRcBDQACYBcwL3nNbn4C+hZWfmxnVcW4XDOrTJRiEgoEk\n5OuG4NJMAGoYJYUEsNC25gJOPF00ri8bdhxh9ZbSZvdnZaRRXetr2P5gcwmrt5Ry6cR+XDVtEJ2z\n0uPVVWPiF9xE5EYgR1X/KCL/BryB84TEE6paLCIvApeJyDKcgiRfiVffopGfm8noQfls3FXe4nGX\nnzeAG2YOi1Ov4svtdnH7NaN588N9vL1qH4ePOTOnOZ3SubioL1dOGcCWvRU8t2g7+8uceSSvL8Ab\nH+xlyboDzLpgMJdMKLSZVBMXtoi3FXbsP8YDf12N19f8fae8nAz+a85kuuZEN8prz/z+AGXHqgn4\nA3TPy2oUsHx+P4vXHeClxTs5dqK20XkFXbP47IxhTJICXC5bxXCmbClIeBbcWmnzriM89upmyo/X\nNGrv070zd10/lt7dsuPZnaRWVePljQ/28PoHe6ita/wHYWjfLtxwyTCG9+uaoN6lBgtu4VlwawOv\nz8+HHx/i0fmbAXC54Ld3T6dTpt1Tak758RpeWryDJesO0PQfa+KIAj47Yyi97I9Cm1hwC8+C2xl4\neoHyzupiLplQyE2XS6K60W7sK6lk3qJtbNhxpFF7mtvFjPGFfOaCQeRmZySod+2TBbfwLLiZuNuw\n8zDz3tnOvtLKRu2dMtO4auogPjGpH+lxWJidCiy4hWfBzSSE3x9g2YaDvPDedo5WNp506N4lk+su\nHsr55/TCbZMOLbLgFp4FN5NQNXU+Fnywh3+9v4eakDVyAAN75zJ75jBGDsxPUO+SnwW38Cy4maRQ\ncaKWl5fs5L21+/E3+Z0sGtaDz84YelYSEqQaC27hWXAzSWV/2Qn+sWg7a7eVNWp3u1xcVNSXqy8c\nTF5nm3SoZ8EtPAtuJil9vLucZxduY/fB443aMzPSuPL8AVx+3gAy023SwYJbeBbcTNLyBwJ8sOkQ\nz7+7ncPHGi+a7pqTwbUXDeGCMX0S/sxtIllwC8+Cm0l6dV4fb63axz+X7aaqpnF2ln4FOcy+ZBij\nB7e/DMhngwW38Cy4mXbj+Mla5i/dxcI1xaeleR8zuBs3zBxGv57xywCcDIu4LbiFZ8HNtDuHjpzk\nH+9uZ5U2Tr3kcsEFY/tw7fQhUaeoaqvqWi93/uI9AsH3feSei8jKiH8GMQtu4aV2uSaTknp1y+bO\na8eydd9R5r2zje37jwEQCMCSdQf4YPMhPjl5AJ86fwCdMmPzK+71BRqekw0EnG2TXCyxlmm3hvfr\nyr/fPJHbrxlDQdeshvbaOj/zl+3i+39cwaK1xfj87bcehWk7G7mZds3lcjF5ZE+KhvVg4Zpi5i/d\nyYlqZ9Lh2Ila/vK68uaHe7lh5jDGDe1uOeQ6EBu5mZSQ7nFz+eT+PHDbVD513gA8aaeC2IHDJ/nV\nP9bx4DNrTls3Z1JXLOuWhi2yLCK9gb+HHF4E3K+qvxeR1TjFYgB2qmpSpRs3ya1zVjo3XDKMmRMK\neeG9Hby/6VDDvo/3HOXHT37I1NG9ue6iIXTPy2rhlUx7F8vL0msIU2RZVQ8CMwBEZCrwE+BREckC\nXE0r1BvTWgVdO/GNz4zm8sn9efadbWzZe7Rh3/KNB/nw4xIun9yfK6cMJDvL7s6kolhell5ISJFl\n4LQiyyLiAn4D3K6qPpxRXraILBCRd4JB0Zg2G9ynC9+7cTx3XTe2UbZfr8/Pv1bs5v4/LOftVfvC\n1sUw7Vcs/2RFU2R5FrBRVeuLYZ7EKfP3GDAceE1EpKX6pfGqOG/at8t7duGSKYN4Y8Vu/vbGxw2F\nayqr6vjrm1tYuKaYOVedw5QxfaKadMhsUvime/ccutgD/UkllsEtmiLLNwG/CtneAmxT1QCwRUQO\nA32AveHeJF4V501qOG9ED8YMmMJr7+9mwYd7qfM6I7b9ZSf43yc/ZHi/PG64ZBhD++a1+DqVVXWN\ntg8frqTmZPxraFjF+fBieVm6FLgSoIUiy5OAZSHbX8W5N4eI9MUZ/R2IYR9NB5Sd5eH6i4fy01un\nMG1Mb0LHaVv3VfCTv6zi9y9voORoVcL6aM5cLEdupxVZblKYuQA4Fhyl1XsceFJEluBUoP9qS5ek\nxpyJbl2y+PpV53DZpP7MW7iNzbtPFdz+YHMJq7SUSyf246ppg8jpZJXN2ht7ttQYIBAIsH7HEZ5b\nuI3ishON9nXO8nDVtEFcMqEf6R43gUCAtVvL+M0Lpy5GfnrrlISUJ7RnS8Oz4GZMCJ/fz9L1B3nx\nvR1UNJk06JGXxafOH8CSdfvZdbBx5S5PmovPXzqcSyb0i2d3Lbi1oMXgJiIXtXSyqr531nvUShbc\nTCxU13p544O9vPb+bmrrol8m8rVPj+KCsX1i2LPGLLiFF+me24+Dn7sDw3AmCXzANJwJggti1zVj\nEicrw8PVFw7m4qK+vLR4B4vXHSCai5wXF+9g6ujeHTo7cLJocbZUVWeq6kxgHzBOVS9T1U8BYwF7\nSM+kvK45mcy5YhQ//up5UaVPOnKsBg15GsIkTrRLQQbWPxcatAcYGIP+GJOU+hXkkJ0Z3WLxisqa\nyAeZmIt2KcgqEfkzMA8nIN4ILI5Zr4xJQjnZGacVqmlObrY9qZAMoh25fR1YB9wG3AIsB+6IVaeM\nSUZTzukV8Zi8nAxkQNc49MZEElVwU9Va4Hng98B1wHxbXGs6munj+tK9S8tpkj4zbRCeNEuTmAyi\n+lcQkdnAfJznQLsBy0Xkplh2zJhkk53l4d4vFNGn++mLdV3A9RcPYcb4wvh3zDQr2j8x38NZ/nFc\nVUuA8cD3Y9YrY5JUr/xs/vtr5/H1T49q1P6jr07m01MHWRrzJBJtcPOpasPSD1U9AFgCLNMhpbnd\njBvWo1Fbfq5l9U020c6WbhSRbwLpIlKEM5mwNnbdMsaYMxPtyO1OoBCoAp7AydVms6XGmKQV7cjt\nFuD/VNXusxlj2oVog1shsEJEFHgaeEFVO3wK3Gf1Jd4rXsZFhdOYLdckujvGmBBRBTdVvQ+4T0Sm\nA7OBH4rI+6p6c0x7l8SqvTUsLl4OwOLi5Vw99AqyPJkJ7pUxkd3w7O2FwBCgElg3b/ZcX4K7FBNR\nZ+INVqpKBzJwZko79AN03oCXAE6aiAABvAEvYMHNJK8bnr19NPAg8CloyK6++4Znb38Q+N282XNT\nKn1YVMFNRH6DU4d0DfBX4FuqWh3Ljhljzp4bnr29CHgXpy5JqIHAb3FSmt3TltcWEQ/wJs5f90+r\nanmEU6J93YOq2rut50c7ctsCTFDV0mhfuKWK88H99+A8s1r/mt8AtrZ0jjGm9W549nYXTrnMpoEt\n1N03PHv7c/Nmz13WwjHh9AW6qOrENnUwRloMbiJyq6r+EeeRq9tFpNF+Vf3vFk4PW3E+aCLwJVVd\nFfJ+10U4xyQRm1BpNyYGPyK5jcbV6KL1e2C4iPwJp5xn92D7t1R1vYhsC77uCOBtIA84D1BVvVlE\nxgC/ANKAHjhF2hv6ISJjgV/jXEofxikcFVoTuVmRRm6uMF9Ho1HFeRFpWnF+IvB9EekNvKqqP43i\nnNMkqihzVk3jH0eP7jnkZubEvR+JUl1XfWpCZf9yvn7+58hK7zir9NtZUeaiKI8b38bXvwP4O1AC\nfKCqc0VkOPAnnP/Tg4BLcMp0HgHOB+4CdohIV2A08J1gILwR+AqNg+yjOAFtk4h8Dfgu8B+ROtVi\ncFPVPwS/rACeUdVDUX6zELni/N+BR3AWBL8oIldFcc5pElWUubKucYWkssOVVKen1P3YFlXWnTg1\noRIIcKD0KDnpnRPcq/hpZ0WZ6yIfAkBt5ENaNBa4JJhoA5wrPoDDqroHQEROqOqm4NcVQBZQDPxA\nRKpwRn7HmrzuKOB3wSvHdJzbVxFF+4RC/Tq310XkJhGJpoZZ2IrzwZnX/1PVsmA6pVdx/mpEU6Xe\nGNM6i4Bo/vK+fYbv8zHwS1WdAdyAsyaWKN7718B/qeqXcWqzNL1KVJxbWDNwRm3/jKYz0eZzu09V\nBwM/AaYAa0XkqQintVRxvguwQURygoHuEmBVhHOMMW0wb/bc3cALEQ6rBeae4Vv9BLhBRBbh3F7a\nEOV5TwPPichinPtyfZvsvx34S7BY+wM4iXMjiuU6t0gV5/8dWBh8nbdV9V/BGdZG50Tbv3g7fPKs\nzHYbEy/fwFnucW4z+7zAzfNmz93ZlhdW1V04gx5wJhKb7u8d5uv6e4G/CH40e15w0nFGa/vVmnVu\nV+NkAnmaKNa5qaofZ/Yl1Mch+58CnorinKRSevIwz255kc1HtjRqf1Zf4gtyHdnpnRLUM2PCmzd7\n7uEbnr39QpxR0NeA4ThPKLwM/HLe7LlrEtm/WIh25HYImNiadW6p6HDVER5e/QjHaytP27e65CPK\nqsq4e8LtZKYl7ayZ6cDmzZ5bifOEwoOJ7ks8RDuh8MWOHtgAXtnxerOBrd6e48W8t68ty4RMe+NJ\nczXc9Xa5nG2TXKIduW0SkR8C7+PkdANAVd+LSa+S0Im6k6wpiTy/sWT/+1w2cEbsO2QSKivDw8wJ\nhbyzupiZ4wvJyoj69rWJk2j/RboBM4Mf9QI4s5wdQlnVYXyByMkTyqoOU1F9jLyslp50MangpsuF\nmy6XyAcmmVnfeblRVpD5D1/dcbOCqOrMyEeltnR39As0/3P5/zKs6xDGF4zl3IIx5GVGtdDSmJia\n9Z2Xm80KMus7Lz8I/G7+w1en1Cr0aGdLF9LMQjxV7TAjt96de9I9qxuHq49EPNYf8LOlfBtbyrcx\nb8tLDMkbyPie4ygqGEN+lhXsNfE36zsvxywrSFuIyBxgpKreH6v3iPay9EchX6fjLAvpUAu93C43\nlwyYznNbXm7xuE6eLKq8p1bJBAiwvWIX2yt28Y+trzCwS3/GF4xlfM+x9OjUvYVXMubsmPWdl6PK\nCjLrOy8/N//hq1NmRizay9J3mzS9JSLvAz88+11KXhcXTuPgiZKGB8abmj3iGi7oez7bK3aypmQD\nH5Wup6L2eKNjdh/by+5je3lp+7/ol9OX8T3HUlQwlt6de8bjWzAdU0yzggRHYbOATkAfnOLtVwNj\ngHuB/sB1QGegDLi2yfl3ATfiXB3+XVV/3do+NCfay9IBIZsunKf4O9yww+VyMXvENZxbMJqFe5ew\n8XDDmmS+Pf4bjMgfCsCI/GGMyB/G50Z8hp0Ve1hbup41Jesprzna6PX2Ve5nX+V+5u94g96dezWM\n6Pp27m3FfduBdpTyKdZZQQByVfVyEfk8zuXtFJynCu7BebTyE6rqF5E3gMn1J4nIOTilCy4MNr0p\nIm+oqp5BX4DoL0vf5dQ9twBO9L3rTN+8PXK5XIzqNoL+uYV8b/GPG9r75pyeMNTtcjO06yCGdh3E\ndcOuYs/xfawpWc+a0vWUVR1udOzBE4d47cQhXtv1Fj079aCo51iKCsYwILefBbok1M5qaMQjK0j9\nEw5Hgc2qGhCRcpzHNWuBZ0SkEuiHc2ur3hic+371D+3n4zw9EfvgFkxF9AlV3S4i1+I8urEaJ62w\niZLL5WJgl/4M7NKfq4deQXHlgYYR3cGTJY2OLakqY8HuhSzYvZBuWfkUFYxhfM+xDOoyALcr2nXX\nJpbaWQ2NRTiDkkh/Jc8kK0i4mdYM4BpVPT+YTWhVk34osBG4IhgQ7yHKB+MjiZSJ916cIeOXRWQc\nznOl3wbOAR4C7j4bnehoXC4X/XL70i+3L1cN+SQHTxxiTckG1pSuo7jyQKNjj1SX887exbyzdzF5\nGV0o6jmGooKxDOs62AKdicr8h6/ePes7L78AXN/CYWcjK0hzvMAJEVka3D5ASNYPVf1IRN4GlohI\nJvABTn63MxZp5HYzMFVVT4rIA8ArqvpYMEPIprPRAQO9O/fiisG9uGLwpZScLOOj0g2sKVnP7uN7\nGx1XUXuMd/ct4919y8hNz2FcwWjGF4xlRP5Q0tzxz0Zs2pWIWUHmP3x1W7OCPBny9eucyqa9Frg8\nivNj8rxrpOAWCCm+PBOneAvB4ePZ7osBemb34LKBM7hs4AwOV5XzUel61pRuYGfF7obLIIDjdZUs\n3f8+S/e/T7anE+N6jKao5xhGdhtButseBTKNzX/46sOzvvNy2Kwg8x++usNlBfEGc5zn4MykLAAQ\nkYE40d7EUPdO+Vwy4CIuGXARR2sq+Kh0I2tL1rP16I5Gge6kt4oVB1ey4uBKstKyGNNjJOMLxnJO\ndyHDMpSYoPkPX92hsoJECm4P4ORw8wCPqeoBEbkB+F/gxy2eac6qrpl5XNxvGhf3m8bx2krWlW1k\nTcl6tHwb/oC/4bhqXzUrD61l5aG1ZLjTGd19JON7jmV095FkeTpOARdjIhWI+UcwK24PVa2fwajE\nqSe6KNadM83Lzcjhgr7nc0Hf8zlZd5J1ZZtYW7qezUe24vWfGlDX+utYU+osPfG4PYzqNoLxBWMZ\n2+McS6p5Bk7WVbGk+P1GbXuO7eOc7narJplEvDmjqvuB/SHb/4rmhaMoyvwFnNlWL06thDuCi/xW\nc6r6zU5VTdpU48kgOz2bKX0mMaXPJKq81Wws28ya0g1sPPwxdf5Ty5u8fi/ryzaxvmwTaa40JH8Y\n43uOZVyP0eRkdJyqVWdqZ8Uefr/uT6dVP3vko8e5oO/5fF6utVnsJBHLO89hizKLSCfg/wFjgzOx\nzwBXicgCwBWscpPUPC4PLlwECODChceV+Jv4nTxZTOo9nkm9x1Pjq2XTYWVt6Xo2lG2m2neq5IUv\n4GPTEWXTEeUZ1wuWwSRKFTXHmfvRE5zwNl9Ocun+9+ma2YUrB18W556Z5sTyf2RLBZZrgGkhM7Ee\noBpnlJcdDHIe4N9VdUUM+9hmWZ5MphdO5b3iZUwvnJp0q9Mz0zIY39N5nKvOV8fH5VtZU7KedWWb\nqPI25BttcwaTA5WNS9gGAimVLadZi4uXhw1s9d7Zu4RPDLjYJnKSgCtWv5Qi8hjwvKq+FtzeAwxp\nWoc0+NDslcGPMTjPpD2GM1X9GiAt1S71en2BRFScb6+8Pi8bSrbw/r41fFi8lmM14dOmD+s2iCn9\nx3N+v/H0yikAoOzEER754M9sLGlcIKdfbm/umvpVBuf3j2n/W8vv91PtraHKW+181Dkf1d4a5+v6\nNm811XU1nPRWUV1XE9yu5mSwvcpbzcm6qshvCNw//Q4m9B0b4++sgT2bF0Ysg9svgBWqOi+4vU9V\n+4XsdwM/x6lT+Png5WkmTiHmquAxHwDXq+re09/BUVp6PPWHDDHi8/tazGASql9OX0Z3F94/sIqj\ntU0Lgjs6ebK4d+I3zzjDidfvpdpXQ423hmpfDdXBzzW+Gqq91U32VYfsq2m8z1dDre9Mi6i33pxz\nvsDk3mfyDHr0CgpyLbiFEcvL0qU4aVDmhSmw/Aecy9NrgiX9AL4KjAXuEJG+OPmnDmBiIs2d1uoM\nJi2p8lbzjy2v8JlhnwoTmGqo9lU3Cj7V3lP76oOXN4p07smse6duie6CIbYjt/rZ0nGcKrA8AWdB\n8Mrgx2JOPXD7K+BV4ElgQLD9e6raYn4pG7mdfYFAoCGDydrS9ZQ2yWDSXrlwkZmWSZYn0/kc/Dor\nLZNMT/12VsO+TE8mnUL2afk25u94o8X36JXdkx+c/524