W01_titanic.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Titanic dataset\n",
    "The [Titanic dataset](https://www.kaggle.com/c/titanic-dataset/data) contains the passengers of [the Titanic](https://en.wikipedia.org/wiki/Titanic#Collecting_passengers) (the actual boat, not the movie), together with features like the sex and age of the passenger. It is also known whether the passenger survived (1) or not (0). Let us build a model to predict if a passenger survived. We will use the same mantra as always:\n",
    "\n",
    "    1. frame the problem\n",
    "    2. load the data\n",
    "    3. inspect the data\n",
    "    4. preprocess the data (clean, normalize)\n",
    "    5. split the data\n",
    "    6. train the model\n",
    "    7. evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# getting the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the data\n",
    "url = 'data/titanic.csv'\n",
    "df = pd.read_csv(url)\n",
    "df.columns = ['PassengerId','Survived','Pclass','Name','Sex','Age','SibSp','Parch','Ticket','Fare','Cabin','Embarked']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# inspect the data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check de class variabele \n",
    "df.Survived.value_counts().plot(kind='barh')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Check missing values\n",
    "# we can check easily with the isnull() method\n",
    "df.isnull().sum()\n",
    "#set contains missing values on Age and Cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--- \n",
    "We cannot remove the feature `Age`, as too much data will be lost. Let see if we can use an intelligent fill method\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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 the data\n",
    "df.Age.hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data is scattered, how to fill the missing data\n",
    "# Could there be a relation with another factor?\n",
    "c = df.corr().abs()\n",
    "sns.heatmap(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sns.boxplot(x='Pclass', y='Age', data=df, palette='hls')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Roughly speaking, we can say that the younger a passenger is, the more likely he or she is to be in third grade. The older a passenger is, the more likely he or she is in first class. So there is a loose relationship between these variables. Let's write a function that approximates the age of the passenger, based on their class. The box plot shows that the average age of first class passengers is approximately 37, second class passengers are 29 and third class passengers are 24.\n",
    "\n",
    "So our function needs to finds every zero value in the variable `Age` and checks the value of the `Pclass` for this observation and assigns an `Age` value based on the average age of passengers in that class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def age_approx(cols):\n",
    "    Age = cols[0]\n",
    "    Pclass = cols[1]\n",
    "    \n",
    "    if pd.isnull(Age):\n",
    "        if Pclass == 1:\n",
    "            return 37\n",
    "        elif Pclass == 2:\n",
    "            return 29\n",
    "        else:\n",
    "            return 24\n",
    "    else:\n",
    "        return Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age              0\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'] = df[['Age', 'Pclass']].apply(age_approx, axis=1)\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>PC 17599</td>\n",
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       "      <td>C85</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>0</td>\n",
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       "      <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",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <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  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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/sh/djdw6j_j1zbfpbcf7k2z77rc0000gp/T/ipykernel_1469/1787645108.py:2: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  df = df.drop(['PassengerId','Name','Ticket','Cabin'], 1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Survived    0\n",
       "Pclass      0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "Embarked    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# most likely the ID, Name, Ticket and Cabin are not relevant; let's remove them\n",
    "df = df.drop(['PassengerId','Name','Ticket','Cabin'], 1)\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Survived    0\n",
       "Pclass      0\n",
       "Sex         0\n",
       "Age         0\n",
       "SibSp       0\n",
       "Parch       0\n",
       "Fare        0\n",
       "Embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#deleting two rows is also not of importance\n",
    "df.dropna(inplace=True)\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 889 entries, 0 to 890\n",
      "Data columns (total 8 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  889 non-null    int64  \n",
      " 1   Pclass    889 non-null    int64  \n",
      " 2   Sex       889 non-null    object \n",
      " 3   Age       889 non-null    float64\n",
      " 4   SibSp     889 non-null    int64  \n",
      " 5   Parch     889 non-null    int64  \n",
      " 6   Fare      889 non-null    float64\n",
      " 7   Embarked  889 non-null    object \n",
      "dtypes: float64(2), int64(4), object(2)\n",
      "memory usage: 62.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0         0       3    male  22.0      1      0   7.2500        S\n",
       "1         1       1  female  38.0      1      0  71.2833        C\n",
