Data_Preprocessing_&_Visualisation_Project.ipynb

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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "BUOhpWi3nNNz",
        "outputId": "79f117de-d68c-408d-9908-c6a95bcdc23d"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-adedd597-fd7c-4ed1-92a2-fe42c896b2a9\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-adedd597-fd7c-4ed1-92a2-fe42c896b2a9\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving train.xlsx to train.xlsx\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "0M67eEtunfpH",
        "outputId": "4f8e499d-cd16-4297-c3a9-c16d788dd934"
      },
      "source": [
        "import io\n",
        "import pandas as pd\n",
        "df= pd.read_excel(io.BytesIO(uploaded['train.xlsx']))\n",
        "df"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>female</td>\n",
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              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
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              "      <td>S</td>\n",
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              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
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              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 12 columns</p>\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",
              "886          887         0       2  ...  13.0000   NaN         S\n",
              "887          888         1       1  ...  30.0000   B42         S\n",
              "888          889         0       3  ...  23.4500   NaN         S\n",
              "889          890         1       1  ...  30.0000  C148         C\n",
              "890          891         0       3  ...   7.7500   NaN         Q\n",
              "\n",
              "[891 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "df.head()"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
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              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
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              "      <th>Age</th>\n",
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              "      <th>0</th>\n",
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              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>7.2500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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              "      <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",
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              "      <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",
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              "      <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": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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      "source": [
        "df.head(8)"
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              "      <th>3</th>\n",
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              "      <td>1</td>\n",
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              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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              "      <td>35.0</td>\n",
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              "      <th>5</th>\n",
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              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330877</td>\n",
              "      <td>8.4583</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>McCarthy, Mr. Timothy J</td>\n",
              "      <td>male</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17463</td>\n",
              "      <td>51.8625</td>\n",
              "      <td>E46</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Palsson, Master. Gosta Leonard</td>\n",
              "      <td>male</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>349909</td>\n",
              "      <td>21.0750</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",
              "5            6         0       3  ...   8.4583   NaN         Q\n",
              "6            7         0       1  ...  51.8625   E46         S\n",
              "7            8         0       3  ...  21.0750   NaN         S\n",
              "\n",
              "[8 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 297
        },
        "id": "GTSE0Q1N03l_",
        "outputId": "f2981486-3cae-40d8-e109-fc4ca2b7e080"
      },
      "source": [
        "df[[\"Name\",\"Age\",\"Sex\"]].head(8)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <td>NaN</td>\n",
              "      <td>male</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>McCarthy, Mr. Timothy J</td>\n",
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              "    </tr>\n",
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              "      <th>7</th>\n",
              "      <td>Palsson, Master. Gosta Leonard</td>\n",
              "      <td>2.0</td>\n",
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              "  </tbody>\n",
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            ],
            "text/plain": [
              "                                                Name   Age     Sex\n",
              "0                            Braund, Mr. Owen Harris  22.0    male\n",
              "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  38.0  female\n",
              "2                             Heikkinen, Miss. Laina  26.0  female\n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  35.0  female\n",
              "4                           Allen, Mr. William Henry  35.0    male\n",
              "5                                   Moran, Mr. James   NaN    male\n",
              "6                            McCarthy, Mr. Timothy J  54.0    male\n",
              "7                     Palsson, Master. Gosta Leonard   2.0    male"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PRuipCuV1wB7",
        "outputId": "f02d40b4-cc7b-4f00-ee59-e1dd6b47bb30"
      },
      "source": [
        "df.Age.max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "80.0"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
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          "height": 297
        },
        "id": "3vYo3QKpvjSf",
        "outputId": "da0ab55d-4551-4627-9a13-a2569f88967d"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
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              "      <td>714.000000</td>\n",
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              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>257.353842</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>223.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>446.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>668.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       PassengerId    Survived      Pclass  ...       SibSp       Parch        Fare\n",
              "count   891.000000  891.000000  891.000000  ...  891.000000  891.000000  891.000000\n",
              "mean    446.000000    0.383838    2.308642  ...    0.523008    0.381594   32.204208\n",
              "std     257.353842    0.486592    0.836071  ...    1.102743    0.806057   49.693429\n",
