titanic-ml.ipynb

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     "text": [
      "/kaggle/input/titanic/train.csv\n",
      "/kaggle/input/titanic/test.csv\n",
      "/kaggle/input/titanic/gender_submission.csv\n"
     ]
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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   },
   "source": [
    "## Importing Required Libraries"
   ]
  },
  {
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   "id": "rocky-spelling",
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "# Models\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "intelligent-coast",
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    "tags": []
   },
   "source": [
    "## Loading the Data"
   ]
  },
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   "source": [
    "train_df = pd.read_csv(\"/kaggle/input/titanic/train.csv\")\n",
    "test_df = pd.read_csv(\"/kaggle/input/titanic/test.csv\")"
   ]
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       "   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",
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       "                                                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",
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       "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",
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    "train_df.head()"
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       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
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       "      <td>1</td>\n",
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       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch   Ticket     Fare Cabin Embarked  \n",
       "0  34.5      0      0   330911   7.8292   NaN        Q  \n",
       "1  47.0      1      0   363272   7.0000   NaN        S  \n",
       "2  62.0      0      0   240276   9.6875   NaN        Q  \n",
       "3  27.0      0      0   315154   8.6625   NaN        S  \n",
       "4  22.0      1      1  3101298  12.2875   NaN        S  "
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   ],
   "source": [
    "test_df.head()"
   ]
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   },
   "source": [
    "## Data Analysis"
   ]
  },
  {
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    "tags": []
   },
   "source": [
    "Getting basic info about our dataframes like Column name, Types, count etc"
   ]
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  {
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   "id": "promotional-overview",
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     "text": [
      "Training Data Columns:  Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
      "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
      "      dtype='object')\n",
      "Testing Data Columns:  Index(['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch',\n",
      "       'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Analysis of Clumns present\n",
    "\n",
    "print(\"Training Data Columns: \",train_df.columns)\n",
    "print(\"Testing Data Columns: \",test_df.columns)"
   ]
  },
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     "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_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aging-continent",
   "metadata": {
    "execution": {
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     "iopub.status.idle": "2021-06-05T08:48:35.319486Z",
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     "shell.execute_reply.started": "2021-06-05T07:38:12.65551Z"
    },
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     "status": "completed"
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    "tags": []
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   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\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>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>204</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
       "      <td>681</td>\n",
       "      <td>147</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Troupiansky, Mr. Moses Aaron</td>\n",
       "      <td>male</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>G6</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Name   Sex    Ticket Cabin Embarked\n",
       "count                            891   891       891   204      889\n",
       "unique                           891     2       681   147        3\n",
       "top     Troupiansky, Mr. Moses Aaron  male  CA. 2343    G6        S\n",
       "freq                               1   577         7     4      644"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "encouraging-currency",
   "metadata": {
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     "start_time": "2021-06-05T08:48:35.415915",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### What is the distribution of categorical features?\n",
    "\n",
    "- Names are unique across the dataset (count=unique=891)\n",
    "- Sex variable as two possible values with 65% male (top=male, freq=577/count=891).\n",
    "- Cabin values have several dupicates across samples. Alternatively several passengers shared a cabin.\n",
    "- Embarked takes three possible values. S port used by most passengers (top=S)\n",
    "- Ticket feature has high ratio (22%) of duplicate values (unique=681)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "double-traffic",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Name         418 non-null    object \n",
      " 3   Sex          418 non-null    object \n",
      " 4   Age          332 non-null    float64\n",
      " 5   SibSp        418 non-null    int64  \n",
      " 6   Parch        418 non-null    int64  \n",
      " 7   Ticket       418 non-null    object \n",
      " 8   Fare         417 non-null    float64\n",
      " 9   Cabin        91 non-null     object \n",
      " 10  Embarked     418 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "test_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "radio-petite",
   "metadata": {
    "execution": {
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     "status": "completed"
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   "outputs": [
    {
     "data": {
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       "    }\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>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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "exclusive-mount",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:36.110302Z",
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     "status": "completed"
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   "outputs": [
    {
     "data": {
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       "        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>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>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>332.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>418.000000</td>\n",
       "      <td>417.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>2.265550</td>\n",
       "      <td>30.272590</td>\n",
       "      <td>0.447368</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>35.627188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>120.810458</td>\n",
       "      <td>0.841838</td>\n",
       "      <td>14.181209</td>\n",
       "      <td>0.896760</td>\n",
       "      <td>0.981429</td>\n",
       "      <td>55.907576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>892.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.170000</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>996.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.895800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1100.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>27.000000</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>1204.750000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1309.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId      Pclass         Age       SibSp       Parch        Fare\n",
       "count   418.000000  418.000000  332.000000  418.000000  418.000000  417.000000\n",
       "mean   1100.500000    2.265550   30.272590    0.447368    0.392344   35.627188\n",
       "std     120.810458    0.841838   14.181209    0.896760    0.981429   55.907576\n",
       "min     892.000000    1.000000    0.170000    0.000000    0.000000    0.000000\n",
       "25%     996.250000    1.000000   21.000000    0.000000    0.000000    7.895800\n",
       "50%    1100.500000    3.000000   27.000000    0.000000    0.000000   14.454200\n",
       "75%    1204.750000    3.000000   39.000000    1.000000    0.000000   31.500000\n",
       "max    1309.000000    3.000000   76.000000    8.000000    9.000000  512.329200"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "transparent-teach",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2021-06-05T04:58:01.305973Z"
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "## Assumtions based on data analysis\n",
    "We arrive at following assumptions based on data analysis done so far. We may validate these assumptions further before taking appropriate actions.\n",
    "\n",
    "### Correlating.\n",
    "     \n",
    "    We want to know how well does each feature correlate with Survival. We want to do this early in our project and match these quick correlations with modelled correlations later in the project.\n",
    "\n",
    "### Completing.\n",
    "\n",
    "- We may want to complete Age feature as it is definitely correlated to survival.\n",
    "- We may want to complete the Embarked feature as it may also correlate with survival or another important feature.\n",
    "\n",
    "### Correcting.\n",
    "\n",
    "- Ticket feature may be dropped from our analysis as it contains high ratio of duplicates (22%) and there may not be a correlation between Ticket and survival.\n",
    "- Cabin feature may be dropped as it is highly incomplete or contains many null values both in training and test dataset.\n",
    "- PassengerId may be dropped from training dataset as it does not contribute to survival.\n",
    "- Name feature is relatively non-standard, may not contribute directly to survival, so maybe dropped.\n",
    "\n",
    "### Creating.\n",
    "\n",
    "- We may want to create a new feature called Family based on Parch and SibSp to get total count of family members on board.\n",
    "- We may want to engineer the Name feature to extract Title as a new feature.\n",
    "- We may want to create new feature for Age bands. This turns a continous numerical feature into an ordinal categorical feature.\n",
    "- We may also want to create a Fare range feature if it helps our analysis.\n",
    "\n",
    "### Classifying.\n",
    "\n",
    "We may also add to our assumptions based on the problem description noted earlier.\n",
    "\n",
    "- Women (Sex=female) were more likely to have survived.\n",
    "- Children (Age<?) were more likely to have survived.\n",
    "- The upper-class passengers (Pclass=1) were more likely to have survived"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "numeric-objective",
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   "source": [
    "### We will quickly verify some of our assumptions by Pivoting features against Survival class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "shaped-moral",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:36.676415Z",
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     "iopub.status.idle": "2021-06-05T08:48:36.688153Z",
     "shell.execute_reply": "2021-06-05T08:48:36.688597Z",
     "shell.execute_reply.started": "2021-06-05T07:38:12.791049Z"
    },
    "papermill": {
     "duration": 0.103173,
     "end_time": "2021-06-05T08:48:36.688787",
     "exception": false,
     "start_time": "2021-06-05T08:48:36.585614",
     "status": "completed"
    },
    "tags": []
   },
   "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>Sex</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>0.742038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>0.188908</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sex  Survived\n",
       "0  female  0.742038\n",
       "1    male  0.188908"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sex against Survival\n",
    "\n",
    "train_df[[\"Sex\", \"Survived\"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "surprised-emperor",
   "metadata": {
    "papermill": {
     "duration": 0.08984,
     "end_time": "2021-06-05T08:48:36.869526",
     "exception": false,
