titanic_dataset.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "titanic_dataset.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/roshank1605A04/Titanic-Passenger-Survival-Prediction/blob/master/titanic_dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "bNXu_KxHIZ-H",
        "colab_type": "code",
        "outputId": "c7abceda-63cd-4dfa-9d2a-670d1bb99713",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "V_DgkJb38Gu7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1QshPii1-PNc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Importing the dataset**"
      ]
    },
    {
      "metadata": {
        "id": "J0aCWX0R9QTZ",
        "colab_type": "code",
        "outputId": "7c7c9d41-f81f-4ec2-ecf7-2eefcc79919a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "train = pd.read_csv('drive/My Drive/Projects/practice/Titanic Passenger Survival/titanic_train.csv')\n",
        "test = pd.read_csv('drive/My Drive/Projects/practice/Titanic Passenger Survival/titanic_test.csv')\n",
        "\n",
        "print(train.shape)\n",
        "print(test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(891, 12)\n",
            "(418, 11)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "HgxKNstMw9vm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**SibSp: No. of siblings/Spouses in abroad\n",
        "   Parch: No. of Parents/Childrens abroad**"
      ]
    },
    {
      "metadata": {
        "id": "iyZjbA5Tl9N6",
        "colab_type": "code",
        "outputId": "b6aee1a0-24f1-48a2-9a0e-07c722eb202c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "train['Survived'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    549\n",
              "1    342\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "3ahPxwk2C0EU",
        "colab_type": "code",
        "outputId": "46666b1d-995b-48b8-c6e6-0f86a938361d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "cell_type": "code",
      "source": [
        "# Checking if our dataset contains any NULL values\n",
        "\n",
        "train.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "-OYZ2urPDCbX",
        "colab_type": "code",
        "outputId": "8a20e0ba-3c23-4d16-f511-61333a10405f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "cell_type": "code",
      "source": [
        "test.isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age             86\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             1\n",
              "Cabin          327\n",
              "Embarked         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "N8RD4iNb-r0C",
        "colab_type": "code",
        "outputId": "358a7cae-ed2e-46f7-e8cc-1c31cb8813b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
              "2            3         1       3   \n",
              "3            4         1       1   \n",
              "4            5         0       3   \n",
              "\n",
              "                                                Name     Sex   Age  SibSp  \\\n",
              "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3      0            113803  53.1000  C123        S  \n",
              "4      0            373450   8.0500   NaN        S  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "CVvHthFbtpam",
        "colab_type": "code",
        "outputId": "752796a7-ba4a-43d7-c11f-a0fcb8969b3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "train['Ticket'].nunique()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "681"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "metadata": {
        "id": "_rB9y-vOueMb",
        "colab_type": "code",
        "outputId": "05a6af65-d53b-4f3b-b4dd-c67a0dec007d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "cell_type": "code",
      "source": [
        "train[[\"SibSp\", \"Survived\"]].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "id": "rD494vulu8Bu",
        "colab_type": "code",
        "outputId": "8907747b-896c-4f9b-d135-f0dcafb18628",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        }
      },
      "cell_type": "code",
      "source": [
        "train[[\"Sex\", \"Survived\"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "Qf8YOsVUvBMY",
        "colab_type": "code",
        "outputId": "859f14b1-f621-4d25-a053-d4746ba07098",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "cell_type": "code",
      "source": [
        "train[[\"Parch\", \"Survived\"]].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "metadata": {
        "id": "LmMaxVxbvLWN",
        "colab_type": "code",
        "outputId": "69a70f92-5ea3-4f2e-8df8-2468e8f4bc55",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "cell_type": "code",
      "source": [
        "train[[\"Embarked\", \"Survived\"]].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Embarked</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>C</td>\n",
              "      <td>0.553571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Q</td>\n",
              "      <td>0.389610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>S</td>\n",
              "      <td>0.336957</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Embarked  Survived\n",
              "0        C  0.553571\n",
              "1        Q  0.389610\n",
              "2        S  0.336957"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "metadata": {
        "id": "ThL_1pKAEblx",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Data Preprocessing**"
      ]
    },
    {
      "metadata": {
        "id": "KsZ0rJC_jmX1",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_copy = train.copy()\n",
        "test_copy = test.copy()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "f0ZuU-KhCZIE",
        "colab_type": "code",
        "outputId": "16b5a65f-06ca-4e9d-e3e5-b05519371de3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "# Removing unnecassary columns\n",
        "\n",
        "train = train.drop(['PassengerId', 'Ticket'], axis = 1)\n",
        "\n",
        "\n",
        "print(train.columns)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare',\n",
            "       'Cabin', 'Embarked'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Fxan20ylKURC",
        "colab_type": "code",
        "outputId": "f2bbf720-7d09-4f26-db07-13c2ce9410e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "test = test.drop(['PassengerId', 'Ticket',], axis = 1)\n",
        "\n",
        "print(test.columns)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Cabin',\n",
            "       'Embarked'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qb3YgJZw9Xlc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "combine = [train, test]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "r7nC6KZRneVA",
        "colab_type": "code",
        "outputId": "3ec1dcfc-2af2-46d5-b749-16caf68c3bef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "train['Cabin'].fillna('U', inplace=True)\n",
        "train['Cabin'] = train['Cabin'].apply(lambda x: x[0])\n",
        "\n",
        "test['Cabin'].fillna('U', inplace=True)\n",
        "test['Cabin'] = test['Cabin'].apply(lambda x: x[0])\n",
        "\n",
        "train['Cabin'].unique()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['U', 'C', 'E', 'G', 'D', 'A', 'B', 'F', 'T'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "metadata": {
        "id": "xOfxrQYVny9N",
        "colab_type": "code",
