Decision_Trees/decision_trees_titanic.ipynb

{
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   "cell_type": "markdown",
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   },
   "source": [
    "# Decision Trees and Random Forests: The Titanic Disaster\n",
    "\n",
    "On 10th April 1912 RMS Titanic began her maiden voyage across the Atlantic Ocean from Southampton in England to New York City. Just four days into the crossing and 375 miles South of Newfoundland the ship hit an iceberg that tore into its hull. Less than 3 hours later the titanic broke apart and sank. Tragically, the number of lifeboats on deck was insufficient to carry all the passengers on board and over half drowned as the ship sank below the icy surface. A disproportionate number of men were left aboard  because of the \"women and children first\" policy for loading lifeboats. But there may be other passenger features that can also be attributed to the chances of survival. \n",
    "\n",
    "Our task today is to create a machine learning model that will accurately predict who did and who didn't survive the Titanic disaster. We will use the dataset [here](https://moodle.essex.ac.uk/course/view.php?id=15076&section=10). Please download train.csv to your computer. The dataset is taken from the [Kaggle \"Titanic - Machine Learning from Disaster\"](https://www.kaggle.com/c/titanic/overview) competition. If you choose to download the data from the Kaggle website then please download the [train.csv](https://www.kaggle.com/c/titanic/data?select=train.csv) file. We will use the training data to both train and test our algorithms. The test.csv file provided on the website is used to enter the competition (which you are welcome to do!) \n",
    "\n",
    "We will begin by implementing a Decision Trees algorithm and then see if this can be improved using a Random Forest.\n",
    "\n",
    "But first, let's take a look at our data and perform an exploratory data analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the Titanic dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "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",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "# Load the training dataset used in the Kaggle \"Titanic - Machine Learning from Disaster\" competition.\n",
    "# This dataset will be used here both for training and testing our algorithms. \n",
    "\n",
    "# The test set on the Kaggle site is used to enter the competition - have a go!\n",
    "titanic = pd.read_csv(\"titanic_dataset.csv\")\n",
    "\n",
    "# Loading the csv file from Github (might take some time depending on internet connection)\n",
    "#url = 'https://raw.githubusercontent.com/mariogutierrezroig/Teaching-Notebooks/main/Decision_Trees/titanic_dataset.csv'\n",
    "#df = pd.read_csv(url)\n",
    "\n",
    "# Print out information about the titanic dataframe\n",
    "titanic.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset contains 891 observations (i.e. passengers), 11 features and the target variable *Survived*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's calculate the probability of surviving without using any feature information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of surviving is: 0.38\n"
     ]
    }
   ],
   "source": [
    "survive_prob = sum(titanic.Survived) / len(titanic)\n",
    "print('Probability of surviving is:', np.round(survive_prob,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thus we could label every passenger *Died* and achieve an accuracy of: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.62\n"
     ]
    }
   ],
   "source": [
    "print(round(1-survive_prob,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the accuracy we need to beat!\n",
    "Discuss class imbalance. (mild 20-40%, moderate 1-20%, extreme <1%)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th>Fare</th>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>A/5 21171</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>STON/O2. 3101282</td>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <td>35.0</td>\n",
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       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
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       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
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       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
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       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
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       "      <td>E46</td>\n",
       "      <td>S</td>\n",
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       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>S</td>\n",
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       "      <td>9</td>\n",
       "      <td>1</td>\n",
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       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
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      "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",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \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",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \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  \n",
       "5      0            330877   8.4583   NaN        Q  \n",
       "6      0             17463  51.8625   E46        S  \n",
       "7      1            349909  21.0750   NaN        S  \n",
       "8      2            347742  11.1333   NaN        S  \n",
       "9      0            237736  30.0708   NaN        C  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at the first 10 rows of the dataframe\n",
    "titanic.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataframe is displayed in a tidy format with observations in rows and with features and the target variable in columns. The features are as follows:\n",
    "\n",
    "*Passengerid*: Passenger ID\n",
    "\n",
    "*Survived*: Survival (0 = 'Died', 1 = 'Survived')\n",
    "\n",
    "*Pclass*: Ticket class (1 = 1st, 2 = 2nd, 3 = 3rd)\n",
    "\n",
    "*Name*: Passenger name, including title.\n",
    "\n",
