Logistic-Regression-Titanic-example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('titanic_train.csv')\n",
    "test = pd.read_csv('titanic_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "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",
       "    </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": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(5)"
   ]
  },
  {
   "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",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x183f6376320>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(), yticklabels=False, cmap='viridis', cbar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x183f964c160>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(test.isnull(), yticklabels=False, cmap='viridis', cbar=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x183f7713dd8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Survived', data=train, hue='Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch',\n",
       "       'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x183fbea3a20>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train[train['Survived'] == 1]['Age'].dropna(), hist=False, bins=40, color='r')\n",
    "sns.distplot(train[train['Survived'] == 0]['Age'].dropna(), hist=False, bins=40)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x183fbf7bb00>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(train[train['Survived'] == 1]['Fare'], hist=False, bins=40, color='r')\n",
    "sns.distplot(train[train['Survived'] == 0]['Fare'], hist=False, bins=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x183fde48a90>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 4))\n",
    "sns.set_style('white', rc={'axes.grid': True})\n",
    "sns.boxplot(x='Pclass', y='Age', data=train, hue='Sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "you can use sns.axes_style() to return all the styling of the axes and then you can override them with set_style()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 male 41.28\n",
      "1 female 34.61\n",
      "2 male 30.74\n",
      "2 female 28.72\n",
      "3 male 26.51\n",
      "3 female 21.75\n"
     ]
    }
   ],
   "source": [
    "# we can read the age mean age value for each category but if you want to be very precise!\n",
    "for pclass in [1, 2, 3]:\n",
    "    for sex in ['male', 'female']:\n",
    "        print(pclass, sex, round(train[(train['Pclass']==pclass) & (train['Sex']==sex)]['Age'].mean(), 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "in order to define our impute function, the best way is to create a nice structured object to avoid many 'if' estatements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "age_class = {1:{'male': 41.28, 'female': 34.61}, 2: {'male': 30.74, 'female': 28.72}, 3: {'male': 25.51, 'female': 21.7}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_age(cols):\n",
    "    Age, Pclass, Sex = tuple(cols)\n",
    "    \n",
    "    if pd.isnull(Age):\n",
    "        return age_class[Pclass][Sex]\n",
    "#        if pclass==1 and sex=='male': return 41.28\n",
    "#        elif pclass==1 and sex=='female': return 34.61\n",
    "#        elif pclass==2 and sex=='male': return 30.74\n",
