Titanic_Survival_ML.ipynb

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  "metadata": {
    "colab": {
      "name": "Titanic_Survival_ML.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lkvFZ7TPGlft"
      },
      "source": [
        "# Titanic Dataset\n",
        "\n",
        "About Dataset: \n",
        "Format/columns of dataset\n",
        "\n",
        "\tPassengerId\tSurvived\tPclass\tName\tSex\tAge\tSibSp\tParch\tTicket\tFare\tCabin\tEmbarked\n",
        "\n",
        "Survival 0 = No , 1 = Yes\n",
        "\n",
        "sibsp = Number of siblings/spouses\n",
        "\n",
        "parch = Number of parents/children\n",
        "\n",
        "embarked = Port of Embarkation : C = Cherbourg, Q = Queenstown, S = Southampton\n",
        "\n",
        "pclass = socio-economic status : 1st = Upper , 2nd = Middle ,3rd = Lower\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BK14uZpTGO1s",
        "outputId": "759427d4-76fd-4dce-b0ce-18b38ed97264"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u40oXRIlH4Yr"
      },
      "source": [
        "# Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4896iSw3Hmm1"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rxyGGP_MH7xq"
      },
      "source": [
        "# Importing Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OZjUScXiHpdV",
        "outputId": "9ae37af2-90b0-4456-df0b-3c865c1bacee"
      },
      "source": [
        "train = pd.read_csv('drive/My Drive/datasets/titanic_train.txt')\n",
        "test = pd.read_csv('drive/My Drive/datasets/titanic_test.txt')\n",
        "print(train.shape)\n",
        "print(test.shape)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(891, 12)\n",
            "(418, 11)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BFooOxdYIIDK"
      },
      "source": [
        "**Number of people survived/not survived**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_qgDiPC2IQkU",
        "outputId": "5cfb4a51-b1d2-4fd3-c500-956d555ded11"
      },
      "source": [
        "train['Survived'].value_counts()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    549\n",
              "1    342\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VeVP-YHlIT_U"
      },
      "source": [
        "**Checking for null values**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F_5lZ0UiIXZE",
        "outputId": "683a7a25-5701-4508-fb87-24142b329ab4"
      },
      "source": [
        "train.isnull().sum()"
      ],
      "execution_count": 5,
      "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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W_uA-ObsIbpb",
        "outputId": "acd4a1c4-0af9-4a35-ce4b-6f08dc5e6ad2"
      },
      "source": [
        "test.isnull().sum()"
      ],
      "execution_count": 6,
      "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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "oyK385TBIeEE",
        "outputId": "285b9386-c003-4852-bed4-e1d062110b7a"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <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  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vHDzakp2Ih_k",
        "outputId": "b76f0a03-3403-4204-8b77-e8fac09f2387"
      },
      "source": [
        "train['Ticket'].nunique()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "681"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "ikMJiiZfImms",
        "outputId": "e5934e0c-5ae3-4648-908f-283719bf9fd7"
      },
      "source": [
        "train[[\"SibSp\", \"Survived\"]].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Survived</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.535885</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0.464286</td>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0.345395</td>\n",
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              "      <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",
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              "    <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": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "id": "LuUXY3DeLxfc",
        "outputId": "387e883b-c42d-4cf1-859e-70426dca4464"
      },
      "source": [
        "train[[\"Sex\", \"Survived\"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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          "height": 254
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        "id": "Ek-xyJI3L0Qb",
        "outputId": "f6da3065-37c7-4afa-8e04-3baa6b23eb0e"
      },
      "source": [
        "train[[\"Parch\", \"Survived\"]].groupby(['Parch'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "id": "dKLjV-fFL3mb",
        "outputId": "65f56785-8486-4db9-a6a3-368349d89fb9"
      },
      "source": [
        "train[[\"Embarked\", \"Survived\"]].groupby(['Embarked'], as_index=False).mean().sort_values(by='Survived', ascending=False)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <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": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CxaSRIlUL6PE"
      },
      "source": [
        "# Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1UJjcZ-nMCq7"
      },
      "source": [
        "train_copy = train.copy()\n",
        "test_copy = test.copy()"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6c__2FIlMFX7",
        "outputId": "b8133732-8c33-4b60-d08d-a7ad204b6a18"
      },
      "source": [
        "# Removing unnecassary columns\n",
        "train = train.drop(['PassengerId', 'Ticket'], axis = 1)\n",
        "print(train.columns)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare',\n",
            "       'Cabin', 'Embarked'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fvpxOEcUMJJM",
        "outputId": "f6c681ba-ae4a-443e-f42f-5437ff15b2da"
      },
      "source": [
        "test = test.drop(['PassengerId', 'Ticket',], axis = 1)\n",
        "print(test.columns)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Cabin',\n",