    "*Sex*: Gender ('male' or 'female')\n",
    "\n",
    "*Age*: Passenger age in years\n",
    "\n",
    "*SibSp*: Number of siblings/spouse on board\n",
    "\n",
    "*Parch*: Number of parents/children on board\n",
    "\n",
    "*Ticket*: Ticket number\n",
    "\n",
    "*Fare*: Passenger ticket fare\n",
    "\n",
    "*Cabin*: Cabin numbers (NaN if no cabin; letter denotes the deck)\n",
    "\n",
    "*Embarked*: Embarkation port ('S' = Southampton, 'C' = Cherbourg, 'Q' = Queenstown)\n",
    "\n",
    "We assume that the variable *Passengerid* is randomly assigned and contains no information about whether or not a passenger survived. Similarly for *Ticket*.\n",
    "\n",
    "The attribute *Pclass* is an indicator of socio-economic status and is likely to be correlated with *Fare*. Other passenger features may also be correlated with each other.\n",
    "\n",
    "We can print out the information given above from the dataframe using, for example, the following commands:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embarked categories are: ['S' 'C' 'Q' nan]\n",
      "Pclass categories are: [3 1 2]\n"
     ]
    }
   ],
   "source": [
    "# \"Embarked\" categories\n",
    "print('Embarked categories are:', titanic.Embarked.unique())\n",
    "# \"Pclass\" categories\n",
    "print('Pclass categories are:', titanic.Pclass.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis (EDA)\n",
    "\n",
    "It is useful before building any machine learning model to perform an EDA on the dataset. This enables us to get to know the data, remove any irrelevant or redundant features and see where [\"Feature Engineering\"](https://en.wikipedia.org/wiki/Feature_engineering) may be helpful.\n",
    "\n",
    "(Notes: \n",
    "Feature extraction, PCA etc. \n",
    "Feature crosses - sythetic features - show example of feature cross from Google Developers on a zoom whiteboard) \n",
    "\n",
    "\n",
    "Let's start by seeing if the \"women and child first\" policy is apparent in the data.\n",
    "\n",
    "We can plot the number of male and female passengers on board using a bar chart, splitting by 'Survived' and 'Died':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# Plot a bar chart showing the number who survived and died by gender\n",
    "labels_Sex = ['male', 'female'] \n",
    "# Survived by gender\n",
    "male_survived = sum(titanic.loc[titanic['Sex']=='male'].Survived) # Sum 'Survived' variable for rows where 'Sex'='male'\n",
    "female_survived = sum(titanic.loc[titanic['Sex']=='female'].Survived) # Sum 'Survived' variable for rows where 'Sex'='female'\n",
    "Sex_survived = [male_survived, female_survived]\n",
    "# Died by gender\n",
    "total_males = len(titanic.loc[titanic['Sex']=='male'].Survived) # Number of males on board (try changing 'Survived' to a different variable - this should work too!)\n",
    "total_females = len(titanic.loc[titanic['Sex']=='female'].Survived) # Number of females on board \n",
    "male_died = total_males - male_survived \n",
    "female_died = total_females - female_survived\n",
    "Sex_died = [male_died, female_died]\n",
    "\n",
    "# Make the plot\n",
    "fig, ax = plt.subplots(1,1)\n",
    "width = 0.3\n",
    "ax.bar(labels_Sex, Sex_survived, width, label='Survived',color='tab:blue')\n",
    "ax.bar(labels_Sex, Sex_died, width, bottom=Sex_survived,label='Died',color='orange')\n",
    "ax.set_xlabel('Sex')\n",
    "ax.set_ylabel('Passengers')\n",
    "ax.set_title('Passengers by Sex and Survival')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there were many more male than female passengers on board the Titanic and that the proportion of women who survived was far greater than for men. We can calculate the percentages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Proportion of male passengers who survived: 18.9 %\n",
      "Proportion of female passengers who survived: 74.2 %\n"
     ]
    }
   ],
   "source": [
    "# Print out the number of male (female) passengers who survived as a percentage of the total nummber of \n",
    "# males (females) on board\n",
    "print('Proportion of male passengers who survived:',round(male_survived*100/(male_survived+male_died),1),'%')\n",
    "print('Proportion of female passengers who survived:',round(female_survived*100/(female_survived+female_died),1),'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can look at the effect of age by plotting histograms of the age distributions for the two target classes 'Survived' and 'Died':"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "ax1.hist(titanic.loc[titanic['Survived']==1].Age,color='tab:blue')\n",
    "ax1.set_xlabel('Age')\n",
    "ax1.set_ylabel('Passengers')\n",
    "ax1.set_title('Survived')\n",
    "ax2.hist(titanic.loc[titanic['Survived']==0].Age,color='orange')\n",
    "ax2.set_xlabel('Age')\n",
    "ax2.set_title('Died')\n",
    "# Set the axis limits to be the same for both both plots\n",
    "xlimits = [min(titanic.Age), max(titanic.Age)]\n",
    "ylimits = [0, 120]\n",
    "ax1.set_xlim(xlimits)\n",
    "ax1.set_ylim(ylimits)\n",
    "ax2.set_xlim(xlimits)\n",
    "ax2.set_ylim(ylimits)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see the effect of age more clearly we can plot the histograms overlaid on each other:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oVRbAMV0bjpmZFanU0Ud1eQdidiAqsm9h1f2sX3BG0VGYdUipt+M8WNJXJS1Ml4dLqqweRzMzO2ClNh99n2Qqik+kyxtIRiRV4L9n1hN5VJRZ1yi1o/mDEfFNYAdARPyJ5L4KZmb2HlJqUnhbUj/euR3nB4E/5xaVmZkVotTmo6uBnwBDJS0lmavob/MKyszMilHq6KMHJT0OjCdpNpqXzodkZmbvIaWOPpoAbI+I+4EBwFWShuUamZmZlV2pfQo3A3+SdDxwGfAccFtuUZmZWSFKTQo7IyJIbo5zU0TcBByaX1hmZlaEUjua/yjpSuBs4GRJBwG98wvLzMyKUGpNoYFkCOp5EfEyyZ3R/jm3qMzMrBAl1xSA6yNiV3rDnY8Ay/ILy8zMilBqTeFRoI+kIcADwGxgcV5BmZlZMUpNCkqntvhr4DsRcRbw0fzCMjOzIpScFCSdBHye5F7KHdnXzMy6iVL/sM8DrgR+FBFPSToGeDi/sMzMrAilTnPxKEm/wp7l54EvdfakkgYA3yNpggrgXGAdcAdQC6wHpkfEa509h5mZdVxJSUFSNXA5cBzQd095REzq5HmvB34SEdMkvQ84GLgKeCgiFki6ArgC+Eonj9993V6pM5L7/gBmPUGpzUdLgaeBOuBakv/k/6szJ5R0OHAysAggIt6OiNdJrpZekm62BPhsZ45vZmadV2pSGBgRi4AdEfHziDgX6GwtoQ5oAb4v6QlJ35N0CHBURGxMt3kZOKqtnSXNldQoqbGlpaWTIZiZWVtKTQo70ueNks6QdAJwRCfP2Qs4Ebg5Ik4A3iJpKsqk8yxFWztHxMKIqI+I+urq6k6GYGZmbSk1KXw9bfb5MvD3JJ3El3bynM1Ac0Q8li7fTZIkXpE0GCB93