titanicprojectw8.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "titanicprojectw8.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNnKyc2u9kZ/OKWtUAgpF7B"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gWco4WaJSRJc",
        "colab_type": "text"
      },
      "source": [
        "#Mount Drive and Read Data Set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oztMRpbaSXmE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1ffcd288-c3f7-4b53-91fd-402691b293b3"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DkwizceMS6Sn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fLwRMHtQS9Au",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = pd.read_csv('gdrive/My Drive/Colab Notebooks/Summer-2020-machine-learning/titanicsurvivorproject/train.csv')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "guUnYI7lcBn_",
        "colab_type": "text"
      },
      "source": [
        "#Show Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gTt0d0WTTONk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "b2ef63dd-a291-408e-958d-fbc47c797a41"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     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": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FEmzBIAlcIFY",
        "colab_type": "text"
      },
      "source": [
        "#Show Shape"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_uytVVpZTnAy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9120dcfb-a07f-4fe0-9497-0c190169f162"
      },
      "source": [
        "train.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zwG4sXQTcLZz",
        "colab_type": "text"
      },
      "source": [
        "#Show All Information with missing Value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lsja15RYTWBh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "230eda58-2612-4d09-c16a-1fae034db689"
      },
      "source": [
        "train.info()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Name         891 non-null    object \n",
            " 4   Sex          891 non-null    object \n",
            " 5   Age          714 non-null    float64\n",
            " 6   SibSp        891 non-null    int64  \n",
            " 7   Parch        891 non-null    int64  \n",
            " 8   Ticket       891 non-null    object \n",
            " 9   Fare         891 non-null    float64\n",
            " 10  Cabin        204 non-null    object \n",
            " 11  Embarked     889 non-null    object \n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 83.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s6KlEfcscRGY",
        "colab_type": "text"
      },
      "source": [
        "#Show Null Value"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WDZa-1eVT2jQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "e58e467c-3b61-4aaa-be7a-e971856c6141"
      },
      "source": [
        "train.isnull().sum()"
      ],
      "execution_count": 8,
      "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": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YInYO4VAcXdg",
        "colab_type": "text"
      },
      "source": [
        "#Import library for data visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "64rmrIdYUuqu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "f1ac046f-dc33-44c7-e3cd-e0c2d1af9277"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "sns.set()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AvIJYPnkccCH",
        "colab_type": "text"
      },
      "source": [
        "#Create a function to see the graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wi6PvhQkUyBX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def bar_chart(feature):\n",
        "    survived = train[train['Survived']==1][feature].value_counts()\n",
        "    dead = train[train['Survived']==0][feature].value_counts()\n",
        "    df = pd.DataFrame([survived,dead])\n",
        "    df.index = ['Survived','Dead']\n",
        "    df.plot(kind='bar',stacked=True, figsize=(10,5))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lz2OIjkqcguS",
        "colab_type": "text"
      },
      "source": [
        "#Passing column as a feature parameter in bar_chart() function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZBDE3dAIVI_H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "7115a7ab-1721-46b5-9608-60bd8c9eaff3"
      },
      "source": [
        "bar_chart('Sex')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4np5XcV0VMnR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "49281836-d9a1-4e1d-ee5d-20693bf70812"
      },
      "source": [
        "bar_chart('Pclass')"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a4M1TeTlVzzY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "4dfc8a99-10a7-4944-a4c1-0cc7ba619de6"
      },
      "source": [
        "bar_chart('SibSp')"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OzTJyKyzWMDo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "28f2019d-05cf-4c10-aaef-f6d49b519afe"
      },
      "source": [
        "bar_chart('Parch')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6gZfkYw0WbZH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "9a8bdffb-b9de-422f-93c1-54c32ccf2559"
      },
      "source": [
        "bar_chart('Embarked')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bWEux97aWixC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "cb856e9f-76e3-41b3-8235-dfb4245d19e7"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     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": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "idYWjaQqcpuF",
        "colab_type": "text"
      },
      "source": [
        "#Find Title from Name Column"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HAIJKa2DYNa9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_test_data = [train] \n",
        "\n",
        "for dataset in train_test_data:\n",
        "    dataset['Title'] = dataset['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TUSqFO8nXwef",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "3cbdb95a-6719-4902-ed5e-25c9dd4bf29a"
