K_Means-With-PCA-Complete Exercise .ipynb

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    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPFKKYkKRVcm3mMiRTZUaeN",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/SACHINSIROHI47/DataScience_with_Python_Programming_Project/blob/main/K_Means-With-PCA-Complete%20Exercise%20.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# **K-means with PCA¶**\n",
        "K-means is an unsupervised learning method for clustering data points. The algorithm iteratively divides data points into K clusters by minimizing the variance in each cluster.\n",
        "\n",
        "Here, we will show you how to estimate the best value for K using the elbow method, then use K-means clustering to group the data points into clusters."
      ],
      "metadata": {
        "id": "k93efNyds1q0"
      }
    },
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)"
      ],
      "metadata": {
        "id": "w8nMJIoas6ck"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "NY5wn5PNs4q6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "1hCDrLldsx0k",
        "outputId": "c3c9abb5-8104-44f6-a66a-0f7c4406e3e8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    0    1    2     3     4     5    6    7    8    9   ...   54   55   56  \\\n",
              "0  0.0  0.0  5.0  13.0   9.0   1.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
              "1  0.0  0.0  0.0  12.0  13.0   5.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
              "2  0.0  0.0  0.0   4.0  15.0  12.0  0.0  0.0  0.0  0.0  ...  5.0  0.0  0.0   \n",
              "3  0.0  0.0  7.0  15.0  13.0   1.0  0.0  0.0  0.0  8.0  ...  9.0  0.0  0.0   \n",
              "4  0.0  0.0  0.0   1.0  11.0   0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
              "\n",
              "    57   58    59    60    61   62   63  \n",
              "0  0.0  6.0  13.0  10.0   0.0  0.0  0.0  \n",
              "1  0.0  0.0  11.0  16.0  10.0  0.0  0.0  \n",
              "2  0.0  0.0   3.0  11.0  16.0  9.0  0.0  \n",
              "3  0.0  7.0  13.0  13.0   9.0  0.0  0.0  \n",
              "4  0.0  0.0   2.0  16.0   4.0  0.0  0.0  \n",
              "\n",
              "[5 rows x 64 columns]"
            ],
            "text/html": [
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              "    <div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>54</th>\n",
              "      <th>55</th>\n",
              "      <th>56</th>\n",
              "      <th>57</th>\n",
              "      <th>58</th>\n",
              "      <th>59</th>\n",
              "      <th>60</th>\n",
              "      <th>61</th>\n",
              "      <th>62</th>\n",
              "      <th>63</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <th>2</th>\n",
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              "      <td>16.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>8.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 64 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-53e9b9ad-a0b7-48a6-8a84-8b5d3245f352')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-53e9b9ad-a0b7-48a6-8a84-8b5d3245f352 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-53e9b9ad-a0b7-48a6-8a84-8b5d3245f352');\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-62a635eb-f7a4-4962-bb9e-31dd5520bf63\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-62a635eb-f7a4-4962-bb9e-31dd5520bf63')\"\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-62a635eb-f7a4-4962-bb9e-31dd5520bf63 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"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "from sklearn.datasets import load_digits\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.decomposition import PCA\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "data=load_digits().data\n",
        "df=pd.DataFrame(data)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**PCA- Principal Component Analysis**\n",
        "\n",
        "reducing the dimensionality of data.\n",
        "\n",
        "**Reduces dimensionality**\n",
        "\n",
        "PCA creates new variables that are uncorrelated and gradually reduce the variance of the system\n"
      ],
      "metadata": {
        "id": "O1tjFYo9wyDV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pca=PCA(2)\n",
        "print(pca)\n",
        "#Transpose the data\n",
        "df=pca.fit_transform(data)\n",
        "df.shape\n",
        "print(df.shape)\n",
        "plt.plot(df)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "h1CG5Yawv7JV",
        "outputId": "834942ba-5809-444d-b85b-cb15c9f2a352"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PCA(n_components=2)\n",
            "(1797, 2)\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": [
        "kmeans=KMeans(n_clusters=10,random_state=0)\n",
        "#fit the model\n",
        "kmeans.fit(df)\n",
        "#predict the lebels of clusters\n",
        "label=kmeans.fit_predict(df)\n",
        "print(label)\n",
        "print(label.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D3SQue9ds1Nt",
        "outputId": "313ccbdf-37cc-4029-cadd-b2329af05567"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[9 4 5 ... 5 3 3]\n",
            "(1797,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#display df dataset  using scatter chart\n",
        "import matplotlib.pyplot as plt\n",
        "flabel=df[label==0]\n",
        "# print(flabel)\n",
        "plt.scatter(flabel[:,0],flabel[:,1])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "-xP-b1aAuv-N",
        "outputId": "59e0fb5b-5ee4-47e0-c70b-59a26510f727"
      },
      "execution_count": 45,
      "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": [
        "flabel2=df[label==0]\n",
        "flabel8=df[label==1]\n",
        "plt.scatter(flabel2[:,0],flabel2[:,1],color='red')\n",
        "plt.scatter(flabel8[:,0],flabel8[:,1],color='black')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "HEJ1icT_xm3N",
        "outputId": "3303518f-74c9-43b2-e9fa-473bf08c2060"
      },
      "execution_count": 37,
      "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": [
        "#show all label using chart\n",
