Data_analysis_parametrs.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2UDg7bjBqEku"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Covariance:\n",
        "\n",
        "Covariance is a statistical term that refers to a systematic relationship between two random variables in which a change in the other reflects a change in one variable.\n",
        "\n",
        "It can range from -∞ to +∞, with a negative value indicating a negative relationship and a positive value indicating a positive relationship.\n",
        "\n",
        "The greater this number, the more reliant the relationship.\n",
        "\n",
        "A positive number, or positive covariance, indicates a direct relationship. Conversely, a negative value signifies negative covariance, signifying an inverse correlation between the two variables.\n",
        "\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "where,\n",
        "\n",
        "xi​ and yi​ are individual data points of variables x and y,\n",
        "\n",
        "ˉxˉ and ˉyˉ​ are the means of variables x and y,\n",
        "\n",
        "n is the number of data points."
      ],
      "metadata": {
        "id": "jQRo8kHFqcoA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# covariance using numpy\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "# Sample data for two variables\n",
        "variable1 = np.array([1, 2, 3, 4, 5])\n",
        "variable2 = np.array([6, 7, 8, 9, 10])\n",
        "\n",
        "# Calculate covariance\n",
        "print(np.cov(variable1, variable2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gr6Wa9EPqy5B",
        "outputId": "16c55992-087b-4caa-e359-63b2740bd50f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2.5 2.5]\n",
            " [2.5 2.5]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# covariance using sklearn\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "iris= pd.read_csv('iris.csv')\n",
        "\n",
        "iris.head(4)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "yzql-VcdrSl4",
        "outputId": "18d62f3d-f9d0-4daa-f29d-6a9d092c838b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   sr.no  sepal_length  sepal_width  petal_length  petal_width species\n",
              "0      1           5.1          3.5           1.4          0.2  setosa\n",
              "1      2           4.9          3.0           1.4          0.2  setosa\n",
              "2      3           4.7          3.2           1.3          0.2  setosa\n",
              "3      4           4.6          3.1           1.5          0.2  setosa"
            ],
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              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f40fb0a2-536a-425a-8122-086a895b3625 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-f40fb0a2-536a-425a-8122-086a895b3625');\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-43b31e22-ab40-44dd-9e9a-9ddccfe42090\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-43b31e22-ab40-44dd-9e9a-9ddccfe42090')\"\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-43b31e22-ab40-44dd-9e9a-9ddccfe42090 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute the covariance matrix\n",
        "cov_matrix = iris.cov()\n",
        "\n",
        "print(\"Covariance Matrix:\")\n",
        "print(cov_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PwgVVYb3rgt2",
        "outputId": "d5b1aafc-96a3-4c5d-b876-5e586ec021cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Covariance Matrix:\n",
            "                    sr.no  sepal_length  sepal_width  petal_length  \\\n",
            "sr.no         1887.500000     25.782886    -7.492282     67.667785   \n",
            "sepal_length    25.782886      0.685694    -0.039268      1.273682   \n",
            "sepal_width     -7.492282     -0.039268     0.188004     -0.321713   \n",
            "petal_length    67.667785      1.273682    -0.321713      3.113179   \n",
            "petal_width     29.832215      0.516904    -0.117981      1.296387   \n",
            "\n",
            "              petal_width  \n",
            "sr.no           29.832215  \n",
            "sepal_length     0.516904  \n",
            "sepal_width     -0.117981  \n",
            "petal_length     1.296387  \n",
            "petal_width      0.582414  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-4-443f62d3488a>:2: FutureWarning: The default value of numeric_only in DataFrame.cov is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  cov_matrix = iris.cov()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The diagonal elements represent the variances of variable1 and variable2 (positive covariance).\n",
        "\n",
        "\n",
        "The off-diagonal elements represent the covariance between variable1 and variable2, and vice versa (negative covariance)"
      ],
      "metadata": {
        "id": "pd-7-DjmrpsD"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "S9WJZyA_rons"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "correlation:\n",
        "\n",
        "Correlation is a statistical measure that quantifies the strength and direction of a linear relationship between two random variables.\n",
        "\n",
        "Unlike covariance, which is not standardized and can take any real value, correlation is normalized and always falls within the range of -1 to 1.\n",
        "\n",
        "A correlation of 1 indicates a perfect positive linear relationship: as one variable increases, the other variable also increases linearly.\n",
        "\n",
        "A correlation of -1 indicates a perfect negative linear relationship: as one variable increases, the other variable decreases linearly.\n",
        "\n",
        "A correlation of 0 indicates no linear relationship between the variables.\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "cov(x,y) is the covariance between x and y\n",
        "\n",
        "σ(x) and σ(y) are the standard deviations of x and y, respectively.\n",
        "\n"
      ],
      "metadata": {