      },
      "source": [
        "train['Title'].value_counts()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mr          517\n",
              "Miss        182\n",
              "Mrs         125\n",
              "Master       40\n",
              "Dr            7\n",
              "Rev           6\n",
              "Mlle          2\n",
              "Col           2\n",
              "Major         2\n",
              "Ms            1\n",
              "Don           1\n",
              "Lady          1\n",
              "Sir           1\n",
              "Countess      1\n",
              "Mme           1\n",
              "Capt          1\n",
              "Jonkheer      1\n",
              "Name: Title, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J6nW0b9zcuaY",
        "colab_type": "text"
      },
      "source": [
        "#Mapping Title into number"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iA_3ite0ZFjT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "title_mapping = {\"Mr\": 0, \"Miss\": 1, \"Mrs\": 2, \n",
        "                 \"Master\": 3, \"Dr\": 3, \"Rev\": 3, \"Col\": 3, \"Major\": 3, \"Mlle\": 3,\"Countess\": 3,\n",
        "                 \"Ms\": 3, \"Lady\": 3, \"Jonkheer\": 3, \"Don\": 3, \"Dona\" : 3, \"Mme\": 3,\"Capt\": 3,\"Sir\": 3 }"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DPFo6Qc-ZX6F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in train_test_data:\n",
        "    dataset['Title'] = dataset['Title'].map(title_mapping)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "laxa2oSIZggR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "c0a27146-9f4d-4fad-e968-3e1cbc881f1b"
      },
      "source": [
        "train.head(5)"
      ],
      "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>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",
              "      <th>Title</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",
              "      <td>0</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",
              "      <td>2</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",
              "      <td>1</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",
              "      <td>2</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",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ... Cabin Embarked  Title\n",
              "0            1         0       3  ...   NaN        S      0\n",
              "1            2         1       1  ...   C85        C      2\n",
              "2            3         1       3  ...   NaN        S      1\n",
              "3            4         1       1  ...  C123        S      2\n",
              "4            5         0       3  ...   NaN        S      0\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdJOxcpPaPAo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "52ad8286-988a-48d1-bc0b-900cb0ad5215"
      },
      "source": [
        "bar_chart('Title')"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5wGvNosecy35",
        "colab_type": "text"
      },
      "source": [
        "#Drop unnecessary Column such as Name."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eNPR8DJ8aiAz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train.drop('Name', axis=1, inplace=True)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wmQLwt6tak9C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "e719dbc5-cd71-40ca-ce07-93130535c7fe"
      },
      "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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass     Sex  ...     Fare  Cabin  Embarked Title\n",
              "0            1         0       3    male  ...   7.2500    NaN         S     0\n",
              "1            2         1       1  female  ...  71.2833    C85         C     2\n",
              "2            3         1       3  female  ...   7.9250    NaN         S     1\n",
              "3            4         1       1  female  ...  53.1000   C123         S     2\n",
              "4            5         0       3    male  ...   8.0500    NaN         S     0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9cEsU5HBc4JM",
        "colab_type": "text"
      },
      "source": [
        "#Mapping Sex Attribute"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B_DJ--MZamZo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sex_mapping = {\"male\": 0, \"female\": 1}\n",
        "for dataset in train_test_data:\n",
        "    dataset['Sex'] = dataset['Sex'].map(sex_mapping)"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uAwf7d_jbAvE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "316c15ed-ce08-4c65-b439-270d7135f4f0"
      },
      "source": [
        "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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  Sex  ...     Fare  Cabin  Embarked Title\n",
              "0            1         0       3    0  ...   7.2500    NaN         S     0\n",
              "1            2         1       1    1  ...  71.2833    C85         C     2\n",
              "2            3         1       3    1  ...   7.9250    NaN         S     1\n",
              "3            4         1       1    1  ...  53.1000   C123         S     2\n",
              "4            5         0       3    0  ...   8.0500    NaN         S     0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GQY9WGmmbLlr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "82a62ac2-0c34-4a3b-fa36-91e35ee252dd"
      },
      "source": [
        "bar_chart('Sex')"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ZsFcwq3fAXF",
        "colab_type": "text"
      },
      "source": [
        "#Missing value fillup of Age"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pHsUG9UoeOvB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train[\"Age\"].fillna(train.groupby(\"Title\")[\"Age\"].transform(\"median\"), inplace=True)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sGWYBjSzfMqX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "16ec87f5-3dc2-4d0f-fc04-3cef71b1819b"