              "min       1.000000    0.000000    1.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     223.500000    0.000000    2.000000  ...    0.000000    0.000000    7.910400\n",
              "50%     446.000000    0.000000    3.000000  ...    0.000000    0.000000   14.454200\n",
              "75%     668.500000    1.000000    3.000000  ...    1.000000    0.000000   31.000000\n",
              "max     891.000000    1.000000    3.000000  ...    8.000000    6.000000  512.329200\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cm2sBzJz13hm",
        "outputId": "1185389d-4a8a-4176-c354-ec9e3ac9d775"
      },
      "source": [
        "df.Age.min()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.42"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "PlLN2Hgi2hcC",
        "outputId": "d648af47-e7e1-4fa3-f95d-840494a1ab7b"
      },
      "source": [
        "df.isnull().head(10)"
      ],
      "execution_count": null,
      "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>Name</th>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <th>6</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "      <th>8</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "      <td>False</td>\n",
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              "  </tbody>\n",
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            "text/plain": [
              "   PassengerId  Survived  Pclass   Name  ...  Ticket   Fare  Cabin  Embarked\n",
              "0        False     False   False  False  ...   False  False   True     False\n",
              "1        False     False   False  False  ...   False  False  False     False\n",
              "2        False     False   False  False  ...   False  False   True     False\n",
              "3        False     False   False  False  ...   False  False  False     False\n",
              "4        False     False   False  False  ...   False  False   True     False\n",
              "5        False     False   False  False  ...   False  False   True     False\n",
              "6        False     False   False  False  ...   False  False  False     False\n",
              "7        False     False   False  False  ...   False  False   True     False\n",
              "8        False     False   False  False  ...   False  False   True     False\n",
              "9        False     False   False  False  ...   False  False   True     False\n",
              "\n",
              "[10 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lrlVQL9w3GtX",
        "outputId": "4bea5d5d-0760-4ec6-96af-f2df89c8eb17"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "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": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "Zey6dvfS7zN_",
        "outputId": "637576c1-3519-4d79-b4bc-52b1d3f2175d"
      },
      "source": [
        "df.Age.mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29.69911764705882"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "k9CL-qbJ72zB",
        "outputId": "d88b9bd0-ada9-4c8d-9ae2-8fbab3c4478b"
      },
      "source": [
        "df.Age.median()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28.0"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SdtAb98077gB",
        "outputId": "8c1fd578-14ed-4ac3-8fa4-d0e3ca8a3ac6"
      },
      "source": [
        "df.Age.mode()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    24.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eY-hski48X04",
        "outputId": "9196312f-d8e0-45fd-a85f-1d0ef81c1e01"
      },
      "source": [
        "df.Cabin.mode()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        B96 B98\n",
              "1    C23 C25 C27\n",
              "2             G6\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EqZ7Bctz_fkK",
        "outputId": "9abaca86-6904-4ce3-c16e-bc308d22ef8f"
      },
      "source": [
        "df.Embarked.mode()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    S\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "KdZyWyl3A2kd",
        "outputId": "284bb5b8-f1dc-4e15-a148-4990f8290417"
      },
      "source": [
        "df['Age'].fillna(df['Age'].mean(), inplace=True)\n",
        "df[:110]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>PC 17599</td>\n",
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              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.000000</td>\n",
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              "      <td>STON/O2. 3101282</td>\n",
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              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.000000</td>\n",
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              "      <th>105</th>\n",
              "      <td>106</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Mionoff, Mr. Stoytcho</td>\n",
              "      <td>male</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>349207</td>\n",
              "      <td>7.8958</td>\n",
              "      <td>0</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>107</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Salkjelsvik, Miss. Anna Kristine</td>\n",
              "      <td>female</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>343120</td>\n",
              "      <td>7.6500</td>\n",
              "      <td>0</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>108</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Moss, Mr. Albert Johan</td>\n",
              "      <td>male</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>312991</td>\n",
              "      <td>7.7750</td>\n",
              "      <td>0</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>109</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Rekic, Mr. Tido</td>\n",
              "      <td>male</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>349249</td>\n",
              "      <td>7.8958</td>\n",
              "      <td>0</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>110</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Moran, Miss. Bertha</td>\n",
              "      <td>female</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>371110</td>\n",
              "      <td>24.1500</td>\n",
              "      <td>0</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>110 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0              1         0       3  ...   7.2500     0         S\n",
              "1              2         1       1  ...  71.2833   C85         C\n",
              "2              3         1       3  ...   7.9250     0         S\n",
              "3              4         1       1  ...  53.1000  C123         S\n",
              "4              5         0       3  ...   8.0500     0         S\n",
              "..           ...       ...     ...  ...      ...   ...       ...\n",
              "105          106         0       3  ...   7.8958     0         S\n",
              "106          107         1       3  ...   7.6500     0         S\n",
              "107          108         1       3  ...   7.7750     0         S\n",
              "108          109         0       3  ...   7.8958     0         S\n",
              "109          110         1       3  ...  24.1500     0         Q\n",
              "\n",
              "[110 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UrIUBa5oCiaa",