            "       'Embarked'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tjaEaCknML7z"
      },
      "source": [
        "combine = [train, test]"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e0nk1LF1MOm0",
        "outputId": "bba2d53e-32c0-4d80-e2e9-0c4578c12f69"
      },
      "source": [
        "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()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['U', 'C', 'E', 'G', 'D', 'A', 'B', 'F', 'T'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "iV_WHU8RMXZO",
        "outputId": "adfa4489-9368-4835-ce67-e5966e8856df"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 18,
      "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  ... Cabin Embarked\n",
              "0         0       3  ...     U        S\n",
              "1         1       1  ...     C        C\n",
              "2         1       3  ...     U        S\n",
              "3         1       1  ...     C        S\n",
              "4         0       3  ...     U        S\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        },
        "id": "oKrNcl42MhEs",
        "outputId": "a338d564-d5df-4212-e53e-e304483cf1b0"
      },
      "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": 19,
      "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": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "ezCgbvkUMlmF",
        "outputId": "dedcede4-afbd-4921-a097-82a849506a89"
      },
      "source": [
        "train[['Cabin', 'Survived']].groupby(['Cabin'], as_index = False).mean().sort_values(by = 'Survived', ascending = True)"
      ],
      "execution_count": 20,
      "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": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "mUDYX8YdMr4B",
        "outputId": "d7048447-09e0-4460-9392-ac38f14872f9"
      },
      "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": 21,
      "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  ... Cabin Embarked\n",
              "0         0       3  ...     1        S\n",
              "1         1       1  ...     4        C\n",
              "2         1       3  ...     1        S\n",
              "3         1       1  ...     4        S\n",
              "4         0       3  ...     1        S\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        },
        "id": "BN3NrHnvMyUN",
        "outputId": "e49afc5c-baf4-49b6-ebff-12d8e67a40f4"
      },
      "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": 22,
      "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": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "03k0cneGM3DU",
        "outputId": "d67fc648-7957-4d8a-a225-8f6da9c93c69"
      },
      "source": [
        "for dataset in combine:\n",
        "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Major', 'Rev', 'Jonkheer', 'Dona'], 'Rare')\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()"
      ],
      "execution_count": 23,
      "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": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "brvlkPHrM9nF",
        "outputId": "3661eb61-9280-4e14-ba7e-0688b80c4918"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 24,
      "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  ... Embarked Title\n",
              "0         0       3  ...        S    Mr\n",
              "1         1       1  ...        C   Mrs\n",
              "2         1       3  ...        S  Miss\n",
              "3         1       1  ...        S   Mrs\n",
              "4         0       3  ...        S    Mr\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "dpMWIpe1NAMk",
        "outputId": "46866529-ec97-49d2-bfc5-237670f32611"
      },
      "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": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>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  ... Embarked Title\n",
              "0         0       3  ...        S   1.0\n",
              "1         1       1  ...        C   4.0\n",
              "2         1       3  ...        S   3.0\n",
              "3         1       1  ...        S   4.0\n",
              "4         0       3  ...        S   1.0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "UUiACVqzNIvz",
        "outputId": "b93270fb-c54e-4335-9354-7a514ef8f303"
      },
      "source": [
        "for dataset in combine:\n",
        "    dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0} ).astype(int)\n",
        "\n",
        "train.head()"
      ],
      "execution_count": 26,
      "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  ... Embarked  Title\n",
              "0         0       3  ...        S    1.0\n",
              "1         1       1  ...        C    4.0\n",
              "2         1       3  ...        S    3.0\n",
              "3         1       1  ...        S    4.0\n",
              "4         0       3  ...        S    1.0\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L9w4BG8ONN_0"
      },
      "source": [
        "**Resolving Missing Values**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hdpvbgqkNRLU"
      },
      "source": [
        "# Imputing Missing Values\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)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "24RhYspnNW2L",
        "outputId": "4179613d-bcd3-4d9a-9eeb-ac81beb00938"
      },
      "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": 28,
      "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": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "gw-XqFwlNaxj",
        "outputId": "c8aa7168-94a4-4c8a-ca30-387e0261c04d"
      },
      "source": [
        "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": 29,
      "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  ... Title           AgeBand\n",
              "0         0       3  ...   1.0  (16.336, 32.252]\n",
              "1         1       1  ...   4.0  (32.252, 48.168]\n",