      },
      "source": [
        "train.groupby(\"Title\")[\"Age\"].transform(\"median\")\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>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</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>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  Sex  ...     Fare  Cabin  Embarked Title\n",
              "0            1         0       3    0  ...   7.2500    NaN         S     0\n",
              "1            2         1       1    1  ...  71.2833    C85         C     2\n",
              "2            3         1       3    1  ...   7.9250    NaN         S     1\n",
              "3            4         1       1    1  ...  53.1000   C123         S     2\n",
              "4            5         0       3    0  ...   8.0500    NaN         S     0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dkF3CdkTfa0H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "9ebc86c6-60a0-4457-df48-69285a2aa610"
      },
      "source": [
        "train.isnull().sum()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Sex              0\n",
              "Age              0\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "Title            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BQc2IcQffsdI",
        "colab_type": "text"
      },
      "source": [
        "#Visualization of Age to see Relationship with survive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VBtmZy1tfhQG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "244044ff-3dbc-4744-ac48-ae89a74f77aa"
      },
      "source": [
        "bar_chart('Age')"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AyKDfbqDgIDh",
        "colab_type": "text"
      },
      "source": [
        "#Different age is existing. Divide in Catagorial Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MMdTjYjBgRP3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "14582f1f-e7ed-432a-e8e5-badedc973976"
      },
      "source": [
        "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
        "facet.map(sns.kdeplot,'Age',shade= True)\n",
        "facet.set(xlim=(0, train['Age'].max()))\n",
        "facet.add_legend()\n",
        "plt.show() "
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 925.55x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6zmKzoFiiX4g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "4467b4f7-3035-4d40-a2bc-c505722e048b"
      },
      "source": [
        "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
        "facet.map(sns.kdeplot,'Age',shade= True)\n",
        "facet.set(xlim=(0, train['Age'].max()))\n",
        "facet.add_legend()\n",
        "plt.xlim(0, 20)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 20.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 925.55x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vhsD2Sjciyel",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "fde998fb-64ca-401e-9292-f84e4873d069"
      },
      "source": [
        "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
        "facet.map(sns.kdeplot,'Age',shade= True)\n",
        "facet.set(xlim=(0, train['Age'].max()))\n",
        "facet.add_legend()\n",
        "plt.xlim(20, 30)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20.0, 30.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 925.55x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wwNc7rvOiycU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "46c9db83-c658-4d6f-ceac-c3c73286ef9b"
      },
      "source": [
        "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
        "facet.map(sns.kdeplot,'Age',shade= True)\n",
        "facet.set(xlim=(0, train['Age'].max()))\n",
        "facet.add_legend()\n",
        "plt.xlim(30, 40)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(30.0, 40.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 925.55x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pg9RrxmQjUCu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "844a4f0b-966e-4be6-d1d0-a942a95d0149"
      },
      "source": [
        "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
        "facet.map(sns.kdeplot,'Age',shade= True)\n",
        "facet.set(xlim=(0, train['Age'].max()))\n",
        "facet.add_legend()\n",
        "plt.xlim(40, 60)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(40.0, 60.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 925.55x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "afiEAv9DjaxH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "707055a8-4bbf-4bb4-bbac-7c25baf5d8a0"
      },
      "source": [
        "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
        "facet.map(sns.kdeplot,'Age',shade= True)\n",
        "facet.set(xlim=(0, train['Age'].max()))\n",
        "facet.add_legend()\n",
        "plt.xlim(60, 80)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60.0, 80.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 925.55x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wk_NcDaSkgQD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "91631b72-a7c5-4962-cb22-f75a68aa53da"
      },
      "source": [
        "train.info()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  891 non-null    int64  \n",
            " 1   Survived     891 non-null    int64  \n",
            " 2   Pclass       891 non-null    int64  \n",
            " 3   Sex          891 non-null    int64  \n",
            " 4   Age          891 non-null    float64\n",
            " 5   SibSp        891 non-null    int64  \n",
            " 6   Parch        891 non-null    int64  \n",
            " 7   Ticket       891 non-null    object \n",
            " 8   Fare         891 non-null    float64\n",
            " 9   Cabin        204 non-null    object \n",
            " 10  Embarked     889 non-null    object \n",
            " 11  Title        891 non-null    int64  \n",
            "dtypes: float64(2), int64(7), object(3)\n",