tTJ45uZWSe9a0ezpL7A+cCHgCHAooj4qwM5YUS8LOklScdGxDrgFGBN+pgDLEif7z2Q85iZWce1N/poCUnT0f8DTgdGklyzcKD+Dliajjx6HjiHpNZyp6TzgD8A07vgPGZm1gHtJYWRETEKQNIi4LddcdKIWAnUt7HqlK44vpmZdU57fQp7OpiJiJ05x2JmZgVrr6ZwvKQ30tcC+qXLIhkkdFiu0Zl1d5V4MeKsNgf2mQHtJIWIqCpXIGZmVjzPdGpmZhknBTMzyzgpmJlZxknBzMwyTgpmZpZxUjAzs4yTgpmZZZwUzMws46RgZmYZJwUzM8s4KZiZWcZJwczMMk4KZmaWcVIwM7OMk4KZmWWcFMzMLOOkYGZmmfZux5kbSVVAI7AhIqZIqgOWAwOBJmB2RLxdVHxFqL3ifmBF0WGYWQ9WZE1hHrC21fI3gG9FxIeA14DzConKzKwHK6SmIKkGOAOYD1wmScAkYFa6yRLgGuDmIuIr2vrRU4oOYR+1q1yDMesJiqop/CtwObA7XR4IvB4RO9PlZmBIWztKmiupUVJjS0tL/pGamfUgZU8KkqYAmyKiqTP7R8TCiKiPiPrq6uoujs7MrGcrovloAjBV0meAvsBhwPXAAEm90tpCDbAhrwCSDl0zM9tb2WsKEXFlRNRERC0wA/jPiPg88DAwLd1sDnBvuWMzM+vpChuS2oavAMslfR14AliU9wnXLzgj71N0iGswZla0QpNCRDwCPJK+fh4YV2Q8ZmY9na9oNjOzjJOCmZllKqlPwew9pyIv+lt1f8X1p1nlcE3BzMwyrimY5ajSpiypyJqLVRTXFMzMLOOkYGZmGScFMzPLOCmYmVnGScHMzDJOCmZmlvGQVLOe6HYVHcG+ZkXRERiuKZiZWStOCmZmlnFSMDOzjJOCmZllnBTMzCzTs0cfVdwIDE9WZmbFck3BzMwyTgpmZpYpe1KQNFTSw5LWSHpK0ry0/AhJD0p6Jn1+f7ljMzPr6YqoKewEvhwRI4HxwEWSRgJXAA9FxHDgoXTZzMzKqOwdzRGxEdiYvv6jpLXAEOBMYGK62RLgEeAr5Y7PrCeoyDuw+d7RFaHQPgVJtcAJwGPAUWnCAHgZOGo/+8yV1CipsaWlpSxxmpn1FIUNSZXUH7gHuCQi3pDeGR4aESGpzdmxImIhsBCgvr7eM2iZdYLvHW37U0hNQVJvkoSwNCJ+mBa/Imlwun4wsKmI2MzMerIiRh8JWASsjYh/abXqPmBO+noOcG+5YzMz6+mKaD6aAMwGnpS0Mi27ClgA3CnpPOAPwPQCYjMz69GKGH30C2B/80ucUs5YzMzsL/mKZjMzyzgpmJlZxknBzMwyTgpmZpbp2fdTMLPKUnH3OAFm9axrZF1TMDOzjJOCmZllnBTMzCzjpGBmZhknBTMzyzgpmJlZxknBzMwyTgpmZpbxxWtmVjEq8g5sPeze0U4KZmbt6UFXWjspmFnF8L2ji+c+BTMzyzgpmJlZxknBzMwyTgpmZpapuI5mSZOB64Eq4HsRsaDgkMysh6vIDuechspWVE1BUhVwE3A6MBKYKWlksVGZmfUciqicuwpJOgm4JiJOS5evBIiI/93W9vX19dHY2Njh89Recf+BhGlmVhE6W1OQ1BQR9W2tq7TmoyHAS62Wm4GPt95A0lxgbrr4pqR1nTzXIGBzJ/fNU6XGBZUbm+PqGMfVMRUbl77R6biG7W9FpSWFdkXEQmDhgR5HUuP+MmWRKjUuqNzYHFfHOK6O6WlxVVSfArABGNpquSYtMzOzMqi0pPBfwHBJdZLeB8wA7is4JjOzHqOimo8iYqeki4GfkgxJvTUinsrpdAfcBJWTSo0LKjc2x9UxjqtjelRcFTX6yMzMilVpzUdmZlYgJwUzM8v0yKQgabKkdZKelXRFgXHcKmmTpNWtyo6Q9KCkZ9Ln9xcQ11BJD0taI+kpSfMqITZJfSX9VtLv0riuTcvrJD2Wfp53pIMUyk5SlaQnJK2olLgkrZf0pKSVkhrTskr4jg2QdLekpyWtlXRS0XFJOjZ9n/Y83pB0SdFxpbFdmn7nV0talv4u5PL96nFJocKm0lgMTN6r7ArgoYgYDjyULpfbTuDLETESGA9clL5HRcf2Z2BSRBwPjAEmSxoPfAP4VkR8CHgNOK/Mce0xD1jbarlS4vqriBjTakx70Z8jJPOb/SQiPgIcT/K+FRpXRKxL36cxwFjgT8CPio5L0hDgS0B9RHyUZBDODPL6fkVEj3oAJwE/bbV8JXBlgfHUAqtbLa8DBqevBwPrKuA9uxf475UUG3Aw8DjJFe+bgV5tfb5ljKeG5A/GJGAFoAqJaz0waK+yQj9H4HDgBdKBLpUS116xnAr8shLi4p2ZHo4gGTG6Ajgtr+9Xj6sp0PZUGkMKiqUtR0XExvT1y8BRRQYjqRY4AXiMCogtbaJZCWwCHgSeA16PiJ3pJkV9nv8KXA7sTpcHVkhcATwgqSmdIgaK/xzrgBbg+2lz2/ckHVIBcbU2A1iWvi40rojYAFwHvAhsBLYCTeT0/eqJSaHbiORfgMLGDEvqD9wDXBIRb7ReV1RsEbErkup9DTAO+Ei5Y9ibpCnApohoKjqWNnwyIk4kaS69SNLJrVcW9Dn2Ak4Ebo6IE4C32KtJpsjvfto2PxW4a+91RcSV9mGcSZJMjwYOYd9m5y7TE5NCpU+l8YqkwQDp86YigpDUmyQhLI2IH1ZSbAAR8TrwMEm1eYCkPRdiFvF5TgCmSloPLCdpQrq+AuLa818mEbGJpH18HMV/js1Ac0Q8li7fTZIkio5rj9OBxyPilXS56Lg+DbwQES0RsQP4Icl3LpfvV09MCpU+lcZ9wJz09RyS9vyykiRgEbA2Iv6lUmKTVC1pQPq6H0k/x1qS5DCtqLgi4sqIqImIWpLv039GxOeLjkvSIZIO3fOapJ18NQV/jhHxMvCSpGPTolOANUXH1cpM3mk6guLjehEYL+ng9Hdzz/uVz/erqI6cIh/AZ4Dfk7RH/2OBcSwjaSPcQfLf03kkbdEPAc8APwOOKCCuT5JUkVcBK9PHZ4qODRgNPJHGtRr4p7T8GOC3wLMkVf4+BX6mE4EVlRBXev7fpY+n9nzXi/4c0xjGAI3pZ/nvwPsrJK5DgC3A4a3KKiGua4Gn0+/9vwF98vp+eZoLMzPL9MTmIzMz2w8nBTMzyzgpmJlZxknBzMwyTgpmZpZxUjDrJEmflRSSCr+q2qyrOCmYdd5M4Bfps9l7gpOCWSek80J9kuSCwxlp2UGSvpPeI+BBST+WNC1dN1bSz9OJ6X66Z9oEs0rjpGDWOWeS3A/g98AWSWOBvyaZCn0kMJtkXqY980jdCEyLiLHArcD8IoI2a0+v9jcxszbMJJn0DpJJ8GaS/D7dFRG7gZclPZyuPxb4KPBgMnUNVSTTm5hVHCcFsw6SdATJTKijJAXJH/kgmYW0zV2ApyLipDKFaNZpbj4y67hpwL9FxLCIqI2IoSR3EnsV+Ju0b+EoksnxILlzV7WkrDlJ0nFFBG7WHicFs46byb61gnuA/0Yy2+0a4AcktwvdGhFvkySSb0j6Hcmss58oX7hmpfMsqWZdSFL/iHhT0kCSaY0nRHL/ALNuwX0KZl1rRXojoPcB/9MJwbob1xTMzCzjPgUzM8s4KZiZWcZJwczMMk4KZmaWcVIwM7PM/wfKpaDWJi0X5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the same bins for both plots\n",
    "bins_age = [0,10,20,30,40,50,60,70,80]\n",
    "\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "ax.hist(titanic.loc[titanic['Survived']==0].Age,bins=bins_age,color='orange',label='Died')\n",
    "ax.hist(titanic.loc[titanic['Survived']==1].Age,bins=bins_age,facecolor='none',edgecolor='tab:blue',linewidth=2,label='Survived')\n",
    "ax.set_xlabel('Age')\n",
    "ax.set_ylabel('Passengers')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above shows that the 0-10 years age group is the only group for which more passengers survived than died, consistent with the \"women and child first\" policy. Passengers in the 20-30 years age group had the lowest chance of survival. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\"><b>Exercise 1:</b><br> \n",
    "    \n",
    "It is rumoured that passengers in third class were kept behind locked gates below deck and prevented from boarding lifeboats. Although this conjecture is generally considered to be false, it seems likely there was some direct effect of socio-economic status on the chances of survival. Plot bar charts depicting how *Pclass* and *Embarked* (categorical variables) relate to survival, as was done for gender. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Exercise 1: Solution ###\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\"><b>Exercise 2:</b><br> \n",
    "    \n",
    "Show the correlation between *Fare* (a continuous variable) and *Survival* using a histogram plot similar to the one used for *Age*. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Exercise 2: Solution ###\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly, let's have a look at the effect of travelling with family members on survival. Were passengers more likely to survive if they had parents or children on board? Did more passengers survive if they were travelling with a spouse or siblings?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Look at Parch and SibSp\n",
    "#print(titanic.SibSp.value_counts())\n",
    "#print(titanic.Parch.value_counts())\n",
    "\n",
    "# SibSp\n",
    "unique_SibSp = sorted(titanic.SibSp.unique()) # Sorts into ascending order\n",
    "#print(unique_SibSp)\n",
    "SibSp_survived = [] # List for number who survived for each Sibsp\n",
    "SibSp_died = [] # List for number who died for each Sibsp\n",
    "for SibSp in unique_SibSp:\n",
    "    tmp_survived = sum(titanic.loc[titanic['SibSp']==SibSp].Survived) # Write over number who survived\n",
