5thJULY.ipynb

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      "collapsed_sections": [
        "8vGdrVXa6Qq9",
        "1cKf9Eps5_Cn"
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      "authorship_tag": "ABX9TyM8MRPDxC85io6iBsccZ0RI"
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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "h11OlkRu8Gyf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#MIN MAX & Z SCORE NORMALIZATION"
      ],
      "metadata": {
        "id": "8vGdrVXa6Qq9"
      }
    },
    {
      "cell_type": "code",
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    X   Y\n",
            "0   4  11\n",
            "1   8   4\n",
            "2  13   5\n",
            "3   7  14\n",
            "[[10.         17.        ]\n",
            " [14.44444444 10.        ]\n",
            " [20.         11.        ]\n",
            " [13.33333333 20.        ]]\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import pandas as pd\n",
        "df=pd.DataFrame({'X':[4,8,13,7],'Y':[11,4,5,14]})\n",
        "scaler=MinMaxScaler(feature_range=(10,20))\n",
        "a=scaler.fit_transform(df)\n",
        "print(df)\n",
        "print(a)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler=StandardScaler() #same as Z-score\n",
        "sdf=scaler.fit_transform(df)\n",
        "print(sdf)"
      ],
      "metadata": {
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        "id": "x4CtIUJckaOQ",
        "outputId": "87242b97-1aba-4536-bf53-5f684a87c2e8"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "[[-1.2344268   0.60192927]\n",
            " [ 0.         -1.08347268]\n",
            " [ 1.5430335  -0.84270097]\n",
            " [-0.3086067   1.32424438]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#PRINCIPAL COMPONENT ANALYSIS"
      ],
      "metadata": {
        "id": "1cKf9Eps5_Cn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#WITHOUT NORMALIZATION\n",
        "from sklearn.decomposition import PCA\n",
        "pca=PCA(n_components=1)\n",
        "principalComponents=pca.fit_transform(df)\n",
        "print(principalComponents)"
      ],
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      "outputs": [
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          "name": "stdout",
          "text": [
            "[[-4.30518692]\n",
            " [ 3.73612869]\n",
            " [ 5.69282771]\n",
            " [-5.12376947]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pdf=pd.DataFrame(data=principalComponents,columns=['PC1'])\n",
        "pdf"
      ],
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          "height": 175
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            "text/plain": [
              "        PC1\n",
              "0 -4.305187\n",
              "1  3.736129\n",
              "2  5.692828\n",
              "3 -5.123769"
            ],
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              "summary": "{\n  \"name\": \"pdf\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"PC1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.512246758265153,\n        \"min\": -5.123769474497616,\n        \"max\": 5.692827710560993,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3.7361286866113286,\n          -5.123769474497616,\n          -4.305186922674705\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
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          "metadata": {},
          "execution_count": 5
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "#WITH NORMALIZATION\n",
        "scaler=MinMaxScaler()\n",
        "a=scaler.fit_transform(df)\n",
        "pca=PCA(n_components=1)\n",
        "p=pca.fit_transform(a)\n",
        "print(p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xFkMTIuc4Ln9",
        "outputId": "c4ea93c7-1834-4b31-dd25-ae69f1261ed4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.47205393]\n",
            " [ 0.35256803]\n",
            " [ 0.61944808]\n",
            " [-0.49996219]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#IRIS DATASET"
      ],
      "metadata": {
        "id": "NoAwJQf26JMz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv('/content/iris_data.csv')"
      ],
      "metadata": {
        "id": "nmAN6Bjc5lXA"
      },
      "execution_count": null,
      "outputs": []
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        "data"
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      "execution_count": null,
      "outputs": [
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            "text/plain": [
              "      Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
              "0      1            5.1           3.5            1.4           0.2   \n",
              "1      2            4.9           3.0            1.4           0.2   \n",
