SMUMSE_ProgWorkshop_Lesson5.5.ipynb

{
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  {
   "cell_type": "markdown",
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   "source": [
    "## Final Lesson - Applied Econometrics with Python\n",
    "\n",
    "The final class is dedicated to Applied Econometrics, specifically using Python. How do we run a linear regression in Python? How can we obtain the results?\n",
    "\n",
    "Before we can run a linear regression, we need to conduct data cleaning, processing and manipulation. This draws on the previous lessons we have learnt. If there are any missing variables in the dataset, we have to impute the missing data, or remove the variable, since running a regression requires the observations to be present."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Case-Study approach\n",
    "\n",
    "Similar to the previous lessons, we will be adopting a case-study approach to learn more about the different regressions that can be done in Python. Specifically, we will be using the Titanic dataset. In addition, we will be using the following techniques taught in the previous lessons:\n",
    "\n",
    "1. Data Manipulation, Processing and Cleaning \n",
    "2. Imputing Missing Data\n",
    "3. Feature Generation\n",
    "\n",
    "The first and second section relies on much that we have learnt in lesson 3 on data manipulation, while the last section relies much on the 4th lesson. Without further ado, let's apply our understanding on the Titanic dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preliminary Data Exploration\n",
    "\n",
    "In this section, we import key libraries and read the training and test data into Python. We look at summary statistics to get a better understanding of what we are currently dealing with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def read_and_combine_data(train_filepath, test_filepath):\n",
    "    # This function combines the training and test dataset into 1 dataset\n",
    "    train = pd.read_csv(train_filepath, index_col=0)\n",
    "    test = pd.read_csv(test_filepath, index_col=0)\n",
    "    \n",
    "    combine = pd.concat([train, test], sort=True)\n",
    "    return combine\n",
    "\n",
    "train_filepath = 'data/titanic/train.csv'\n",
    "test_filepath = 'data/titanic/test.csv'\n",
    "train = pd.read_csv(train_filepath, index_col=0)\n",
    "test = pd.read_csv(test_filepath, index_col=0)\n",
    "\n",
    "\n",
    "combine = read_and_combine_data(train_filepath, test_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
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       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
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       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th>1</th>\n",
       "      <td>22.0</td>\n",
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       "      <td>7.2500</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5 21171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>PC 17599</td>\n",
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       "      <th>3</th>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age Cabin Embarked     Fare  \\\n",
       "PassengerId                                 \n",
       "1            22.0   NaN        S   7.2500   \n",
       "2            38.0   C85        C  71.2833   \n",
       "3            26.0   NaN        S   7.9250   \n",
       "4            35.0  C123        S  53.1000   \n",
       "5            35.0   NaN        S   8.0500   \n",
       "\n",
       "                                                          Name  Parch  Pclass  \\\n",
       "PassengerId                                                                     \n",
       "1                                      Braund, Mr. Owen Harris      0       3   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...      0       1   \n",
       "3                                       Heikkinen, Miss. Laina      0       3   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)      0       1   \n",
       "5                                     Allen, Mr. William Henry      0       3   \n",
       "\n",
       "                Sex  SibSp  Survived            Ticket  \n",
       "PassengerId                                             \n",
       "1              male      1       0.0         A/5 21171  \n",
       "2            female      1       1.0          PC 17599  \n",
       "3            female      0       1.0  STON/O2. 3101282  \n",
       "4            female      1       1.0            113803  \n",
       "5              male      0       0.0            373450  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "combine.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
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       "      <th>count</th>\n",
       "      <td>1046.000000</td>\n",
       "      <td>295</td>\n",
       "      <td>1307</td>\n",
       "      <td>1308.000000</td>\n",
       "      <td>1309</td>\n",
       "      <td>1309.000000</td>\n",
       "      <td>1309.000000</td>\n",
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       "      <td>891.000000</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>929</td>\n",
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       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Connolly, Miss. Kate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CA. 2343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "      <td>914</td>\n",
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       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>843</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
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       "      <td>29.881138</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>33.295479</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.385027</td>\n",
       "      <td>2.294882</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.498854</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>std</th>\n",
       "      <td>14.413493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51.758668</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.865560</td>\n",
       "      <td>0.837836</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.041658</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.170000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.895800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.275000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>max</th>\n",
       "      <td>80.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "                Age        Cabin Embarked         Fare                  Name  \\\n",
