Assignment 2.ipynb

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              "   CustomerID Title FirstName MiddleName LastName Suffix    AddressLine1  \\\n",
              "0           1   Mr.      John         A.      Doe    NaN      123 Elm St   \n",
              "1           2   Ms.      Jane         B.    Smith    NaN     456 Oak Ave   \n",
              "2           3   Dr.     Emily         C.  Johnson    PhD    789 Maple Rd   \n",
              "3           4   Mr.   Michael         D.    Brown    NaN     101 Pine Ln   \n",
              "4           5  Mrs.     Susan         E.    Davis    NaN  202 Birch Blvd   \n",
              "\n",
              "  AddressLine2         City StateProvinceName    Education  Occupation Gender  \\\n",
              "0          NaN  Springfield          Illinois      Masters    Engineer      M   \n",
              "1       Unit 4   Metropolis          New York    Bachelors     Teacher      F   \n",
              "2    Suite 100       Gotham        California          PhD   Scientist      F   \n",
              "3          NaN    Smalltown             Texas  High School  Technician      M   \n",
              "4       Apt 12    Rivertown           Florida    Bachelors       Nurse      F   \n",
              "\n",
              "  MaritalStatus  HomeOwnerFlag  NumberCarsOwned  NumberChildrenAtHome  \\\n",
              "0       Married              1                2                     1   \n",
              "1        Single              1                1                     0   \n",
              "2       Married              1                2                     2   \n",
              "3      Divorced              0                3                     2   \n",
              "4       Married              1                1                     1   \n",
              "\n",
              "   TotalChildren  YearlyIncome LastUpdated  \n",
              "0              3         75000  2024-08-21  \n",
              "1              0         55000  2024-08-21  \n",
              "2              2        120000  2024-08-21  \n",
              "3              3         45000  2024-08-21  \n",
              "4              1         60000  2024-08-21  "
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              "      <th>CustomerID</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Mr.</td>\n",
              "      <td>John</td>\n",
              "      <td>A.</td>\n",
              "      <td>Doe</td>\n",
              "      <td>NaN</td>\n",
              "      <td>123 Elm St</td>\n",
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              "      <td>Springfield</td>\n",
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              "      <td>75000</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>456 Oak Ave</td>\n",
              "      <td>Unit 4</td>\n",
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              "      <td>New York</td>\n",
              "      <td>Bachelors</td>\n",
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              "      <td>1</td>\n",
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              "      <td>55000</td>\n",
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              "      <th>2</th>\n",
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              "      <td>Emily</td>\n",
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              "      <td>PhD</td>\n",
              "      <td>789 Maple Rd</td>\n",
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              "      <td>Gotham</td>\n",
              "      <td>California</td>\n",
              "      <td>PhD</td>\n",
              "      <td>Scientist</td>\n",
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              "      <td>Married</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>120000</td>\n",
              "      <td>2024-08-21</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Mr.</td>\n",
              "      <td>Michael</td>\n",
              "      <td>D.</td>\n",
              "      <td>Brown</td>\n",
              "      <td>NaN</td>\n",
              "      <td>101 Pine Ln</td>\n",
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              "      <td>Smalltown</td>\n",
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              "      <td>45000</td>\n",
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              "      <td>Davis</td>\n",
              "      <td>NaN</td>\n",
              "      <td>202 Birch Blvd</td>\n",
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              "      <td>60000</td>\n",
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],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Bachelors\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupation\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Teacher\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"F\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MaritalStatus\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Married\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HomeOwnerFlag\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumberCarsOwned\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumberChildrenAtHome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalChildren\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearlyIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29453,\n        \"min\": 45000,\n        \"max\": 120000,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          55000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LastUpdated\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2024-08-21\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "import pandas as pd\n",
        "# Using double backslashes\n",
        "df = pd.read_csv(\"//content//something.csv\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from io import StringIO\n",
        "\n",
        "# CSV content\n",
        "csv_data = \"\"\"CustomerID,Title,FirstName,MiddleName,LastName,Suffix,AddressLine1,AddressLine2,City,StateProvinceName,Education,Occupation,Gender,MaritalStatus,HomeOwnerFlag,NumberCarsOwned,NumberChildrenAtHome,TotalChildren,YearlyIncome,LastUpdated\n",
        "1,Mr.,John,A.,Doe,,123 Elm St,,Springfield,Illinois,Masters,Engineer,M,Married,1,2,1,3,75000,2024-08-21\n",
        "2,Ms.,Jane,B.,Smith,,456 Oak Ave,Unit 4,Metropolis,New York,Bachelors,Teacher,F,Single,1,1,0,0,55000,2024-08-21\n",
        "3,Dr.,Emily,C.,Johnson,PhD,789 Maple Rd,Suite 100,Gotham,California,PhD,Scientist,F,Married,1,2,2,2,120000,2024-08-21\n",
        "4,Mr.,Michael,D.,Brown,,101 Pine Ln,,Smalltown,Texas,High School,Technician,M,Divorced,0,3,2,3,45000,2024-08-21\n",
        "5,Mrs.,Susan,E.,Davis,,202 Birch Blvd,Apt 12,Rivertown,Florida,Bachelors,Nurse,F,Married,1,1,1,1,60000,2024-08-21\"\"\"\n",
        "\n",
        "# Convert CSV content to DataFrame\n",
        "df = pd.read_csv(StringIO(csv_data))\n",
        "\n",
        "# Save DataFrame to CSV file in the Google Colab local environment\n",
        "df.to_csv('/content/something.csv', index=False)\n",
        "\n",
        "# Verify by reading the saved file\n",
        "df_saved = pd.read_csv('/content/something.csv')\n",
        "df_saved.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "collapsed": true,
        "id": "AvPmyXJ04fHS",
        "outputId": "f1d3bdff-2b33-4fec-8a47-6511929418d0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CustomerID Title FirstName MiddleName LastName Suffix    AddressLine1  \\\n",
              "0           1   Mr.      John         A.      Doe    NaN      123 Elm St   \n",
              "1           2   Ms.      Jane         B.    Smith    NaN     456 Oak Ave   \n",
