Handling_Missing_Values.ipynb

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        "# **Missing Values**"
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        "### **1) Missing completely at random (MCAR):**\n",
        "- mechanism in which the probability of value being missing is unrelated to both the observed data and missing data.\n",
        "- There is no symmetric reason for why they are missing values present in the dataset.\n",
        "\n",
        "ex. In disease survery, some unrealated question may be asked which is not linked or related to the actual questions related to diseases."
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        "### **2) Missing at Random (MAR):**\n",
        "\n",
        "- type of missing data mechanism in which the probability of value being missing depends only on observed data, not on the missing data itself.\n",
        "- The missing data is depended on another column of dataset.\n",
        "\n",
        "ex. if patient don't fill the data of their blood pressure then, the column of blood pressure will remain missing at random."
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        "### **3) Missing data not at Random (MNAR):**\n",
        "\n",
        "- type of missing data in which probability of missing values depends on the value of missing data itself.\n",
        "\n",
        "ex. job satisfaction is directly related to the income or salary of person."
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      "source": [
        "import seaborn as sns"
      ]
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      "source": [
        "df = sns.load_dataset(\"titanic\")"
      ],
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        "df.head()"
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              "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
              "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
              "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
              "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
              "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
              "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
              "\n",
              "     who  adult_male deck  embark_town alive  alone  \n",
              "0    man        True  NaN  Southampton    no  False  \n",
              "1  woman       False    C    Cherbourg   yes  False  \n",
              "2  woman       False  NaN  Southampton   yes   True  \n",
              "3  woman       False    C  Southampton   yes  False  \n",
              "4    man        True  NaN  Southampton    no   True  "
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        "df.isnull()"
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      "execution_count": null,
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              "     survived  pclass    sex    age  sibsp  parch   fare  embarked  class  \\\n",
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              "\n",
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              "..     ...         ...    ...          ...    ...    ...  \n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pclass\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sibsp\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"who\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adult_male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deck\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embark_town\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alive\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ERNUtFA5Kwjj",
        "outputId": "8d19a83b-4a4b-414a-e2a2-660cd9b12c2d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "survived         0\n",
              "pclass           0\n",
              "sex              0\n",
              "age            177\n",
              "sibsp            0\n",
              "parch            0\n",
              "fare             0\n",
              "embarked         2\n",
              "class            0\n",
              "who              0\n",
              "adult_male       0\n",
              "deck           688\n",
              "embark_town      2\n",
              "alive            0\n",
              "alone            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.isnull())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "Qc06SA8PKzzr",
        "outputId": "30378950-563b-4ec7-c05e-5ad92a064d12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Handling with NAN data - by droping the Null dataset\n",
        "df.dropna()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "vdPcOuO_K7LW",
        "outputId": "0638c5ab-af44-466c-8c7e-a8d36982e7ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
              "1           1       1  female  38.0      1      0  71.2833        C  First   \n",
              "3           1       1  female  35.0      1      0  53.1000        S  First   \n",
              "6           0       1    male  54.0      0      0  51.8625        S  First   \n",
              "10          1       3  female   4.0      1      1  16.7000        S  Third   \n",
              "11          1       1  female  58.0      0      0  26.5500        S  First   \n",
              "..        ...     ...     ...   ...    ...    ...      ...      ...    ...   \n",
              "871         1       1  female  47.0      1      1  52.5542        S  First   \n",
              "872         0       1    male  33.0      0      0   5.0000        S  First   \n",
              "879         1       1  female  56.0      0      1  83.1583        C  First   \n",
              "887         1       1  female  19.0      0      0  30.0000        S  First   \n",
              "889         1       1    male  26.0      0      0  30.0000        C  First   \n",
              "\n",
              "       who  adult_male deck  embark_town alive  alone  \n",
              "1    woman       False    C    Cherbourg   yes  False  \n",
              "3    woman       False    C  Southampton   yes  False  \n",
              "6      man        True    E  Southampton    no   True  \n",
              "10   child       False    G  Southampton   yes  False  \n",
              "11   woman       False    C  Southampton   yes   True  \n",
              "..     ...         ...  ...          ...   ...    ...  \n",
              "871  woman       False    D  Southampton   yes  False  \n",
              "872    man        True    B  Southampton    no   True  \n",
              "879  woman       False    C    Cherbourg   yes  False  \n",
              "887  woman       False    B  Southampton   yes   True  \n",
              "889    man        True    C    Cherbourg   yes   True  \n",
              "\n",
              "[182 rows x 15 columns]"
            ],
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              "      <th></th>\n",
              "      <th>survived</th>\n",
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              "      <td>58.0</td>\n",
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              "      <td>26.5500</td>\n",
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              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
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              "      <th>871</th>\n",
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              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
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              "      <td>1</td>\n",
              "      <td>52.5542</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>D</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>872</th>\n",
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              "      <td>33.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5.0000</td>\n",
              "      <td>S</td>\n",
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              "      <td>B</td>\n",
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              "    <tr>\n",
              "      <th>879</th>\n",
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              "      <td>56.0</td>\n",
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              "      <td>83.1583</td>\n",
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              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>182 rows × 15 columns</p>\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 182,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"male\",\n          \"female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.671615357379704,\n        \"min\": 0.92,\n        \"max\": 80.0,\n        \"num_unique_values\": 63,\n        \"samples\": [\n          11.0,\n          6.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sibsp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 76.49077401159936,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          29.7,\n          66.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"C\",\n          \"S\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"First\",\n          \"Third\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"who\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"woman\",\n          \"man\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adult_male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deck\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"C\",\n          \"E\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embark_town\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Cherbourg\",\n          \"Southampton\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alive\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"no\",\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bdoCuM2lLN2R",
        "outputId": "452515f4-64e7-4dc4-e441-87bf9adab4ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(891, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`dropna()` is not effective, becuase it leads to loss of important dataset which can be used for more parameter features."