       "count   1046.000000          295     1307  1308.000000                  1309   \n",
       "unique          NaN          186        3          NaN                  1307   \n",
       "top             NaN  C23 C25 C27        S          NaN  Connolly, Miss. Kate   \n",
       "freq            NaN            6      914          NaN                     2   \n",
       "mean      29.881138          NaN      NaN    33.295479                   NaN   \n",
       "std       14.413493          NaN      NaN    51.758668                   NaN   \n",
       "min        0.170000          NaN      NaN     0.000000                   NaN   \n",
       "25%       21.000000          NaN      NaN     7.895800                   NaN   \n",
       "50%       28.000000          NaN      NaN    14.454200                   NaN   \n",
       "75%       39.000000          NaN      NaN    31.275000                   NaN   \n",
       "max       80.000000          NaN      NaN   512.329200                   NaN   \n",
       "\n",
       "              Parch       Pclass   Sex        SibSp    Survived    Ticket  \n",
       "count   1309.000000  1309.000000  1309  1309.000000  891.000000      1309  \n",
       "unique          NaN          NaN     2          NaN         NaN       929  \n",
       "top             NaN          NaN  male          NaN         NaN  CA. 2343  \n",
       "freq            NaN          NaN   843          NaN         NaN        11  \n",
       "mean       0.385027     2.294882   NaN     0.498854    0.383838       NaN  \n",
       "std        0.865560     0.837836   NaN     1.041658    0.486592       NaN  \n",
       "min        0.000000     1.000000   NaN     0.000000    0.000000       NaN  \n",
       "25%        0.000000     2.000000   NaN     0.000000    0.000000       NaN  \n",
       "50%        0.000000     3.000000   NaN     0.000000    0.000000       NaN  \n",
       "75%        0.000000     3.000000   NaN     1.000000    1.000000       NaN  \n",
       "max        9.000000     3.000000   NaN     8.000000    1.000000       NaN  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imputing Missing Data\n",
    "\n",
    "In our case, we are tasked to predict whether a passenger survived based on the features in the model. However, some of these features are not as useful as others, and we can generate new features using a combination of different features. Before we do that however, we check for missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age          263\n",
       "Cabin       1014\n",
       "Embarked       2\n",
       "Fare           1\n",
       "Name           0\n",
       "Parch          0\n",
       "Pclass         0\n",
       "Sex            0\n",
       "SibSp          0\n",
       "Survived     418\n",
       "Ticket         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(pd.isnull(combine))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that there are quite a significant number of observations with missing data. To circumvent this, we can create a new column for the \"Cabin\" variable, to indicate that the data are missing. For the \"Embarked\" and \"Fare\" feature, we can impute the most popular embarkation point and the average fare."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    914\n",
       "C    270\n",
       "Q    123\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine.Embarked.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.2954792813456"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine.Fare.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_missing_cabins():\n",
    "    return combine.Cabin.fillna(\"N\", inplace=True)\n",
    "\n",
    "def impute_missing_embarkation_point():\n",
    "    return combine.Embarked.fillna(\"S\", inplace=True)\n",
    "\n",
    "def impute_missing_fare():\n",
    "    return combine.Fare.fillna(combine.Fare.mean(), inplace=True)\n",
    "\n",
    "impute_missing_cabins()\n",
    "impute_missing_embarkation_point()\n",
    "impute_missing_fare()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age         263\n",
       "Cabin         0\n",
       "Embarked      0\n",
       "Fare          0\n",
       "Name          0\n",
       "Parch         0\n",
       "Pclass        0\n",
       "Sex           0\n",
       "SibSp         0\n",
       "Survived    418\n",
       "Ticket        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(pd.isnull(combine))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are left with the age variable. There are a few ways to resolve this issue:\n",
    "\n",
    "1. Drop the age variable.\n",
    "2. Impute the missing ages with the average age of the dataset\n",
    "3. Use conditional information to determine the missing age\n",
    "\n",
    "Of the 3, the third way is the hardest - we will do the third way. \n",
    "\n",
    "We first split the Name column into First and Last Name, bearing in mind that the designation of the passenger may contain some important information on the age of the passenger. Let's take a look at an example. The first element in the Name column is \"Braund, Mr. Owen Harris\". Splitting on space yields the following list: [\"Braund,\", \"Mr.\", \"Owen\", \"Harris\"].\n",
    "\n",
    "What happens when we split on \", \"?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Braund', 'Mr. Owen Harris']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine.Name.iloc[0].split(\", \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done.\n"
     ]
    }
   ],
   "source": [
    "def get_desg_first_name(name):\n",
    "    # This returns the second element after splitting on the pattern \", \"\n",
    "    return name.split(\", \")[1]\n",
    "\n",
    "def get_first_name():\n",
    "    # Fist, we ob\n",
    "    first_name = combine.Name.apply(lambda x: get_desg_first_name(x))\n",
    "    combine['FirstName'] = first_name.apply(lambda x: x.split(\". \")[1])\n",
    "    combine['Designation'] = first_name.apply(lambda x: x.split(\". \")[0])\n",
    "    return combine\n",
    "    \n",
    "def get_last_name():\n",
    "    combine['LastName'] = combine.Name.apply(lambda x: x.split(\", \")[0])\n",
    "    return combine\n",
    "\n",
    "get_last_name()\n",
    "get_first_name()\n",
    "print('Done.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>LastName</th>\n",
       "      <th>FirstName</th>\n",
       "      <th>Designation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.0</td>\n",
       "      <td>N</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>Braund</td>\n",
       "      <td>Owen Harris</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>Cumings</td>\n",