     "start_time": "2021-06-05T08:48:36.779686",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "This implies that Women survived most"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "strange-passage",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:37.057584Z",
     "iopub.status.busy": "2021-06-05T08:48:37.056942Z",
     "iopub.status.idle": "2021-06-05T08:48:37.071914Z",
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     "shell.execute_reply.started": "2021-06-05T07:38:12.814389Z"
    },
    "papermill": {
     "duration": 0.109012,
     "end_time": "2021-06-05T08:48:37.072045",
     "exception": false,
     "start_time": "2021-06-05T08:48:36.963033",
     "status": "completed"
    },
    "tags": []
   },
   "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>Pclass</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.629630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.472826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pclass  Survived\n",
       "0       1  0.629630\n",
       "1       2  0.472826\n",
       "2       3  0.242363"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Passenger Class(Pclass) Against Survival\n",
    "\n",
    "train_df[['Pclass','Survived']].groupby(['Pclass'], as_index = False).mean().sort_values(by='Survived',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hybrid-complex",
   "metadata": {
    "papermill": {
     "duration": 0.092737,
     "end_time": "2021-06-05T08:48:37.255379",
     "exception": false,
     "start_time": "2021-06-05T08:48:37.162642",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "This implies that 1st class Passengers survived most"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "successful-heavy",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:37.452455Z",
     "iopub.status.busy": "2021-06-05T08:48:37.451707Z",
     "iopub.status.idle": "2021-06-05T08:48:37.454685Z",
     "shell.execute_reply": "2021-06-05T08:48:37.455204Z",
     "shell.execute_reply.started": "2021-06-05T07:38:12.835646Z"
    },
    "papermill": {
     "duration": 0.107377,
     "end_time": "2021-06-05T08:48:37.455361",
     "exception": false,
     "start_time": "2021-06-05T08:48:37.347984",
     "status": "completed"
    },
    "tags": []
   },
   "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>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.535885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.345395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SibSp  Survived\n",
       "1      1  0.535885\n",
       "2      2  0.464286\n",
       "0      0  0.345395\n",
       "3      3  0.250000\n",
       "4      4  0.166667\n",
       "5      5  0.000000\n",
       "6      8  0.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No. of siblings/spouses aboard(sibsp) vs Survival\n",
    "\n",
    "train_df[['SibSp', 'Survived']].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "literary-circuit",
   "metadata": {
    "papermill": {
     "duration": 0.090188,
     "end_time": "2021-06-05T08:48:37.636658",
     "exception": false,
     "start_time": "2021-06-05T08:48:37.546470",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "This Implies that Passengers with 0,1 or 2 siblings/spouses survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "alien-oxford",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:37.823526Z",
     "iopub.status.busy": "2021-06-05T08:48:37.822920Z",
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     "shell.execute_reply.started": "2021-06-05T07:38:12.854189Z"
    },
    "papermill": {
     "duration": 0.107847,
     "end_time": "2021-06-05T08:48:37.835807",
     "exception": false,
     "start_time": "2021-06-05T08:48:37.727960",
     "status": "completed"
    },
    "tags": []
   },
   "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>Parch</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.550847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.343658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Parch  Survived\n",
       "3      3  0.600000\n",
       "1      1  0.550847\n",
       "2      2  0.500000\n",
       "0      0  0.343658\n",
       "5      5  0.200000\n",
       "4      4  0.000000\n",
       "6      6  0.000000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# No. of parents/children aboard(Parch) vs Survival\n",
    "\n",
    "train_df[['Parch','Survived']].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "suburban-incidence",
   "metadata": {
    "papermill": {
     "duration": 0.092614,
     "end_time": "2021-06-05T08:48:38.018956",
     "exception": false,
     "start_time": "2021-06-05T08:48:37.926342",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Lets verify some feature Correlation using Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "answering-titanium",
   "metadata": {
    "papermill": {
     "duration": 0.090637,
     "end_time": "2021-06-05T08:48:38.202162",
     "exception": false,
     "start_time": "2021-06-05T08:48:38.111525",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Correlating numerical features**\n",
    "\n",
    "Age vs Survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "civilian-listing",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:38.387244Z",
     "iopub.status.busy": "2021-06-05T08:48:38.386588Z",
     "iopub.status.idle": "2021-06-05T08:48:38.786954Z",
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     "shell.execute_reply.started": "2021-06-05T07:38:12.873742Z"
    },
    "papermill": {
     "duration": 0.493728,
     "end_time": "2021-06-05T08:48:38.787099",
     "exception": false,
     "start_time": "2021-06-05T08:48:38.293371",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f579d4b70d0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(train_df, col='Survived')\n",
    "g.map(plt.hist, 'Age', bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "auburn-nancy",
   "metadata": {
    "papermill": {
     "duration": 0.093312,
     "end_time": "2021-06-05T08:48:38.974398",
     "exception": false,
     "start_time": "2021-06-05T08:48:38.881086",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observation**\n",
    "1. Children(Age<4) survived most\n",
    "2. Oldest Passengers (Age 80) also survived.\n",
    "3. Large number of 15-25 year olds did not survive.\n",
    "4. Most passengers are in 15-35 age range.\n",
    "\n",
    "**Conclusion**\n",
    "1. We need Age column in our Dataset\n",
    "2. We can group age column to form age categories instead of actual age\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "statewide-irrigation",
   "metadata": {
    "papermill": {
     "duration": 0.091878,
     "end_time": "2021-06-05T08:48:39.157694",
     "exception": false,
     "start_time": "2021-06-05T08:48:39.065816",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "iraqi-hello",
   "metadata": {
    "papermill": {
     "duration": 0.092962,
     "end_time": "2021-06-05T08:48:39.351973",
     "exception": false,
     "start_time": "2021-06-05T08:48:39.259011",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Correlating numerical and ordinal features**\n",
    "\n",
    "\n",
    "We can combine multiple features for identifying correlations using a single plot. This can be done with numerical and categorical features which have numeric values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "finite-silly",
   "metadata": {
    "papermill": {
     "duration": 0.091613,
     "end_time": "2021-06-05T08:48:39.536269",
     "exception": false,
     "start_time": "2021-06-05T08:48:39.444656",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Age and Passenger Class against Survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "known-growing",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:39.760677Z",
     "iopub.status.busy": "2021-06-05T08:48:39.725877Z",
     "iopub.status.idle": "2021-06-05T08:48:41.039255Z",
     "shell.execute_reply": "2021-06-05T08:48:41.038723Z",
     "shell.execute_reply.started": "2021-06-05T07:38:13.39837Z"
    },
    "papermill": {
     "duration": 1.409369,
     "end_time": "2021-06-05T08:48:41.039390",
     "exception": false,
     "start_time": "2021-06-05T08:48:39.630021",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, col='Survived', row='Pclass', height=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
    "grid.add_legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "disciplinary-charge",
   "metadata": {
    "papermill": {
     "duration": 0.094927,
     "end_time": "2021-06-05T08:48:41.229555",
     "exception": false,
     "start_time": "2021-06-05T08:48:41.134628",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observations**\n",
    "1. Pclass=3 had most passengers, however most did not survive.\n",
    "2. Infant passengers in Pclass=2 and Pclass=3 mostly survived.\n",
    "3. Most passengers in Pclass=1 survived. \n",
    "4. Pclass varies in terms of Age distribution of passengers.\n",
    "\n",
    "**Decisions.**\n",
    "\n",
    "- Consider Pclass for model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "outside-retro",
   "metadata": {
    "papermill": {
     "duration": 0.093248,
     "end_time": "2021-06-05T08:48:41.415873",
     "exception": false,
     "start_time": "2021-06-05T08:48:41.322625",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "weird-chorus",
   "metadata": {
    "papermill": {
     "duration": 0.093351,
     "end_time": "2021-06-05T08:48:41.602625",
     "exception": false,
     "start_time": "2021-06-05T08:48:41.509274",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Correlating categorical features**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "thrown-marriage",
   "metadata": {
    "papermill": {
     "duration": 0.093249,
     "end_time": "2021-06-05T08:48:41.788998",
     "exception": false,
     "start_time": "2021-06-05T08:48:41.695749",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Embarked Point and Age and Pclass vs Survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "spread-missile",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:41.979050Z",
     "iopub.status.busy": "2021-06-05T08:48:41.978424Z",
     "iopub.status.idle": "2021-06-05T08:48:43.351917Z",
     "shell.execute_reply": "2021-06-05T08:48:43.351413Z",
     "shell.execute_reply.started": "2021-06-05T07:38:15.6621Z"
    },
    "papermill": {
     "duration": 1.471184,
     "end_time": "2021-06-05T08:48:43.352086",
     "exception": false,
     "start_time": "2021-06-05T08:48:41.880902",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/axisgrid.py:316: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/conda/lib/python3.7/site-packages/seaborn/axisgrid.py:643: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "/opt/conda/lib/python3.7/site-packages/seaborn/axisgrid.py:648: UserWarning: Using the pointplot function without specifying `hue_order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f5792f29e10>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 323.94x475.2 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, row='Embarked', size=2.2, aspect=1.6)\n",
    "grid.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='deep')\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "extreme-thirty",
   "metadata": {
    "papermill": {
     "duration": 0.094436,
     "end_time": "2021-06-05T08:48:43.543897",
     "exception": false,
     "start_time": "2021-06-05T08:48:43.449461",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observations.**\n",
    "- Female passengers had much better survival rate than males.\n",
    "- Exception in Embarked=C where males had higher survival rate. This could be a correlation between Pclass and Embarked and in turn Pclass and Survived, not necessarily direct correlation between Embarked and Survived.\n",
    "- Males had better survival rate in Pclass=3 when compared with Pclass=2 for C and Q ports.\n",
    "- Ports of embarkation have varying survival rates for Pclass=3 and among male passengers.\n",
    "\n",
    "**Decisions.**\n",
    "\n",
    "- Add Sex feature to model training.\n",
    "- Complete and add Embarked feature to model training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lucky-craps",
   "metadata": {
    "papermill": {
     "duration": 0.09577,
     "end_time": "2021-06-05T08:48:43.734490",
     "exception": false,
     "start_time": "2021-06-05T08:48:43.638720",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Correlating categorical and numerical features**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "delayed-engagement",
   "metadata": {
    "papermill": {
     "duration": 0.093681,
     "end_time": "2021-06-05T08:48:43.921646",
     "exception": false,
     "start_time": "2021-06-05T08:48:43.827965",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Fare with Sex and Embarked Point vs Survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "differential-cartoon",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:44.116657Z",