ZXKxkVt4MQtu8WLBLbYCgQDFlQdYeWgt\nb+5ZFPf3d7vcdErLCgk+mU2Cz6l9mSHBquE4T1bDvoy09DNapuH1e/m/1X9g57Hdze534eL2c7/C\n6O4j2/werWXBLTwLbiYq/oCfuxf9R1QVwNLdHrKCQSd05NMo2HiajJxC9oUGMI/bk1T57Kq8Vfz1\n4+dZU9I4K0+XjFxuHHk9Y3ucE9f+WHALL/GLs0y74Ha5GdfjHNaUtly7tbBzH75/3t1JFZDOpk6e\nTnx9zE3sObaPn608lQ37+5O/TZdMK+eYTGwptYnaJwZeHPGy7pODLknZwBaqW6f8RttuSzmVdCy4\nmagN6jKAOed8Ho+r+f/I1wy9kom9mksXZkz82WWpaZWJvYoYkjeId/Yu4Z297zW0/9uEOxjadVDi\nOmZMEzZyM62Wn9WVTw6a2aitV+eCBPXGmOZZcDPGpCQLbsaYlGTBzRiTkiy4GWNSkgU3Y0xKsuBm\nTBvUZ2IGkiYTs2nMgpsxbVCfiRlIykzMxhbxGtNms+UaZss1ie6GCcNGbsaYlGTBzRiTkiy4GWNS\nkgU3Y0xKitmEQhQV52cBP8SpOP+Eqj4a6RxjjIlWLEduDRXngftxKs4DICLpwC+By4GLgVtFpFdL\n5xhjTGskquL8KGCbqpYDiMgS4CJgagvnNCs/Pxsryhx/uXXpuHARIIDL5aJPQVey0rMS3S1jGsQy\nuHUBKkK2fSLiCVaPb7rvOJAX4ZxmlZefPItdNq0xvXAq7xUvY3rfqRw/Wsdx6hLdpQ6noCA30V1I\nWrEMbseA0J+8OyRINd2XCxyNcI5JMraI1SSzWN5zWwpcCdBMxfnNwHAR6SYiGTiXpMsjnGOMMVFL\nSMV5Vf1jyGypG2e29JHmzlHVj1t6H6tbajoyq1sanhVlNqYds+AWni3iNcakJAtuxpiUZMHNGJOS\n2v09N2OMaY6N3IwxKcmCmzEmJVlwM8akJAtuxpiUZMHNGJOSLLgZY1KSBTdjTEqy4GaMSUkW3Iwx\nKcmCmzEmJVlwM8akpFimGTcxICKDgC3Apia7HlXVR6J8jUXAj1R1URv78CSwSFWfbMO5c4AZqjqn\nje89GHgIGAPUAR8D96rqrra8nkldFtzap/2qWpToTsSbiPQAlgD3qer1wbabgCUicq6qHk5oB01S\nseCWYkTkIDAfmA4cwEnb/i2gHzBHVd8NHnqriPwCJ537Paq6SEQKgceBrkAf4BlVvT842voy0CP4\n2vXvlQ0sCB73iIh8Cbgb53bHKuBOVa0WkZuB/8QpALQbqGzS57Tg8U3NVlUN2b4NeE9V/1bfoKpP\ni8hngvt+0rqflkllFtzap74isrZJ282quh7oBfxTVW8RkYXAtao6XUS+jBN46oNbpapOEJFxwKsi\nMgz4Ak6g+rOI5AF7ReSh4PH9gFGq6g1elmYALwD/CAa20cAtwLRgQPspcK+IPAH8HCgCDgOv0iS4\nqaovuD+S84CFzbS/C3wyivNNB2LBrX2KdFn6WvDzbpzLuPqv80OOeRxAVdeJSCkwUlUfEpGZInIv\nzj2tDKBz8PjVTcos/g/gB64Lbs8EhgMrRITguauBacAyVT0EICJPA5eGdrYVI7dwOgFWmds0YsEt\nBalqbchmuLqvoe0uoE5EHgaGAH8DXgI+EdwHUNXk/GeAHODHwH04wWWeqn4LQERycH6/LqXxrPxp\n/WnFyO1D4Pz6DRHpqaolwBRgZRTnmw7EloJ0XF8EEJFJQBdgK3AZ8KCqPgf0BwoJPyJaC3wXuElE\nioBFwLUi0lNEXMBcnMvgJcAUESkMlm6cfQZ9ngtcKCI3Brd/LyJvABfg3Fs0poEFt/apr4isbfLx\n61a+Ro6IrAF+D9yoqnXAT4GnRGQVzmhsJTA43Auo6hHgfuBRYAPOKO4dYCPO79YDwcvRu4C3gA9w\nJhXaRFXLcAp4f05EtgCjgWrgEPDptr6uSU1WQ8G0eyKSC5ynqm8nui8meVhwM8akJLssNcakJAtu\nxpiUZMHNGJOS2v06t9LS43bT0HRYBQW5rshHdUw2cjPGpCQLbsaYlNTuL0sT5XhtJcWVB3C73AzI\nLSTLk5XoLsVVlbeavceL8Qf8FOb0ITcjJ9FdMqYRC26tdLSmghe2/pM1pevxB/wAZKZlMLXPZK4e\negUZaRkJ7mFsVXtreGXHayw/sJJan/MIa5orjfE9x3LdsFnkZeYmuIfGONr9It54TihU1Bzj4VWP\ncHaDxrYAABaMSURBVLi6vNn9w7oO5ptFt5DuTs2/GbW+On6z9o/sqNjd7P4eWd24d9I3bRQXRzah\nEF5M77mJyPnBlNZN22eJyIcislxEbgm2uUXk98G2RcH8YknllR2vhw1sANuO7mRJ8Yo49ii+3ite\nFjawAZRVH2H+jjfi2CNjwovZEENEvgvcDJxo0p4O/BKYHNy3VERewcnskKWqU0VkCvAwcHWs+tda\nVd4qVh1qmh/ydK9sf431ZU3LG6SGlgJbvQ8Prua6YVeR5cmMQ4+MCS+W10/bcRIZPtWkfRSwTVXL\nAURkCU6mh6nA6wCquiKYiidplJwso84fLjXaKbX+OrR8Wxx6lJxq/XUcrj5CYU6fRHfFdHAxC26q\n+nywUlNTXYCKkO3jQF4z7T4R8TTJ/nqa/PxsPJ7YJ2GtSs+L+XukivfLPuCzvT5N9+z8yAcbEyOJ\nuPN9DAidUssFjjbT7o4U2ADKy0+e3d6FkeHvTNfMPI7WVLR4XK/snszoNy0ufYq3hXuXUlJVGvG4\nt3csZeHO5RQVjGFm/wsZ3GUgLpfd946FggKbnQ4nEcFtMzBcRLrhFAq5CKcOZQCYBcwL3nNbn4C+\nhZXmTmNGvwt4afu/Wjzuc8M/w6juI+LUq/jqlpXP3HV/iupYf8DP6pJ1rC5Zx4DcfszsfyETeo7D\nk6IzySb5xO0JBRG5UURuDWZ8/TfgDWA58ISqFgMvAtUisgxnwuGeePUtWpcOuIhJvcKn+p815FMp\nG9gAxvQYxZWDLwu7f3zPcVw1+JPkZXRp1L7n+D7+vOnv/GDZT3lt51scr60M8wrGnD22zq2V/AE/\na0rWs7h4OXuPF+N2uRmeP5SZ/S5geP7QeHYlYbaUb2PR3qVsObqDQMDPgC79uahwKkUFY3C5XPj8\nPtaUrmfR3iXsPLbntPM9rjQm9RrPjP4X0j+3bwK+g9Rh69zCs+BmYmrXsT0s3LuE1SXrGp7oCDWs\n62Bm9ruQsT3OIc1t1flay4JbeBbczP9v776j4yrPPI5/R8UqtiUsuVsuOJiHalwxLhg7EBJYyJJK\nlrKUQxqbZEklcDZkk01CkgMpm2RPICxpZFMgBEioCbjgigvGYPBjsCmW3OUiS1bX7B93ZEuWZjSW\nNDPSzO9zjs6ZO/dezTPH8Oht932S4mD9IZ6vWMWyilVUN9Z0OF+SP4QLyuYwZ9RMCnMLUxBh/6Tk\nFp2SmyRVY3Mja3dvYFH5Miqqd3Y4PyArl1mjZrCgbC4jBw5PQYT9i5JbdEpukhLhcJg3Dm5jUfly\nNu7dRJiO/4ynl5zKwrHzOL3kVLJC2p2rM0pu0Sm5Scrtq93PkvLlrNy5htqmug7nhxcOZUHZPGaN\nnK7Huo6j5Badkpv0GXVN9azetY7F5cvYc2Rfh/MFOfnMHjWTC8rmMrSgJAUR9j1KbtEpuUmf0xJu\n4bX9W1i0fRmv7d/S4XyIEJOHnsGCsfOYdNLElD398Ed/hKUVK5g/Zg5X2hUpiUHJLTolN+nTdtXs\nYXH5clbvXEtDS2OH82MGjWJB2TxmjphCbnZu0uKqa6rnS0vvIEyYECHumv/NlHSZldyiU3KTfuFI\n4xFW7FzDkvIV7O9kT71BuQOZN3oW55fN5qS8xG9yUN1Yw63Pf+Po8ffO/zqDcgcm/HOPp+QWnR70\nk36hMLeQi8ZdwMKyeby871UWlS/jjYNvHj1f3VjDU28/xzPvLGba8MksKJvHycXjUhixpJqSm/Qr\n2VnZTBl+NlOGn832wxUs3r6ctbtfpCncDATjdWt3b2Dt7g1MKBrHwrK5TB0+WU8/ZCB1S6XfO9xQ\nzbKKVSytWElVw+EO54sHFDG/bDZzR8/qtfoO6pb2fWq5Sb83eMAgLjn5It4zfgHr92xk8fblvH14\n+9Hzhxqq+Ou2p3nyrWeZOWIqC8fO007BGUDJTdJGTlYO546cxswRU3mz6h0Wb1/WrgRjU0sTK3eu\nYeXONUw6aSILx57P2UNP19MPaUrJTdJOKBRiYvF4JhaP50DdQZZWrGT5jtXUNB7btfn1g9t4/eA2\nSvNLWFA2h9mjZ1KQU5DCqKW3acxNMkJDcyNrdq9n8fbl7KjZ1eF8XvYAzhs1gwvK5jKicFiXv09j\nbn2fWm6SEQZk5zJ39CzmjDqXLQe2sqh8Ga/se+3oA/v1zQ0sKV/BkvIVnFl6GgvL5nFaySTVfujH\nlNwko4RCIazkFKzkFPYeqTz6wH5dc/3RazZVbmZT5WZGFg5nwdi5nDtyOnnZA9r9ngO1B9sdt7Q0\nJyV+iV/CuqVmlgX8D3AOUA/c5O5vRM6NBP7Q5vIpwFfd/edmtp6gEhbAm+5+Q6zPUbdUeqq2qY5V\nO9eypHw5e2srO5wvyClg7uhzmT9mDoW5Bfx+859Zt+eldtcUDyjiqtM+xFlDT09W2IC6pbEkMrl9\nEHi/u18fqWZ1m7t3qCBvZrOBbwPvAXKBle4+Nd7PUXKT3tISbmFT5WYWb1/O5gOvdzgfIkRhTgE1\nTZ2Xk8wKZfHpyTdwRqklOtSjlNyiS2Ry+wHwgrv/IXJc4e5jjrsmBKwBrnZ3N7NZwG+Atwm6zLe7\n+6pYn9PU1BxORlFmySzbD+3gyS2LWPr2ahqaOz6wH82YopH84H13JHOsTsktikSOucVTQf5yYJO7\ne+T4CEEN0/uAScCTZmaxijMnqyizZJZ8BvOBCe/n4jEXsXzHapaWr+RA/cEu76uo2sWarZs4uXh8\nEqJUUeZYErl6MZ4K8tcA97Y53gI84O5hd98CVAJaSi4pMzC3kIvHL+Qbs29lYE58hWv21e5PcFQS\nj0Qmt+XApQAxKsjPAFa0Ob4RuDtyz2iC1l/HKiIiSZadlU1xXlHXFxLsGCypl8jk1qGCfGvVeQAz\nGwZUuXvbQb//BU4ys2XAH4EbY3VJRZLpnGFndXlNQU5+xhTn7uv0hIJInA7VV/Ffq++mtqk26jWX\nTriIf5p4cdJi0mxpdHpiWCROxXlF3HzOjQyMUjR6zqiZXHLyRUmOSqKJ2XIzs/mxbnb3pb0e0QlS\ny02SrabxCEvKV/D4m88cfe/Tk2/krKGnJT0Wtdyi62opSOuTwaXAKQSTBM3AHIIJgrmJC02kbxqY\nW8j8stntktuE4rEpjEg6EzO5uftCADN7Avhgm8enxgP3JD48EZHuiXfMbXxrYot4B0jOKkURkW6I\n9wmFdWb2a+BPBAnxKuD5hEUlItJD8Sa3m4DPAp8CwsA/CHb8EBHpk+JKbu7eYGZ/BjYDTwNjtbhW\nRPqyuMbczOxK4K/Aj4ESYKWZXZPIwEREeiLeCYVbCZZ/HHb3PcBU4LaERSUi0kPxJrdmdz9a7dbd\ndwItiQlJRKTn4p1Q2GRmnwFyzWwKcDOwIXFhiYj0TLwtt38DxgC1wP0Ee7XdnKigRER6Kt6W28eB\nH7m7xtlEpF+IN7mNAVaZmQMPAA+7u/b3FpE+K65uqbt/2d1PJqhSdR6wwcx+m9DIRPqwnFAOoUht\nlhAhckIqAdzXxL2fW6RSVS4wgGCmtD72HSLpKz8nj/PHzAbg/DGzyc/JS3FEcry4duI1s58AVwAv\nAr8DHnX3ui7uiVqUOXL+8wSPde2NvPVJ4PVY93RG+7lJJtN+btHF25beAkxz971dXnnMFUC+u8+O\nFIi5G2hblHk68K/uvq71jUgh51j3iIjEJWZyM7NPuPu9BI9cfdqsfSVtd/9mjNvnAU9FrltlZjOO\nOz8duM3MRgKPu/udcdwjIhKXrlpuoSiv49FVUeY/AD8jWDP3FzO7LI57OhgypBBVnBeR43W1E2/r\nbruHgN+7++4T+N1RizJHJid+5O6HIsePEzyvGk8h53ZUcV4ymSrORxfvbGnrOrenzOwaM4un9Has\nosxFwCtmNiiS6N4NrOviHhGRuJ1Q3VIzOx+4ErgYWO3u18a4tnW2dDJBl/YGYBowyN3vNbNrgc8R\nzIo+6+5f7+wed98cKybNlkom02xpdHEnt0gLayHwMWA+sMzdb0pgbHFRcpNMpuQWXVxLQSLr3P6Z\nYCeQB4DPdbXOTUQkleJd57YbmH6C69xERFIm3gmFq5XYRKQ/ibfl9qqZ3QGsJtjTDQB3X5qQqERE\neije5FZCMJmwsM17YYIlHCIifc4JLQXpizRbKplMs6XRxTtbuoigpdaOu6vlJiJ9Urzd0v9s8zqX\nYFnIgV6PRkSkl3S7W2pmq919Vi/Hc8LULZVMpm5pdPF2S8e1OQwBZwKlCYlIRKQXxNstXcKxMbcw\nsA/4bEIiEhHpBV0u4o3ss3aRu08Evgi8BjwN/D3BsYmIdFvM5GZmXwK+DuSZ2WSC50ofAQYBdyU+\nPBGR7umq5XYtcIG7vwpcBTzm7vcRtODem+jgRES6q6vkFm5TfHkhx+obaIZSRPq0riYUmszsJIJu\n6FTgGQAzGw/E3P5bRCSVumq5fZdgD7dVwH3uvtPMPgo8C3w/0cGJiHRXl4t4zWw0MNTdN0aOLwWO\nuPvixIfXNS3ilUymRbzRJezB+Tgqzv8LcAtB9/Zl4GZ3bzGz9QRVsADedPcbYn2OkptkMiW36OJd\nxNsdUSvOm1kB8C3gbHc/Yma/By4zs2eAkLsvSGBcIpIBEpncYlWPrwfmtJmJzQHqCFp5hZEklwPc\n7u6rYn2IijKLSGcSmdyiVo939xaCugyY2WcJZmP/DpxFsDj4PmAS8KSZWazCzCrKLJlMRZmjS2Ry\ni1k9PjIm933gVOBD7h42sy3AG5F1dFvMrBIYBWxPYJwikobiLRDTHV1Vj78HyAeuaNM9vZFgbK51\nlrYI2JnAGEUkTSVjtrRDxXlgbeTneY7tNvJj4HHgV8C4yPu3uvuKWJ+j2VLJZJotjU41FET6MSW3\n6BLZLRURSRklN5FueuAZ58bvPscDz3iqQ5FOKLmJdENdQxOL1lcAsOjFCuoatI9EX6PkJtINTc3h\nY/vuh4Nj6VuU3EQkLSm5iUhaUnITkbSk5CZygja8vo8fP/hSu/cefX4b1bWNKYpIOqNFvCIn4IlV\nb/PQ4q2dnhtRUshXr55G8cABSYtHi3ijU8tNJE5v7qyKmtgAdu8/wgNPa81bX6HkJhKnf6ztenOa\n9a/vZX9VXRKika4kcssjkX6lqbmFA4fr2XeojspDdVRW1bHvUO3R13sPdp20wmHYuqOKkqL8JEQs\nsSi5ScZoaGymsqo1aR1LYJWHguOD1fX0xhB0fx/HThdKbpI2auubOiSutq+rahoSHkMImDiqKOGf\nI11TcuummrpGduyrISsrxNhhgxiQm1l1HOobmynfU01LOMyYoQMpzM9N6OeFw2Fq6pqOtrIqD9Wy\nr6p966umrnee7xyYn0NpcT5DiwsoLcqPvM6npq6RXz6xOea9k99VytCTCnolDukZJbcTVFXTwIOL\n32D1q3toam4Bgv8Z5k8ZzRXzTiY3zYvVNDQ288iyN1myYQe19UEyyc3JYtYZI/jIgncxuLB7yyDC\n4TBVNQ1B4mrtKrYmr8jr+obmXvkOxQMHUFqc3y5xtb4uLcqnIC/6/xZ7D9bxtxVvdXqutCifa99r\nvRKj9JzWuZ2AqpoGvvPAOvYcqO30/BkThnDLR84hJzs9J6Ebm1r44Z82sPmdg52eH1lSyG3XTOs0\nwbW0hDlwuL7NGFdtu/Guyqr6o38seiIUgiGD8ygtiiStSMIaWlwQeZ3X4z9Aq17dxeMr36Zib83R\n9+adPZIPXfAuigfl9fQrnBCtc4suYS23OIoyXw7cQVCU+X53/0VX96Taw0u3RU1sAK++dYAlG3Zw\n4fSyJEaVPIterIia2AB27T/C/U+8xrRJw45LXHUcOFxPc0vP/w5lZ4XatbLaJrDS4nyGDM5L+B+X\n884YyZkTSvj3/1529L2PvnsSgwoS2zWXE5Oqosy5wA+BmUANsNzMHgPmRrsn1Wrrm1i1aVeX1z20\nZCsvb6tMQkTJt/mdA11e89Iblbz0Rve//4CcrE4TV2vLq3jgALKyUt9YCYVSH4PElqqizKcTlPA7\nAGBmy4D5wOwY96TU7gNHaGjquttU39DMxq3pmdx6Q0FeNqVFBe3GuY4mseJ8BhfkKnFIr0hJUeZO\nzh0Giru4p1PJqjhf3djz8aBMMW7EYMpGDGL4kEKGDSlg+JBCRpQUMmxIYdp03QbVNxEKBYt2s0Iw\nckRRzIkISb5UFWU+/txg4GAX93QqWRXn87OgeNAADlXHXis1YkgBF0wZk5SYku259eXsOxR7lX5J\nUR53XDej065jbXUdtdXp82jSwqljeG59BQumjqG6qpbqFMSgivPRJTK5LQcuB/7USVHm14BJZlYC\nVBN0Se8iqFUa7Z6UysnO4sJpZTy8dFvM6z524STOOWVokqJKruFDCvjpw7H/SS6cXtYnxsSS4ZqL\njWsu1tKPviqR00p/AerMbAXB5MHnzewqM/uEuzcCXwCeBlYSzJZWdHZPAuM7YZecN47ppw6Lev7y\nORPSNrEBTDt1GJeeNz7q+ZmnDee9M8clMSKR6LTO7QS1tIRZuWkXi1+s4K1dh8nOCmHjhvCeGWWc\nNbE0maGkzMatlfxj3Xb8nYO0tISZMGow755axqwzR5ClyYCk0jq36JTcRPoxJbfo0nMpvYhkPCU3\nEUlLSm4ikpb6/ZibiEhn1HITkbSk5CYiaUnJTUTSkpKbiKQlJTcRSUtKbiKSlpTcRCQtaXe9HjCz\nWcD33H1BqmNJtshW8fcDE4A84Fvu/lhKg0oiM8sGfgEYwVZdn3L3V1IblbSllls3mdlXgPuA/FTH\nkiLXAJXufj7wPuCnKY4n2S4HcPe5wH8A305tOHI8Jbfu2wp8MNVBpNCDwNcir0MEVcwyhrs/Anwi\ncjieYCdp6UPULe0md/+zmU1IdRyp4u7VAGY2GHiIoPWSUdy9ycx+DXwA+HCq45H21HKTbjOzscAi\n4Lfu/n+pjicV3P064FTgF2Y2MNXxyDFquUm3mNkI4BngM+7+bKrjSTYzuxYoc/c7gSNAS+RH+ggl\nN+mu24EhwNfMrHXs7RJ3r01hTMn0MPBLM1sK5AK3ZNB37xe05ZGIpCWNuYlIWlJyE5G0pOQmImlJ\nyU1E0pKSm4ikJS0FESJPWmwBXiV4CHwAsAO4wd3LO7n+emCBu1+fvChFToySm7Ta4e5TWg/M7E7g\nJwSPFon0O0puEs1S4P1mdhFwN8EQxtvAVW0vMrOPAF8ECiI/N7n7UjP7AnAdwar9F9z9k2Y2GbiX\n4L+7OoKW4evJ+kKSWTTmJh1E9mq7EngB+B1wnbufDWwkSFit12UBnwIuc/dzgO8CXzazHOA2YAYw\nHWgxszHA54G73X0GQavwvOR9K8k0ekJBjh9zg2DzyReAnwE/d/dpx11/PZExNzMrItjbzIAFQLO7\nLzSzRwm2AnoUeNDdXzGzD0d+598iP4+5e3OCv55kKHVLpVW7MTcAMzvnuONiYHCb40HAGuC3BN3Y\njcBnIqevIGiZXQI8ZWZXu/tDZrYSuAy4BbgU+Hhivo5kOnVLJRYHhpnZGZHjrxB0Q1udSjCm9h3g\nOYJElm1mw4DXgJfd/Q6C3UMmm9kfgXPd/R6CjS7btQhFepOSm0Tl7nUE24n/xsw2AmcQjKu1egnY\nAGwG1gPVwHh33wvcA6wxs3UEu4f8iiAJ3m5m64G7gC8k6atIBtKYm4ikJbXcRCQtKbmJSFpSchOR\ntKTkJiJpSclNRNKSkpuIpCUlNxFJS/8PT4hQI5ZrcdkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11302c250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Embarked')\n",
    "grid = sns.FacetGrid(train_df, row='Embarked', size=2.2, aspect=1.6)\n",
    "grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6b3f73f4-4600-c1ce-34e0-bd7d9eeb074a",
    "_uuid": "dd1c43bf6381c9cf4c426f1b9ba4ee89a6f5e990"
   },
   "source": [
    "### Correlating categorical and numerical features\n",
    "\n",
    "We may also want to correlate categorical features (with non-numeric values) and numeric features. We can consider correlating Embarked (Categorical non-numeric), Sex (Categorical non-numeric), Fare (Numeric continuous), with Survived (Categorical numeric).\n",
    "\n",
    "**Observations.**\n",
    "\n",
    "- Higher fare paying passengers had better survival. Confirms our assumption for creating (#4) fare ranges.\n",
    "- Port of embarkation correlates with survival rates. Confirms correlating (#1) and completing (#2).\n",
    "\n",
    "**Decisions.**\n",
    "\n",
    "- Consider banding Fare feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "_cell_guid": "a21f66ac-c30d-f429-cc64-1da5460d16a9",
    "_uuid": "3651478fae6fad777d19b4690504de14a8cbbd7f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1130127d0>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AvB34HkBm3ga8qc3rlyRJQEd/f3/bVhYR/wD8c2YuLZ//DNghM9e1rQhJktT2HoCngO7G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\ncsltHCL9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112e01f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Embarked', hue='Survived', palette={0: 'k', 1: 'w'})\n",
    "grid = sns.FacetGrid(train_df, row='Embarked', col='Survived', size=2.2, aspect=1.6)\n",
    "grid.map(sns.barplot, 'Sex', 'Fare', alpha=.5, ci=None)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "cfac6291-33cc-506e-e548-6cad9408623d",
    "_uuid": "31b38baf9620a8f40c7e62db8877bba62ddb5bd2"
   },
   "source": [
    "## Wrangle data\n",
    "\n",
    "We have collected several assumptions and decisions regarding our datasets and solution requirements. So far we did not have to change a single feature or value to arrive at these. Let us now execute our decisions and assumptions for correcting, creating, and completing goals.\n",
    "\n",
    "### Correcting by dropping features\n",
    "\n",
    "This is a good starting goal to execute. By dropping features we are dealing with fewer data points. Speeds up our notebook and eases the analysis.\n",
    "\n",
    "Based on our assumptions and decisions we want to drop the Cabin (correcting #2) and Ticket (correcting #1) features.\n",
    "\n",
    "Note that where applicable we perform operations on both training and testing datasets together to stay consistent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "_cell_guid": "da057efe-88f0-bf49-917b-bb2fec418ed9",
    "_uuid": "4ce3cc4ffa231935c5ae7f15d1124b628063faa9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Before', (891, 12), (418, 11), (891, 12), (418, 11))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('After', (891, 10), (418, 9), (891, 10), (418, 9))"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Before\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)\n",
    "\n",
    "train_df = train_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "test_df = test_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "\n",
    "\"After\", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6b3a1216-64b6-7fe2-50bc-e89cc964a41c",
    "_uuid": "4f385afabdc686c31d2e9ab41784c7e1a484dc30"
   },
   "source": [
    "### Creating new feature extracting from existing\n",
    "\n",
    "We want to analyze if Name feature can be engineered to extract titles and test correlation between titles and survival, before dropping Name and PassengerId features.\n",
    "\n",
    "In the following code we extract Title feature using regular expressions. The RegEx pattern `(\\w+\\.)` matches the first word which ends with a dot character within Name feature. The `expand=False` flag returns a DataFrame.\n",
    "\n",
    "**Observations.**\n",
    "\n",
    "When we plot Title, Age, and Survived, we note the following observations.\n",
    "\n",
    "- Most titles band Age groups accurately. For example: Master title has Age mean of 5 years.\n",
    "- Survival among Title Age bands varies slightly.\n",
    "- Certain titles mostly survived (Mme, Lady, Sir) or did not (Don, Rev, Jonkheer).\n",
    "\n",
    "**Decision.**\n",
    "\n",
    "- We decide to retain the new Title feature for model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "_cell_guid": "df7f0cd4-992c-4a79-fb19-bf6f0c024d4b",
    "_uuid": "f5c309b58765b931683d19d5db76a9277ee17772"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capt</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Countess</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonkheer</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lady</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Title                 \n",
       "Capt           0     1\n",
       "Col            0     2\n",
       "Countess       1     0\n",
       "Don            0     1\n",
       "Dr             1     6\n",
       "Jonkheer       0     1\n",
       "Lady           1     0\n",
       "Major          0     2\n",
       "Master         0    40\n",
