titanic-ml_v_2.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"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "speaking-burner",
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    "tags": []
   },
   "source": [
    "## Importing Required Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "interracial-bowling",
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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": "infinite-lodge",
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    "tags": []
   },
   "source": [
    "## Loading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "crude-finance",
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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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   "id": "muslim-outside",
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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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   "id": "experienced-helping",
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       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <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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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
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    "tags": []
   },
   "source": [
    "## Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
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    "tags": []
   },
   "source": [
    "Getting basic info about our dataframes like Column name, Types, count etc"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "advisory-findings",
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    {
     "name": "stdout",
     "output_type": "stream",
     "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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   "id": "amended-pacific",
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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": "upset-austria",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2021-06-05T11:17:15.666103Z",
     "shell.execute_reply.started": "2021-06-05T11:13:02.306330Z"
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     "status": "completed"
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    "tags": []
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   "outputs": [
    {
     "data": {
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       "<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>Peuchen, Major. Arthur Godfrey</td>\n",
       "      <td>male</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>C23 C25 C27</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     Peuchen, Major. Arthur Godfrey  male  CA. 2343  C23 C25 C27        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": "expensive-portfolio",
   "metadata": {
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     "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": "prescribed-alpha",
   "metadata": {
    "execution": {
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     "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 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": "developmental-alberta",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2021-06-05T11:13:02.368805Z"
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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": "pleased-character",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:16.547500Z",
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     "start_time": "2021-06-05T11:17:16.415428",
     "status": "completed"
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   "outputs": [
    {
     "data": {
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       "<div>\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>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": "working-boards",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T04:58:01.306009Z",
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     "iopub.status.idle": "2021-06-05T04:58:01.318831Z",
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     "shell.execute_reply.started": "2021-06-05T04:58:01.305973Z"
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     "start_time": "2021-06-05T11:17:16.649034",
     "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"
   ]
  },
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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": "covered-milan",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:17.147270Z",
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     "iopub.status.idle": "2021-06-05T11:17:17.164680Z",
     "shell.execute_reply": "2021-06-05T11:17:17.165374Z",
     "shell.execute_reply.started": "2021-06-05T11:13:02.443308Z"
    },
    "papermill": {
     "duration": 0.12186,
     "end_time": "2021-06-05T11:17:17.165575",
     "exception": false,
     "start_time": "2021-06-05T11:17:17.043715",
     "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": "cardiovascular-runner",
   "metadata": {
    "papermill": {
     "duration": 0.099827,
     "end_time": "2021-06-05T11:17:17.363426",
     "exception": false,
     "start_time": "2021-06-05T11:17:17.263599",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "This implies that Women survived most"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "parental-disabled",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:17.570867Z",
     "iopub.status.busy": "2021-06-05T11:17:17.569814Z",
     "iopub.status.idle": "2021-06-05T11:17:17.587749Z",
     "shell.execute_reply": "2021-06-05T11:17:17.587224Z",
     "shell.execute_reply.started": "2021-06-05T11:13:02.465036Z"
    },
    "papermill": {
     "duration": 0.125918,
     "end_time": "2021-06-05T11:17:17.587923",
     "exception": false,
     "start_time": "2021-06-05T11:17:17.462005",
     "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": "consecutive-trainer",
   "metadata": {
    "papermill": {
     "duration": 0.098189,
     "end_time": "2021-06-05T11:17:17.785135",
     "exception": false,
     "start_time": "2021-06-05T11:17:17.686946",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "This implies that 1st class Passengers survived most"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fitting-chart",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:18.002697Z",
     "iopub.status.busy": "2021-06-05T11:17:18.001604Z",
     "iopub.status.idle": "2021-06-05T11:17:18.006096Z",
     "shell.execute_reply": "2021-06-05T11:17:18.006602Z",
     "shell.execute_reply.started": "2021-06-05T11:13:02.485795Z"
    },
    "papermill": {
     "duration": 0.123212,
     "end_time": "2021-06-05T11:17:18.006804",
     "exception": false,
     "start_time": "2021-06-05T11:17:17.883592",
     "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": "nuclear-upset",
   "metadata": {
    "papermill": {
     "duration": 0.100381,
     "end_time": "2021-06-05T11:17:18.207679",
     "exception": false,
     "start_time": "2021-06-05T11:17:18.107298",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "This Implies that Passengers with 0,1 or 2 siblings/spouses survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "velvet-store",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:18.413073Z",
     "iopub.status.busy": "2021-06-05T11:17:18.411936Z",
     "iopub.status.idle": "2021-06-05T11:17:18.428243Z",
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     "shell.execute_reply.started": "2021-06-05T11:13:02.505766Z"
    },
    "papermill": {
     "duration": 0.122086,
     "end_time": "2021-06-05T11:17:18.428951",
     "exception": false,
     "start_time": "2021-06-05T11:17:18.306865",
     "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": "private-adolescent",
   "metadata": {
    "papermill": {
     "duration": 0.105058,
     "end_time": "2021-06-05T11:17:18.636467",
     "exception": false,
     "start_time": "2021-06-05T11:17:18.531409",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Lets verify some feature Correlation using Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "level-portuguese",
   "metadata": {
    "papermill": {
     "duration": 0.102906,
     "end_time": "2021-06-05T11:17:18.840614",
     "exception": false,
     "start_time": "2021-06-05T11:17:18.737708",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Correlating numerical features**\n",
    "\n",
    "Age vs Survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "radical-aruba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:19.058550Z",
     "iopub.status.busy": "2021-06-05T11:17:19.057509Z",
     "iopub.status.idle": "2021-06-05T11:17:19.577877Z",
     "shell.execute_reply": "2021-06-05T11:17:19.577264Z",
     "shell.execute_reply.started": "2021-06-05T11:13:02.522950Z"