       "      <td>John Bradley (Florence Briggs Thayer)</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.0</td>\n",
       "      <td>N</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>Heikkinen</td>\n",
       "      <td>Laina</td>\n",
       "      <td>Miss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113803</td>\n",
       "      <td>Futrelle</td>\n",
       "      <td>Jacques Heath (Lily May Peel)</td>\n",
       "      <td>Mrs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35.0</td>\n",
       "      <td>N</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373450</td>\n",
       "      <td>Allen</td>\n",
       "      <td>William Henry</td>\n",
       "      <td>Mr</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age Cabin Embarked     Fare  \\\n",
       "PassengerId                                 \n",
       "1            22.0     N        S   7.2500   \n",
       "2            38.0   C85        C  71.2833   \n",
       "3            26.0     N        S   7.9250   \n",
       "4            35.0  C123        S  53.1000   \n",
       "5            35.0     N        S   8.0500   \n",
       "\n",
       "                                                          Name  Parch  Pclass  \\\n",
       "PassengerId                                                                     \n",
       "1                                      Braund, Mr. Owen Harris      0       3   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...      0       1   \n",
       "3                                       Heikkinen, Miss. Laina      0       3   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)      0       1   \n",
       "5                                     Allen, Mr. William Henry      0       3   \n",
       "\n",
       "                Sex  SibSp  Survived            Ticket   LastName  \\\n",
       "PassengerId                                                         \n",
       "1              male      1       0.0         A/5 21171     Braund   \n",
       "2            female      1       1.0          PC 17599    Cumings   \n",
       "3            female      0       1.0  STON/O2. 3101282  Heikkinen   \n",
       "4            female      1       1.0            113803   Futrelle   \n",
       "5              male      0       0.0            373450      Allen   \n",
       "\n",
       "                                         FirstName Designation  \n",
       "PassengerId                                                     \n",
       "1                                      Owen Harris          Mr  \n",
       "2            John Bradley (Florence Briggs Thayer)         Mrs  \n",
       "3                                            Laina        Miss  \n",
       "4                    Jacques Heath (Lily May Peel)         Mrs  \n",
       "5                                    William Henry          Mr  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the column, \"Designation\" can be used to impute missing data for the \"Age\" variable. Suppose we have a missing data with the designation, \"Miss\". We can look at the mean value for all observations with the same designation and impute that as the missing value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Designation\n",
       "Capt            70.000000\n",
       "Col             54.000000\n",
       "Don             40.000000\n",
       "Dona            39.000000\n",
       "Dr              43.571429\n",
       "Jonkheer        38.000000\n",
       "Lady            48.000000\n",
       "Major           48.500000\n",
       "Master           5.482642\n",
       "Miss            21.774238\n",
       "Mlle            24.000000\n",
       "Mme             24.000000\n",
       "Mr              32.252151\n",
       "Mrs             36.994118\n",
       "Ms              28.000000\n",
       "Rev             41.250000\n",
       "Sir             49.000000\n",
       "the Countess    33.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine.groupby(\"Designation\")['Age'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def impute_missing_age():\n",
    "    '''\n",
    "    Generate a pd.Series using information on Designation to impute missing age data\n",
    "    '''\n",
    "    age_list = []\n",
    "    for age, desg in zip(combine.Age, combine.Designation):\n",
    "        if pd.isnull(age):\n",
    "            age_list.append(combine.groupby(\"Designation\")['Age'].mean()[desg])\n",
    "        else: age_list.append(age)\n",
    "    return age_list\n",
    "\n",
    "combine['NewAge'] = impute_missing_age()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We check for missing data once again. If everything turns out to be fine, we can begin to explore the relationships inherent in the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Age            263\n",
       "Cabin            0\n",
       "Embarked         0\n",
       "Fare             0\n",
       "Name             0\n",
       "Parch            0\n",
       "Pclass           0\n",
       "Sex              0\n",
       "SibSp            0\n",
       "Survived       418\n",
       "Ticket           0\n",
       "LastName         0\n",
       "FirstName        0\n",
       "Designation      0\n",
       "NewAge           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(pd.isnull(combine))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It turns out that the new age column, \"NewAge\" has no more missing observations. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Exploration and Plotting\n",
    "Everything seems good. We can proceed with data exploration using plots. We begin with some hypotheses that we are interested in, and see what that leads to. Here are some possible hypotheses one may be interested in testing:\n",
    "\n",
    "1. Age is correlated with Survival Rate\n",
    "2. Fare is correlated with Survival Rate\n",
    "3. Designation is correlated with Survival Rate\n",
    "\n",
    "From there, we can generate additional hypotheses: can an interaction term between Gender x Age lead to even better results? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAACgS0QCAAAAoEtEAgAAAKBLRAIAAACgS0QCAAAAoEtEAgAAAKBLRAIAAACg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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f986e63c160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Hypothesis 1 - Age is correlated with Survival Rate\n",
    "plt.figure(figsize=(20, 12))\n",
    "sns.distplot(combine[combine.Survived == 0].NewAge, bins=range(0, 81, 1), color = 'red', label='Perished')\n",