    "    SibSp_survived.append(tmp_survived)\n",
    "    tmp_died = len(titanic.loc[titanic['SibSp']==SibSp].Survived)- tmp_survived\n",
    "    SibSp_died.append(tmp_died)\n",
    "labels_SibSp = [str(x) for x in unique_SibSp] # Convert each value in SibSp to a string for plot labels\n",
    "\n",
    "# Parch\n",
    "unique_Parch = sorted(titanic.Parch.unique())\n",
    "Parch_survived = [] # List for number who survived for each Parch\n",
    "Parch_died = [] # List for number who died for each Parch\n",
    "for Parch in unique_Parch:\n",
    "    tmp_survived = sum(titanic.loc[titanic['Parch']==Parch].Survived) # Write over number who survived\n",
    "    Parch_survived.append(tmp_survived)\n",
    "    tmp_died = len(titanic.loc[titanic['Parch']==Parch].Survived)- tmp_survived\n",
    "    Parch_died.append(tmp_died)\n",
    "labels_Parch = [str(x) for x in unique_Parch]\n",
    "\n",
    "# Make the plots\n",
    "fig, (ax1, ax2) = plt.subplots(1,2)\n",
    "width = 0.5\n",
    "\n",
    "# SibSp\n",
    "ax1.bar(labels_SibSp, SibSp_survived, width, label='Survived',color='tab:blue')\n",
    "ax1.bar(labels_SibSp, SibSp_died, width, bottom=SibSp_survived,label='Died',color='orange')\n",
    "ax1.set_xlabel('SibSp')\n",
    "ax1.set_ylabel('Passengers')\n",
    "\n",
    "# Parch\n",
    "ax2.bar(labels_Parch, Parch_survived, width, label='Survived',color='tab:blue')\n",
    "ax2.bar(labels_Parch, Parch_died, width, bottom=Parch_survived,label='Died',color='orange')\n",
    "ax2.set_xlabel('Parch')\n",
    " \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The chances of survival were less for passengers travelling without any siblings/spouse or without children/parents. How does the picture look if we add these variables together to make an engineered feature *Family*? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Family</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",
       "      <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>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>1</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",
       "      <td>0</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",
       "      <td>1</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>McCarthy, Mr. Timothy J</td>\n",
       "      <td>male</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17463</td>\n",
       "      <td>51.8625</td>\n",
       "      <td>E46</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Palsson, Master. Gosta Leonard</td>\n",
       "      <td>male</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>349909</td>\n",
       "      <td>21.0750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>347742</td>\n",
       "      <td>11.1333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
       "      <td>female</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>237736</td>\n",
       "      <td>30.0708</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C</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",
       "5            6         0       3   \n",
       "6            7         0       1   \n",
       "7            8         0       3   \n",
       "8            9         1       3   \n",
       "9           10         1       2   \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",
       "5                                   Moran, Mr. James    male   NaN      0   \n",
       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  Family  \n",
       "0      0         A/5 21171   7.2500   NaN        S       1  \n",
       "1      0          PC 17599  71.2833   C85        C       1  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S       0  \n",
       "3      0            113803  53.1000  C123        S       1  \n",
       "4      0            373450   8.0500   NaN        S       0  \n",
       "5      0            330877   8.4583   NaN        Q       0  \n",
       "6      0             17463  51.8625   E46        S       0  \n",
       "7      1            349909  21.0750   NaN        S       4  \n",
       "8      2            347742  11.1333   NaN        S       2  \n",
       "9      0            237736  30.0708   NaN        C       1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Family = titanic.Parch + titanic.SibSp\n",
    "titanic['Family'] = Family\n",
    "titanic.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cnVtVZmZWiHJDYWsaxnoicFtE3AYcmF9ZZmZWhHJPNL8r6bvAV4GTJfWg9MxlMzP7CCn3SKGJ0iWo0yLidUpPTPun3KoyM7NClH2kANwcEdvSA3c+DdyTX1lmZlaEco8UHgP2kzQYeBiYAtyVV1FmZlaMckNBaWiLvwP+NSLOAf4mv7LMzKwIZYeCpJOArwA/38Ntzcysmyj3F/sM4LvAgxHxrKTDgSX5lWVmZkUod5iLxyidV2iffxn4Rl5FmZlZMcoKBUn1wLeB/wb0bm+PiHE51WVmZgUot/toLvA8MBy4BngF+FNONZmZWUHKDYX+ETEb2BIRv4mIrwE+SjAz+4gp9+a1Lel9taQzgf8LHJpPSWZmVpRyQ+FaSQcD/wDcChwEfDO3qszMrBC7DQVJvYGvA0cAg4HZEfGFahRmZmbV19U5hTlAI/A0cAbww9wrMjOzwnTVffSZiBgJIGk28ET+JZmZWVG6OlJoP8FMRGzNuRYzMytYV6FwtKT16fUuMKp9WtL63W0oaaikJZKek/SspBmp/VBJj0h6Mb0fktol6RZJKyUtl3RsZb6imZmVa7ehEBF1EXFQeh0YET07TB/Uxb63Av8QEZ8BTgQukfQZ4CpgcUQcCSxO81A6Z3Fkek2n9FxoMzOrotxGOo2I1RGxNE2/C6ygdAXTREonsEnvZ6XpicDdUfI40E/SoLzqMzOznVVl+GtJDcAxwB+BgRGxOi16HRiYpgcDr3XYrDW17biv6ZKaJTW3tbXlVrOZ2b4o91CQ1Bd4ALg8Ij5wHiIiAog92V9EzIqIxohorK+vr2ClZmaWayhI6kUpEOZGxL+n5jfau4XS+5rUvgoY2mHzIanNzMyqJLdQkCRgNrAiIv65w6KFwNQ0PRV4qEP7+ekqpBOBdR26mczMrArKHfvowxgDTAGelrQstV0NXA8skDQNeBU4Ny37BfAlYCXwHnBBjrWZmVkncguFiPgtoF0sPqWT9QO4JK96zMysa1W5+sjMzLoHh4KZmWUcCmZmlnEomJlZxqFgZmYZh4KZmWUcCmZmlnEomJlZJs87mmtaw/JFFdnPK5Mrshszs5qwz4ZCzZm3q5u/99DkPRp01szsA9x9ZGZmGYeCmZllHApmZpZxKJiZWcahYGZmGYeCmZllHApmZpZxKJiZWcahYGZmGYeCmZllHApmZpZxKJiZWcahYGZmGYeCmZllHApmZpZxKJiZWcahYGZmGYeCmZllHApmZpbJLRQk/VTSGknPdGg7VNIjkl5M74ekdkm6RdJKScslHZtXXWZmtmt5HincBZy+Q9tVwOKIOBJYnOYBzgCOTK/pwO051mVmZrvQM68dR8Rjkhp2aJ4IjE3Tc4BHge+k9rsjIoDHJfWTNCgiVudVn3Vhniqzn8lRmf2YWVVU+5zCwA6/6F8HBqbpwcBrHdZrTW07kTRdUrOk5ra2tvwqNTPbBxV2ojkdFezxn5ERMSsiGiOisb6+PofKzMz2XdUOhTckDQJI72tS+ypgaIf1hqQ2MzOromqHwkJgapqeCjzUof38dBXSicA6n08wM6u+3E40S7qH0knlAZJage8D1wMLJE0DXgXOTav/AvgSsBJ4D7ggr7pqVcPyRRXZzyuTK7IbM9tH5Xn10Xm7WHRKJ+sGcEletZiZWXl8R7OZmWUcCmZmlnEomJlZxqFgZmYZh4KZmWUcCmZmlnEomJlZxqFgZmYZh4KZmWUcCmZmlsltmAvr3jwWk9m+yUcKZmaWcSiYmVnGoWBmZhmHgpmZZRwKZmaWcSiYmVnGoWBmZhmHgpmZZRwKZmaWcSiYmVnGoWBmZhmPfWTdwzxVZj+TozL7MfuI8pGCmZllfKRg1t35KMoqyKFg3YKH8jarDncfmZlZxkcKZh9GJbps3F1jNcihYGaV5XMc3Zq7j8zMLFNTRwqSTgduBuqAOyPi+oJLMrPurtaOXGqtnh3UTChIqgNuA74ItAJ/krQwIp4rtjIz2xO+Uqx7q5lQAI4HVkbEywCS5gMTAYeC1ZxK/OKr1C89/xK2SlJEbZzMkXQ2cHpE/M80PwU4ISIu3WG96cD0NHsU8EKOZQ0A3sxx/3vK9exeLdVTS7WA6+nKvlbPsIio72xBLR0plCUiZgGzqvFZkpojorEan1UO17N7tVRPLdUCrqcrruf/q6Wrj1YBQzvMD0ltZmZWJbUUCn8CjpQ0XNLHgEnAwoJrMjPbp9RM91FEbJV0KfCflC5J/WlEPFtwWVXpptoDrmf3aqmeWqoFXE9XXE9SMyeazcyseLXUfWRmZgVzKJiZWcahsAuSTpf0gqSVkq4quJafSloj6Zki60i1DJW0RNJzkp6VNKPgenpLekLSU6mea4qsp52kOklPSqrMnWV7V8srkp6WtExScw3U00/S/ZKel7RC0kkF1nJU+rm0v9ZLurzKNez0/1vSoZIekfRiej+kavX4nMLO0pAbf6bDkBvAeUUNuSHpZGADcHdE/E0RNXSoZRAwKCKWSjoQaAHOKvBnI+CAiNggqRfwW2BGRDxeRD0d6roCaAQOiojxBdfyCtAYETVxc5akOcD/iYg705WG+0fEOzVQVx2ly+BPiIhXq/i5O/3/lnQj8FZEXJ/+KD0kIr5TjXp8pNC5bMiNiHgfaB9yoxAR8RjwVlGf31FErI6IpWn6XWAFMLjAeiIiNqTZXulV6F86koYAZwJ3FllHLZJ0MHAyMBsgIt6vhUBITgFeqmYgwC7/f08E5qTpOcBZ1arHodC5wcBrHeZbKfAXX62S1AAcA/yx4DrqJC0D1gCPRESh9QD/Anwb2F5wHe0CeFhSSxompkjDgTbgZ6l77U5JBxRcU7tJwD1FF5EMjIjVafp1YGC1PtihYB+KpL7AA8DlEbG+yFoiYltEjKZ0F/zxkgrrYpM0HlgTES1F1dCJz0XEscAZwCWpu6IoPYFjgdsj4hhgI1DoOTuA1I01Abiv6Fp2FKU+/qod/ToUOuchN3Yj9d0/AMyNiH8vup52qRtiCXB6gWWMASakfvz5wDhJ/1ZgPUTEqvS+BniQUvdoUVqB1g5Hc/dTCominQEsjYg3ii4keSOdv2s/j7emWh/sUOich9zYhXRidzawIiL+uQbqqZfUL033oXRxwPNF1RMR342IIRHRQOnfza8j4qtF1SPpgHRBAKmb5lSgsKvYIuJ14DVJR6WmU6iN4fHPo3a6jqD0+2Zqmp4KPFStD66ZYS5qSa0NuSHpHmAsMEBSK/D9iJhdUDljgCnA06kfH+DqiPhFQfUMAuakK0d6AAsiovDLQGvIQODBUpbTE5gXEb8stiQuA+amP7heBi4ospgUll8ELizo83f6/w1cDyyQNA14FTi3avX4klQzM2vn7iMzM8s4FMzMLONQMDOzjEPBzMwyDgUzM8s4FMx2QdK2HUbQbNjL/U1oH3FX0g8kfasSdZpVku9TMNu1TWn4jIqIiIX4JkircT5SMCuTpL6SFktamp5PMDG1N6RnA9wl6c+S5kr675J+l8bDPz6t9/eSfrzDPj8paWmH+SM7zptVm0PBbNf6dOg6ehDYDHw5DS73BeCHadgPgCOAHwKfTq/JwOeAbwFX7+oDIuIlYJ2k9iOSC4Cf5fJtzMrg7iOzXftA91EaCPAf0yij2ykNp94+pPFfIuLptN6zwOKICElPAw1dfM6dwAXpwTxNFDtgne3jfKRgVr6vAPXAcSks3gB6p2X/1WG97R3mt9P1H18PUBqlczzQEhFrK1ax2R5yKJiV72BKz0rYIukLwLBK7DQiNlMafPF23HVkBXMomJVvLtCYuoTOp7JDdM+ldFTxcAX3abbHPEqqWQ1I9ywcHBEzi67F9m0+0WxWsHRl0yeBcUXXYuYjBTMzy/icgpmZZRwKZmaWcSiYmVnGoWBmZhmHgpmZZf4fZaaKXO4hh80AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a plot by survival for \"Family\"\n",
    "unique_Family = sorted(titanic.Family.unique())\n",
    "Family_survived = [] # List for number who survived for each Family size\n",
    "Family_died = [] # List for number who died for each Family size\n",
    "for Family in unique_Family:\n",
    "    tmp_survived = sum(titanic.loc[titanic['Family']==Family].Survived) # Write over number who survived\n",
    "    Family_survived.append(tmp_survived)\n",
    "    tmp_died = len(titanic.loc[titanic['Family']==Family].Survived)- tmp_survived\n",
    "    Family_died.append(tmp_died)\n",
    "labels_Family = [str(x) for x in unique_Family]\n",
    "\n",
    "# Make the plot\n",
    "fig, ax = plt.subplots(1,1)\n",
    "width = 0.5\n",
    "\n",
    "ax.bar(labels_Family, Family_survived, width, label='Survived',color='tab:blue')\n",
    "ax.bar(labels_Family, Family_died, width, bottom=Family_survived,label='Died',color='orange')\n",
    "ax.set_xlabel('Family')\n",
    "ax.set_ylabel('Passengers')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that passengers travelling alone were far more likely to die than to survive. Passenegers travelling with 1-3 other family members had a better than even chance of survival, but belonging to a family with 4 or more members appeared to be detrimental.\n",
    "\n",
    "How about *Name*? This variable may be useful for various reasons. If a passenger has a title other than \"Mr\" or \"Mrs\" then this may indicate class or profession and could be a predictor of survival. *Name* may also be useful to fill in missing data. For example, the name may indicate gender or port of embarkation, and the title will give a clue to age (e.g. a female with title \"Miss\" is likely to be younger than a passenger with title \"Mrs\"). For simplicity we will omit *Name* in this model, but check [here](https://triangleinequality.wordpress.com/2013/09/08/basic-feature-engineering-with-the-titanic-data/) to see an example of using the engineered feature *Title*. \n",
    "\n",
    "For now, we will perform a simple feature selection using the results from our EDA. Let's remove *PassengerID*, *Ticket* and *Name* and replace *Parch* and *SibSp* with the engineered feature *Family*. (Note that it may be that *Parch* and *SibSp* contain useful information beyond that provided by *Family*. I will leave you to look into this further.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Family</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</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>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age     Fare Cabin Embarked  Family\n",
       "0         0       3    male  22.0   7.2500   NaN        S       1\n",
       "1         1       1  female  38.0  71.2833   C85        C       1\n",
       "2         1       3  female  26.0   7.9250   NaN        S       0\n",
       "3         1       1  female  35.0  53.1000  C123        S       1\n",
       "4         0       3    male  35.0   8.0500   NaN        S       0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic = titanic.drop(['PassengerId','Name','Ticket','SibSp','Parch'],axis=1)\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning the data\n",
    "\n",
    "Before running our data through our machine learning algorithm we must clean it: we need to decide what to do with missing data (i.e. NaNs) and convert categorical variables to numbers. \n",
    "\n",
    "Let's start by looking at where we have NaNs in the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Survived', 'Pclass', 'Sex', 'Age', 'Fare', 'Cabin', 'Embarked', 'Family']\n",
      "NaNs for Survived is 0\n",
      "NaNs for Pclass is 0\n",
      "NaNs for Sex is 0\n",
      "NaNs for Age is 177\n",
      "NaNs for Fare is 0\n",
      "NaNs for Cabin is 687\n",
      "NaNs for Embarked is 2\n",
      "NaNs for Family is 0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# Go through each input feature checking if there are any (and how many) NaNs \n",
    "Features = titanic.columns.tolist() # Make a list of the feature names\n",