              "2           3   Dr.     Emily         C.  Johnson    PhD    789 Maple Rd   \n",
              "3           4   Mr.   Michael         D.    Brown    NaN     101 Pine Ln   \n",
              "4           5  Mrs.     Susan         E.    Davis    NaN  202 Birch Blvd   \n",
              "\n",
              "  AddressLine2         City StateProvinceName    Education  Occupation Gender  \\\n",
              "0          NaN  Springfield          Illinois      Masters    Engineer      M   \n",
              "1       Unit 4   Metropolis          New York    Bachelors     Teacher      F   \n",
              "2    Suite 100       Gotham        California          PhD   Scientist      F   \n",
              "3          NaN    Smalltown             Texas  High School  Technician      M   \n",
              "4       Apt 12    Rivertown           Florida    Bachelors       Nurse      F   \n",
              "\n",
              "  MaritalStatus  HomeOwnerFlag  NumberCarsOwned  NumberChildrenAtHome  \\\n",
              "0       Married              1                2                     1   \n",
              "1        Single              1                1                     0   \n",
              "2       Married              1                2                     2   \n",
              "3      Divorced              0                3                     2   \n",
              "4       Married              1                1                     1   \n",
              "\n",
              "   TotalChildren  YearlyIncome LastUpdated  \n",
              "0              3         75000  2024-08-21  \n",
              "1              0         55000  2024-08-21  \n",
              "2              2        120000  2024-08-21  \n",
              "3              3         45000  2024-08-21  \n",
              "4              1         60000  2024-08-21  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6fe62c0a-fea4-4b22-8ac7-2c10acac8dfa\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Title</th>\n",
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              "      <th>TotalChildren</th>\n",
              "      <th>YearlyIncome</th>\n",
              "      <th>LastUpdated</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Mr.</td>\n",
              "      <td>John</td>\n",
              "      <td>A.</td>\n",
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              "      <td>123 Elm St</td>\n",
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              "      <td>75000</td>\n",
              "      <td>2024-08-21</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
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              "      <td>B.</td>\n",
              "      <td>Smith</td>\n",
              "      <td>NaN</td>\n",
              "      <td>456 Oak Ave</td>\n",
              "      <td>Unit 4</td>\n",
              "      <td>Metropolis</td>\n",
              "      <td>New York</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>Teacher</td>\n",
              "      <td>F</td>\n",
              "      <td>Single</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>55000</td>\n",
              "      <td>2024-08-21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Dr.</td>\n",
              "      <td>Emily</td>\n",
              "      <td>C.</td>\n",
              "      <td>Johnson</td>\n",
              "      <td>PhD</td>\n",
              "      <td>789 Maple Rd</td>\n",
              "      <td>Suite 100</td>\n",
              "      <td>Gotham</td>\n",
              "      <td>California</td>\n",
              "      <td>PhD</td>\n",
              "      <td>Scientist</td>\n",
              "      <td>F</td>\n",
              "      <td>Married</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>120000</td>\n",
              "      <td>2024-08-21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Mr.</td>\n",
              "      <td>Michael</td>\n",
              "      <td>D.</td>\n",
              "      <td>Brown</td>\n",
              "      <td>NaN</td>\n",
              "      <td>101 Pine Ln</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Smalltown</td>\n",
              "      <td>Texas</td>\n",
              "      <td>High School</td>\n",
              "      <td>Technician</td>\n",
              "      <td>M</td>\n",
              "      <td>Divorced</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>45000</td>\n",
              "      <td>2024-08-21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Mrs.</td>\n",
              "      <td>Susan</td>\n",
              "      <td>E.</td>\n",
              "      <td>Davis</td>\n",
              "      <td>NaN</td>\n",
              "      <td>202 Birch Blvd</td>\n",
              "      <td>Apt 12</td>\n",
              "      <td>Rivertown</td>\n",
              "      <td>Florida</td>\n",
              "      <td>Bachelors</td>\n",
              "      <td>Nurse</td>\n",
              "      <td>F</td>\n",
              "      <td>Married</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>60000</td>\n",
              "      <td>2024-08-21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6fe62c0a-fea4-4b22-8ac7-2c10acac8dfa')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "\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",
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              "      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",
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              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-6fe62c0a-fea4-4b22-8ac7-2c10acac8dfa 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-6fe62c0a-fea4-4b22-8ac7-2c10acac8dfa');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "      --bg-color: #E8F0FE;\n",
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              "\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    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",
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              "      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",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_saved",
              "summary": "{\n  \"name\": \"df_saved\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Ms.\",\n          \"Mrs.\",\n          \"Mr.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FirstName\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Jane\",\n          \"Susan\",\n          \"Emily\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MiddleName\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"B.\",\n          \"E.\",\n          \"C.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LastName\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Smith\",\n          \"Davis\",\n          \"Johnson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Suffix\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"PhD\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AddressLine1\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"456 Oak Ave\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AddressLine2\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Unit 4\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Metropolis\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StateProvinceName\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"New York\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Bachelors\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupation\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Teacher\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"F\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MaritalStatus\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Married\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HomeOwnerFlag\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumberCarsOwned\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumberChildrenAtHome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalChildren\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearlyIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29453,\n        \"min\": 45000,\n        \"max\": 120000,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          55000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LastUpdated\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2024-08-21\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "-jVeLfmA3AWp",
