titanic_survival_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab: Titanic Survival Exploration with Decision Trees"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Started\n",
    "In this lab, you will see how decision trees work by implementing a decision tree in sklearn.\n",
    "\n",
    "We'll start by loading the dataset and displaying some of its rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <td>PC 17599</td>\n",
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       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
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       "      <th>4</th>\n",
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       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import libraries necessary for this project\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import display # Allows the use of display() for DataFrames\n",
    "\n",
    "# Pretty display for notebooks\n",
    "%matplotlib inline\n",
    "\n",
    "# Set a random seed\n",
    "import random\n",
    "random.seed(42)\n",
    "\n",
    "# Load the dataset\n",
    "in_file = 'titanic_data.csv'\n",
    "full_data = pd.read_csv(in_file)\n",
    "\n",
    "# Print the first few entries of the RMS Titanic data\n",
    "display(full_data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall that these are the various features present for each passenger on the ship:\n",
    "- **Survived**: Outcome of survival (0 = No; 1 = Yes)\n",
    "- **Pclass**: Socio-economic class (1 = Upper class; 2 = Middle class; 3 = Lower class)\n",
    "- **Name**: Name of passenger\n",
    "- **Sex**: Sex of the passenger\n",
    "- **Age**: Age of the passenger (Some entries contain `NaN`)\n",
    "- **SibSp**: Number of siblings and spouses of the passenger aboard\n",
    "- **Parch**: Number of parents and children of the passenger aboard\n",
    "- **Ticket**: Ticket number of the passenger\n",
    "- **Fare**: Fare paid by the passenger\n",
    "- **Cabin** Cabin number of the passenger (Some entries contain `NaN`)\n",
    "- **Embarked**: Port of embarkation of the passenger (C = Cherbourg; Q = Queenstown; S = Southampton)\n",
    "\n",
    "Since we're interested in the outcome of survival for each passenger or crew member, we can remove the **Survived** feature from this dataset and store it as its own separate variable `outcomes`. We will use these outcomes as our prediction targets.  \n",
    "Run the code cell below to remove **Survived** as a feature of the dataset and store it in `outcomes`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</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",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
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       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>S</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>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",
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      ],
      "text/plain": [
       "   PassengerId  Pclass                                               Name  \\\n",
       "0            1       3                            Braund, Mr. Owen Harris   \n",
       "1            2       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
       "2            3       3                             Heikkinen, Miss. Laina   \n",
       "3            4       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
       "4            5       3                           Allen, Mr. William Henry   \n",
       "\n",
       "      Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0    male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "1  female  38.0      1      0          PC 17599  71.2833   C85        C  \n",
       "2  female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3  female  35.0      1      0            113803  53.1000  C123        S  \n",
       "4    male  35.0      0      0            373450   8.0500   NaN        S  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Store the 'Survived' feature in a new variable and remove it from the dataset\n",
    "outcomes = full_data['Survived']\n",
    "features_raw = full_data.drop('Survived', axis = 1)\n",
    "\n",
    "# Show the new dataset with 'Survived' removed\n",
    "display(features_raw.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The very same sample of the RMS Titanic data now shows the **Survived** feature removed from the DataFrame. Note that `data` (the passenger data) and `outcomes` (the outcomes of survival) are now *paired*. That means for any passenger `data.loc[i]`, they have the survival outcome `outcomes[i]`.\n",
    "\n",
    "## Preprocessing the data\n",
    "\n",
    "Now, let's do some data preprocessing. First, we'll one-hot encode the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = pd.get_dummies(features_raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now we'll fill in any blanks with zeroes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
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       "      <th>Name_Abbing, Mr. Anthony</th>\n",
       "      <th>Name_Abbott, Mr. Rossmore Edward</th>\n",
       "      <th>Name_Abbott, Mrs. Stanton (Rosa Hunt)</th>\n",
       "      <th>Name_Abelson, Mr. Samuel</th>\n",
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       "<p>5 rows × 1730 columns</p>\n",