            "memory usage: 83.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rRQvgobrkgOS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in train_test_data:\n",
        "    dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0,\n",
        "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 26), 'Age'] = 1,\n",
        "    dataset.loc[(dataset['Age'] > 26) & (dataset['Age'] <= 36), 'Age'] = 2,\n",
        "    dataset.loc[(dataset['Age'] > 36) & (dataset['Age'] <= 62), 'Age'] = 3,\n",
        "    dataset.loc[ dataset['Age'] > 62, 'Age'] = 4"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WUh0PKdkk6UD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "e718ae14-259a-4eea-af4d-55b67b860d1e"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>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",
              "      <th>Title</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>0</td>\n",
              "      <td>1.0</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",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</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",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</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",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</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",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</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",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  Sex  ...     Fare  Cabin  Embarked Title\n",
              "0            1         0       3    0  ...   7.2500    NaN         S     0\n",
              "1            2         1       1    1  ...  71.2833    C85         C     2\n",
              "2            3         1       3    1  ...   7.9250    NaN         S     1\n",
              "3            4         1       1    1  ...  53.1000   C123         S     2\n",
              "4            5         0       3    0  ...   8.0500    NaN         S     0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DR2F6gt7lN6M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "33b917e9-0e93-421c-d435-30e60156384a"
      },
      "source": [
        "bar_chart('Age')"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2N-UMFHslVp5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "outputId": "0f0dada7-4dac-4995-885b-2e7b57ffb839"
      },
      "source": [
        "Pclass1 = train[train['Pclass']==1]['Embarked'].value_counts()\n",
        "Pclass2 = train[train['Pclass']==2]['Embarked'].value_counts()\n",
        "Pclass3 = train[train['Pclass']==3]['Embarked'].value_counts()\n",
        "df = pd.DataFrame([Pclass1, Pclass2, Pclass3])\n",
        "df.index = ['1st class','2nd class', '3rd class']\n",
        "df.plot(kind='bar',stacked=True, figsize=(10,5))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb317949278>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZuDNz0fqoC5Q",
        "colab_type": "text"
      },
      "source": [
        "#Filling Missing data of Embarked"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z96svtOAnYRF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in train_test_data:\n",
        "    dataset['Embarked'] = dataset['Embarked'].fillna('S')"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cwdv5N8aoUMl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "e2718cc0-ed8a-470c-cc10-37042f5607fa"
      },
      "source": [
        "train.isnull().sum()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Sex              0\n",
              "Age              0\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         0\n",
              "Title            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nNCqFrciobNG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "000a8070-3c33-4814-f876-d08b616a3d77"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 45,
      "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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              "      <th></th>\n",
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              "      <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",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</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",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</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",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  Sex  ...     Fare  Cabin  Embarked Title\n",
              "0            1         0       3    0  ...   7.2500    NaN         S     0\n",
              "1            2         1       1    1  ...  71.2833    C85         C     2\n",
              "2            3         1       3    1  ...   7.9250    NaN         S     1\n",
              "3            4         1       1    1  ...  53.1000   C123         S     2\n",
              "4            5         0       3    0  ...   8.0500    NaN         S     0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZdC0CCsxopIx",
        "colab_type": "text"
      },
      "source": [
        "#Mapping Embarked"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4vhA1tQMoknZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "embarked_mapping = {\"S\": 0, \"C\": 1, \"Q\": 2}\n",
        "for dataset in train_test_data:\n",
        "    dataset['Embarked'] = dataset['Embarked'].map(embarked_mapping)"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5aE6idQ3oxPF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "7bfaaee9-c86d-43d8-fbeb-ab907c85591d"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 47,
      "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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              "<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>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
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              "      <td>PC 17599</td>\n",
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              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  Sex  ...     Fare  Cabin  Embarked Title\n",