        "outputId": "0bb1b4a4-6e6f-4e5b-a260-1c0683b828a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>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>U</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\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",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\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>U</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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>C</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\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>U</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass                                               Name  \\\n",
              "0         0       3                            Braund, Mr. Owen Harris   \n",
              "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
              "2         1       3                             Heikkinen, Miss. Laina   \n",
              "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
              "4         0       3                           Allen, Mr. William Henry   \n",
              "\n",
              "      Sex   Age  SibSp  Parch     Fare Cabin Embarked  \n",
              "0    male  22.0      1      0   7.2500     U        S  \n",
              "1  female  38.0      1      0  71.2833     C        C  \n",
              "2  female  26.0      0      0   7.9250     U        S  \n",
              "3  female  35.0      1      0  53.1000     C        S  \n",
              "4    male  35.0      0      0   8.0500     U        S  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "metadata": {
        "id": "bq-0rARQpk2w",
        "colab_type": "code",
        "outputId": "013621ca-8935-49dc-a9e5-156f8250f258",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "cell_type": "code",
      "source": [
        "for dataset in combine:\n",
        "  dataset['Cabin'] = dataset['Cabin'].fillna('U')\n",
        "  dataset['Cabin'] = dataset['Cabin'].apply(lambda x: x[0])\n",
        "  \n",
        "pd.crosstab(train['Cabin'], train['Survived'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Survived</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cabin</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>A</th>\n",
              "      <td>8</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>12</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C</th>\n",
              "      <td>24</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>D</th>\n",
              "      <td>8</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>E</th>\n",
              "      <td>8</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F</th>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>G</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>T</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>U</th>\n",
              "      <td>481</td>\n",
              "      <td>206</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Survived    0    1\n",
              "Cabin             \n",
              "A           8    7\n",
              "B          12   35\n",
              "C          24   35\n",
              "D           8   25\n",
              "E           8   24\n",
              "F           5    8\n",
              "G           2    2\n",
              "T           1    0\n",
              "U         481  206"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "metadata": {
        "id": "uWl-cYbQqSHy",
        "colab_type": "code",
        "outputId": "4bc582b9-dabf-4494-e113-e44560c10e93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        }
      },
      "cell_type": "code",
      "source": [
        "train[['Cabin', 'Survived']].groupby(['Cabin'], as_index = False).mean().sort_values(by = 'Survived', ascending = True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Cabin</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>T</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>U</td>\n",
              "      <td>0.299854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A</td>\n",
              "      <td>0.466667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>G</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>C</td>\n",
              "      <td>0.593220</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>F</td>\n",
              "      <td>0.615385</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>B</td>\n",
              "      <td>0.744681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>E</td>\n",
              "      <td>0.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>D</td>\n",
              "      <td>0.757576</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Cabin  Survived\n",
              "7     T  0.000000\n",
              "8     U  0.299854\n",
              "0     A  0.466667\n",
              "6     G  0.500000\n",
              "2     C  0.593220\n",
              "5     F  0.615385\n",
              "1     B  0.744681\n",
              "4     E  0.750000\n",
              "3     D  0.757576"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "metadata": {
        "id": "H36nGDBNsMuX",
        "colab_type": "code",
        "outputId": "7b40e72b-ff4f-47b5-e7f1-ef00c9713308",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "cabin_mapping = {\"T\": 0, \"U\": 1, \"A\": 2, \"G\": 3, \"C\": 4, \"F\": 5, \"B\": 6, \"E\": 7, \"D\": 8}\n",
        "for dataset in combine:\n",
        "    dataset['Cabin'] = dataset['Cabin'].map(cabin_mapping)\n",
        "    dataset['Cabin'] = dataset['Cabin'].fillna(0)\n",
        "\n",
        "train.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>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>1</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\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>4</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\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>1</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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>4</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\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>1</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass                                               Name  \\\n",
              "0         0       3                            Braund, Mr. Owen Harris   \n",
              "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
              "2         1       3                             Heikkinen, Miss. Laina   \n",
              "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
              "4         0       3                           Allen, Mr. William Henry   \n",
              "\n",
              "      Sex   Age  SibSp  Parch     Fare  Cabin Embarked  \n",
              "0    male  22.0      1      0   7.2500      1        S  \n",
              "1  female  38.0      1      0  71.2833      4        C  \n",
              "2  female  26.0      0      0   7.9250      1        S  \n",
              "3  female  35.0      1      0  53.1000      4        S  \n",
              "4    male  35.0      0      0   8.0500      1        S  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "metadata": {
        "id": "MdNWXRUD9nOp",
        "colab_type": "code",
        "outputId": "ad73440d-5a60-419f-cfb4-23258ba1e736",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        }
      },
      "cell_type": "code",
      "source": [
        "for dataset in combine:\n",
        "    dataset['Title'] = dataset['Name'].map(lambda x: x.split(',')[1].split('.')[0].strip())\n",
        "\n",
        "pd.crosstab(train['Title'], train['Sex'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "    </tr>\n",