     "iopub.status.busy": "2021-06-05T08:48:44.115986Z",
     "iopub.status.idle": "2021-06-05T08:48:45.019219Z",
     "shell.execute_reply": "2021-06-05T08:48:45.019676Z",
     "shell.execute_reply.started": "2021-06-05T07:38:17.055936Z"
    },
    "papermill": {
     "duration": 1.003267,
     "end_time": "2021-06-05T08:48:45.019849",
     "exception": false,
     "start_time": "2021-06-05T08:48:44.016582",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/axisgrid.py:316: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/conda/lib/python3.7/site-packages/seaborn/axisgrid.py:643: UserWarning: Using the barplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f5792da4e90>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, row='Embarked', col='Survived', size=2.2, aspect=1.6)\n",
    "grid.map(sns.barplot, 'Sex', 'Fare', alpha=.5, ci=None)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "seasonal-appendix",
   "metadata": {
    "papermill": {
     "duration": 0.094942,
     "end_time": "2021-06-05T08:48:45.213560",
     "exception": false,
     "start_time": "2021-06-05T08:48:45.118618",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Observations**\n",
    "\n",
    "- Higher fare paying passengers had better survival.\n",
    "- Port of embarkation correlates with survival rates.\n",
    "\n",
    "**Decisions.**\n",
    "\n",
    "- Consider banding Fare feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "personal-croatia",
   "metadata": {
    "papermill": {
     "duration": 0.097598,
     "end_time": "2021-06-05T08:48:45.408672",
     "exception": false,
     "start_time": "2021-06-05T08:48:45.311074",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "certified-thickness",
   "metadata": {
    "papermill": {
     "duration": 0.095669,
     "end_time": "2021-06-05T08:48:45.599673",
     "exception": false,
     "start_time": "2021-06-05T08:48:45.504004",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Data Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "annoying-football",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T06:02:13.753353Z",
     "iopub.status.busy": "2021-06-05T06:02:13.752939Z",
     "iopub.status.idle": "2021-06-05T06:02:13.758386Z",
     "shell.execute_reply": "2021-06-05T06:02:13.757015Z",
     "shell.execute_reply.started": "2021-06-05T06:02:13.753315Z"
    },
    "papermill": {
     "duration": 0.094834,
     "end_time": "2021-06-05T08:48:45.789565",
     "exception": false,
     "start_time": "2021-06-05T08:48:45.694731",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Now, lets drop the unwanted columns from our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "worst-baghdad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:45.985217Z",
     "iopub.status.busy": "2021-06-05T08:48:45.984537Z",
     "iopub.status.idle": "2021-06-05T08:48:45.990336Z",
     "shell.execute_reply": "2021-06-05T08:48:45.990786Z",
     "shell.execute_reply.started": "2021-06-05T07:38:18.123306Z"
    },
    "papermill": {
     "duration": 0.105717,
     "end_time": "2021-06-05T08:48:45.990949",
     "exception": false,
     "start_time": "2021-06-05T08:48:45.885232",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df = train_df.drop(['Ticket', 'Cabin'], axis=1)\n",
    "test_df = test_df.drop(['Ticket', 'Cabin'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cross-cancer",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:46.190384Z",
     "iopub.status.busy": "2021-06-05T08:48:46.189780Z",
     "iopub.status.idle": "2021-06-05T08:48:46.202881Z",
     "shell.execute_reply": "2021-06-05T08:48:46.203371Z",
     "shell.execute_reply.started": "2021-06-05T07:38:18.131686Z"
    },
    "papermill": {
     "duration": 0.11493,
     "end_time": "2021-06-05T08:48:46.203522",
     "exception": false,
     "start_time": "2021-06-05T08:48:46.088592",
     "status": "completed"
    },
    "tags": []
   },
   "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>Fare</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>7.2500</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>71.2833</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>7.9250</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>53.1000</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>8.0500</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     Fare Embarked  \n",
       "0      0   7.2500        S  \n",
       "1      0  71.2833        C  \n",
       "2      0   7.9250        S  \n",
       "3      0  53.1000        S  \n",
       "4      0   8.0500        S  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "blocked-highway",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2021-06-05T08:48:46.422398Z",
     "shell.execute_reply.started": "2021-06-05T07:38:18.155893Z"
    },
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     "end_time": "2021-06-05T08:48:46.423382",
     "exception": false,
     "start_time": "2021-06-05T08:48:46.301198",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>Kelly, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
       "      <td>female</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
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       "      <td>Myles, Mr. Thomas Francis</td>\n",
       "      <td>male</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>Wirz, Mr. Albert</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.6625</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass                                          Name     Sex  \\\n",
       "0          892       3                              Kelly, Mr. James    male   \n",
       "1          893       3              Wilkes, Mrs. James (Ellen Needs)  female   \n",
       "2          894       2                     Myles, Mr. Thomas Francis    male   \n",
       "3          895       3                              Wirz, Mr. Albert    male   \n",
       "4          896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female   \n",
       "\n",
       "    Age  SibSp  Parch     Fare Embarked  \n",
       "0  34.5      0      0   7.8292        Q  \n",
       "1  47.0      1      0   7.0000        S  \n",
       "2  62.0      0      0   9.6875        Q  \n",
       "3  27.0      0      0   8.6625        S  \n",
       "4  22.0      1      1  12.2875        S  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "tough-cleanup",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T06:03:01.644416Z",
     "iopub.status.busy": "2021-06-05T06:03:01.644019Z",
     "iopub.status.idle": "2021-06-05T06:03:01.648885Z",
     "shell.execute_reply": "2021-06-05T06:03:01.647845Z",
     "shell.execute_reply.started": "2021-06-05T06:03:01.644382Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Now, Let's create new feature by extracting info from existing features**\n",
    "\n",
    "- Lets drop the Name column from the Datase and Instead Create a New Column with Title of them Passenger\n",
    "            We want to analyze if Name feature can be engineered to extract titles and test correlation between titles and survival, before dropping Name and PassengerId features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "behind-coordinator",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:46.855901Z",
     "iopub.status.busy": "2021-06-05T08:48:46.855246Z",
     "iopub.status.idle": "2021-06-05T08:48:46.858418Z",
     "shell.execute_reply": "2021-06-05T08:48:46.857948Z",
     "shell.execute_reply.started": "2021-06-05T07:38:18.176463Z"
    },
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     "start_time": "2021-06-05T08:48:46.743242",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df['Title'] = train_df.Name.str.extract('([A-Za-z]+)\\.', expand=False)\n",
    "test_df['Title'] = test_df.Name.str.extract('([A-Za-z]+)\\.', expand=False)\n",
    "# i.e Find the First string ending with '.' in our Name column. Also,expand=False flag returns a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "hungry-graphic",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:47.079513Z",
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     "shell.execute_reply.started": "2021-06-05T07:38:18.195147Z"
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     "start_time": "2021-06-05T08:48:46.959571",
     "status": "completed"
    },
    "tags": []
   },
   "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>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capt</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>Countess</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>Don</th>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonkheer</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>Lady</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex       female  male\n",
       "Title                 \n",
       "Capt           0     1\n",
       "Col            0     2\n",
       "Countess       1     0\n",
       "Don            0     1\n",
       "Dr             1     6\n",
       "Jonkheer       0     1\n",
       "Lady           1     0\n",
       "Major          0     2\n",
       "Master         0    40\n",
       "Miss         182     0\n",
       "Mlle           2     0\n",
       "Mme            1     0\n",
       "Mr             0   517\n",
       "Mrs          125     0\n",
       "Ms             1     0\n",
       "Rev            0     6\n",
       "Sir            0     1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(train_df['Title'], train_df['Sex'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "stunning-track",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:47.302945Z",
     "iopub.status.busy": "2021-06-05T08:48:47.302330Z",
     "iopub.status.idle": "2021-06-05T08:48:47.304890Z",
     "shell.execute_reply": "2021-06-05T08:48:47.304293Z",
     "shell.execute_reply.started": "2021-06-05T07:38:18.235869Z"
    },
    "papermill": {
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     "start_time": "2021-06-05T08:48:47.199305",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Now, lets combine the Similar Titles into one common category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "shared-burden",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:47.514239Z",
     "iopub.status.busy": "2021-06-05T08:48:47.513460Z",
     "iopub.status.idle": "2021-06-05T08:48:47.516485Z",
     "shell.execute_reply": "2021-06-05T08:48:47.515933Z",
     "shell.execute_reply.started": "2021-06-05T07:38:18.244002Z"
    },
    "papermill": {
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     "start_time": "2021-06-05T08:48:47.402997",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df['Title'] = train_df['Title'].replace('Mlle', 'Miss')\n",
    "train_df['Title'] = train_df['Title'].replace('Ms', 'Miss')\n",
    "train_df['Title'] = train_df['Title'].replace('Mme', 'Mrs')\n",
    "\n",
    "test_df['Title'] = test_df['Title'].replace('Mlle', 'Miss')\n",
    "test_df['Title'] = test_df['Title'].replace('Ms', 'Miss')\n",
    "test_df['Title'] = test_df['Title'].replace('Mme', 'Mrs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "opponent-niger",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:47.721624Z",
     "iopub.status.busy": "2021-06-05T08:48:47.720911Z",
     "iopub.status.idle": "2021-06-05T08:48:47.723636Z",
     "shell.execute_reply": "2021-06-05T08:48:47.723128Z",
     "shell.execute_reply.started": "2021-06-05T07:38:18.259346Z"
    },
    "papermill": {
     "duration": 0.108025,
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     "start_time": "2021-06-05T08:48:47.615745",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Now let's combine the Remaining Less frequent Titles into a New Title 'Rare'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "accurate-absorption",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:47.936248Z",
     "iopub.status.busy": "2021-06-05T08:48:47.935535Z",
     "iopub.status.idle": "2021-06-05T08:48:47.941173Z",
     "shell.execute_reply": "2021-06-05T08:48:47.941678Z",
     "shell.execute_reply.started": "2021-06-05T07:38:18.268241Z"
    },
    "papermill": {
     "duration": 0.117772,
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     "exception": false,
     "start_time": "2021-06-05T08:48:47.824067",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df['Title'] = train_df['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "test_df['Title'] = test_df['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ruled-analysis",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:48.144409Z",
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     "iopub.status.idle": "2021-06-05T08:48:48.163784Z",
     "shell.execute_reply": "2021-06-05T08:48:48.164289Z",
     "shell.execute_reply.started": "2021-06-05T07:38:18.285574Z"
    },
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     "exception": false,