        "outputId": "4b93a1f1-4938-4faa-ebb8-8217b395960a"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.1.4)\n",
            "Requirement already satisfied: numpy<2,>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.26.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.1)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df[['Education','Occupation','Gender','NumberCarsOwned','TotalChildren','YearlyIncome','MiddleName']]"
      ],
      "metadata": {
        "id": "RMqwMENA3CkT"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "null_values = df.isnull().sum()\n",
        "print(null_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fr7B9V8z5-x4",
        "outputId": "e21c2c16-ecf6-4f32-d62f-0d9986c7c831"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CustomerID              0\n",
            "Title                   0\n",
            "FirstName               0\n",
            "MiddleName              0\n",
            "LastName                0\n",
            "Suffix                  4\n",
            "AddressLine1            0\n",
            "AddressLine2            2\n",
            "City                    0\n",
            "StateProvinceName       0\n",
            "Education               0\n",
            "Occupation              0\n",
            "Gender                  0\n",
            "MaritalStatus           0\n",
            "HomeOwnerFlag           0\n",
            "NumberCarsOwned         0\n",
            "NumberChildrenAtHome    0\n",
            "TotalChildren           0\n",
            "YearlyIncome            0\n",
            "LastUpdated             0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "BfwnnWgG6AVF",
        "outputId": "d857be55-61e8-49e4-b1b2-dddb88359813"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Education  Occupation Gender  NumberCarsOwned  TotalChildren  \\\n",
              "0      Masters    Engineer      M                2              3   \n",
              "1    Bachelors     Teacher      F                1              0   \n",
              "2          PhD   Scientist      F                2              2   \n",
              "3  High School  Technician      M                3              3   \n",
              "4    Bachelors       Nurse      F                1              1   \n",
              "\n",
              "   YearlyIncome MiddleName  \n",
              "0         75000         A.  \n",
              "1         55000         B.  \n",
              "2        120000         C.  \n",
              "3         45000         D.  \n",
              "4         60000         E.  "
            ],
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            ],
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              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Bachelors\",\n          \"High School\",\n          \"Masters\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupation\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Teacher\",\n          \"Nurse\",\n          \"Scientist\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"F\",\n          \"M\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumberCarsOwned\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalChildren\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearlyIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29453,\n        \"min\": 45000,\n        \"max\": 120000,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          55000,\n          60000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MiddleName\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"B.\",\n          \"E.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "null_values = df1.isnull().sum()\n",
        "print(null_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hri4Fuvr6DVS",
        "outputId": "b6a4bf4e-d92e-4790-c5f5-ffdb91d0fd3f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Education          0\n",
            "Occupation         0\n",
            "Gender             0\n",
            "NumberCarsOwned    0\n",
            "TotalChildren      0\n",
            "YearlyIncome       0\n",
            "MiddleName         0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Find the mode of the \"Middle Name\" column\n",
        "mode_value = df1['MiddleName'].mode()[0]\n",
        "print(f\"The mode of the 'Middle Name' column is: {mode_value}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vGRe1BQN6Hzz",
        "outputId": "38b60d3a-f189-445b-d842-fb8d67e21da2"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The mode of the 'Middle Name' column is: A.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Replace null values in the \"Middle Name\" column with the mode\n",
        "df1['MiddleName'].fillna(mode_value, inplace=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FXU1WEbO6JCu",
        "outputId": "97340b02-d657-4474-9eca-1c2b60632441"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-cf7f4d9d4790>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df1['MiddleName'].fillna(mode_value, inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "null_values = df1.isnull().sum()\n",
        "print(null_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dipwJKtt6NVd",
        "outputId": "aa397796-fce5-4a5c-a484-c31e0f5092f8"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Education          0\n",
            "Occupation         0\n",
            "Gender             0\n",
            "NumberCarsOwned    0\n",
            "TotalChildren      0\n",
            "YearlyIncome       0\n",
            "MiddleName         0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "HgsofVMb6Sqp",
        "outputId": "4e07be8b-9851-4099-87fe-4517bd59cece"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Education  Occupation Gender  NumberCarsOwned  TotalChildren  \\\n",
              "0      Masters    Engineer      M                2              3   \n",
              "1    Bachelors     Teacher      F                1              0   \n",
              "2          PhD   Scientist      F                2              2   \n",
              "3  High School  Technician      M                3              3   \n",
              "4    Bachelors       Nurse      F                1              1   \n",
              "\n",
              "   YearlyIncome MiddleName  \n",
              "0         75000         A.  \n",
              "1         55000         B.  \n",
              "2        120000         C.  \n",
              "3         45000         D.  \n",
              "4         60000         E.  "
            ],
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    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",