              "2         1       3  ...   3.0  (16.336, 32.252]\n",
              "3         1       1  ...   4.0  (32.252, 48.168]\n",
              "4         0       3  ...   1.0  (32.252, 48.168]\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "9jHOz_czNlqz",
        "outputId": "e9637c03-0f4d-4c48-8fbe-ccb20cf2a757"
      },
      "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": 30,
      "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": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "LmJKjzQyNqN7",
        "outputId": "bc67327c-98c0-4beb-8151-0244cd1f47e2"
      },
      "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": 31,
      "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  ...           AgeBand  FamilySize\n",
              "0         0       3  ...  (16.336, 32.252]           2\n",
              "1         1       1  ...  (32.252, 48.168]           2\n",
              "2         1       3  ...  (16.336, 32.252]           1\n",
              "3         1       1  ...  (32.252, 48.168]           2\n",
              "4         0       3  ...  (32.252, 48.168]           1\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "FFBI322-NwoL",
        "outputId": "6577c98b-aadd-401f-e820-6b4be1a5f928"
      },
      "source": [
        "train['FareBand'] = pd.qcut(train['Fare'], 4)\n",
        "train[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False).mean().sort_values(by='FareBand', ascending=True)"
      ],
      "execution_count": 32,
      "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": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "MBh_wHnoN0Yt",
        "outputId": "5a275dd3-8fb1-46e2-d07d-2ff2f7abd775"
      },
      "source": [
        "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",
        "train.head()"
      ],
      "execution_count": 33,
      "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  ...           AgeBand  FamilySize\n",
              "0         0       3  ...  (16.336, 32.252]           2\n",
              "1         1       1  ...  (32.252, 48.168]           2\n",
              "2         1       3  ...  (16.336, 32.252]           1\n",
              "3         1       1  ...  (32.252, 48.168]           2\n",
              "4         0       3  ...  (32.252, 48.168]           1\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-1vBvJUgN6_7"
      },
      "source": [
        "**Dropping non relevant columns**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C9eSMzqYN_8E"
      },
      "source": [
        "train = train.drop(['AgeBand', 'Name', 'SibSp', 'Parch' ], axis = 1)\n",
        "test = test.drop(['Name', 'SibSp', 'Parch'], axis = 1)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g0zP89QfOCrk",
        "outputId": "60567420-906a-483d-cd15-159d27eda047"
      },
      "source": [
        "# splitting the dataset into x(independent variables) and y(dependent variables)\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": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(891, 8)\n",
            "(891,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "CYeVXDTCOd3P",
        "outputId": "33fdc978-1c5b-4dcd-d235-0f9d65f5a838"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 39,
      "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": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JUU_XKpaOf97",
        "outputId": "bfe69944-eb15-4784-8d23-ef3be49430b0"
      },
      "source": [
        "x_test = test\n",
        "print(x_test.shape)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(418, 8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Nuez6AROk2j"
      },
      "source": [
        "**Scaling data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uwgS1YhCOpKl"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "sc = StandardScaler()\n",
        "x_train = sc.fit_transform(x_train)\n",
        "x_test = sc.transform(x_test)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yR2dd2KBOtMN",
        "outputId": "e0f63827-c477-414b-9442-3594f8c42e66"
      },
      "source": [
        "from sklearn.decomposition import PCA\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": 42,
      "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"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-RQj5C5EOwyN",
        "outputId": "9aa3457d-2271-4d1f-adad-29f4036f5243"
      },
      "source": [
        "from sklearn.decomposition import PCA\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": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.33922101 0.2096452  0.16874812 0.11269072 0.08894929]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F0OVDBZ8O1zF",
        "outputId": "cedd6caf-2841-4332-9e91-cb86ffe82561"
      },
      "source": [
        "x_train"
      ],
      "execution_count": 44,
      "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": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kEEFvHVCO6qT"
      },
      "source": [
        "# Machine Learning Models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WICS2UhVO9uU"
      },
      "source": [
        "**Logistic Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Colf-0QPCNd",
        "outputId": "86683944-6a78-4afd-f7bc-977b9d39fd5c"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "model = LogisticRegression()\n",
        "model.fit(x_train, y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "print('Training Accuracy :', model.score(x_train, y_train))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy : 0.8092031425364759\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9NhZzF5KPHpc",
        "outputId": "92056550-7127-4e0b-b97a-dce876cfd89a"
      },
      "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",
        "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": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Accuracy: 0.8024731655263324\n",
            "Mean Standard Deviation:  0.011378470011165464\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oq04qEdlPL60"