              "0            1         0       3    0  ...   7.2500    NaN         0     0\n",
              "1            2         1       1    1  ...  71.2833    C85         1     2\n",
              "2            3         1       3    1  ...   7.9250    NaN         0     1\n",
              "3            4         1       1    1  ...  53.1000   C123         0     2\n",
              "4            5         0       3    0  ...   8.0500    NaN         0     0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FllEWQwAoyvt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "576f767e-108c-4757-ace6-f4ebdff427d7"
      },
      "source": [
        "train[\"Fare\"].fillna(train.groupby(\"Pclass\")[\"Fare\"].transform(\"median\"), inplace=True)\n",
        "train.head(5)"
      ],
      "execution_count": 48,
      "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",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>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",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>A/5 21171</td>\n",
              "      <td>7.2500</td>\n",
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              "    <tr>\n",
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              "      <td>3.0</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>1</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  Sex  ...     Fare  Cabin  Embarked Title\n",
              "0            1         0       3    0  ...   7.2500    NaN         0     0\n",
              "1            2         1       1    1  ...  71.2833    C85         1     2\n",
              "2            3         1       3    1  ...   7.9250    NaN         0     1\n",
              "3            4         1       1    1  ...  53.1000   C123         0     2\n",
              "4            5         0       3    0  ...   8.0500    NaN         0     0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cnt5KaY5pPCe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "69d4ef95-1b14-4747-8597-300aac0ac978"
      },
      "source": [
        "bar_chart('Fare')"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WoVLHxFppX-z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "26880d52-b8cf-490b-efed-199e479dd487"
      },
      "source": [
        "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
        "facet.map(sns.kdeplot,'Fare',shade= True)\n",
        "facet.set(xlim=(0, train['Fare'].max()))\n",
        "facet.add_legend()\n",
        " \n",
        "plt.show() "
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xTIM9+5o4cKCpX+eQ4augIKR2IWmlNifppjYn6aY2J+mW6jankHpiPQ6FDYfD7Nu3D9u2geQkPfv37yccDh9Vrrq6uut1JBJh/PjxR52vqKiIM888k+eee67flW6KxnGBUVn9z8WGYRAK+jjUpCVHREREREQk86xcuZL7778/5eddu3Yt3/jGN/p0TI/BMj8/n+LiYioqKgCoqKiguLi42zBYgAULFrBu3Tocx6Guro6nn36a0tJSAHbs2NFVrq6ujq1btzJz5sw+VfRIjS0xAAL+gXW4Zge8CpYiIiIiIpJSr7zyCjfeeCPnnnsuF1xwATfeeCNvvvlmyt/nrrvuYvny5Sk/b3/0KpndeeedrFixggceeICcnBzWrFkDwLJly7j99ts588wzKS8vZ9u2bcyfPx+A5cuXM2nSJAAee+wxtmzZgsfjwXVdbrrpJi699NJ+V7qhM1j6rH6fA5Izw+6vbx3QOURERERERDo1Nzfz5S9/mTvvvJPPfOYzxONxXnnlFXw+X5/O47ourutimr1aIXLI9SpYzpgxo9u6lJ1+9rOfdT23LIvVq1cf8/h//Md/7Gf1jq2hJdnLGPD3f1ZYSM4M+8GeelzXHfAstSIiIiIiIlVVVQBdq2pYltXVqbZ27Vo+/vhjvv/97wOwZ88errrqKt5++208Hg8333wz55xzDlu3bqWyspKvfvWrbNy4kf/5n//pOv9//ud/8uc//5kf//jHrFixgnHjxvH1r3+dz3zmM/zDP/wDn/70pwFIJBJceumlPPjgg8yePZs33niD733ve3z44YcUFRXxrW99iwsvvBCA3bt3881vfpO3336bOXPmMG3atD5fd2bE30/oHAo7kHssIdljmbBdWtriqaiWiIiIiIiMcNOmTcOyLO644w7++Mc/0tDQ0Kfjn3jiCe6++25ee+01/uIv/oKqqip27tzZtX/9+vUsXLjwqOOuvfbartsXAV544QVyc3OZPXs2+/bt49Zbb+UrX/kKL730EnfccQe333571xKS3/jGN5g9ezZbt27ltttu43//93/7fN0ZGSwbWmJk+Swsa2C9jNnBZHf0oeZYKqolIiIiIiIjXHZ2Nr/4xS8wDIPvfOc7zJ07ly9/+cscPHiwV8d/9rOf5dRTT8Xj8RAKhbjqqqu6AuPOnTv56KOPmDdv3lHHLVy4kGeeeYbW1uStfuvXr+faa68FkmH18ssv54orrsA0TS655BLOOOMM/vjHP1JdXc327dv5m7/5G3w+H+eff/4xz9+TjAyWjS0xckb5cJ2BnScUSA6lPdTYloJaiYiIiIiIJG8l/N73vsfzzz/P+vXr2b9/P//0T//Uq2M/ufrGwoULefLJJ4HkRKolJSUEAoGjjpsyZQozZszg2WefpbW1lWeeeaarZ7O6upqNGzdy3nnndT1effVVDhw4wP79+8nJySEYDHadq6ioqM/XnLZ1LFOpoTlGKOjDcd0BnScUTAbL2kbNDCsiIiIiIqk3Y8YMrr/+eh577DFOP/102toOd2odqxfzk3O/XHzxxdTV1fHOO+9QUVHBN7/5zeO+V1lZGRUVFTiOwymnnMKUKVOAZFgtLy/nnnvuOeqYvXv30tjYSDQa7QqX1dXVfZ6DJjN7LKOxrlA4EKOyvFimwb5D0RTUSkRERERERrodO3bw85//nJqaGgAikQgVFRWcffbZFBcX8/LLL1NdXU1TUxM/+clPejyf1+tlwYIF3HvvvTQ0NHDJJZcct+w111zDli1b+OUvf9k1eRDAddddx7PPPsvmzZuxbZv29na2bt1KTU0NEyZM4IwzzmDt2rXEYjFeeeUVnn322T5fd0YGy4bmGNmBgQdL0zTIDfmpqVWwFBERERGRgcvOzmbbtm0sXryYOXPmcMMNNzBz5kxWrFjBJZdcwjXXXMN1113H9ddf3zWDa08WLlzIiy++yIIFC/B4jj/otLCwkDlz5vD6669zzTXXdG0Ph8M88MAD/OQnP2Hu3LlcccUVPPjggzhO8t7Cf/3Xf2Xbtm1ceOGF3H///SxatKjP12247gDHk6ZJbW0zjuMST9jc+v0/UnrBZM6ekT/g8/7v5o9obInxz7fOTUEtZbgoKAhx4EDTUFdDRhC1OUk3tTlJN7U5SbdUt7mCglDKzjUcZVyPZUOKlhrplBfK4mBDG7YzwJmARERERERERqiMC5aNLck1JwP+FAXLHD+241LboAl8RERERERE+iPjgmVDSzIABvxWSs6XF/IDaAIfERERERGRfsrAYJkcChv0D3zyHoDcnCwA9h5sScn5RERERERERpqMC5aNzclgmaoey4DPIstnaWZYERERERGRfsq4YNkQjTEq4MHs44Kdx2MYBnkhPzV1CpYiIiIiIiL9kXHBsrE5Rk7Qh5PCVVJyQ1m6x1JERERERKSfMi5YNrTECAV9pHL1zbwcPw3NMdrjidSdVEREREREZITIuGDZ2BIjFEzNxD2dOmeG3X+oLaXnFREREREROZlUVVWxZMkSSktLWbJkCTt37kzJeTMuWDa0xMgOpDZY5oaSM8NGajUzrIiIiIiIDF+rVq1i6dKlPPXUUyxdupSVK1em5LyelJwlTdpiCdrjNsGs1FY7t6PHMqKZYUVEREREZBA888oufv/SrkE599UXTGbeeZN7LFdbW0tlZSUPPfQQAGVlZdx9993U1dWRl5c3oDr0qseyN92ltm2zevVqSkpKuPrqq1m3bl3Xvvvvv59rr72WhQsXcv3117N58+Z+VbZzDctRKVrDspPXY5IT9BGpU4+liIiIiIgMT5FIhHHjxmFZyaUbLcuisLCQSCQy4HP3quuvs7u0vLycJ554gpUrV/Lwww93K7N+/Xp27drFpk2bqK+vZ9GiRcydO5eJEydy1lln8YUvfIFAIMC7777LTTfdxAsvvEBWVlafKtvYESyzUtxjCcley321rSk/r4iIiIiIyLzzetermKl67LHs7C4tKysDkt2llZWV1NXVdSu3YcMGFi9ejGma5OXlUVJSwsaNGwG47LLLCAQCAMyaNQvXdamvr+9zZRuak8Ey4LP6fGxP8nL87DsUxU3ldLMiIiIiIiIniXA4zL59+7BtG0iOOt2/fz/hcHjA5+4xWPa2uzQSiVBUVNSt0jU1NUed7/HHH2fy5MmMHz++z5XtHAqb6nssITkzbFvMpqk1nvJzi4iIiIiIDLX8/HyKi4upqKgAoKKiguLi4gHfXwlpnrznpZde4oc//CE///nP+3xsfn42CcAwYPzYEKnuV5wUHg3spc2GGQWhFJ9dMlGB2oGkmdqcpJvanKSb2pykm9rc0e68805WrFjBAw88QE5ODmvWrEnJeXsMlkd2l1qWddzu0nA4THV1NWeddRZwdA/m66+/zt///d/zwAMPMH369D5XtLa2mcj+ZkJBH42NrdhOaqOlr6Pv