        "outputId": "438aaf7e-bb8b-4fe6-8428-09e25f37bacc"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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          891 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "3uD68aisrayc",
        "outputId": "be6cc5bd-1ea1-4f15-9850-0c911d1ef751"
      },
      "source": [
        "df[\"Embarked\"]=df[\"Embarked\"].fillna(\"S\")\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>PassengerId</th>\n",
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              "      <td>Heikkinen, Miss. Laina</td>\n",
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              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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              "      <td>8.0500</td>\n",
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              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
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              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>S</td>\n",
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              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
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              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
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              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
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              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>0</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0              1         0       3  ...   7.2500     0         S\n",
              "1              2         1       1  ...  71.2833   C85         C\n",
              "2              3         1       3  ...   7.9250     0         S\n",
              "3              4         1       1  ...  53.1000  C123         S\n",
              "4              5         0       3  ...   8.0500     0         S\n",
              "..           ...       ...     ...  ...      ...   ...       ...\n",
              "886          887         0       2  ...  13.0000     0         S\n",
              "887          888         1       1  ...  30.0000   B42         S\n",
              "888          889         0       3  ...  23.4500     0         S\n",
              "889          890         1       1  ...  30.0000  C148         C\n",
              "890          891         0       3  ...   7.7500     0         Q\n",
              "\n",
              "[891 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7f7SgkV0r_wP",
        "outputId": "37f894a1-e1c8-4837-ae2f-159665cf0e83"
      },
      "source": [
        "df.Embarked.count"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method Series.count of 0      S\n",
              "1      C\n",
              "2      S\n",
              "3      S\n",
              "4      S\n",
              "      ..\n",
              "886    S\n",
              "887    S\n",
              "888    S\n",
              "889    C\n",
              "890    Q\n",
              "Name: Embarked, Length: 891, dtype: object>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "6qqryWaJ0f71",
        "outputId": "0a0a6edf-f228-495f-a200-dd92e6b12767"
      },
      "source": [
        "df['Cabin']= df['Cabin'].fillna(0)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>0</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>0</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0              1         0       3  ...   7.2500     0         S\n",
              "1              2         1       1  ...  71.2833   C85         C\n",
              "2              3         1       3  ...   7.9250     0         S\n",
              "3              4         1       1  ...  53.1000  C123         S\n",
              "4              5         0       3  ...   8.0500     0         S\n",
              "..           ...       ...     ...  ...      ...   ...       ...\n",
              "886          887         0       2  ...  13.0000     0         S\n",
              "887          888         1       1  ...  30.0000   B42         S\n",
              "888          889         0       3  ...  23.4500     0         S\n",
              "889          890         1       1  ...  30.0000  C148         C\n",
              "890          891         0       3  ...   7.7500     0         Q\n",
              "\n",
              "[891 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tbNPo7nI7z5P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d3346c5-c0ce-4899-f255-ad5eceee3fba"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId    0\n",
              "Survived       0\n",
              "Pclass         0\n",
              "Name           0\n",
              "Sex            0\n",
              "Age            0\n",
              "SibSp          0\n",
              "Parch          0\n",
              "Ticket         0\n",
              "Fare           0\n",
              "Cabin          0\n",
              "Embarked       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5oyjvXqk4K_8",
        "outputId": "2d814b9c-714a-45df-ca7c-b95aee126080"
      },
      "source": [
        "df.isnull().sum().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bXzxp0WwRbff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "outputId": "bf08d21f-983c-4fc3-daa9-b74ee8c2935b"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "x = df[\"Sex\"]\n",
        "y= df[\"Age\"]\n",
        "plt.xlabel=\"Sex\"\n",
        "plt.ylabel=\"Age\"\n",
        "g=sns.FacetGrid(df, col=\"Sex\")\n",
        "g.map(sns.histplot, \"Age\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Om5PB9UEypez"
      },
      "source": [
        "The correlation shows that Males who ranged from 18-35years of Age survived more than the other age group, where as females who ranged from 18-40years of Age survived more than the others. Also, infants (0-2age) has a high survival rate too."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "_o3O_BObONfd",
        "outputId": "3e9974d1-aae5-43d5-c639-0a46877250b3"
      },
      "source": [
        "sns.barplot(x=\"Pclass\", y=\"Survived\", data=df)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5775817110>"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jlPUCJgCxsqc"
      },
      "source": [
        "This barplot shows that the categorical data \"Pclass\" has a correalation with \"Survival\", People in Pclass 1 survived more than those in other places."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "Kec1ugdOA2Ce",
        "outputId": "704d66c5-48e8-413d-cdd4-49b6b77aaac1"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from seaborn import FacetGrid\n",
        "\n",
        "g = sns.FacetGrid(df, col='Embarked',size=4.5, aspect=1.6)\n",
        "g.map(sns.pointplot, 'Pclass', 'Survived', 'Sex', palette='autumn_r',  order=None, hue_order=None )\n",
        "g.add_legend()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
            "  warnings.warn(msg, UserWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f57736cffd0>"
            ]
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1625.7x324 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_QqZm5U2wXXA"
      },
      "source": [
        "The above plot shows that there is a correlation between Embarked and Survival depending on the gender. More females who were on S and Q survived compared to the males.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "TPN2nm9v056d",