      ],
      "metadata": {
        "id": "yXi1sgfCLTMt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#row-wise deletion\n",
        "df.dropna().shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TvVXzinELQMM",
        "outputId": "0bc2f653-3a12-4171-9e92-ab45c3769006"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(182, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#handling missing values by deleting the column wise dataset."
      ],
      "metadata": {
        "id": "z8grFWR3L5mM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#column-wise deletion\n",
        "df.dropna(axis = 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "oFoK4GR_Lrlj",
        "outputId": "8a166995-03bb-4759-9ff2-0d2597a9d87d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "     survived  pclass     sex  sibsp  parch     fare   class    who  \\\n",
              "0           0       3    male      1      0   7.2500   Third    man   \n",
              "1           1       1  female      1      0  71.2833   First  woman   \n",
              "2           1       3  female      0      0   7.9250   Third  woman   \n",
              "3           1       1  female      1      0  53.1000   First  woman   \n",
              "4           0       3    male      0      0   8.0500   Third    man   \n",
              "..        ...     ...     ...    ...    ...      ...     ...    ...   \n",
              "886         0       2    male      0      0  13.0000  Second    man   \n",
              "887         1       1  female      0      0  30.0000   First  woman   \n",
              "888         0       3  female      1      2  23.4500   Third  woman   \n",
              "889         1       1    male      0      0  30.0000   First    man   \n",
              "890         0       3    male      0      0   7.7500   Third    man   \n",
              "\n",
              "     adult_male alive  alone  \n",
              "0          True    no  False  \n",
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              "..          ...   ...    ...  \n",
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              "887       False   yes   True  \n",
              "888       False    no  False  \n",
              "889        True   yes   True  \n",
              "890        True    no   True  \n",
              "\n",
              "[891 rows x 11 columns]"
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7e275764-5b2d-4d04-8535-f3355589fee2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sibsp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Third\",\n          \"First\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"who\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"man\",\n          \"woman\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adult_male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alive\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "`dropna()` is not effective, becuase it leads to loss of important dataset which can be used for more parameter features."
      ],
      "metadata": {
        "id": "NSADXljRMImV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Imputation Techinques**\n",
        "\n",
        "- techniques to remove or fill the null values present in the dataset\n",
        "\n",
        "ie. Categorical or numericals.\n",
        "- In machine learning, imputation techniques are used to handle missing values in datasets.\n",
        "- Missing data can arise due to various reasons such as data collection errors, equipment malfunctions, or human errors.\n",
        "- Imputation techniques aim to estimate or fill in the missing values with plausible substitutes based on the available data."
      ],
      "metadata": {
        "id": "fAgmmjSeMKbx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **1) Mean value Imputation**\n",
        "\n",
        "- Mean value imputation is a simple imputation technique used to fill in missing values in a dataset.\n",
        "- It involves replacing the missing values with the mean of the non-missing values for the respective feature.\n",
        "- This technique is commonly used for numerical features with missing values.\n",
        "- this technique work well when your data is normally distributed. (like below diagram)"
      ],
      "metadata": {
        "id": "SVAALjvnMY7n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(df[\"age\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "hc-aHEC0L1W_",
        "outputId": "2a599650-d246-411f-f03d-bb070b685c8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-eef84e7ff8f0>:1: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(df[\"age\"])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='age', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.age.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FWCYmovJMjTO",
        "outputId": "81162445-6626-49bc-bc4d-15230bf95f3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "177"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"age_mean\"] = df[\"age\"].fillna(df[\"age\"].mean())"
      ],
      "metadata": {
        "id": "Qru6t_tGMnnc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[[\"age_mean\", \"age\"]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "LG7ksy81Mup2",
        "outputId": "b44f11b6-94b9-46c7-ccfe-77e6bd6f45dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      age_mean   age\n",
              "0    22.000000  22.0\n",
              "1    38.000000  38.0\n",
              "2    26.000000  26.0\n",
              "3    35.000000  35.0\n",
              "4    35.000000  35.0\n",
              "..         ...   ...\n",
              "886  27.000000  27.0\n",
              "887  19.000000  19.0\n",
              "888  29.699118   NaN\n",
              "889  26.000000  26.0\n",
              "890  32.000000  32.0\n",
              "\n",
              "[891 rows x 2 columns]"
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age_mean</th>\n",
              "      <th>age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>22.000000</td>\n",
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              "    <tr>\n",
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              "      <td>38.000000</td>\n",
              "      <td>38.0</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
              "      <td>26.000000</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>35.000000</td>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>35.000000</td>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>27.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>19.000000</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>29.699118</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>26.000000</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>32.000000</td>\n",
              "      <td>32.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 2 columns</p>\n",
              "</div>\n",
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              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-28a4cfbc-b3ca-44e2-998e-1a0ac257bfbd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-28a4cfbc-b3ca-44e2-998e-1a0ac257bfbd');\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",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-06af2d21-e866-4788-97a6-898e54d30538\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-06af2d21-e866-4788-97a6-898e54d30538')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-06af2d21-e866-4788-97a6-898e54d30538 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[[\\\"age_mean\\\", \\\"age\\\"]]\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"age_mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.002015226002882,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          59.0,\n          36.5,\n          28.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0,\n          49.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **2) Median value Imputation**\n",
        "- Median value imputation is a technique used to fill in missing values in a dataset by replacing them with the median of the non-missing values for the respective feature.\n",
        "- Similar to mean value imputation, median value imputation is commonly used for numerical features with missing values."
      ],
      "metadata": {
        "id": "y8yDEJA-NM7O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"age_median\"] = df[\"age\"].fillna(df[\"age\"].median())"
      ],
      "metadata": {
        "id": "J-tnqWkiM5xo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[[\"age_median\", \"age_mean\", \"age\"]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "rnlBFY1rNalD",
        "outputId": "1129470f-3162-4436-8055-69255652af9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     age_median   age_mean   age\n",
              "0          22.0  22.000000  22.0\n",
              "1          38.0  38.000000  38.0\n",
              "2          26.0  26.000000  26.0\n",
              "3          35.0  35.000000  35.0\n",
              "4          35.0  35.000000  35.0\n",
              "..          ...        ...   ...\n",
              "886        27.0  27.000000  27.0\n",
              "887        19.0  19.000000  19.0\n",
              "888        28.0  29.699118   NaN\n",
              "889        26.0  26.000000  26.0\n",
              "890        32.0  32.000000  32.0\n",
              "\n",
              "[891 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-22103b1b-ad30-420c-b3f5-435036477b19\" class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age_median</th>\n",
              "      <th>age_mean</th>\n",
              "      <th>age</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>22.0</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>22.0</td>\n",
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              "      <th>1</th>\n",
              "      <td>38.0</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>38.0</td>\n",
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              "      <th>2</th>\n",
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              "      <td>26.000000</td>\n",
              "      <td>26.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>35.0</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>35.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>35.0</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>886</th>\n",
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              "      <td>27.000000</td>\n",
              "      <td>27.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>19.0</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>28.0</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>26.0</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>32.0</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>32.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-22103b1b-ad30-420c-b3f5-435036477b19')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-22103b1b-ad30-420c-b3f5-435036477b19 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-22103b1b-ad30-420c-b3f5-435036477b19');\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",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
              "<div id=\"df-a53949f0-edcd-463a-abcc-8cff584bd118\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a53949f0-edcd-463a-abcc-8cff584bd118')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
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              "summary": "{\n  \"name\": \"df[[\\\"age_median\\\", \\\"age_mean\\\", \\\"age\\\"]]\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"age_median\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.019696550973201,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0,\n          49.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.002015226002882,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          59.0,\n          36.5,\n          28.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0,\n          49.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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    {
      "cell_type": "markdown",
      "source": [
        "### **3) Mode value Imputation**\n",
        "\n",
        "- Mode value imputation is a technique used to fill in missing values in a dataset by replacing them with the mode, which is the most frequently occurring value, of the non-missing values for the respective feature.\n",
        "- Mode value imputation is commonly used for categorical features with missing values."