              "    <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",
              "    <tr>\n",
              "      <th>the Countess</th>\n",
              "      <td>1</td>\n",
              "      <td>0</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",
              "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\n",
              "the Countess       1     0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "metadata": {
        "id": "eNq-t_k0-LZp",
        "colab_type": "code",
        "outputId": "d0162e62-fd8e-47ed-b222-aed87329e1f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "for dataset in combine:\n",
        "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Major', 'Rev', 'Jonkheer', 'Dona'], 'Rare')\n",
        "\n",
        "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
        "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
        "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
        "    dataset['Title'] = dataset['Title'].replace('Sir', 'Mr')\n",
        "    dataset['Title'] = dataset['Title'].replace('Dr', 'Mr')\n",
        "    \n",
        "train[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>0.161905</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>0.214286</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>the Countess</td>\n",
              "      <td>1.000000</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  0.161905\n",
              "3           Mrs  0.793651\n",
              "4          Rare  0.214286\n",
              "5  the Countess  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "metadata": {
        "id": "EUg2TZ3xAjSq",
        "colab_type": "code",
        "outputId": "2d6f78f7-b295-4f9c-aedd-704d50eb80d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</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>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>1</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\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>4</td>\n",
              "      <td>C</td>\n",
              "      <td>Mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\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>1</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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>4</td>\n",
              "      <td>S</td>\n",
              "      <td>Mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\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>1</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass                                               Name  \\\n",
              "0         0       3                            Braund, Mr. Owen Harris   \n",
              "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
              "2         1       3                             Heikkinen, Miss. Laina   \n",
              "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
              "4         0       3                           Allen, Mr. William Henry   \n",
              "\n",
              "      Sex   Age  SibSp  Parch     Fare  Cabin Embarked Title  \n",
              "0    male  22.0      1      0   7.2500      1        S    Mr  \n",
              "1  female  38.0      1      0  71.2833      4        C   Mrs  \n",
              "2  female  26.0      0      0   7.9250      1        S  Miss  \n",
              "3  female  35.0      1      0  53.1000      4        S   Mrs  \n",
              "4    male  35.0      0      0   8.0500      1        S    Mr  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "metadata": {
        "id": "VyicfVSg_cVM",
        "colab_type": "code",
        "outputId": "2d6198c6-4732-4ae5-e1c5-e45ee75750c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "title_mapping = {\"Mrs\": 4, \"Miss\": 3, \"Mr\": 1, \"Master\": 2, \"Rare\": 0}\n",
        "for dataset in combine:\n",
        "    dataset['Title'] = dataset['Title'].map(title_mapping)\n",
        "    dataset['Title'] = dataset['Title'].fillna(0)\n",
        "\n",
        "train.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</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>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>1</td>\n",
              "      <td>S</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\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>4</td>\n",
              "      <td>C</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\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>1</td>\n",
              "      <td>S</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\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>4</td>\n",
              "      <td>S</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\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>1</td>\n",
              "      <td>S</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass                                               Name  \\\n",
              "0         0       3                            Braund, Mr. Owen Harris   \n",
              "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...   \n",
              "2         1       3                             Heikkinen, Miss. Laina   \n",
              "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)   \n",
              "4         0       3                           Allen, Mr. William Henry   \n",
              "\n",
              "      Sex   Age  SibSp  Parch     Fare  Cabin Embarked  Title  \n",
              "0    male  22.0      1      0   7.2500      1        S    1.0  \n",
              "1  female  38.0      1      0  71.2833      4        C    4.0  \n",
              "2  female  26.0      0      0   7.9250      1        S    3.0  \n",
              "3  female  35.0      1      0  53.1000      4        S    4.0  \n",
              "4    male  35.0      0      0   8.0500      1        S    1.0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "metadata": {
        "id": "Ca-eus1_Aoo-",
        "colab_type": "code",
        "outputId": "2214a806-db3d-408a-a027-f306373f744e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "for dataset in combine:\n",
        "    dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n",
        "\n",
        "train.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</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>Braund, Mr. Owen Harris</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>1</td>\n",
              "      <td>S</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</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>4</td>\n",
              "      <td>C</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</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>1</td>\n",
              "      <td>S</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</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>4</td>\n",
              "      <td>S</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</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>1</td>\n",
              "      <td>S</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass                                               Name  Sex  \\\n",
              "0         0       3                            Braund, Mr. Owen Harris    0   \n",
              "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...    1   \n",
              "2         1       3                             Heikkinen, Miss. Laina    1   \n",
              "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)    1   \n",