    },
    "papermill": {
     "duration": 0.637063,
     "end_time": "2021-06-05T11:17:19.578147",
     "exception": false,
     "start_time": "2021-06-05T11:17:18.941084",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f98ee35ce10>"
      ]
     },
     "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": "accurate-lafayette",
   "metadata": {
    "papermill": {
     "duration": 0.100451,
     "end_time": "2021-06-05T11:17:19.779614",
     "exception": false,
     "start_time": "2021-06-05T11:17:19.679163",
     "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": "perfect-enterprise",
   "metadata": {
    "papermill": {
     "duration": 0.102413,
     "end_time": "2021-06-05T11:17:19.991642",
     "exception": false,
     "start_time": "2021-06-05T11:17:19.889229",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "speaking-adobe",
   "metadata": {
    "papermill": {
     "duration": 0.100222,
     "end_time": "2021-06-05T11:17:20.193657",
     "exception": false,
     "start_time": "2021-06-05T11:17:20.093435",
     "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": "collected-regression",
   "metadata": {
    "papermill": {
     "duration": 0.100827,
     "end_time": "2021-06-05T11:17:20.394374",
     "exception": false,
     "start_time": "2021-06-05T11:17:20.293547",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Age and Passenger Class against Survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "colonial-conjunction",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:20.720638Z",
     "iopub.status.busy": "2021-06-05T11:17:20.606235Z",
     "iopub.status.idle": "2021-06-05T11:17:22.176699Z",
     "shell.execute_reply": "2021-06-05T11:17:22.176104Z",
     "shell.execute_reply.started": "2021-06-05T11:13:02.942282Z"
    },
    "papermill": {
     "duration": 1.68029,
     "end_time": "2021-06-05T11:17:22.176866",
     "exception": false,
     "start_time": "2021-06-05T11:17:20.496576",
     "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": "tested-reunion",
   "metadata": {
    "papermill": {
     "duration": 0.104228,
     "end_time": "2021-06-05T11:17:22.382143",
     "exception": false,
     "start_time": "2021-06-05T11:17:22.277915",
     "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": "third-going",
   "metadata": {
    "papermill": {
     "duration": 0.101523,
     "end_time": "2021-06-05T11:17:22.586661",
     "exception": false,
     "start_time": "2021-06-05T11:17:22.485138",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "urban-stroke",
   "metadata": {
    "papermill": {
     "duration": 0.101752,
     "end_time": "2021-06-05T11:17:22.789857",
     "exception": false,
     "start_time": "2021-06-05T11:17:22.688105",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Correlating categorical features**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "changed-cookbook",
   "metadata": {
    "papermill": {
     "duration": 0.10129,
     "end_time": "2021-06-05T11:17:22.993125",
     "exception": false,
     "start_time": "2021-06-05T11:17:22.891835",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Embarked Point and Age and Pclass vs Survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "sealed-cooler",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:23.206454Z",
     "iopub.status.busy": "2021-06-05T11:17:23.205615Z",
     "iopub.status.idle": "2021-06-05T11:17:24.616511Z",
     "shell.execute_reply": "2021-06-05T11:17:24.615617Z",
     "shell.execute_reply.started": "2021-06-05T11:13:04.252841Z"
    },
    "papermill": {
     "duration": 1.516501,
     "end_time": "2021-06-05T11:17:24.617020",
     "exception": false,
     "start_time": "2021-06-05T11:17:23.100519",
     "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 0x7f98e5de9e10>"
      ]
     },
     "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": "taken-johnson",
   "metadata": {
    "papermill": {
     "duration": 0.107317,
     "end_time": "2021-06-05T11:17:24.830871",
     "exception": false,
     "start_time": "2021-06-05T11:17:24.723554",
     "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": "invalid-handy",
   "metadata": {
    "papermill": {
     "duration": 0.131934,
     "end_time": "2021-06-05T11:17:25.089189",
     "exception": false,
     "start_time": "2021-06-05T11:17:24.957255",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Correlating categorical and numerical features**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "advanced-startup",
   "metadata": {
    "papermill": {
     "duration": 0.105074,
     "end_time": "2021-06-05T11:17:25.303483",
     "exception": false,
     "start_time": "2021-06-05T11:17:25.198409",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Fare with Sex and Embarked Point vs Survival"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "little-liabilities",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:25.526951Z",
     "iopub.status.busy": "2021-06-05T11:17:25.525621Z",
     "iopub.status.idle": "2021-06-05T11:17:26.602693Z",
     "shell.execute_reply": "2021-06-05T11:17:26.602111Z",
     "shell.execute_reply.started": "2021-06-05T11:13:05.403308Z"
    },
    "papermill": {
     "duration": 1.191248,
     "end_time": "2021-06-05T11:17:26.602864",
     "exception": false,
     "start_time": "2021-06-05T11:17:25.411616",
     "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 0x7f98e5b9bb90>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8Ksk+s12HJEl9NooegAOB1VV1Z1U9DpwPHDWCOiRJ6q1RBIDdgXvHja9pp0mSpFkyf9QFrE+SpcDSdvTHSW4fZT0dehrwwKiLGNZrR13ApmVzfu+WV9XiYWbsUVuFzfs935xtzu/b0G11olTVdJabtiQvBN5RVUe0428HqKr3zGohm4gkK6tq0ajr0NT53vWP7/nc5Ps22ChOAVwL7JVkYZKtgFcCl4ygDkmSemvWTwFU1dokrwcuB+YBH6mqW2e7DkmS+mwk1wBU1WXAZaPY9iborFEXoGnzvesf3/O5yfdtgFm/BkCSJI2eXwUsSVIPGQAkSeohA4AkST1kAJAkqYcMAJIk9ZABQJKkHjIASJLUQwYASZJ6yAAgSVIPGQAkSeohA4AkST1kAJiBJD9LcuO4n5OnsOwhSS6d4favTjKte1wnOSfJMTPZfrueZye5LMkdSa5PcmGSXQfMd/d6lj8oyTXt67cqyTtmWtO4dX9pI61nY71WS9rX6Y4kSzZGbRqe7dX2OsX1LE/y0Ezf903ZSO4GuBl5tKr2G8WGk8wbxXYn1LA18CngLVX1f9tphwALgO8OuZplwLFV9bX2Oe09he2H5oZWTwx6vKr+07Dr6lqSnYFTgEVAAdcluaSqHhxtZb1ie7W9TsWpwLbAiaMupCv2AHQgyd1J3tOm5JVJDkhyeZJvJnntuFmfmuRTSW5P8sEkW7TLf6Bd7tYkfzVhve9Ncj3w++Omb9Gm3nclmZfk1CTXJrkpyYntPEny/nZbnwGevhGe6h8AX163MwGoqqur6pYprOPpwP3tsj+rqtvaet+R5K3jnuMtSfZsf25P8v8BtwB/keTUcfMdn+T97fCP29/nJ3nZuHnOSXLMLL9WRwBXVtUP2n/6VwKLN8J6NUO2V9vrIFW1Anh4Y6xrU2UPwMxsk+TGcePvqaoL2uFvVdV+SU4DzgEOBramaQQfbOc5ENgHuAdYDvwucBHw51X1gzZhr0iyb1Xd1C7z/ao6AKDdOc0HzgVuqap3J1kK/LCqXpDkl4AvJrkC2J8mre8D7ArcBnxk4hNK8qfAcQOe6+er6o0Tpj0XuG7SV2nDTgNuT3I1zWuwrKr+bZJl9gKWVNVXkiwAvgz8afvYK4B3T5j/AuBY4FNJtgIOBf4IOIHZe612B+4dN76mnabZY3u1vQ77WvWCAWBmNtSleEn7+2Zg+6p6GHg4yWNJdmwf+2pV3QmQ5DzgRTQ7lGPbHcN8YDeaP+x1O5R1O6x1zgQurKp1jehwYN88eQ5sB5oG+GLgvKr6GfDtJJ8dVHRVnUrT9TUrquqvk5xLU/cfAK8CDplksXuq6ivt8mNJ7kxyEHAH8BzgixPm/zRwRrvTWEzT4B9NMqdeK82Y7XWGbK+bFwNAdx5rfz8xbnjd+LrXvSYsU0kWAm8FXlBVDyY5h+ZIZJ1HJizzJeAlSf6+TeIB3lBVl4+fKclvDVP0FFPyrcB/GWa9G1JV3wQ+kORDwFiSXYC1/Pwpqg29BufTHDF8HfhEVf3c61pV/9YesRxBc8RxfvvQbL5W9/HzO8o9gKuH2Y5mhe11SD1pr/1QVf5M8wf48Xqm3w08rR0+Hnj/xMdo/hk8CiykaTiXA78HPB/4WjttV5qLc46fuN52/Gqai8reQnMEMx9YCnwS2LKd59nAdjTdlZcD82iOUh4Ejpnh898GWA28bNy0FwPPHfSarGcdL6O5MAjgPwIPtDX+N+D8dvoBwM+APdufWyasYyfgm8BVwIGD3p92O5+g6Ybfqp02m6/VzsBdba07tcM7j/pvuE8/tlfb6zRes0OAS0f9t9vVjz0AMzPxnOLyqhr6o0XAtcD7gWfRNIZPVNUTSW6gScf38ovdY7+gqt6XZAfgn2kS7p7A9UkCjAFH0zSml9KcH/sWzXm4GammW+7lwOlJTgd+StP1+aYprOYPgdOS/ITmKOK4qvpZko8B/0+SW4FrgG9soI4Hk6wC9qmqr65ntitoXp+Lq+rxdtqHmb3X6gdJ3knzngP8dVX9YKbr1ZTYXm2vQ0vyrzSnKLZPsgY4oSb0Psx165Kc1Kkkd1fVnqOuQ9LkbK/94McAJUnqIQOAZsvpoy5A0tBOH3UB6p6nACRJ6iF7ACRJ6iEDgCRJPTQnPga4ePHiWr58+ajLkPok01nItirNumm1VZgjPQAPPPDAqEuQNATbqjR3zIkAIEmSNi4DgCRJPWQAkCSph+bERYCSNj9nXnHjqEvY7J14+H6jLkGbsE57AJK8OcmtSW5Jcl6SrZMsTHJNktVJLkiyVZc1SJKkX9RZAEiyO/BGYFFVPZfmVo2vBN4LnFZVz6K5beMJXdUgSZIG6/oagPk0t+CcD2wL3E9z28aL2seX0dzOUZIkzaLOAkBV3Qf8Hc39me8HfghcBzxUVWvb2dYAu3dVgyRJGqzLUwA7AUcBC4FnANsBi6ew/NIkK5OsHBsb66hKSTNlW5Xmpi5PARwG3FVVY1X1U+DjwMHAju0pAYA9gPsGLVxVZ1XVoqpatGDBgg7LlDQTtlVpbuoyAHwLOCjJtkkCHArcBlwFHNPOswS4uMMaJEnSAF1eA3ANzcV+1wM3t9s6CzgJeEuS1cAuwNld1SBJkgbr9IuAquoU4JQJk+8EDuxyu5IkacP8KmBJknrIACBJUg8ZACRJ6iEDgCRJPWQAkCSphwwAkiT1UKcfA5QkbX7OvOLGUZewWTvx8P1mZTv2AEiS1EMGAEmSesgAIElSDxkAJEnqIQOAJEk9ZACQJKmHDACSJPWQAUCSpB4yAEiS1EMGAEmSeqjTAJBkxyQXJfl6klVJXphk5yRXJrmj/b1TlzVIkqRf1HUPwBnA8qp6DvB8YBVwMrCiqvYCVrTjkiRpFnUWAJLsALwYOBugqh6vqoeAo4Bl7WzLgKO7qkGSJA3WZQ/AQmAM+KckNyT5cJLtgF2r6v52nu8Au3ZYgyRJGqDLADAfOAD4QFXtDzzChO7+qiqgBi2cZGmSlUlWjo2NdVimpJmwrUpzU5cBYA2wpqquaccvogkE302yG0D7+3uDFq6qs6pqUVUtWrBgQYdlSpoJ26o0N3UWAKrqO8C9SfZuJx0K3AZcAixppy0BLu6qBkmSNNj8jtf/BuDcJFsBdwKvpgkdFyY5AbgHOLbjGiRJ0gSdBoCquhFYNOChQ7vcriRJ2jC/CVCSpB4yAEiS1EMGAEmSesgAIElSDxkAJEnqIQOAJEk9ZACQJKmHDACSJPXQ0AEgyYuSvLodXpBkYXdlSZKkLg0VAJKcApwEvL2dtCXwL10VJUmSujVsD8DvAEfS3NKXqvo28JSuipIkSd0aNgA8XlUFFECS7borSZIkdW3YAHBhkjOBHZO8BvgM8KHuypIkSV2a9G6ASQJcADwH+BGwN/CXVXVlx7VJkqSOTBoAqqqSXFZVzwP8