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              "  }\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",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "      } catch (error) {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Bachelors\",\n          \"High School\",\n          \"Masters\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupation\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Teacher\",\n          \"Nurse\",\n          \"Scientist\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"F\",\n          \"M\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumberCarsOwned\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalChildren\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 3,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearlyIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29453,\n        \"min\": 45000,\n        \"max\": 120000,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          55000,\n          60000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MiddleName\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"B.\",\n          \"E.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Identify numerical columns\n",
        "numerical_cols = df1.select_dtypes(include=['float64', 'int64']).columns\n",
        "print(\"Numerical columns to be normalized:\", numerical_cols)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jo3rUf6l6UHp",
        "outputId": "fa66c310-fade-4252-d58a-9719023df00f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Numerical columns to be normalized: Index(['NumberCarsOwned', 'TotalChildren', 'YearlyIncome'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "df1[numerical_cols] = scaler.fit_transform(df[numerical_cols])\n",
        "\n",
        "df1.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "-jACSLYv6Yx1",
        "outputId": "1fcfb999-6bce-4b60-ef0a-f9b543032972"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-17-33a7738e66a1>:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df1[numerical_cols] = scaler.fit_transform(df[numerical_cols])\n"
          ]
        },
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Education  Occupation Gender  NumberCarsOwned  TotalChildren  \\\n",
              "0      Masters    Engineer      M              0.5       1.000000   \n",
              "1    Bachelors     Teacher      F              0.0       0.000000   \n",
              "2          PhD   Scientist      F              0.5       0.666667   \n",
              "3  High School  Technician      M              1.0       1.000000   \n",
              "4    Bachelors       Nurse      F              0.0       0.333333   \n",
              "\n",
              "   YearlyIncome MiddleName  \n",
              "0      0.400000         A.  \n",
              "1      0.133333         B.  \n",
              "2      1.000000         C.  \n",
              "3      0.000000         D.  \n",
              "4      0.200000         E.  "
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Bachelors\",\n          \"High School\",\n          \"Masters\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupation\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Teacher\",\n          \"Nurse\",\n          \"Scientist\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"F\",\n          \"M\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumberCarsOwned\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4183300132670378,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.5,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalChildren\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4346134936801766,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0,\n          0.3333333333333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearlyIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3927113726672838,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.1333333333333333,\n          0.20000000000000007\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MiddleName\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"B.\",\n          \"E.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df1[numerical_cols].describe())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sl9uW1Yh6cBq",
        "outputId": "c5dc664c-95e5-4060-b8c3-f9031e15ee76"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       NumberCarsOwned  TotalChildren  YearlyIncome\n",
            "count          5.00000       5.000000      5.000000\n",
            "mean           0.40000       0.600000      0.346667\n",
            "std            0.41833       0.434613      0.392711\n",
            "min            0.00000       0.000000      0.000000\n",
            "25%            0.00000       0.333333      0.133333\n",
            "50%            0.50000       0.666667      0.200000\n",
            "75%            0.50000       1.000000      0.400000\n",
            "max            1.00000       1.000000      1.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df1['YearlyIncome_Binned'] = pd.cut(df1['YearlyIncome'], bins=3, labels=[\"Low\",\"Moderate\",\"High\"])\n",
        "df1[['YearlyIncome', 'YearlyIncome_Binned']].head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "ZPtoMDQC6ekO",
        "outputId": "ed56cefb-06d0-46e2-fafd-7725bd8eea07"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-19-528817792f08>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df1['YearlyIncome_Binned'] = pd.cut(df1['YearlyIncome'], bins=3, labels=[\"Low\",\"Moderate\",\"High\"])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   YearlyIncome YearlyIncome_Binned\n",
              "0      0.400000            Moderate\n",
              "1      0.133333                 Low\n",
              "2      1.000000                High\n",
              "3      0.000000                 Low\n",
              "4      0.200000                 Low"
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              "      <td>Moderate</td>\n",
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              "      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-60504d13-bec2-4eb6-9275-e8fdc9c1bbd1 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-60504d13-bec2-4eb6-9275-e8fdc9c1bbd1');\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-340d5229-ace2-4992-aa39-9519595a0c24\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-340d5229-ace2-4992-aa39-9519595a0c24')\"\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-340d5229-ace2-4992-aa39-9519595a0c24 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\": \"df1[['YearlyIncome', 'YearlyIncome_Binned']]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"YearlyIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3927113726672838,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.1333333333333333,\n          0.20000000000000007,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearlyIncome_Binned\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Moderate\",\n          \"Low\",\n          \"High\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "column_to_standardize = 'YearlyIncome'\n",
        "df[column_to_standardize] = scaler.fit_transform(df[[column_to_standardize]])\n",
        "df[[column_to_standardize]].head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "WfD7_r696haz",
        "outputId": "4c44f4ac-9c5d-4381-b290-d995226fbbf9"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   YearlyIncome\n",
              "0      0.151838\n",
              "1     -0.607352\n",
              "2      1.860015\n",
              "3     -0.986947\n",
              "4     -0.417554"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fe7a726c-464c-4b94-a175-747966cc5156\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>YearlyIncome</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.151838</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.607352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.860015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.986947</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.417554</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fe7a726c-464c-4b94-a175-747966cc5156')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\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-fe7a726c-464c-4b94-a175-747966cc5156 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-fe7a726c-464c-4b94-a175-747966cc5156');\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-4189116c-a18b-4b71-a3dc-3c8a76a448fc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4189116c-a18b-4b71-a3dc-3c8a76a448fc')\"\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",
