Case_Study_Marketing_Analysis.ipynb

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      "source": [
        "# Dataset Link - https://www.kaggle.com/datasets/jackdaoud/marketing-data/versions/2\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd"
      ]
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      "source": [
        "df = pd.read_csv('/content/marketing_data.csv')\n",
        "\n",
        "df.head()"
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              "      ID  Year_Birth   Education Marital_Status      Income   Kidhome  \\\n",
              "0   1826        1970  Graduation       Divorced  $84,835.00         0   \n",
              "1      1        1961  Graduation         Single  $57,091.00         0   \n",
              "2  10476        1958  Graduation        Married  $67,267.00         0   \n",
              "3   1386        1967  Graduation       Together  $32,474.00         1   \n",
              "4   5371        1989  Graduation         Single  $21,474.00         1   \n",
              "\n",
              "   Teenhome Dt_Customer  Recency  MntWines  ...  NumStorePurchases  \\\n",
              "0         0     6/16/14        0       189  ...                  6   \n",
              "1         0     6/15/14        0       464  ...                  7   \n",
              "2         1     5/13/14        0       134  ...                  5   \n",
              "3         1     5/11/14        0        10  ...                  2   \n",
              "4         0      4/8/14        0         6  ...                  2   \n",
              "\n",
              "   NumWebVisitsMonth  AcceptedCmp3  AcceptedCmp4  AcceptedCmp5  AcceptedCmp1  \\\n",
              "0                  1             0             0             0             0   \n",
              "1                  5             0             0             0             0   \n",
              "2                  2             0             0             0             0   \n",
              "3                  7             0             0             0             0   \n",
              "4                  7             1             0             0             0   \n",
              "\n",
              "   AcceptedCmp2  Response  Complain  Country  \n",
              "0             0         1         0       SP  \n",
              "1             1         1         0       CA  \n",
              "2             0         0         0       US  \n",
              "3             0         0         0      AUS  \n",
              "4             0         1         0       SP  \n",
              "\n",
              "[5 rows x 28 columns]"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2240 entries, 0 to 2239\n",
            "Data columns (total 28 columns):\n",
            " #   Column               Non-Null Count  Dtype \n",
            "---  ------               --------------  ----- \n",
            " 0   ID                   2240 non-null   int64 \n",
            " 1   Year_Birth           2240 non-null   int64 \n",
            " 2   Education            2240 non-null   object\n",
            " 3   Marital_Status       2240 non-null   object\n",
            " 4    Income              2216 non-null   object\n",
            " 5   Kidhome              2240 non-null   int64 \n",
            " 6   Teenhome             2240 non-null   int64 \n",
            " 7   Dt_Customer          2240 non-null   object\n",
            " 8   Recency              2240 non-null   int64 \n",
            " 9   MntWines             2240 non-null   int64 \n",
            " 10  MntFruits            2240 non-null   int64 \n",
            " 11  MntMeatProducts      2240 non-null   int64 \n",
            " 12  MntFishProducts      2240 non-null   int64 \n",
            " 13  MntSweetProducts     2240 non-null   int64 \n",
            " 14  MntGoldProds         2240 non-null   int64 \n",
            " 15  NumDealsPurchases    2240 non-null   int64 \n",
            " 16  NumWebPurchases      2240 non-null   int64 \n",
            " 17  NumCatalogPurchases  2240 non-null   int64 \n",
            " 18  NumStorePurchases    2240 non-null   int64 \n",
            " 19  NumWebVisitsMonth    2240 non-null   int64 \n",
            " 20  AcceptedCmp3         2240 non-null   int64 \n",
            " 21  AcceptedCmp4         2240 non-null   int64 \n",
            " 22  AcceptedCmp5         2240 non-null   int64 \n",
            " 23  AcceptedCmp1         2240 non-null   int64 \n",
            " 24  AcceptedCmp2         2240 non-null   int64 \n",
            " 25  Response             2240 non-null   int64 \n",
            " 26  Complain             2240 non-null   int64 \n",
            " 27  Country              2240 non-null   object\n",
            "dtypes: int64(23), object(5)\n",
            "memory usage: 490.1+ KB\n"
          ]
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      "source": [
        "# Clean the column names ?\n",
        "\n",
        "df.columns = df.columns.str.replace(' ', '')"
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        "df.columns"
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            "text/plain": [
              "Index(['ID', 'Year_Birth', 'Education', 'Marital_Status', 'Income', 'Kidhome',\n",
              "       'Teenhome', 'Dt_Customer', 'Recency', 'MntWines', 'MntFruits',\n",
              "       'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',\n",
              "       'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',\n",
              "       'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',\n",
              "       'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',\n",
              "       'AcceptedCmp2', 'Response', 'Complain', 'Country'],\n",