py9J0mZg2FMA1yd5QaeVSJKkWTNpD0DrN4DjktxD80mA0HQO7NtZZZIkqTPDBoAjOq1CkiTNqqFOAVTVPVV1D/AozUcB//0jgZNJMi/JDUkubccXJrkmyeokFyTZarrFS5Kk6Rn2mwCPTHIHcBfwOeBu4NNDbuNNwKpx4+8FTquqZwEPAicMXa0kSdoohr0I8J3AQcA3qmohcCjwlckWSrIH8DLgw+14gJcCF7WzLAOOnlrJkiRppoYNAD+tqu8DWyTZoqquAhYNsdzpwNuAJ9rxXYCHqmptO74G2H0K9UqSpI1g2ADwUJLtgc8D5yY5g/a+AOuT5OXA96rquukUlmRpkpVJVo6NjU1nFZJmgW1Vmps2GACS/Eo7eBTwE+DNwHLgm8BvT7Lug4Ejk9wNnE/T9X8GzdcJr/v0wR7AfYMWrqqzqmpRVS1asGDBEE9F0ijYVqW5abKPAX4SOKCqHknysar6PZrz9pOqqrfT3j44ySHAW6vquCQfBY6hCQVLgIunV/pgZ15x48ZcnSY48fD9Rl2CJGkjmOwUQMYNP3MjbfMk4C1JVtNcE3D2RlqvJEka0mQ9ALWe4SmpqquBq9vhO4EDp7suSZI0c5MFgOcn+RFNT8A27TA8+VXAT+20OkmS1IkNBoCqmjdbhUiSpNkz7McAJUnSZsQAIElSDxkAJEnqIQOAJEk9ZACQJKmHDACSJPWQAUCSpB4yAEiS1EMGAEmSesgAIElSDxkAJEnqIQOAJEk9ZACQJKmHDACSJPWQAUCSpB7qLAAk+eUkVyW5LcmtSd7UTt85yZVJ7mh/79RVDZIkabAuewDWAn9SVfsABwGvS7IPcDKwoqr2Ala045IkaRZ1FgCq6v6qur4dfhhYBewOHAUsa2dbBhzdVQ2SJGmwWbkGIMmewP7ANcCuVXV/+9B3gF1nowZJkvSkzgNAku2BjwF/XFU/Gv9YVRVQ61luaZKVSVaOjY11XaakabKtSnNTpwEgyZY0//zPraqPt5O/m2S39vHdgO8NWraqzqqqRVW1aMGCBV2WKWkGbKvS3NTlpwACnA2sqqr3jXvoEmBJO7wEuLirGiRJ0mDzO1z3wcAfAjcnubGd9mfA3wIXJjkBuAc4tsMaJEnSAJ0FgKr6ApD1PHxoV9uVJEmT67IHQBramVfcOOoSNnsnHr7fqEuQtAnxq4AlSeohA4AkST1kAJAkqYcMAJIk9ZABQJKkHjIASJLUQwYASZJ6yAAgSVIPGQAkSeohA4AkST1kAJAkqYcMAJIk9ZABQJKkHjIASJLUQwYASZJ6yAAgSVIPjSQAJFmc5PYkq5OcPIoaJEnqs1kPAEnmAf8I/CawD/CqJPvMdh2SJPXZKHoADgRWV9WdVfU4cD5w1AjqkCSpt0YRAHYH7h03vqadJkmSZsn8URewPkmWAkvb0R8nuX2U9XToacADoy5iWK8ddQGbls35vVteVYuHmbFHbRU27/d8c7Y5v29Dt9WJUlXTWW7akrwQeEdVHdGOvx2gqt4zq4VsIpKsrKpFo65DU+d71z++53OT79tgozgFcC2wV5KFSbYCXglcMoI6JEnqrVk/BVBVa5O8HrgcmAd8pKpune06JEnqs5FcA1BVlwGXjWLbm6CzRl2Aps33rn98z+cm37cBZv0aAEmSNHp+FbAkST1kAJAkqYcMAJIk9ZABQJKkHjIASJLUQwYASZJ6yAAgSVIPGQAkSeohA4AkST1kAJAkqYcMAJIk9ZABQJKkHjIAzECSnyW5cdzPyVNY9pAkl85w+1cnWTTNZc9JcsxMtt+u59eSfDbJ7Um+meSvkgz8u0py93qmvzzJDUm+luS2JCfOtK52vc9IctFGWte0X+sJ63l7ktXt63XExqhNk7Ot2lanuI5dklyV5MdJ3r8x6toUjeR2wJuRR6tqv1FsOMm8UWx3Qg3bAJcAf1RVVyTZFvgY8CbgtCHXsSXNrToPrKo1SX4J2HMKNcyvqrWDHquqbwMz3nFuLEn2AV4J/BrwDOAzSZ5dVT8bbWW9YFu1rU7FvwF/ATy3/dks2QPQgSR3J3lPe6SxMskBSS5vU/drx8361CSfahP5B9el8SQfaJe7NclfTVjve5NcD/z+uOlbtEcJ70oyL8mpSa5NctO6hJ7G+9ttfQZ4+kZ4qn8AfLGqrgCoqp8Arwf+dArreApNEP1+u47Hqur2tuafO/JJ8uP29yFJ/jXJJcBtSf42yevGzfeOJG9NsmeSW9ppX0nya+PmuTrJoiTbJflIkq+2RzZHtY9vk+T8JKuSfALYZjov0ARHAee3z/EuYDVw4EZYr6bJtmpbHaSqHqmqL9AEgc2WPQAzs02SG8eNv6eqLmiHv1VV+yU5DTgHOBjYGrgF+GA7z4HAPsA9wHLgd4GLgD+vqh+0Rw4rkuxbVTe1y3y/qg4AaHdQ84FzgVuq6t1JlgI/rKoXtAn9i0muAPYH9m63tytwG/CRiU8oyZ8Cxw14rp+vqjdOmPZrwHXjJ1TVN9sGuWNVPTT4Zfu5+X/Q7hzuSbICuBQ4r6qemGTRA4DnVtVdSfYHTgf+sX3sWOAIYPyR1wXt9FOS7AbsVlUrk/wN8Nmq+u9JdgS+2u50TwR+UlX/Mcm+wPWDimjf35cMeOj8qvrbCdN2B74ybnxNO03ds63aVqfSVnvBADAzG+pWvKT9fTOwfVU9DDyc5LH2jxfgq1V1J0CS84AX0exUjm13DvOB3Wh2BOt2Kut2WuucCVxYVe9uxw8H9h2XxncA9gJeTNNYfwZ8O8lnBxVdVacCp076zDeiqvofSZ4HHAa8FfivwPGTLPbV9iiaqrohydOTPANYADxYVfcm2XPc/BcCVwCn0Oxc1p1vPBw4Mslb2/GtgV+heb3+oV3/TUluYoCqevNUnqtGxra6EdhWNy8GgO481v5+YtzwuvF1r3tNWKaSLKRpWC+oqgeTnEPzh77OIxOW+RLwkiR/X1X/BgR4Q1VdPn6mJL81TNFTPKq4jabxjV/+mTRHPg8Ns711qupm4OYk/wzcRbNTWUt7mqrtct1q3CITX4eP0pxD/A/84o6XqrovyffbI4RXAOu6dwP83rquzHHPY6i6p3hUcR/wy+PG92inabRsq1PQk7baC14DMFoHJlnYNphXAF8AnkrTYH6YZFfgNydZx9nAZcCFSeYDlwN/lOaCHZI8O8l2wOeBV6Q577gbgxsCVXVqVe034GfiDgWa7swXJTms3dY2NEn8lGFfgCTbJzlk3KT9aLpZAe4Gfr0dPhLYcgOruoDmArtjaHYw65vnbcAO47ppLwfekHYv0nZRQvN6/UE77bnAvoNWWFVvXs/rNWiHcgnwyiS/1P7z2Av46gaekzYdttV+tdVesAdgZiaeV1xeVUN/vAi4Fng/8CzgKuATVfVEkhuArwP3Al+cbCVV9b4kOwD/THNEsCdwfdtQxoCjgU8AL6U5EvgW8OUp1Lm+7T6a5Ejgfyf5PzTns99VVedOYTUB3pbkTOBRmh3q8e1jHwIuTvI1mvOuE48kxtdya5KnAPdV1f3rme0i4AzgneOmvZPmnORN7c79LuDlwAeAf0qyCljFhPOn09HWeCHNe7AWeF35CYDZYlu1rU5Jmo9CPhXYKsnRwOFVddvGWPemIlUTe7ak6WkbyfuAl1TVPQMev7uq9pztuiT9PNuqwACgWeRORZobbKv94DUAmk2nj7oASUM5fdQFqHv2AEiS1EP2AEiS1ENz4lMAixcvruXLl4+6DKlPhvtw9QS2VWnWTautwhzpAXjggQdGXYKkIdhWpbljTgQASZK0cRkAJEnqIQOAJEk9ZACQJKmHOg0ASd6c5NYktyQ5L8nW7Q01rkmyOskFSbaafE2SJGlj6iwAJNkdeCOwqKqeC8yjuQPUe4HTqupZwIPACV3VIEmSBuv6FMB8mrtwzQe2Be6nucvVRe3jy2jufiVJkmZRZwGgqu4D/o7mdpb3Az+kuU3jQ1W1tp1tDc1tKSVJ0izq8hTATsBRwELgGcB2wOIpLL80ycokK8fGxjqqUtJM2ValuanLUwCHAXdV1VhV/RT4OHAwsGN7SgBgD+C+QQtX1VlVtaiqFi1YsKDDMiXNhG1Vmpu6DADfAg5Ksm2SAIcCtwFXAce08ywBLu6wBkmSNECX1wBcQ3Ox3/XAze22zgJOAt6SZDWwC3B2VzVIkqTBOr0bYFWdApwyYfKdwIFdbleSJG2Y3wQoSVIPGQAkSeohA4AkST1kAJAkqYcMAJIk9ZABQJKkHjIASJLUQwYASZJ6yAAgSVIPGQAkSeohA4AkST1kAJAkqYcMAJIk9ZABQJKkHjIASJLUQwYASZJ6yAAgSVIPGQAkSeqhTgNAkh2TXJTk60lWJXlhkp2TXJnkjvb3Tl3WIEmSflHXPQBnAMur6jnA84FVwMnAiqraC1jRjkuSpFnUWQBIsgPwYuBsgKp6vKoeAo4ClrWzLQOO7qoGSZI0WJc9AAuBMeCfktyQ5MNJtgN2rar723m+A+zaYQ2SJGmALgPAfOAA4ANVtT/wCBO6+6uqgBq0cJKlSVYmWTk2NtZhmZJmwrYqzU1dBoA1wJqquqYdv4gmEHw3yW4A7e/vDVq4qs6qqkVVtWjBggUdlilpJmyr0tzUWQCoqu8A9ybZu510KHAbcAmwpJ22BLi4qxokSdJg8zte/xuAc5NsBdwJvJomdFyY5ATgHuDYjmuQJEkTdBoAqupGYNGAhw7tcruSJGnD/CZASZJ6yAAgSVIPGQAkSeohA4AkST1kAJAkqYcMAJIk9ZABQJKkHho6ACR5UZJXt8MLkizsrixJktSloQJAklOAk4C3t5O2BP6lq6IkSVK3hu0B+B3gSJo7+lFV3wae0lVRkiSpW8MGgMfH37o3yXbdlSRJkro2bAC4MMmZwI5JXgN8BvhQd2VJkqQuTXozoCQBLgCeA/wI2Bv4y6q6suPaJElSRyYNAFVVSS6rqucB/tOXJGkzMOwpgOuTvKDTSiRJ0qyZtAeg9RvAcUnuofkkQGg6B/btrDJJktSZYQPAEZ1WIUmSZtVQpwCq6p6qugd4lOajgP/+kcDJJJmX5IYkl7bjC5Nck2R1kguSbDXd4iVJ0vQM+02ARya5A7gL+BxwN/DpIbfxJmDVuPH3AqdV1bOAB4EThq5WkiRtFMNeBPhO4CDgG1W1EDgU+MpkCyXZA3gZ8OF2PMBLgYvaWZYBR0+tZEmSNFPDBoCfVtX3gS2SbFFVVwGLhljudOBtwBPt+C7AQ1W1th1fA+w+hXolSdJGMGwAeCjJ9sDngXOTnEF7X4D1SfJy4HtVdd10CkuyNMnKJCvHxsamswpJs8C2Ks1NGwwASX6lHTwK+AnwZmA58E3gtydZ98HAkUnuBs6n6fo/g+brhNd9+mAP4L5BC1fVWVW1qKoWLViwYIinImkUbKvS3DRZD8AnAarqEeCjVbW2qpZV1T+0pwTWq6reXlV7VNWewCuBz1bVccBVwDHtbEuAi2fyBCRJ0tRNFgAybviZG2mbJwFvSbKa5pqAszfSeiVJ0pAm+yKgWs/wlFTV1cDV7fCdwIHTXZckSZq5yQLA85P8iKYnYJt2GJ78KuCndlqdJEnqxAYDQFXNm61CJEnS7Bn2Y4CSJGkzYgCQJKmHDACSJPWQAUCSpB4yAEiS1EMGAEmSesgAIElSDxkAJEnqIQOAJEk9ZACQJKmHDACSJPWQAUCSpB4yAEiS1EMGAEmSesgAIElSD3UWAJL8cpKrktyW5NYkb2qn75zkyiR3tL936qoGSZI0WJc9AGuBP6mqfYCDgNcl2Qc4GVhRVXsBK9pxSZI0izoLAFV1f1Vd3w4/DKwCdgeOApa1sy0Dju6qBkmSNNisXAOQZE9gf+AaYNequr996DvArrNRgyRJelLnASDJ9sDHgD+uqh+Nf6yqCqj1LLc0ycokK8fGxrouU9I02ValuanTAJBkS5p//udW1cfbyd9Nslv7+G7A9wYtW1VnVdWiqlq0YMGCLsuUNAO2VWlu6vJTAAHOBlZV1fvGPXQJsKQdXgJc3FUNkiRpsPkdrvtg4A+Bm5Pc2E77M+BvgQuTnADcAxzbYQ2SJGmAzgJAVX0ByHoePrSr7UqSpMn5TYCSJPWQAUCSpB4yAEiS1ENdXgQ4EmdeceOoS9isnXj4fqMuQZK0EWx2AUDS3GBY756BXRtiAJAkTYnhrVuzFdy8BkCSpB6yB0CbBI8oumd3sKTx7AGQJKmHDACSJPWQAUCSpB4yAEiS1EMGAEmSesgAIElSDxkAJEnqIQOAJEk9ZACQJKmHDACSJPXQSAJAksVJbk+yOsnJo6hBkqQ+m/UAkGQe8I/AbwL7AK9Kss9s1yFJUp+NogfgQGB1Vd1ZVY8D5wNHjaAOSZJ6axQBYHfg3nHja9ppkiRplmyytwNOshRY2o7+OMnto6ynQ08DHhh1EcN67agL2LRszu/d8qpaPMyMPWqrsHm/55uzzfl9G7qtTpSqms5y05bkhcA7quqIdvztAFX1nlktZBORZGVVLRp1HZo637v+8T2fm3zfBhvFKYBrgb2SLEyyFfBK4JIR1CFJUm/N+imAqlqb5PXA5cA84CNVdets1yFJUp+N5BqAqroMuGwU294EnTXqAjRtvnf943s+N/m+DTDr1wBIkqTR86uAJUnqIQPADCV5Y5JVSc7taP3vSPLWLtatjSfJIUkuHXUd2jDbq8D2us4m+z0Ac8j/CxxWVWtGXYikSdlepZY9ADOQ5IPAM4FPJ/nzJB9J8tUkNyQ5qp3n+CSfTHJlkruTvD7JW9p5vpJk53a+1yS5NsnXknwsybYDtverSZYnuS7JvyZ5zuw+481bkj2TfD3JOUm+keTcJIcl+WKSO5Ic2P58uX3/vpRk7wHr2W7Q34JGy/a6ebG9bgRV5c8MfoC7ab5l6m+A/9ZO2xH4BrAdcDywGngKsAD4IfDadr7TgD9uh3cZt853AW9oh98BvLUdXgHs1Q7/BvDZUT//zekH2BNYCzyPJhxfB3wECM39Kj4JPBWY385/GPCxdvgQ4NJ2eODfwqifnz+2183px/Y68x9PAWw8hwNHjjv/tzXwK+3wVVX1MPBwkh8C/7edfjOwbzv83CTvovkD3J7mexL+XZLtgf8EfDTJusm/1MHz6Lu7qupmgCS3AiuqqpLcTLPD2QFYlmQvoIAtB6xjfX8Lq7ouXkOzvW4ebK8zYADYeAL8XlX93PegJ/kN4LFxk54YN/4ET74H5wBHV9XXkhxPk1DH2wJ4qKr226hVa6LJ3qt30vyD+J0kewJXD1jHwL8FbVJsr5sH2+sMeA3AxnM58Ia0cT/J/lNc/inA/Um2BI6b+GBV/Qi4K8nvt+tPkufPsGZN3Q7Afe3w8euZZ6Z/C+qe7bUfbK8bYADYeN5J0710U9sV9c4pLv8XwDXAF4Gvr2ee44ATknwNuJXmPJdm1/8C3pPkBtbfgzbTvwV1z/baD7bXDfCbACVJ6iF7ACRJ6iEDgCRJPWQAkCSphwwAkiT1kAFAkqQeMgBoRtrvVL81yU1Jbmy/SEXSJsa2qon8JkBNW5IXAi8HDqiqx5I8DdhqxGVJmsC2qkHsAdBM7AY8UFWPAVTVA1X17SS/nuRz7V3QLk+yW5Idkty+7m5cSc5L8pqRVi/1h21Vv8AvAtK0tTc8+QKwLfAZ4ALgS8DngKOqaizJK4Ajquq/J/mvwF8DZwDHV9XiEZUu9YptVYN4CkDTVlU/TvLrwH8GXkKzU3kX8FzgyvartecB97fzX9l+N/o/An4vujRLbKsaxB4AbTRJjgFeB2xdVS8c8PgWNEccewK/te42npJml21V4DUAmoEke7f32V5nP5p7aC9oLzoiyZZJfq19/M3t438A/FN7JzVJHbOtahB7ADRtbZfi/wZ2BNYCq4GlwB7AP9DcinM+cDrweeCTwIFV9XCS9wEPV9Ups1641DO2VQ1iAJAkqYc8BSBJUg8ZACRJ6iEDgCRJPWQAkCSphwwAkiT1kAFAkqQeMgBIktRDBgBJknro/wfujdgRcWM3iwAAAABJRU5ErkJggg==\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": "universal-elder",
   "metadata": {
    "papermill": {
     "duration": 0.107509,
     "end_time": "2021-06-05T11:17:26.820715",
     "exception": false,
     "start_time": "2021-06-05T11:17:26.713206",
     "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": "nearby-mission",
   "metadata": {
    "papermill": {
     "duration": 0.109648,
     "end_time": "2021-06-05T11:17:27.036844",
     "exception": false,
     "start_time": "2021-06-05T11:17:26.927196",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "valid-sudan",
   "metadata": {
    "papermill": {
     "duration": 0.167093,
     "end_time": "2021-06-05T11:17:27.314535",
     "exception": false,
     "start_time": "2021-06-05T11:17:27.147442",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Data Wrangling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "persistent-darkness",