    "sns.distplot(combine[combine.Survived == 1].NewAge, bins=range(0, 81, 1), color = 'blue', label='Survived')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title(\"Age Distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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d91fUAAAAAJhBJrVmUWvtyiRXjtv31qHvqzL2etr4865NsmgX5wgAAADAHjKZ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\nA8edO4MQYNGitO3444v7H36IsOB4QqB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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f986d434ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Hypothesis 2 - Fare is correlated with Survival Rate\n",
    "plt.figure(figsize=(20, 12))\n",
    "sns.distplot(combine[combine.Survived == 0].Fare, bins=range(0, 81, 1), color = 'red', label='Perished')\n",
    "sns.distplot(combine[combine.Survived == 1].Fare, bins=range(0, 81, 1), color = 'blue', label='Survived')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title(\"Age Distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the graphs above, it appears that passengers who are young tend to survive the Titanic crash. In addition, passengers who paid more for their fare tended to have a higher rate of survival. However, something stands out. There appears to be anomaly in the dataset - some passengers paid way more than \\$100 for their fare.\n",
    "\n",
    "We **may** need to conduct a secondary analysis to ensure that this is indeed an anomaly.\n",
    "\n",
    "---\n",
    "\n",
    "Let's test the third hypothesis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gTVQCAAAAoG3dvAdg6Zx/1Y6Zn+P9p8z8FAAAAMAq4EolAAAAANpEJQAAAADa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\nmdXNjUpmZmZmZmZmZla3/wcDkrPXxCaEwAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f986e61fd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Hypothesis 3 - Designation is correlated with Survival Rate\n",
    "plt.figure(figsize=(20, 12))\n",
    "sns.countplot(\"Designation\", hue='Survived', data=combine)\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot seems to imply the following:\n",
    "\n",
    "1. Male passengers have a relatively low survival rate, compared to females\n",
    "2. Children tend to have a relatively high survival rate (their Designation is Master) \n",
    "3. There is not enough data about the other Designations to draw a decisive conclusion\n",
    "\n",
    "---\n",
    "\n",
    "Other hypotheses that we can carry out are as follows:\n",
    "\n",
    "1. Could the feature Ticket be correlated with the Survival Variable?\n",
    "2. Are passengers with Siblings/Spouses and Parents/Children more likely to survive than passengers travelling alone?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "929"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.unique(combine.Ticket))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1309"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(combine.Ticket)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that there are 929 unique value for tickets, but there are 1309 entries. Some passengers might be sharing tickets! To determine how many people are sharing tickets, we can use the DataFrame method, `groupby`. This can be done using the following function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ticket\n",
       "110152               3\n",
       "110413               3\n",
       "110465               2\n",
       "110469               1\n",
       "110489               1\n",
       "110564               1\n",
       "110813               2\n",
       "111163               1\n",
       "111240               1\n",
       "111320               1\n",
       "111361               2\n",
       "111369               1\n",
       "111426               1\n",
       "111427               1\n",
       "111428               1\n",
       "112050               1\n",
       "112051               1\n",
       "112052               1\n",
       "112053               1\n",
       "112058               2\n",
       "112059               1\n",
       "112277               1\n",
       "112377               1\n",
       "112378               2\n",
       "112379               1\n",
       "112901               1\n",
       "113028               1\n",
       "113038               1\n",
       "113043               1\n",
       "113044               1\n",
       "                    ..\n",
       "STON/O 2. 3101273    1\n",
       "STON/O 2. 3101274    1\n",
       "STON/O 2. 3101275    1\n",
       "STON/O 2. 3101280    1\n",
       "STON/O 2. 3101285    1\n",
       "STON/O 2. 3101286    1\n",
       "STON/O 2. 3101288    1\n",
       "STON/O 2. 3101289    1\n",
       "STON/O 2. 3101291    1\n",
       "STON/O 2. 3101292    1\n",
       "STON/O 2. 3101293    1\n",
       "STON/O 2. 3101294    1\n",
       "STON/O2. 3101270     1\n",
       "STON/O2. 3101271     1\n",
       "STON/O2. 3101279     2\n",
       "STON/O2. 3101282     1\n",
       "STON/O2. 3101283     1\n",
       "STON/O2. 3101290     1\n",
       "STON/OQ. 369943      1\n",
       "SW/PP 751            1\n",
       "W./C. 14258          1\n",
       "W./C. 14260          1\n",
       "W./C. 14263          1\n",
       "W./C. 14266          1\n",
       "W./C. 6607           4\n",
       "W./C. 6608           5\n",
       "W./C. 6609           1\n",
       "W.E.P. 5734          2\n",
       "W/C 14208            1\n",
       "WE/P 5735            2\n",
       "Length: 929, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine.groupby('Ticket').size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that there are 3 people sharing the same ticket, \"110152\". The following function returns the number of passengers that are holding a ticket number similar to the passenger ID, including himself:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId\n",
       "1    1\n",
       "2    2\n",
       "3    1\n",
       "4    2\n",
       "5    1\n",
       "Name: Name, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We group by the ticket number, and count the number of times the same ticket number appears in the column\n",
    "combine.groupby('Ticket')['Name'].transform(\"count\").head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JSUnuX1WnDs/EvnlSkiumh2C//UB3n+S2aVvO05K8srvvmuRu8Xtwy+ju9y2/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\nBAAAADBGmAAAAADGCBMAAADAmP8PN8pMBG8W8NQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f986e64f940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combine['SharedTickets'] = combine.groupby('Ticket')['Name'].transform(\"count\")\n",
    "\n",
    "plt.figure(figsize=(18, 10))\n",
