Clustering_Species.ipynb

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        "# Import Required Packages\n",
        "import pandas as pd  # Import pandas for data manipulation\n",
        "import matplotlib.pyplot as plt  # Import matplotlib for visualization\n",
        "from sklearn.preprocessing import StandardScaler  # Import StandardScaler for feature scaling\n",
        "from sklearn.decomposition import PCA  # Import PCA for dimensionality reduction\n",
        "from sklearn.cluster import KMeans  # Import KMeans for clustering"
      ]
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        "# Loading and examining the dataset\n",
        "df = pd.read_csv(\"/content/drive/MyDrive/penguins.csv\")  # Load the penguins dataset from the CSV file into a pandas DataFrame\n",
        "df.head()  # Display the first 5 rows of the DataFrame to examine its structure and contents\n"
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              "   culmen_length_mm  culmen_depth_mm  flipper_length_mm  body_mass_g     sex\n",
              "0              39.1             18.7              181.0       3750.0    MALE\n",
              "1              39.5             17.4              186.0       3800.0  FEMALE\n",
              "2              40.3             18.0              195.0       3250.0  FEMALE\n",
              "3               NaN              NaN                NaN          NaN     NaN\n",
              "4              36.7             19.3              193.0       3450.0  FEMALE"
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 344,\n  \"fields\": [\n    {\n      \"column\": \"culmen_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.4595837139265315,\n        \"min\": 32.1,\n        \"max\": 59.6,\n        \"samples\": [\n          48.2,\n          49.8,\n          45.1\n        ],\n        \"num_unique_values\": 164,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"culmen_depth_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9747931568167816,\n        \"min\": 13.1,\n        \"max\": 21.5,\n        \"samples\": [\n          16.9,\n          18.7,\n          18.6\n        ],\n        \"num_unique_values\": 80,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flipper_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 260.5580573536498,\n        \"min\": -132.0,\n        \"max\": 5000.0,\n        \"samples\": [\n          181.0,\n          5000.0,\n          199.0\n        ],\n        \"num_unique_values\": 57,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body_mass_g\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 801.9545356980956,\n        \"min\": 2700.0,\n        \"max\": 6300.0,\n        \"samples\": [\n          4350.0,\n          4150.0,\n          3525.0\n        ],\n        \"num_unique_values\": 94,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"samples\": [\n          \"MALE\",\n          \"FEMALE\",\n          \".\"\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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        "Columns in the dataset\n",
        "\n",
        "\n",
        "-\n",
        "Species: penguin species (Chinstrap, Adélie, or Gentoo)\n",
        "-\n",
        "Island: island name (Dream, Torgersen, or Biscoe) in the Palmer Archipelago (Antarctica)\n",
        "-\n",
        "culmen_length_mm: culmen length (mm)\n",
        "-\n",
        "culmen_depth_mm: culmen depth (mm)\n",
        "-\n",
        "flipper_length_mm: flipper length (mm)\n",
        "-\n",
        "body_mass_g: body mass (g)\n",
        "-\n",
        "Sex: penguin sex\n",
        "-"
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      "execution_count": 62,
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            "text/plain": [
              "(344, 5)"
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              "Index(['culmen_length_mm', 'culmen_depth_mm', 'flipper_length_mm',\n",
              "       'body_mass_g', 'sex'],\n",
              "      dtype='object')"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 344 entries, 0 to 343\n",
            "Data columns (total 5 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   culmen_length_mm   342 non-null    float64\n",
            " 1   culmen_depth_mm    342 non-null    float64\n",
            " 2   flipper_length_mm  342 non-null    float64\n",
            " 3   body_mass_g        342 non-null    float64\n",
            " 4   sex                335 non-null    object \n",
            "dtypes: float64(4), object(1)\n",
            "memory usage: 13.6+ KB\n"
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      "source": [
        "penguins_df.boxplot()  # Generate a boxplot to visualize the distribution of numerical variables in the DataFrame\n",
        "plt.show()  # Display the boxplot"
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#df.dropna()"
      ],
      "metadata": {
        "id": "itpvzVSkeCze"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna()  # Remove rows with missing values from the DataFrame"
