casestudypreprocssing.ipynb

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        "<a href=\"https://colab.research.google.com/github/josmyrose/Assignment1/blob/main/casestudypreprocssing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "Do the following on titanic dataset.\n",
        "1. Load the dataset into python environment\n",
        "2. Make ‘PassengerId’ as the index column\n",
        "3. Check the basic details of the dataset\n",
        "4. Fill in all the missing values present in all the columns in the dataset\n",
        "5. Check and handle outliers in at least 3 columns in the dataset\n",
        "6. Do min max scaling on the feature set (Take ‘Survived’ as target)\n",
        "\n",
        "\n"
      ],
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        "id": "nM2Q_kdMpQaj"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Load the dataset into python environment\n"
      ],
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        "id": "s94h5kwTq3ih"
      }
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    {
      "cell_type": "code",
      "source": [
        "df=pd.read_csv('/content/drive/MyDrive/titanic_dataset.csv')"
      ],
      "metadata": {
        "id": "5750thAnptla"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Details of Dataset"
      ],
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        "id": "qXEmy7mjrYts"
      },
      "execution_count": 3,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "df.head()"
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              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
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              "3            4         1       1   \n",
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              "                                                Name     Sex   Age  SibSp  \\\n",
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              "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                           Allen, Mr. William Henry    male  35.0      0   \n",
              "\n",
              "   Parch            Ticket     Fare Cabin Embarked  \n",
              "0      0         A/5 21171   7.2500   NaN        S  \n",
              "1      0          PC 17599  71.2833   C85        C  \n",
              "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
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              "<bound method DataFrame.info of      PassengerId  Survived  Pclass  \\\n",
              "0              1         0       3   \n",
              "1              2         1       1   \n",
              "2              3         1       3   \n",
              "3              4         1       1   \n",
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              "                                                  Name     Sex   Age  SibSp  \\\n",
              "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
              "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
              "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
              "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
              "4                             Allen, Mr. William Henry    male  35.0      0   \n",
              "..                                                 ...     ...   ...    ...   \n",
              "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
              "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
              "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
              "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
              "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
              "\n",
              "     Parch            Ticket     Fare Cabin Embarked  \n",
              "0        0         A/5 21171   7.2500   NaN        S  \n",
              "1        0          PC 17599  71.2833   C85        C  \n",
              "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "3        0            113803  53.1000  C123        S  \n",
              "4        0            373450   8.0500   NaN        S  \n",
              "..     ...               ...      ...   ...      ...  \n",
              "886      0            211536  13.0000   NaN        S  \n",
              "887      0            112053  30.0000   B42        S  \n",
              "888      2        W./C. 6607  23.4500   NaN        S  \n",
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              "[891 rows x 12 columns]>"
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      "source": [
        "2. Make ‘PassengerId’ as the index column\n"
      ],
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        "id": "wRdM2tx9q-hh"
      }
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      "source": [
        "#The index attribute is used to display the row labels of a data frame object\n",
        "df.set_index('PassengerId',inplace= True)"
      ],
      "metadata": {
        "id": "RJaxHWverNQR"
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      "execution_count": 6,
      "outputs": []
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              "             Survived  Pclass  \\\n",
              "PassengerId                     \n",
              "1                   0       3   \n",
              "2                   1       1   \n",
              "3                   1       3   \n",
              "4                   1       1   \n",
              "5                   0       3   \n",
              "\n",
              "                                                          Name     Sex   Age  \\\n",
              "PassengerId                                                                    \n",
              "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
              "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
              "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
              "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
              "5                                     Allen, Mr. William Henry    male  35.0   \n",
              "\n",
              "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
              "PassengerId                                                          \n",
              "1                1      0         A/5 21171   7.2500   NaN        S  \n",
              "2                1      0          PC 17599  71.2833   C85        C  \n",
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              "      <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>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e4e17792-35ea-4cbf-a3e2-f16e57065b4e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-e4e17792-35ea-4cbf-a3e2-f16e57065b4e button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e4e17792-35ea-4cbf-a3e2-f16e57065b4e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3. Check the basic details of the dataset"