      },
      "source": [
        "**AdaBoost Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C6XpiY3OPPiz",
        "outputId": "204907ef-dc71-4ffe-be81-b08e01827f37"
      },
      "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",
        "print(\"Training Accuracy :\", model.score(x_train, y_train))"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy : 0.8406285072951739\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QYzViWEKPVi9",
        "outputId": "3600566d-0b32-428d-e457-7efc291dbe79"
      },
      "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": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Accuracy: 0.7912874270290629\n",
            "Mean Standard Deviation:  0.023769465913831115\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rQadSPuAPZJl"
      },
      "source": [
        "**Gradient Boosting**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ioyuqoifPcH7",
        "outputId": "6a7f890e-6c46-4a2e-e403-e37e94a2b6d1"
      },
      "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",
        "print(\"Training Accuracy :\", model.score(x_train, y_train))"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy : 0.8866442199775533\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fBhYCmBVPh-k",
        "outputId": "a1e2de7a-9df6-4e3a-c826-16f8ac1e6852"
      },
      "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": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Accuracy: 0.8013495700207145\n",
            "Mean Standard Deviation:  0.015280695999664674\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ITPSwOatPmAn"
      },
      "source": [
        "**Decision Tree Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dIBsJu09PpZu",
        "outputId": "90617dfd-d692-458a-9985-67316cb4716b"
      },
      "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",
        "print(\"Training Accuracy :\", model.score(x_train, y_train))"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy : 0.9057239057239057\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JvaN3sIgPvRc",
        "outputId": "f7aae1c9-9a44-4270-ce77-2aa1ab1facc2"
      },
      "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 = 10)\n",
        "print('Mean Accuracy:', cvs.mean())\n",
        "print('Mean Standard Deviation: ', cvs.std())"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Accuracy: 0.8036204744069912\n",
            "Mean Standard Deviation:  0.04458187591737531\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lCpc1wxfP14d"
      },
      "source": [
        "**Extra Tree Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "70TJ5VIOP48N",
        "outputId": "3e9970ec-6a80-4ba0-d80f-b1cda436396f"
      },
      "source": [
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "\n",
        "model = ExtraTreesClassifier()\n",
        "model.fit(x_train, y_train)\n",
        "y_pred = model.predict(x_test)\n",
        "print(\"Training Accuracy: \", model.score(x_train, y_train))"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy:  0.9057239057239057\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VsUH7IL0P-pU",
        "outputId": "feac6198-7a25-4c03-d282-3a3edfdc45f0"
      },
      "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": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Accuracy: 0.8125842696629213\n",
            "Mean Standard Deviation:  0.034009140516660874\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3r-x0K3EQBhe"
      },
      "source": [
        "**Random Forest Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JPCnWJbwQFXs",
        "outputId": "34c1b40e-39d6-42f4-cb4e-a5625384f2ee"
      },
      "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",
        "print(\"Training Accuracy :\", model.score(x_train, y_train))"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy : 0.9057239057239057\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LcJAJKs1QKau",
        "outputId": "c5fdfe14-a398-49b9-f1a4-06cf53394034"
      },
      "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": 57,
      "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:  0.8226790534178645\n",
            "Best Parameters:  {'bootstrap': 'False', 'max_depth': 8, 'max_features': 'log2', 'min_samples_leaf': 3, 'min_samples_split': 10, 'n_estimators': 10}\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Done 1620 out of 1620 | elapsed:  1.4min finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6bKwbddQQpu0",
        "outputId": "6267aa21-67b9-470c-9c69-3c45a0f26547"
      },
      "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",
        "print(\"Training Accuracy :\", model.score(x_train, y_train))"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy : 0.8619528619528619\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J35ZdpJgQuLY",
        "outputId": "174a0aa1-88e6-4a7d-8d4b-eeb66cc6e061"
      },
      "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": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Accuracy: 0.8182022471910113\n",
            "Mean Standard Deviation:  0.035338105770964356\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rbDyBjIUQwGE"
      },
      "source": [
        "**XG Boost**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rMN33oIlQyf1",
        "outputId": "af88d978-2f86-4793-a00c-9c7339b7cae4"
      },
      "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",
        "print(\"Training Accuracy :\", model.score(x_train, y_train))"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy : 0.8866442199775533\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d7GMGd_IQ3f9",
        "outputId": "c32cadb9-1cc7-41c8-8511-944b7647e5e5"
      },
      "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": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Accuracy: 0.8193133583021222\n",
            "Mean Standard Deviation:  0.03455711294630714\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}