        "outputId": "8769a4df-3a8b-440a-ec63-b98bf2741006"
      },
      "source": [
        "grid = sns.FacetGrid(df, col='Survived', row='Pclass', height=3.2, aspect=1.6)\n",
        "grid.map(plt.hist, 'Age', alpha=.5, bins=20)\n",
        "grid.add_legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f5772ece710>"
            ]
          },
          "metadata": {},
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SJEmNMJxLkiRJjTCcS5IkSY0wnEuSJEmNMJxLkiRJjRg6nCfZJcnNST7RrS9PsiHJpiQXJ9l1+DIlSZKk8TeKkfO3Anf3rb8XOKuqngd8CzhxBM8hSZIkjb2hwnmSZcCrgHO69QAvAz7W7bIOOHqY55AkSZIWi2FHzv8SeCfwH936s4BvV9Uj3fpmYOlMByZZnWRjko1TU1NDliFJmi/7YUlqz8DhPMmvAFur6vODHF9Va6pqsqomJyYmBi1DkjQg+2FJas+SIY49HHh1klcCTwV+Ajgb2DPJkm70fBmwZfgyJUmSpPE38Mh5VZ1aVcuq6gDgGODTVfV64FrgNd1uxwOXD12lJEmStAgsxHXO3wX8XpJN9Oagn7sAzyFJkiSNnWGmtTymqq4DruuW7wUOHcV5JUmSpMXEO4RKkiRJjTCcS5IkSY0wnEuSJEmNMJxLkiRJjTCcS5IkSY0wnEuSJEmNMJxLkiRJjTCcS5IkSY0wnEuSJEmNMJxLkiRJjTCcS5IkSY0wnEuSJEmNMJxLkiRJjTCcS5IkSY1YsqMLkCRJT66zrv7SUMe/7Zd/ekSVSJrOkXNJkiSpEYZzSZIkqREDh/Mk+yW5NsldSe5M8taufa8kVye5p3t85ujKlSRJksbXMCPnjwBvr6qDgMOAk5McBJwCXFNVK4BrunVJkiRJsxg4nFfV/VV1U7f8b8DdwFJgFbCu220dcPSwRUqSJEmLwUiu1pLkAODFwAZgn6q6v9v0ALDPdo5ZDawG2H///UdRhnZig1w5wKsFSMOxH9aghrnai3239MSG/kFokqcDHwd+t6q+27+tqgqomY6rqjVVNVlVkxMTE8OWIUmaJ/thSWrPUOE8yY/TC+YXVNWlXfODSfbttu8LbB2uREmSJGlxGOZqLQHOBe6uqvf3bboCOL5bPh64fPDyJEmSpMVjmDnnhwNvAG5PckvX9m7gTOCSJCcCXwVeO1yJkiRJ0uIwcDivqn8Gsp3NRw56XkmSJGmx8g6hkiRJUiMM55IkSVIjRnKdc0mSNH9eL1zSdI6cS5IkSY0wnEuSJEmNcFqLJEl60jiVR3pihnNJkqRZ+I8KPVmc1iJJkiQ1wnAuSZIkNcJpLZIkDWiYqQ4783NLWjiGc6khg37YOp9RkmbnP2i0M3BaiyRJktQIR86lBeIIjSSNlv2qFgPDuaQFN8gHqlN1JEmLkeFcI9X6qIZzuv+T74XU03q/pZ3fjvx/zD575+Occ0mSJKkRhnNJkiSpEU5r2ck8mVMR/Kr3P/le/KfW3wvnt0uSdmYLFs6TrATOBnYBzqmqMxfquTS71gPVIMbxNQ1qHN+LcXxN426Y/2b+A0laGP693PksyLSWJLsAHwKOAg4Cjk1y0EI8lyRJkjQuFmrk/FBgU1XdC5DkImAVcNcCPd+M/HpbkhYHv2mRtM3O/m1Bqmr0J01eA6ysqt/q1t8AvLSq3tK3z2pgdbd6IPDFAZ5qb+DrQ5a7UFqtrdW6wNoG1WptrdYF41vb16tq5Vx3HkE/PK7v40KztvlrtS6wtkG1Wtuwdc2rH57JDvtBaFWtAdYMc44kG6tqckQljVSrtbVaF1jboFqtrdW6wNq2GbYf9n0cjLXNX6t1gbUNqtXaWqhroS6luAXYr299WdcmSZIkaTsWKpzfCKxIsjzJrsAxwBUL9FySJEnSWFiQaS1V9UiStwCfpHcpxfOq6s4FeKqhpsUssFZra7UusLZBtVpbq3WBtY1Ky7Va22Bara3VusDaBtVqbTu8rgX5QagkSZKk+VuoaS2SJEmS5slwLkmSJDXCcC5JkiQ1wnAuSZIkNcJwLkmSJDXCcC5JkiQ1wnAuSZIkNcJwLkmSJDXCcC5JkiQ1wnAuSZIkNcJwLkmSJDXCcC5JkiQ1wnCueUvyaJJbktyR5KNJdn+Cfd+T5PefzPq2U8frk9yW5PYkn03ywu3stzbJETO075PkE0luTXJXkitHWNs5SQ4awXlOSPLBEZznJd37tCnJB5Jk2HNKGh374LHvg89Icl+S7w17Lu2cDOcaxA+q6kVVdTDwQ+CkHV3QHHwZ+G9V9f8AfwKsmefx/xO4uqpeWFUHAafM5+Aku2xvW1X9VlXdNc96FtKHgTcBK7o/K3dsOZKmsQ8e7z74/wCH7ugitOMYzjWszwDPA0hyXDcycmuSv5++Y5I3Jbmx2/7xbaM9SX6tGwG6Ncn1Xdvzk9zQjQ7dlmTFMEVW1Wer6lvd6npg2TxPsS+wue98t3V1HpHkE32v8YNJTuiWv5LkvUluAt6R5Ia+/Q5Icnu3fF2SySQnJXlf3z6PjcIk+Y2+9+Nvtn3QJHljki915z58nq/pcZLsC/xEVa2vqgLOB44e9rySFox98Bj1wd1rW19V94/iXNo5Gc41sCRLgKOA25M8H/gD4GVV9ULgrTMccmlV/Zdu+93AiV37HwGv6Npf3bWdBJxdVS8CJunrlPue/+Kuo5z+57hZSj8R+Md5vtwPAecmuTbJaUmeM8fjvlFVh1TVmcCuSZZ37a8DLp6278eBX+1bfx1wUZKf7ZYP796PR4HXd0H6j+l9IPw8MOPXskl+cTvv02dn2H0pP/peb+7aJDXGPnhOdrY+WGLJji5AO6XdktzSLX8GOBd4M/DRqvo6QFV9c4bjDk7yp8CewNOBT3bt/wKsTXIJcGnX9jngtCTL6H2g3DP9ZFX1uvkWnuQX6X0w/Px8jquqTyb5KXpTPI4Cbk5y8BwO7e/8L6HXwZ/ZPf5I/VU1leTeJIcB9wA/Q++9ORl4CXBjetO/dwO2Ai8Frquqqe61XQz89Ay1Xwu8aO6vVlLj7IPtgzXGDOcaxA+60YPHZG6/GVwLHF1Vt3ZfOx4BUFUnJXkp8Crg80leUlUfSbKha7syyZur6tPTnvNi4MAZnuf9VXX+9MYkLwDOAY6qqm/MpeB+3YfdR4CPdF+j/gLwID/6DdRTpx32/b7li4GPJrm0d7rHf9gBFwGvBb4AXFZVld6bu66qTp32euY03aT7MDxrhk0PVdXPTWvbwo9+3bysa5PUDvvg8e2DJcO5RubTwGVJ3l9V30iy1wwjN3sA9yf5ceD1dKEvyXOragOwIclRwH5JngHcW1UfSLI/8ILuOR4zn1Gb7hyXAm+oqi/N98UleRmwvqoeSrIH8Fzga8ADwEFJnkJvNOVI4J9nOkdV/WuSR4E/5PFfp25zGXAa8GLgXV3bNcDlSc6qqq1J9qL3Xm4Azk7yLOC7wK8Bt87wvHMetamq+5N8txs52gAcB/zVXI6VtEPZB49BHyyB4VwjUlV3JjkD+Keu87sZOGHabn9IrzOb6h736Nrfl96PjUKvE7yVXqf4hiQP0+t8/2zIEv8IeBbw190I0yNVNTmP418CfDDJI/RGac6pqhsBuq+C76B3NYKbZznPxcD7gOUzbayqbyW5Gzioqm7o2u5K8gfAp5L8GPAwcHJVrU/yHnpfP38buGWmcw7gt+mNsO1Gb17ofOeGSnqS2QePTx+c5H8Bvw7snmQzvdf6nlGcWzuH9C7IIAl619gF1lbVdTu4FEladOyDJa/WIkmSJDXDcC79qH8AvrKji5CkRco+WIverOE8yXlJtia5Y4Ztb09SSfbu1pPe7b43pXfTgkMWomhpoVTVP1TVV3Z0HZK0GNkHS3MbOV/LDLfvTrIf8HJ6v5be5ij+85bfq+ndBlySJEnSHMx6tZaquj7JATNsOgt4J3B5X9sq4Pzutt/rk+yZZN/ZbkO7cuXKuuqqq+ZetSRpJnO62PVM7IclaSQG7oe3GehSiklWAVu6Gxn0b1oK3Ne3vu3W348L50lW0xtdZ//99x+kDEnSEOyHJak98/5BaJLdgXfTu2bpwKpqTVVNVtXkxMTEMKeSJA3AfliS2jPIyPlz6V28f9uo+TLgpiSH0rvb2H59+3rrb0mSJGmO5j1yXlW3V9Wzq+qAqjqA3tSVQ6rqAeAK4Ljuqi2HAd+Zbb65JEmSpJ65XErxQnq3pj0wyeYkJz7B7lcC9wKbgL+ldxtwSZIkSXMwl6u1HDvL9gP6lgs4efiyJEmSpMXHO4RKkiRJjTCcS5IkSY0wnEuSJEmNMJxLkiRJjTCcS5IkSY0wnEuSJEmNMJxLkiRJjTCcS5IkSY0wnEuSJEmNMJxLkiRJjTCcS5IkSY0wnEuSJEmNMJxLkiRJjTCcS5IkSY0wnEuSJEmNMJxLkiRJjTCcS5IkSY2YNZwnOS/J1iR39LW9L8kXktyW5LIke/ZtOzXJpiRfTPKKhSpckiRJGjdzGTlfC6yc1nY1cHBVvQD4EnAqQJKDgGOA53fH/HWSXUZWrSRJkjTGZg3nVXU98M1pbZ+qqke61fXAsm55FXBRVf17VX0Z2AQcOsJ6JUmSpLE1ijnnvwn8Y7e8FLivb9vmru1xkqxOsjHJxqmpqRGUIUmaD/thSWrPUOE8yWnAI8AF8z22qtZU1WRVTU5MTAxThiRpAPbDktSeJYMemOQE4FeAI6uquuYtwH59uy3r2iRJkiTNYqCR8yQrgXcCr66qh/o2XQEck+QpSZYDK4Abhi9TkiRJGn+zjpwnuRA4Atg7yWbgdHpXZ3kKcHUSgPVVdVJV3ZnkEuAuetNdTq6qRxeqeEmSJGmczBrOq+rYGZrPfYL9zwDOGKYoSZIkaTHyDqGSJElSIwznkiRJUiMM55IkSVIjDOeSJElSIwznkiRJUiMM55IkSVIjDOeSJElSIwznkiRJUiMM55IkSVIjDOeSJElSIwznkiRJUiMM55IkSVIjDOeSJElSIwznkiRJUiMM55IkSVIjDOeSJElSIwznkiRJUiMM55IkSVIjZg3nSc5LsjXJHX1teyW5Osk93eMzu/Yk+UCSTUluS3LIQhYvSZIkjZO5jJyvBVZOazsFuKaqVgDXdOsARwEruj+rgQ+PpkxJkiRp/M0azqvqeuCb05pXAeu65XXA0X3t51fPemDPJPuOqlhJkiRpnA0653yfqrq/W34A2KdbXgrc17ff5q7tcZKsTrIxycapqakBy5AkDcp+WJLaM/QPQquqgBrguDVVNVlVkxMTE8OWIUmaJ/thSWrPoOH8wW3TVbrHrV37FmC/vv2WdW2SJEmSZjFoOL8COL5bPh64vK/9uO6qLYcB3+mb/iJJkiTpCSyZbYckFwJHAHsn2QycDpwJXJLkROCrwGu73a8EXglsAh4C3rgANUuSJEljadZwXlXHbmfTkTPsW8DJwxYlSZIkLUbeIVSSJElqhOFckiRJaoThXJIkSWqE4VySJElqhOFckiRJaoThXJIkSWqE4VySJElqhOFckiRJaoThXJIkSWqE4VySJElqhOFckiRJaoThXJIkSWqE4VySJElqhOFckiRJaoThXJIkSWqE4VySJElqhOFckiRJaoThXJIkSWrEUOE8yduS3JnkjiQXJnlqkuVJNiTZlOTiJLuOqlhJkiRpnA0czpMsBX4HmKyqg4FdgGOA9wJnVdXzgG8BJ46iUEmSJGncDTutZQmwW5IlwO7A/cDLgI9129cBRw/5HJIkSdKiMHA4r6otwF8AX6MXyr8DfB74dlU90u22GVg60/FJVifZmGTj1NTUoGVIkgZkPyxJ7RlmWsszgVXAcuA5wNOAlXM9vqrWVNVkVU1OTEwMWoYkaUD2w5LUnmGmtfwS8OWqmqqqh4FLgcOBPbtpLgDLgC1D1ihJkiQtCsOE868BhyXZPUmAI4G7gGuB13T7HA9cPlyJkiRJ0uIwzJzzDfR++HkTcHt3rjXAu4DfS7IJeBZw7gjqlCRJksbektl32b6qOh04fVrzvcChw5xXkiRJWoy8Q6gkSZLUCMO5JEmS1AjDuSRJktQIw7kkSZLUCMO5JEmS1AjDuSRJktQIw7kkSZLUCMO5JEmS1AjDuSRJktQIw7kkSZLUCMO5JEmS1AjDuSRJktQIw7kkSZLUCMO5JEmS1AjDuSRJktQIw7kkSZLUCMO5JEmS1IihwnmSPZN8LMkXktyd5L8m2SvJ1Unu6R6fOapiJUmSpHE27Mj52cBVVfUzwAuBu4FTgGuqagVwTbcuSZIkaRYDh/MkzwB+ATgXoKp+WFXfBlYB67rd1gFHD1ukJEmStBgMM2sSoW8AACAASURBVHK+HJgC/i7JzUnOSfI0YJ+qur/b5wFgn5kOTrI6ycYkG6empoYoQ5I0CPthSWrPMOF8CXAI8OGqejHwfaZNYamqAmqmg6tqTVVNVtXkxMTEEGVIkgZhPyxJ7RkmnG8GNlfVhm79Y/TC+oNJ9gXoHrcOV6IkSZK0OAwczqvqAeC+JAd2TUcCdwFXAMd3bccDlw9VoSRJkrRILBny+P8BXJBkV+Be4I30Av8lSU4Evgq8dsjnkCRJkhaFocJ5Vd0CTM6w6chhzitJkiQtRt4hVJIkSWqE4VySJElqhOFckiRJaoThXJIkSWqE4VySJElqhOFckiRJaoThXJIkSWqE4VySJElqhOFckiRJaoThXJIkSWqE4VySJElqhOFckiRJaoThXJIkSWqE4VySJElqhOFckiRJaoThXJIkSWqE4VySJElqhOFckiRJasTQ4TzJLkluTvKJbn15kg1JNiW5OMmuw5cpSZIkjb9RjJy/Fbi7b/29wFlV9TzgW8CJI3gOSZIkaewNFc6TLANeBZzTrQd4GfCxbpd1wNHDPIckSZK0WAw7cv6XwDuB/+jWnwV8u6oe6dY3A0tnOjDJ6iQbk2ycmpoasgxJ0nzZD0tSewYO50l+BdhaVZ8f5PiqWlNVk1U1OTExMWgZkqQB2Q9LUnuWDHHs4cCrk7wSeCrwE8DZwJ5JlnSj58uALcOXKUmSJI2/gUfOq+rUqlpWVQcAxwCfrqrXA9cCr+l2Ox64fOgqJUmSpEVgIa5z/i7g95JsojcH/dwFeA5JkiRp7AwzreUxVXUdcF23fC9w6CjOK0mSJC0m3iFUkiRJaoThXJIkSWqE4VySJElqhOFckiRJaoThXJIkSWqE4VySJElqhOFckiRJaoThXJIkSWqE4VySJElqhOFckiRJaoThXJIkSWqE4VySJElqhOFckiRJaoThXJIkSWqE4VySJElqhOFckiRJaoThXJIkSWqE4VySJElqxMDhPMl+Sa5NcleSO5O8tWvfK8nVSe7pHp85unIlSZKk8TXMyPkjwNur6iDgMODkJAcBpwDXVNUK4JpuXZIkSdIsBg7nVXV/Vd3ULf8bcDewFFgFrOt2WwccPWyRkiRJ0mIwkjnnSQ4AXgxsAPapqvu7TQ8A+2znmNVJNibZODU1NYoyJEnzYD8sSe0ZOpwneTrwceB3q+q7/duqqoCa6biqWlNVk1U1OTExMWwZkqR5sh+WpPYMFc6T/Di9YH5BVV3aNT+YZN9u+77A1uFKlCRJkhaHYa7WEuBc4O6qen/fpiuA47vl44HLBy9PkiRJWjyWDHHs4cAbgNuT3NK1vRs4E7gkyYnAV4HXDleixt1ZV39pzvu+7Zd/egErkSRJ2rEGDudV9c9AtrP5yEHPK0mSJC1W3iFUkiRJaoThXJIkSWqE4VySJElqhOFckiRJasQwV2uRJEk7mflcIQu8Spb0ZHPkXJIkSWqEI+daMPMdnZEkSVrsDOcC5h6k/XpT0kycKjG+/G8rPbkM55IkNcQwLC1uzjmXJEmSGuHIucbSfEaeHHWSJEmtMJxrXnb0jzx39PNLUmvTTuwXpfFiON8J2RFLkiSNJ+ecS5IkSY0wnEuSJEmNcFqLFj2v8S49+Vqbt63RWej/tv7gX+POcC4tgFH/LmAhPmAW4rcLfhBqoezMYd7fCT2xnfn92Zn/v1S7FmxaS5KVSb6YZFOSUxbqeSRJkqRxsSAj50l2AT4E/DKwGbgxyRVVdddCPJ/0ZNiRozs788iStDPw79h4am1ku7V61KaFmtZyKLCpqu4FSHIRsAownEtjbCHmgjr9RtKTZWf/R5rhfzykqkZ/0uQ1wMqq+q1u/Q3AS6vqLX37rAZWd6sHAl8c4Kn2Br4+ZLkLpdXaWq0LrG1QrdbWal0wvrV9vapWznXnEfTD4/o+LjRrm79W6wJrG1SrtQ1b17z64ZnssB+EVtUaYM0w50iysaomR1TSSLVaW6t1gbUNqtXaWq0LrG2bYfth38fBWNv8tVoXWNugWq2thboW6gehW4D9+taXdW2SJEmStmOhwvmNwIoky5PsChwDXLFAzyVJkiSNhQWZ1lJVjyR5C/BJYBfgvKq6cwGeaqhpMQus1dparQusbVCt1tZqXWBto9JyrdY2mFZra7UusLZBtVrbDq9rQX4QKkmSJGn+FuwmRJIkSZLmx3AuSZIkNcJwLkmSJDXCcC5JkiQ1wnAuSZIkNcJwLkmSJDXCcC5JkiQ1wnAuSZIkNcJwLkmSJDXCcC5JkiQ1wnAuSZIkNcJwLkmSJDXCcC5JkiQ1wnCueUvyaJJbktyR5KNJdn+Cfd+T5PefzPq2U8eqJLd1dW9M8vPb2e+6JAfM0H5gt+2WJHcnWTPC2q5MsucIzjOS9zrJyiRfTLIpySnDnk/SaNkHj30ffF6SrUnuGPZc2jkZzjWIH1TVi6rqYOCHwEk7uqA5uAZ4YVW9CPhN4Jx5Hv8B4Kzudf8s8FfzOTjJLtvbVlWvrKpvz7OeBdHV+SHgKOAg4NgkB+3YqiRNYx88pn1wZy2wckcXoR3HcK5hfQZ4HkCS47qRkVuT/P30HZO8KcmN3faPbxvtSfJr3QjQrUmu79qen+SGbpTktiQrhimyqr5XVdWtPg2oJ9p/BvsCm/vOd3tX5wlJPtj3Gj+R5Ihu+XtJ/neSW4FTk3y0b78jknyiW/5Kkr2TnJnk5L59HhuFSfKO7r27Lckf9+1zWpIvJfln4MB5vqaZHApsqqp7q+qHwEXAqhGcV9LCsA8erz6Yqroe+OYozqWd05IdXYB2XkmW0BthvSrJ84E/AH6uqr6eZK8ZDrm0qv62O/ZPgRPpjX78EfCKqtrS99XiScDZVXVBkl2Bx416JLmYmTvD91fV+TPs/6vAnwPPBl41z5d7FvDpJJ8FPgX83RxGWp4GbKiqt3fv1b1JnlZV3wdeRy/49rsY+Et6I9cArwVekeTlwAp6wTnAFUl+Afg+cAzwInp/l28CPj+9iCSvB94xQ32bquo109qWAvf1rW8GXjrL65S0A9gHj2UfLBnONZDdktzSLX8GOBd4M/DRqvo6QFXN9K/+g7sPhD2BpwOf7Nr/BVib5BLg0q7tc8BpSZbR+0C5Z/rJqup18ym6qi4DLus61T8Bfmkex/5dkk/S+6pxFfDmJC+c5bBHgY93xz+S5Crgvyf5GL0PpndOe46bkzw7yXOACeBbVXVfkrcCLwdu7nZ9Or0Pij2Ay6rqIYAkV2yn9guAC+b6WiU1zz7YPlhjzHCuQfygmzf4mCRzOW4tcHRV3ZrkBOAIgKo6KclL6XWWn0/ykqr6SJINXduVSd5cVZ+e9pzzGrXZpqquT/JTSfbe9kE2F1X1/wHnAeel90Odg4FH+NHpYU/tW/6/VfVo3/pFwFvofV25sar+bYan+SjwGuAn6Y3iQG+k5s+r6m/6d0zyu3Ope56jNluA/frWl3VtktphHzy+fbBkONfIfJreiMj7q+obSfaaYeRmD+D+JD8OvJ4u9CV5blVtADYkOQrYL8kzgHur6gNJ9gde0D3HY+YzapPkecC/VlUlOQR4CvCNeRy/Erimqh5O8pPAs7r6dwN+O8mP0ZsScugTnOaf6H2wvInHf526zcXA3wJ7A/+ta/sk8CdJLqiq7yVZCjwMXE9vtOvP6f1d/u/A30w/4TxHbW4EViRZ3r2+Y4Bfn+OxknYc++Dx6IMlw7lGo6ruTHIG8E9JHqX39d8J03b7Q2ADMNU97tG1vy+9HxuF3i/6bwXeBbwhycPAA8CfDVni/wsc153vB8Dr+n6cNBcvB85O8n+79XdU1QNJHgS+DNwF3E1vzuGMqurR9H6AdAJw/Hb2uTPJHsCWqrq/a/tUkp8FPteNjn0P+I2quqkbuboV2EovWA+l++r3LfQ+jHYBzquqO4c9r6SFZR88Hn0wQJIL6X2rsXeSzcDpVXXuKM6tnUPm93dDGm9JrgNOqKqv7OBSJGnRsQ+WvJSiJEmS1IxZw3me4E5VSd6epJLs3a0nyQfSu7Pgbd28MmlnshZo6WYUkrSYrMU+WIvcXEbO1zLDnaqS7EdvDtjX+pqPond5oRXAauDDw5coPXmqam1jd4qTpEXDPliaww9Cu0seHTDDprPoXSP08r62VcD53Y881ifZM8m+235UsT0rV66sq666au5VS5JmMqfr6c3EfliSRmLgfnibga7WkmQVvV8y3zrt2qoz3V1wKfC4cJ5kNb3Rdfbff/9BypAkDcF+WJLaM+8fhCbZHXg3vdv9Dqyq1lTVZFVNTkxMDHMqSdIA7IclqT2DjJw/F1gObBs1XwbclORQvLugJEmSNLB5j5xX1e1V9eyqOqCqDqA3deWQqnoAuILeTQaS5DDgO7PNN5ckSZLUM5dLKV4IfA44MMnmJCc+we5XAvcCm+jd/va3R1KlJEmStAjM5Wotx86y/YC+5QJOHr4sSZIkafHxDqGSJElSIwznkiRJUiMGus65NJuzrv7SQMe97Zd/esSVSJIk7TwcOZckSZIaYTiXJEmSGmE4lyRJkhphOJckSZIaYTiXJEmSGmE4lyRJkhphOJckSZIaYTiXJEmSGmE4lyRJkhphOJckSZIaYTiXJEmSGmE4lyRJkhphOJckSZIaYTiXJEmSGmE4lyRJkhoxazhPcl6SrUnu6Gt7X5IvJLktyWVJ9uzbdmqSTUm+mOQVC1W4JEmSNG7mMnK+Flg5re1q4OCqegHwJeBUgCQHAccAz++O+esku4ysWkmSJGmMzRrOq+p64JvT2j5VVY90q+uBZd3yKuCiqvr3qvoysAk4dIT1SpIkSWNrFHPOfxP4x255KXBf37bNXdvjJFmdZGOSjVNTUyMoQ5I0H/bDktSeocJ5ktOAR4AL5ntsVa2pqsmqmpyYmBimDEnSAOyHJak9SwY9MMkJwK8AR1ZVdc1bgP36dlvWtUmSJEmaxUAj50lWAu8EXl1VD/VtugI4JslTkiwHVgA3DF+mJEmSNP5mHTlPciFwBLB3ks3A6fSuzvIU4OokAOur6qSqujPJJcBd9Ka7nFxVjy5U8ZIkSdI4mTWcV9WxMzSf+wT7nwGcMUxRkiRJ0mLkHUIlSZKkRhjOJUmSpEYYziVJkqRGGM4lSZKkRhjOJUmSpEYYziVJkqRGGM4lSZKkRhjOJUmSpEYYziVJkqRGGM4lSZKkRhjOJUmSpEYYziVJkqRGGM4lSZKkRhjOJUmSpEYYziVJkqRGGM4lSZKkRhjOJUmSpEbMGs6TnJdka5I7+tr2SnJ1knu6x2d27UnygSSbktyW5JCFLF6SJEkaJ3MZOV8LrJzWdgpwTVWtAK7p1gGOAlZ0f1YDHx5NmZIkSdL4mzWcV9X1wDenNa8C1nXL64Cj+9rPr571wJ5J9h1VsZIkSdI4G3TO+T5VdX+3/ACwT7e8FLivb7/NXdvjJFmdZGOSjVNTUwOWIUkalP2wJLVn6B+EVlUBNcBxa6pqsqomJyYmhi1DkjRP9sOS1J5Bw/mD26ardI9bu/YtwH59+y3r2iRJkiTNYtBwfgVwfLd8PHB5X/tx3VVbDgO+0zf9RZIkSdITWDLbDkkuBI4A9k6yGTgdOBO4JMmJwFeB13a7Xwm8EtgEPAS8cQFqliRJksbSrOG8qo7dzqYjZ9i3gJOHLUqSJElajLxDqCRJktQIw7kkSZLUCMO5JEmS1AjDuSRJktQIw7kkSZLUCMO5JEmS1AjDuSRJktQIw7kkSZLUCMO5JEmS1AjDuSRJktQIw7kkSZLUCMO5JEmS1AjDuSRJktQIw7kkSZLUCMO5JEmS1AjDuSRJktQIw7kkSZLUCMO5JEmS1IihwnmStyW5M8kdSS5M8tQky5NsSLIpycVJdh1VsZIkSdI4GzicJ1kK/A4wWVUHA7sAxwDvBc6qqucB3wJOHEWhkiRJ0rgbdlrLEmC3JEuA3YH7gZcBH+u2rwOOHvI5JEmSpEVh4HBeVVuAvwC+Ri+Ufwf4PPDtqnqk220zsHSm45OsTrIxycapqalBy5AkDch+WJLaM8y0lmcCq4DlwHOApwEr53p8Va2pqsmqmpyYmBi0DEnSgOyHJak9w0xr+SXgy1U1VVUPA5cChwN7dtNcAJYBW4asUZIkSVoUhgnnXwMOS7J7kgBHAncB1wKv6fY5Hrh8uBIlSZKkxWGYOecb6P3w8ybg9u5ca4B3Ab+XZBPwLODcEdQpSZIkjb0ls++yfVV1OnD6tOZ7gUOHOa8kSZK0GHmHUEmSJKkRhnNJkiSpEYZzSZIkqRGGc0mSJKkRhnNJkiSpEYZzSZIkqRGGc0n6/9u7/1i767uO48+XLd0cKKWsqZWCxYxB6pRfDQNZlgmiDBcg2iyQhTSmpv8wBVwyQROTxZiMxIw1Zi5pgI1MAtuASdMYsHbMZP5RaKE4aCkgsFFCaTfH0JnIkLd/nG/hpraU+z333PO55z4fyc093+/58X3d8/3e93mf7/dzzleSpEbYnEuSJEmNsDmXJEmSGmFzLkmSJDXC5lySJElqhM25JEmS1Aibc0mSJKkRNueSJElSI2zOJUmSpEbYnEuSJEmNsDmXJEmSGmFzLkmSJDViqOY8yeIk9yR5KsnuJBckWZJkS5Jnut8nzFRYSZIkaZINu+d8A/BAVZ0BnAnsBm4EtlbVacDWblqSJEnSUfRuzpMcD3wUuA2gql6vqleBK4A7upvdAVw5bEhJkiRpPhhmz/mpwAHgK0keS3JrkmOBZVX1cnebfcCyw905yfok25NsP3DgwBAxJEl9WIclqT3DNOcLgXOAL1fV2cBPOWQIS1UVUIe7c1VtrKrVVbV66dKlQ8SQJPVhHZak9gzTnO8F9lbVtm76HgbN+itJlgN0v/cPF1GSJEmaH3o351W1D3gxyendrIuBXcAmYG03by1w/1AJJUmSpHli4ZD3/2PgziSLgOeAP2TQ8H8jyTrg+8Anh1yGJEmSNC8M1ZxX1U5g9WGuuniYx5UkSZLmI88QKkmSJDXC5lySJElqhM25JEmS1Aibc0mSJKkRNueSJElSI2zOJUmSpEbYnEuSJEmNsDmXJEmSGmFzLkmSJDXC5lySJElqhM25JEmS1Aibc0mSJKkRNueSJElSI2zOJUmSpEbYnEuSJEmNsDmXJEmSGrFw3AE0O27Z8nSv+91wyQdnOIkkadym+5rga4E0e4bec55kQZLHkmzupk9Nsi3Js0m+nmTR8DElSZKkyTcTw1quA3ZPmb4ZuKWqPgD8GFg3A8uQJEmSJt5QzXmSFcDvAbd20wEuAu7pbnIHcOUwy5AkSZLmi2H3nH8R+CzwZjd9IvBqVb3RTe8FTjrcHZOsT7I9yfYDBw4MGUOSNF3WYUlqT+/mPMkngP1VtaPP/atqY1WtrqrVS5cu7RtDktSTdViS2jPMt7VcCFye5DLgvcAvAhuAxUkWdnvPVwAvDR9T0P8bVyRJkjQ39N5zXlU3VdWKqloJXAV8u6o+BTwErOlutha4f+iUkiRJ0jwwipMQ/Rnwp0meZTAG/bYRLEOSJEmaODNyEqKq+g7wne7yc8B5M/G4kiRJ0nziGUL1jhznLkmSNHtGMaxFkiRJUg8255IkSVIjbM4lSZKkRticS5IkSY2wOZckSZIaYXMuSZIkNcLmXJIkSWqEzbkkSZLUCJtzSZIkqRE255IkSVIjbM4lSZKkRticS5IkSY2wOZckSZIaYXMuSZIkNcLmXJIkSWqEzbkkSZLUCJtzSZIkqRG9m/MkJyd5KMmuJE8mua6bvyTJliTPdL9PmLm4kiRJ0uQaZs/5G8BnqmoVcD5wbZJVwI3A1qo6DdjaTUuSJEk6it7NeVW9XFWPdpf/E9gNnARcAdzR3ewO4MphQ0qSJEnzwcKZeJAkK4GzgW3Asqp6ubtqH7DsCPdZD6wHOOWUU2YihibALVue7nW/Gy754AwnkSafdViS2jN0c57kOOBe4Pqqei3JW9dVVSWpw92vqjYCGwFWr1592NtIo+abAc1n1mFJas9Q39aS5BgGjfmdVXVfN/uVJMu765cD+4eLKEmSJM0Pw3xbS4DbgN1V9YUpV20C1naX1wL3948nSZIkzR/DDGu5ELgG+F6Snd28Pwc+D3wjyTrg+8Anh4soHV3f4SmSJEkt6d2cV9V3gRzh6ov7Pq4kSZI0X3mGUEmSJKkRNueSJElSI2zOJUmSpEbYnEuSJEmNsDmXJEmSGjH0GUJb4tkeJUmSNJdNVHM+23wzIEmSpJlkcy71MI6THvmmTpq7plszpvv/7onYpMnhmHNJkiSpEe45lyQNZdR7hSVpPrE5lyTNqhabeYeFSGqFzbkkSY3xzYI0fznmXJIkSWqEe84lSdI7anEokjSpbM6Z/cOHHq5UH36vviRJk89hLZIkSVIjbM4lSZKkRticS5IkSY0Y2ZjzJJcCG4AFwK1V9flRLUuS+nAc/9zghxHnntn4bJXrWZNqJM15kgXAl4BLgL3AI0k2VdWuUSxP0pFN+geQfYHWoSZ9m9fAqNfzXK8tfZ6fuf43T4pRDWs5D3i2qp6rqteBu4ErRrQsSZIkaSKkqmb+QZM1wKVV9Ufd9DXAh6vq01Nusx5Y302eDuzpsaj3Az8cMu6otJqt1Vxgtr5azdZqLpjcbD+sqkvf7Y1noA5P6vM4amabvlZzgdn6ajXbsLmmVYcPZ2zfc15VG4GNwzxGku1VtXqGIs2oVrO1mgvM1ler2VrNBWY7aNg67PPYj9mmr9VcYLa+Ws3WQq5RDWt5CTh5yvSKbp4kSZKkIxhVc/4IcFqSU5MsAq4CNo1oWZIkSdJEGMmwlqp6I8mngQcZfJXi7VX15AgWNdSwmBFrNVurucBsfbWardVcYLaZ0nJWs/XTarZWc4HZ+mo129hzjeQDoZIkSZKmzzOESpIkSY2wOZckSZIaMSeb8ySXJtmT5NkkNzaQ5/Yk+5M8MWXekiRbkjzT/T5hDLlOTvJQkl1JnkxyXUPZ3pvk4SSPd9k+180/Ncm2bt1+vftA8axLsiDJY0k2N5brhSTfS7IzyfZu3tjXZ5djcZJ7kjyVZHeSC1rIluT07vk6+PNakusbyXZDt/0/keSu7v+iiW3taFqqw9bgXtmarsFdFuvw9HJZg/vla64Oz7nmPMkC4EvAx4FVwNVJVo03FV8FDv3C+RuBrVV1GrC1m55tbwCfqapVwPnAtd1z1UK2/wEuqqozgbOAS5OcD9wM3FJVHwB+DKwbQzaA64DdU6ZbyQXwW1V11pTvYW1hfQJsAB6oqjOAMxk8f2PPVlV7uufrLOBc4L+Bb407W5KTgD8BVlfVhxh8eP4q2trWDqvBOvxVrMHT1XoNBuvwdFmDp6nZOlxVc+oHuAB4cMr0TcBNDeRaCTwxZXoPsLy7vBzY00DG+4FLWssGvA94FPgwg7NyLTzcup7FPCsYFIqLgM1AWsjVLfsF4P2HzBv7+gSOB56n+5B5S9kOyfM7wL+2kA04CXgRWMLgm7M2A7/byrZ2lOzN1WFr8FC5mqrB3bKtw9PLZA3ul6fJOjzn9pzz9hN50N5uXmuWVdXL3eV9wLJxhkmyEjgb2EYj2bpDljuB/cAW4N+BV6vqje4m41q3XwQ+C7zZTZ/YSC6AAv4pyY4MTr0ObazPU4EDwFe6w9C3Jjm2kWxTXQXc1V0ea7aqegn4G+AHwMvAT4AdtLOtvZO5UIeb2vaswdNmHZ4ea3APrdbhudiczzk1eOs1tu+sTHIccC9wfVW9NvW6cWarqv+twWGuFcB5wBnjyDFVkk8A+6tqx7izHMFHquocBsMJrk3y0alXjnF9LgTOAb5cVWcDP+WQQ5QN/B8sAi4HvnnodePI1o2vvILBi+ovA8fy/4dmaAY0sO1Zg6fBOtyLNbhfpibr8Fxszl8CTp4yvaKb15pXkiwH6H7vH0eIJMcweFG4s6ruaynbQVX1KvAQg0NHi5McPDnWONbthcDlSV4A7mZwSHVDA7mAt97lU1X7GYzZO4821udeYG9Vbeum72HwQtFCtoM+DjxaVa900+PO9tvA81V1oKp+BtzHYPtrYls7irlQh8e9fumWbQ2ePuvw9FmD+2myDs/F5vwR4LTuk7SLGBwi2TTmTIezCVjbXV7LYKzhrEoS4DZgd1V9obFsS5Ms7i7/PINxmLsZvECsGVe2qrqpqlZU1UoG29a3q+pT484FkOTYJL9w8DKDsXtP0MD6rKp9wItJTu9mXQzsaiHbFFfz9uFUGH+2HwDnJ3lf97968Dkb+7b2LsyFOjzu9WsN7sk6PH3W4N7arMOzOcB9pn6Ay4CnGYyP+4sG8tzFYKzSzxi8e13HYHzcVuAZ4J+BJWPI9REGh4n+DdjZ/VzWSLbfAB7rsj0B/GU3/1eBh4FnGRz6es8Y1+vHgM2t5OoyPN79PHlw229hfXY5zgK2d+v0H4ATGsp2LPAj4Pgp88aeDfgc8FT3P/A14D0tbGvvMnszddga3Ctb8zW4y2MdfvfZrMH9sjVXh9MFkyRJkjRmc3FYiyRJkjSRbM4lSZKkRticS5IkSY2wOZckSZIaYXMuSZIkNcLmXOokuTJJJWniLHmSNN9YhyWbc2mqq4Hvdr8lSbPPOqx5z+ZcApIcx+CEIesYnJGOJD+X5O+SPJVkS5J/TLKmu+7cJP+SZEeSBw+egliS1I91WBqwOZcGrgAeqKqngR8lORf4fWAlsAq4BrgAIMkxwN8Ca6rqXOB24K/HEVqSJoh1WAIWjjuA1IirgQ3d5bu76YXAN6vqTWBfkoe6608HPgRsSQKwgMGpwyVJ/VmHJWzOJZIsAS4Cfj1JMSjyBXzrSHcBnqyqC2YpoiRNNOuw9DaHtUiwBvhaVf1KVa2sqpOB54H/AP6gG/O4DPhYd/s9wNIkbx1eTfJr4wguSRPCOix1bM6lwaHTQ/fO3Av8ErAX2AX8PfAoACzSnQAAAH5JREFU8JOqep3BC8nNSR4HdgK/OXtxJWniWIelTqpq3BmkZiU5rqr+K8mJwMPAhVW1b9y5JGm+sA5rvnHMufTONidZDCwC/soXBEmaddZhzSvuOZckSZIa4ZhzSZIkqRE255IkSVIjbM4lSZKkRticS5IkSY2wOZckSZIa8X8MyttOPuOO2AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 745.28x691.2 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4QNTVHzy1R4U"
      },
      "source": [
        "The plot also has shown the what the previous barplot showed us, In Pclass 1 more people survived and Pclass 3 there are more chances of one dying. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "id": "-YVEyJnj_kuw",
        "outputId": "9bd545b1-dc7f-414c-c2f8-8cc72ef519e6"
      },
      "source": [
        "data =[df]\n",
        "titles = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n",
        "\n",
        "for dataset in data:\n",
        "    # extract titles\n",
        "    dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
        "    # replace titles with a more common title or as Rare\n",
        "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Don',\\\n",
        "                                              'Sir', 'Jonkheer', 'Dona'], 'Royalty')\n",
        "    dataset['Title'] = dataset['Title'].replace(['Capt', 'Col','Rev',\\\n",
        "                                              'Dr', 'Major'], 'Officer')\n",
        "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
        "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
        "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
        "  \n",
        "    \n",
        "df.head(20)"
      ],
      "execution_count": 12,
      "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>Mr</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>Mrs</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>Miss</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>Mrs</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>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Moran, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330877</td>\n",
              "      <td>8.4583</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>McCarthy, Mr. Timothy J</td>\n",
              "      <td>male</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17463</td>\n",
              "      <td>51.8625</td>\n",
              "      <td>E46</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Palsson, Master. Gosta Leonard</td>\n",
              "      <td>male</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>349909</td>\n",
              "      <td>21.0750</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Master</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
              "      <td>female</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>347742</td>\n",
              "      <td>11.1333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
              "      <td>female</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>237736</td>\n",
              "      <td>30.0708</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "      <td>Mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Sandstrom, Miss. Marguerite Rut</td>\n",
              "      <td>female</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>PP 9549</td>\n",
              "      <td>16.7000</td>\n",
              "      <td>G6</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Bonnell, Miss. Elizabeth</td>\n",
              "      <td>female</td>\n",
              "      <td>58.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>113783</td>\n",
              "      <td>26.5500</td>\n",
              "      <td>C103</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Saundercock, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>A/5. 2151</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Andersson, Mr. Anders Johan</td>\n",
              "      <td>male</td>\n",
              "      <td>39.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>347082</td>\n",
              "      <td>31.2750</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
              "      <td>female</td>\n",
              "      <td>14.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>350406</td>\n",
              "      <td>7.8542</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Hewlett, Mrs. (Mary D Kingcome)</td>\n",
              "      <td>female</td>\n",
              "      <td>55.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>248706</td>\n",
              "      <td>16.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>17</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Rice, Master. Eugene</td>\n",
              "      <td>male</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>382652</td>\n",
              "      <td>29.1250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>Master</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Williams, Mr. Charles Eugene</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>244373</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Vander Planke, Mrs. Julius (Emelia Maria Vande...</td>\n",