     "start_time": "2021-06-05T08:48:48.041126",
     "status": "completed"
    },
    "tags": []
   },
   "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>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>185</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>0</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>126</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rare</th>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Sex     female  male\n",
       "Title               \n",
       "Master       0    40\n",
       "Miss       185     0\n",
       "Mr           0   517\n",
       "Mrs        126     0\n",
       "Rare         3    20"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(train_df['Title'], train_df['Sex'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chicken-exposure",
   "metadata": {
    "papermill": {
     "duration": 0.103916,
     "end_time": "2021-06-05T08:48:48.388902",
     "exception": false,
     "start_time": "2021-06-05T08:48:48.284986",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now, Lets see the Title vs Survival rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "expired-creek",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:48.588613Z",
     "iopub.status.busy": "2021-06-05T08:48:48.588048Z",
     "iopub.status.idle": "2021-06-05T08:48:48.600446Z",
     "shell.execute_reply": "2021-06-05T08:48:48.600928Z",
     "shell.execute_reply.started": "2021-06-05T07:38:18.318429Z"
    },
    "papermill": {
     "duration": 0.113623,
     "end_time": "2021-06-05T08:48:48.601103",
     "exception": false,
     "start_time": "2021-06-05T08:48:48.487480",
     "status": "completed"
    },
    "tags": []
   },
   "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>Title</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Master</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Miss</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mr</td>\n",
       "      <td>0.156673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mrs</td>\n",
       "      <td>0.793651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Rare</td>\n",
       "      <td>0.347826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Title  Survived\n",
       "0  Master  0.575000\n",
       "1    Miss  0.702703\n",
       "2      Mr  0.156673\n",
       "3     Mrs  0.793651\n",
       "4    Rare  0.347826"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "adult-shepherd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:48.829398Z",
     "iopub.status.busy": "2021-06-05T08:48:48.828595Z",
     "iopub.status.idle": "2021-06-05T08:48:48.834526Z",
     "shell.execute_reply": "2021-06-05T08:48:48.835098Z",
     "shell.execute_reply.started": "2021-06-05T07:38:18.339165Z"
    },
    "papermill": {
     "duration": 0.133895,
     "end_time": "2021-06-05T08:48:48.835275",
     "exception": false,
     "start_time": "2021-06-05T08:48:48.701380",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Now lets drop the Name column also.\n",
    "train_df = train_df.drop(['Name'], axis = 1)\n",
    "test_df = test_df.drop(['Name'], axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "social-consumer",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:49.066593Z",
     "iopub.status.busy": "2021-06-05T08:48:49.065990Z",
     "iopub.status.idle": "2021-06-05T08:48:49.070901Z",
     "shell.execute_reply": "2021-06-05T08:48:49.071454Z",
     "shell.execute_reply.started": "2021-06-05T07:38:18.350227Z"
    },
    "papermill": {
     "duration": 0.110574,
     "end_time": "2021-06-05T08:48:49.071620",
     "exception": false,
     "start_time": "2021-06-05T08:48:48.961046",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Also, in our Training Dataset, we don't need Passenger ID. SO lets drop it\n",
    "train_df = train_df.drop(['PassengerId'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "norwegian-daisy",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:49.278304Z",
     "iopub.status.busy": "2021-06-05T08:48:49.277679Z",
     "iopub.status.idle": "2021-06-05T08:48:49.282165Z",
     "shell.execute_reply": "2021-06-05T08:48:49.282641Z",
     "shell.execute_reply.started": "2021-06-05T07:38:18.362506Z"
    },
    "papermill": {
     "duration": 0.109081,
     "end_time": "2021-06-05T08:48:49.282804",
     "exception": false,
     "start_time": "2021-06-05T08:48:49.173723",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 9), (418, 9))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape, test_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "invisible-ambassador",
   "metadata": {
    "papermill": {
     "duration": 0.100593,
     "end_time": "2021-06-05T08:48:49.488374",
     "exception": false,
     "start_time": "2021-06-05T08:48:49.387781",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Converting Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "knowing-incidence",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:49.693831Z",
     "iopub.status.busy": "2021-06-05T08:48:49.693181Z",
     "iopub.status.idle": "2021-06-05T08:48:49.710809Z",
     "shell.execute_reply": "2021-06-05T08:48:49.711315Z",
     "shell.execute_reply.started": "2021-06-05T07:38:18.376979Z"
    },
    "papermill": {
     "duration": 0.121129,
     "end_time": "2021-06-05T08:48:49.711476",
     "exception": false,
     "start_time": "2021-06-05T08:48:49.590347",
     "status": "completed"
    },
    "tags": []
   },
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    male  22.0      1      0   7.2500        S      1\n",
       "1         1       1  female  38.0      1      0  71.2833        C      3\n",
       "2         1       3  female  26.0      0      0   7.9250        S      2\n",
       "3         1       1  female  35.0      1      0  53.1000        S      3\n",
       "4         0       3    male  35.0      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets convert the Title Feature into numerical ones:\n",
    "title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
    "train_df['Title'] = train_df['Title'].map(title_mapping)\n",
    "test_df['Title'] = test_df['Title'].map(title_mapping)\n",
    "train_df['Title'] = train_df['Title'].fillna(0)\n",
    "test_df['Title'] = test_df['Title'].fillna(0)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "arranged-clone",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:49.921558Z",
     "iopub.status.busy": "2021-06-05T08:48:49.920859Z",
     "iopub.status.idle": "2021-06-05T08:48:49.924316Z",
     "shell.execute_reply": "2021-06-05T08:48:49.923612Z",
     "shell.execute_reply.started": "2021-06-05T07:38:18.403912Z"
    },
    "papermill": {
     "duration": 0.114582,
     "end_time": "2021-06-05T08:48:49.924455",
     "exception": false,
     "start_time": "2021-06-05T08:48:49.809873",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Lets convert Sex into Numerical Feature. Female = 1, Male=0\n",
    "\n",
    "train_df['Sex'] = train_df['Sex'].map({'female': 1, 'male':0}).astype(int)\n",
    "test_df['Sex'] = test_df['Sex'].map({'female': 1, 'male':0}).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "republican-recording",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:50.154122Z",
     "iopub.status.busy": "2021-06-05T08:48:50.153171Z",
     "iopub.status.idle": "2021-06-05T08:48:50.156724Z",
     "shell.execute_reply": "2021-06-05T08:48:50.157217Z",
     "shell.execute_reply.started": "2021-06-05T07:38:18.415623Z"
    },
    "papermill": {
     "duration": 0.129324,
     "end_time": "2021-06-05T08:48:50.157369",
     "exception": false,
     "start_time": "2021-06-05T08:48:50.028045",
     "status": "completed"
    },
    "tags": []
   },
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\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>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0  22.0      1      0   7.2500        S      1\n",
       "1         1       1    1  38.0      1      0  71.2833        C      3\n",
       "2         1       3    1  26.0      0      0   7.9250        S      2\n",
       "3         1       1    1  35.0      1      0  53.1000        S      3\n",
       "4         0       3    0  35.0      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "unlikely-agriculture",
   "metadata": {
    "papermill": {
     "duration": 0.099703,
     "end_time": "2021-06-05T08:48:50.357191",
     "exception": false,
     "start_time": "2021-06-05T08:48:50.257488",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "facial-tolerance",
   "metadata": {
    "papermill": {
     "duration": 0.099347,
     "end_time": "2021-06-05T08:48:50.555295",
     "exception": false,
     "start_time": "2021-06-05T08:48:50.455948",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Completing a numerical continuous feature**\n",
    "\n",
    "Now we should start estimating and completing features with missing or null values. We will first do this for the Age feature.\n",
    "\n",
    "We can consider three methods to complete a numerical continuous feature.\n",
    "\n",
    "1. A simple way is to generate random numbers between mean and standard deviation.\n",
    "\n",
    "2. More accurate way of guessing missing values is to use other correlated features. In our case we note correlation among Age, Gender, and Pclass. Guess Age values using median values for Age across sets of Pclass and Gender feature combinations. So, median Age for Pclass=1 and Gender=0, Pclass=1 and Gender=1, and so on...\n",
    "\n",
    "3. Combine methods 1 and 2. So instead of guessing age values based on median, use random numbers between mean and standard deviation, based on sets of Pclass and Gender combinations.\n",
    "\n",
    "Method 1 and 3 will introduce random noise into our models. The results from multiple executions might vary. We will prefer method 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "sharp-matter",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:50.753602Z",
     "iopub.status.busy": "2021-06-05T08:48:50.752657Z",
     "iopub.status.idle": "2021-06-05T08:48:52.025129Z",
     "shell.execute_reply": "2021-06-05T08:48:52.024552Z",
     "shell.execute_reply.started": "2021-06-05T07:38:18.442299Z"
    },
    "papermill": {
     "duration": 1.37289,
     "end_time": "2021-06-05T08:48:52.025257",
     "exception": false,
     "start_time": "2021-06-05T08:48:50.652367",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f5792a0f3d0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 514.88x475.2 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = sns.FacetGrid(train_df, row='Pclass', col='Sex', height=2.2, aspect=1.6)\n",
    "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
    "grid.add_legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "elder-minneapolis",
   "metadata": {
    "papermill": {
     "duration": 0.106749,
     "end_time": "2021-06-05T08:48:52.234865",
     "exception": false,
     "start_time": "2021-06-05T08:48:52.128116",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let us start by preparing an empty array to contain guessed Age values based on Pclass x Gender combinations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "varying-frost",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:52.475828Z",
     "iopub.status.busy": "2021-06-05T08:48:52.474921Z",
     "iopub.status.idle": "2021-06-05T08:48:52.478492Z",
     "shell.execute_reply": "2021-06-05T08:48:52.478952Z",
     "shell.execute_reply.started": "2021-06-05T07:38:19.925852Z"
    },
    "papermill": {
     "duration": 0.133513,
     "end_time": "2021-06-05T08:48:52.479119",
     "exception": false,
     "start_time": "2021-06-05T08:48:52.345606",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guess_ages = np.zeros((2,3))\n",
    "guess_ages"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "frequent-isolation",
   "metadata": {
    "papermill": {
     "duration": 0.099966,
     "end_time": "2021-06-05T08:48:52.678663",
     "exception": false,
     "start_time": "2021-06-05T08:48:52.578697",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now we iterate over Sex (0 or 1) and Pclass (1, 2, 3) to calculate guessed values of Age for the six combinations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "peaceful-understanding",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:52.886345Z",
     "iopub.status.busy": "2021-06-05T08:48:52.885395Z",
     "iopub.status.idle": "2021-06-05T08:48:52.917473Z",
     "shell.execute_reply": "2021-06-05T08:48:52.918005Z",
     "shell.execute_reply.started": "2021-06-05T07:38:19.935533Z"
    },
    "papermill": {
     "duration": 0.138096,
     "end_time": "2021-06-05T08:48:52.918190",
     "exception": false,
     "start_time": "2021-06-05T08:48:52.780094",
     "status": "completed"
    },
    "tags": []
   },