    "print(Features)\n",
    "for feature in Features:\n",
    "    num = 0\n",
    "    for i in range(0,len(titanic)):\n",
    "        if pd.isnull(titanic.iloc[i][feature]) == True:\n",
    "            num = num + 1\n",
    "    print('NaNs for', feature ,'is', num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Age*, *Cabin* and *Embarked* all contain entries with NaNs. \n",
    "\n",
    "*Cabin* is the most straight-forward to deal with, so we'll do that first. Print out all unique *Cabin* entries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cabins are: [nan 'C85' 'C123' 'E46' 'G6' 'C103' 'D56' 'A6' 'C23 C25 C27' 'B78' 'D33'\n",
      " 'B30' 'C52' 'B28' 'C83' 'F33' 'F G73' 'E31' 'A5' 'D10 D12' 'D26' 'C110'\n",
      " 'B58 B60' 'E101' 'F E69' 'D47' 'B86' 'F2' 'C2' 'E33' 'B19' 'A7' 'C49'\n",
      " 'F4' 'A32' 'B4' 'B80' 'A31' 'D36' 'D15' 'C93' 'C78' 'D35' 'C87' 'B77'\n",
      " 'E67' 'B94' 'C125' 'C99' 'C118' 'D7' 'A19' 'B49' 'D' 'C22 C26' 'C106'\n",
      " 'C65' 'E36' 'C54' 'B57 B59 B63 B66' 'C7' 'E34' 'C32' 'B18' 'C124' 'C91'\n",
      " 'E40' 'T' 'C128' 'D37' 'B35' 'E50' 'C82' 'B96 B98' 'E10' 'E44' 'A34'\n",
      " 'C104' 'C111' 'C92' 'E38' 'D21' 'E12' 'E63' 'A14' 'B37' 'C30' 'D20' 'B79'\n",
      " 'E25' 'D46' 'B73' 'C95' 'B38' 'B39' 'B22' 'C86' 'C70' 'A16' 'C101' 'C68'\n",
      " 'A10' 'E68' 'B41' 'A20' 'D19' 'D50' 'D9' 'A23' 'B50' 'A26' 'D48' 'E58'\n",
      " 'C126' 'B71' 'B51 B53 B55' 'D49' 'B5' 'B20' 'F G63' 'C62 C64' 'E24' 'C90'\n",
      " 'C45' 'E8' 'B101' 'D45' 'C46' 'D30' 'E121' 'D11' 'E77' 'F38' 'B3' 'D6'\n",
      " 'B82 B84' 'D17' 'A36' 'B102' 'B69' 'E49' 'C47' 'D28' 'E17' 'A24' 'C50'\n",
      " 'B42' 'C148']\n"
     ]
    }
   ],
   "source": [
    "print('Cabins are:', titanic.Cabin.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's assign a '1' to those passengers with a cabin and '0' to those without a cabin (i.e. all those with NaN entries). I will leave it to you to investigate whether or not the deck location of the cabin is a useful predictor of survival - look [here](https://triangleinequality.wordpress.com/2013/09/08/basic-feature-engineering-with-the-titanic-data/) to see how to strip the numbers from the cabin name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of passengers with a cabin: 204\n"
     ]
    }
   ],
   "source": [
    "# Loop over passengers and assign '0' for no cabin and '1' for cabin\n",
    "import math\n",
    "Cabin_binary = [] \n",
    "for cabin in titanic.Cabin:\n",
    "    if str(cabin) == 'nan': # Convert cabin to str and check if nan\n",
    "        Cabin_binary.append(0)\n",
    "    else:\n",
    "        Cabin_binary.append(1)\n",
    "print('Number of passengers with a cabin:',sum(Cabin_binary))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace *Cabin* with *Cabin_binary* in the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Family</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</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>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age     Fare  Cabin Embarked  Family\n",
       "0         0       3    male  22.0   7.2500      0        S       1\n",
       "1         1       1  female  38.0  71.2833      1        C       1\n",
       "2         1       3  female  26.0   7.9250      0        S       0\n",
       "3         1       1  female  35.0  53.1000      1        S       1\n",
       "4         0       3    male  35.0   8.0500      0        S       0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace 'Cabin' with Cabin_binary (but still named 'Cabin')\n",
    "titanic['Cabin'] = Cabin_binary\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are various ways to proceed in order to deal with missing data for *Age* and *Embarked*. Bear in mind that if you are planning to enter the Kaggle 'Titanic - Machine Learning from Disaster' competition then you will need a method for dealing with NaNs in the Kaggle test.csv file [here](https://www.kaggle.com/c/titanic/data?select=test.csv) used to submit results.\n",
    "\n",
    "For example, we could:\n",
    "\n",
    "1. Remove all rows containing missing data to train our model and for each passenger in the test set with an NaN entry simply assign *Survive* according to the probability of survival from the training set.\n",
    "2. Replace each NaN entry in the dataframe with the mean for that feature (for continuous varibles) or the mode (for categorical variables).\n",
    "3. Replace each NaN using a more sophisticated method that takes into account clues from the other variables (e.g. different distibutions of *Age* for men and women, *Name* may indicate age group etc).\n",
    "\n",
    "For now we will proceed with route (2). However, note that this method may bias our predictions and it is worth trying other ways to better deal with missing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean age over all passengers is: 29.7 years\n"
     ]
    }
   ],
   "source": [
    "# Replace NaNs in \"Age\" with the mean value over all passengers (improve on this later!)\n",
    "import numpy as np\n",
    "mean_age = titanic.Age.mean()\n",
    "print('Mean age over all passengers is:', np.round(mean_age,2), 'years')\n",
    "#titanic = titanic\n",
    "titanic['Age'] = titanic['Age'].fillna(mean_age)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replace NaNs in *Embarked* by the mode, 'S'. (Perhaps we could inprove on this later by using the information in *Name* to indicate nationality?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Family</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</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>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>C</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age     Fare  Cabin Embarked  Family\n",
       "0         0       3    male  22.0   7.2500      0        S       1\n",
       "1         1       1  female  38.0  71.2833      1        C       1\n",
       "2         1       3  female  26.0   7.9250      0        S       0\n",
       "3         1       1  female  35.0  53.1000      1        S       1\n",