      ],
      "metadata": {
        "id": "rfaC3dO9cFLt"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GFiN0w1UeKBN",
        "outputId": "e44a8d7e-c8d3-4e0b-d034-68c297412875"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(335, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "ebNKI_KAg4sd",
        "outputId": "84d1d22c-c478-4d30-b053-c665670ef7d8"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       culmen_length_mm  culmen_depth_mm  flipper_length_mm  body_mass_g\n",
              "count        335.000000       335.000000         335.000000   335.000000\n",
              "mean          43.988358        17.169552         214.355224  4209.179104\n",
              "std            5.453430         1.971966         263.253508   803.633495\n",
              "min           32.100000        13.100000        -132.000000  2700.000000\n",
              "25%           39.500000        15.600000         190.000000  3550.000000\n",
              "50%           44.500000        17.300000         197.000000  4050.000000\n",
              "75%           48.550000        18.700000         213.000000  4787.500000\n",
              "max           59.600000        21.500000        5000.000000  6300.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ba645c36-a0b1-4fd3-a1e9-693d2ebe18c3\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>culmen_length_mm</th>\n",
              "      <th>culmen_depth_mm</th>\n",
              "      <th>flipper_length_mm</th>\n",
              "      <th>body_mass_g</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>335.000000</td>\n",
              "      <td>335.000000</td>\n",
              "      <td>335.000000</td>\n",
              "      <td>335.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>43.988358</td>\n",
              "      <td>17.169552</td>\n",
              "      <td>214.355224</td>\n",
              "      <td>4209.179104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.453430</td>\n",
              "      <td>1.971966</td>\n",
              "      <td>263.253508</td>\n",
              "      <td>803.633495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>32.100000</td>\n",
              "      <td>13.100000</td>\n",
              "      <td>-132.000000</td>\n",
              "      <td>2700.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>39.500000</td>\n",
              "      <td>15.600000</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>3550.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>44.500000</td>\n",
              "      <td>17.300000</td>\n",
              "      <td>197.000000</td>\n",
              "      <td>4050.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>48.550000</td>\n",
              "      <td>18.700000</td>\n",
              "      <td>213.000000</td>\n",
              "      <td>4787.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>59.600000</td>\n",
              "      <td>21.500000</td>\n",
              "      <td>5000.000000</td>\n",
              "      <td>6300.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      buttonEl.style.display =\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"culmen_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 105.8008077806276,\n        \"min\": 5.453429563406746,\n        \"max\": 335.0,\n        \"samples\": [\n          43.98835820895522,\n          44.5,\n          335.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"culmen_depth_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 113.27104065597187,\n        \"min\": 1.9719662767183503,\n        \"max\": 335.0,\n        \"samples\": [\n          17.169552238805966,\n          17.3,\n          335.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flipper_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1708.567562789759,\n        \"min\": -132.0,\n        \"max\": 5000.0,\n        \"samples\": [\n          214.355223880597,\n          197.0,\n          335.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body_mass_g\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2001.188193629,\n        \"min\": 335.0,\n        \"max\": 6300.0,\n        \"samples\": [\n          4209.179104477612,\n          4050.0,\n          335.0\n        ],\n        \"num_unique_values\": 8,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Detecting outliers in a dataset can be done using various statistical methods and visualization techniques"
      ],
      "metadata": {
        "id": "ORtsx18oip77"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#detect outliers using descriptive statistics\n",
        "# Compute summary statistics\n",
        "mean = df[\"flipper_length_mm\"].mean()\n",
        "median = df[\"flipper_length_mm\"].median()\n",
        "std_dev = df[\"flipper_length_mm\"].std()\n",
        "Q1 = df[\"flipper_length_mm\"].quantile(0.25)\n",
        "Q3 = df[\"flipper_length_mm\"].quantile(0.75)\n",
        "IQR = Q3 - Q1"
      ],
      "metadata": {