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0   22      1      0   7.2500        S      1\n",
       "1         1       1    1   38      1      0  71.2833        C      3\n",
       "2         1       3    1   26      0      0   7.9250        S      2\n",
       "3         1       1    1   35      1      0  53.1000        S      3\n",
       "4         0       3    0   35      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(0, 2):\n",
    "    for j in range(0, 3):\n",
    "        guess_df = train_df[(train_df['Sex'] == i) & \\\n",
    "                                  (train_df['Pclass'] == j+1)]['Age'].dropna()\n",
    "        \n",
    "        age_guess = guess_df.median()\n",
    "\n",
    "        # Convert random age float to nearest .5 age\n",
    "        guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5\n",
    "\n",
    "for i in range(0, 2):\n",
    "    for j in range(0, 3):\n",
    "        train_df.loc[ (train_df.Age.isnull()) & (train_df.Sex == i) & (train_df.Pclass == j+1),\\\n",
    "                'Age'] = guess_ages[i,j]\n",
    "\n",
    "train_df['Age'] = train_df['Age'].astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dynamic-robert",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:53.129657Z",
     "iopub.status.busy": "2021-06-05T08:48:53.129027Z",
     "iopub.status.idle": "2021-06-05T08:48:53.134117Z",
     "shell.execute_reply": "2021-06-05T08:48:53.134642Z",
     "shell.execute_reply.started": "2021-06-05T07:38:19.976735Z"
    },
    "papermill": {
     "duration": 0.111491,
     "end_time": "2021-06-05T08:48:53.134848",
     "exception": false,
     "start_time": "2021-06-05T08:48:53.023357",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[40. , 30. , 25. ],\n",
       "       [35. , 28. , 21.5]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guess_ages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "responsible-participant",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:53.344443Z",
     "iopub.status.busy": "2021-06-05T08:48:53.343863Z",
     "iopub.status.idle": "2021-06-05T08:48:53.367030Z",
     "shell.execute_reply": "2021-06-05T08:48:53.367511Z",
     "shell.execute_reply.started": "2021-06-05T07:38:19.985528Z"
    },
    "papermill": {
     "duration": 0.128627,
     "end_time": "2021-06-05T08:48:53.367670",
     "exception": false,
     "start_time": "2021-06-05T08:48:53.239043",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for i in range(0, 2):\n",
    "    for j in range(0, 3):\n",
    "        guess_df = test_df[(test_df['Sex'] == i) & \\\n",
    "                                  (test_df['Pclass'] == j+1)]['Age'].dropna()\n",
    "        \n",
    "        age_guess = guess_df.median()\n",
    "\n",
    "        # Convert random age float to nearest .5 age\n",
    "        guess_ages[i,j] = int( age_guess/0.5 + 0.5 ) * 0.5\n",
    "\n",
    "for i in range(0, 2):\n",
    "    for j in range(0, 3):\n",
    "        test_df.loc[ (test_df.Age.isnull()) & (test_df.Sex == i) & (test_df.Pclass == j+1),\\\n",
    "                'Age'] = guess_ages[i,j]\n",
    "\n",
    "test_df['Age'] = test_df['Age'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "built-emphasis",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:53.578300Z",
     "iopub.status.busy": "2021-06-05T08:48:53.577722Z",
     "iopub.status.idle": "2021-06-05T08:48:53.582266Z",
     "shell.execute_reply": "2021-06-05T08:48:53.582831Z",
     "shell.execute_reply.started": "2021-06-05T07:38:20.016003Z"
    },
    "papermill": {
     "duration": 0.110479,
     "end_time": "2021-06-05T08:48:53.582992",
     "exception": false,
     "start_time": "2021-06-05T08:48:53.472513",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[42., 28., 24.],\n",
       "       [41., 24., 22.]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "guess_ages"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alpha-scoop",
   "metadata": {
    "papermill": {
     "duration": 0.10177,
     "end_time": "2021-06-05T08:48:53.788333",
     "exception": false,
     "start_time": "2021-06-05T08:48:53.686563",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let us create Age bands and determine correlations with Survived."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "looking-pressure",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:53.997579Z",
     "iopub.status.busy": "2021-06-05T08:48:53.996955Z",
     "iopub.status.idle": "2021-06-05T08:48:54.022837Z",
     "shell.execute_reply": "2021-06-05T08:48:54.022178Z",
     "shell.execute_reply.started": "2021-06-05T07:38:20.026149Z"
    },
    "papermill": {
     "duration": 0.132516,
     "end_time": "2021-06-05T08:48:54.022981",
     "exception": false,
     "start_time": "2021-06-05T08:48:53.890465",
     "status": "completed"
    },
    "tags": []
   },
   "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>AgeBand</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(-0.08, 16.0]</td>\n",
       "      <td>0.550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "      <td>0.337374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "      <td>0.412037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(48.0, 64.0]</td>\n",
       "      <td>0.434783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(64.0, 80.0]</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         AgeBand  Survived\n",
       "0  (-0.08, 16.0]  0.550000\n",
       "1   (16.0, 32.0]  0.337374\n",
       "2   (32.0, 48.0]  0.412037\n",
       "3   (48.0, 64.0]  0.434783\n",
       "4   (64.0, 80.0]  0.090909"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['AgeBand'] = pd.cut(train_df['Age'], 5)\n",
    "train_df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "universal-tablet",
   "metadata": {
    "papermill": {
     "duration": 0.103869,
     "end_time": "2021-06-05T08:48:54.235543",
     "exception": false,
     "start_time": "2021-06-05T08:48:54.131674",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let us replace Age with ordinals based on these bands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "fuzzy-robertson",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:54.454033Z",
     "iopub.status.busy": "2021-06-05T08:48:54.452959Z",
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     "shell.execute_reply.started": "2021-06-05T07:38:20.054437Z"
    },
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     "duration": 0.133448,
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     "exception": false,
     "start_time": "2021-06-05T08:48:54.336147",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>AgeBand</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>(16.0, 32.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>(32.0, 48.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title  \\\n",
       "0         0       3    0    1      1      0   7.2500        S      1   \n",
       "1         1       1    1    2      1      0  71.2833        C      3   \n",
       "2         1       3    1    1      0      0   7.9250        S      2   \n",
       "3         1       1    1    2      1      0  53.1000        S      3   \n",
       "4         0       3    0    2      0      0   8.0500        S      1   \n",
       "\n",
       "        AgeBand  \n",
       "0  (16.0, 32.0]  \n",
       "1  (32.0, 48.0]  \n",
       "2  (16.0, 32.0]  \n",
       "3  (32.0, 48.0]  \n",
       "4  (32.0, 48.0]  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.loc[ train_df['Age'] <= 16, 'Age'] = 0\n",
    "train_df.loc[(train_df['Age'] > 16) & (train_df['Age'] <= 32), 'Age'] = 1\n",
    "train_df.loc[(train_df['Age'] > 32) & (train_df['Age'] <= 48), 'Age'] = 2\n",
    "train_df.loc[(train_df['Age'] > 48) & (train_df['Age'] <= 64), 'Age'] = 3\n",
    "train_df.loc[ train_df['Age'] > 64, 'Age'] = 4\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "postal-parliament",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:54.682115Z",
     "iopub.status.busy": "2021-06-05T08:48:54.681175Z",
     "iopub.status.idle": "2021-06-05T08:48:54.700181Z",
     "shell.execute_reply": "2021-06-05T08:48:54.700606Z",
     "shell.execute_reply.started": "2021-06-05T07:38:20.082899Z"
    },
    "papermill": {
     "duration": 0.126753,
     "end_time": "2021-06-05T08:48:54.700802",
     "exception": false,
     "start_time": "2021-06-05T08:48:54.574049",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>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",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>3</td>\n",
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       "      <td>Q</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12.2875</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0          892       3    0    2      0      0   7.8292        Q      1\n",
       "1          893       3    1    2      1      0   7.0000        S      3\n",
       "2          894       2    0    3      0      0   9.6875        Q      1\n",
       "3          895       3    0    1      0      0   8.6625        S      1\n",
       "4          896       3    1    1      1      1  12.2875        S      3"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.loc[ test_df['Age'] <= 16, 'Age'] = 0\n",
    "test_df.loc[(test_df['Age'] > 16) & (test_df['Age'] <= 32), 'Age'] = 1\n",
    "test_df.loc[(test_df['Age'] > 32) & (test_df['Age'] <= 48), 'Age'] = 2\n",
    "test_df.loc[(test_df['Age'] > 48) & (test_df['Age'] <= 64), 'Age'] = 3\n",
    "test_df.loc[ test_df['Age'] > 64, 'Age'] = 4\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "organized-shape",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T07:49:24.660139Z",
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     "shell.execute_reply.started": "2021-06-05T07:49:24.660109Z"
    },
    "papermill": {
     "duration": 0.145865,
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     "exception": false,
     "start_time": "2021-06-05T08:48:54.807116",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "improving-cornell",
   "metadata": {
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    },
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   },
   "source": [
    "We can now remove the AgeBand feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "declared-pixel",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
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   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
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      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0         0       3    0    1      1      0   7.2500        S      1\n",
       "1         1       1    1    2      1      0  71.2833        C      3\n",
       "2         1       3    1    1      0      0   7.9250        S      2\n",
       "3         1       1    1    2      1      0  53.1000        S      3\n",
       "4         0       3    0    2      0      0   8.0500        S      1"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['AgeBand'], axis=1)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "resident-farmer",
   "metadata": {
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "- Now, lets repeat the Same for the Fare Feature and convert it into Numeric feature.\n",
    "\n",
    "- But, First we need to fill the one missing Value present there.\n",
    "    - For this, we will simply use the Median value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "completed-shipping",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:55.796574Z",
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    {
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       "0"
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     },
     "execution_count": 47,
     "metadata": {},
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   ],
   "source": [
    "#Replace NA to median value \n",
    "test_df['Fare'].fillna(test_df['Fare'].dropna().median(), inplace=True)\n",
    "test_df['Fare'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "crucial-understanding",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:56.015614Z",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.4542"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "test_df['Fare'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "mature-brush",
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    {
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       "   PassengerId  Pclass  Sex  Age  SibSp  Parch     Fare Embarked  Title\n",
       "0          892       3    0    2      0      0   7.8292        Q      1\n",
       "1          893       3    1    2      1      0   7.0000        S      3\n",
       "2          894       2    0    3      0      0   9.6875        Q      1\n",
       "3          895       3    0    1      0      0   8.6625        S      1\n",