       "2         1       3  female  26.0      0      0   7.9250        S"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   Survived  Pclass Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0         0       3   0  22.0      1      0   7.2500        S\n",
       "1         1       1   1  38.0      1      0  71.2833        C\n",
       "2         1       3   1  26.0      0      0   7.9250        S\n",
       "3         1       1   1  35.0      1      0  53.1000        S\n",
       "4         0       3   0  35.0      0      0   8.0500        S"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we need to make some columns numerical\n",
    "df['Sex'] = df['Sex'].map({'female': '1', 'male': '0'})\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Embarked.value_counts().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we need to have numerical data for our classifiers, we still need to assign the embarkment-place to an integer. We (kind of arbitrarily) map Cherbourg to 1, Southampton to 2 and Queenstown to 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   Survived  Pclass Sex   Age  SibSp  Parch     Fare Embarked\n",
       "0         0       3   0  22.0      1      0   7.2500        2\n",
       "1         1       1   1  38.0      1      0  71.2833        1\n",
       "2         1       3   1  26.0      0      0   7.9250        2\n",
       "3         1       1   1  35.0      1      0  53.1000        2\n",
       "4         0       3   0  35.0      0      0   8.0500        2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Embarked'] = df['Embarked'].map({'C': '1', 'S': '2', 'Q': '3'})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 889 entries, 0 to 890\n",
      "Data columns (total 8 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  889 non-null    int64  \n",
      " 1   Pclass    889 non-null    int64  \n",
      " 2   Sex       889 non-null    object \n",
      " 3   Age       889 non-null    float64\n",
      " 4   SibSp     889 non-null    int64  \n",
      " 5   Parch     889 non-null    int64  \n",
      " 6   Fare      889 non-null    float64\n",
      " 7   Embarked  889 non-null    object \n",
      "dtypes: float64(2), int64(4), object(2)\n",
      "memory usage: 62.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 889 entries, 0 to 890\n",
      "Data columns (total 8 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  889 non-null    int64  \n",
      " 1   Pclass    889 non-null    int64  \n",
      " 2   Sex       889 non-null    int64  \n",
      " 3   Age       889 non-null    float64\n",
      " 4   SibSp     889 non-null    int64  \n",
      " 5   Parch     889 non-null    int64  \n",
      " 6   Fare      889 non-null    float64\n",
      " 7   Embarked  889 non-null    int64  \n",
      "dtypes: float64(2), int64(6)\n",
      "memory usage: 62.5 KB\n"
     ]
    }
   ],
   "source": [
    "# change the features Sex and Embarked to numbers\n",
    "df['Sex'] = pd.to_numeric(df['Sex'])\n",
    "df['Embarked'] = pd.to_numeric(df['Embarked'])\n",
    "df.info()"
   ]
  },
  {
   "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",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>3</td>\n",
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       "      <td>22.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>2</td>\n",
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       "      <td>1</td>\n",
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       "      <td>38.0</td>\n",
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       "      <td>26.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
       "0         0       3    0  22.0      1      0   7.2500         2\n",
       "1         1       1    1  38.0      1      0  71.2833         1\n",
       "2         1       3    1  26.0      0      0   7.9250         2\n",
       "3         1       1    1  35.0      1      0  53.1000         2\n",
       "4         0       3    0  35.0      0      0   8.0500         2"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# preparing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(889, 7)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get X and y\n",
    "y = np.array(df['Survived']) # this is what we want to predict (vector)\n",
    "X = np.array(df.iloc[:,1:8]) # these are the features (matrix)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize the data\n",
    "def normalize(X):\n",
    "    scaler = StandardScaler()\n",
    "    scaler = scaler.fit(X)\n",
    "    X = scaler.transform(X)\n",
    "    return X\n",
    "\n",
    "X = normalize(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3, random_state=42)\n",
    "\n",
    "# train the model\n",
    "logreg = LogisticRegression()\n",
    "history = logreg.fit(X_train, y_train)\n",
    "y_pred = logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[138  29]\n",
      " [ 26  74]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.83      0.83       167\n",
      "           1       0.72      0.74      0.73       100\n",
      "\n",
      "    accuracy                           0.79       267\n",
      "   macro avg       0.78      0.78      0.78       267\n",
      "weighted avg       0.80      0.79      0.79       267\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Evaluate\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(confusion_matrix)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.92207297  1.31794904 -0.65232495 -0.48205671 -0.05747359  0.12832454\n",
      "  -0.16918916]]\n"
     ]
    }
   ],
   "source": [
    "print(logreg.coef_)"
   ]
  }
 ],
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