              "      <td>female</td>\n",
              "      <td>31.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>345763</td>\n",
              "      <td>18.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Mrs</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Masselmani, Mrs. Fatima</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2649</td>\n",
              "      <td>7.2250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "      <td>Mrs</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    PassengerId  Survived  Pclass  ... Cabin Embarked   Title\n",
              "0             1         0       3  ...   NaN        S      Mr\n",
              "1             2         1       1  ...   C85        C     Mrs\n",
              "2             3         1       3  ...   NaN        S    Miss\n",
              "3             4         1       1  ...  C123        S     Mrs\n",
              "4             5         0       3  ...   NaN        S      Mr\n",
              "5             6         0       3  ...   NaN        Q      Mr\n",
              "6             7         0       1  ...   E46        S      Mr\n",
              "7             8         0       3  ...   NaN        S  Master\n",
              "8             9         1       3  ...   NaN        S     Mrs\n",
              "9            10         1       2  ...   NaN        C     Mrs\n",
              "10           11         1       3  ...    G6        S    Miss\n",
              "11           12         1       1  ...  C103        S    Miss\n",
              "12           13         0       3  ...   NaN        S      Mr\n",
              "13           14         0       3  ...   NaN        S      Mr\n",
              "14           15         0       3  ...   NaN        S    Miss\n",
              "15           16         1       2  ...   NaN        S     Mrs\n",
              "16           17         0       3  ...   NaN        Q  Master\n",
              "17           18         1       2  ...   NaN        S      Mr\n",
              "18           19         0       3  ...   NaN        S     Mrs\n",
              "19           20         1       3  ...   NaN        C     Mrs\n",
              "\n",
              "[20 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "eDSX8lFMCt_6",
        "outputId": "7843c785-2b04-4437-e34c-3ced78f0a812"
      },
      "source": [
        "sns.barplot(x=\"Title\",y=\"Age\", data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f577263de90>"
            ]
          },
          "metadata": {},
          "execution_count": 141
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AcCebaOOGAq3"
      },
      "source": [
        "The above barplot is showing that Officers above the age of 40years survived, followed by Royalty and Mrs. Master has lowest survival rate."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "VemBItiTFQ9I",
        "outputId": "74bf48e0-1cc7-4dff-87e1-0989e7b69f36"
      },
      "source": [
        "sns.barplot(x=\"Title\", y=\"Survived\",data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5772600c10>"
            ]
          },
          "metadata": {},
          "execution_count": 142
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "02BpcBf028fx",
        "outputId": "852eca96-1488-4096-a21f-619be6c3e695"
      },
      "source": [
        "# plot a correlation map the database df \n",
        "def plot_correlation_map(df):\n",
        "\n",
        "  # input the correlation for the database\n",
        "  corr=df.corr()\n",
        "\n",
        "  # Set up matplot\n",
        "  s,ax=plt.subplots(figsize=(12,10))\n",
        "\n",
        "  # set up diverging color map \n",
        "  cmap= sns.diverging_palette(250,15,as_cmap=True)\n",
        "\n",
        "  # \n",
        "  s= sns.heatmap(corr,cmap=cmap, square=True, cbar_kws={'shrink':.9}, ax=ax, annot=True, annot_kws={\"fontsize\":12})\n",
        "\n",
        "plot_correlation_map(df)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q0zdKBKNq_tx"
      },
      "source": [
        "Correlation coefficient is used to check values which have a relation with each other. When the value between two variable is 1, that means one variable increases, the other one also increases. Note that when you have 1 as the correlation between two variables, its a perfect correlation and we have can see perfect correlations in the following variables.\n",
        "\n",
        "familySize and relatives\n",
        "\n",
        "\n",
        " Also the closer the correlation is to 1 between two variables, that means both variables are correlated.\n",
        "\n",
        "Example of such are: Fare_Per_Person and Fare which have a correlation of 0.84.\n",
        " Relatives and Parch which have a correlation of 0.78.\n",
        "\n",
        "When the value is 0, there is no correlation. This simply means that the variables changes in a random manner with respect to each other.\n",
        "From the map, we see that \"Survived\" does not correlate with any other variables "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "AxrqSrrivn6O",
        "outputId": "503c263f-5477-41a5-e444-6bcf5f4c9cb5"
      },
      "source": [
        "g = df.groupby(\"Pclass\").mean()\n",
        "g"
      ],
      "execution_count": 7,
      "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>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>461.597222</td>\n",
              "      <td>0.629630</td>\n",
              "      <td>38.233441</td>\n",
              "      <td>0.416667</td>\n",
              "      <td>0.356481</td>\n",
              "      <td>84.154687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>445.956522</td>\n",
              "      <td>0.472826</td>\n",
              "      <td>29.877630</td>\n",
              "      <td>0.402174</td>\n",
              "      <td>0.380435</td>\n",
              "      <td>20.662183</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>439.154786</td>\n",
              "      <td>0.242363</td>\n",
              "      <td>25.140620</td>\n",
              "      <td>0.615071</td>\n",
              "      <td>0.393075</td>\n",
              "      <td>13.675550</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        PassengerId  Survived        Age     SibSp     Parch       Fare\n",
              "Pclass                                                                 \n",
              "1        461.597222  0.629630  38.233441  0.416667  0.356481  84.154687\n",
              "2        445.956522  0.472826  29.877630  0.402174  0.380435  20.662183\n",
              "3        439.154786  0.242363  25.140620  0.615071  0.393075  13.675550"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242
        },
        "id": "MSCviuC3bSuF",
        "outputId": "45269783-de24-4de3-d157-968b0202fe10"
      },
      "source": [
        "grid= sns.FacetGrid(df, col=\"Pclass\")\n",
        "grid.map(sns.histplot, \"Survived\")\n",
        "grid.add_legend()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f45bd9a3b10>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 656x216 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 932
        },
        "id": "nxUsRImhOW1G",
        "outputId": "80e27c1a-5fea-43b5-b81b-e55d40b505e0"
      },
      "source": [
        "data = [df]\n",