       "4          896       3    1    1      1      1  12.2875        S      3"
      ]
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     "execution_count": 49,
     "metadata": {},
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   ],
   "source": [
    "test_df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "several-satin",
   "metadata": {
    "execution": {
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     "status": "completed"
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    {
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       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
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       "      <td>2.308642</td>\n",
       "      <td>0.352413</td>\n",
       "      <td>1.322110</td>\n",
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       "      <td>0.820081</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>1.030039</td>\n",
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       "    <tr>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
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       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
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       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>2.000000</td>\n",
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       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>5.000000</td>\n",
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      "text/plain": [
       "         Survived      Pclass         Sex         Age       SibSp       Parch  \\\n",
       "count  891.000000  891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean     0.383838    2.308642    0.352413    1.322110    0.523008    0.381594   \n",
       "std      0.486592    0.836071    0.477990    0.820081    1.102743    0.806057   \n",
       "min      0.000000    1.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000    2.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "50%      0.000000    3.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "75%      1.000000    3.000000    1.000000    2.000000    1.000000    0.000000   \n",
       "max      1.000000    3.000000    1.000000    4.000000    8.000000    6.000000   \n",
       "\n",
       "             Fare       Title  \n",
       "count  891.000000  891.000000  \n",
       "mean    32.204208    1.728395  \n",
       "std     49.693429    1.030039  \n",
       "min      0.000000    1.000000  \n",
       "25%      7.910400    1.000000  \n",
       "50%     14.454200    1.000000  \n",
       "75%     31.000000    2.000000  \n",
       "max    512.329200    5.000000  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "opening-times",
   "metadata": {
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    },
    "tags": []
   },
   "source": [
    "Now, Lets create the Fare band"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "rapid-currency",
   "metadata": {
    "execution": {
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     "start_time": "2021-06-05T08:48:56.794792",
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    },
    "tags": []
   },
   "outputs": [
    {
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       "      <th>3</th>\n",
       "      <td>(31.0, 512.329]</td>\n",
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      ],
      "text/plain": [
       "          FareBand  Survived\n",
       "0   (-0.001, 7.91]  0.197309\n",
       "1   (7.91, 14.454]  0.303571\n",
       "2   (14.454, 31.0]  0.454955\n",
       "3  (31.0, 512.329]  0.581081"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['FareBand'] = pd.qcut(train_df['Fare'], 4)\n",
    "train_df[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
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   "id": "mysterious-feedback",
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   "outputs": [
    {
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       "   Survived  Pclass  Sex  Age  SibSp  Parch  Fare Embarked  Title  \\\n",
       "0         0       3    0    1      1      0   0.0        S      1   \n",
       "1         1       1    1    2      1      0   3.0        C      3   \n",
       "2         1       3    1    1      0      0   1.0        S      2   \n",
       "3         1       1    1    2      1      0   3.0        S      3   \n",
       "4         0       3    0    2      0      0   1.0        S      1   \n",
       "\n",
       "          FareBand  \n",
       "0   (-0.001, 7.91]  \n",
       "1  (31.0, 512.329]  \n",
       "2   (7.91, 14.454]  \n",
       "3  (31.0, 512.329]  \n",
       "4   (7.91, 14.454]  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.loc[ train_df['Fare'] <= 7.91, 'Fare'] = 0\n",
    "train_df.loc[(train_df['Fare'] > 7.91) & (train_df['Fare'] <= 14.454), 'Fare'] = 1\n",
    "train_df.loc[(train_df['Fare'] > 14.454) & (train_df['Fare'] <= 31), 'Fare'] = 2\n",
    "train_df.loc[(train_df['Fare'] > 31), 'Fare'] = 3\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "unlimited-somerset",
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   "outputs": [
    {
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       "   PassengerId  Pclass  Sex  Age  SibSp  Parch  Fare Embarked  Title\n",
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     },
     "execution_count": 53,
     "metadata": {},
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   ],
   "source": [
    "test_df.loc[ test_df['Fare'] <= 7.91, 'Fare'] = 0\n",
    "test_df.loc[(test_df['Fare'] > 7.91) & (test_df['Fare'] <= 14.454), 'Fare'] = 1\n",
    "test_df.loc[(test_df['Fare'] > 14.454) & (test_df['Fare'] <= 31), 'Fare'] = 2\n",
    "test_df.loc[(test_df['Fare'] > 31), 'Fare'] = 3\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "universal-belgium",
   "metadata": {
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    "tags": []
   },
   "source": [
    "Lets remove the FareBand now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "prompt-learning",
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    },
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   "outputs": [
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      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  SibSp  Parch  Fare Embarked  Title\n",
       "0         0       3    0    1      1      0   0.0        S      1\n",
       "1         1       1    1    2      1      0   3.0        C      3\n",
       "2         1       3    1    1      0      0   1.0        S      2\n",
       "3         1       1    1    2      1      0   3.0        S      3\n",
       "4         0       3    0    2      0      0   1.0        S      1"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['FareBand'], axis=1)\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "backed-basis",
   "metadata": {
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    },
    "tags": []
   },
   "source": [
    "**Create new feature combining existing features**\n",
    "\n",
    "We can create a new feature for FamilySize which combines Parch and SibSp. This will enable us to drop Parch and SibSp from our datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "alternate-chile",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FamilySize  Survived\n",
       "3           4  0.724138\n",
       "2           3  0.578431\n",
       "1           2  0.552795\n",
       "6           7  0.333333\n",
       "0           1  0.303538\n",
       "4           5  0.200000\n",
       "5           6  0.136364\n",
       "7           8  0.000000\n",
       "8          11  0.000000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['FamilySize'] = train_df['SibSp'] + train_df['Parch'] + 1\n",
    "test_df['FamilySize'] = test_df['SibSp'] + test_df['Parch'] + 1\n",
    "\n",
    "train_df[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "regulated-pioneer",
   "metadata": {
    "papermill": {
     "duration": 0.105198,
     "end_time": "2021-06-05T08:48:58.523395",
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     "start_time": "2021-06-05T08:48:58.418197",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We can create another feature called IsAlone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "middle-eclipse",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:58.748698Z",
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     "shell.execute_reply": "2021-06-05T08:48:58.755570Z",
     "shell.execute_reply.started": "2021-06-05T07:56:26.977012Z"
    },
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     "duration": 0.124756,
     "end_time": "2021-06-05T08:48:58.755726",
     "exception": false,
     "start_time": "2021-06-05T08:48:58.630970",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Survived</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>0.505650</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "   IsAlone  Survived\n",
       "0        0  0.505650\n",
       "1        1  0.303538"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['IsAlone'] = 0\n",
    "train_df.loc[train_df['FamilySize'] == 1, 'IsAlone'] = 1\n",
    "\n",
    "train_df[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "meaningful-gibraltar",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:58.974288Z",
     "iopub.status.busy": "2021-06-05T08:48:58.973568Z",
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     "shell.execute_reply": "2021-06-05T08:48:58.976492Z",
     "shell.execute_reply.started": "2021-06-05T07:56:42.939515Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_df['IsAlone'] = 0\n",
    "test_df.loc[test_df['FamilySize'] == 1, 'IsAlone'] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "korean-richmond",
   "metadata": {
    "papermill": {
     "duration": 0.111567,
     "end_time": "2021-06-05T08:48:59.200971",
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     "start_time": "2021-06-05T08:48:59.089404",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let us drop Parch, SibSp, and FamilySize features in favor of IsAlone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "vocational-punishment",
   "metadata": {
    "execution": {
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     "start_time": "2021-06-05T08:48:59.312521",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <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>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>S</td>\n",
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       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>S</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  Fare Embarked  Title  IsAlone\n",
       "0         0       3    0    1   0.0        S      1        0\n",
       "1         1       1    1    2   3.0        C      3        0\n",
       "2         1       3    1    1   1.0        S      2        1\n",
       "3         1       1    1    2   3.0        S      3        0\n",
       "4         0       3    0    2   1.0        S      1        1"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\n",
    "test_df = test_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "seasonal-england",
   "metadata": {
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     "duration": 0.110525,
     "end_time": "2021-06-05T08:48:59.665552",
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     "start_time": "2021-06-05T08:48:59.555027",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Completing a categorical feature**\n",
    "\n",
    "Embarked feature takes S, Q, C values based on port of embarkation. Our training dataset has two missing values. We simply fill these with the most common occurance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "animal-jamaica",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:48:59.889721Z",
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     "shell.execute_reply.started": "2021-06-05T08:02:04.737257Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'S'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq_port = train_df.Embarked.dropna().mode()[0]\n",
    "freq_port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "drawn-display",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:49:00.121340Z",
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     "start_time": "2021-06-05T08:49:00.003704",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Embarked</th>\n",
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       "      <td>C</td>\n",
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       "      <th>1</th>\n",
       "      <td>Q</td>\n",
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       "  Embarked  Survived\n",
       "0        C  0.553571\n",
       "1        Q  0.389610\n",
       "2        S  0.339009"
      ]
     },
     "execution_count": 60,
     "metadata": {},