   "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": 0.10937,
     "end_time": "2021-06-05T11:17:27.533556",
     "exception": false,
     "start_time": "2021-06-05T11:17:27.424186",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Now, lets drop the unwanted columns from our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "responsible-roman",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:27.756096Z",
     "iopub.status.busy": "2021-06-05T11:17:27.755299Z",
     "iopub.status.idle": "2021-06-05T11:17:27.759023Z",
     "shell.execute_reply": "2021-06-05T11:17:27.758091Z",
     "shell.execute_reply.started": "2021-06-05T11:13:06.278656Z"
    },
    "papermill": {
     "duration": 0.118371,
     "end_time": "2021-06-05T11:17:27.759478",
     "exception": false,
     "start_time": "2021-06-05T11:17:27.641107",
     "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": "continental-manner",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:27.991042Z",
     "iopub.status.busy": "2021-06-05T11:17:27.990075Z",
     "iopub.status.idle": "2021-06-05T11:17:27.994802Z",
     "shell.execute_reply": "2021-06-05T11:17:27.994148Z",
     "shell.execute_reply.started": "2021-06-05T11:13:06.287813Z"
    },
    "papermill": {
     "duration": 0.128246,
     "end_time": "2021-06-05T11:17:27.994962",
     "exception": false,
     "start_time": "2021-06-05T11:17:27.866716",
     "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": "sitting-wilderness",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:28.239344Z",
     "iopub.status.busy": "2021-06-05T11:17:28.238261Z",
     "iopub.status.idle": "2021-06-05T11:17:28.244925Z",
     "shell.execute_reply": "2021-06-05T11:17:28.244267Z",
     "shell.execute_reply.started": "2021-06-05T11:13:06.310246Z"
    },
    "papermill": {
     "duration": 0.132367,
     "end_time": "2021-06-05T11:17:28.245071",
     "exception": false,
     "start_time": "2021-06-05T11:17:28.112704",
     "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",
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       "  </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",
       "      <td>0</td>\n",
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       "      <td>S</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>2</td>\n",
       "      <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": "african-syndrome",
   "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"
    },
    "papermill": {
     "duration": 0.112915,
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     "start_time": "2021-06-05T11:17:28.356019",
     "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": "composed-bristol",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:28.706449Z",
     "iopub.status.busy": "2021-06-05T11:17:28.705391Z",
     "iopub.status.idle": "2021-06-05T11:17:28.709612Z",
     "shell.execute_reply": "2021-06-05T11:17:28.709093Z",
     "shell.execute_reply.started": "2021-06-05T11:13:06.327685Z"
    },
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     "end_time": "2021-06-05T11:17:28.709766",
     "exception": false,
     "start_time": "2021-06-05T11:17:28.578452",
     "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": "weekly-requirement",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:28.936161Z",
     "iopub.status.busy": "2021-06-05T11:17:28.935465Z",
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     "shell.execute_reply": "2021-06-05T11:17:28.967071Z",
     "shell.execute_reply.started": "2021-06-05T11:13:06.344525Z"
    },
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     "duration": 0.144736,
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     "exception": false,
     "start_time": "2021-06-05T11:17:28.822516",
     "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",
       "      <td>1</td>\n",
       "    </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",
       "    </tr>\n",
       "    <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": "entertaining-american",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:29.196497Z",
     "iopub.status.busy": "2021-06-05T11:17:29.195526Z",
     "iopub.status.idle": "2021-06-05T11:17:29.199193Z",
     "shell.execute_reply": "2021-06-05T11:17:29.199727Z",
     "shell.execute_reply.started": "2021-06-05T11:13:06.389345Z"
    },
    "papermill": {
     "duration": 0.118834,
     "end_time": "2021-06-05T11:17:29.199937",
     "exception": false,
     "start_time": "2021-06-05T11:17:29.081103",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Now, lets combine the Similar Titles into one common category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "administrative-export",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:29.433162Z",
     "iopub.status.busy": "2021-06-05T11:17:29.432410Z",
     "iopub.status.idle": "2021-06-05T11:17:29.443294Z",
     "shell.execute_reply": "2021-06-05T11:17:29.443916Z",
     "shell.execute_reply.started": "2021-06-05T11:13:06.394323Z"
    },
    "papermill": {
     "duration": 0.12721,
     "end_time": "2021-06-05T11:17:29.444121",
     "exception": false,
     "start_time": "2021-06-05T11:17:29.316911",
     "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": "catholic-reducing",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:29.669376Z",
     "iopub.status.busy": "2021-06-05T11:17:29.668312Z",
     "iopub.status.idle": "2021-06-05T11:17:29.673270Z",
     "shell.execute_reply": "2021-06-05T11:17:29.673832Z",
     "shell.execute_reply.started": "2021-06-05T11:13:06.411075Z"
    },
    "papermill": {
     "duration": 0.11934,
     "end_time": "2021-06-05T11:17:29.674043",
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     "start_time": "2021-06-05T11:17:29.554703",
     "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": "contemporary-pocket",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:29.897651Z",
     "iopub.status.busy": "2021-06-05T11:17:29.896608Z",
     "iopub.status.idle": "2021-06-05T11:17:29.907772Z",
     "shell.execute_reply": "2021-06-05T11:17:29.908364Z",
     "shell.execute_reply.started": "2021-06-05T11:13:06.423723Z"
    },
    "papermill": {
     "duration": 0.12247,
     "end_time": "2021-06-05T11:17:29.908579",
     "exception": false,
     "start_time": "2021-06-05T11:17:29.786109",
     "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": "civil-throw",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:30.146369Z",
     "iopub.status.busy": "2021-06-05T11:17:30.145536Z",
     "iopub.status.idle": "2021-06-05T11:17:30.170056Z",
     "shell.execute_reply": "2021-06-05T11:17:30.169421Z",
     "shell.execute_reply.started": "2021-06-05T11:13:06.444844Z"
    },
    "papermill": {
     "duration": 0.150256,
     "end_time": "2021-06-05T11:17:30.170200",
     "exception": false,
     "start_time": "2021-06-05T11:17:30.019944",
     "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": "incoming-response",
   "metadata": {
    "papermill": {
     "duration": 0.11094,
     "end_time": "2021-06-05T11:17:30.392573",
     "exception": false,
     "start_time": "2021-06-05T11:17:30.281633",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now, Lets see the Title vs Survival rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "short-palace",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:30.619182Z",
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     "iopub.status.idle": "2021-06-05T11:17:30.634357Z",
     "shell.execute_reply": "2021-06-05T11:17:30.634913Z",
     "shell.execute_reply.started": "2021-06-05T11:13:06.473141Z"
    },
    "papermill": {
     "duration": 0.131203,
     "end_time": "2021-06-05T11:17:30.635148",
     "exception": false,
     "start_time": "2021-06-05T11:17:30.503945",
     "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": "uniform-thousand",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:30.864740Z",
     "iopub.status.busy": "2021-06-05T11:17:30.864002Z",
     "iopub.status.idle": "2021-06-05T11:17:30.867424Z",
     "shell.execute_reply": "2021-06-05T11:17:30.866864Z",
     "shell.execute_reply.started": "2021-06-05T11:13:06.489591Z"
    },
    "papermill": {
     "duration": 0.123906,
     "end_time": "2021-06-05T11:17:30.867593",
     "exception": false,
     "start_time": "2021-06-05T11:17:30.743687",
     "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": "blocked-capability",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:31.096498Z",
     "iopub.status.busy": "2021-06-05T11:17:31.095764Z",
     "iopub.status.idle": "2021-06-05T11:17:31.099412Z",
     "shell.execute_reply": "2021-06-05T11:17:31.100022Z",
     "shell.execute_reply.started": "2021-06-05T11:13:06.502710Z"
    },
    "papermill": {
     "duration": 0.123999,