              "4         0       3                           Allen, Mr. William Henry    0   \n",
              "\n",
              "    Age  SibSp  Parch     Fare  Cabin Embarked  Title  \n",
              "0  22.0      1      0   7.2500      1        S    1.0  \n",
              "1  38.0      1      0  71.2833      4        C    4.0  \n",
              "2  26.0      0      0   7.9250      1        S    3.0  \n",
              "3  35.0      1      0  53.1000      4        S    4.0  \n",
              "4  35.0      0      0   8.0500      1        S    1.0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "metadata": {
        "id": "D-R6NBssFFwl",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Imputing Missing Values\n",
        "\n",
        "train['Age'].fillna(train['Age'].dropna().median(), inplace=True)\n",
        "test['Age'].fillna(train['Age'].mean(), inplace = True)\n",
        "test['Fare'].fillna(train['Fare'].dropna().median(), inplace = True)\n",
        "train['Embarked'].fillna('C', inplace = True)\n",
        "test['Embarked'].fillna('C', inplace = True)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "SScxN3EqBnKF",
        "colab_type": "code",
        "outputId": "a2069777-063d-45e8-9c8a-42509228fc0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "train['AgeBand'] = pd.cut(train['Age'], 5)\n",
        "train[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False).mean().sort_values(by='AgeBand', ascending=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>(0.34, 16.336]</td>\n",
              "      <td>0.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(16.336, 32.252]</td>\n",
              "      <td>0.344168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(32.252, 48.168]</td>\n",
              "      <td>0.404255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(48.168, 64.084]</td>\n",
              "      <td>0.434783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(64.084, 80.0]</td>\n",
              "      <td>0.090909</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            AgeBand  Survived\n",
              "0    (0.34, 16.336]  0.550000\n",
              "1  (16.336, 32.252]  0.344168\n",
              "2  (32.252, 48.168]  0.404255\n",
              "3  (48.168, 64.084]  0.434783\n",
              "4    (64.084, 80.0]  0.090909"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "metadata": {
        "id": "U9g1CmrNBwQC",
        "colab_type": "code",
        "outputId": "101f55e1-8b1e-4263-f374-1e7de02c5df3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "for dataset in combine:    \n",
        "    dataset.loc[ dataset['Age'] <= 16, 'Age'] = 4\n",
        "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n",
        "    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n",
        "    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n",
        "    dataset.loc[ dataset['Age'] > 64, 'Age'] = 0\n",
        "    \n",
        "train.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</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>Braund, Mr. Owen Harris</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>1</td>\n",
              "      <td>S</td>\n",
              "      <td>1.0</td>\n",
              "      <td>(16.336, 32.252]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>4</td>\n",
              "      <td>C</td>\n",
              "      <td>4.0</td>\n",
              "      <td>(32.252, 48.168]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>1</td>\n",
              "      <td>S</td>\n",
              "      <td>3.0</td>\n",
              "      <td>(16.336, 32.252]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>4</td>\n",
              "      <td>S</td>\n",
              "      <td>4.0</td>\n",
              "      <td>(32.252, 48.168]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>1</td>\n",
              "      <td>S</td>\n",
              "      <td>1.0</td>\n",
              "      <td>(32.252, 48.168]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass                                               Name  Sex  \\\n",
              "0         0       3                            Braund, Mr. Owen Harris    0   \n",
              "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...    1   \n",
              "2         1       3                             Heikkinen, Miss. Laina    1   \n",
              "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)    1   \n",
              "4         0       3                           Allen, Mr. William Henry    0   \n",
              "\n",
              "   Age  SibSp  Parch     Fare  Cabin Embarked  Title           AgeBand  \n",
              "0  1.0      1      0   7.2500      1        S    1.0  (16.336, 32.252]  \n",
              "1  2.0      1      0  71.2833      4        C    4.0  (32.252, 48.168]  \n",
              "2  1.0      0      0   7.9250      1        S    3.0  (16.336, 32.252]  \n",
              "3  2.0      1      0  53.1000      4        S    4.0  (32.252, 48.168]  \n",
              "4  2.0      0      0   8.0500      1        S    1.0  (32.252, 48.168]  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "metadata": {
        "id": "PfEbyQKHQG04",
        "colab_type": "code",
        "outputId": "9252fdc2-a8d3-4c2e-974b-afd07eafe1e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        }
      },
      "cell_type": "code",
      "source": [
        "for dataset in combine:\n",
        "    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\n",
        "\n",
        "train[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>FamilySize</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.724138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.578431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.552795</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.303538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0.136364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>11</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   FamilySize  Survived\n",
              "3           4  0.724138\n",
              "2           3  0.578431\n",
              "1           2  0.552795\n",
              "6           7  0.333333\n",
              "0           1  0.303538\n",
              "4           5  0.200000\n",
              "5           6  0.136364\n",
              "7           8  0.000000\n",
              "8          11  0.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "metadata": {
        "id": "barC6_TAHZqw",
        "colab_type": "code",
        "outputId": "5b5ccff5-916b-49a5-af68-508e8554679b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "cell_type": "code",
      "source": [
        "'''\n",
        "for dataset in combine:    \n",
        "    dataset.loc[ dataset['FamilySize'] > 4, 'FamilySize'] = 0  \n",
        "    dataset.loc[ dataset['FamilySize'] <= 4, 'FamilySize'] = 1\n",
        "    \n",
        "train.head()\n",
        "\n",
        "'''"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\nfor dataset in combine:    \\n    dataset.loc[ dataset['FamilySize'] > 4, 'FamilySize'] = 0  \\n    dataset.loc[ dataset['FamilySize'] <= 4, 'FamilySize'] = 1\\n    \\ntrain.head()\\n\\n\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "metadata": {
        "id": "aoGeYmE4wcx4",
        "colab_type": "code",
        "outputId": "b4947f90-aaeb-4d5f-ad31-0bdc95c269b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "for dataset in combine:\n",