        "for dataset in data:\n",
        "    dataset['familySize']= dataset['SibSp'] + dataset['Parch']\n",
        "    dataset.loc[dataset['familySize'] > 0, \"alone\"] = 0\n",
        "    dataset.loc[dataset['familySize'] == 0, \"alone\"] = 1\n",
        "df"
      ],
      "execution_count": null,
      "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",
              "      <th>relatives</th>\n",
              "      <th>alone</th>\n",
              "      <th>Fare_Per_Person</th>\n",
              "      <th>familySize</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>Mr</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</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>Mrs</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>1</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>Miss</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>0</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>Mrs</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "      <td>1</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>Mr</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Officer</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Miss</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Behr, Mr. Karl Howell</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>111369</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "      <td>Mr</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>Mr</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 17 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     PassengerId  Survived  Pclass  ... alone Fare_Per_Person  familySize\n",
              "0              1         0       3  ...     0               3           1\n",
              "1              2         1       1  ...     0              35           1\n",
              "2              3         1       3  ...     1               7           0\n",
              "3              4         1       1  ...     0              26           1\n",
              "4              5         0       3  ...     1               8           0\n",
              "..           ...       ...     ...  ...   ...             ...         ...\n",
              "886          887         0       2  ...     1              13           0\n",
              "887          888         1       1  ...     1              30           0\n",
              "888          889         0       3  ...     0               5           3\n",
              "889          890         1       1  ...     1              30           0\n",
              "890          891         0       3  ...     1               7           0\n",
              "\n",
              "[891 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "svp2eGapDDi7",
        "outputId": "a08ebea5-bb88-4b95-8fb7-fa7a4e9bd1bb"
      },
      "source": [
        "data = [df]\n",
        "for dataset in data:\n",
        "    dataset['familySize']= dataset['SibSp'] + dataset['Parch']\n",
        "    dataset.loc[dataset['familySize'] > 0, \"alone\"] = 0\n",
        "    dataset.loc[dataset['familySize'] == 0, \"alone\"] = 1\n",
        "  \n",
        "df['alone'].value_counts()\n",
        "    \n",
        "  \n",
        "  \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    537\n",
              "0    354\n",
              "Name: alone, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kxJiu9hoS46Z"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 906
        },
        "id": "cQKndaCiJoaA",
        "outputId": "cd7a0e2f-315f-4846-a961-4e2f47361df1"
      },
      "source": [
        "data= [df]\n",
        "for dataset in data:\n",
        "    dataset['Fare_Per_Person'] = dataset['Fare']/(dataset['familySize']+1)\n",
        "    dataset['Fare_Per_Person'] = dataset['Fare_Per_Person'].astype(int)\n",
        "\n",
        "df.head(10)"
      ],
      "execution_count": null,
      "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",
              "      <th>relatives</th>\n",
              "      <th>alone</th>\n",
              "      <th>Fare_Per_Person</th>\n",
              "      <th>familySize</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>Mr</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</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>Mrs</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>1</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>Miss</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>0</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>Mrs</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>26</td>\n",
              "      <td>1</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>Mr</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Moran, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330877</td>\n",
              "      <td>8.4583</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "      <td>Mr</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>McCarthy, Mr. Timothy J</td>\n",
              "      <td>male</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17463</td>\n",
              "      <td>51.8625</td>\n",
              "      <td>E46</td>\n",
              "      <td>S</td>\n",
              "      <td>Mr</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>51</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Palsson, Master. Gosta Leonard</td>\n",
              "      <td>male</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>349909</td>\n",
              "      <td>21.0750</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Master</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
              "      <td>female</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>347742</td>\n",
              "      <td>11.1333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "      <td>Mrs</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
              "      <td>female</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>237736</td>\n",
              "      <td>30.0708</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "      <td>Mrs</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PassengerId  Survived  Pclass  ... alone Fare_Per_Person  familySize\n",
              "0            1         0       3  ...     0               3           1\n",
              "1            2         1       1  ...     0              35           1\n",
              "2            3         1       3  ...     1               7           0\n",
              "3            4         1       1  ...     0              26           1\n",
              "4            5         0       3  ...     1               8           0\n",
              "5            6         0       3  ...     1               8           0\n",
              "6            7         0       1  ...     1              51           0\n",
              "7            8         0       3  ...     0               4           4\n",
              "8            9         1       3  ...     0               3           2\n",
              "9           10         1       2  ...     0              15           1\n",
              "\n",
              "[10 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wSbt-9EOS5HG"
      },
      "source": [
        "The column familySize has been used to create a new feature fare_per_person. This feature can be used to find total amount titanic got for the journey."
      ]
    }
  ]
}