    "sns.countplot(combine.SharedTickets)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Are passengers who share tickets more or less likely to perish? To find out, we can use the `sns.distplot` function to find out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3q+rbVbWqqv6uqhaMflQAAAAARmHaIFRVY0nOTnJskgVJTpoi+HyqtXZga+2Q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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f986eb09128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "\n",
    "sns.distplot(combine[(combine.Survived == 0)].SharedTickets.dropna(),\n",
    "             bins = range(1, 12, 1), kde = False, norm_hist = True, color = 'red', label='Perished')\n",
    "\n",
    "sns.distplot(combine[(combine.Survived == 1)].SharedTickets.dropna(),\n",
    "             bins = range(1, 12, 1), kde = False, norm_hist = True, color = 'blue', label='Survived')\n",
    "\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Distribution of Shared Tickets')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that passengers who shared tickets with 1, 2 or 3 other friends or relative had a higher chance of survival. To capture this, we can create a new feature called \"GoodSharedTicket\". In addition, we can also create a new column called, \"Alone\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "combine['GoodSharedTickets'] = ((combine.SharedTickets >= 2) & (combine.SharedTickets <= 4))\n",
    "combine['Alone'] = combine.SharedTickets == 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
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       "      <th>Designation</th>\n",
       "      <th>NewAge</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
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       "      <th>1</th>\n",
       "      <td>22.0</td>\n",
       "      <td>N</td>\n",
       "      <td>S</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>Braund</td>\n",
       "      <td>Owen Harris</td>\n",
       "      <td>Mr</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38.0</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>PC 17599</td>\n",
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       "      <td>Mrs</td>\n",
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       "      <td>26.0</td>\n",
       "      <td>N</td>\n",
       "      <td>S</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>Heikkinen</td>\n",
       "      <td>Laina</td>\n",
       "      <td>Miss</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113803</td>\n",
       "      <td>Futrelle</td>\n",
       "      <td>Jacques Heath (Lily May Peel)</td>\n",
       "      <td>Mrs</td>\n",
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       "      <td>False</td>\n",
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       "      <th>5</th>\n",
       "      <td>35.0</td>\n",
       "      <td>N</td>\n",
       "      <td>S</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>373450</td>\n",
       "      <td>Allen</td>\n",
       "      <td>William Henry</td>\n",
       "      <td>Mr</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age Cabin Embarked     Fare  \\\n",
       "PassengerId                                 \n",
       "1            22.0     N        S   7.2500   \n",
       "2            38.0   C85        C  71.2833   \n",
       "3            26.0     N        S   7.9250   \n",
       "4            35.0  C123        S  53.1000   \n",
       "5            35.0     N        S   8.0500   \n",
       "\n",
       "                                                          Name  Parch  Pclass  \\\n",
       "PassengerId                                                                     \n",
       "1                                      Braund, Mr. Owen Harris      0       3   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...      0       1   \n",
       "3                                       Heikkinen, Miss. Laina      0       3   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)      0       1   \n",
       "5                                     Allen, Mr. William Henry      0       3   \n",
       "\n",
       "                Sex  SibSp  Survived            Ticket   LastName  \\\n",
       "PassengerId                                                         \n",
       "1              male      1       0.0         A/5 21171     Braund   \n",
       "2            female      1       1.0          PC 17599    Cumings   \n",
       "3            female      0       1.0  STON/O2. 3101282  Heikkinen   \n",
       "4            female      1       1.0            113803   Futrelle   \n",
       "5              male      0       0.0            373450      Allen   \n",
       "\n",
       "                                         FirstName Designation  NewAge  \\\n",
       "PassengerId                                                              \n",
       "1                                      Owen Harris          Mr    22.0   \n",
       "2            John Bradley (Florence Briggs Thayer)         Mrs    38.0   \n",
       "3                                            Laina        Miss    26.0   \n",
       "4                    Jacques Heath (Lily May Peel)         Mrs    35.0   \n",
       "5                                    William Henry          Mr    35.0   \n",
       "\n",
       "             SharedTickets  GoodSharedTickets  Alone  \n",
       "PassengerId                                           \n",
       "1                        1              False   True  \n",
       "2                        2               True  False  \n",
       "3                        1              False   True  \n",
       "4                        2               True  False  \n",
       "5                        1              False   True  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall that we had tickets costing more than \\$500. Let's take a closer look at those tickets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
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       "      <th>Fare</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>35.0</td>\n",
       "      <td>N</td>\n",
       "      <td>C</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>Ward, Miss. Anna</td>\n",
       "      <td>0</td>\n",
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       "      <td>female</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>Ward</td>\n",
       "      <td>Anna</td>\n",
       "      <td>Miss</td>\n",
       "      <td>35.0</td>\n",
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>36.0</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>C</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>Cardeza, Mr. Thomas Drake Martinez</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>Cardeza</td>\n",
       "      <td>Thomas Drake Martinez</td>\n",
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       "      <td>36.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>35.0</td>\n",