    "#        elif pclass==2 and sex=='female': return 28.72\n",
    "#        elif pclass==3 and sex=='male': return 26.51\n",
    "#        else:\n",
    "#            return 21.75\n",
    "    else:\n",
    "        return Age\n",
    "train['Age'] = train[['Age', 'Pclass', 'Sex']].apply(impute_age, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x183ff06e7b8>"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(), yticklabels=False, cbar=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('Cabin', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.get_dummies(train, columns=['Sex', 'Embarked'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</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>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "\n",
       "                                                Name   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare  Sex_male  Embarked_Q  Embarked_S  \n",
       "0         A/5 21171   7.2500         1           0           1  \n",
       "1          PC 17599  71.2833         0           0           0  \n",
       "2  STON/O2. 3101282   7.9250         0           0           1  \n",
       "3            113803  53.1000         0           0           1  \n",
       "4            373450   8.0500         1           0           1  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>1</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>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</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>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age  SibSp  Parch     Fare  Sex_male  Embarked_Q  \\\n",
       "0         0       3  22.0      1      0   7.2500         1           0   \n",
       "1         1       1  38.0      1      0  71.2833         0           0   \n",
       "2         1       3  26.0      0      0   7.9250         0           0   \n",
       "3         1       1  35.0      1      0  53.1000         0           0   \n",
       "4         0       3  35.0      0      0   8.0500         1           0   \n",
       "\n",
       "   Embarked_S  \n",
       "0           1  \n",
       "1           0  \n",
       "2           1  \n",
       "3           1  \n",
       "4           1  "
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we will not use test dataset because it is basically for kaggle competition. But, let's take a look at it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x183ff0bd0b8>"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aeH6peyZNmjTR9u3btX379vDnbIZuaDteZWWlnnvuOeXm5qpt27YaM2aMtZony8/P19NPP63PPvtM3bp107x58zyv4fl4YdiwYVqyZInXf+z/KCkpSf369dOMGTM0ZcoU3XLLLc5q33rrrRo5cqTatGkT/pyLjfMTJ07U+PHjtXPnTg0fPlxPPfWU9ZpSVTcwZswYHTx4UDExMUpJSdH777+v3/zmN9Zq1qhRQzVr1tS2bduUkJAQnh276u4nTJigESNG6JtvvlHTpk01efJkJ3WffPJJTZo0SYmJifr444/11FNPadWqVU5qn7y//uDBg07qjh8/XkOGDNE111yjjRs3avz48Vq4cKGT2hUVFSouLlYgEFBJSYmVRsbz0HV9SRIycuRIjRw5UpJ01VVXOVnkCHnjjTd08803O+vwt23bpgkTJmjZsmUaPHiwMjIydPToUe3fv1/t27e3Xj8xMVEZGRlatGiR/va3v+mbb77RQw89ZL3u7t27tXz58vCCzo4dO5x19xs3btQ777zjpNaJGjRooOnTp6u0tFQ7duzQK6+84qz27Nmz9cYbb+j777/X8ePH1apVK2VlZVmvW7NmTV1//fWSqm5aeO2116zXDBkxYoT69OmjwsJC9e7dWxMmTPC8hueh6/qSJGT16tXhF4gxRocOHdJ7