kaggle/titanic_competition.ipynb

{
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    "collapsed": true
   },
   "source": [
    "## Problem Definition\n",
    "\n",
    "We are given a dataset of passengers of Titanic. The goal is to predict whether a passenger survived based on attributes such as their age, sex, passenger class, source of journey and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import os # for file operations\n",
    "import matplotlib.pyplot as plt # for plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and Explore the data.\n",
    "\n",
    "We have 3 files provided in the [dataset](https://www.kaggle.com/c/titanic/data). Unlike the train dataset, we don't have labels in the test dataset.The goal is to train the best model you can using the training data, then make your predictions on the test data and upload them to Kaggle to see your final score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data\\gender_submission.csv\n",
      "data\\test.csv\n",
      "data\\train.csv\n"
     ]
    }
   ],
   "source": [
    "for dirname, _, filenames in os.walk('data'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`gender_submission.csv` is a set of predictions that assume all and only female passengers survive, as an example of what a submission file should look like.\n",
    "\n",
    "Let's first read the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>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",
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       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv(\"data/train.csv\")\n",
    "df_test = pd.read_csv(\"data/test.csv\")\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the documentation on the [competition page](https://www.kaggle.com/c/titanic/data) the attributes have following meaning.\n",
    "\n",
    "* **Survived**: The target label, 0 means the passenger did not survive, while 1 means passenger survived.\n",
    "* **PClass**: The passenger class.\n",
    "* **Name**: Name of the passenger.\n",
    "* **Age**: Age of the passenger.\n",
    "* **Sex**: Gender of the passenger.\n",
    "* **SibSp**: The number of siblings and spouses of the passenger aborad the Titanic.\n",
    "* **Parch**: The number of children and parents of the passenger aborad the Titanic.\n",
    "* **Ticket**: Ticket id\n",
    "* **Fare**: The fare paid in pounds\n",
    "* **Cabin**: Passenger's cabin number\n",
    "* **Embarked**: Where did the passenger embarked the Titanic.\n",
    "\n",
    "Let's explore the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *Age*, *Cabin* and *Embarked* have `null` values, *Cabin* has lot of nulls (77 %), we will ignore the cabin for now.\n",
    "Although *Name* and *Ticket* have valid values, but that will be tricky to convert to numerical values for model to use, so we will also ignore them.\n",
    "\n",
    "For *Age*, we can replace the `null` values with median age. For *Embarked*, only two values are missing, we can choose the most frequent for that one.\n",
    "\n",
    "Before, that let's take a look at numerical attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The mean age is ~30 years. \n",
    "* Around 38% passengers survived.\n",
    "* The mean fare was around $ \\def\\euro{\\unicode{x20AC}} \\euro$32.20.\n",
    "\n",
    "Let's explore some values and ensure they are categorical. First the target variable. Also plot them for visual understanding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Survived.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": [
    "df_train.Survived.value_counts().sort_values(ascending=False).plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's do the same for passenger class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    491\n",
       "1    216\n",
       "2    184\n",
       "Name: Pclass, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Pclass.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "df_train.Pclass.value_counts().sort_values(ascending=False).plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": [
    "df_train.Sex.value_counts().sort_values(ascending=False).plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Embarked` attribute represents where did the passenger boarded Titanic.\n",
    "* C = Cherbourg\n",
    "* Q = Queenstown\n",
    "* S = Southampton"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    644\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.Embarked.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": [
    "df_train.Embarked.value_counts().sort_values(ascending=False).plot(kind='bar')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's buid the `Scikit-Learn` pipeline to preprocess and then train the data like we did in our end-to-end machine learning project. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22.    ,  1.    ,  0.    ,  7.25  ],\n",
       "       [38.    ,  1.    ,  0.    , 71.2833],\n",
       "       [26.    ,  0.    ,  0.    ,  7.925 ],\n",
       "       ...,\n",
       "       [28.    ,  1.    ,  2.    , 23.45  ],\n",
       "       [26.    ,  0.    ,  0.    , 30.    ],\n",
       "       [32.    ,  0.    ,  0.    ,  7.75  ]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "\n",
    "num_attributes = [\"Age\", \"SibSp\", \"Parch\", \"Fare\"]\n",
    "cat_attributes = [\"Pclass\", \"Sex\", \"Embarked\"]\n",
    "\n",
    "num_pipeline = Pipeline([\n",
    "    ('median_imputer', SimpleImputer(strategy='median'))\n",
    "])\n",
    "\n",
    "num_pipeline.fit_transform(df_train[num_attributes])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's build a pipeline for categorical attributes, First let's build a most frequent imputer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 1., ..., 0., 0., 1.],\n",
       "       [0., 1., 1., ..., 0., 1., 0.],\n",
       "       [1., 0., 1., ..., 0., 0., 1.],\n",
       "       ...,\n",
       "       [1., 0., 1., ..., 0., 0., 1.],\n",
       "       [0., 1., 1., ..., 0., 1., 0.],\n",
       "       [1., 0., 1., ..., 1., 1., 0.]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_pipeline = Pipeline([\n",
    "        (\"most_frequent_imputer\", SimpleImputer(strategy='most_frequent')),\n",
    "        (\"cat_encoder\", OneHotEncoder(sparse=False))\n",
    "   ])\n",
    "\n",
    "cat_pipeline.fit_transform(cat_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's combine the numerical and categorical pipelines using `ColumnTransformer`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "\n",
    "preprocessing_pipeline = ColumnTransformer([\n",
    "    (\"num\", num_pipeline, num_attributes),\n",
    "    (\"cat\", cat_pipeline, cat_attributes)\n",
    "])\n",
    "\n",
    "X_train = preprocessing_pipeline.fit_transform(df_train)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = df_train['Survived']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Awesome! We have preprocessing pipeline now. Let's train our first classifier `SVC`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "    max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "    tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm_clf = SVC(gamma='auto')\n",
    "svm_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, we have a trained classifier now, let's read the test data and see the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = preprocessing_pipeline.fit_transform(df_test)\n",
    "y_pred = svm_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From `y_pred` we can build a `csv` expected by kaggle and get our score. We will do that later. Let's analyze our classifier's performance by using cross validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7329588014981274"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "svm_scores = cross_val_score(svm_clf, X_train, y_train, cv=10)\n",
    "svm_scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy is right metric here, as we have know approximately 38% people survived. So we are doing better than random prediction. Let's try another classifier `RandomForestClassifier`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8126466916354558"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "forest_clf = RandomForestClassifier(n_estimators = 100, random_state=42)\n",
    "forest_scores = cross_val_score(forest_clf, X_train, y_train, cv=10)\n",
    "forest_scores.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cool, the performance improved to 81%. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To be continued..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
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