     "end_time": "2021-06-05T11:17:31.100230",
     "exception": false,
     "start_time": "2021-06-05T11:17:30.976231",
     "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": "rotary-professional",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:31.321681Z",
     "iopub.status.busy": "2021-06-05T11:17:31.320007Z",
     "iopub.status.idle": "2021-06-05T11:17:31.325698Z",
     "shell.execute_reply": "2021-06-05T11:17:31.325070Z",
     "shell.execute_reply.started": "2021-06-05T11:13:06.515299Z"
    },
    "papermill": {
     "duration": 0.117748,
     "end_time": "2021-06-05T11:17:31.325884",
     "exception": false,
     "start_time": "2021-06-05T11:17:31.208136",
     "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": "ordinary-scenario",
   "metadata": {
    "papermill": {
     "duration": 0.107439,
     "end_time": "2021-06-05T11:17:31.544864",
     "exception": false,
     "start_time": "2021-06-05T11:17:31.437425",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Converting Categorical Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "wooden-oracle",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:31.796776Z",
     "iopub.status.busy": "2021-06-05T11:17:31.795805Z",
     "iopub.status.idle": "2021-06-05T11:17:31.800336Z",
     "shell.execute_reply": "2021-06-05T11:17:31.799714Z",
     "shell.execute_reply.started": "2021-06-05T11:13:06.524641Z"
    },
    "papermill": {
     "duration": 0.138424,
     "end_time": "2021-06-05T11:17:31.800476",
     "exception": false,
     "start_time": "2021-06-05T11:17:31.662052",
     "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": "sonic-culture",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:32.038032Z",
     "iopub.status.busy": "2021-06-05T11:17:32.035535Z",
     "iopub.status.idle": "2021-06-05T11:17:32.041610Z",
     "shell.execute_reply": "2021-06-05T11:17:32.042408Z",
     "shell.execute_reply.started": "2021-06-05T11:13:06.545672Z"
    },
    "papermill": {
     "duration": 0.132047,
     "end_time": "2021-06-05T11:17:32.042623",
     "exception": false,
     "start_time": "2021-06-05T11:17:31.910576",
     "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": "narrow-sculpture",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:32.280109Z",
     "iopub.status.busy": "2021-06-05T11:17:32.279163Z",
     "iopub.status.idle": "2021-06-05T11:17:32.284822Z",
     "shell.execute_reply": "2021-06-05T11:17:32.284117Z",
     "shell.execute_reply.started": "2021-06-05T11:13:06.563021Z"
    },
    "papermill": {
     "duration": 0.130529,
     "end_time": "2021-06-05T11:17:32.284972",
     "exception": false,
     "start_time": "2021-06-05T11:17:32.154443",
     "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": "julian-filter",
   "metadata": {
    "papermill": {
     "duration": 0.107925,
     "end_time": "2021-06-05T11:17:32.505965",
     "exception": false,
     "start_time": "2021-06-05T11:17:32.398040",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bright-senator",
   "metadata": {
    "papermill": {
     "duration": 0.113435,
     "end_time": "2021-06-05T11:17:32.731357",
     "exception": false,
     "start_time": "2021-06-05T11:17:32.617922",
     "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": "formed-calvin",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:32.968755Z",
     "iopub.status.busy": "2021-06-05T11:17:32.968040Z",
     "iopub.status.idle": "2021-06-05T11:17:34.551396Z",
     "shell.execute_reply": "2021-06-05T11:17:34.550804Z",
     "shell.execute_reply.started": "2021-06-05T11:13:06.583151Z"
    },
    "papermill": {
     "duration": 1.708889,
     "end_time": "2021-06-05T11:17:34.551540",
     "exception": false,
     "start_time": "2021-06-05T11:17:32.842651",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f98e58c66d0>"
      ]
     },
     "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": "popular-helicopter",
   "metadata": {
    "papermill": {
     "duration": 0.109802,
     "end_time": "2021-06-05T11:17:34.773321",
     "exception": false,
     "start_time": "2021-06-05T11:17:34.663519",
     "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": "musical-density",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:34.998336Z",
     "iopub.status.busy": "2021-06-05T11:17:34.997484Z",
     "iopub.status.idle": "2021-06-05T11:17:35.001836Z",
     "shell.execute_reply": "2021-06-05T11:17:35.001282Z",
     "shell.execute_reply.started": "2021-06-05T11:13:07.854137Z"
    },
    "papermill": {
     "duration": 0.119262,
     "end_time": "2021-06-05T11:17:35.002039",
     "exception": false,
     "start_time": "2021-06-05T11:17:34.882777",
     "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": "stable-visitor",
   "metadata": {
    "papermill": {
     "duration": 0.111372,
     "end_time": "2021-06-05T11:17:35.223175",
     "exception": false,
     "start_time": "2021-06-05T11:17:35.111803",
     "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": "automotive-village",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:35.479483Z",
     "iopub.status.busy": "2021-06-05T11:17:35.478591Z",
     "iopub.status.idle": "2021-06-05T11:17:35.485383Z",
     "shell.execute_reply": "2021-06-05T11:17:35.485859Z",
     "shell.execute_reply.started": "2021-06-05T11:13:07.861395Z"
    },
    "papermill": {
     "duration": 0.154545,
     "end_time": "2021-06-05T11:17:35.486038",
     "exception": false,
     "start_time": "2021-06-05T11:17:35.331493",
     "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": "front-ribbon",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:35.712798Z",
     "iopub.status.busy": "2021-06-05T11:17:35.711756Z",
     "iopub.status.idle": "2021-06-05T11:17:35.716243Z",
     "shell.execute_reply": "2021-06-05T11:17:35.715630Z",
     "shell.execute_reply.started": "2021-06-05T11:13:07.899749Z"
    },
    "papermill": {
     "duration": 0.11922,
     "end_time": "2021-06-05T11:17:35.716378",
     "exception": false,
     "start_time": "2021-06-05T11:17:35.597158",
     "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": "seeing-polish",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:35.954115Z",
     "iopub.status.busy": "2021-06-05T11:17:35.953072Z",
     "iopub.status.idle": "2021-06-05T11:17:35.983779Z",
     "shell.execute_reply": "2021-06-05T11:17:35.984459Z",
     "shell.execute_reply.started": "2021-06-05T11:13:07.905731Z"
    },
    "papermill": {
     "duration": 0.159493,
     "end_time": "2021-06-05T11:17:35.984719",
     "exception": false,
     "start_time": "2021-06-05T11:17:35.825226",
     "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": "coastal-position",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:36.220517Z",
     "iopub.status.busy": "2021-06-05T11:17:36.219582Z",
     "iopub.status.idle": "2021-06-05T11:17:36.225300Z",
     "shell.execute_reply": "2021-06-05T11:17:36.225870Z",
     "shell.execute_reply.started": "2021-06-05T11:13:07.935628Z"
    },
    "papermill": {
     "duration": 0.120137,
     "end_time": "2021-06-05T11:17:36.226047",
     "exception": false,
     "start_time": "2021-06-05T11:17:36.105910",
     "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": "reserved-barrel",
   "metadata": {
    "papermill": {
     "duration": 0.109727,
     "end_time": "2021-06-05T11:17:36.446139",
     "exception": false,
     "start_time": "2021-06-05T11:17:36.336412",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let us create Age bands and determine correlations with Survived."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cellular-swimming",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:36.669422Z",
     "iopub.status.busy": "2021-06-05T11:17:36.668735Z",
     "iopub.status.idle": "2021-06-05T11:17:36.694203Z",
     "shell.execute_reply": "2021-06-05T11:17:36.694712Z",
     "shell.execute_reply.started": "2021-06-05T11:13:07.951934Z"
    },
    "papermill": {
     "duration": 0.139478,
     "end_time": "2021-06-05T11:17:36.694905",
     "exception": false,
     "start_time": "2021-06-05T11:17:36.555427",
     "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": "standard-validation",
   "metadata": {
    "papermill": {
     "duration": 0.110909,
     "end_time": "2021-06-05T11:17:36.916758",
     "exception": false,
     "start_time": "2021-06-05T11:17:36.805849",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let us replace Age with ordinals based on these bands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "uniform-somalia",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:37.168243Z",
     "iopub.status.busy": "2021-06-05T11:17:37.164527Z",
     "iopub.status.idle": "2021-06-05T11:17:37.173501Z",
     "shell.execute_reply": "2021-06-05T11:17:37.172871Z",
     "shell.execute_reply.started": "2021-06-05T11:13:07.984608Z"
    },
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     "duration": 0.143818,