       "Miss         182     0\n",
       "Mlle           2     0\n",
       "Mme            1     0\n",
       "Mr             0   517\n",
       "Mrs          125     0\n",
       "Ms             1     0\n",
       "Rev            0     6\n",
       "Sir            0     1"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "\n",
    "pd.crosstab(train_df['Title'], train_df['Sex'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "908c08a6-3395-19a5-0cd7-13341054012a",
    "_uuid": "6d406f0aefa01b0512623f24fe1b62e53b002825"
   },
   "source": [
    "We can replace many titles with a more common name or classify them as `Rare`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "_cell_guid": "553f56d7-002a-ee63-21a4-c0efad10cfe9",
    "_uuid": "b58afe0c2eea21534e2719b171922a9452f3d9dc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rare</td>\n",
       "      <td>0.347826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title  Survived\n",
       "0  Master  0.575000\n",
       "1    Miss  0.702703\n",
       "2      Mr  0.156673\n",
       "3     Mrs  0.793651\n",
       "4    Rare  0.347826"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\\\n",
    " \t'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "\n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
    "    \n",
    "train_df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6d46be9a-812a-f334-73b9-56ed912c9eca",
    "_uuid": "493af795ca1da1aecce3e9dab35daaaf4f98e52f"
   },
   "source": [
    "We can convert the categorical titles to ordinal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "_cell_guid": "67444ebc-4d11-bac1-74a6-059133b6e2e8",
    "_uuid": "a7dca77165256f202ba12e769b21448b92a86707"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch     Fare Embarked  Title  \n",
       "0      0   7.2500        S      1  \n",
       "1      0  71.2833        C      3  \n",
       "2      0   7.9250        S      2  \n",
       "3      0  53.1000        S      3  \n",
       "4      0   8.0500        S      1  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f27bb974-a3d7-07a1-f7e4-876f6da87e62",
    "_uuid": "9895ac2ae42bfe34034578a68b1cf2381f6d3c39"
   },
   "source": [
    "Now we can safely drop the Name feature from training and testing datasets. We also do not need the PassengerId feature in the training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "_cell_guid": "9d61dded-5ff0-5018-7580-aecb4ea17506",
    "_uuid": "01270064ebe67f7d2eae99f658188f4646af9eec"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 9), (418, 9))"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['Name', 'PassengerId'], axis=1)\n",
    "test_df = test_df.drop(['Name'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2c8e84bb-196d-bd4a-4df9-f5213561b5d3",
    "_uuid": "0ed2f5d32834b64084bf492a9aefd45fe5e40ba5"
   },
   "source": [
    "### Converting a categorical feature\n",
    "\n",
    "Now we can convert features which contain strings to numerical values. This is required by most model algorithms. Doing so will also help us in achieving the feature completing goal.\n",
    "\n",
    "Let us start by converting Sex feature to a new feature called Gender where female=1 and male=0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "_cell_guid": "c20c1df2-157c-e5a0-3e24-15a828095c96",
    "_uuid": "7fb4f6b8569d388efe65764515511f3a30f1b3d0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0  22.0      1      0   7.2500        S      1\n",
       "1         1       1    1  38.0      1      0  71.2833        C      3\n",
       "2         1       3    1  26.0      0      0   7.9250        S      2\n",
       "3         1       1    1  35.0      1      0  53.1000        S      3\n",
       "4         0       3    0  35.0      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d72cb29e-5034-1597-b459-83a9640d3d3a",
    "_uuid": "8691f070177fa364489347c60b1f8bff401dc50f"
   },
   "source": [
    "### Completing a numerical continuous feature\n",
    "\n",
    "Now we should start estimating and completing features with missing or null values. We will first do this for the Age feature.\n",
    "\n",
    "We can consider three methods to complete a numerical continuous feature.\n",
    "\n",
    "1. A simple way is to generate random numbers between mean and [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation).\n",
    "\n",
    "2. More accurate way of guessing missing values is to use other correlated features. In our case we note correlation among Age, Gender, and Pclass. Guess Age values using [median](https://en.wikipedia.org/wiki/Median) values for Age across sets of Pclass and Gender feature combinations. So, median Age for Pclass=1 and Gender=0, Pclass=1 and Gender=1, and so on...\n",
    "\n",
    "3. Combine methods 1 and 2. So instead of guessing age values based on median, use random numbers between mean and standard deviation, based on sets of Pclass and Gender combinations.\n",
    "\n",
    "Method 1 and 3 will introduce random noise into our models. The results from multiple executions might vary. We will prefer method 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "_cell_guid": "c311c43d-6554-3b52-8ef8-533ca08b2f68",
    "_uuid": "2a472a56f251cb0cd194024c87d9d464a9b2a5c3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x113b1a490>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jGrXxwPzGtCthYa5OwGnAdESsBA4Drgb2avn5BPB421uXJEl9M+s5AZn52sxc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113ba8950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# grid = sns.FacetGrid(train_df, col='Pclass', hue='Gender')\n",
    "grid = sns.FacetGrid(train_df, row='Pclass', col='Sex', size=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a4f166f9-f5f9-1819-66c3-d89dd5b0d8ff",
    "_uuid": "3fa4cd9c76cadc708ab86c0f7bb6f6014f45563a"
   },
   "source": [
    "Let us start by preparing an empty array to contain guessed Age values based on Pclass x Gender combinations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "_cell_guid": "9299523c-dcf1-fb00-e52f-e2fb860a3920",
    "_uuid": "4843fa70e9220c49c779f54025dae6232a2354a5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.]])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guess_ages = np.zeros((2,3))\n",
    "guess_ages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ec9fed37-16b1-5518-4fa8-0a7f579dbc82",
    "_uuid": "542537e1e94b550269b2b8937d9a266eb6999f36"
   },
   "source": [
    "Now we iterate over Sex (0 or 1) and Pclass (1, 2, 3) to calculate guessed values of Age for the six combinations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "_cell_guid": "a4015dfa-a0ab-65bc-0cbe-efecf1eb2569",
    "_uuid": "120fb07faad9805f46f7db3c5d7635c556045b1c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0   22      1      0   7.2500        S      1\n",
       "1         1       1    1   38      1      0  71.2833        C      3\n",
       "2         1       3    1   26      0      0   7.9250        S      2\n",
       "3         1       1    1   35      1      0  53.1000        S      3\n",
       "4         0       3    0   35      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            guess_df = dataset[(dataset['Sex'] == i) & \\\n",
    "                                  (dataset['Pclass'] == j+1)]['Age'].dropna()\n",
    "\n",
    "            # age_mean = guess_df.mean()\n",
    "            # age_std = guess_df.std()\n",
    "            # age_guess = rnd.uniform(age_mean - age_std, age_mean + age_std)\n",
    "\n",
    "            age_guess = guess_df.median()\n",
    "\n",
    "            # Convert random age float to nearest .5 age\n",
    "            guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5\n",
    "            \n",
    "    for i in range(0, 2):\n",