              "      dtype='object')"
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      "source": [
        "# Income feature is converted to Numerical\n",
        "\n",
        "df['Income'] = df['Income'].str.replace('$', '')\n",
        "\n",
        "df['Income'] = df['Income'].str.replace(',', '')\n",
        "\n",
        "df.head()"
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      "execution_count": 10,
      "outputs": [
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            "<ipython-input-10-65791d42be3c>:3: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  df['Income'] = df['Income'].str.replace('$', '')\n"
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              "      ID  Year_Birth   Education Marital_Status     Income  Kidhome  Teenhome  \\\n",
              "0   1826        1970  Graduation       Divorced  84835.00         0         0   \n",
              "1      1        1961  Graduation         Single  57091.00         0         0   \n",
              "2  10476        1958  Graduation        Married  67267.00         0         1   \n",
              "3   1386        1967  Graduation       Together  32474.00         1         1   \n",
              "4   5371        1989  Graduation         Single  21474.00         1         0   \n",
              "\n",
              "  Dt_Customer  Recency  MntWines  ...  NumStorePurchases  NumWebVisitsMonth  \\\n",
              "0     6/16/14        0       189  ...                  6                  1   \n",
              "1     6/15/14        0       464  ...                  7                  5   \n",
              "2     5/13/14        0       134  ...                  5                  2   \n",
              "3     5/11/14        0        10  ...                  2                  7   \n",
              "4      4/8/14        0         6  ...                  2                  7   \n",
              "\n",
              "   AcceptedCmp3  AcceptedCmp4  AcceptedCmp5  AcceptedCmp1  AcceptedCmp2  \\\n",
              "0             0             0             0             0             0   \n",
              "1             0             0             0             0             1   \n",
              "2             0             0             0             0             0   \n",
              "3             0             0             0             0             0   \n",
              "4             1             0             0             0             0   \n",
              "\n",
              "   Response  Complain  Country  \n",
              "0         1         0       SP  \n",
              "1         1         0       CA  \n",
              "2         0         0       US  \n",
              "3         0         0      AUS  \n",
              "4         1         0       SP  \n",
              "\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1826</td>\n",
              "      <td>1970</td>\n",
              "      <td>Graduation</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>84835.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6/16/14</td>\n",
              "      <td>0</td>\n",
              "      <td>189</td>\n",
              "      <td>...</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>SP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1961</td>\n",
              "      <td>Graduation</td>\n",
              "      <td>Single</td>\n",
              "      <td>57091.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6/15/14</td>\n",
              "      <td>0</td>\n",
              "      <td>464</td>\n",
              "      <td>...</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>CA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10476</td>\n",
              "      <td>1958</td>\n",
              "      <td>Graduation</td>\n",
              "      <td>Married</td>\n",
              "      <td>67267.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5/13/14</td>\n",
              "      <td>0</td>\n",
              "      <td>134</td>\n",
              "      <td>...</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>US</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1386</td>\n",
              "      <td>1967</td>\n",
              "      <td>Graduation</td>\n",
              "      <td>Together</td>\n",
              "      <td>32474.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5/11/14</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>AUS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5371</td>\n",
              "      <td>1989</td>\n",
              "      <td>Graduation</td>\n",
              "      <td>Single</td>\n",
              "      <td>21474.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>4/8/14</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>SP</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 28 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6bc4b3cc-f09f-49d9-b9a6-a9613f18b89c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "      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",
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              "          document.querySelector('#df-6bc4b3cc-f09f-49d9-b9a6-a9613f18b89c 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-6bc4b3cc-f09f-49d9-b9a6-a9613f18b89c');\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",
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            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Income'] = df['Income'].astype('float')\n",
        "\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0iQCwYChTt2T",
        "outputId": "1b6c7a5a-6ca8-4560-9c89-5d0a0a477c22"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2240 entries, 0 to 2239\n",