        "for i in range(10):\n",
        "  flabel=df[label==i]\n",
        "  plt.scatter(flabel[:,0],flabel[:,1])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "id": "prj4O5asx8JL",
        "outputId": "8f9f1158-c2e0-4ccb-c35c-9979eedefdfe"
      },
      "execution_count": 38,
      "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": [
        "#use centroid above chart\n",
        "centroids=kmeans.cluster_centers_\n",
        "print(centroids)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YAVgMAI1yEt7",
        "outputId": "d5db68b0-c6db-4061-d79f-3ee4e6ee0ac0"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-14.92697626   6.22301013]\n",
            " [  6.33863008 -20.46071373]\n",
            " [ 22.39514607   1.0909264 ]\n",
            " [ -5.8931547   -4.29648396]\n",
            " [  9.03171611  17.12545736]\n",
            " [  1.72902028   6.13651096]\n",
            " [-18.72406468  -9.2969905 ]\n",
            " [ 17.38799275 -11.79728926]\n",
            " [ -3.78756593  17.11790078]\n",
            " [ -3.4582747  -20.92358434]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#display centroid using scattter chart\n",
        "for i in range(10):\n",
        "  flabel=df[label==i]\n",
        "  plt.scatter(flabel[:,0],flabel[:,1])\n",
        "plt.scatter(centroids[:,0],centroids[:,1],marker='x',s=150,linewidths=5,zorder=10)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 433
        },
        "id": "HxUtAYmayLOs",
        "outputId": "ae43e15f-0d55-4ed0-b6be-707c9115fb83"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **K-MEANS Second Example using PCA**"
      ],
      "metadata": {
        "id": "Z3qO0hfy3iUB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import necessary libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Load the dataset\n",
        "data = pd.read_csv('DataforStandardScaler.csv')\n",
        "data\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "PbiBXrYQyOhX",
        "outputId": "fbd4414d-dfa7-4221-f29f-0ad31b9c6fe2"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Feature1   Feature2   Feature3\n",
              "0  54.967142  48.617357  56.476885\n",
              "1  65.230299  47.658466  47.658630\n",
              "2  65.792128  57.674347  45.305256\n",
              "3  55.425600  45.365823  45.342702\n",
              "4  52.419623  30.867198  32.750822\n",
              "5  44.377125  39.871689  53.142473\n",
              "6  40.919759  35.876963  64.656488\n",
              "7  47.742237  50.675282  35.752518\n",
              "8  44.556173  51.109226  38.490064\n",
              "9  53.756980  43.993613  47.083063"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-783c7713-a689-468f-bdfc-91ab77b0f20c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\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>Feature1</th>\n",
              "      <th>Feature2</th>\n",
              "      <th>Feature3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>54.967142</td>\n",
              "      <td>48.617357</td>\n",
              "      <td>56.476885</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>65.230299</td>\n",
              "      <td>47.658466</td>\n",
              "      <td>47.658630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>65.792128</td>\n",
              "      <td>57.674347</td>\n",
              "      <td>45.305256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>55.425600</td>\n",
              "      <td>45.365823</td>\n",
              "      <td>45.342702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>52.419623</td>\n",
              "      <td>30.867198</td>\n",
              "      <td>32.750822</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>44.377125</td>\n",
              "      <td>39.871689</td>\n",
              "      <td>53.142473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>40.919759</td>\n",
              "      <td>35.876963</td>\n",
              "      <td>64.656488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>47.742237</td>\n",
              "      <td>50.675282</td>\n",
              "      <td>35.752518</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>44.556173</td>\n",
              "      <td>51.109226</td>\n",
              "      <td>38.490064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>53.756980</td>\n",
              "      <td>43.993613</td>\n",
              "      <td>47.083063</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "\n",
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              "        document.querySelector('#df-783c7713-a689-468f-bdfc-91ab77b0f20c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-783c7713-a689-468f-bdfc-91ab77b0f20c');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "  @keyframes spin {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1a4770f8-b6f7-44c0-806e-1d0142d78e39 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
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              "\n",
              "  <div id=\"id_fb518bd2-6070-4195-a606-42757fed97bf\">\n",
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              "      .colab-df-generate {\n",
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              "        cursor: pointer;\n",
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              "        height: 32px;\n",
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              "\n",
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              "        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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Feature1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.437968667387546,\n        \"min\": 40.91975924478789,\n        \"max\": 65.79212815507391,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          44.556172754748175,\n          65.23029856408026,\n          44.37712470759027\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Feature2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.89122571527187,\n        \"min\": 30.86719755342202,\n        \"max\": 57.67434729152909,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          51.10922589709866,\n          47.65846625276664,\n          39.87168879665576\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Feature3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.69650579880754,\n        \"min\": 32.75082167486967,\n        \"max\": 64.65648768921554,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          38.490064225776976,\n          47.6586304305082,\n          53.142473325952736\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Step 2: Apply StandardScaler\n",