       "      <td>B101</td>\n",
       "      <td>C</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>Lesurer, Mr. Gustave J</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>Lesurer</td>\n",
       "      <td>Gustave J</td>\n",
       "      <td>Mr</td>\n",
       "      <td>35.0</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1235</th>\n",
       "      <td>58.0</td>\n",
       "      <td>B51 B53 B55</td>\n",
       "      <td>C</td>\n",
       "      <td>512.3292</td>\n",
       "      <td>Cardeza, Mrs. James Warburton Martinez (Charlo...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PC 17755</td>\n",
       "      <td>Cardeza</td>\n",
       "      <td>James Warburton Martinez (Charlotte Wardle Drake)</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>58.0</td>\n",
       "      <td>4</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age        Cabin Embarked      Fare  \\\n",
       "PassengerId                                         \n",
       "259          35.0            N        C  512.3292   \n",
       "680          36.0  B51 B53 B55        C  512.3292   \n",
       "738          35.0         B101        C  512.3292   \n",
       "1235         58.0  B51 B53 B55        C  512.3292   \n",
       "\n",
       "                                                          Name  Parch  Pclass  \\\n",
       "PassengerId                                                                     \n",
       "259                                           Ward, Miss. Anna      0       1   \n",
       "680                         Cardeza, Mr. Thomas Drake Martinez      1       1   \n",
       "738                                     Lesurer, Mr. Gustave J      0       1   \n",
       "1235         Cardeza, Mrs. James Warburton Martinez (Charlo...      1       1   \n",
       "\n",
       "                Sex  SibSp  Survived    Ticket LastName  \\\n",
       "PassengerId                                               \n",
       "259          female      0       1.0  PC 17755     Ward   \n",
       "680            male      0       1.0  PC 17755  Cardeza   \n",
       "738            male      0       1.0  PC 17755  Lesurer   \n",
       "1235         female      0       NaN  PC 17755  Cardeza   \n",
       "\n",
       "                                                     FirstName Designation  \\\n",
       "PassengerId                                                                  \n",
       "259                                                       Anna        Miss   \n",
       "680                                      Thomas Drake Martinez          Mr   \n",
       "738                                                  Gustave J          Mr   \n",
       "1235         James Warburton Martinez (Charlotte Wardle Drake)         Mrs   \n",
       "\n",
       "             NewAge  SharedTickets  GoodSharedTickets  Alone  \n",
       "PassengerId                                                   \n",
       "259            35.0              4               True  False  \n",
       "680            36.0              4               True  False  \n",
       "738            35.0              4               True  False  \n",
       "1235           58.0              4               True  False  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combine[combine.Fare >= 500]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is something similar across all ticket-holders; they all share the same ticket! One theory might be that the ticket fare, \\$512.3292, might reflect the joint total instead of the fare each person paid. If we believe this to be true, we can simply create a new fare column by using the fare to divide by the number of people that shared the same ticket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "combine['NormalisedFare'] = combine.Fare / combine.SharedTickets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Fare is 512.329200\n",
      "Max Normalised Fare is 128.082300\n"
     ]
    }
   ],
   "source": [
    "print('Max Fare is %f' % max(combine.Fare))\n",
    "print('Max Normalised Fare is %f' % max(combine.NormalisedFare))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2zD3SBtf2LAIAAGBKRpksOivJna21u1prO5Jck+T5Sw9orX24tbZl+PRTSY4d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\nMyMiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f986eb0ce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Revised Hypothesis 2\n",
    "plt.figure(figsize=(20, 12))\n",
    "sns.distplot(combine[combine.Survived == 0].NormalisedFare, bins=range(0, 131, 1), color = 'red', label='Perished')\n",
    "sns.distplot(combine[combine.Survived == 1].NormalisedFare, bins=range(0, 131, 1), color = 'blue', label='Survived')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title(\"Age Distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All in all, the observation that passengers who paid higher fares has a higher rate of survival still stands.\n",
    "\n",
    "---\n",
    "\n",
    "#### Investing the Cabin Variable\n",
    "\n",
    "Finally, we investigate the cabin variable, and check whether we are able to generate any new features from it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A10', 'A11', 'A14', 'A16', 'A18', 'A19', 'A20', 'A21', 'A23',\n",
       "       'A24', 'A26', 'A29', 'A31', 'A32', 'A34', 'A36', 'A5', 'A6', 'A7',\n",
       "       'A9', 'B10', 'B101', 'B102', 'B11', 'B18', 'B19', 'B20', 'B22',\n",
       "       'B24', 'B26', 'B28', 'B3', 'B30', 'B35', 'B36', 'B37', 'B38',\n",
       "       'B39', 'B4', 'B41', 'B42', 'B45', 'B49', 'B5', 'B50',\n",
       "       'B51 B53 B55', 'B52 B54 B56', 'B57 B59 B63 B66', 'B58 B60', 'B61',\n",
       "       'B69', 'B71', 'B73', 'B77', 'B78', 'B79', 'B80', 'B82 B84', 'B86',\n",
       "       'B94', 'B96 B98', 'C101', 'C103', 'C104', 'C105', 'C106', 'C110',\n",
       "       'C111', 'C116', 'C118', 'C123', 'C124', 'C125', 'C126', 'C128',\n",
       "       'C130', 'C132', 'C148', 'C2', 'C22 C26', 'C23 C25 C27', 'C28',\n",
       "       'C30', 'C31', 'C32', 'C39', 'C45', 'C46', 'C47', 'C49', 'C50',\n",
       "       'C51', 'C52', 'C53', 'C54', 'C55 C57', 'C6', 'C62 C64', 'C65',\n",
       "       'C68', 'C7', 'C70', 'C78', 'C80', 'C82', 'C83', 'C85', 'C86',\n",
       "       'C87', 'C89', 'C90', 'C91', 'C92', 'C93', 'C95', 'C97', 'C99', 'D',\n",
       "       'D10 D12', 'D11', 'D15', 'D17', 'D19', 'D20', 'D21', 'D22', 'D26',\n",
       "       'D28', 'D30', 'D33', 'D34', 'D35', 'D36', 'D37', 'D38', 'D40',\n",
       "       'D43', 'D45', 'D46', 'D47', 'D48', 'D49', 'D50', 'D56', 'D6', 'D7',\n",
       "       'D9', 'E10', 'E101', 'E12', 'E121', 'E17', 'E24', 'E25', 'E31',\n",