     "end_time": "2021-06-05T11:17:37.173667",
     "exception": false,
     "start_time": "2021-06-05T11:17:37.029849",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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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": "dying-major",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:37.410617Z",
     "iopub.status.busy": "2021-06-05T11:17:37.409901Z",
     "iopub.status.idle": "2021-06-05T11:17:37.426153Z",
     "shell.execute_reply": "2021-06-05T11:17:37.426678Z",
     "shell.execute_reply.started": "2021-06-05T11:13:08.011354Z"
    },
    "papermill": {
     "duration": 0.139381,
     "end_time": "2021-06-05T11:17:37.426869",
     "exception": false,
     "start_time": "2021-06-05T11:17:37.287488",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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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>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",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6875</td>\n",
       "      <td>Q</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <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",
       "      <td>8.6625</td>\n",
       "      <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": "defined-catalog",
   "metadata": {
    "papermill": {
     "duration": 0.113518,
     "end_time": "2021-06-05T11:17:37.653443",
     "exception": false,
     "start_time": "2021-06-05T11:17:37.539925",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "romantic-belgium",
   "metadata": {
    "papermill": {
     "duration": 0.111809,
     "end_time": "2021-06-05T11:17:37.878971",
     "exception": false,
     "start_time": "2021-06-05T11:17:37.767162",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We can now remove the AgeBand feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "global-horror",
   "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",
       "      <th>Age</th>\n",
       "      <th>SibSp</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": "continued-oakland",
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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": "traditional-thailand",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:38.585942Z",
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    {
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       "0"
      ]
     },
     "execution_count": 47,
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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": "chubby-moral",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:38.821334Z",
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    {
     "data": {
      "text/plain": [
       "14.4542"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df['Fare'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "fancy-combat",
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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": "visible-vienna",
   "metadata": {
    "execution": {
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     "status": "completed"
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    {
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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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       "      <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",
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       "      <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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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>1.000000</td>\n",
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       "      <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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       "      <th>max</th>\n",
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       "      <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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       "         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": "earlier-philippines",
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    },
    "tags": []
   },
   "source": [
    "Now, Lets create the Fare band"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "celtic-nudist",
   "metadata": {
    "execution": {
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    },
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   },
   "outputs": [
    {
     "data": {
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       "      <td>0.454955</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(31.0, 512.329]</td>\n",
       "      <td>0.581081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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",
   "execution_count": 52,
   "id": "polished-astrology",
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    {
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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   \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()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 53,
   "id": "mineral-presentation",
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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   0.0        Q      1\n",
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       "4          896       3    1    1      1      1   1.0        S      3"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": "tribal-coordination",
   "metadata": {
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     "start_time": "2021-06-05T11:17:40.453802",
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    },
    "tags": []
   },
   "source": [
    "Lets remove the FareBand now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "dominant-arnold",
   "metadata": {
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     "status": "completed"
    },
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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": "intimate-pound",
   "metadata": {
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     "status": "completed"
    },
    "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": "advance-estonia",
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      ],
      "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": "posted-bangkok",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "We can create another feature called IsAlone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "respiratory-minute",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:41.824993Z",
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     "shell.execute_reply.started": "2021-06-05T11:13:08.244523Z"
    },
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     "exception": false,
     "start_time": "2021-06-05T11:17:41.696689",
     "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",
       "    .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>IsAlone</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.505650</td>\n",
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       "      <td>0.303538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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": "possible-assault",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:42.097897Z",
     "iopub.status.busy": "2021-06-05T11:17:42.096880Z",
     "iopub.status.idle": "2021-06-05T11:17:42.099194Z",
     "shell.execute_reply": "2021-06-05T11:17:42.099651Z",
     "shell.execute_reply.started": "2021-06-05T11:13:08.269505Z"
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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": "after-commonwealth",
   "metadata": {
    "papermill": {
     "duration": 0.123761,
     "end_time": "2021-06-05T11:17:42.344494",
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     "start_time": "2021-06-05T11:17:42.220733",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Let us drop Parch, SibSp, and FamilySize features in favor of IsAlone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "entertaining-mission",
   "metadata": {
    "execution": {
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     "start_time": "2021-06-05T11:17:42.466694",
     "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",
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       "      <th>1</th>\n",
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       "      <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",
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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",
       "    <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>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": "interior-teens",
   "metadata": {