      ],
      "metadata": {
        "id": "PBWUDaHvrwKW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#No.of rows and columns in the dataset\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hBXr_Pger7wa",
        "outputId": "30d6df62-7ab3-4aff-d5c2-3f7397df6ef9"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Get the details of dataset\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "id": "tEJm9r7XsOxp",
        "outputId": "2bb7f14a-c241-49ed-f012-d98ee03e0017"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Survived  Pclass  \\\n",
              "PassengerId                     \n",
              "1                   0       3   \n",
              "2                   1       1   \n",
              "3                   1       3   \n",
              "4                   1       1   \n",
              "5                   0       3   \n",
              "\n",
              "                                                          Name     Sex   Age  \\\n",
              "PassengerId                                                                    \n",
              "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
              "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
              "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
              "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
              "5                                     Allen, Mr. William Henry    male  35.0   \n",
              "\n",
              "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
              "PassengerId                                                          \n",
              "1                1      0         A/5 21171   7.2500   NaN        S  \n",
              "2                1      0          PC 17599  71.2833   C85        C  \n",
              "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
              "4                1      0            113803  53.1000  C123        S  \n",
              "5                0      0            373450   8.0500   NaN        S  "
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
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              "      <th>Cabin</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassengerId</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></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>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",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>PC 17599</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C85</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>STON/O2. 3101282</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>113803</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Allen, Mr. William Henry</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>373450</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
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              "  </tbody>\n",
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              "</div>\n",
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              "\n",
              "      <script>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9c9a715e-6a32-4082-b89f-72c338cbce0b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4.Fill in all the missing values present in all the columns in the dataset"
      ],
      "metadata": {
        "id": "vatKSZFosvfR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Find the columns having null values"
      ],
      "metadata": {
        "id": "lzJphDDXs6-8"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qg-wFwxrtEkf",
        "outputId": "8b93b33b-7e0e-45c1-ab38-ee21d6b0e344"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "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": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "TO7e3nmDq4yO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#To drop Name and Ticket\n",
        "df.drop(['Name','Ticket'],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "65lhHnPm_PZa"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Find datatype of columns ,its used for knowing datatype of null values\n",
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IbYHWC2eG5Gp",
        "outputId": "194c6784-2842-4faa-8792-dd2a33d0a5db"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived      int64\n",
              "Pclass        int64\n",
              "Sex          object\n",
              "Age         float64\n",
              "SibSp         int64\n",
              "Parch         int64\n",
              "Fare        float64\n",
              "Cabin        object\n",
              "Embarked     object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dgraph=df.select_dtypes(include=['float'])\n",
        "dgraph.hist(figsize=(20,15))\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 656
        },
        "id": "E8WBoKDbHb70",
        "outputId": "ec8a3c2f-24a2-40f5-faa7-54b845896842"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1080 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The graph shows that people having age between 20 and 3o.The null field can be filled with mean of age."
      ],
      "metadata": {
        "id": "QX_yUJSLHrrQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "medianage=df.groupby(['Pclass'])['Age'].median"
      ],
      "metadata": {
        "id": "Il62e4YHfDWx"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age']=df['Age'].fillna(medianage)"
      ],
      "metadata": {
        "id": "L0TqaXTDMsv1"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qNvjQaZvQwU3",
        "outputId": "22d31dce-6b54-48e8-ca4a-26d24910baf7"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived      0\n",
              "Pclass        0\n",
              "Sex           0\n",
              "Age           0\n",
              "SibSp         0\n",
              "Parch         0\n",
              "Fare          0\n",
              "Cabin       687\n",
              "Embarked      2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "5.Check and handle outliers in at least 3 columns in the dataset"
      ],
      "metadata": {
        "id": "D7h-LtmRW6Fp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot(kind ='box',subplots =True,layout = (2,4), sharey =False, figsize =(10,8))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "SoOxLFZDXYPC",
        "outputId": "faa1f34e-0944-43d6-eb60-55462841a72f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here Sibsp,Parch and Fare have outliers ."