        "    dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n",
        "\n",
        "train.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "      <th>AgeBand</th>\n",
              "      <th>FamilySize</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>(16.336, 32.252]</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>(32.252, 48.168]</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>(16.336, 32.252]</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>(32.252, 48.168]</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>(32.252, 48.168]</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass                                               Name  Sex  \\\n",
              "0         0       3                            Braund, Mr. Owen Harris    0   \n",
              "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...    1   \n",
              "2         1       3                             Heikkinen, Miss. Laina    1   \n",
              "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)    1   \n",
              "4         0       3                           Allen, Mr. William Henry    0   \n",
              "\n",
              "   Age  SibSp  Parch     Fare  Cabin  Embarked  Title           AgeBand  \\\n",
              "0  1.0      1      0   7.2500      1         0    1.0  (16.336, 32.252]   \n",
              "1  2.0      1      0  71.2833      4         1    4.0  (32.252, 48.168]   \n",
              "2  1.0      0      0   7.9250      1         0    3.0  (16.336, 32.252]   \n",
              "3  2.0      1      0  53.1000      4         0    4.0  (32.252, 48.168]   \n",
              "4  2.0      0      0   8.0500      1         0    1.0  (32.252, 48.168]   \n",
              "\n",
              "   FamilySize  \n",
              "0           2  \n",
              "1           2  \n",
              "2           1  \n",
              "3           2  \n",
              "4           1  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "metadata": {
        "id": "9T6faF8HEHAA",
        "colab_type": "code",
        "outputId": "20bfcc6f-6f63-4ea1-99ba-810fb4b61a16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "train['FareBand'] = pd.qcut(train['Fare'], 4)\n",
        "train[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>FareBand</th>\n",
              "      <th>Survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(-0.001, 7.91]</td>\n",
              "      <td>0.197309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(7.91, 14.454]</td>\n",
              "      <td>0.303571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(14.454, 31.0]</td>\n",
              "      <td>0.454955</td>\n",
              "    </tr>\n",
              "    <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"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "metadata": {
        "id": "njZhd9MVETiv",
        "colab_type": "code",
        "outputId": "1a2be765-836c-4807-bd75-4ebbe185069d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "for dataset in combine:\n",
        "    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0\n",
        "    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n",
        "    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare']   = 2\n",
        "    dataset.loc[ dataset['Fare'] > 31, 'Fare'] = 3\n",
        "    dataset['Fare'] = dataset['Fare'].astype(int)\n",
        "\n",
        "train = train.drop(['FareBand'], axis=1)\n",
        "combine = [train, test]\n",
        "    \n",
        "train.head(5)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "      <th>AgeBand</th>\n",
              "      <th>FamilySize</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>(16.336, 32.252]</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>(32.252, 48.168]</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>(16.336, 32.252]</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>(32.252, 48.168]</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>(32.252, 48.168]</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass                                               Name  Sex  \\\n",
              "0         0       3                            Braund, Mr. Owen Harris    0   \n",
              "1         1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...    1   \n",
              "2         1       3                             Heikkinen, Miss. Laina    1   \n",
              "3         1       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)    1   \n",
              "4         0       3                           Allen, Mr. William Henry    0   \n",
              "\n",
              "   Age  SibSp  Parch  Fare  Cabin  Embarked  Title           AgeBand  \\\n",
              "0  1.0      1      0     0      1         0    1.0  (16.336, 32.252]   \n",
              "1  2.0      1      0     3      4         1    4.0  (32.252, 48.168]   \n",
              "2  1.0      0      0     1      1         0    3.0  (16.336, 32.252]   \n",
              "3  2.0      1      0     3      4         0    4.0  (32.252, 48.168]   \n",
              "4  2.0      0      0     1      1         0    1.0  (32.252, 48.168]   \n",
              "\n",
              "   FamilySize  \n",
              "0           2  \n",
              "1           2  \n",
              "2           1  \n",
              "3           2  \n",
              "4           1  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "metadata": {
        "id": "wrX0urcOFGJb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train = train.drop(['AgeBand', 'Name', 'SibSp', 'Parch' ], axis = 1)\n",
        "test = test.drop(['Name', 'SibSp', 'Parch'], axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_w2HT4-NFjiZ",
        "colab_type": "code",
        "outputId": "fda2bbaa-e327-4469-cbb1-5a4c69a47d6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "# splitting the dataset into x(independent variables) and y(dependent variables)\n",
        "\n",
        "x_train = train.drop('Survived', axis = 1)\n",
        "y_train = train.Survived\n",
        "\n",
        "print(x_train.shape)\n",
        "print(y_train.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(891, 8)\n",
            "(891,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "8Fy5FXAfMhqK",
        "colab_type": "code",
        "outputId": "0e9810dc-411c-4375-8f93-295d263ec39d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "      <th>FamilySize</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>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</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>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Survived  Pclass  Sex  Age  Fare  Cabin  Embarked  Title  FamilySize\n",
              "0         0       3    0  1.0     0      1         0    1.0           2\n",
              "1         1       1    1  2.0     3      4         1    4.0           2\n",
              "2         1       3    1  1.0     1      1         0    3.0           1\n",
              "3         1       1    1  2.0     3      4         0    4.0           2\n",
              "4         0       3    0  2.0     1      1         0    1.0           1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "metadata": {
        "id": "w_n7NrKl0dao",
        "colab_type": "code",
        "outputId": "d3d697c0-6815-4860-c579-b169e6c2f6bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "x_test = test\n",
        "\n",