              "2      3            4.7           3.2            1.3           0.2   \n",
              "3      4            4.6           3.1            1.5           0.2   \n",
              "4      5            5.0           3.6            1.4           0.2   \n",
              "..   ...            ...           ...            ...           ...   \n",
              "145  146            6.7           3.0            5.2           2.3   \n",
              "146  147            6.3           2.5            5.0           1.9   \n",
              "147  148            6.5           3.0            5.2           2.0   \n",
              "148  149            6.2           3.4            5.4           2.3   \n",
              "149  150            5.9           3.0            5.1           1.8   \n",
              "\n",
              "            Species  \n",
              "0       Iris-setosa  \n",
              "1       Iris-setosa  \n",
              "2       Iris-setosa  \n",
              "3       Iris-setosa  \n",
              "4       Iris-setosa  \n",
              "..              ...  \n",
              "145  Iris-virginica  \n",
              "146  Iris-virginica  \n",
              "147  Iris-virginica  \n",
              "148  Iris-virginica  \n",
              "149  Iris-virginica  \n",
              "\n",
              "[150 rows x 6 columns]"
            ],
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              "      <th>Id</th>\n",
              "      <th>SepalLengthCm</th>\n",
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              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "      <th>Species</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>Iris-setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>146</td>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>147</td>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>148</td>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>149</td>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>150</td>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "      <td>Iris-virginica</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 6 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43,\n        \"min\": 1,\n        \"max\": 150,\n        \"num_unique_values\": 150,\n        \"samples\": [\n          74,\n          19,\n          119\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.828066127977863,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335943113621737,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522626,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008411,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Iris-setosa\",\n          \"Iris-versicolor\",\n          \"Iris-virginica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data1=data[['SepalLengthCm','SepalWidthCm','PetalLengthCm','PetalWidthCm']]\n",
        "data1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "7vZ9d5_b8eES",
        "outputId": "fd8ac0c8-e1dc-4ae2-de25-9a5fe753926d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
              "0              5.1           3.5            1.4           0.2\n",
              "1              4.9           3.0            1.4           0.2\n",
              "2              4.7           3.2            1.3           0.2\n",
              "3              4.6           3.1            1.5           0.2\n",
              "4              5.0           3.6            1.4           0.2\n",
              "..             ...           ...            ...           ...\n",
              "145            6.7           3.0            5.2           2.3\n",
              "146            6.3           2.5            5.0           1.9\n",
              "147            6.5           3.0            5.2           2.0\n",
              "148            6.2           3.4            5.4           2.3\n",
              "149            5.9           3.0            5.1           1.8\n",
              "\n",
              "[150 rows x 4 columns]"
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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>SepalLengthCm</th>\n",
              "      <th>SepalWidthCm</th>\n",
              "      <th>PetalLengthCm</th>\n",
              "      <th>PetalWidthCm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>6.7</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>6.3</td>\n",
              "      <td>2.5</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>6.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.2</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>6.2</td>\n",
              "      <td>3.4</td>\n",
              "      <td>5.4</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>5.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 4 columns</p>\n",