    "        for j in range(0, 3):\n",
    "            dataset.loc[ (dataset.Age.isnull()) & (dataset.Sex == i) & (dataset.Pclass == j+1),\\\n",
    "                    'Age'] = guess_ages[i,j]\n",
    "\n",
    "    dataset['Age'] = dataset['Age'].astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "dbe0a8bf-40bc-c581-e10e-76f07b3b71d4",
    "_uuid": "7b9b9aa4c4d052e348531bd6f34bdf7ef96680e5"
   },
   "source": [
    "Let us create Age bands and determine correlations with Survived."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "_cell_guid": "725d1c84-6323-9d70-5812-baf9994d3aa1",
    "_uuid": "fa388e22dccd8fae9b3279c9d5fc7e25fa746d41"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AgeBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "      <td>0.337374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "      <td>0.412037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(48.0, 64.0]</td>\n",
       "      <td>0.434783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(64.0, 80.0]</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         AgeBand  Survived\n",
       "0  (-0.08, 16.0]  0.550000\n",
       "1   (16.0, 32.0]  0.337374\n",
       "2   (32.0, 48.0]  0.412037\n",
       "3   (48.0, 64.0]  0.434783\n",
       "4   (64.0, 80.0]  0.090909"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['AgeBand'] = pd.cut(train_df['Age'], 5)\n",
    "train_df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ba4be3a0-e524-9c57-fbec-c8ecc5cde5c6",
    "_uuid": "70f0c1ccec5789e8793d986b4bb2ac6b1cfc46aa"
   },
   "source": [
    "Let us replace Age with ordinals based on these bands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "_cell_guid": "797b986d-2c45-a9ee-e5b5-088de817c8b2",
    "_uuid": "6c78c0f2b97c366a24a408404045418edacf58fe"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>AgeBand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title  \\\n",
       "0         0       3    0    1      1      0   7.2500        S      1   \n",
       "1         1       1    1    2      1      0  71.2833        C      3   \n",
       "2         1       3    1    1      0      0   7.9250        S      2   \n",
       "3         1       1    1    2      1      0  53.1000        S      3   \n",
       "4         0       3    0    2      0      0   8.0500        S      1   \n",
       "\n",
       "        AgeBand  \n",
       "0  (16.0, 32.0]  \n",
       "1  (32.0, 48.0]  \n",
       "2  (16.0, 32.0]  \n",
       "3  (32.0, 48.0]  \n",
       "4  (32.0, 48.0]  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:    \n",
    "    dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n",
    "    dataset.loc[ dataset['Age'] > 64, 'Age']\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "004568b6-dd9a-ff89-43d5-13d4e9370b1d",
    "_uuid": "a280a569d1b6c0a564647492736d3c1b93236f6d"
   },
   "source": [
    "We can not remove the AgeBand feature. Why?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "_cell_guid": "875e55d4-51b0-5061-b72c-8a23946133a3",
    "_uuid": "e5459c36f6e6d9495c57818531a5f6aa099f14b0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0    1      1      0   7.2500        S      1\n",
       "1         1       1    1    2      1      0  71.2833        C      3\n",
       "2         1       3    1    1      0      0   7.9250        S      2\n",
       "3         1       1    1    2      1      0  53.1000        S      3\n",
       "4         0       3    0    2      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['AgeBand'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1c237b76-d7ac-098f-0156-480a838a64a9",
    "_uuid": "b2f4f2ce66345087eaa6ddeb26b9fcc184ed5e1b"
   },
   "source": [
    "### Create new feature combining existing features\n",
    "\n",
    "We can create a new feature for FamilySize which combines Parch and SibSp. This will enable us to drop Parch and SibSp from our datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "_cell_guid": "7e6c04ed-cfaa-3139-4378-574fd095d6ba",
    "_uuid": "21e2049e3a0697de1a817bc23c6d91796bc56447"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.724138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.578431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.552795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FamilySize  Survived\n",
       "3           4  0.724138\n",
       "2           3  0.578431\n",
       "1           2  0.552795\n",
       "6           7  0.333333\n",
       "0           1  0.303538\n",
       "4           5  0.200000\n",
       "5           6  0.136364\n",
       "7           8  0.000000\n",
       "8          11  0.000000"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\n",
    "\n",
    "train_df[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "842188e6-acf8-2476-ccec-9e3451e4fa86",
    "_uuid": "a0a214100aefc5617d66904fa2fca783fb21a2f6"
   },
   "source": [
    "We can create another feature called IsAlone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "_cell_guid": "5c778c69-a9ae-1b6b-44fe-a0898d07be7a",
    "_uuid": "1b365d7dc1022c008c06b04aac9a9822fce4b2a5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.505650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   IsAlone  Survived\n",
       "0        0  0.505650\n",
       "1        1  0.303538"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['IsAlone'] = 0\n",
    "    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1\n",
    "\n",
    "train_df[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e6b87c09-e7b2-f098-5b04-4360080d26bc",
    "_uuid": "60a2d4ee294d48b8aefc78325acf61e7fed4fb6d"
   },
   "source": [
    "Let us drop Parch, SibSp, and FamilySize features in favor of IsAlone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "_cell_guid": "74ee56a6-7357-f3bc-b605-6c41f8aa6566",
    "_uuid": "608f80080638d2ec0004e5638b223cb568eafa3d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age     Fare Embarked  Title  IsAlone\n",
       "0         0       3    0    1   7.2500        S      1        0\n",
       "1         1       1    1    2  71.2833        C      3        0\n",
       "2         1       3    1    1   7.9250        S      2        1\n",
       "3         1       1    1    2  53.1000        S      3        0\n",
       "4         0       3    0    2   8.0500        S      1        1"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\n",
    "test_df = test_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\n",
    "combine = [train_df, test_df]\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f890b730-b1fe-919e-fb07-352fbd7edd44",
    "_uuid": "5544f96479ee28ee261eebb94d8f47db233c9cb4"
   },
   "source": [
    "We can also create an artificial feature combining Pclass and Age."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "13292c1b-020d-d9aa-525c-941331bb996a",
    "_uuid": "27b2e3f9da4a7a02f8a11519ec7c70ecb9deac47"
   },
   "source": [
    "### Completing a categorical feature\n",
    "\n",
    "Embarked feature takes S, Q, C values based on port of embarkation. Our training dataset has two missing values. We simply fill these with the most common occurance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "_cell_guid": "bf351113-9b7f-ef56-7211-e8dd00665b18",
    "_uuid": "314d397afe349a0056c51ade70dea4ebab5c0c06"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'S'"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_port = train_df.Embarked.dropna().mode()[0]\n",
    "freq_port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "_cell_guid": "51c21fcc-f066-cd80-18c8-3d140be6cbae",
    "_uuid": "d5d3f879f54934be4b3ac952ad449960a797d2cc"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
       "      <td>0.553571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Q</td>\n",