        "print(x_test.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(418, 8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "LzCMs62VuYGb",
        "colab_type": "code",
        "outputId": "d26f9cd7-6dea-481f-8a2c-ad5492f1abb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "'''\n",
        "from sklearn.model_selection import train_test_split \n",
        "\n",
        "x_train, x_valid, y_train, y_valid = train_test_split(x, y, test_size = 0.2, random_state = 42)\n",
        "'''"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nfrom sklearn.model_selection import train_test_split \\n\\nx_train, x_valid, y_train, y_valid = train_test_split(x, y, test_size = 0.2, random_state = 42)\\n'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "metadata": {
        "id": "M__dvoNRK9UL",
        "colab_type": "code",
        "outputId": "b4d2440e-65db-4425-d4ae-7df7c9d04a11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "sc = StandardScaler()\n",
        "x_train = sc.fit_transform(x_train)\n",
        "#x_valid = sc.transform(x_valid)\n",
        "x_test = sc.transform(x_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
            "  return self.partial_fit(X, y)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:464: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
            "  return self.fit(X, **fit_params).transform(X)\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "A8tF842M7Tkg",
        "colab_type": "code",
        "outputId": "fff14255-8e55-4ca3-d759-f52c0d9246c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(n_components = None)\n",
        "x_train = pca.fit_transform(x_train)\n",
        "x_test = pca.transform(x_test)\n",
        "print(pca.explained_variance_ratio_)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.33922101 0.2096452  0.16874812 0.11269072 0.08894929 0.05149289\n",
            " 0.02183786 0.00741492]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qmO54R9T9ZZW",
        "colab_type": "code",
        "outputId": "7cdf5f54-588d-4192-ee65-5c91e5d18c98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(n_components = 5)\n",
        "x_train = pca.fit_transform(x_train)\n",
        "x_test = pca.transform(x_test)\n",
        "print(pca.explained_variance_ratio_)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.33922101 0.2096452  0.16874812 0.11269072 0.08894929]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "lmpsLArq_yFq",
        "colab_type": "code",
        "outputId": "d30fc7b3-b0d4-4fa2-ad14-40d2a010411f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "cell_type": "code",
      "source": [
        "x_train"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.90386147, -0.16874302,  0.32791026, -0.51782161,  0.22073179],\n",
              "       [ 3.00663252, -0.13171522, -1.28605086,  0.46436509, -0.04010024],\n",
              "       [-0.04875718, -1.57226679, -0.77558194, -1.2972408 , -0.11996154],\n",
              "       ...,\n",
              "       [ 0.90242887, -1.76222116,  0.44071742, -0.8645806 ,  1.11984568],\n",
              "       [ 0.21098649,  1.96279915, -1.18041621,  0.85647786,  0.48103077],\n",
              "       [-2.08836924, -0.54849005, -1.36075869,  2.13493504,  0.23086413]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "metadata": {
        "id": "bhXfWJGiQqN9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Modelling**"
      ]
    },
    {
      "metadata": {
        "id": "4jgpAJwxiR5O",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Logistic Regression**"
      ]
    },
    {
      "metadata": {
        "id": "yWuXzEuAcSxn",
        "colab_type": "code",
        "outputId": "8f25f3e2-b801-40d9-dca3-333bab44887d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "model = LogisticRegression()\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "print('Training Accuracy :', model.score(x_train, y_train))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy : 0.8092031425364759\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "1hWU730Eemea",
        "colab_type": "code",
        "outputId": "460d2a90-dbf3-433a-a0ab-0111ac7b3526",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "\n",
        "cross_validation = StratifiedKFold(n_splits = 5)\n",
        "\n",
        "cvs = cross_val_score(estimator = model, X = x_train, y = y_train, cv = cross_validation)\n",
        "print('Mean Accuracy:', cvs.mean())\n",
        "print('Mean Standard Deviation: ', cvs.std())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Accuracy: 0.8013724086251905\n",
            "Mean Standard Deviation:  0.012709974632745872\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "N7XSuKeSiNSs",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**AdaBoost Classifier**"
      ]
    },
    {
      "metadata": {
        "id": "o2wd-sbGhT2C",
        "colab_type": "code",
        "outputId": "d19c1117-391a-466b-9dcf-811cdcb4c863",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "\n",
        "model = AdaBoostClassifier()\n",
        "model.fit(x_train, y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "\n",
        "print(\"Training Accuracy :\", model.score(x_train, y_train))\n",
        "#print(\"Validation Accuracy :\", model.score(x_valid, y_valid)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy : 0.8406285072951739\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "7nEmUT1rhjKl",
        "colab_type": "code",
        "outputId": "f08ebc7b-881f-4be0-c929-de270bf9aacb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "\n",
        "cvs = cross_val_score(estimator = model, X = x_train, y = y_train, cv = StratifiedKFold(n_splits = 5))\n",
        "print('Mean Accuracy:', cvs.mean())\n",
        "print('Mean Standard Deviation: ', cvs.std())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Accuracy: 0.7924401382090208\n",
            "Mean Standard Deviation:  0.0223128948895535\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "RrfGwOzuiJ4Z",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Gradient Boosting**"
      ]
    },
    {
      "metadata": {
        "id": "Nux7xBg3h17U",
        "colab_type": "code",
        "outputId": "a3ce5206-1e74-4c34-feae-9fc781ee11c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "model = GradientBoostingClassifier()\n",
        "model.fit(x_train, y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "\n",
        "print(\"Training Accuracy :\", model.score(x_train, y_train))\n",
        "#print(\"Validation Accuracy :\", model.score(x_valid, y_valid)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy : 0.8866442199775533\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_ce62kk0iDNu",
        "colab_type": "code",
        "outputId": "2826d48a-080b-449a-861c-88bc301c9aa1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "\n",
        "cvs = cross_val_score(estimator = model, X = x_train, y = y_train, cv = StratifiedKFold(n_splits = 5))\n",
        "print('Mean Accuracy:', cvs.mean())\n",
        "print('Mean Standard Deviation: ', cvs.std())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Accuracy: 0.7969032058095168\n",
            "Mean Standard Deviation:  0.015879224455638417\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3K16LoqXj2Dh",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Decision Tree Classifier**"
      ]
    },
    {
      "metadata": {
        "id": "4q6280zij6l0",
        "colab_type": "code",
        "outputId": "098077e9-ee9d-475c-eea3-c32f39bcab1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "model = DecisionTreeClassifier()\n",