            "Data columns (total 28 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   ID                   2240 non-null   int64  \n",
            " 1   Year_Birth           2240 non-null   int64  \n",
            " 2   Education            2240 non-null   object \n",
            " 3   Marital_Status       2240 non-null   object \n",
            " 4   Income               2216 non-null   float64\n",
            " 5   Kidhome              2240 non-null   int64  \n",
            " 6   Teenhome             2240 non-null   int64  \n",
            " 7   Dt_Customer          2240 non-null   object \n",
            " 8   Recency              2240 non-null   int64  \n",
            " 9   MntWines             2240 non-null   int64  \n",
            " 10  MntFruits            2240 non-null   int64  \n",
            " 11  MntMeatProducts      2240 non-null   int64  \n",
            " 12  MntFishProducts      2240 non-null   int64  \n",
            " 13  MntSweetProducts     2240 non-null   int64  \n",
            " 14  MntGoldProds         2240 non-null   int64  \n",
            " 15  NumDealsPurchases    2240 non-null   int64  \n",
            " 16  NumWebPurchases      2240 non-null   int64  \n",
            " 17  NumCatalogPurchases  2240 non-null   int64  \n",
            " 18  NumStorePurchases    2240 non-null   int64  \n",
            " 19  NumWebVisitsMonth    2240 non-null   int64  \n",
            " 20  AcceptedCmp3         2240 non-null   int64  \n",
            " 21  AcceptedCmp4         2240 non-null   int64  \n",
            " 22  AcceptedCmp5         2240 non-null   int64  \n",
            " 23  AcceptedCmp1         2240 non-null   int64  \n",
            " 24  AcceptedCmp2         2240 non-null   int64  \n",
            " 25  Response             2240 non-null   int64  \n",
            " 26  Complain             2240 non-null   int64  \n",
            " 27  Country              2240 non-null   object \n",
            "dtypes: float64(1), int64(23), object(4)\n",
            "memory usage: 490.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "preSLoDJUXGj",
        "outputId": "13e6f5bb-8eb1-40fb-d32b-d6dd9c0af473"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Income                 24\n",
              "ID                      0\n",
              "NumDealsPurchases       0\n",
              "Complain                0\n",
              "Response                0\n",
              "AcceptedCmp2            0\n",
              "AcceptedCmp1            0\n",
              "AcceptedCmp5            0\n",
              "AcceptedCmp4            0\n",
              "AcceptedCmp3            0\n",
              "NumWebVisitsMonth       0\n",
              "NumStorePurchases       0\n",
              "NumCatalogPurchases     0\n",
              "NumWebPurchases         0\n",
              "MntGoldProds            0\n",
              "Year_Birth              0\n",
              "MntSweetProducts        0\n",
              "MntFishProducts         0\n",
              "MntMeatProducts         0\n",
              "MntFruits               0\n",
              "MntWines                0\n",
              "Recency                 0\n",
              "Dt_Customer             0\n",
              "Teenhome                0\n",
              "Kidhome                 0\n",
              "Marital_Status          0\n",
              "Education               0\n",
              "Country                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Income'] = df['Income'].fillna(df['Income'].median())\n",
        "\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UvsTxOPhU6Yb",
        "outputId": "29463e92-c61a-4d06-950c-b36d4b48320d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2240 entries, 0 to 2239\n",
            "Data columns (total 28 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   ID                   2240 non-null   int64  \n",
            " 1   Year_Birth           2240 non-null   int64  \n",
            " 2   Education            2240 non-null   object \n",
            " 3   Marital_Status       2240 non-null   object \n",
            " 4   Income               2240 non-null   float64\n",
            " 5   Kidhome              2240 non-null   int64  \n",
            " 6   Teenhome             2240 non-null   int64  \n",
            " 7   Dt_Customer          2240 non-null   object \n",
            " 8   Recency              2240 non-null   int64  \n",
            " 9   MntWines             2240 non-null   int64  \n",
            " 10  MntFruits            2240 non-null   int64  \n",
            " 11  MntMeatProducts      2240 non-null   int64  \n",
            " 12  MntFishProducts      2240 non-null   int64  \n",
            " 13  MntSweetProducts     2240 non-null   int64  \n",
            " 14  MntGoldProds         2240 non-null   int64  \n",
            " 15  NumDealsPurchases    2240 non-null   int64  \n",
            " 16  NumWebPurchases      2240 non-null   int64  \n",
            " 17  NumCatalogPurchases  2240 non-null   int64  \n",
            " 18  NumStorePurchases    2240 non-null   int64  \n",
            " 19  NumWebVisitsMonth    2240 non-null   int64  \n",
            " 20  AcceptedCmp3         2240 non-null   int64  \n",
            " 21  AcceptedCmp4         2240 non-null   int64  \n",
            " 22  AcceptedCmp5         2240 non-null   int64  \n",
            " 23  AcceptedCmp1         2240 non-null   int64  \n",
            " 24  AcceptedCmp2         2240 non-null   int64  \n",
            " 25  Response             2240 non-null   int64  \n",
            " 26  Complain             2240 non-null   int64  \n",
            " 27  Country              2240 non-null   object \n",
            "dtypes: float64(1), int64(23), object(4)\n",
            "memory usage: 490.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VnyrG9PCVqjB",