        "id": "mt4Aw84hr6cl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# covariance using numpy\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "# Sample data for two variables\n",
        "variable1 = np.array([1, 2, 3, 4, 5])\n",
        "variable2 = np.array([5, 4, 3, 2, 1])\n",
        "\n",
        "# Calculate correlation coefficient\n",
        "print(np.corrcoef(variable1, variable2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tVu5DYsGsgYS",
        "outputId": "20877b9f-5a7e-422b-b886-13b31161eb98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1. -1.]\n",
            " [-1.  1.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# correlation using sklearn\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "iris = pd.read_csv('iris.csv')\n",
        "\n",
        "# Compute the correlation matrix\n",
        "correlation_matrix = iris.corr()\n",
        "\n",
        "print(\"Correlation Matrix:\")\n",
        "print(correlation_matrix)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D4sJnzPmsucN",
        "outputId": "6fd14a0b-9388-41ff-a06d-f28de4501c7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlation Matrix:\n",
            "                 sr.no  sepal_length  sepal_width  petal_length  petal_width\n",
            "sr.no         1.000000      0.716676    -0.397729      0.882747     0.899759\n",
            "sepal_length  0.716676      1.000000    -0.109369      0.871754     0.817954\n",
            "sepal_width  -0.397729     -0.109369     1.000000     -0.420516    -0.356544\n",
            "petal_length  0.882747      0.871754    -0.420516      1.000000     0.962757\n",
            "petal_width   0.899759      0.817954    -0.356544      0.962757     1.000000\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-6-f38ce9fff321>:8: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  correlation_matrix = iris.corr()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The diagonal elements represent the correlation of variable1 and variable2.\n",
        "\n",
        "The off-diagonal elements represent the correlation between variable1 and variable2, and vice versa.\n",
        "\n",
        "Hence, Covariance gives us a directional sense of this relationship, while correlation, a standardized measure, provides a clearer comparison."
      ],
      "metadata": {
        "id": "22YEAXEQtONM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# new topic :"
      ],
      "metadata": {
        "id": "iT_3TRUhJbhF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Higehr dimensional data analysis:\n",
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "JIw9Yd79JU5_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "omuxRe08IzVt"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def hypersphere_volume(dim):\n",
        "    return math.pi ** (dim / 2) / math.gamma(dim / 2 + 1)"
      ],
      "metadata": {
        "id": "6FBDpWzII0ZC"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def hypercube_volume(dim):\n",
        "    return 2 ** dim"
      ],
      "metadata": {
        "id": "I7jGkXM5I9mP"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Number of dimensions to consider\n",
        "max_dim = 20"
      ],
      "metadata": {
        "id": "WsvxwozEJAFa"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dimensions = range(1, max_dim + 1)\n",
        "\n",
        "sphere_volumes = [hypersphere_volume(dim) for dim in dimensions]\n",
        "\n",
        "cube_volumes = [hypercube_volume(dim) for dim in dimensions]\n",
        "\n",
        "ratios = [sphere_volumes[i] / cube_volumes[i] for i in range(max_dim)]"
      ],
      "metadata": {
        "id": "2TmLRXZMJDWh"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 3))\n",
        "plt.plot(dimensions, ratios, marker='o')\n",
        "\n",
        "plt.xlabel('Number of Dimensions')\n",
        "plt.ylabel('Ratio of Volumes (Hypersphere/Hypercube)')\n",
        "\n",
        "plt.title('Volume Concentration in Higher Dimensions')\n",
        "\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "386X13pBJJUI",
        "outputId": "1a25a2c4-8a9f-4983-f3bf-023559cef847"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "SVM :"
      ],
      "metadata": {
        "id": "4wCfEw0DKocP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "x = np.array([1,2,3,4,5,6,7,8,9,11,12,13,14,15,16,17,18,19,20,21,23,24,25,26,27,28,29,30]).reshape(-1, 1)\n",
        "y = np.array([1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1])"
      ],
      "metadata": {
        "id": "rSSFuFPkKp4p"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def transform_to_2d(X):\n",
        "    return np.c_[X, X**2]"
      ],
      "metadata": {
        "id": "twf1E_vGLAyH"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_transformed = transform_to_2d(x)"
      ],
      "metadata": {
        "id": "RwqmbY4RLEWW"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svm = SVC(kernel='linear')\n",
        "svm.fit(X_transformed, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "TMNOsyLjLI1r",
        "outputId": "4808c784-36c2-4755-9dff-f0eb2a0bfb7a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(kernel='linear')"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(kernel=&#x27;linear&#x27;)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
        "\n",
        "# 1D data plot\n",
        "axes[0].scatter(x, np.zeros_like(x), c=y, cmap='plasma', edgecolors='k')\n",
        "axes[0].set_title('Original 1D Data')\n",
        "axes[0].set_xlabel('Feature')\n",
        "axes[0].set_yticks([])\n",
        "\n",
        "# 2D transformed data plot\n",
        "axes[1].scatter(X_transformed[:, 0], X_transformed[:, 1], c=y, cmap='plasma', edgecolors='k')\n",
        "axes[1].set_title('Transformed 2D Data')\n",
        "axes[1].set_xlabel('Original Feature')\n",
        "axes[1].set_ylabel('Transformed Feature (X^2)')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "BTmYmd7ILMja",
        "outputId": "a12159ae-d392-4977-ed45-71ca6cc886fb"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Transformed Feature (X^2)')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}