        "id": "CVlpVS1miZTg"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define outlier boundaries\n",
        "lower_bound = Q1 - 1.5 * IQR\n",
        "upper_bound = Q3 + 1.5 * IQR"
      ],
      "metadata": {
        "id": "s91FJ2_ai2Dh"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Detect outliers\n",
        "outliers = df[(df[\"flipper_length_mm\"] < lower_bound) | (df[\"flipper_length_mm\"] > upper_bound)]\n",
        "\n",
        "print(\"Mean:\", mean)\n",
        "print(\"Median:\", median)\n",
        "print(\"Standard Deviation:\", std_dev)\n",
        "print(\"Q1:\", Q1)\n",
        "print(\"Q3:\", Q3)\n",
        "print(\"IQR:\", IQR)\n",
        "print(\"Lower Bound:\", lower_bound)\n",
        "print(\"Upper Bound:\", upper_bound)\n",
        "print(\"Outliers:\")\n",
        "print(outliers)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jB1RlSrKivQK",
        "outputId": "e22e4831-4c5f-4ab3-eaa9-82fa648249b6"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean: 214.355223880597\n",
            "Median: 197.0\n",
            "Standard Deviation: 263.25350779494266\n",
            "Q1: 190.0\n",
            "Q3: 213.0\n",
            "IQR: 23.0\n",
            "Lower Bound: 155.5\n",
            "Upper Bound: 247.5\n",
            "Outliers:\n",
            "    culmen_length_mm  culmen_depth_mm  flipper_length_mm  body_mass_g   sex\n",
            "9               42.0             20.2             5000.0       4250.0  MALE\n",
            "14              34.6             21.1             -132.0       4400.0  MALE\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot boxplot\n",
        "plt.boxplot(df[\"flipper_length_mm\"])\n",
        "plt.title('Boxplot of Data')\n",
        "plt.ylabel('Values for flipper_length_mm')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "njNwujqtjUX3",
        "outputId": "b9254f50-7e12-4fb4-9e9e-d88e949b141a"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "df.boxplot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "YdthDjGCljd6",
        "outputId": "7f14dd9d-ccb6-43b5-9140-d7ca21d6660c"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[df[\"flipper_length_mm\"] > 4000]\n",
        "df[df[\"flipper_length_mm\"] < 0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "nw8K2rfIkoOH",
        "outputId": "cd80fd3a-38cc-4b59-9202-ebe147530580"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    culmen_length_mm  culmen_depth_mm  flipper_length_mm  body_mass_g   sex\n",
              "14              34.6             21.1             -132.0       4400.0  MALE"
            ],
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              "      <th>culmen_length_mm</th>\n",
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              "      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-daefa86e-c029-497d-8769-04977e70350e 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-daefa86e-c029-497d-8769-04977e70350e');\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>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[df[\\\"flipper_length_mm\\\"] < 0]\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"culmen_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": NaN,\n        \"min\": 34.6,\n        \"max\": 34.6,\n        \"samples\": [\n          34.6\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"culmen_depth_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": NaN,\n        \"min\": 21.1,\n        \"max\": 21.1,\n        \"samples\": [\n          21.1\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flipper_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": NaN,\n        \"min\": -132.0,\n        \"max\": -132.0,\n        \"samples\": [\n          -132.0\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body_mass_g\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": NaN,\n        \"min\": 4400.0,\n        \"max\": 4400.0,\n        \"samples\": [\n          4400.0\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"MALE\"\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop specific rows (index 9 and 14) from the DataFrame to remove outliers\n",
        "df_cleaned = df.drop([9, 14])"
      ],
      "metadata": {
        "id": "pqpFCX9tkoQB"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "df_cleaned.boxplot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "id": "6ZaKha0VmVIu",
        "outputId": "8d0700ea-9270-4e7f-e33f-c5e21b887c76"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert categorical variables into dummy/indicator variables and drop one of the resulting columns to avoid multicollinearity\n",
        "# Drop the column 'sex_.' which corresponds to the category 'sex' with the lowest lexicographic order\n",
        "df = pd.get_dummies(df_cleaned).drop(\"sex_.\", axis=1)\n",
        "\n",
        "# Display the first few rows of the DataFrame\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Q-P6T0IQmVTD",
        "outputId": "bfe8055e-3ea3-4a50-e0a7-0fe31151dc9c"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   culmen_length_mm  culmen_depth_mm  flipper_length_mm  body_mass_g  \\\n",