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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",
              "  .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-4189116c-a18b-4b71-a3dc-3c8a76a448fc 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[[column_to_standardize]]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"YearlyIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.118033988749895,\n        \"min\": -0.9869465034014604,\n        \"max\": 1.8600145641027523,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -0.6073516944008988,\n          -0.4175542899006179,\n          1.8600145641027523\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example: One-Hot Encoding the 'Gender' column\n",
        "df1= pd.get_dummies(df1, columns=['Gender'])\n",
        "\n",
        "# Display the first few rows to verify the encoding\n",
        "df1.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wm0a0Hws6kPy",
        "outputId": "00a3d22c-6037-45d0-bf7f-85a6229db80f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Education  Occupation  NumberCarsOwned  TotalChildren  YearlyIncome  \\\n",
              "0      Masters    Engineer              0.5       1.000000      0.400000   \n",
              "1    Bachelors     Teacher              0.0       0.000000      0.133333   \n",
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              "4    Bachelors       Nurse              0.0       0.333333      0.200000   \n",
              "\n",
              "  MiddleName YearlyIncome_Binned  Gender_F  Gender_M  \n",
              "0         A.            Moderate     False      True  \n",
              "1         B.                 Low      True     False  \n",
              "2         C.                High      True     False  \n",
              "3         D.                 Low     False      True  \n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Bachelors\",\n          \"High School\",\n          \"Masters\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupation\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Teacher\",\n          \"Nurse\",\n          \"Scientist\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumberCarsOwned\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4183300132670378,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.5,\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalChildren\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4346134936801766,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0,\n          0.3333333333333333,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearlyIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3927113726672838,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.1333333333333333,\n          0.20000000000000007,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MiddleName\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"B.\",\n          \"E.\",\n          \"C.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearlyIncome_Binned\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Moderate\",\n          \"Low\",\n          \"High\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender_F\",\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\": \"Gender_M\",\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}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Find the mode of the \"Middle Name\" column\n",
        "mode_value = df1['MiddleName'].mode()[0]\n",
        "print(f\"The mode of the 'Middle Name' column is: {mode_value}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Ol-SjqA69UT",
        "outputId": "0220167d-d498-4be8-a2d6-e905888ea31d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The mode of the 'Middle Name' column is: A.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Replace null values in the \"Middle Name\" column with the mode\n",
        "df1['MiddleName'].fillna(mode_value, inplace=True)\n"
      ],
      "metadata": {
        "id": "imX8AR746-iB"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "null_values = df1.isnull().sum()\n",
        "print(null_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kVGE9-yM7BBj",
        "outputId": "b9423994-50c6-471f-9fa7-96422fe81fc3"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Education              0\n",
            "Occupation             0\n",
            "NumberCarsOwned        0\n",
            "TotalChildren          0\n",
            "YearlyIncome           0\n",
            "MiddleName             0\n",
            "YearlyIncome_Binned    0\n",
            "Gender_F               0\n",
            "Gender_M               0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "R6ZGy6cx7Dup",
        "outputId": "8f9c36fc-7778-48bf-df96-7d5207550e5f"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Education  Occupation  NumberCarsOwned  TotalChildren  YearlyIncome  \\\n",
              "0      Masters    Engineer              0.5       1.000000      0.400000   \n",
              "1    Bachelors     Teacher              0.0       0.000000      0.133333   \n",
              "2          PhD   Scientist              0.5       0.666667      1.000000   \n",
              "3  High School  Technician              1.0       1.000000      0.000000   \n",
              "4    Bachelors       Nurse              0.0       0.333333      0.200000   \n",
              "\n",
              "  MiddleName YearlyIncome_Binned  Gender_F  Gender_M  \n",
              "0         A.            Moderate     False      True  \n",
              "1         B.                 Low      True     False  \n",
              "2         C.                High      True     False  \n",
              "3         D.                 Low     False      True  \n",
              "4         E.                 Low      True     False  "
            ],
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              "      <td>Scientist</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>C.</td>\n",
              "      <td>High</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>High School</td>\n",
              "      <td>Technician</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>D.</td>\n",
              "      <td>Low</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bachelors</td>\n",
              "      <td>Nurse</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>E.</td>\n",
              "      <td>Low</td>\n",
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              "      <td>False</td>\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Bachelors\",\n          \"High School\",\n          \"Masters\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupation\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Teacher\",\n          \"Nurse\",\n          \"Scientist\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumberCarsOwned\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4183300132670378,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.5,\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalChildren\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4346134936801766,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0,\n          0.3333333333333333,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearlyIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3927113726672838,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.1333333333333333,\n          0.20000000000000007,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MiddleName\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"B.\",\n          \"E.\",\n          \"C.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearlyIncome_Binned\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Moderate\",\n          \"Low\",\n          \"High\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender_F\",\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\": \"Gender_M\",\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}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Identify numerical columns\n",