771nva5U9UbzwAMPOKklSTNnztQzzzyj2rVr64UXXtD8+fPVsmVLDRkyRDfeeKO1uuXl5crKytLixYsVDAZVUlKi1atXKyYmxlrNkEcffVSPPfaYLrroIqWlpWnDhg0aM2aMZs2aZb22JK1du1YDBw50enkvVTUTzz77rPLy8vT66687rZ2dna3s7GxNmTJFgwYN0hNPPGG1XmhEWbduXc2fP1/XXXedtm7d6uwuQEm6+uqrtWrVKhUXF6tJkybas2eP5zU8D92pU6dq9+7dys/PV7t27dS0aVOvS5zSiy++qEmTJmnp0qVKSkrSp59+6qSuVPVGk56ervbt24dXXXv37m2tnjFGP/nJT3TgwAEdO3ZMV1xxhSRZ7/q6deumO+64Q9OnT1erVq00ZMgQJ4ErVXXXy5YtC//+pz/9qT788ENnVzTffvutOnfurBYtWoRvFrC5on5yo1JUVBT+nIuFaanqRpRgMKijR4+qZcuWEedO2BDqohs3bqxdu3Zp165dktzdbCRV3e49e/ZsxcXFaenSpVq4cKHn4xzPQ/fEcwhSUlKUl5fn5ByCJk2a6Oqrr9bSpUvVo0cPLV++3HrNkNDe2KKiIif1Qos4H3/8sZKTkyVVdaFHjx61WnfAgAFasWKFvv76a919991ROV3siy++0OOPP66ioiI1b95cTzzxhJN90Ta3wp3KicFaWlqqevXq6cCBA7rgggucfQ/NmjXTn//8Z9WtW1czZsxQSUmJ1XqnO6PF1SxZkpKTkzVmzBgdOXJEsbGxdvZEe31fcbTOIRg2bJjZsGGDSUtLM9nZ2eaWW25xUjfkwIED5uuvvzZ79+41mzZtslrr5ZdfNr179zbXX3+9+de//mXy8vLMoEGDzNy5c63WDVm/fr0ZPXq06dixo3nuuefMl19+6aSuMVXnAezYscMYY8z27dtNnz59nNTNzc01c+fONXPmzDFz5swxkyZNclJ3zpw5ZurUqcYYY37729+al19+2XrNF1980RhjTEVFhdm0aZM5cuSIyczMDP+72zZr1iyTlJRkrrnmGtO+fXtz++23W69ZVlYW/vXKK6+YwYMHh3/vNc87XROlcwieeOIJ7dq1Sw8++KBmzZqlRx55xEldqWq19R//+IeOHTum48ePKyEhwepdQw888IBuvPFGxcXFqUmTJsrPz1efPn108803W6t5oo4dO6pjx4767rvv9M477+ixxx7T22+/7aR2nTp1dOmll0qqukff1Xhh7NixuuGGG7Rp0yY1bdrU6va4E61ZsyZ81TZ79mylpqZaXz9Yt26dhg8frho1amjmzJnKzMx0ti9Zcj9LlhQ+RU36fxkW+pzXtyB7Hrqhw1BcnUNw4oEUzZo1k1S1+ODqnFVJ2rVrl7KyspSenq6RI0fq0UcftV7zxO1pF198sS6++GLrNU/WsGFD9e/f38kP5J/+9CdJVUfuZWRkhBdZbB94ExITE6Nhw4YpNzdXU6dO1b333uukbiAQUHl5uYLBYHiR2LYTa7iodzLXs2RJ4bMe3nnnHd11111Wa3keuq4PQzlxXhwIBGSMCQeuqwNg6tevr0AgoNLSUsXFxTk75MdPCgsLJVWtLktVb7axsbG6/PLLndQ3xqiwsFClpaUqLS11dvNNamqqfv3rX+uyyy7Trl27NGTIEOs1T2xYXDYvISfOkqdPn259lnyiZcuWWQ9dzw+8idZhKNE6kESqum2xUaNGKioqUkFBgfbu3Rux0o7/u4KCAjVr1uyUR+1dcsklVmuXlJTo3//+t3bu3KmmTZtq4sSJ6t69u8aOHWu1bkhxcbH27NmjhIQEz88BOJUOHTqobdu2MsZo586d4Y9t79iQqq5oUlJSVFhYqP3792v79u1KTk6OuLKz6Z577lF5eXnEfQZen7HheacbrcNQonEgSWiO2bp1a5WUlKh169Zq3ry5kxsU/GbhwoUaN26c0tPTw7d516xZUw0aNLB6RbNo0SItWLBAtWrV0sSJE9WlSxere6FD/vCHP2j48OFKS0v7Ubdp+3X97rvvWv3zT2fOnDnasWOH7rzzTl100UUyxujVV1/V4cOHndx8I0mjR4+2XsPz0I3GYShSdA4kCZ17EGKM0fLlyxUTE6OhQ4dar+8nd955p7p3764333xTH330kTIyMhQbG2v9h3HFihVauXKlSkpK9Nhjjzk7cevzzz+XJKdnS4TYOEP2fyM7O1tvvvlm+E2mRYsWmjlzplJTU52F7mWXXaZPPvlEP/zwg4wxOnjwoDp27OhpDc9D9+TDUI4ePWr9MJQQ1weSjBo1KvxxXl6efve736lr164aP3689dp+E7oLLxgMOr0LLxgMKhgMOp/Vh2p5/QNfndWrV+9HXX3t2rVVv359Z9/DI488olatWumrr75SnTp1rDwtw/PQPfEwlAsvvFDp6enWD0MpKSnR6NGjo3IgiSQtXrxYr732msaNG6cbbrjBWV0/Mae5C8/lQo/Llfw9e/ac9ohDG0/IqA5iYmLCs+uQPXv2OF/Mmzx5ssaNG6enn37ayhW656GbmJj4o7vBbD7m41QzN1cOHDigcePGqVGjRlq2bJn1pyb42enuwrN9BbVz506NGjUqvKh04tWNzdlqTEyM9QXC6mb06NEaPny4kpOTw0/2/uSTT5w91TukrKxMx44dC+9I8prnuxfefvttzZs3T2VlZeHP2Xy+UWpqqjIzM8MzN5enMF133XWqXbu2fvaznzlf7PCbefPmac2aNSooKNBLL72k+vXrKyMjQ0lJSRo2bJi1uhs2bDjt12xe+ocez+M3R44c0erVq3Xw4EE1b95cXbt2dbYXW5JWrVql3NxcxcXFac6cOerQoYNmzpzpaQ3PO9358+frpZdecvaMsmjN3KSqQ3bgRrTuwovWTPXKK6+MSt1oi42NVffu3aNW/9Zbb5VUdcDRbbfdZiXwPQ/dhIQEJw9HPBXXd8/4aZGjOqgOd+G54moPMCKtXbtWkydPVsOGDVVaWqrJkycrKSnJ0xqejxdGjBihkpISXX755eFLbpuD/06dOik5OVnGGK1bty4875O4xAfw/6dXr156+eWXFRcXp8LCQj300EOen6PieacbOvHdlRdeeCH8cTT2NAI4d9SvXz9819/5559vZcuY553uDz/8oLfeekv79+9XUlKS2rZt6+TWRQD4T4W2523evFn16tVThw4dtHXrVpWVlVXfpwGHPP7