       "4         0       3    male  35.0   8.0500      0        S       0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic['Embarked'] = titanic['Embarked'].fillna('S')\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we need to convert categorical data to numerical. The categorical features have Dtype \"object\".\n",
    "\n",
    "*Sex* is given as male or female, so lets change this to 0 (male) and 1 (female). *Embarked* will be given outputs 0, 1 and 2 for 'S', 'C' and 'Q', respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Family</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>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age     Fare  Cabin  Embarked  Family\n",
       "0         0       3    0  22.0   7.2500      0         0       1\n",
       "1         1       1    1  38.0  71.2833      1         1       1\n",
       "2         1       3    1  26.0   7.9250      0         0       0\n",
       "3         1       1    1  35.0  53.1000      1         0       1\n",
       "4         0       3    0  35.0   8.0500      0         0       0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert object types to numerical\n",
    "if titanic.Sex[0] == 'male' or titanic.Sex[0] == 'female':\n",
    "    titanic['Sex']=titanic['Sex'].map({'male' : 0, 'female' : 1})\n",
    "    titanic['Embarked']=titanic['Embarked'].map({'S' : 0, 'C' : 1, 'Q' : 2})\n",
    "\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cleaned dataframe contains 7 features and 891 observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 8 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  891 non-null    int64  \n",
      " 1   Pclass    891 non-null    int64  \n",
      " 2   Sex       891 non-null    int64  \n",
      " 3   Age       891 non-null    float64\n",
      " 4   Fare      891 non-null    float64\n",
      " 5   Cabin     891 non-null    int64  \n",
      " 6   Embarked  891 non-null    int64  \n",
      " 7   Family    891 non-null    int64  \n",
      "dtypes: float64(2), int64(6)\n",
      "memory usage: 55.8 KB\n"
     ]
    }
   ],
   "source": [
    "titanic.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test set: 0.81\n",
      "Features in order of importance:\n",
      "Sex\n",
      "Pclass\n",
      "Cabin\n",
      "Age\n",
      "Fare\n",
      "Embarked\n",
      "Family\n"
     ]
    }
   ],
   "source": [
    "# Now run the ML decision tree algorithm\n",
    "\n",
    "# Begin by separating into training and test datasets so we can test our algorithm's performance\n",
    "from sklearn.model_selection import train_test_split\n",
    "X=titanic.drop(['Survived'],axis=1) # Input variables\n",
    "X_names = Features[1:] # Remove 'Survived' from feature names\n",
    "y=titanic['Survived'] # Target variable\n",
    "y_names=['Died','Survived'] # Target labels\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=2, stratify=y)  \n",
    "\n",
    "from sklearn import tree\n",
    "decision_tree = tree.DecisionTreeClassifier(max_depth = 3, random_state=7) # Choose tree depth\n",
    "decision_tree.fit(X_train,y_train)\n",
    "y_pred=decision_tree.predict(X_test)\n",
    "\n",
    "from sklearn import metrics\n",
    "# Print the model accuracy (how often is the classifier correct?)\n",
    "print(\"Accuracy on test set:\",np.round(metrics.accuracy_score(y_test, y_pred),2))\n",
    "\n",
    "# Print out the feature importances\n",
    "feature_importances_dict = {}\n",
    "for i, name in enumerate(X_names): feature_importances_dict.update({name : decision_tree.feature_importances_[i]})\n",
    "\n",
    "# Sort features in descending order of importance (need to use reverse=True for descending)  \n",
    "feature_importances_sorted = dict(sorted(feature_importances_dict.items(), key=lambda x:x[1], reverse=True)) \n",
    "# items() method returns (key,value) tuple\n",
    "print('Features in order of importance:')   \n",
    "for key in feature_importances_sorted:\n",
    "    print(key)\n",
    "    \n",
    "# Run the above code with different random_state seeds and max_depth values to see the effect on feature importance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualise the Decision Tree\n",
    "\n",
    "Visualise the model using the plot_tree function (note that it will be difficult to see if you have used max_depth > 3)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "_ = tree.plot_tree(decision_tree,feature_names=X_names,class_names=y_names,filled=True) # _ stops annotations print-out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the nodes and try to understand what is written in each one. At the top of each node is the decision rule used for the split into the two child nodes e.g. in the root node the data is divided by *Sex* with male passengers (*Sex* = 0 <= 0.5) put in the left-hand child node and female passengers in the right-hand child node. The 'gini' value is the Gini Impurity for that node (see these [lecture slides](https://moodle.essex.ac.uk/pluginfile.php/1771417/mod_resource/content/1/MA336_ML_Lectures2and3.pdf) for a description of the Gini Impurity). The higher the Gini Impurity, the greater the mixing between classes. The 'samples' value is the total number of observations in that node (which for the root node is all of the training data - check this is correct using the command len(X_train)). The 'value' list is the number of samples in each class, in this case ['Died', 'Survived']. The class of the node is assigned according to which class has the greater number of samples. This is helpful to see how the classifier would perform if the tree was terminated at that depth. The leaf nodes give the final classification of each sample.\n",
    "\n",
    "We can calculate the model performance using an accuracy metric by adding up all the correct classifications from the leaf nodes and dividing by the total number of samples. Check you get the same accuracy as output by the accuracy_score() function from sklearn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\"><b>Exercise 3:</b><br>\n",
    "    \n",