        "numerical_cols = df1.select_dtypes(include=['float64', 'int64']).columns\n",
        "print(\"Numerical columns to be normalized:\", numerical_cols)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6aY2xsIS7Gvb",
        "outputId": "d107883d-9fcf-454a-8831-e04321d738a8"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Numerical columns to be normalized: Index(['NumberCarsOwned', 'TotalChildren', 'YearlyIncome'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "df1[numerical_cols] = scaler.fit_transform(df[numerical_cols])\n",
        "\n",
        "df1.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9oGu9utu7LLl",
        "outputId": "478b7319-3427-439d-cb00-47ed12892610"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Education  Occupation  NumberCarsOwned  TotalChildren  YearlyIncome  \\\n",
              "0      Masters    Engineer              0.5       1.000000      0.400000   \n",
              "1    Bachelors     Teacher              0.0       0.000000      0.133333   \n",
              "2          PhD   Scientist              0.5       0.666667      1.000000   \n",
              "3  High School  Technician              1.0       1.000000      0.000000   \n",
              "4    Bachelors       Nurse              0.0       0.333333      0.200000   \n",
              "\n",
              "  MiddleName YearlyIncome_Binned  Gender_F  Gender_M  \n",
              "0         A.            Moderate     False      True  \n",
              "1         B.                 Low      True     False  \n",
              "2         C.                High      True     False  \n",
              "3         D.                 Low     False      True  \n",
              "4         E.                 Low      True     False  "
            ],
            "text/html": [
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              "    <div>\n",
              "<style scoped>\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e6fb3984-a882-4978-b025-756bf53e1e72');\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-d0b3e6c8-3532-4e89-b44c-ed10cde1d4e5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d0b3e6c8-3532-4e89-b44c-ed10cde1d4e5')\"\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-d0b3e6c8-3532-4e89-b44c-ed10cde1d4e5 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",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Bachelors\",\n          \"High School\",\n          \"Masters\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupation\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Teacher\",\n          \"Nurse\",\n          \"Scientist\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumberCarsOwned\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4183300132670378,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.5,\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalChildren\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4346134936801766,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0,\n          0.3333333333333333,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearlyIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.39271137266728373,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.1333333333333333,\n          0.19999999999999996,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MiddleName\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"B.\",\n          \"E.\",\n          \"C.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearlyIncome_Binned\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Moderate\",\n          \"Low\",\n          \"High\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender_F\",\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\": \"Gender_M\",\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}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df1[numerical_cols].describe())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xrMd_z7X7OSS",
        "outputId": "7225a42b-471d-457c-937f-6058659b44f7"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       NumberCarsOwned  TotalChildren  YearlyIncome\n",
            "count          5.00000       5.000000      5.000000\n",
            "mean           0.40000       0.600000      0.346667\n",
            "std            0.41833       0.434613      0.392711\n",
            "min            0.00000       0.000000      0.000000\n",
            "25%            0.00000       0.333333      0.133333\n",
            "50%            0.50000       0.666667      0.200000\n",
            "75%            0.50000       1.000000      0.400000\n",
            "max            1.00000       1.000000      1.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df1['YearlyIncome_Binned'] = pd.cut(df1['YearlyIncome'], bins=5, labels=[\"Very Low\",\"Low\",\"Moderate\",\"High\",\"Very High\"])\n",
        "df1[['YearlyIncome', 'YearlyIncome_Binned']].head(20)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ZDzlLFXT7Q8Y",
        "outputId": "19e84904-7688-4b2f-eddc-a352e01e8941"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   YearlyIncome YearlyIncome_Binned\n",
              "0      0.400000                 Low\n",
              "1      0.133333            Very Low\n",
              "2      1.000000           Very High\n",
              "3      0.000000            Very Low\n",
              "4      0.200000            Very Low"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-825399ac-c3a0-4f5f-83b9-8634704370bf\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>YearlyIncome</th>\n",
              "      <th>YearlyIncome_Binned</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>0.400000</td>\n",
              "      <td>Low</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.133333</td>\n",
              "      <td>Very Low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>Very High</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>Very Low</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.200000</td>\n",
              "      <td>Very Low</td>\n",
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              "</table>\n",
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              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-825399ac-c3a0-4f5f-83b9-8634704370bf')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      width: 32px;\n",
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              "\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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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-825399ac-c3a0-4f5f-83b9-8634704370bf 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-825399ac-c3a0-4f5f-83b9-8634704370bf');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\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",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    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",
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              "    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",