        "model.fit(x_train, y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "\n",
        "print(\"Training Accuracy :\", model.score(x_train, y_train))\n",
        "#print(\"Validation Accuracy :\", model.score(x_valid, y_valid)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy : 0.9057239057239057\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "baHKQ4tokG7d",
        "colab_type": "code",
        "outputId": "832709f8-0fb4-40b5-b0b4-a0d2f66e96ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import Strat\n",
        "\n",
        "cvs = cross_val_score(estimator = model, X = x_train, y = y_train, cv = 10)\n",
        "print('Mean Accuracy:', cvs.mean())\n",
        "print('Mean Standard Deviation: ', cvs.std())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Accuracy: 0.7924049483600046\n",
            "Mean Standard Deviation:  0.04037263479207128\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "3U3MEYQ4jxFW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Extra Trees Classifier**"
      ]
    },
    {
      "metadata": {
        "id": "8A-dCj6cjYD2",
        "colab_type": "code",
        "outputId": "4a847226-483f-47b1-eb6e-c8e1a4bbe9db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "\n",
        "model = ExtraTreesClassifier()\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "print(\"Training Accuracy: \", model.score(x_train, y_train))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy:  0.9057239057239057\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "F7ee4I0hjrkk",
        "colab_type": "code",
        "outputId": "0db3a9f5-b856-487b-bc75-03186b2bd81c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "cvs = cross_val_score(estimator = model, X = x_train, y = y_train, cv = 10)\n",
        "print('Mean Accuracy:', cvs.mean())\n",
        "print('Mean Standard Deviation: ', cvs.std())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Accuracy: 0.8081358529111338\n",
            "Mean Standard Deviation:  0.026852840097782778\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "4mmpe2ftjA94",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Random Forest Classifier**"
      ]
    },
    {
      "metadata": {
        "id": "D81eobSajARa",
        "colab_type": "code",
        "outputId": "3bbbd842-0eaa-4954-83c5-72eefe5147fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "model = RandomForestClassifier()\n",
        "model.fit(x_train, y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "\n",
        "print(\"Training Accuracy :\", model.score(x_train, y_train))\n",
        "#print('Validation Accuracy: ', model.score(x_valid, y_valid))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy : 0.9034792368125701\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
            "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "dMTZ4-kamW5Q",
        "colab_type": "code",
        "outputId": "bba57902-d97f-48ac-a1ca-25280447ec35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "\n",
        "cross_validation = StratifiedKFold(n_splits = 5)\n",
        "\n",
        "param_grid = [{'max_depth': [4, 6, 8], \n",
        "                'max_features': ['sqrt', 'auto', 'log2'], \n",
        "                'n_estimators': [50, 10],\n",
        "               'min_samples_split': [2, 3, 10],\n",
        "               'min_samples_leaf': [1, 3, 10],\n",
        "               'bootstrap': ['True', 'False']}]\n",
        "\n",
        "grid_search = GridSearchCV(estimator = model, param_grid = param_grid, cv = cross_validation, verbose = 1)\n",
        "grid_search.fit(x_train, y_train)\n",
        "\n",
        "print(\"Best Accuracy: \", grid_search.best_score_)\n",
        "print(\"Best Parameters: \", grid_search.best_params_)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 324 candidates, totalling 1620 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Best Accuracy:  0.8215488215488216\n",
            "Best Parameters:  {'bootstrap': 'True', 'max_depth': 8, 'max_features': 'log2', 'min_samples_leaf': 10, 'min_samples_split': 3, 'n_estimators': 50}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done 1620 out of 1620 | elapsed:  1.3min finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "Xj-vd1r8qMUt",
        "colab_type": "code",
        "outputId": "45ab332b-2306-4d34-c991-574281cf3b78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "model = RandomForestClassifier(bootstrap = 'True', max_depth = 6, max_features = 'log2', min_samples_leaf = 3, min_samples_split = 10, n_estimators = 10)\n",
        "model.fit(x_train, y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "\n",
        "print(\"Training Accuracy :\", model.score(x_train, y_train))\n",
        "#print('Validation Accuracy: ', model.score(x_valid, y_valid))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy : 0.856341189674523\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "FbmFLeedrIr_",
        "colab_type": "code",
        "outputId": "df2b5ba1-8e68-4d44-d68d-06df8a7f04bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "feature = pd.DataFrame()\n",
        "x_train = pd.DataFrame(x_train)\n",
        "\n",
        "feature['name'] = x_train.columns\n",
        "feature['importances'] = model.feature_importances_\n",
        "\n",
        "feature.sort_values(by = ['importances'], ascending = True, inplace = True)\n",
        "feature.set_index('name', inplace = True)\n",
        "\n",
        "feature.plot(kind = 'barh', figsize = (7, 7), title = 'Feature Importances', fontsize = 15)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f118bb8ac18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbIAAAGuCAYAAAD1Z4P8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkdF6lQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "4hpFHyrmjOUY",
        "colab_type": "code",
        "outputId": "24266e69-75dd-4144-d81a-2f8323cb5087",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "cvs = cross_val_score(estimator = model, X = x_train, y = y_train, cv = 10)\n",
        "print('Mean Accuracy:', cvs.mean())\n",
        "print('Mean Standard Deviation: ', cvs.std())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Accuracy: 0.8137282941777324\n",
            "Mean Standard Deviation:  0.03157949583575602\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ShdFUl5JSDle",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Xg-Boost**"
      ]
    },
    {
      "metadata": {
        "id": "zQwM2E7bR653",
        "colab_type": "code",
        "outputId": "7110bb3a-984e-4ce5-ba5b-82555bdfcdd4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "from xgboost.sklearn import XGBClassifier\n",
        "\n",
        "model = XGBClassifier(max_depth = 4)\n",
        "model.fit(x_train, y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "\n",
        "print(\"Training Accuracy :\", model.score(x_train, y_train))\n",
        "#print(\"Validation Accuracy :\", model.score(x_valid, y_valid))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy : 0.8810325476992144\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "xKEjuVLCfNQV",
        "colab_type": "code",
        "outputId": "20380236-ce93-489a-aa8f-e84c3f392362",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "cvs = cross_val_score(estimator = model, X = x_train, y = y_train, cv = 10)\n",
        "print('Mean Accuracy:', cvs.mean())\n",
        "print('Mean Standard Deviation: ', cvs.std())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Accuracy: 0.8002457155827942\n",