    "Now that you know how to train a Decision Tree with a specific max_depth it's time to explore what happens when you change this hyperparamter. Create a loop that iterates over different values of max_depth and train each time a decision tree. Then, plot in a line chart the accuracy as a function of the max_depth parameter for both the train set and test set. What happens as you increase the value of the hyperparameter max_depth?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Exercise 3: Solution ###\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accuracy using k-fold cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores are: [0.75977654 0.82022472 0.80898876 0.78651685 0.85393258]\n",
      "Average performance for a tree depth of 4 is: 80.6 +/- 3.5 %\n"
     ]
    }
   ],
   "source": [
    "# Use 5-fold cross-validation to compute the mean accuracy at a tree depth of 4\n",
    "from sklearn.model_selection import cross_val_score\n",
    "maxDepth = 4\n",
    "k=5\n",
    "decision_tree = tree.DecisionTreeClassifier(max_depth=maxDepth, random_state=2)\n",
    "cv_scores = cross_val_score(decision_tree, X, y, cv=k)\n",
    "print('Cross-validation scores are:',cv_scores)\n",
    "\n",
    "# Compute the average of the accuracies and its error\n",
    "avg = sum(cv_scores)/len(cv_scores)\n",
    "sd = math.sqrt(sum((cv_scores-avg)**2)/(len(cv_scores)-1))\n",
    "print('Average performance for a tree depth of',maxDepth,'is:',np.round(avg*100,1),'+/-',np.round(sd*100,1),'%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\"><b>Exercise 4:</b><br>\n",
    "    \n",
    "Select a k-fold value and recreate a similar line chart than in previous exercise, accuracy vs max_depth for the test accuracy, but this time use the average values of the cross-validation scores and its error. You can also try with different values of k and see if there any difference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Exercise 4: Solution ###\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a histogram of feature importances using a fixed tree depth\n",
    "\n",
    "def get_feature_importances(X,y,randomState,maxDepth):\n",
    "    from sklearn import tree, model_selection\n",
    "    X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.25, \n",
    "                                                                        random_state=randomState, stratify=y)  \n",
    "    decision_tree = tree.DecisionTreeClassifier(max_depth = maxDepth, random_state=randomState)\n",
    "    decision_tree.fit(X_train,y_train)\n",
    "    return decision_tree.feature_importances_\n",
    "\n",
    "maxDepth = 6\n",
    "\n",
    "# Get feature importances for different train/test splits (by changing random_state in train_test_split()) and compute the average importance for each feature.\n",
    "import random\n",
    "random.seed(2)\n",
    "feature_importances_summed = np.zeros(len(X_names))\n",
    "n=100\n",
    "for i in range(0,n):\n",
    "    randomState = random.randint(0,1000)\n",
    "    feature_importances = get_feature_importances(X,y,randomState,maxDepth)\n",
    "    # Commented out the below 2 lines and replaced with 1 (better to use less lines of code!)\n",
    "    #tmp = zip(feature_importances_summed,feature_importances)\n",
    "    #feature_importances_summed = [(x + y) for (x, y) in tmp]\n",
    "    feature_importances_summed = feature_importances_summed + feature_importances\n",
    "feature_importances_mean = [number / n for number in feature_importances_summed]\n",
    "\n",
    "fig, ax = plt.subplots(1,1)\n",
    "ax.bar(X_names, feature_importances_mean,color='tab:green')\n",
    "ax.set_ylabel('Feature importance')    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Individual decision trees are prone to overfitting, especically if they are grown too deep. Random Forests allow trees to be grown deeper as they rely on \"majority vote\" and so are less exposed to the peculiarities of the training set. Below we run a Random Forest classifier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.99\n",
      "Accuracy on test set: 0.83\n",
      "OOB score: 0.8\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "forest = RandomForestClassifier(n_estimators=100,bootstrap=True,max_features='auto',criterion='gini',max_depth=None,random_state=3,oob_score=True)\n",
    "forest.fit(X_train,y_train)\n",
    "y_train_pred = forest.predict(X_train)\n",
    "print(\"Accuracy on training set:\",round(metrics.accuracy_score(y_train, y_train_pred),2))\n",
    "y_test_pred = forest.predict(X_test)\n",
    "print(\"Accuracy on test set:\",round(metrics.accuracy_score(y_test, y_test_pred),2))\n",
    "print(\"OOB score:\",round(forest.oob_score_,2)) # Alternative to test set validation method "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Out-of-bag error](https://en.wikipedia.org/wiki/Out-of-bag_error) can be computed when bootstrap aggregating (bagging) it is used. This adds an extra layer of validation as described in the [documentation](https://scikit-learn.org/stable/auto_examples/ensemble/plot_ensemble_oob.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\"><b>Exercise 5:</b><br>\n",
    "    \n",
    "Plot the mean performance of the Random Forest algorithm as a function of tree depth using different numbers of trees. Show that each individual tree can be grown deeper when an ensemble method is used and a greater accuracy achieved overall. Also show on the plot the results from cross-validation using a single decision tree for comparison. (Note that you should not set n_estimators=1 in the Random Forest algorithm to represent a single decision tree unless max_features is set equal to the total number of features in the dataframe and bootStrap=False)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Exercise 5: Solution ###\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<div style=\"text-align: right ;font-size: small; color: gray\"> Notebook by <a href=\"http://mariogutierrezroig.net\">Mario Gutiérrez-Roig</a> and <a href=\"https://www.essex.ac.uk/people/voigt40704/lisa-voigt\">Lisa Voigt</a>, Lecturers in Data Science and Statistics at the University of Essex <a href=\"http://creativecommons.org/licenses/by-sa/4.0/\" rel=\"license\"><img src=\"https://i.creativecommons.org/l/by-sa/4.0/88x31.png\" alt=\"Licencia de Creative Commons\" hspace=\"10\" align=\"right\"></a></div>"
   ]
  }
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