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              "    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",
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            ],
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df1[['YearlyIncome', 'YearlyIncome_Binned']]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"YearlyIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.39271137266728373,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.1333333333333333,\n          0.19999999999999996,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearlyIncome_Binned\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Low\",\n          \"Very Low\",\n          \"Very High\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "column_to_standardize = 'YearlyIncome'\n",
        "df[column_to_standardize] = scaler.fit_transform(df[[column_to_standardize]])\n",
        "df[[column_to_standardize]].head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PGnHHhex7UD_",
        "outputId": "5e732069-93fb-4664-94cf-c0abece42d79"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   YearlyIncome\n",
              "0      0.151838\n",
              "1     -0.607352\n",
              "2      1.860015\n",
              "3     -0.986947\n",
              "4     -0.417554"
            ],
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[[column_to_standardize]]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"YearlyIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.118033988749895,\n        \"min\": -0.9869465034014604,\n        \"max\": 1.8600145641027523,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -0.6073516944008988,\n          -0.4175542899006179,\n          1.8600145641027523\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1= pd.get_dummies(df1, columns=['Occupation'])\n",
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "nvynQoLl7iDR",
        "outputId": "639b0fec-26cb-4f45-f7e5-277f9650a76f"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Education  NumberCarsOwned  TotalChildren  YearlyIncome MiddleName  \\\n",
              "0      Masters              0.5       1.000000      0.400000         A.   \n",
              "1    Bachelors              0.0       0.000000      0.133333         B.   \n",
              "2          PhD              0.5       0.666667      1.000000         C.   \n",
              "3  High School              1.0       1.000000      0.000000         D.   \n",
              "4    Bachelors              0.0       0.333333      0.200000         E.   \n",
              "\n",
              "  YearlyIncome_Binned  Gender_F  Gender_M  Occupation_Engineer  \\\n",
              "0                 Low     False      True                 True   \n",
              "1            Very Low      True     False                False   \n",
              "2           Very High      True     False                False   \n",
              "3            Very Low     False      True                False   \n",
              "4            Very Low      True     False                False   \n",
              "\n",
              "   Occupation_Nurse  Occupation_Scientist  Occupation_Teacher  \\\n",
              "0             False                 False               False   \n",
              "1             False                 False                True   \n",
              "2             False                  True               False   \n",
              "3             False                 False               False   \n",
              "4              True                 False               False   \n",
              "\n",
              "   Occupation_Technician  \n",
              "0                  False  \n",
              "1                  False  \n",
              "2                  False  \n",
              "3                   True  \n",
              "4                  False  "
            ],
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              "      <th>3</th>\n",
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              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Bachelors\",\n          \"High School\",\n          \"Masters\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumberCarsOwned\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4183300132670378,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.5,\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalChildren\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4346134936801766,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0,\n          0.3333333333333333,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearlyIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.39271137266728373,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.1333333333333333,\n          0.19999999999999996,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MiddleName\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"B.\",\n          \"E.\",\n          \"C.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearlyIncome_Binned\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Low\",\n          \"Very Low\",\n          \"Very High\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender_F\",\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\": \"Gender_M\",\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\": \"Occupation_Engineer\",\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\": \"Occupation_Nurse\",\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\": \"Occupation_Scientist\",\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\": \"Occupation_Teacher\",\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\": \"Occupation_Technician\",\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": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numerical_cols = df1.select_dtypes(include=['float64', 'int64']).columns\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "df1[numerical_cols] = scaler.fit_transform(df[numerical_cols])\n",
        "\n",
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "BMKVc94V7lEg",
        "outputId": "9ed4b03a-2e81-491e-81b2-4cc18787c0f4"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Education  NumberCarsOwned  TotalChildren  YearlyIncome MiddleName  \\\n",
              "0      Masters              0.5       1.000000      0.400000         A.   \n",
              "1    Bachelors              0.0       0.000000      0.133333         B.   \n",
              "2          PhD              0.5       0.666667      1.000000         C.   \n",
              "3  High School              1.0       1.000000      0.000000         D.   \n",
              "4    Bachelors              0.0       0.333333      0.200000         E.   \n",
              "\n",
              "  YearlyIncome_Binned  Gender_F  Gender_M  Occupation_Engineer  \\\n",
              "0                 Low     False      True                 True   \n",
              "1            Very Low      True     False                False   \n",
              "2           Very High      True     False                False   \n",
              "3            Very Low     False      True                False   \n",
              "4            Very Low      True     False                False   \n",
              "\n",
              "   Occupation_Nurse  Occupation_Scientist  Occupation_Teacher  \\\n",
              "0             False                 False               False   \n",
              "1             False                 False                True   \n",
              "2             False                  True               False   \n",
              "3             False                 False               False   \n",
              "4              True                 False               False   \n",
              "\n",
              "   Occupation_Technician  \n",