        "scaler = StandardScaler()\n",
        "scaled_data = scaler.fit_transform(df)\n",
        "\n",
        "scaled_data_df = pd.DataFrame(scaled_data, columns=df.columns)\n",
        "scaled_data_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "k_Yb6Neq0IHW",
        "outputId": "af5e5a08-18af-432a-b544-f988ed59bf7a"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Feature1  Feature2  Feature3\n",
              "0  0.305865  0.460357  1.066539\n",
              "1  1.587964  0.332271  0.107919\n",
              "2  1.658150  1.670170 -0.147912\n",
              "3  0.363137  0.026025 -0.143842\n",
              "4 -0.012378 -1.910670 -1.512686\n",
              "5 -1.017067 -0.707870  0.704060\n",
              "6 -1.448970 -1.241477  1.955731\n",
              "7 -0.596689  0.735250 -1.186376\n",
              "8 -0.994700  0.793216 -0.888782\n",
              "9  0.154688 -0.157272  0.045350"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2dc7e71d-9112-4f3a-b53f-361b39073ac2\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
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              "        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>Feature1</th>\n",
              "      <th>Feature2</th>\n",
              "      <th>Feature3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.305865</td>\n",
              "      <td>0.460357</td>\n",
              "      <td>1.066539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.587964</td>\n",
              "      <td>0.332271</td>\n",
              "      <td>0.107919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.658150</td>\n",
              "      <td>1.670170</td>\n",
              "      <td>-0.147912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.363137</td>\n",
              "      <td>0.026025</td>\n",
              "      <td>-0.143842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.012378</td>\n",
              "      <td>-1.910670</td>\n",
              "      <td>-1.512686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-1.017067</td>\n",
              "      <td>-0.707870</td>\n",
              "      <td>0.704060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-1.448970</td>\n",
              "      <td>-1.241477</td>\n",
              "      <td>1.955731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>-0.596689</td>\n",
              "      <td>0.735250</td>\n",
              "      <td>-1.186376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>-0.994700</td>\n",
              "      <td>0.793216</td>\n",
              "      <td>-0.888782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.154688</td>\n",
              "      <td>-0.157272</td>\n",
              "      <td>0.045350</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2dc7e71d-9112-4f3a-b53f-361b39073ac2 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-2dc7e71d-9112-4f3a-b53f-361b39073ac2');\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-c3eeacc9-8077-4f99-8974-9a455e3553b0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c3eeacc9-8077-4f99-8974-9a455e3553b0')\"\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-c3eeacc9-8077-4f99-8974-9a455e3553b0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_50cbd6a9-7f72-4f48-9e83-b2e6a960f4e7\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('scaled_data_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_50cbd6a9-7f72-4f48-9e83-b2e6a960f4e7 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('scaled_data_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "scaled_data_df",
              "summary": "{\n  \"name\": \"scaled_data_df\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Feature1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0540925533894598,\n        \"min\": -1.4489700591483416,\n        \"max\": 1.6581496620730476,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -0.9947000146637223,\n          1.5879644775239312,\n          -1.0170671530885804\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Feature2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0540925533894598,\n        \"min\": -1.910669950639052,\n        \"max\": 1.6701700780333935,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.7932156195423374,\n          0.3322707453401271,\n          -0.7078698396012604\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Feature3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0540925533894596,\n        \"min\": -1.5126861586694955,\n        \"max\": 1.9557307798104557,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          -0.8887817377850878,\n          0.10791929681378447,\n          0.7040596054965942\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#label the scaled values\n",
        "label=kmeans.fit_predict(scaled_data)\n",
        "print(label)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UIhqVhJy2gVs",
        "outputId": "5d4a0282-c2a2-4235-e8c2-8802a5f70e0d"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[7 6 5 1 2 0 4 3 8 9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#apply the pca\n",
        "pca=PCA(2)\n",
        "df=pca.fit_transform(scaled_data)\n",
        "df.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k-WSa2vV3Idb",
        "outputId": "5e7e5b97-8ebb-40fe-f020-e091c247e6e4"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#display the data using chart\n",
        "for i in range(10):\n",
        "  flabel=df[label==i]\n",
        "  plt.scatter(flabel[:,0],flabel[:,1])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "sGVmNPYi3b_i",
        "outputId": "e0c637c6-37f0-41b0-c579-a03575a11214"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}