       "      <td>0.389610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S</td>\n",
       "      <td>0.339009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Embarked  Survived\n",
       "0        C  0.553571\n",
       "1        Q  0.389610\n",
       "2        S  0.339009"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)\n",
    "    \n",
    "train_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f6acf7b2-0db3-e583-de50-7e14b495de34",
    "_uuid": "df53eb42900d01e25c44018303864e07b2ba15de"
   },
   "source": [
    "### Converting categorical feature to numeric\n",
    "\n",
    "We can now convert the EmbarkedFill feature by creating a new numeric Port feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "_cell_guid": "89a91d76-2cc0-9bbb-c5c5-3c9ecae33c66",
    "_uuid": "cf7ac7e8adcc6b69e9dae591cb31fab767fac4b6"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age     Fare  Embarked  Title  IsAlone\n",
       "0         0       3    0    1   7.2500         0      1        0\n",
       "1         1       1    1    2  71.2833         1      3        0\n",
       "2         1       3    1    1   7.9250         0      2        1\n",
       "3         1       1    1    2  53.1000         0      3        0\n",
       "4         0       3    0    2   8.0500         0      1        1"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for dataset in combine:\n",
    "    dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e3dfc817-e1c1-a274-a111-62c1c814cecf",
    "_uuid": "c2c95fb1ae6968f961e5b36b66f030a6371d4653"
   },
   "source": [
    "### Quick completing and converting a numeric feature\n",
    "\n",
    "We can now complete the Fare feature for single missing value in test dataset using mode to get the value that occurs most frequently for this feature. We do this in a single line of code.\n",
    "\n",
    "Note that we are not creating an intermediate new feature or doing any further analysis for correlation to guess missing feature as we are replacing only a single value. The completion goal achieves desired requirement for model algorithm to operate on non-null values.\n",
    "\n",
    "We may also want round off the fare to two decimals as it represents currency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "_cell_guid": "3600cb86-cf5f-d87b-1b33-638dc8db1564",
    "_uuid": "40c12b484fa23421a7b3ccffb4baed7b994645b6"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age     Fare  Embarked  Title  IsAlone\n",
       "0          892       3    0    2   7.8292         2      1        1\n",
       "1          893       3    1    2   7.0000         0      3        0\n",
       "2          894       2    0    3   9.6875         2      1        1\n",
       "3          895       3    0    1   8.6625         0      1        1\n",
       "4          896       3    1    1  12.2875         0      3        0"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['Fare'].fillna(test_df['Fare'].dropna().median(), inplace=True)\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d65901a5-3684-6869-e904-5f1a7cce8a6d",
    "_uuid": "a2959d7cc5ae1ddc671557f3d1310370cf452a90"
   },
   "source": [
    "Convert the Fare feature to ordinal values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "_cell_guid": "385f217a-4e00-76dc-1570-1de4eec0c29c",
    "_uuid": "ef36434fce94ab049cc4c9cde5d6e8ee31705cf7"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
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       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone\n",
       "0         0       3    0    1     0         0      1        0\n",
       "1         1       1    1    2     0         1      3        0\n",
       "2         1       3    1    1     0         0      2        1\n",
       "3         1       1    1    2     0         0      3        0\n",
       "4         0       3    0    2     0         0      1        1\n",
       "5         0       3    0    1     0         2      1        1\n",
       "6         0       1    0    3     0         0      1        1\n",
       "7         0       3    0    0     0         0      4        0\n",
       "8         1       3    1    1     0         0      3        0\n",
       "9         1       2    1    0     0         1      3        0"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#train_df['FareBand'] = pd.qcut(train_df['Fare'], 4)\n",
    "#train_df[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)\n",
    "for dataset in combine:\n",
    "    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0\n",
    "    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n",
    "    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare']   = 2\n",
    "    dataset.loc[ dataset['Fare'] > 31, 'Fare'] = 3\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)\n",
    "\n",
    "combine = [train_df, test_df]\n",
    "    \n",
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "27272bb9-3c64-4f9a-4a3b-54f02e1c8289",
    "_uuid": "e09b9d63940ef5ff1295295a522a4fa3df435d04"
   },
   "source": [
    "And the test dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "_cell_guid": "d2334d33-4fe5-964d-beac-6aa620066e15",
    "_uuid": "603ac6180321892be699426ef257e6a0c09ea001"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>894</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>897</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>899</td>\n",
       "      <td>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>3</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone\n",
       "0          892       3    0    2     0         2      1        1\n",
       "1          893       3    1    2     0         0      3        0\n",
       "2          894       2    0    3     0         2      1        1\n",
       "3          895       3    0    1     0         0      1        1\n",
       "4          896       3    1    1     0         0      3        0\n",
       "5          897       3    0    0     0         0      1        1\n",
       "6          898       3    1    1     0         2      2        1\n",
       "7          899       2    0    1     0         0      1        0\n",
       "8          900       3    1    1     0         1      3        1\n",
       "9          901       3    0    1     0         0      1        0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "69783c08-c8cc-a6ca-2a9a-5e75581c6d31",
    "_uuid": "37c357803dbc22a3fdf21061dfaf9dd39fb26e96"
   },
   "source": [
    "## Model, predict and solve\n",
    "\n",
    "All of the data has now been collated, cleaned and is ready for use\n",
    "\n",
    "** In this example we use all data as training and testing, with test being our predicting data **\n",
    "\n",
    "Now we are ready to train a model and predict the required solution. There are 60+ predictive modelling algorithms to choose from. We must understand the type of problem and solution requirement to narrow down to a select few models which we can evaluate. Our problem is a classification and regression problem. We want to identify relationship between output (Survived or not) with other variables or features (Gender, Age, Port...). We are also perfoming a category of machine learning which is called supervised learning as we are training our model with a given dataset. With these two criteria - Supervised Learning plus Classification and Regression, we can narrow down our choice of models to a few. These include:\n",
    "\n",
    "- Logistic Regression\n",
    "- KNN or k-Nearest Neighbors\n",
    "- Support Vector Machines\n",
    "- Naive Bayes classifier\n",
    "- Decision Tree\n",
    "- Random Forrest\n",
    "- Perceptron\n",
    "- Artificial neural network\n",