              "0                  False  \n",
              "1                  False  \n",
              "2                  False  \n",
              "3                   True  \n",
              "4                  False  "
            ],
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              "      <td>0.333333</td>\n",
              "      <td>0.200000</td>\n",
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              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Bachelors\",\n          \"High School\",\n          \"Masters\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumberCarsOwned\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4183300132670378,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.5,\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalChildren\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4346134936801766,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0,\n          0.3333333333333333,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearlyIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.39271137266728373,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.1333333333333333,\n          0.19999999999999996,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MiddleName\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"B.\",\n          \"E.\",\n          \"C.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearlyIncome_Binned\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Low\",\n          \"Very Low\",\n          \"Very High\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender_F\",\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\": \"Gender_M\",\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\": \"Occupation_Engineer\",\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\": \"Occupation_Nurse\",\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\": \"Occupation_Scientist\",\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\": \"Occupation_Teacher\",\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\": \"Occupation_Technician\",\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": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.metrics.pairwise import pairwise_distances, cosine_similarity\n",
        "import numpy as np\n",
        "\n",
        "# Assuming df1 is already defined and contains your data\n",
        "df1_encoded = pd.get_dummies(df1)\n",
        "\n",
        "obj1 = df1_encoded.iloc[0].values\n",
        "obj2 = df1_encoded.iloc[1].values\n",
        "\n",
        "obj1 = np.array(obj1, dtype=float)\n",
        "obj2 = np.array(obj2, dtype=float)\n",
        "\n",
        "# Simple Matching Coefficient (SMC)\n",
        "smc = 1 - pairwise_distances([obj1], [obj2], metric='hamming')[0][0]\n",
        "print(f\"Simple Matching Coefficient: {smc}\")\n",
        "\n",
        "# Cosine Similarity\n",
        "cos_sim = cosine_similarity([obj1], [obj2])[0][0]\n",
        "print(f\"Cosine Similarity: {cos_sim}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t9wTxCVd7mPm",
        "outputId": "ebe4ab29-4e6c-4b07-d09c-775dff9493d8"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Simple Matching Coefficient: 0.45833333333333337\n",
            "Cosine Similarity: 0.009404029899585773\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "a6UsdSko7xvM",
        "outputId": "ba6a0cc5-77e8-46c7-fa5a-5f42ce97bbc1"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Education  NumberCarsOwned  TotalChildren  YearlyIncome MiddleName  \\\n",
              "0      Masters              0.5       1.000000      0.400000         A.   \n",
              "1    Bachelors              0.0       0.000000      0.133333         B.   \n",
              "2          PhD              0.5       0.666667      1.000000         C.   \n",
              "3  High School              1.0       1.000000      0.000000         D.   \n",
              "4    Bachelors              0.0       0.333333      0.200000         E.   \n",
              "\n",
              "  YearlyIncome_Binned  Gender_F  Gender_M  Occupation_Engineer  \\\n",
              "0                 Low     False      True                 True   \n",
              "1            Very Low      True     False                False   \n",
              "2           Very High      True     False                False   \n",
              "3            Very Low     False      True                False   \n",
              "4            Very Low      True     False                False   \n",
              "\n",
              "   Occupation_Nurse  Occupation_Scientist  Occupation_Teacher  \\\n",
              "0             False                 False               False   \n",
              "1             False                 False                True   \n",
              "2             False                  True               False   \n",
              "3             False                 False               False   \n",
              "4              True                 False               False   \n",
              "\n",
              "   Occupation_Technician  \n",
              "0                  False  \n",
              "1                  False  \n",
              "2                  False  \n",
              "3                   True  \n",
              "4                  False  "
            ],
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              "      --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-41e72ff4-7151-4cea-af85-a3f3ce859ac9 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",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Bachelors\",\n          \"High School\",\n          \"Masters\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumberCarsOwned\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4183300132670378,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.5,\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TotalChildren\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4346134936801766,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.0,\n          0.3333333333333333,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearlyIncome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.39271137266728373,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.1333333333333333,\n          0.19999999999999996,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MiddleName\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"B.\",\n          \"E.\",\n          \"C.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"YearlyIncome_Binned\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Low\",\n          \"Very Low\",\n          \"Very High\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender_F\",\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\": \"Gender_M\",\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\": \"Occupation_Engineer\",\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\": \"Occupation_Nurse\",\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\": \"Occupation_Scientist\",\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\": \"Occupation_Teacher\",\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\": \"Occupation_Technician\",\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": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "correlation = df1['NumberCarsOwned'].corr(df1['YearlyIncome'])\n",
        "print(f\"Correlation between Number of Cars Owned and Yearly Income: {correlation}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SqcJcYkt7n8M",
        "outputId": "d9fa3367-43ee-4e29-94dc-cb08ccfa1639"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlation between Number of Cars Owned and Yearly Income: 0.010145098007699985\n"
          ]
        }
      ]
    }
  ]
}