            "Mean Standard Deviation:  0.033149503069303025\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "C-Qk4Y_0VLit",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Create a  DataFrame with the passengers ids and our prediction regarding whether they survived or not\n",
        "submission = pd.DataFrame({'PassengerId': test_copy['PassengerId'],'Survived': y_pred})\n",
        "\n",
        "#Visualize the first 5 rows\n",
        "submission.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Aj79MFz9V0SZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#Convert DataFrame to a csv file that can be uploaded\n",
        "#This is saved in the same directory as your notebook\n",
        "filename = 'submission.csv'\n",
        "\n",
        "submission.to_csv(filename,index=False)\n",
        "\n",
        "print('Saved file: ' + filename)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9dG8TtFaXMP7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.download('submission.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "R1gH-OioiX61",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**K nearest neighbors**"
      ]
    },
    {
      "metadata": {
        "id": "NjLxlq4MidqW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "model = KNeighborsClassifier()\n",
        "model.fit(x_train, y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "\n",
        "print(\"Training Accuracy :\", model.score(x_train, y_train))\n",
        "#print('Validation Accuracy: ', model.score(x_valid, y_valid))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XhQjLZFAicpn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "cvs = cross_val_score(estimator = model, X = x_train, y = y_train, cv = 10)\n",
        "print('Mean Accuracy:', cvs.mean())\n",
        "print('Mean Standard Deviation: ', cvs.std())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "X3r1PZrLSczK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Support Vector Machine**"
      ]
    },
    {
      "metadata": {
        "id": "rizbjjCUSas6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "model = SVC(C = 9, gamma = 0.1)\n",
        "model.fit(x_train, y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "\n",
        "print(\"Training Accuracy :\", model.score(x_train, y_train))\n",
        "#print('Validation Accuracy: ', model.score(x_valid, y_valid))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kEnTpxYkfSOI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "cvs = cross_val_score(estimator = model, X = x_train, y = y_train, cv = 10)\n",
        "print('Mean Accuracy:', cvs.mean())\n",
        "print('Mean Standard Deviation: ', cvs.std())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ocnVrRiFghC4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "'''\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "parameters =[{'C':[9, 11, 15], 'kernel': ['rbf', 'poly', 'sigmoid'], 'gamma': [1, 1.5, 2]}]\n",
        "\n",
        "grid_search = GridSearchCV(estimator = model, param_grid = parameters, cv = 2, scoring = 'accuracy' )\n",
        "\n",
        "grid_search.fit(x_train, y_train)\n",
        "print(\"Best Parameters: \", grid_search.best_params_)\n",
        "print(\"Best Accuracy: \", grid_search.best_score_)\n",
        "'''"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NearfL-l5Fxp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "'''from sklearn.svm import SVC\n",
        "\n",
        "model = SVC(C = 13, gamma = 0.00001, kernel = 'poly')\n",
        "model.fit(x_train, y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "\n",
        "print(\"Training Accuracy :\", model.score(x_train, y_train))\n",
        "#print('Validation Accuracy: ', model.score(x_valid, y_valid))\n",
        "'''"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HaPBnwuSSu-B",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Multi Layer Perceptron**"
      ]
    },
    {
      "metadata": {
        "id": "VQFdtx6VSs-C",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "model = MLPClassifier(hidden_layer_sizes = (100, 100), max_iter = 100)\n",
        "model.fit(x_train, y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "\n",
        "print(\"Training Accuracy :\", model.score(x_train, y_train))\n",
        "#print(\"Validation Accuracy :\", model.score(x_valid, y_valid))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8ctg1gzPfXCO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "cvs = cross_val_score(estimator = model, X = x_train, y = y_train, cv = 10)\n",
        "print('Mean Accuracy:', cvs.mean())\n",
        "print('Mean Standard Deviation: ', cvs.std())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "S8jFdareTNKq",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Artificial Neural Networks**"
      ]
    },
    {
      "metadata": {
        "id": "GM6-HFMfTaD3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Importing libraries**"
      ]
    },
    {
      "metadata": {
        "id": "kXQxB2c9TFqz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "d8eRZDMUTK03",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "'''\n",
        "# creating the model\n",
        "model = Sequential()\n",
        "\n",
        "# first hidden layer\n",
        "model.add(Dense(output_dim = 5, init = 'uniform', activation = 'relu', input_dim = 8))\n",
        "\n",
        "# second hidden layer\n",
        "model.add(Dense(output_dim = 5, init = 'uniform', activation = 'relu'))\n",
        "\n",
        "# output layer\n",
        "model.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid'))\n",
        "\n",
        "# Compiling the NN\n",
        "# binary_crossentropy loss function used when a binary output is expected\n",
        "model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy']) \n",
        "\n",
        "model.fit(x_train, y_train, batch_size = 10, nb_epoch = 500)\n",
        "\n",
        "'''"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rGgo5iOYJb_Z",
        "colab_type": "code",
        "outputId": "67a5a2d4-f714-49a4-ec2a-1ee2c47cf822",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "cell_type": "code",
      "source": [
        "#Create a  DataFrame with the passengers ids and our prediction regarding whether they survived or not\n",
        "submission = pd.DataFrame({'PassengerId':test_copy['PassengerId'],'Survived': y_pred})\n",
        "\n",
        "#Visualize the first 5 rows\n",
        "submission.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <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>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived\n",
              "0          892         0\n",
              "1          893         0\n",
              "2          894         0\n",
              "3          895         0\n",
              "4          896         0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "metadata": {
        "id": "kKD-05WZdXfF",
        "colab_type": "code",
        "outputId": "36053614-846d-494d-e547-df93bc15f800",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "#Convert DataFrame to a csv file that can be uploaded\n",
        "#This is saved in the same directory as your notebook\n",
        "filename = 'submission.csv'\n",
        "\n",
        "submission.to_csv(filename,index=False)\n",
        "\n",
        "print('Saved file: ' + filename)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Saved file: submission.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "acTY2TIGddC5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.download('submission.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}