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      ],
      "text/plain": [
       "   PassengerId  Pclass   Age  SibSp  Parch     Fare  Name_Abbing, Mr. Anthony  \\\n",
       "0            1       3  22.0      1      0   7.2500                         0   \n",
       "1            2       1  38.0      1      0  71.2833                         0   \n",
       "2            3       3  26.0      0      0   7.9250                         0   \n",
       "3            4       1  35.0      1      0  53.1000                         0   \n",
       "4            5       3  35.0      0      0   8.0500                         0   \n",
       "\n",
       "   Name_Abbott, Mr. Rossmore Edward  Name_Abbott, Mrs. Stanton (Rosa Hunt)  \\\n",
       "0                                 0                                      0   \n",
       "1                                 0                                      0   \n",
       "2                                 0                                      0   \n",
       "3                                 0                                      0   \n",
       "4                                 0                                      0   \n",
       "\n",
       "   Name_Abelson, Mr. Samuel     ...      Cabin_F G73  Cabin_F2  Cabin_F33  \\\n",
       "0                         0     ...                0         0          0   \n",
       "1                         0     ...                0         0          0   \n",
       "2                         0     ...                0         0          0   \n",
       "3                         0     ...                0         0          0   \n",
       "4                         0     ...                0         0          0   \n",
       "\n",
       "   Cabin_F38  Cabin_F4  Cabin_G6  Cabin_T  Embarked_C  Embarked_Q  Embarked_S  \n",
       "0          0         0         0        0           0           0           1  \n",
       "1          0         0         0        0           1           0           0  \n",
       "2          0         0         0        0           0           0           1  \n",
       "3          0         0         0        0           0           0           1  \n",
       "4          0         0         0        0           0           0           1  \n",
       "\n",
       "[5 rows x 1730 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = features.fillna(0.0)\n",
    "display(features.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (TODO) Training the model\n",
    "\n",
    "Now we're ready to train a model in sklearn. First, let's split the data into training and testing sets. Then we'll train the model on the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, outcomes, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the classifier from sklearn\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# TODO: Define the classifier, and fit it to the data\n",
    "model= DecisionTreeClassifier()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing the model\n",
    "Now, let's see how our model does, let's calculate the accuracy over both the training and the testing set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training accuracy is 1.0\n",
      "The test accuracy is 0.815642458101\n"
     ]
    }
   ],
   "source": [
    "# Making predictions\n",
    "y_train_pred = model.predict(X_train)\n",
    "y_test_pred = model.predict(X_test)\n",
    "\n",
    "# Calculate the accuracy\n",
    "from sklearn.metrics import accuracy_score\n",
    "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
    "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
    "print('The training accuracy is', train_accuracy)\n",
    "print('The test accuracy is', test_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise: Improving the model\n",
    "\n",
    "Ok, high training accuracy and a lower testing accuracy. We may be overfitting a bit.\n",
    "\n",
    "So now it's your turn to shine! Train a new model, and try to specify some parameters in order to improve the testing accuracy, such as:\n",
    "- `max_depth`\n",
    "- `min_samples_leaf`\n",
    "- `min_samples_split`\n",
    "\n",
    "You can use your intuition, trial and error, or even better, feel free to use Grid Search!\n",
    "\n",
    "**Challenge:** Try to get to 85% accuracy on the testing set. If you'd like a hint, take a look at the solutions notebook next."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=8,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=5, min_samples_split=10,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# TODO: Define the classifier, and fit it to the data\n",
    "model = DecisionTreeClassifier(max_depth=8, min_samples_leaf=5, min_samples_split=10)\n",
    "model.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The training accuracy is 0.886235955056\n",
      "The test accuracy is 0.843575418994\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = model.predict(X_train)\n",
    "y_test_pred = model.predict(X_test)\n",
    "\n",
    "# Calculate the accuracy\n",
    "from sklearn.metrics import accuracy_score\n",
    "train_accuracy = accuracy_score(y_train, y_train_pred)\n",
    "test_accuracy = accuracy_score(y_test, y_test_pred)\n",
    "print('The training accuracy is', train_accuracy)\n",
    "print('The test accuracy is', test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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