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    }
   ],
   "source": [
    "train_df['Embarked'] = train_df['Embarked'].fillna(freq_port)\n",
    "\n",
    "train_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "acoustic-fifth",
   "metadata": {
    "execution": {
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   },
   "outputs": [
    {
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone\n",
       "0         0       3    0    1   0.0         0      1        0\n",
       "1         1       1    1    2   3.0         1      3        0\n",
       "2         1       3    1    1   1.0         0      2        1\n",
       "3         1       1    1    2   3.0         0      3        0\n",
       "4         0       3    0    2   1.0         0      1        1"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Embarked'] = train_df['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n",
    "test_df['Embarked'] = test_df['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n",
    "\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "nearby-average",
   "metadata": {
    "papermill": {
     "duration": 0.107805,
     "end_time": "2021-06-05T08:49:00.587200",
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     "start_time": "2021-06-05T08:49:00.479395",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now, lets see our Training and Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "automated-nelson",
   "metadata": {
    "execution": {
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    },
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   },
   "outputs": [
    {
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       "<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>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Title</th>\n",
       "      <th>IsAlone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone\n",
       "0         0       3    0    1   0.0         0      1        0\n",
       "1         1       1    1    2   3.0         1      3        0\n",
       "2         1       3    1    1   1.0         0      2        1\n",
       "3         1       1    1    2   3.0         0      3        0\n",
       "4         0       3    0    2   1.0         0      1        1\n",
       "5         0       3    0    1   1.0         2      1        1\n",
       "6         0       1    0    3   3.0         0      1        1\n",
       "7         0       3    0    0   2.0         0      4        0\n",
       "8         1       3    1    1   1.0         0      3        0\n",
       "9         1       2    1    0   2.0         1      3        0"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "generic-scientist",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2021-06-05T08:19:50.116751Z"
    },
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     "end_time": "2021-06-05T08:49:01.059778",
     "exception": false,
     "start_time": "2021-06-05T08:49:00.934347",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 8 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  891 non-null    int64  \n",
      " 1   Pclass    891 non-null    int64  \n",
      " 2   Sex       891 non-null    int64  \n",
      " 3   Age       891 non-null    int64  \n",
      " 4   Fare      891 non-null    float64\n",
      " 5   Embarked  891 non-null    int64  \n",
      " 6   Title     891 non-null    int64  \n",
      " 7   IsAlone   891 non-null    int64  \n",
      "dtypes: float64(1), int64(7)\n",
      "memory usage: 55.8 KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "acknowledged-circulation",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "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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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>897</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>898</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>7</th>\n",
       "      <td>899</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>900</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>901</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Pclass  Sex  Age  Fare  Embarked  Title  IsAlone\n",
       "0          892       3    0    2   0.0         2      1        1\n",
       "1          893       3    1    2   0.0         0      3        0\n",
       "2          894       2    0    3   1.0         2      1        1\n",
       "3          895       3    0    1   1.0         0      1        1\n",
       "4          896       3    1    1   1.0         0      3        0\n",
       "5          897       3    0    0   1.0         0      1        1\n",
       "6          898       3    1    1   0.0         2      2        1\n",
       "7          899       2    0    1   2.0         0      1        0\n",
       "8          900       3    1    1   0.0         1      3        1\n",
       "9          901       3    0    1   2.0         0      1        0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "affected-chemistry",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:49:01.550872Z",
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     "shell.execute_reply.started": "2021-06-05T08:19:52.605578Z"
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     "exception": false,
     "start_time": "2021-06-05T08:49:01.418528",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 8 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  418 non-null    int64  \n",
      " 1   Pclass       418 non-null    int64  \n",
      " 2   Sex          418 non-null    int64  \n",
      " 3   Age          418 non-null    int64  \n",
      " 4   Fare         418 non-null    float64\n",
      " 5   Embarked     418 non-null    int64  \n",
      " 6   Title        418 non-null    int64  \n",
      " 7   IsAlone      418 non-null    int64  \n",
      "dtypes: float64(1), int64(7)\n",
      "memory usage: 26.2 KB\n"
     ]
    }
   ],
   "source": [
    "test_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alone-brush",
   "metadata": {
    "papermill": {
     "duration": 0.11295,
     "end_time": "2021-06-05T08:49:01.780835",
     "exception": false,
     "start_time": "2021-06-05T08:49:01.667885",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ignored-heavy",
   "metadata": {
    "papermill": {
     "duration": 0.109908,
     "end_time": "2021-06-05T08:49:02.001642",
     "exception": false,
     "start_time": "2021-06-05T08:49:01.891734",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Models Used:\n",
    "1. Logistic Regression\n",
    "2. KNN or k-Nearest Neighbors\n",
    "3. Support Vector Machines\n",
    "4. Naive Bayes classifier\n",
    "5. Decision Tree\n",
    "6. Random Forrest\n",
    "7. Perceptron"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "derived-accreditation",
   "metadata": {
    "papermill": {
     "duration": 0.109556,
     "end_time": "2021-06-05T08:49:02.223794",
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     "start_time": "2021-06-05T08:49:02.114238",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "1. Logistic Regression is a useful model to run early in the workflow. Logistic regression measures the relationship between the categorical dependent variable (feature) and one or more independent variables (features) by estimating probabilities using a logistic function, which is the cumulative logistic distribution. Reference [Wikipedia](https://en.wikipedia.org/wiki/Logistic_regression).\n",
    "\n",
    "2. Support Vector Machines are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training samples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new test samples to one category or the other, making it a non-probabilistic binary linear classifier. Reference [Wikipedia](https://en.wikipedia.org/wiki/Support_vector_machine).\n",
    "\n",
    "3. In pattern recognition, the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression. A sample is classified by a majority vote of its neighbors, with the sample being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small). If k = 1, then the object is simply assigned to the class of that single nearest neighbor. Reference [Wikipedia](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm).\n",
    "\n",
    "4. In machine learning, naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features. Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features) in a learning problem. Reference [Wikipedia](https://en.wikipedia.org/wiki/Naive_Bayes_classifier).\n",
    "\n",
    "5. The perceptron is an algorithm for supervised learning of binary classifiers (functions that can decide whether an input, represented by a vector of numbers, belongs to some specific class or not). It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector. The algorithm allows for online learning, in that it processes elements in the training set one at a time. Reference [Wikipedia](https://en.wikipedia.org/wiki/Perceptron).\n",
    "\n",
    "6. Decision Tree is a predictive model which maps features (tree branches) to conclusions about the target value (tree leaves). Tree models where the target variable can take a finite set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. Reference [Wikipedia](https://en.wikipedia.org/wiki/Decision_tree_learning).\n",
    "\n",
    "7. Random Forests is one of the most popular. Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks, that operate by constructing a multitude of decision trees (n_estimators=100) at training time and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. Reference [Wikipedia](https://en.wikipedia.org/wiki/Random_forest).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "congressional-movement",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:49:02.453309Z",
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     "iopub.status.idle": "2021-06-05T08:49:02.459211Z",
     "shell.execute_reply": "2021-06-05T08:49:02.458601Z",
     "shell.execute_reply.started": "2021-06-05T08:19:54.653778Z"
    },
    "papermill": {
     "duration": 0.126538,
     "end_time": "2021-06-05T08:49:02.459337",
     "exception": false,
     "start_time": "2021-06-05T08:49:02.332799",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((891, 7), (891,), (418, 7))"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = train_df.drop(\"Survived\", axis=1)\n",
    "Y_train = train_df[\"Survived\"]\n",
    "X_test  = test_df.drop(\"PassengerId\", axis=1).copy()\n",
    "X_train.shape, Y_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "empirical-dependence",
   "metadata": {
    "papermill": {
     "duration": 0.108309,
     "end_time": "2021-06-05T08:49:02.677944",
     "exception": false,
     "start_time": "2021-06-05T08:49:02.569635",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "worthy-settlement",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:49:02.905606Z",
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     "shell.execute_reply": "2021-06-05T08:49:02.926170Z",
     "shell.execute_reply.started": "2021-06-05T08:19:57.973942Z"
    },
    "papermill": {
     "duration": 0.1416,
     "end_time": "2021-06-05T08:49:02.926893",
     "exception": false,
     "start_time": "2021-06-05T08:49:02.785293",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.37"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, Y_train)\n",
    "Y_pred = logreg.predict(X_test)\n",
    "acc_log = round(logreg.score(X_train, Y_train) * 100, 2)\n",
    "acc_log"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "clean-vaccine",
   "metadata": {
    "papermill": {
     "duration": 0.111445,
     "end_time": "2021-06-05T08:49:03.150718",
     "exception": false,
     "start_time": "2021-06-05T08:49:03.039273",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**KNN**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "plastic-trick",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:49:03.381837Z",
     "iopub.status.busy": "2021-06-05T08:49:03.380953Z",
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     "shell.execute_reply.started": "2021-06-05T08:21:41.765315Z"
    },
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     "duration": 0.194306,
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     "exception": false,
     "start_time": "2021-06-05T08:49:03.261674",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.62"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 3)\n",
    "knn.fit(X_train, Y_train)\n",
    "Y_pred = knn.predict(X_test)\n",
    "acc_knn = round(knn.score(X_train, Y_train) * 100, 2)\n",
    "acc_knn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "latest-kernel",
   "metadata": {
    "papermill": {
     "duration": 0.126714,
     "end_time": "2021-06-05T08:49:03.704155",
     "exception": false,