    "- RVM or Relevance Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "_cell_guid": "0acf54f9-6cf5-24b5-72d9-29b30052823a",
    "_uuid": "f5bdd9da299f051934d3da38f665333fad387fc8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 7), (891,), (418, 7))"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = train_df.drop(\"Survived\", axis=1)\n",
    "Y_train = train_df[\"Survived\"]\n",
    "X_test  = test_df.drop(\"PassengerId\", axis=1).copy()\n",
    "X_train.shape, Y_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "579bc004-926a-bcfe-e9bb-c8df83356876",
    "_uuid": "71cc3307dca44bf584f0b4e662f8c7205451b73a"
   },
   "source": [
    "Logistic Regression is a useful model to run early in the workflow. Logistic regression measures the relationship between the categorical dependent variable (feature) and one or more independent variables (features) by estimating probabilities using a logistic function, which is the cumulative logistic distribution. Reference [Wikipedia](https://en.wikipedia.org/wiki/Logistic_regression).\n",
    "\n",
    "Note the confidence score generated by the model based on our training dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Sex</th>\n",
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       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Sex  Age  Fare  Embarked  Title  IsAlone\n",
       "0       3    0    1     0         0      1        0\n",
       "1       1    1    2     0         1      3        0\n",
       "2       3    1    1     0         0      2        1\n",
       "3       1    1    2     0         0      3        0\n",
       "4       3    0    2     0         0      1        1"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    0\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   Pclass  Sex  Age  Fare  Embarked  Title  IsAlone\n",
       "0       3    0    2     0         2      1        1\n",
       "1       3    1    2     0         0      3        0\n",
       "2       2    0    3     0         2      1        1\n",
       "3       3    0    1     0         0      1        1\n",
       "4       3    1    1     0         0      3        0"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
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    "_uuid": "9d61a3fb0d404d59e646acf4b24e1d7625981887"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.36"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, Y_train)\n",
    "Y_pred = logreg.predict(X_test)\n",
    "acc_log = round(logreg.score(X_train, Y_train) * 100, 2)\n",
    "acc_log\n",
    "\n",
    "#\n",
    "#Score Returns the coefficient of determination R^2 of the prediction.\n",
    "#The coefficient R^2 is defined as (1 - u/v), \n",
    "#where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() \n",
    "#and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). \n",
    "#The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). \n",
    "#A constant model that always predicts the expected value of y, disregarding the input features, \n",
    "#would get a R^2 score of 0.0.\n",
    "#http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "3af439ae-1f04-9236-cdc2-ec8170a0d4ee",
    "_uuid": "ee4252852a14cf0c50a8d8d88332483778d9f313"
   },
   "source": [
    "We can use Logistic Regression to validate our assumptions and decisions for feature creating and completing goals. This can be done by calculating the coefficient of the features in the decision function.\n",
    "\n",
    "Positive coefficients increase the log-odds of the response (and thus increase the probability), and negative coefficients decrease the log-odds of the response (and thus decrease the probability).\n",
    "\n",
    "- Sex is highest positivie coefficient, implying as the Sex value increases (male: 0 to female: 1), the probability of Survived=1 increases the most.\n",
    "- Inversely as Pclass increases, probability of Survived=1 decreases the most.\n",
    "- This way Age*Class is a good artificial feature to model as it has second highest negative correlation with Survived.\n",
    "- So is Title as second highest positive correlation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "_cell_guid": "e545d5aa-4767-7a41-5799-a4c5e529ce72",
    "_uuid": "868139e48e40e9d7c14308fea565df9a1776c59b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>Correlation/Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sex</td>\n",
       "      <td>2.201527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Title</td>\n",
       "      <td>0.398234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Age</td>\n",
       "      <td>0.287163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Embarked</td>\n",
       "      <td>0.261762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>IsAlone</td>\n",
       "      <td>0.129140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fare</td>\n",
       "      <td>-0.085150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pclass</td>\n",
       "      <td>-0.749007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Feature  Correlation/Coefficient\n",
       "1       Sex                 2.201527\n",
       "5     Title                 0.398234\n",
       "2       Age                 0.287163\n",
       "4  Embarked                 0.261762\n",
       "6   IsAlone                 0.129140\n",
       "3      Fare                -0.085150\n",
       "0    Pclass                -0.749007"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeff_df = pd.DataFrame(train_df.columns.delete(0))\n",
    "coeff_df.columns = ['Feature']\n",
    "coeff_df[\"Correlation/Coefficient\"] = pd.Series(logreg.coef_[0])\n",
    "\n",
    "coeff_df.sort_values(by='Correlation/Coefficient', ascending=False)\n",
    "\n",
    "#Why these values?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "ac041064-1693-8584-156b-66674117e4d0",
    "_uuid": "3fc915a8bd57608fa2f0312bb50e47c813a8a187"
   },
   "source": [
    "Next we model using Support Vector Machines which are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training samples, each marked as belonging to one or the other of **two categories**, an SVM training algorithm builds a model that assigns new test samples to one category or the other, making it a non-probabilistic binary linear classifier. Reference [Wikipedia](https://en.wikipedia.org/wiki/Support_vector_machine).\n",
    "\n",
    "Note that the model generates a confidence score which is higher than Logistics Regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "_cell_guid": "7a63bf04-a410-9c81-5310-bdef7963298f",
    "_uuid": "67ccd3413cb9847b9dd2b6cf5ce22d775a992718"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82.83"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Support Vector Machines\n",
    "\n",
    "svc = SVC()\n",
    "svc.fit(X_train, Y_train)\n",
    "Y_pred = svc.predict(X_test)\n",
    "acc_svc = round(svc.score(X_train, Y_train) * 100, 2)\n",
    "acc_svc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.34"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear SVC\n",
    "\n",
    "linear_svc = LinearSVC()\n",
    "linear_svc.fit(X_train, Y_train)\n",
    "Y_pred = linear_svc.predict(X_test)\n",
    "acc_linear_svc = round(linear_svc.score(X_train, Y_train) * 100, 2)\n",
    "acc_linear_svc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "172a6286-d495-5ac4-1a9c-5b77b74ca6d2",
    "_uuid": "ec1502b83305418546a3b6eb1545ab3c416c0042"
   },
   "source": [
    "In pattern recognition, the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression. A sample is classified by a majority vote of its neighbors, with the sample being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. Reference [Wikipedia](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm).\n",
    "\n",