      ],
      "metadata": {
        "id": "nBny1fZCNwPV"
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        "df[df[\"embarked\"].isnull()]"
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          "height": 112
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        "id": "IrZV_GixNh-9",
        "outputId": "69ac37a4-9d06-4c02-8fe2-0af71daf8aba"
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      "execution_count": null,
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              "     survived  pclass     sex   age  sibsp  parch  fare embarked  class  \\\n",
              "61          1       1  female  38.0      0      0  80.0      NaN  First   \n",
              "829         1       1  female  62.0      0      0  80.0      NaN  First   \n",
              "\n",
              "       who  adult_male deck embark_town alive  alone  age_man  age_median  \\\n",
              "61   woman       False    B         NaN   yes   True     38.0        38.0   \n",
              "829  woman       False    B         NaN   yes   True     62.0        62.0   \n",
              "\n",
              "     age_mean  \n",
              "61       38.0  \n",
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              "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
              "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
              "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
              "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
              "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
              "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
              "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
              "885         0       3  female  39.0      0      5  29.1250        Q   Third   \n",
              "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
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              "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
              "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
              "\n",
              "       who  adult_male deck  embark_town alive  alone  age_man  age_median  \\\n",
              "0      man        True  NaN  Southampton    no  False     22.0        22.0   \n",
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              "4      man        True  NaN  Southampton    no   True     35.0        35.0   \n",
              "..     ...         ...  ...          ...   ...    ...      ...         ...   \n",
              "885  woman       False  NaN   Queenstown    no  False     39.0        39.0   \n",
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              "890    man        True  NaN   Queenstown    no   True     32.0        32.0   \n",
              "\n",
              "     age_mean  \n",
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              "4        35.0  \n",
              "..        ...  \n",
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              "886      27.0  \n",
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              "890      32.0  \n",
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              "[714 rows x 18 columns]"
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              "      <th>age_man</th>\n",
              "      <th>age_median</th>\n",
              "      <th>age_mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>False</td>\n",
              "      <td>22.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>22.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "      <td>38.0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>38.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "      <td>26.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "      <td>35.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>35.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "      <td>35.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>35.0</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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>885</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>39.0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>29.1250</td>\n",
              "      <td>Q</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Queenstown</td>\n",
              "      <td>no</td>\n",
              "      <td>False</td>\n",
              "      <td>39.0</td>\n",
              "      <td>39.0</td>\n",
              "      <td>39.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>S</td>\n",
              "      <td>Second</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "      <td>27.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>27.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>B</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "      <td>26.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>Q</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Queenstown</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "      <td>32.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>32.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>714 rows × 18 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
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              "summary": "{\n  \"name\": \"df[df[\\\"age\\\"]\",\n  \"rows\": 714,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        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\"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"who\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"man\",\n          \"woman\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adult_male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deck\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"C\",\n          \"E\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embark_town\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Southampton\",\n          \"Cherbourg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alive\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_man\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_median\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age_mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334042,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df[\"age\"].notna()][\"embarked\"].mode()[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "w2LDF-n1OL36",
        "outputId": "4773f552-0e75-409f-d480-70d04b0739f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'S'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mode = df[df[\"age\"].notna()][\"embarked\"].mode()[0]"
      ],
      "metadata": {
        "id": "q2-3-uxzPAOA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mode"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "vuQ-uvjfPADf",
        "outputId": "e3ab8c83-cbae-4f30-a97f-978e1aeb9948"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'S'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"embarked_mode\"] = df[\"embarked\"].fillna(mode)"
      ],
      "metadata": {
        "id": "9r6nZO72Pme0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[[\"embarked_mode\", \"embarked\"]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "GZpTIv3aPP29",
        "outputId": "f798942e-d563-4fcb-cbb6-a50e37727dc2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    embarked_mode embarked\n",
              "0               S        S\n",
              "1               C        C\n",
              "2               S        S\n",
              "3               S        S\n",
              "4               S        S\n",
              "..            ...      ...\n",
              "886             S        S\n",
              "887             S        S\n",
              "888             S        S\n",
              "889             C        C\n",
              "890             Q        Q\n",
              "\n",
              "[891 rows x 2 columns]"
            ],
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              "<p>891 rows × 2 columns</p>\n",
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              "      background-color: #434B5C;\n",
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              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "\n",
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              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-color: transparent;\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d38c1207-ecf9-4b1b-b5d9-1bbed76cc392 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[[\\\"embarked_mode\\\", \\\"embarked\\\"]]\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"embarked_mode\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\",\n          \"Q\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\",\n          \"Q\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"embarked_mode\"].isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1f6xmqiEOWP1",
        "outputId": "745728e7-9395-4b4a-d6ca-c9bb7c973363"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    }
  ]
}