     "start_time": "2021-06-05T08:49:03.577441",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Support Vector Machines (SVM)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "imperial-inclusion",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:49:03.943859Z",
     "iopub.status.busy": "2021-06-05T08:49:03.942945Z",
     "iopub.status.idle": "2021-06-05T08:49:04.008241Z",
     "shell.execute_reply": "2021-06-05T08:49:04.007676Z",
     "shell.execute_reply.started": "2021-06-05T08:21:15.133205Z"
    },
    "papermill": {
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     "end_time": "2021-06-05T08:49:04.008377",
     "exception": false,
     "start_time": "2021-06-05T08:49:03.822986",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83.28"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Support Vector Machines\n",
    "\n",
    "svc = SVC()\n",
    "svc.fit(X_train, Y_train)\n",
    "Y_pred = svc.predict(X_test)\n",
    "acc_svc = round(svc.score(X_train, Y_train) * 100, 2)\n",
    "acc_svc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "irish-british",
   "metadata": {
    "papermill": {
     "duration": 0.111294,
     "end_time": "2021-06-05T08:49:04.232694",
     "exception": false,
     "start_time": "2021-06-05T08:49:04.121400",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Naive Bayes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "violent-orange",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:49:04.461573Z",
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     "shell.execute_reply.started": "2021-06-05T08:22:13.101701Z"
    },
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     "start_time": "2021-06-05T08:49:04.344425",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77.33"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gaussian Naive Bayes\n",
    "\n",
    "gaussian = GaussianNB()\n",
    "gaussian.fit(X_train, Y_train)\n",
    "Y_pred = gaussian.predict(X_test)\n",
    "acc_gaussian = round(gaussian.score(X_train, Y_train) * 100, 2)\n",
    "acc_gaussian"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "accomplished-billy",
   "metadata": {
    "papermill": {
     "duration": 0.114847,
     "end_time": "2021-06-05T08:49:04.701821",
     "exception": false,
     "start_time": "2021-06-05T08:49:04.586974",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Decision Trees**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "looking-reserve",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:49:04.932857Z",
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     "shell.execute_reply.started": "2021-06-05T08:23:00.238148Z"
    },
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     "duration": 0.128591,
     "end_time": "2021-06-05T08:49:04.942319",
     "exception": false,
     "start_time": "2021-06-05T08:49:04.813728",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decision Tree\n",
    "\n",
    "decision_tree = DecisionTreeClassifier()\n",
    "decision_tree.fit(X_train, Y_train)\n",
    "Y_pred = decision_tree.predict(X_test)\n",
    "acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)\n",
    "acc_decision_tree"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "studied-estimate",
   "metadata": {
    "papermill": {
     "duration": 0.111423,
     "end_time": "2021-06-05T08:49:05.165804",
     "exception": false,
     "start_time": "2021-06-05T08:49:05.054381",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Random Forest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "optical-assumption",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:49:05.400567Z",
     "iopub.status.busy": "2021-06-05T08:49:05.395349Z",
     "iopub.status.idle": "2021-06-05T08:49:05.645279Z",
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     "shell.execute_reply.started": "2021-06-05T08:23:31.811214Z"
    },
    "papermill": {
     "duration": 0.366332,
     "end_time": "2021-06-05T08:49:05.645414",
     "exception": false,
     "start_time": "2021-06-05T08:49:05.279082",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest\n",
    "\n",
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "Y_pred = random_forest.predict(X_test)\n",
    "random_forest.score(X_train, Y_train)\n",
    "acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\n",
    "acc_random_forest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "continued-concern",
   "metadata": {
    "papermill": {
     "duration": 0.109009,
     "end_time": "2021-06-05T08:49:05.866969",
     "exception": false,
     "start_time": "2021-06-05T08:49:05.757960",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Preceptron**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "mineral-affiliation",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:49:06.101736Z",
     "iopub.status.busy": "2021-06-05T08:49:06.100716Z",
     "iopub.status.idle": "2021-06-05T08:49:06.110777Z",
     "shell.execute_reply": "2021-06-05T08:49:06.110319Z",
     "shell.execute_reply.started": "2021-06-05T08:24:14.968425Z"
    },
    "papermill": {
     "duration": 0.132785,
     "end_time": "2021-06-05T08:49:06.110916",
     "exception": false,
     "start_time": "2021-06-05T08:49:05.978131",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "78.34"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perceptron\n",
    "\n",
    "perceptron = Perceptron()\n",
    "perceptron.fit(X_train, Y_train)\n",
    "Y_pred = perceptron.predict(X_test)\n",
    "acc_perceptron = round(perceptron.score(X_train, Y_train) * 100, 2)\n",
    "acc_perceptron"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "processed-success",
   "metadata": {
    "papermill": {
     "duration": 0.108723,
     "end_time": "2021-06-05T08:49:06.333629",
     "exception": false,
     "start_time": "2021-06-05T08:49:06.224906",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Linear SVC**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "hungarian-nancy",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:49:06.552590Z",
     "iopub.status.busy": "2021-06-05T08:49:06.551978Z",
     "iopub.status.idle": "2021-06-05T08:49:06.604608Z",
     "shell.execute_reply": "2021-06-05T08:49:06.605045Z",
     "shell.execute_reply.started": "2021-06-05T08:24:48.423419Z"
    },
    "papermill": {
     "duration": 0.163739,
     "end_time": "2021-06-05T08:49:06.605217",
     "exception": false,
     "start_time": "2021-06-05T08:49:06.441478",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/sklearn/svm/_base.py:986: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "80.13"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Linear SVC\n",
    "\n",
    "linear_svc = LinearSVC()\n",
    "linear_svc.fit(X_train, Y_train)\n",
    "Y_pred = linear_svc.predict(X_test)\n",
    "acc_linear_svc = round(linear_svc.score(X_train, Y_train) * 100, 2)\n",
    "acc_linear_svc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "blind-accident",
   "metadata": {
    "papermill": {
     "duration": 0.112862,
     "end_time": "2021-06-05T08:49:06.830126",
     "exception": false,
     "start_time": "2021-06-05T08:49:06.717264",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Stochastic Gradient Descent**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "falling-mediterranean",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:49:07.065620Z",
     "iopub.status.busy": "2021-06-05T08:49:07.064738Z",
     "iopub.status.idle": "2021-06-05T08:49:07.076037Z",
     "shell.execute_reply": "2021-06-05T08:49:07.075568Z",
     "shell.execute_reply.started": "2021-06-05T08:25:21.921962Z"
    },
    "papermill": {
     "duration": 0.133012,
     "end_time": "2021-06-05T08:49:07.076189",
     "exception": false,
     "start_time": "2021-06-05T08:49:06.943177",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79.57"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Stochastic Gradient Descent\n",
    "\n",
    "sgd = SGDClassifier()\n",
    "sgd.fit(X_train, Y_train)\n",
    "Y_pred = sgd.predict(X_test)\n",
    "acc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)\n",
    "acc_sgd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "super-elizabeth",
   "metadata": {
    "papermill": {
     "duration": 0.114946,
     "end_time": "2021-06-05T08:49:07.306194",
     "exception": false,
     "start_time": "2021-06-05T08:49:07.191248",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Model evaluation**\n",
    "\n",
    "We can now rank our evaluation of all the models to choose the best one for our problem. While both Decision Tree and Random Forest score the same, we choose to use Random Forest as they correct for decision trees' habit of overfitting to their training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "british-collar",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:49:07.541974Z",
     "iopub.status.busy": "2021-06-05T08:49:07.541356Z",
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     "shell.execute_reply.started": "2021-06-05T08:31:57.77091Z"
    },
    "papermill": {
     "duration": 0.129863,
     "end_time": "2021-06-05T08:49:07.554463",
     "exception": false,
     "start_time": "2021-06-05T08:49:07.424600",
     "status": "completed"
    },
    "tags": []
   },
   "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>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>86.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>86.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>84.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>83.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>81.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>80.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Stochastic Gradient Decent</td>\n",
       "      <td>79.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Perceptron</td>\n",
       "      <td>78.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>77.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Model  Score\n",
       "3               Random Forest  86.64\n",
       "8               Decision Tree  86.64\n",
       "1                         KNN  84.62\n",
       "0     Support Vector Machines  83.28\n",
       "2         Logistic Regression  81.37\n",
       "7                  Linear SVC  80.13\n",
       "6  Stochastic Gradient Decent  79.57\n",
       "5                  Perceptron  78.34\n",
       "4                 Naive Bayes  77.33"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', \n",
    "              'Random Forest', 'Naive Bayes', 'Perceptron', \n",
    "              'Stochastic Gradient Decent', 'Linear SVC', \n",
    "              'Decision Tree'],\n",
    "    'Score': [acc_svc, acc_knn, acc_log, \n",
    "              acc_random_forest, acc_gaussian, acc_perceptron, \n",
    "              acc_sgd, acc_linear_svc, acc_decision_tree]})\n",
    "models.sort_values(by='Score', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "meaningful-convention",
   "metadata": {
    "papermill": {
     "duration": 0.114059,
     "end_time": "2021-06-05T08:49:07.782152",
     "exception": false,
     "start_time": "2021-06-05T08:49:07.668093",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Submission**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "annual-clause",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:49:08.008096Z",
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     "exception": false,
     "start_time": "2021-06-05T08:49:07.891443",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random Forest\n",
    "\n",
    "random_forest = RandomForestClassifier(n_estimators=100)\n",
    "random_forest.fit(X_train, Y_train)\n",
    "Y_pred = random_forest.predict(X_test)\n",
    "random_forest.score(X_train, Y_train)\n",
    "acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\n",
    "acc_random_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "selective-cowboy",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:49:08.539351Z",
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   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": test_df[\"PassengerId\"],\n",
    "        \"Survived\": Y_pred\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "recreational-guitar",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T08:49:08.774314Z",
     "iopub.status.busy": "2021-06-05T08:49:08.773598Z",
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     "shell.execute_reply.started": "2021-06-05T08:44:41.100251Z"
    },
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     "start_time": "2021-06-05T08:49:08.656504",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "regional-colon",
   "metadata": {
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     "end_time": "2021-06-05T08:49:09.009475",
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     "status": "completed"
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   "source": []
  }
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