Survival prediction with titanic dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(r'C:\\Users\\Shan\\Documents\\GitHub\\ML-in-python\\Kaggle titanic competitions/train.csv')\n",
    "test_df = pd.read_csv(r'C:\\Users\\Shan\\Documents\\GitHub\\ML-in-python\\Kaggle titanic competitions/test.csv')\n",
    "labels = train.Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### So there are three columns with missing value that is Age, Cabin and Embarked. Among them Cabin has very less values so I might reject it all together. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data-Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bar_chart(feature):\n",
    "    survived = train[train['Survived']==1][feature].value_counts()\n",
    "    dead = train[train['Survived']==0][feature].value_counts()\n",
    "    df = pd.DataFrame([survived,dead])\n",
    "    df.index = ['Survived','Dead']\n",
    "    df.plot(kind='bar',stacked=True, figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Sex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Here, blue represents the women and orrange represents the dead clearly womens have survived more than men un the crash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_chart('Pclass')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Here 3rd class - green, 2nd class - orange, 1st class - blue, clearly the 1st class people survived more than the 3rd class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xf2d9708>"
      ]
     },
     "execution_count": 65,
     "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": [
    "plt.scatter(train.Age, train.Survived)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Not much information from the data apart from the fact that people between the age 63 - 80 have all died"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "plt.subplot(121)\n",
    "sns.barplot(train.SibSp, train.Survived)\n",
    "plt.subplot(122)\n",
    "sns.barplot(train.Parch, train.Survived)\n",
    "plt.subplots_adjust(wspace=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From here it is clear that people who were alone or with more than one company have all died"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### So after the exploration I have selected 5 features which are Pclass, Age, Gender, fare, SibSp(sibling or spouse ) and Parch ( parent or children ) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data cleaning and processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inpute_age(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: return 24\n",
    "    else: return Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Age']=train[['Age','Pclass']].apply(inpute_age, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xf270488>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.heatmap(train.isnull(),cbar=False, yticklabels=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### So I have cleaned the Age column and I would reject the Cabin column completely, left is embarked. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop('Cabin', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x5559e48>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.heatmap(train.isnull(),cbar=False, yticklabels=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x55ba108>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "sns.heatmap(train.isnull(),cbar=False, yticklabels=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The data is now completely cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 889 entries, 0 to 890\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  889 non-null    int64  \n",
      " 1   Survived     889 non-null    int64  \n",
      " 2   Pclass       889 non-null    int64  \n",
      " 3   Name         889 non-null    object \n",
      " 4   Sex          889 non-null    object \n",
      " 5   Age          889 non-null    float64\n",
      " 6   SibSp        889 non-null    int64  \n",
      " 7   Parch        889 non-null    int64  \n",
      " 8   Ticket       889 non-null    object \n",
      " 9   Fare         889 non-null    float64\n",
      " 10  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(4)\n",
      "memory usage: 83.3+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "train['Male'] = pd.get_dummies(train['Sex'], drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "embarked = pd.get_dummies(train['Embarked'], drop_first=True)\n",
    "train = pd.concat([train, embarked], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['PassengerId', 'Name', 'Sex', 'Ticket', 'Embarked'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Male</th>\n",
       "      <th>Q</th>\n",
       "      <th>S</th>\n",
       "      <th>Q</th>\n",
       "      <th>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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass   Age  SibSp  Parch     Fare  Male  Q  S  Q  S\n",
       "0         0       3  22.0      1      0   7.2500     1  0  1  0  1\n",
       "1         1       1  38.0      1      0  71.2833     0  0  0  0  0\n",
       "2         1       3  26.0      0      0   7.9250     0  0  1  0  1\n",
       "3         1       1  35.0      1      0  53.1000     0  0  1  0  1\n",
       "4         0       3  35.0      0      0   8.0500     1  0  1  0  1"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 889 entries, 0 to 890\n",
      "Data columns (total 11 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  889 non-null    int64  \n",
      " 1   Pclass    889 non-null    int64  \n",
      " 2   Age       889 non-null    float64\n",
      " 3   SibSp     889 non-null    int64  \n",
      " 4   Parch     889 non-null    int64  \n",
      " 5   Fare      889 non-null    float64\n",
      " 6   Male      889 non-null    uint8  \n",
      " 7   Q         889 non-null    uint8  \n",
      " 8   S         889 non-null    uint8  \n",
      " 9   Q         889 non-null    uint8  \n",
      " 10  S         889 non-null    uint8  \n",
      "dtypes: float64(2), int64(4), uint8(5)\n",
      "memory usage: 53.0 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "train.drop(['Q','S'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 889 entries, 0 to 890\n",
      "Data columns (total 7 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  889 non-null    int64  \n",
      " 1   Pclass    889 non-null    int64  \n",
      " 2   Age       889 non-null    float64\n",
      " 3   SibSp     889 non-null    int64  \n",
      " 4   Parch     889 non-null    int64  \n",
      " 5   Fare      889 non-null    float64\n",
      " 6   Male      889 non-null    uint8  \n",
      "dtypes: float64(2), int64(4), uint8(1)\n",
      "memory usage: 49.5 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Seperate the feature columns from the target column\n",
    "X = train.drop('Survived', axis=1)\n",
    "y = train['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split the data into two. I don't think this is necessary since there are two files.\n",
    "#I will keep this here for now\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logmodel = LogisticRegression()\n",
    "logmodel.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clean the test data the same way we did the training data\n",
    "test_df['Age']=test_df[['Age','Pclass']].apply(inpute_age, axis=1)\n",
    "test_df.drop('Cabin', axis=1, inplace=True)\n",
    "test_df.dropna(inplace=True)\n",
    "test_df['Male'] = pd.get_dummies(test_df['Sex'], drop_first=True)\n",
    "embarked = pd.get_dummies(test_df['Embarked'], drop_first=True)\n",
    "test_df = pd.concat([test_df, embarked], axis=1)\n",
    "pass_ids = test_df['PassengerId']\n",
    "test_df.drop(['PassengerId', 'Name', 'Sex', 'Ticket', 'Embarked'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 417 entries, 0 to 417\n",
      "Data columns (total 8 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Pclass  417 non-null    int64  \n",
      " 1   Age     417 non-null    float64\n",
      " 2   SibSp   417 non-null    int64  \n",
      " 3   Parch   417 non-null    int64  \n",
      " 4   Fare    417 non-null    float64\n",
      " 5   Male    417 non-null    uint8  \n",
      " 6   Q       417 non-null    uint8  \n",
      " 7   S       417 non-null    uint8  \n",
      "dtypes: float64(2), int64(3), uint8(3)\n",
      "memory usage: 20.8 KB\n"
     ]
    }
   ],
   "source": [
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df.drop(['Q','S'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 417 entries, 0 to 417\n",
      "Data columns (total 6 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   Pclass  417 non-null    int64  \n",
      " 1   Age     417 non-null    float64\n",
      " 2   SibSp   417 non-null    int64  \n",
      " 3   Parch   417 non-null    int64  \n",
      " 4   Fare    417 non-null    float64\n",
      " 5   Male    417 non-null    uint8  \n",
      "dtypes: float64(2), int64(3), uint8(1)\n",
      "memory usage: 20.0 KB\n"
     ]
    }
   ],
   "source": [
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = logmodel.predict(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": pass_ids,\n",
    "        \"Survived\": predictions\n",
    "    })\n",
    "submission.to_csv('titanic.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reference taken from - https://github.com/minsuk-heo/kaggle- "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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