              "0              39.1             18.7              181.0       3750.0   \n",
              "1              39.5             17.4              186.0       3800.0   \n",
              "2              40.3             18.0              195.0       3250.0   \n",
              "4              36.7             19.3              193.0       3450.0   \n",
              "5              39.3             20.6              190.0       3650.0   \n",
              "\n",
              "   sex_FEMALE  sex_MALE  \n",
              "0           0         1  \n",
              "1           1         0  \n",
              "2           1         0  \n",
              "4           1         0  \n",
              "5           0         1  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>culmen_length_mm</th>\n",
              "      <th>culmen_depth_mm</th>\n",
              "      <th>flipper_length_mm</th>\n",
              "      <th>body_mass_g</th>\n",
              "      <th>sex_FEMALE</th>\n",
              "      <th>sex_MALE</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>39.1</td>\n",
              "      <td>18.7</td>\n",
              "      <td>181.0</td>\n",
              "      <td>3750.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>39.5</td>\n",
              "      <td>17.4</td>\n",
              "      <td>186.0</td>\n",
              "      <td>3800.0</td>\n",
              "      <td>1</td>\n",
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              "      <th>2</th>\n",
              "      <td>40.3</td>\n",
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              "      <td>3250.0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>36.7</td>\n",
              "      <td>19.3</td>\n",
              "      <td>193.0</td>\n",
              "      <td>3450.0</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>39.3</td>\n",
              "      <td>20.6</td>\n",
              "      <td>190.0</td>\n",
              "      <td>3650.0</td>\n",
              "      <td>0</td>\n",
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              "      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-1c94d5aa-a56c-4219-b8bd-546804bbc5a8 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-1c94d5aa-a56c-4219-b8bd-546804bbc5a8');\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-49d7a699-007f-4685-8889-8a1574bd5b29\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-49d7a699-007f-4685-8889-8a1574bd5b29')\"\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-49d7a699-007f-4685-8889-8a1574bd5b29 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": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 333,\n  \"fields\": [\n    {\n      \"column\": \"culmen_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.444307594541216,\n        \"min\": 32.1,\n        \"max\": 59.6,\n        \"samples\": [\n          59.6,\n          48.1,\n          46.3\n        ],\n        \"num_unique_values\": 163,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"culmen_depth_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9589398810340501,\n        \"min\": 13.1,\n        \"max\": 21.5,\n        \"samples\": [\n          19.5,\n          18.7,\n          21.1\n        ],\n        \"num_unique_values\": 79,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flipper_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.04229924534464,\n        \"min\": 172.0,\n        \"max\": 231.0,\n        \"samples\": [\n          196.0,\n          231.0,\n          224.0\n        ],\n        \"num_unique_values\": 54,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body_mass_g\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 805.9789922954343,\n        \"min\": 2700.0,\n        \"max\": 6300.0,\n        \"samples\": [\n          4725.0,\n          4400.0,\n          3575.0\n        ],\n        \"num_unique_values\": 93,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex_FEMALE\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"samples\": [\n          1,\n          0\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex_MALE\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"samples\": [\n          0,\n          1\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform preprocessing steps on the dataset - scaling\n",
        "scaler = StandardScaler()  # Initialize the StandardScaler object for scaling the data\n",
        "scaled_data = scaler.fit_transform(df)  # Fit the scaler to the data and transform it"
      ],
      "metadata": {
        "id": "w7OifQaLnFXP"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the scaled data array back to a DataFrame\n",
        "df_preprocessed = pd.DataFrame(data=scaled_data, columns=df.columns)\n",
        "df_preprocessed.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "Mj3Y1-p7n5GM",
        "outputId": "155937af-5ec7-4111-eab6-d65f8e448460"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   culmen_length_mm  culmen_depth_mm  flipper_length_mm  body_mass_g  \\\n",