       "       'E33', 'E34', 'E36', 'E38', 'E39 E41', 'E40', 'E44', 'E45', 'E46',\n",
       "       'E49', 'E50', 'E52', 'E58', 'E60', 'E63', 'E67', 'E68', 'E77',\n",
       "       'E8', 'F', 'F E46', 'F E57', 'F E69', 'F G63', 'F G73', 'F2',\n",
       "       'F33', 'F38', 'F4', 'G6', 'N', 'T'], dtype=object)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(combine.Cabin)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that there are many different types of cabins, and some passengers \"share\" cabins with other passengers. To capture these effects, we can create a new column, \"CabinType\" (for example, the cabin type of cabin \"A35\" is \"A\"), using the first character of the Cabin feature, and another, \"NoCabin\" to capture the number of cabins that the passengers were staying in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ngbolin/anaconda3/lib/python3.6/site-packages/seaborn/categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
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E4w+pqt9YPP6nVXX6lPMAAGwH1mAAwFQmC0lVdXySS5I8I8mZSc6rqjM37fac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\nKMRDMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f986e2a2a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combine['CabinType'] = combine.Cabin.apply(lambda x: x[0])\n",
    "\n",
    "def number_of_cabins(cabin):\n",
    "    if cabin == 'N': return 0\n",
    "    else: return len(cabin.split(' '))\n",
    "    \n",
    "combine['NoCabin'] = combine.Cabin.apply(lambda x: number_of_cabins(x))\n",
    "\n",
    "fig = plt.figure(figsize=(20, 10))\n",
    "ax1 = fig.add_subplot(121); ax2 = fig.add_subplot(122)\n",
    "\n",
    "g1 = sns.factorplot('NoCabin', 'Survived', \n",
    "                    kind = 'bar', data = combine, ax = ax1)\n",
    "plt.close(g1.fig)\n",
    "g2 = sns.factorplot('CabinType', 'Survived', \n",
    "                    kind = 'bar', data = combine, ax = ax2)\n",
    "plt.close(g2.fig)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it turns out, passengers staying in cabins had a higher possibility of surviving, but the effect does not increase with the number of cabins. In fact, the higher number of cabins, the lower the probability of the passenger surviving. We can then convert this column to categorical instead of numerical to capture the asymmetric effects.\n",
    "\n",
    "From the Cabin types, we note that passengers staying in the \"E\", \"D\", and \"B\" cabins had a higher survival rate. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "category\n"
     ]
    }
   ],
   "source": [
    "combine['NoCabin'] = pd.Categorical(combine.NoCabin)\n",
    "print(combine.NoCabin.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection\n",
    "\n",
    "After all this, we can begin to select features which we believe are informative of the response variable. We typically do so using a heatmap/correlation plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FEtJT9zHzvS/YtjmZV559jGdnvNtk2zvuf5xuPfu4LEvbn8JH/57FP195i5DQ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\nsyLhjhozMzMzMzMzsyLx/wHP8idMZ0c7hQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f986e0d79e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 12))\n",
    "sns.heatmap(combine.corr(), cmap='Blues', annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the correlation plot, we note that the following features seem to be fairly highly correlated with the \"Survived\" feature:\n",
    "\n",
    "1. NormalisedFare\n",
    "2. GoodSharedTickets\n",
    "3. NewAge\n",
    "4. PassengerClass\n",
    "\n",
    "In addition, we will include some other categorical features not included in the correlation plots:\n",
    "\n",
    "1. Gender\n",
    "2. CabinType\n",
    "3. NoCabin\n",
    "4. Designation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['NormalisedFare', 'GoodSharedTickets', 'NewAge', 'Pclass', 'Alone',\n",
    "            'Sex', 'CabinType', 'NoCabin', 'Designation']\n",
    "\n",
    "X = combine[features]\n",
    "y = combine['Survived']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next section, we introduce several libaries that are useful for the purpose of our analysis. In addition, we start off with a basic example to get a better understanding of the libraries.\n",
    "\n",
    "This section was lifted from [Real Python](https://realpython.com/linear-regression-in-python/).\n",
    "\n",
    "We begin with a gentle introduction to Simple Linear Regression with Python. \n",
    "\n",
    "Note that we have to provide a 2-dimensional array for the independent variables, since 1 dimension is used as a count of the number of observations, while the other stores the potential independent variables (could be more than 1). We can do this easily with the `reshape` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5]\n",
      " [15]\n",
      " [25]\n",
      " [35]\n",
      " [45]\n",
      " [55]]\n",
      "[ 5 20 14 32 22 38]\n"
     ]
    }
   ],
   "source": [
    "# Importing the linear_model library from sklearn\n",
    "from sklearn import linear_model\n",
    "\n",
    "# Define x and y\n",
    "x0 = np.array([5, 15, 25, 35, 45, 55]).reshape([-1,1]) # Reshape here reshapes the array into 2 dimensions.\n",
    "y0 = np.array([5, 20, 14, 32, 22, 38])\n",
    "\n",
    "print(x0)\n",
    "print(y0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First, we tell Python that the model of choice is the linear regression model\n",
    "model = linear_model.LinearRegression(fit_intercept=True)\n",
    "model.fit(x0,y0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.715875613747954\n",
      "5.633333333333333\n",
      "0.5399999999999999\n"
     ]
    }
   ],
   "source": [
    "# After fitting, we can obtain the R-sq, intercept and coefficient of the parameter of interest\n",
    "r_sq = model.score(x0,y0)\n",
    "print(r_sq)\n",
    "print(model.intercept_)\n",
    "print(model.coef_[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8.33333333, 13.73333333, 19.13333333, 24.53333333, 29.93333333,\n",
       "       35.33333333])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finally, to use the fitted model for prediction, we can simply call the prediction function of the model\n",
    "model.predict(x0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we look at an example that uses multiple variables - Multiple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1 = [[0, 1], [5, 1], [15, 2], [25, 5], [35, 11], [45, 15], [55, 34], [60, 35]]\n",
    "y1 = [4, 5, 20, 14, 32, 22, 38, 43]\n",
    "\n",
    "# We have 8 observations and 2 independent variables\n",
    "len(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To run a multiple linear regression, we can do the same\n",