        "outputId": "fe21c8a3-5e78-48ad-a0ba-2b9c0219487e"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                     0\n",
              "Year_Birth             0\n",
              "Complain               0\n",
              "Response               0\n",
              "AcceptedCmp2           0\n",
              "AcceptedCmp1           0\n",
              "AcceptedCmp5           0\n",
              "AcceptedCmp4           0\n",
              "AcceptedCmp3           0\n",
              "NumWebVisitsMonth      0\n",
              "NumStorePurchases      0\n",
              "NumCatalogPurchases    0\n",
              "NumWebPurchases        0\n",
              "NumDealsPurchases      0\n",
              "MntGoldProds           0\n",
              "MntSweetProducts       0\n",
              "MntFishProducts        0\n",
              "MntMeatProducts        0\n",
              "MntFruits              0\n",
              "MntWines               0\n",
              "Recency                0\n",
              "Dt_Customer            0\n",
              "Teenhome               0\n",
              "Kidhome                0\n",
              "Income                 0\n",
              "Marital_Status         0\n",
              "Education              0\n",
              "Country                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Analysing the Income Feature"
      ],
      "metadata": {
        "id": "lF6zSMhfVzG5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# On average what is the household income? \n",
        "\n",
        "df['Income'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T0MsxxeWVuRf",
        "outputId": "e9a16f45-773a-49fa-a78e-aaaa273074a4"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "52237.97544642857"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['Income'].min())\n",
        "\n",
        "print(df['Income'].max())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V4Zqcy3IWW4x",
        "outputId": "4e0c3a46-e8dd-48a6-ada0-33ef0e32a30f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1730.0\n",
            "666666.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Income'].std()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6uWFCAH0Yk57",
        "outputId": "c30c16d8-55a2-4b54-ab19-1bd9a1b72b11"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25037.9558906219"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Income'].median()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sZRWGOf9Y1Gn",
        "outputId": "c4c0a86e-f002-4c26-e954-5c178c6d4823"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "51381.5"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['Income'].quantile(0.25))\n",
        "\n",
        "print(df['Income'].quantile(0.75))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cdWJ9DGrZKu3",
        "outputId": "689f3453-53b4-4215-db19-28786b950406"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "35538.75\n",
            "68289.75\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Detection of Outlier\n",
        "\n",
        "df['Income'].plot(kind='box')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "Y3uuEte0Zt9W",
        "outputId": "92caa9a1-ba9c-45d8-be5b-8e483087b4fe"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "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": "code",
      "source": [
        "# Treat\n",
        "\n",
        "clean_df = df[df.Income <= 600000]\n",
        "\n",
        "clean_df['Income'].plot(kind='box')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "O4ejYYHdZ7Av",
        "outputId": "bc648be7-12ad-4a54-c8e3-04026f2a0de1"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "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": "code",
      "source": [
        "# 1.5 * IQR \n",
        "\n",
        "q1 = df['Income'].quantile(0.25)\n",
        "q3 = df['Income'].quantile(0.75)\n",
        "\n",
        "IQR = q3 - q1\n",
        "\n",
        "income_lower_bound = q1 - 1.5 * IQR\n",
        "income_upper_bound = q3 + 1.5 * IQR\n",
        "\n",
        "clean_df_2 = df[(df['Income'] >= income_lower_bound) & (df['Income'] <= income_upper_bound)]\n",
        "\n",
        "clean_df_2['Income'].plot(kind='box')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "99suijBNbvWr",
        "outputId": "6459f060-c6a9-4b17-f30e-a2f060d7293f"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "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": "code",
      "source": [
        "clean_df_2['Income'].std()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wF0meaILfeTS",
        "outputId": "28fd3dab-b743-4186-9ac3-d0460ad759cb"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20601.6795422375"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Income'].std()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v5V0fBaPgHHb",
        "outputId": "569853cb-8d35-4908-b2c0-4cfb58e25c0d"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25037.9558906219"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clean_df_2['Income'].plot(kind='kde')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "olqyeWmkgJp1",
        "outputId": "f4e08c71-20ed-423f-de50-b52f8397079f"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "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": "code",
      "source": [
        "df['Income'].plot(kind='kde')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "VKrKYqiRgmw5",
        "outputId": "e86544d6-ce1b-4c80-a45f-27dc2ef11257"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "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": "code",
      "source": [],
      "metadata": {
        "id": "75xYBepmgr6i"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}