              "</div>\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "      spin 1s steps(1) infinite;\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    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",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "  <div id=\"id_c37e0176-0199-4af1-b588-b496fe93e8ed\">\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data1')\"\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",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data1');\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data1",
              "summary": "{\n  \"name\": \"data1\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"SepalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.828066127977863,\n        \"min\": 4.3,\n        \"max\": 7.9,\n        \"num_unique_values\": 35,\n        \"samples\": [\n          6.2,\n          4.5,\n          5.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SepalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4335943113621737,\n        \"min\": 2.0,\n        \"max\": 4.4,\n        \"num_unique_values\": 23,\n        \"samples\": [\n          2.3,\n          4.0,\n          3.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalLengthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7644204199522626,\n        \"min\": 1.0,\n        \"max\": 6.9,\n        \"num_unique_values\": 43,\n        \"samples\": [\n          6.7,\n          3.8,\n          3.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PetalWidthCm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7631607417008411,\n        \"min\": 0.1,\n        \"max\": 2.5,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          0.2,\n          1.2,\n          1.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "S=StandardScaler()"
      ],
      "metadata": {
        "id": "dUJnGvQp3Qf4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "std_data=S.fit_transform(data1)"
      ],
      "metadata": {
        "id": "v3tgVdNb76iA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "std_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8MGcy6858ZYY",
        "outputId": "53288a07-c775-4ffb-aefa-831687b5d97a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       [-9.00681170e-01,  5.69251294e-01, -1.17067529e+00,\n",
              "        -9.18557817e-01],\n",
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              "        -1.31297673e+00],\n",
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              "        -1.31297673e+00],\n",
              "       [-1.02184904e+00,  8.00654259e-01, -1.22754100e+00,\n",
              "        -1.05003079e+00],\n",
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              "        -1.31297673e+00],\n",
              "       [-7.79513300e-01,  8.00654259e-01, -1.34127240e+00,\n",
              "        -1.31297673e+00],\n",
              "       [-1.38535265e+00,  3.37848329e-01, -1.22754100e+00,\n",
              "        -1.31297673e+00],\n",
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              "        -1.31297673e+00],\n",
              "       [-5.37177559e-01,  8.00654259e-01, -1.28440670e+00,\n",
              "        -1.05003079e+00],\n",
              "       [-7.79513300e-01,  2.42047502e+00, -1.28440670e+00,\n",
              "        -1.44444970e+00],\n",
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              "        -1.31297673e+00],\n",
              "       [-1.14301691e+00,  1.06445364e-01, -1.28440670e+00,\n",
              "        -1.44444970e+00],\n",
              "       [-1.02184904e+00,  3.37848329e-01, -1.45500381e+00,\n",
              "        -1.31297673e+00],\n",
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              "        -1.31297673e+00],\n",
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              "        -1.31297673e+00],\n",
              "       [-6.58345429e-01,  1.49486315e+00, -1.28440670e+00,\n",
              "        -1.31297673e+00],\n",
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              "         3.96171883e-01],\n",
              "       [-1.73673948e-01, -5.87763531e-01,  4.21564419e-01,\n",
              "         1.33225943e-01],\n",
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              "        -2.61192967e-01],\n",
              "       [ 3.10997534e-01, -3.56360566e-01,  5.35295827e-01,\n",
              "         2.64698913e-01],\n",
              "       [-2.94841818e-01, -3.56360566e-01, -9.02269170e-02,\n",
              "         1.33225943e-01],\n",
              "       [ 1.03800476e+00,  1.06445364e-01,  3.64698715e-01,\n",
              "         2.64698913e-01],\n",
              "       [-2.94841818e-01, -1.24957601e-01,  4.21564419e-01,\n",
              "         3.96171883e-01],\n",