    "KNN confidence score is better than Logistics Regression but worse than SVM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "_cell_guid": "ca14ae53-f05e-eb73-201c-064d7c3ed610",
    "_uuid": "023ce86354ffe6d25bf4a88b88724d8706af45f9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80.36"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X_train, Y_train)\n",
    "Y_pred = knn.predict(X_test)\n",
    "acc_knn = round(knn.score(X_train, Y_train) * 100, 2)\n",
    "acc_knn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "810f723d-2313-8dfd-e3e2-26673b9caa90",
    "_uuid": "121d93bfed00e97798813040d15dee557ec843d1"
   },
   "source": [
    "In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features. Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features) in a learning problem. Reference [Wikipedia](https://en.wikipedia.org/wiki/Naive_Bayes_classifier).\n",
    "\n",
    "The model generated confidence score is the lowest among the models evaluated so far."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "_cell_guid": "50378071-7043-ed8d-a782-70c947520dae",
    "_uuid": "509a13a214236c62fae9f47666a093fcbb237f21"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.23"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gaussian Naive Bayes\n",
    "\n",
    "gaussian = GaussianNB()\n",
    "gaussian.fit(X_train, Y_train)\n",
    "Y_pred = gaussian.predict(X_test)\n",
    "acc_gaussian = round(gaussian.score(X_train, Y_train) * 100, 2)\n",
    "acc_gaussian"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1e286e19-b714-385a-fcfa-8cf5ec19956a",
    "_uuid": "f49906c64fb74e453f7a64e89e23fe5a4983919b"
   },
   "source": [
    "The perceptron is an algorithm for supervised learning of binary classifiers (functions that can decide whether an input, represented by a vector of numbers, belongs to some specific class or not). It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector. The algorithm allows for online learning, in that it processes elements in the training set one at a time. Reference [Wikipedia](https://en.wikipedia.org/wiki/Perceptron)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "_cell_guid": "ccc22a86-b7cb-c2dd-74bd-53b218d6ed0d",
    "_uuid": "ea6fa205c1a610012775ff59bd43bcb2c23ef07d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.34"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perceptron\n",
    "\n",
    "perceptron = Perceptron()\n",
    "perceptron.fit(X_train, Y_train)\n",
    "Y_pred = perceptron.predict(X_test)\n",
    "acc_perceptron = round(perceptron.score(X_train, Y_train) * 100, 2)\n",
    "acc_perceptron"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SGD\n",
    "Reference [Wikipedia](https://en.wikipedia.org/wiki/Stochastic_gradient_descent).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "_cell_guid": "dc98ed72-3aeb-861f-804d-b6e3d178bf4b",
    "_uuid": "0f8fc96b0c45f66f99dad035f7ffbbc9a317d5d4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.89"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Stochastic Gradient Descent\n",
    "\n",
    "sgd = SGDClassifier()\n",
    "sgd.fit(X_train, Y_train)\n",
    "Y_pred = sgd.predict(X_test)\n",
    "acc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)\n",
    "acc_sgd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "bae7f8d7-9da0-f4fd-bdb1-d97e719a18d7",
    "_uuid": "b28fa051cd057b7fc6ea5e13e6f598ebcce71112"
   },
   "source": [
    "This model uses a decision tree as a predictive model which maps features (tree branches) to conclusions about the target value (tree leaves). Tree models where the target variable can take a finite set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. Reference [Wikipedia](https://en.wikipedia.org/wiki/Decision_tree_learning).\n",
    "\n",
    "The model confidence score is the highest among models evaluated so far."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "_cell_guid": "dd85f2b7-ace2-0306-b4ec-79c68cd3fea0",
    "_uuid": "d7866134fb03d557449d9de9981c1f00813856af"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.29"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree\n",
    "\n",
    "decision_tree = DecisionTreeClassifier()\n",
    "decision_tree.fit(X_train, Y_train)\n",
    "Y_pred = decision_tree.predict(X_test)\n",
    "acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)\n",
    "acc_decision_tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "85693668-0cd5-4319-7768-eddb62d2b7d0",
    "_uuid": "ff5e5b08f907ad1e0d5dfbaedb46deda6ae485b5"
   },
   "source": [
    "The next model Random Forests is one of the most popular. Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees (n_estimators=100) at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Reference [Wikipedia](https://en.wikipedia.org/wiki/Random_forest).\n",
    "\n",
    "The model confidence score is the highest among models evaluated so far. We decide to use this model's output (Y_pred) for creating our competition submission of results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "_cell_guid": "f0694a8e-b618-8ed9-6f0d-8c6fba2c4567",
    "_uuid": "3559de22a4ab6d952a8401da977acf0d3ec22d34"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.29"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest\n",
    "\n",
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "Y_pred = random_forest.predict(X_test)\n",
    "random_forest.score(X_train, Y_train)\n",
    "acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\n",
    "acc_random_forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "f6c9eef8-83dd-581c-2d8e-ce932fe3a44d",
    "_uuid": "b385cd5e8e10604ba7070ed9c4ebc1b1c7efe4e3"
   },
   "source": [
    "### Model evaluation\n",
    "\n",
    "We can now rank our evaluation of all the models to choose the best one for our problem. While both Decision Tree and Random Forest score the same, we choose to use Random Forest as they correct for decision trees' habit of overfitting to their training set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "_cell_guid": "1f3cebe0-31af-70b2-1ce4-0fd406bcdfc6",
    "_uuid": "2f67e300f2543da99d375071b1261f78702348bf"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>84.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>84.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>82.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>80.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>80.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Perceptron</td>\n",
       "      <td>78.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>78.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>78.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Stochastic Gradient Decent</td>\n",
       "      <td>77.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model  Score\n",
       "3               Random Forest  84.29\n",
       "8               Decision Tree  84.29\n",
       "0     Support Vector Machines  82.83\n",
       "1                         KNN  80.36\n",
       "2         Logistic Regression  80.36\n",
       "5                  Perceptron  78.34\n",
       "7                  Linear SVC  78.34\n",
       "4                 Naive Bayes  78.23\n",
       "6  Stochastic Gradient Decent  77.89"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', \n",
    "              'Random Forest', 'Naive Bayes', 'Perceptron', \n",
    "              'Stochastic Gradient Decent', 'Linear SVC', \n",
    "              'Decision Tree'],\n",
    "    'Score': [acc_svc, acc_knn, acc_log, \n",
    "              acc_random_forest, acc_gaussian, acc_perceptron, \n",
    "              acc_sgd, acc_linear_svc, acc_decision_tree]})\n",
    "models.sort_values(by='Score', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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