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     "duration": 0.121088,
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     "start_time": "2021-06-05T11:17:42.730536",
     "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": "adjacent-saturday",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:43.104673Z",
     "iopub.status.busy": "2021-06-05T11:17:43.103724Z",
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     "shell.execute_reply.started": "2021-06-05T11:13:08.298664Z"
    },
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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": "realistic-supplier",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:43.364048Z",
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     "shell.execute_reply.started": "2021-06-05T11:13:08.305681Z"
    },
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     "start_time": "2021-06-05T11:17:43.236112",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Embarked</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
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       "      <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": "demographic-involvement",
   "metadata": {
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    },
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   },
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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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": "defensive-genre",
   "metadata": {
    "papermill": {
     "duration": 0.12552,
     "end_time": "2021-06-05T11:17:43.898535",
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     "start_time": "2021-06-05T11:17:43.773015",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Now, lets see our Training and Testing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "arctic-arbitration",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:44.148254Z",
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     "status": "completed"
    },
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   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Survived</th>\n",
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       "      <th>Age</th>\n",
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       "  <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": "answering-vitamin",
   "metadata": {
    "execution": {
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    },
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     "exception": false,
     "start_time": "2021-06-05T11:17:44.284578",
     "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": "incorporated-payday",
   "metadata": {
    "execution": {
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     "status": "completed"
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   },
   "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",
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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",
       "      <th>Title</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <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",
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       "      <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>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": "dental-prediction",
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2021-06-05T11:13:08.409705Z"
    },
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     "duration": 0.143557,
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     "exception": false,
     "start_time": "2021-06-05T11:17:44.818744",
     "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": "demanding-financing",
   "metadata": {
    "papermill": {
     "duration": 0.124353,
     "end_time": "2021-06-05T11:17:45.215755",
     "exception": false,
     "start_time": "2021-06-05T11:17:45.091402",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mexican-impossible",
   "metadata": {
    "papermill": {
     "duration": 0.125788,
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     "start_time": "2021-06-05T11:17:45.338696",
     "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": "several-realtor",
   "metadata": {
    "papermill": {
     "duration": 0.1237,
     "end_time": "2021-06-05T11:17:45.712172",
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     "start_time": "2021-06-05T11:17:45.588472",
     "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": "english-values",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:45.987124Z",
     "iopub.status.busy": "2021-06-05T11:17:45.986113Z",
     "iopub.status.idle": "2021-06-05T11:17:45.997752Z",
     "shell.execute_reply": "2021-06-05T11:17:45.998394Z",
     "shell.execute_reply.started": "2021-06-05T11:13:08.430320Z"
    },
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     "duration": 0.163331,
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     "exception": false,
     "start_time": "2021-06-05T11:17:45.835262",
     "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": "serial-explorer",
   "metadata": {
    "papermill": {
     "duration": 0.144048,
     "end_time": "2021-06-05T11:17:46.277256",
     "exception": false,
     "start_time": "2021-06-05T11:17:46.133208",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "painted-zoning",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:46.550764Z",
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     "shell.execute_reply.started": "2021-06-05T11:13:08.446559Z"
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     "start_time": "2021-06-05T11:17:46.411295",
     "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": "signed-world",
   "metadata": {
    "papermill": {
     "duration": 0.129101,
     "end_time": "2021-06-05T11:17:46.839812",
     "exception": false,
     "start_time": "2021-06-05T11:17:46.710711",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**KNN**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "harmful-timer",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:47.124284Z",
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    },
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     "exception": false,
     "start_time": "2021-06-05T11:17:46.976226",
     "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": "bacterial-uruguay",
   "metadata": {
    "papermill": {
     "duration": 0.123971,
     "end_time": "2021-06-05T11:17:47.445339",
     "exception": false,
     "start_time": "2021-06-05T11:17:47.321368",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Support Vector Machines (SVM)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "flying-photography",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:47.701606Z",
     "iopub.status.busy": "2021-06-05T11:17:47.700660Z",
     "iopub.status.idle": "2021-06-05T11:17:47.773809Z",
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     "shell.execute_reply.started": "2021-06-05T11:13:08.545176Z"
    },
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     "exception": false,
     "start_time": "2021-06-05T11:17:47.571489",
     "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": "interior-sunglasses",
   "metadata": {
    "papermill": {
     "duration": 0.122931,
     "end_time": "2021-06-05T11:17:48.020528",
     "exception": false,
     "start_time": "2021-06-05T11:17:47.897597",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Naive Bayes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "silent-rouge",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:48.272175Z",
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     "shell.execute_reply.started": "2021-06-05T11:13:08.614947Z"
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     "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": "renewable-score",
   "metadata": {
    "papermill": {
     "duration": 0.125203,
     "end_time": "2021-06-05T11:17:48.532970",
     "exception": false,
     "start_time": "2021-06-05T11:17:48.407767",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Decision Trees**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "wireless-estimate",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:48.787351Z",
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     "shell.execute_reply.started": "2021-06-05T11:13:08.628239Z"
    },
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     "exception": false,
     "start_time": "2021-06-05T11:17:48.654568",
     "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": "north-congo",
   "metadata": {
    "papermill": {
     "duration": 0.127296,
     "end_time": "2021-06-05T11:17:49.054689",
     "exception": false,
     "start_time": "2021-06-05T11:17:48.927393",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Random Forest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "improved-experiment",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:49.310966Z",