    "model = linear_model.LinearRegression(fit_intercept=True)\n",
    "model.fit(x1,y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8615939258756776\n",
      "5.522579275198183\n",
      "[0.44706965 0.25502548]\n"
     ]
    }
   ],
   "source": [
    "r_sq = model.score(x1,y1)\n",
    "print(r_sq)\n",
    "print(model.intercept_)\n",
    "print(model.coef_) # Note that model.coef_ returns a list since there might be more than 1X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.77760476,  8.012953  , 12.73867497, 17.9744479 , 23.97529728,\n",
       "       29.4660957 , 38.78227633, 41.27265006])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a working understanding of how to run a linear regression, let's apply this knowledge to the Titanic dataset. Before we can run the analysis, recall that we have categorical data - we have to One-Hot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "enc = OneHotEncoder() # We drop the first category to avoid multicollinearity issues\n",
    "\n",
    "catfeatures = ['GoodSharedTickets', 'NoCabin', 'Pclass', 'Alone', 'Sex', 'CabinType', 'Designation']\n",
    "for feature in catfeatures:\n",
    "    Z = pd.get_dummies(X[feature])\n",
    "    X = pd.merge(X,Z,left_index=True,right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "for feature in catfeatures:\n",
    "    del X[feature]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NormalisedFare</th>\n",
       "      <th>NewAge</th>\n",
       "      <th>False_x</th>\n",
       "      <th>True_x</th>\n",
       "      <th>0_y</th>\n",
       "      <th>1_y</th>\n",
       "      <th>2_x</th>\n",
       "      <th>3_x</th>\n",
       "      <th>4</th>\n",
       "      <th>1_x</th>\n",
       "      <th>...</th>\n",
       "      <th>Master</th>\n",
       "      <th>Miss</th>\n",
       "      <th>Mlle</th>\n",
       "      <th>Mme</th>\n",
       "      <th>Mr</th>\n",
       "      <th>Mrs</th>\n",
       "      <th>Ms</th>\n",
       "      <th>Rev</th>\n",
       "      <th>Sir</th>\n",
       "      <th>the Countess</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.25000</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.64165</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.92500</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.55000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.05000</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             NormalisedFare  NewAge  False_x  True_x  0_y  1_y  2_x  3_x  4  \\\n",
       "PassengerId                                                                   \n",
       "1                   7.25000    22.0        1       0    1    0    0    0  0   \n",
       "2                  35.64165    38.0        0       1    0    1    0    0  0   \n",
       "3                   7.92500    26.0        1       0    1    0    0    0  0   \n",
       "4                  26.55000    35.0        0       1    0    1    0    0  0   \n",
       "5                   8.05000    35.0        1       0    1    0    0    0  0   \n",
       "\n",
       "             1_x  ...  Master  Miss  Mlle  Mme  Mr  Mrs  Ms  Rev  Sir  \\\n",
       "PassengerId       ...                                                   \n",
       "1              0  ...       0     0     0    0   1    0   0    0    0   \n",
       "2              1  ...       0     0     0    0   0    1   0    0    0   \n",
       "3              0  ...       0     1     0    0   0    0   0    0    0   \n",
       "4              1  ...       0     0     0    0   0    1   0    0    0   \n",
       "5              0  ...       0     0     0    0   1    0   0    0    0   \n",
       "\n",
       "             the Countess  \n",
       "PassengerId                \n",
       "1                       0  \n",
       "2                       0  \n",
       "3                       0  \n",
       "4                       0  \n",
       "5                       0  \n",
       "\n",
       "[5 rows x 43 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, to avoid the issue of multicollinearity, we remove several columns, such as \"False_x\" and \"False_y\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['NormalisedFare',         'NewAge',        'False_x',         'True_x',\n",
       "                  '0_y',            '1_y',            '2_x',            '3_x',\n",
       "                      4,            '1_x',            '2_y',            '3_y',\n",
       "                  False,         'True_y',         'female',           'male',\n",
       "                    'A',              'B',              'C',              'D',\n",
       "                    'E',              'F',              'G',              'N',\n",
       "                    'T',           'Capt',            'Col',            'Don',\n",
       "                 'Dona',             'Dr',       'Jonkheer',           'Lady',\n",
       "                'Major',         'Master',           'Miss',           'Mlle',\n",
       "                  'Mme',             'Mr',            'Mrs',             'Ms',\n",
       "                  'Rev',            'Sir',   'the Countess'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "multicollinear_cols = [\"False_x\", \"0_y\", \"1_x\", False, \"female\", \"A\", \"Capt\"]\n",
    "for col in multicollinear_cols:\n",
    "    del X[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in X.columns:\n",
    "    X[col] = X[col].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = linear_model.LinearRegression()\n",
    "# Only the training data is used to fit a line of best fit\n",
    "model.fit(X[:len(train)].values,y[:len(train)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.46078577060095083\n",
      "0.39607485392098774\n",
      "[ 3.95920225e-03 -4.01081539e-03  3.14903383e-01 -6.08759260e-02\n",
      " -1.32668817e-01  1.32192835e-02  2.63741750e-01 -4.35892134e-02\n",
      " -1.69902674e-01  3.19345181e-01 -5.62145934e-01  5.22490210e-02\n",
      "  2.41799029e-02  1.41582282e-01  2.13284221e-01  9.28195425e-02\n",
      " -2.04388277e-01 -8.34162899e-02 -3.27196488e-01  5.28665050e-01\n",
      " -1.91774491e-02 -8.19467995e-16  2.97744616e-01  8.25531739e-02\n",
      "  3.81190621e-01  4.50975665e-01  7.61813090e-01  2.17745891e-01\n",
      "  2.39130148e-01  2.56721880e-01  2.74733326e-01  3.06199306e-01\n",
      "  4.72418670e-01  9.89379881e-02  9.95876469e-01  3.15848578e-01]\n"
     ]
    }
   ],
   "source": [
    "r_sq = model.score(X[:len(train)].values,y[:len(train)])\n",
    "print(r_sq)\n",
    "print(model.intercept_)\n",
    "print(model.coef_)"
   ]
  }
 ],
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