      ],
      "metadata": {
        "id": "u0ksUD_zsrLv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#df['Age']=df['Age'].astype('Int64')"
      ],
      "metadata": {
        "id": "RMhItNF2vlrO"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plt.boxplot(df['Age'].astype(int))\n",
        "plt.title('find outliers in beer servings')"
      ],
      "metadata": {
        "id": "7t6dSHAGYLfk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "outputId": "0da7ae34-1948-417b-9363-252d0f9d023e"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'find outliers in beer servings')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Handling outliers "
      ],
      "metadata": {
        "id": "h6za3K_0Nh04"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Q1=np.percentile(df['SibSp'],25,interpolation='midpoint')\n",
        "Q2=np.percentile(df['SibSp'],50,interpolation='midpoint')\n",
        "Q3=np.percentile(df['SibSp'],75,interpolation='midpoint')"
      ],
      "metadata": {
        "id": "kKsesMjYNZhm"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Q2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BgU1vv3GN4Pw",
        "outputId": "7dfc0c56-b3a3-4cab-ff7e-16b7387f2ba1"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "q1= df['Fare'].quantile(.25)\n",
        "q3 = df['Fare'].quantile(.75)\n",
        "iqr = q3 -q1\n",
        "up = q3 +1.5*iqr\n",
        "print(\"q3,iqr,up\\n\",q3,iqr,up)\n",
        "df[df['Fare']>up].groupby(['Survived','Sex'])['Fare'].describe()\n",
        "q3,iqr,up"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxXPzfzNHtox",
        "outputId": "51d0cf1b-73b8-42d5-d176-fa2336149660"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "q3,iqr,up\n",
            " 31.0 23.0896 65.6344\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(31.0, 23.0896, 65.6344)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df[df['Fare']>up].groupby(['Survived','Sex'])['Fare'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "b8qvr_OZJVqU",
        "outputId": "cf544f2a-db46-400d-aad4-d50e9f04e8d2"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 count        mean         std      min        25%        50%  \\\n",
              "Survived Sex                                                                    \n",
              "0        female    5.0  102.350000   44.913250  69.5500  69.550000   69.55000   \n",
              "         male     32.0  114.791403   64.406333  66.6000  73.500000   79.42500   \n",
              "1        female   65.0  130.020189   75.776007  66.6000  79.650000   93.50000   \n",
              "         male     14.0  160.388700  150.850639  76.7292  83.669775  100.98125   \n",
              "\n",
              "                        75%       max  \n",
              "Survived Sex                           \n",
              "0        female  151.550000  151.5500  \n",
              "         male    118.864575  263.0000  \n",
              "1        female  151.550000  512.3292  \n",
              "         male    130.237500  512.3292  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6334fb20-1c5a-4372-bc12-bb7f4cef4a7a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Survived</th>\n",
              "      <th>Sex</th>\n",
              "      <th></th>\n",
              "      <th></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 rowspan=\"2\" valign=\"top\">0</th>\n",
              "      <th>female</th>\n",
              "      <td>5.0</td>\n",
              "      <td>102.350000</td>\n",
              "      <td>44.913250</td>\n",
              "      <td>69.5500</td>\n",
              "      <td>69.550000</td>\n",
              "      <td>69.55000</td>\n",
              "      <td>151.550000</td>\n",
              "      <td>151.5500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>32.0</td>\n",
              "      <td>114.791403</td>\n",
              "      <td>64.406333</td>\n",
              "      <td>66.6000</td>\n",
              "      <td>73.500000</td>\n",
              "      <td>79.42500</td>\n",
              "      <td>118.864575</td>\n",
              "      <td>263.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
              "      <th>female</th>\n",
              "      <td>65.0</td>\n",
              "      <td>130.020189</td>\n",
              "      <td>75.776007</td>\n",
              "      <td>66.6000</td>\n",
              "      <td>79.650000</td>\n",
              "      <td>93.50000</td>\n",
              "      <td>151.550000</td>\n",
              "      <td>512.3292</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>14.0</td>\n",
              "      <td>160.388700</td>\n",
              "      <td>150.850639</td>\n",
              "      <td>76.7292</td>\n",
              "      <td>83.669775</td>\n",
              "      <td>100.98125</td>\n",
              "      <td>130.237500</td>\n",
              "      <td>512.3292</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6334fb20-1c5a-4372-bc12-bb7f4cef4a7a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Among the records with outlier fare values , fare is a factor for seggregating the survived status .So we do not drop the outliers from dataset."
      ],
      "metadata": {
        "id": "exJB03_sJdyq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "6.Do min max scaling on the feature set (Take ‘Survived’ as target)"
      ],
      "metadata": {
        "id": "tma6t-3kaan8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1=df.drop('Survived',axis=1)"
      ],
      "metadata": {
        "id": "tPCzkHUlQicS"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Standardscaler function is in the preprocessing of sklearn\n",
        "from sklearn import preprocessing\n",
        "minmax=preprocessing.MinMaxScaler()\n",
        "st=minmax.fit_transform(df1)"
      ],
      "metadata": {
        "id": "_u3s33GMUBH5"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}