              "0         -0.905520         0.793126          -1.428125    -0.569709   \n",
              "1         -0.831938         0.128503          -1.071522    -0.507579   \n",
              "2         -0.684775         0.435252          -0.429637    -1.191006   \n",
              "\n",
              "   sex_FEMALE  sex_MALE  \n",
              "0   -0.991031  0.997001  \n",
              "1    1.009050 -1.003008  \n",
              "2    1.009050 -1.003008  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-18021b21-40ec-4df9-8813-0ed4c0be3c0d\" 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",
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              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>culmen_length_mm</th>\n",
              "      <th>culmen_depth_mm</th>\n",
              "      <th>flipper_length_mm</th>\n",
              "      <th>body_mass_g</th>\n",
              "      <th>sex_FEMALE</th>\n",
              "      <th>sex_MALE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.905520</td>\n",
              "      <td>0.793126</td>\n",
              "      <td>-1.428125</td>\n",
              "      <td>-0.569709</td>\n",
              "      <td>-0.991031</td>\n",
              "      <td>0.997001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.831938</td>\n",
              "      <td>0.128503</td>\n",
              "      <td>-1.071522</td>\n",
              "      <td>-0.507579</td>\n",
              "      <td>1.009050</td>\n",
              "      <td>-1.003008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.684775</td>\n",
              "      <td>0.435252</td>\n",
              "      <td>-0.429637</td>\n",
              "      <td>-1.191006</td>\n",
              "      <td>1.009050</td>\n",
              "      <td>-1.003008</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-18021b21-40ec-4df9-8813-0ed4c0be3c0d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-18021b21-40ec-4df9-8813-0ed4c0be3c0d 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-18021b21-40ec-4df9-8813-0ed4c0be3c0d');\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-51f44fbb-5c77-4ebc-a1ee-cff71dd10eec\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-51f44fbb-5c77-4ebc-a1ee-cff71dd10eec')\"\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-51f44fbb-5c77-4ebc-a1ee-cff71dd10eec 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": "df_preprocessed",
              "summary": "{\n  \"name\": \"df_preprocessed\",\n  \"rows\": 333,\n  \"fields\": [\n    {\n      \"column\": \"culmen_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0015048917468008,\n        \"min\": -2.193201692027821,\n        \"max\": 2.8655471102352323,\n        \"samples\": [\n          2.8655471102352323,\n          0.7500703383797738,\n          0.41895223495891853\n        ],\n        \"num_unique_values\": 163,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"culmen_depth_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0015048917468008,\n        \"min\": -2.0698651683202,\n        \"max\": 2.22462144258713,\n        \"samples\": [\n          1.2021246304663373,\n          0.7931259056180198,\n          2.0201220801629725\n        ],\n        \"num_unique_values\": 79,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"flipper_length_mm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0015048917468008,\n        \"min\": -2.0700101550587138,\n        \"max\": 2.1379039180337394,\n        \"samples\": [\n          -0.35831629481771576,\n          2.1379039180337394,\n          1.6386598754634483\n        ],\n        \"num_unique_values\": 54,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"body_mass_g\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0015048917468006,\n        \"min\": -1.874432959505961,\n        \"max\": 2.5989064758312126,\n        \"samples\": [\n          0.6418204728711991,\n          0.23797732940325983,\n          -0.7871629578615091\n        ],\n        \"num_unique_values\": 93,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex_FEMALE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0015048917468008,\n        \"min\": -0.9910312089651149,\n        \"max\": 1.009049958219026,\n        \"samples\": [\n          1.009049958219026,\n          -0.9910312089651149\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex_MALE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0015048917468008,\n        \"min\": -1.003007525587691,\n        \"max\": 0.9970014925003394,\n        \"samples\": [\n          -1.003007525587691,\n          0.9970014925003394\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Before PCA:\n",
        "\n",
        "\n",
        "The number of features in your original dataset represents its dimensionality.\n",
        "\n",
        "\n",
        "After PCA:\n",
        "\n",
        "\n",
        "The number of principal components you retain after performing PCA will determine the new dimensionality of your dataset."