449YfYQwAXgptzztxm56tm248f75Lfn6+Hn30UQWDQXXr1k1HjhzxugQAeColJUUpKSm66aab1LBhQ9WpUyf8y2ued7ouHmEMADbcf//9uvTSSxUbGyup6o7H22+/3dManofuyY8w5hwCAGeL2NjY0z6rzSueL6SFFBcXq2HDhqpVy/NcBwArFixYoLp164YfCSVV3XnqJc8T8YMPPlBlZaXKy8s1bdo0DR48WIMHD/a6DAB47rPPPlN5ebk2btwoqWq8UO1Dd8GCBZo3b57S0tL00Ucf6f777yd0AZwVSktL9eqrr1qt4Xnohlb76tevH364HACcDdq2basVK1aoffv24TtqvT7tzfPQbdGihXr27KlJkybp97//vRITE70uAQBWbN++XV9++WX4HJfy8vLwk6i9YmUh7ejRo6pfv76KiooUHx/v9R8PAJ4aMWJE+EiBP/7xj+GRqI0jNj3fRLtx40Z9/vnnWrt2rVJTU/Xee+95XQIAPPXNN9+EP167dm34YxtPrfA8dKdNm6ZWrVopMzNTS5Yssf7IZgDwku0jYj0P3Tp16ui8885TrVq1dP7556u8vNzrEgDgqRM7WtvPZPN8Ia1BgwYaNGiQ7r33Xi1evNjZEyQA4D91qmfhGWOUk5PjeS3PF9LKy8uVn5+vSy+9VF999ZVatWqlYDDoZQkA8JTLZ+F5Hrp5eXlauXJl+HllBw8edPo4dACozjyf6Yae7bRp0ybt3btXhw4d8roEAJy1PA/dmJgYDRs2TBdccIGeeeYZFRUVeV0CAM5anoeuMUaFhYUqLS1VaWmpDh8+7HUJADhreRq6JSUlevjhh/Xhhx/qzjvv1I033qguXbp4WQIAzmqeLaQtWrRICxYsUK1atTRx4kTCFgBOwbNOd8WKFVq5cqWWLl2qzMxMr/5YADineBa6wWBQwWBQcXFx4e1iAIBIVp4aafveZQA4W3k20+3UqZOSk5NljNG6deuUnJwc/tqMGTO8KAEAZz3PQtflbXQAcLay9jRgAMCPWZnpAgBOjdAFAIcIXQBwiNAFAIf+G4IBt/6iXCctAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(test.isnull(), yticklabels=False, cbar=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['Age'] = test[['Age', 'Pclass', 'Sex']].apply(impute_age, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.drop('Cabin', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to convert categorical columns to numerical values using dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.get_dummies(test, columns=['Sex', 'Embarked'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "test.drop(['PassengerId', 'Name', 'Ticket'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train.drop('Survived', axis=1)\n",
    "y = train['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = logreg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "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>Predicted 0</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual 0</th>\n",
       "      <td>TN</td>\n",
       "      <td>FP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>FN</td>\n",
       "      <td>TP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Predicted 0 Predicted 1\n",
       "Actual 0          TN          FP\n",
       "Actual 1          FN          TP"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con_mat = np.array([['TN', 'FP'],\n",
    "                    ['FN', 'TP']])\n",
    "\n",
    "pd.DataFrame(data=con_mat, index=['Actual 0', 'Actual 1'], columns=['Predicted 0', 'Predicted 1'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "if you go horizontally, you can calculate recall, and vertically yeilds the precision value. avg/Total is the accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[144,  23],\n",
       "       [ 31,  69]], dtype=int64)"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, predictions) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.82      0.86      0.84       167\n",
      "          1       0.75      0.69      0.72       100\n",
      "\n",
      "avg / total       0.80      0.80      0.80       267\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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