     "iopub.status.busy": "2021-06-05T11:17:49.309975Z",
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     "shell.execute_reply.started": "2021-06-05T11:13:08.643204Z"
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     "exception": false,
     "start_time": "2021-06-05T11:17:49.182013",
     "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": "arabic-ferry",
   "metadata": {
    "papermill": {
     "duration": 0.128346,
     "end_time": "2021-06-05T11:17:49.866950",
     "exception": false,
     "start_time": "2021-06-05T11:17:49.738604",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Preceptron**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "straight-potential",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:50.129699Z",
     "iopub.status.busy": "2021-06-05T11:17:50.128726Z",
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     "shell.execute_reply": "2021-06-05T11:17:50.147338Z",
     "shell.execute_reply.started": "2021-06-05T11:13:08.859675Z"
    },
    "papermill": {
     "duration": 0.152908,
     "end_time": "2021-06-05T11:17:50.147531",
     "exception": false,
     "start_time": "2021-06-05T11:17:49.994623",
     "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": "binary-ceramic",
   "metadata": {
    "papermill": {
     "duration": 0.128714,
     "end_time": "2021-06-05T11:17:50.403969",
     "exception": false,
     "start_time": "2021-06-05T11:17:50.275255",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Linear SVC**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "composed-american",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:50.665572Z",
     "iopub.status.busy": "2021-06-05T11:17:50.664888Z",
     "iopub.status.idle": "2021-06-05T11:17:50.717931Z",
     "shell.execute_reply": "2021-06-05T11:17:50.718424Z",
     "shell.execute_reply.started": "2021-06-05T11:13:08.877538Z"
    },
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     "end_time": "2021-06-05T11:17:50.718608",
     "exception": false,
     "start_time": "2021-06-05T11:17:50.533331",
     "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": [
       "80.02"
      ]
     },
     "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": "informational-sweet",
   "metadata": {
    "papermill": {
     "duration": 0.129603,
     "end_time": "2021-06-05T11:17:50.977678",
     "exception": false,
     "start_time": "2021-06-05T11:17:50.848075",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Stochastic Gradient Descent**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "mighty-zimbabwe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:51.254639Z",
     "iopub.status.busy": "2021-06-05T11:17:51.253524Z",
     "iopub.status.idle": "2021-06-05T11:17:51.271729Z",
     "shell.execute_reply": "2021-06-05T11:17:51.271076Z",
     "shell.execute_reply.started": "2021-06-05T11:13:08.932275Z"
    },
    "papermill": {
     "duration": 0.161121,
     "end_time": "2021-06-05T11:17:51.271974",
     "exception": false,
     "start_time": "2021-06-05T11:17:51.110853",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76.66"
      ]
     },
     "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": "special-relative",
   "metadata": {
    "papermill": {
     "duration": 0.131565,
     "end_time": "2021-06-05T11:17:51.531262",
     "exception": false,
     "start_time": "2021-06-05T11:17:51.399697",
     "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": "super-university",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:51.808570Z",
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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",
       "    }\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>80.02</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",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Stochastic Gradient Decent</td>\n",
       "      <td>76.66</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  80.02\n",
       "5                  Perceptron  78.34\n",
       "4                 Naive Bayes  77.33\n",
       "6  Stochastic Gradient Decent  76.66"
      ]
     },
     "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": "recent-journal",
   "metadata": {
    "papermill": {
     "duration": 0.128548,
     "end_time": "2021-06-05T11:17:52.074317",
     "exception": false,
     "start_time": "2021-06-05T11:17:51.945769",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Submission**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "successful-hindu",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:52.349808Z",
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     "exception": false,
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     "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": "imperial-manufacturer",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:52.897178Z",
     "iopub.status.busy": "2021-06-05T11:17:52.896472Z",
     "iopub.status.idle": "2021-06-05T11:17:52.900147Z",
     "shell.execute_reply": "2021-06-05T11:17:52.899437Z",
     "shell.execute_reply.started": "2021-06-05T11:13:09.192443Z"
    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "submission = pd.DataFrame({\n",
    "        \"PassengerId\": test_df[\"PassengerId\"],\n",
    "        \"Survived\": Y_pred\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "tender-growing",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:53.164330Z",
     "iopub.status.busy": "2021-06-05T11:17:53.163586Z",
     "iopub.status.idle": "2021-06-05T11:17:53.166634Z",
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#submission.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "nearby-windows",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:53.429752Z",
     "iopub.status.busy": "2021-06-05T11:17:53.429100Z",
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     "shell.execute_reply.started": "2021-06-05T11:16:25.918162Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_data = pd.read_csv(\"/kaggle/input/titanic/train.csv\")\n",
    "test_data = pd.read_csv(\"/kaggle/input/titanic/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "asian-store",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:53.737843Z",
     "iopub.status.busy": "2021-06-05T11:17:53.728306Z",
     "iopub.status.idle": "2021-06-05T11:17:53.961015Z",
     "shell.execute_reply": "2021-06-05T11:17:53.960295Z",
     "shell.execute_reply.started": "2021-06-05T11:16:30.464802Z"
    },
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     "end_time": "2021-06-05T11:17:53.961170",
     "exception": false,
     "start_time": "2021-06-05T11:17:53.580005",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "#data[\"Age\"] = data[\"Age\"].astype(int)\n",
    "#if [\"Age\"]\n",
    "\n",
    "y = train_data[\"Survived\"]\n",
    "\n",
    "features = [\"Pclass\", \"Sex\", \"SibSp\", \"Parch\"]\n",
    "X = pd.get_dummies(train_data[features])\n",
    "X_test = pd.get_dummies(test_data[features])\n",
    "\n",
    "model = RandomForestClassifier(n_estimators=100, max_depth=3, random_state=2)\n",
    "model.fit(X, y)\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions})\n",
    "output.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "approximate-synthesis",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-06-05T11:17:54.233842Z",
     "iopub.status.busy": "2021-06-05T11:17:54.233140Z",
     "iopub.status.idle": "2021-06-05T11:17:54.237905Z",
     "shell.execute_reply": "2021-06-05T11:17:54.237336Z",
     "shell.execute_reply.started": "2021-06-05T11:16:43.386241Z"
    },
    "papermill": {
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     "end_time": "2021-06-05T11:17:54.238048",
     "exception": false,
     "start_time": "2021-06-05T11:17:54.095454",
     "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1305</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>1306</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>1307</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>1308</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>1309</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>418 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived\n",
       "0            892         0\n",
       "1            893         1\n",
       "2            894         0\n",
       "3            895         0\n",
       "4            896         1\n",
       "..           ...       ...\n",
       "413         1305         0\n",
       "414         1306         1\n",
       "415         1307         0\n",
       "416         1308         0\n",
       "417         1309         0\n",
       "\n",
       "[418 rows x 2 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "output"
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