9t2qgxSGfCk9t2SegoIQBw40DXU1ZARRm5N0U5uTdFObk3RLdZsbLiF1xowZ3SZaTZUeh8L2trt0wYIFrFu3DsdxqKur4+mnn6a0tBSAN998k69//ev8+7//O7Nnz+53ZRtbYuQEvTiDcB9kTtCHZRpUH9TMsCIiIiIiIn3Rq6Gwx+suXbZsGbfffjtnnnkm5eXlbNu2jfnz5wOwfPlyJk2aBMDq1atpa2vrtvjmvffey6xZs/pU2YaWdkJBHynJla6LkWjDTLRixlsx4lHmhqrxRQ7h2tMwrIxa4lNERERERGTIGG6GTINaW9vM3/3oBWZMGM2Vcyb07yROgpwPNhDc+xJmPIpxnDs1jeyx+M5ZiHfmJRimAuZIpOE6km5qc5JuanOSbmpzkm4aCpteGZOaXNeloSVGMMvbr+OtlgPkvfkwvsY9JMJnEQ+MwbV8OJYfLB+O5eXFj20+3nuIZbnv0/78Q8Rer8D/qYV4Zl6sgCkiIiIiInIcGZOW2mI2CdtlVD+WGglUv8yYyt+AadE2ZwktWYU4tnNUuZy8BNurRvHhlDM5fWaE+AdbaHv+5xhvVOA/5zo8p8zFMFO/hqaIiIiIiEgmy5hg2bm+ZF/WsDQSbYyp/G+CkVdJ5E2nbWYprbYJxwiVAJPzLLwWbNvTzhkXTcM3dirOwSoS72+h7bn/wHx9PVnzvoJVMDUVlyQiIiIiIjIs9Dgr7MmiqSUGQNDfu2DpbdhN4Z/+lUDkNWKnXkXTrLJkqDwBj2kwLd/D9r1tuK6LYRhYBdPxXXwTvgs+hxtvJ7r+n0jsemPA1yMiIiIiIpJOa9asYd68ecyaNYv3338/pefOnGAZTfZYBnoRLIN7t1Kw9YeYboLWc2+mMe90Egm7V+9zyliLuhaHfY2Jrm2GYWAVnoL/4v+LmZ1P61M/JPbOc/26DhERERERkaFw1VVX8eijjzJhQj8nQz2BjAmWdY1tAIQCJ568x3foI8a8/Wvs/Ok0z/m/tJghXOfYQ1+PZcbYZHB9c0/7UfuMrGx8F92IVTiD9s3/SftL/02GTKorIiIiIiIj3HnnnUc4HB6Uc2fMPZYHG1oZk+3DY5nYzrHDnNneQN4b/4kTzKfl1FJiib6HvtEBk8Jskzf3tDN/dvZR+w2PD++5n8V4+/fE3qjAba7Ff8UtWvdSRERERESOq+nN52ja9sygnDt09jxCZ105KOfurYzpsTzQ0EZhbvC4oRLHJu+N/8K022mbXd6vUNnplAIPH+6P0Ro7dk+nYZp4zpiP97QriH/4J1p/96+47S39fj8REREREZFMljHdbAfq2yjKDx53/+j3nsBfX0Xb2f+HKFnAQIKlxYtV8G5NjE9NzjpmGcMw8JxyEQRyiG/bQPS3/0TgM3+HmZ3X7/cVEREREZHhKXTWlUPeqziYMqbHMtoWJy/Hf8x9gepXyd61mfi0S2gJhGGA9z1OHG3h98C23W09lvVMOB3fhYtxmg7S+uQanGjDgN5bREREREQk02RMsAQYnX10sPQ0VTOm8tck8qbTPP4cnOOsUdkXpmkwY6yHt/a292pyHit/Ct7zP4fTXEfrhntx25oHXAcREREREZFUuueee7j88supqanh85//PNdee23Kzp0xQ2EBRo/ydXttxFvJf/0h8GTROmsBicTAQ2WnGWM9VNa0sedQgkl5x5+JtupgjN++0UxltYfPTV3AZfW/I7rhXwhe+w8Y/lEpq4+IiIiIiMhAfPvb3+bb3/72oJw7Y3osLdMgJ3hEsHQdcrc/itVWR9uZn6UtYaT0/WaMtQD43VvNJOyjey1318X50TN1/POGWnbWxpgzyc9vPs7l4dYrsWt3E/3dfbjxnofSioiIiIiIZLqM6bEcE+o+DDb7oz8QOPA27adfS9QMDfi+yk/K9ptcPsPH8zvaqI/WcusVuYwOWLTGHJ54o4ln34sS8Bpcc0aQT03wYOBy7gSL3707iYcaL+Ov3edpferfCCz4WwyPr+c3FBERERERyVAZEyzzc7JwOpYa8TbuJmfHRuJFc2jJmY6bgvsqj+XyU/wUZJv89q02vvvkQa4+fRS/f7uFhlaHS04JcNl0L5bh0jkD7fjRFn91QYA/vH8qj1Tb3Fz9Aq2/X0tg/u0Y1vGH04qIiIiIiGSyjBkKmxfqWPbDSZC7/Ze4/hDRyZdiD1Ko7FQ83stfXxDEANa90kR2lsltV+Qwb4anI1R2ZxoGV57iY4c1g43OJdi7t9P2hx/jOvag1lNERERERGSoZEywHJ2dHE4a2rEJb3OEWPFniB3j3sfBMC7H4vMXBvmLc4N8/sIAuVkuJ4qzXsugZJafjfUz+Gjs5SR2vkrbsz9VuBQRERERkWEpY4JldtCLt3E3oao/EJ94Di3+wrS+f9BnMGOs1etbOWcVepieb/HTqmnYp15BYsdW2p/7D1xncHtYRURERERE0q1XwbKqqoolS5ZQWlrKkiVL2Llz51FlbNtm9erVlJSUcPXVV7Nu3bqufS+88ALXX389Z5xxBmvWrOlXRXOyzMNDYCdejGOf3L1/hmEw/zQ/7QmX39Sehrf4CuIf/on25x9UuBQRERERkWGlV8Fy1apVLF26lKeeeoqlS5eycuXKo8qsX7+eXbt2sWnTJh577DHWrl3Lnj17AJg0aRLf/e53ueWWW/pd0SXKCWsAABkcSURBVPzIi2kfAjtQY7MtLpjiZcuHrewdcy7eWZcRf38L7ZsfwnUVLkVEREREZHjoMVjW1tZSWVlJWVkZAGVlZVRWVlJXV9et3IYNG1i8eDGmaZKXl0dJSQkbN24EYMqUKRQXF+Px9H8S2uCuLcQnnpv2IbADddkMPwGvwW+3NeE59WI8My8h/t5m2jf/l8KliIiIiIgMCz0Gy0gkwrhx47AsCwDLsigsLCQSiRxVrqioqOt1OBympqYmZRV1faOITpx70g+B/SS/x+DCKV6272ln58EYnlMvwXPqxcTf/SPtWx7BTfH6myIiIiIiIumWMetYGqdfjc/w4RvqivTDlad72brrEL97O8o3y4twL5hPk8+i5e1nCAT95M+/BcMwhrqa8gkFBaGhroKMMGpzkm5qc5JuanOSbmpz6dNjsAyHw+zbtw/btrEsC9u22b9/P+Fw+Khy1dXVnHXWWcDRPZgDVU8Obc3RlJ0v3S6c7OW5D1t548N6po714U6di6ctRuMrv6M12ob/4pswTGuoqykdCgpCHDjQNNTVkBFEbU7STW1O0k1tTtIt1W1OIfXEehwKm5+fT3FxMRUVFQBUVFRQXFxMXl5et3ILFixg3bp1OI5DXV0dTz/9NKWlpSmrqGNn9v2I50/xEfAarH+zGUjOGus57Qo8p1xEvPJZWn93H257yxDXUkREREREpO96NSvsnXfeySOPPEJpaSmPPPIIq1evBmDZsmVs374dgPLyciZOnMj8+fO54YYbWL58OZMmTQLglVde4fLLL+ehhx7iV7/6FZdffjmbN28epEs6OX3yXktIhkvvaVfgm3MNduRdoo/fhV1fPcQ1FRERERER6RvDzZDZY15//kVibW1DXY0BaU+4/Oj5FmYU+rj9qu49vnbdHuKvPo7rOgSuug3PpDOHqJYCGq4j6ac2J+mmNifppjYn6aahsOnVqx5LSQ2/x+DiaT7e2tvOm3u6h2QrbyK+S27GDIymdeN9xLY/pRljRUREREQkIyhYptkFU7wUZJv8Ymsj7fHu942awdH45v4FVngW7X/6Je2bH8K1E0NUUxERERERkd5RsEwzyzT4zOl+6lpsNmxvPmq/4fHh/dR1eGZeQvzd54n+9rskaj4YgpqKiIiIiIj0joLlEJic6+HsCR6eeruF6vr4UfsNw8A781J8534Wt7mW1t9+l9ZNa3EaaoagtiIiIiIiIiemYDlE5s304/cYPLq18bj3UlrhmfivuAXvaVeQ2LOdll9/i7Ytj+C06cZ3ERERERE5eShYDpFRPpN5M318sC/G0+8cf/1Kw+PDc8pFZF25DM/UOcQr/0DLL/+B9jeexE3E0lhjERERERGRY/MMdQVGsjkTvHx00GbdK01k+03mzgget6yRlY13dgnWlE+ReG8zsZfWEd/+FJ4pc7Amz8EzYTaG15/G2ouIiIiIiCQpWA4hwzAoPyuL9tda+a8XGwj4TOZMyjrhMWZ2Pr5zF+HU7cbevZ34jpeIv/s8WF6somI8U+bgmXw2ZnZ+mq5CRERERERGOgXLIeYxDf7PnAC/eLWVn/7xEH9Tkses8T33PJp5kzDzJuE504GGauz9Vdg179O++03aATN3QvIxelzykTMOY/Q4jKwQhmEM/oWJiIiIiMiIoWB5EvB5DG74VID/93KUf/9DHZ87J4dPnxbsVQA0TBNyJ+LJnYg181KI1uMcqMKp241z4CMSVa+Ae8R6mb5AMmQGx2AGcjACORjB0cmfgRyMwGiMrGwM/ygM0xrEqxYRERERkeFCwfIkEfQZ3HRegIrKdn71ciPb97bzVxePZkyw9+HOMAwYlYs1Khdr6jkdW11obcSJ1kO0ASdaj9tch9N8gMSBj3DbmrsHzyP5Ahj+jpDZGTa9WeDxY3h84PVjePwdP31gWMn3c93DP7tmvD382uWI7a4DholhecHjS57nyJ/eLIxRuRimmqqIiIiIyMlK/1o/iYzym9wwJ4vX98T5/XvtrF5/gPI5IS45JYjX6u/wVQMCozEDoyH/E9MAd/aIxlsh1orbHsWNRSHRDvE23Fg7bjyKG2vFbWvGadiHm2hPzkabaD8iNA4yw8AI5mKGxmKECjBDY7ueW2MnY/iOP+mRiIiIiIgMPgXLk4xhGJwzyceUPA8bKtv4xdZGnnyzmatPH8XlM4NkeVO4QkxnMPRkgScLI5hLj/HVMDAMI7n2puuAnQA7nnx0ns8wAONwcDU6/3Pkts7XHb2Xtg12AtfpOJ+TAMfBjbfhtjXhRhtwWuqxqytJtNQDh0OtOXo8ZsE0rI6HOXZKssdTRERERETSQsHyJJU/yuSm8wLsrrfZUhXnv19tYv22Zk4p9DFzXPKRl21hGWCZBoYBsYRLe8KlPe5impDtN8n2m3j63dt5DK6bDJUAGGB5k4+B8nadsZuja24ADrQ140QP4Tbux6mvSQbOD//UUcTEzJuANe5UrPEzscafqllyRUREREQGkYLlScwwDCbnepic6yHSYPNWjc3HdQn+t7q9T+cJeA2mF/g4bbyP08J+JuV6MM30zAzb3OZwKGrj9xiM8psEfAbmgGaldQEDskKYWSHIm4xFMpe6sShuwz7cpn04hyLE399CvPIZAIzsfKzxnUFzJmZuEYaRwt5fEREREZERTMEyQ4RHW4RHW4CPtrjDnnqXdhscF2wXXNfFZxl4LPBa4DgQjUNr3KWpzaGqNsFvXmsHmsjJMjlvahYXTQ8wJd+bsuVHHMfl/f0xXt3Zxsd1cfY3JojGut+HaRiQF7SYlOdhcp6Xyflepo31Esoa+Ay0hi+IUTANCqZhAR7XheZanPq9OIeqsfdWkvjwz8nC3iys/MnJIbRjp2AWTMUcPV5hU0RERESkHxQsM1CW1+SUgr4e5SPa7vBxvcN7+22efz/KM+9GKcyxuHBagAunBSjM6XtzcFyXHfvjvLKzlVd3tdHY6uDzGEzN9/KpSVnkjTLI9hvEEy5tHUG3NmpTXZ9g2+72rjslx+dYnFLo49RxPmYX+ckJpCBoGgaExmKFxmJNOjsZNNubcA9V4zTux6mPEK98hrgdTx7gzUqGzNwJ1IcnEjdCmDkFmKECDP+oAddHRERERGS4Mlw3XVN7Dszrz79IrK1tqKsxbLTHXd4/YPN2TYIdB+K4wNSxXs6dnMX0Ah+T8734PcfuyWxpd9h9KM623e28+nEr9VEHrwWzi/zMDnuZmmtiJhcVOaGE7bKv2aG6wWF3vcPO2jjRmIsBTMn3cuZEP2dO8DM53zvA4bPH57ouRvQQTsM+nKYDOIeqcRoPJGfHPZIvmAyYo8Ykl13xBTH8QQzfKAx/EHzB5FIslie5/qdpgekB00q+tjwd26zk0imdZQwzZT3GktkKCkIcONA01NWQEURtTtJNbU7SLdVtrqAglLJzDUcKlkJzu8M7+23eqk6wtz4BgGlA0RgPoSwTr2XgMQ1itsveQ3EORZPrXnpMKA77OKPIx7RcE8voOUyeiOu6HGh22VFr8+GBBLvqErhAKMvkjCI/Z0zwUxz2k501yMNVDYOcoEnDvv0QbcBtb8KJNuBG63Fbm3BjrTixKMTaYEBXDGAcDpnHC6WmmVwj1DQxjM7XZlcwxTC7lzOMjmM7ynUeY1rJob6dx3aUN8zuZZJht3uZw+9tgcebXHfU8natN4rlPbwWqTnw3uaRSP/gknRTm5N0U5uTdFOwTK9ejX2sqqpixYoV1NfXM2bMGNasWcPUqVO7lbFtm3vuuYfNmzdjGAZf+tKXWLx4cY/7ZOhl+03On2Ry/iQvrTGHSKNLpNkh0uDQFndoboOE42IaBtMLvIwLWYzNNpkQMvGYnWFyYKESkkNXC0MGhSGTuVOTddlZ57Cj1mbbnjb+9FErBsme1dlFfk4p9DE5z5v6oOm6mF4/RnY+ZOdjcKz1Pw3ABTsG8XbceDvYMWzbJmE7mK6DxwTDdcB1cF0Hw7GTs+o6dnKpFsfGdZyu54e3HX6OY+O6nWU6z2Unl2OJ213bnSPLOHbHcjD2Ecd0Hn9426AxzGTA7Jwx2OPD8HgxvAHwZmH4Asnnvqxkz68vkOzt7dhu+I7Y5w0kj1evroiIiMhJrVfBctWqVSxdupTy8nKeeOIJVq5cycMPP9ytzPr169m1axebNm2ivr6eRYsWMXfuXCZOnHjCfXJyCfhMpo+F6WOP7nXqiFJHGHiY7KkuxeNNisd7cE73sa/JoarOYceBBE9ub+5aNjN/lMXEXA/52RZ5oyzysy1ysixG+ZMz0Y7yDWzJFcdNToB0qMXmUDQ5y219i82haPJ1fdSmsc0hngDbtYDk784A/F6DgNdgTNAiN2iRO8pM/gxa5I6yyA2ajA5aePo9S6/R7ceR24+fxTrWIaVjLVLXSa4lekR4PbzPPRxO6dhvJ3DtOK3tCVqiMVpaY7S2xWhvi9EeixGPxSGRwBNPYGHjIUHAcsgyo2QZDfiI43VjeOw2DCfRi0s0oCNwdgbRI0Po0a+DyWDqDXT0rvqSPzt6VjE9J2VQdR07uW5rIpZcyzURx7XjYMdwE8m1Yl07Bl3P45CIdf/Z7bh41/9Dt/P/JR3rxrpH/P/FxXUcXLfzpwMYuIaJY5i4JB+YFqZlYZoWhmVhWRamx9O9x9uwjuhpt44Y/t3Da8M61tpCHb8YOr4YOeILF9dJfgHT7cuSji9qelnOsRMk4gkSieTDTiS/1DEcBxcX1/RgWR4srxfL68Xr9eHxeTE9XgyPv+MLE1/HzxO99h/ert58EZEh47pu8ov5jn/HYCfA+eTzRLe/n51/N93Ov50FVw71ZZzUegyWtbW1VFZW8tBDDwFQVlbG3XffTV1dHXl5eV3lNmzYwOLFizFNk7y8PEpKSti4cSNf/OIXT7ivt3x+fz8uT4aTKQGYUghXngZx2+Vgs8vBqMOBZoeDzTbv1zrE9iWAo8OKz2MQ9CWXO8nymHg9Bj4LvFYygHX+m9Z2IZ5wcWijpd2mqc2hsdXG/kQHn2VCTpZFTraPCWNNgj4DrwleT3Kf40A8ATHHpT2eHG7c2Obw7kGHeKJ7HQ1gVJbJ6IDFKJ+J16Jjdt/kEGSvlXyY5uFMAODgdr22neQ9q3HHJZ5wiTvJ60g4LrGES9zufAC4WKbR8QDLSJ7bMg08JnhMA8sy8ZomDh3ZsuP3Eo05tMQcWmPOMX8nowMWOaNNfJ7ksjKmkTy2Ne7Q3O7Q0u7QFj/8dYSFg8+Ik5vlkJ/lkOO1GeWxCZoJ/GYCH3E8bgKPGz/iEcNqi2FFW7CcGJYTw3TtXrcjFwPH8OCYFq7hwe567sUxLRw8JIOV0VG+8/+SgWscfp7cbOB2tR6jo4faxnQdDByMzueu3fFwMDuf43Tb1kr/e5FdFxJ4SGCScE3irofkO5i4GLgd1538UiH52un8XWAAVsdzwDXAcDu2dMZKB5PE4edG53YXyzj82jI6yyZ/DiWno/bJn8nrdDBxXAO74+Fg4Lgd2zv340n+RmwHM96O1d6KhUMcB8tw8BoOHmwsox/XZ3o6hpB7wfId/rLD4z1iyPkRw9A7Ar1hGsljO4eydxv+fmQiP1Y6/8S2I8o3fOwnEY0dUeQYKwd32zTYX8gM3leVmXHTz/EMYuUH9fdy9MkbPvYl29yAT53J/0Mzte6DXO8jv8g+8otut/MLQZJfEH5yFJdjJ7fbiY5waHcERBucOHtcBycRTwZKp/f/VjhuNc+9FMPS3KfH0+NvJhKJMG7cOCwr+U2rZVkUFhYSiUS6BctIJEJRUVHX63A4TE1NTY/7emv2hef2qbyIiIic2OihroCMOGpzIsOXFu0TERERERGRAekxWIbDYfbt24dtJ7uPbdtm//79hMPho8pVV1d3vY5EIowfP77HfSIiIiIiIpLZegyW+fn5FBcXU1FRAUBFRQXFxcXdhsECLFiwgHXr1uE4DnV1dTz99NOUlpb2uE9EREREREQyW6/WsdyxYwcrVqygsbGRnJwc1qxZw/Tp01m2bBm33347Z555JrZtc9ddd7FlyxYAli1bxpIlSwBOuE9EREREREQyW6+CpYiIiIiIiMjxaPIeERERERERGRAFSxERERERERkQBUsREREREREZEAVLERERERERGZCTOlhWVVWxZMkSSktLWbJkCTt37hzqKskwsGbNGubNm8esWbN4//33u7afqL2pLcpAHDp0iGXLllFaWsrChQv56le/Sl1dHQBvvPEG1113HaWlpXzhC1+gtra267gT7RPpyW233cZ1113HokWLWLp0Ke+88w6gzzoZXD/60Y+6/X3VZ5wMpnnz5rFgwQLKy8spLy9n8+bNgNrdkHFPYjfffLP7+OOPu67ruo8//rh78803D3GNZDh4+eWX3erqavfTn/60+95773VtP1F7U1uUgTh06JD75z//uev19773Pfeb3/yma9u2W1JS4r788suu67ru/fff765YscJ1XfeE+0R6o7Gxsev573//e3fRokWu6+qzTgbPW2+95d5yyy1df1/1GSeD7ZP/lnPdE7cttbvBddL2WNbW1lJZWUlZWRkAZWVlVFZWdn3LL9Jf5513HuFwuNu2E7U3tUUZqDFjxnDhhRd2vZ4zZw7V1dW89dZb+P1+zjvvPABuvPFGNm7cCHDCfSK9EQqFup43NzdjGIY+62TQxGIx7rrrLu68886ubfqMk6Ggdjd0PENdgeOJRCKMGzcOy7IAsCyLwsJCIpEIeXl5Q1w7GW5O1N5c11VblJRxHIdf/vKXzJs3j0gkQlFRUde+vLw8HMehvr7+hPvGjBkzFFWXDPStb32LLVu24Lou//Ef/6HPOhk0P/zhD7nuuuuYOHFi1zZ9xkk6fOMb38B1Xc4991z+9m//Vu1uCJ20PZYiIsPR3XffTTAY5KabbhrqqsgI8N3vfpfnnnuOr3/969x7771DXR0Zpl5//XXeeustli5dOtRVkRHm0Ucf5be//S2/+c1vcF2Xu+66a6irNKKdtMEyHA6zb98+bNsGwLZt9u/ff9QQRpFUOFF7U1uUVFmzZg0ff/wx//Zv/4ZpmoTDYaqrq7v219XVYZomY8aMOeE+kb5atGgRW7duZfz48fqsk5R7+eWX2bFjB1dddRXz5s2jpqaGW265hY8//lifcTKoOj+ffD4fS5cu5bXXXtPf1iF00gbL/Px8iouLqaioAKCiooLi4mINx5FBcaL2prYoqXDffffx1ltvcf/99+Pz+QA444wzaGtr45VXXgHgV7/6FQsWLOhxn0hPWlpaiEQiXa+feeYZRo8erc86GRRf+tKXeOGFF3jmmWd45plnGD9+PA8++CBf/OIX9RkngyYajdLU1ASA67ps2LCB4uJi/W0dQobruu5QV+J4duzYwYoVK2hsbCQnJ4c1a9Ywffr0oa6WZLh77rmHTZs2cfDgQXJzcxkzZgxPPvnkCdub2qIMxAcffEBZWRlTp04lKysLgIkTJ3L//ffz2muvsWrVKtrb25kwYQL/8i//wtixYwFOuE/kRA4ePMhtt91Ga2srpmkyevRo7rjjDmbPnq3POhl08+bN48c//jEzZ87UZ5wMmt27d/O1r30N27ZxHIcZM2bw7W9/m8LCQrW7IXJSB0sRERERERE5+Z20Q2FFREREREQkMyhYioiIiIiIyIAoWIqIiIiIiMiAKFiKiIiIiIjIgChYioiIiIiIyIAoWIqIiIiIiMiAeIa6AiIiIv0xb948Dh48iGVZXds2btzIuHHjhrBWIiIiI5OCpYiIZKwf//jHXHzxxX0+znVdXNfFNDVwR0REJBX0F1VERIaFhoYGbr31Vi666CLOP/98br31Vmpqarr233zzzfzgBz/gxhtv5Oyzz2b37t3s2LGDz3/+81xwwQWUlpayYcOGIbwCERGRzKVgKSIiw4LjOFx//fU8++yzPPvss/j9fu66665uZZ544gnuvvtuXnvtNfLy8vjCF75AWVkZL774Ij/4wQ9YvXo1H3744RBdgYiISObSUFgREclYy5cv77rH8oILLuCBBx7o2veVr3yFv/zLv+xW/rOf/SynnnoqAJs3b2bChAl87nOfA+D000+ntLSUjRs38tWvfjVNVyAiIjI8KFiKiEjGuv/++7vusWxtbWXlypVs3ryZhoYGAFpaWrBtuyt8hsPhrmP37t3Lm2++yXnnnde1zbZtrrvuujRegYiIyPCgYCkiIsPCz3/+c6qqqvj1r39NQUEB77zzDosWLcJ13a4yhmF0PQ+Hw5x//vk89NBDQ1FdERGRYUX3WIqIyLDQ0tKC3+8nJyeH+vp6fvSjH52w/JVXXsnOnTt5/PHHicfjxONx3nzzTXbs2JGmGouIiAwfCpYiIjIs/NVf/RXt7e1cdNFFLFmyhMsuu+yE5bOzs3nwwQfZsGEDl112GZdeeinf//73icViaaqxiIjI8GG4R44REhEREREREekj9ViKiIiIiIjIgChYioiIiIiIyIAoWIqIiIiIiMiAKFiKiIiIiIjIgChYioiIiIiIyIAoWIqIiIiIiMiAKFiKiIiIiIjIgChYioiIiIiIyIAoWIqIiIiIiMiA/H8Tf9KPBMDbFAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 925.55x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qLbsTt5Gpj36",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "4e1c5df1-0cc2-4a86-9143-70b04d07b9e7"
      },
      "source": [
        "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
        "facet.map(sns.kdeplot,'Fare',shade= True)\n",
        "facet.set(xlim=(0, train['Fare'].max()))\n",
        "facet.add_legend()\n",
        "plt.xlim(0, 20)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 20.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 925.55x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tOcbGqiWpxEW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "76a56fc8-bdb1-4859-fabe-8a1283bf1bca"
      },
      "source": [
        "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=4)\n",