      ],
      "metadata": {
        "id": "BTQ3ZgkQpCe_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the dimensionality of the original dataset\n",
        "original_dimension = df_preprocessed.shape[1]  # Number of columns/features\n",
        "original_dimension"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ZBfmj7j0U8Z",
        "outputId": "df691e24-a7c6-4c73-d031-d80a18180c1d"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize PCA with n_components=None to keep all components\n",
        "pca = PCA(n_components=None)\n",
        "\n",
        "# Fit PCA to the preprocessed the dataset\n",
        "dfx_pca = pca.fit(df_preprocessed)"
      ],
      "metadata": {
        "id": "HWx6ZmXj0oVh"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the explained variance ratio of each principal component\n",
        "explained_variance_ratio = dfx_pca.explained_variance_ratio_\n",
        "\n",
        "# Calculate the number of components needed to explain at least 10% of the variance\n",
        "n_components = sum(explained_variance_ratio > 0.1)\n",
        "\n",
        "# Initialize PCA with the determined number of components\n",
        "pca = PCA(n_components=n_components)\n",
        "\n",
        "# Fit PCA to the preprocessed penguins dataset again with the determined number of components\n",
        "penguins_PCA = pca.fit_transform(df_preprocessed)\n",
        "\n",
        "# Print the number of components\n",
        "print(\"Number of components:\", n_components)\n"
      ],
      "metadata": {
        "id": "Ipxh5jYrnFh3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d760e9e6-c9d1-4f88-e851-82194f6c0c53"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of components: 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize an empty list to store the inertia values\n",
        "inertia = []\n",
        "\n",
        "# Iterate over a range of cluster numbers from 1 to 9\n",
        "for k in range(1, 10):\n",
        "    # Fit KMeans clustering algorithm to the data with 'k' clusters\n",
        "    kmeans = KMeans(n_clusters=k, random_state=42).fit(penguins_PCA)\n",
        "    # Append the inertia (sum of squared distances of samples to their closest cluster center) to the list\n",
        "    inertia.append(kmeans.inertia_)\n",
        "\n",
        "# Plot the inertia values against the number of clusters\n",
        "plt.plot(range(1, 10), inertia, marker=\"o\")\n",
        "plt.xlabel(\"Number of clusters\")\n",
        "plt.ylabel(\"Inertia\")\n",
        "plt.title(\"Elbow Method\")\n",
        "plt.show()\n",
        "\n",
        "# Based on the elbow method, choose the number of clusters\n",
        "n_clusters = 4"
      ],
      "metadata": {
        "id": "Dtqk2db3nFmC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        },
        "outputId": "27a5673b-87fc-43b4-8ef2-5fccd277c620"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply K-means clustering algorithm\n",
        "kmeans = KMeans(n_clusters=n_clusters, random_state=42).fit(penguins_PCA)\n",
        "\n",
        "# Scatter plot of the data points colored by their cluster labels\n",
        "plt.scatter(penguins_PCA[:, 0], penguins_PCA[:, 1], c=kmeans.labels_, cmap=\"viridis\")\n",
        "\n",
        "# Set labels for x-axis and y-axis\n",
        "plt.xlabel(\"First Principal Component\")\n",
        "plt.ylabel(\"Second Principal Component\")\n",
        "\n",
        "# Set title for the plot including the number of clusters\n",
        "plt.title(f\"K-means Clustering (K={n_clusters})\")\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "CWmaTYv3nFqD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "outputId": "da738c82-cf56-4eb7-a5ee-90ae5c05e172"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "To evaluate the quality of the clusters produced by the K-means algorithm,\n",
        "we can use various metrics. One commonly used metric is the silhouette score,\n",
        " which measures how similar an object is to its own cluster compared to other clusters.\n",
        "  Higher silhouette scores indicate better clusterings.\n",
        "\"\"\"\n"
      ],
      "metadata": {
        "id": "Y0VAQ52T94r7"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "The silhouette score ranges from -1 to 1.\n",
        "A score close to +1 indicates that the sample is far away from the neighboring clusters.\n",
        "A score of 0 indicates that the sample is on or very close to the decision boundary between two neighboring clusters.\n",
        "A score close to -1 indicates that the samples might have been assigned to the wrong clusters.\n",
        "\"\"\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "id": "CTp1IROQ9FXx",
        "outputId": "1ebf4864-98e0-428d-f9e2-e2795147d02b"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nThe silhouette score ranges from -1 to 1.\\nA score close to +1 indicates that the sample is far away from the neighboring clusters.\\nA score of 0 indicates that the sample is on or very close to the decision boundary between two neighboring clusters.\\nA score close to -1 indicates that the samples might have been assigned to the wrong clusters.\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import silhouette_score\n",
        "silhouette_avg = silhouette_score(penguins_PCA, kmeans.labels_)\n",
        "print(\"Evaluate the quality of the clusters:\", silhouette_avg)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lRG4q8lF853O",
        "outputId": "4b6a19a0-e826-4b43-93c3-d7de236ff86f"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluate the quality of the clusters: 0.738950165133926\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "P5AT03Yn9Fds"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}