        "facet.map(sns.kdeplot,'Fare',shade= True)\n",
        "facet.set(xlim=(0, train['Fare'].max()))\n",
        "facet.add_legend()\n",
        "plt.xlim(0, 30)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 30.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 925.55x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2T6n9q6dp9Gk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for dataset in train_test_data:\n",
        "    dataset.loc[ dataset['Fare'] <= 17, 'Fare'] = 0,\n",
        "    dataset.loc[(dataset['Fare'] > 17) & (dataset['Fare'] <= 30), 'Fare'] = 1,\n",
        "    dataset.loc[(dataset['Fare'] > 30) & (dataset['Fare'] <= 100), 'Fare'] = 2,\n",
        "    dataset.loc[ dataset['Fare'] > 100, 'Fare'] = 3\n"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6SJaYoh7qGKv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "03953a07-e8b6-487b-aad8-13a4ae0474ef"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>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",
              "      <th>Title</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>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5 21171</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>2.0</td>\n",
              "      <td>C85</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>2.0</td>\n",
              "      <td>C123</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  Sex  ...  Fare  Cabin  Embarked Title\n",
              "0            1         0       3    0  ...   0.0    NaN         0     0\n",
              "1            2         1       1    1  ...   2.0    C85         1     2\n",
              "2            3         1       3    1  ...   0.0    NaN         0     1\n",
              "3            4         1       1    1  ...   2.0   C123         0     2\n",
              "4            5         0       3    0  ...   0.0    NaN         0     0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h4RW7XOqq6XP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "features_drop = ['Ticket', 'Cabin']\n",
        "train = train.drop(features_drop, axis=1)"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YDfwxeqbqLzs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "e8713df7-6afc-453f-a46f-1c744a4568ad"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  Sex  Age  SibSp  Parch  Fare  Embarked  Title\n",
              "0            1         0       3    0  1.0      1      0   0.0         0      0\n",
              "1            2         1       1    1  3.0      1      0   2.0         1      2\n",
              "2            3         1       3    1  1.0      0      0   0.0         0      1\n",
              "3            4         1       1    1  2.0      1      0   2.0         0      2\n",
              "4            5         0       3    0  2.0      0      0   0.0         0      0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pG4T3aiKsNQ2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b29bc02d-edb3-4885-fe49-81d0d81573b5"
      },
      "source": [
        "# (X_TRAIN->train_data, Y_TRAIN->target), X_TEST, Y_TEST\n",
        "train_data = train.drop('Survived', axis=1)     \n",
        "target = train['Survived']\n",
        "\n",
        "train_data.shape, target.shape"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((891, 9), (891,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YSkwzOFHsNOx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "f8e5f966-de44-4a27-ddc7-47c49591f3c6"
      },
      "source": [
        "#Start Confusion Matrix Evaluation\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(train_data, target, test_size=.30, random_state=42)\n",
        "print(\"X_train: \", X_train.shape)\n",
        "print(\"X_test: \", X_test.shape)\n",
        "print(\"y_train: \", y_train.shape)\n",
        "print(\"y_test: \", y_test.shape)\n",
        "print(y_test)\n",
        "\n",
        "y_test.value_counts()\n",
        "\n"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train:  (623, 9)\n",
            "X_test:  (268, 9)\n",
            "y_train:  (623,)\n",
            "y_test:  (268,)\n",
            "709    1\n",
            "439    0\n",
            "840    0\n",
            "720    1\n",
            "39     1\n",
            "      ..\n",
            "821    1\n",
            "633    0\n",
            "456    0\n",
            "500    0\n",
            "430    1\n",
            "Name: Survived, Length: 268, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    157\n",
              "1    111\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kzmjGtLrkt8w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Importing Classifier Modules\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "dt = DecisionTreeClassifier()\n",
        "dt.fit(X_train, y_train)\n",
        "y_pred = dt.predict(X_test)"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eWZ1b55UlzUW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "d53efa0d-e287-494f-958e-8fbc09c0b970"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix,classification_report\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print(classification_report(y_test,y_pred))"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[124  33]\n",
            " [ 34  77]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.78      0.79      0.79       157\n",
            "           1       0.70      0.69      0.70       111\n",
            "\n",
            "    accuracy                           0.75       268\n",
            "   macro avg       0.74      0.74      0.74       268\n",
            "weighted avg       0.75      0.75      0.75       268\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wuHhOtOXmFpp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3513c431-a9cd-45c3-a84c-f214f70785af"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "print(accuracy_score(y_test, y_pred))"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.75\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}