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              "        -2.61192967e-01],\n",
              "       [ 4.32165405e-01, -1.97618132e+00,  4.21564419e-01,\n",
              "         3.96171883e-01],\n",
              "       [-2.94841818e-01, -1.28197243e+00,  8.03701950e-02,\n",
              "        -1.29719997e-01],\n",
              "       [ 6.86617933e-02,  3.37848329e-01,  5.92161531e-01,\n",
              "         7.90590793e-01],\n",
              "       [ 3.10997534e-01, -5.87763531e-01,  1.37235899e-01,\n",
              "         1.33225943e-01],\n",
              "       [ 5.53333275e-01, -1.28197243e+00,  6.49027235e-01,\n",
              "         3.96171883e-01],\n",
              "       [ 3.10997534e-01, -5.87763531e-01,  5.35295827e-01,\n",
              "         1.75297293e-03],\n",
              "       [ 6.74501145e-01, -3.56360566e-01,  3.07833011e-01,\n",
              "         1.33225943e-01],\n",
              "       [ 9.16836886e-01, -1.24957601e-01,  3.64698715e-01,\n",
              "         2.64698913e-01],\n",
              "       [ 1.15917263e+00, -5.87763531e-01,  5.92161531e-01,\n",
              "         2.64698913e-01],\n",
              "       [ 1.03800476e+00, -1.24957601e-01,  7.05892939e-01,\n",
              "         6.59117823e-01],\n",
              "       [ 1.89829664e-01, -3.56360566e-01,  4.21564419e-01,\n",
              "         3.96171883e-01],\n",
              "       [-1.73673948e-01, -1.05056946e+00, -1.47092621e-01,\n",
              "        -2.61192967e-01],\n",
              "       [-4.16009689e-01, -1.51337539e+00,  2.35044910e-02,\n",
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              "       [-4.16009689e-01, -1.51337539e+00, -3.33612130e-02,\n",
              "        -2.61192967e-01],\n",
              "       [-5.25060772e-02, -8.19166497e-01,  8.03701950e-02,\n",
              "         1.75297293e-03],\n",
              "       [ 1.89829664e-01, -8.19166497e-01,  7.62758643e-01,\n",
              "         5.27644853e-01],\n",
              "       [-5.37177559e-01, -1.24957601e-01,  4.21564419e-01,\n",
              "         3.96171883e-01],\n",
              "       [ 1.89829664e-01,  8.00654259e-01,  4.21564419e-01,\n",
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              "         1.33225943e-01],\n",
              "       [-4.16009689e-01, -1.28197243e+00,  1.37235899e-01,\n",
              "         1.33225943e-01],\n",
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              "       [ 5.53333275e-01, -3.56360566e-01,  1.04708716e+00,\n",
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              "       [ 7.95669016e-01, -1.24957601e-01,  1.16081857e+00,\n",
              "         1.31648267e+00],\n",
              "       [ 2.12851559e+00, -1.24957601e-01,  1.61574420e+00,\n",
              "         1.18500970e+00],\n",
              "       [-1.14301691e+00, -1.28197243e+00,  4.21564419e-01,\n",
              "         6.59117823e-01],\n",
              "       [ 1.76501198e+00, -3.56360566e-01,  1.44514709e+00,\n",
              "         7.90590793e-01],\n",
              "       [ 1.03800476e+00, -1.28197243e+00,  1.16081857e+00,\n",
              "         7.90590793e-01],\n",
              "       [ 1.64384411e+00,  1.26346019e+00,  1.33141568e+00,\n",
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              "       [ 7.95669016e-01,  3.37848329e-01,  7.62758643e-01,\n",
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              "       [ 6.74501145e-01, -8.19166497e-01,  8.76490051e-01,\n",
              "         9.22063763e-01],\n",
              "       [ 1.15917263e+00, -1.24957601e-01,  9.90221459e-01,\n",
              "         1.18500970e+00],\n",
              "       [-1.73673948e-01, -1.28197243e+00,  7.05892939e-01,\n",
              "         1.05353673e+00],\n",
              "       [-5.25060772e-02, -5.87763531e-01,  7.62758643e-01,\n",
              "         1.57942861e+00],\n",
              "       [ 6.74501145e-01,  3.37848329e-01,  8.76490051e-01,\n",
              "         1.44795564e+00],\n",
              "       [ 7.95669016e-01, -1.24957601e-01,  9.90221459e-01,\n",
              "         7.90590793e-01],\n",
              "       [ 2.24968346e+00,  1.72626612e+00,  1.67260991e+00,\n",
              "         1.31648267e+00],\n",
              "       [ 2.24968346e+00, -1.05056946e+00,  1.78634131e+00,\n",
              "         1.44795564e+00],\n",
              "       [ 1.89829664e-01, -1.97618132e+00,  7.05892939e-01,\n",
              "         3.96171883e-01],\n",
              "       [ 1.28034050e+00,  3.37848329e-01,  1.10395287e+00,\n",
              "         1.44795564e+00],\n",
              "       [-2.94841818e-01, -5.87763531e-01,  6.49027235e-01,\n",
              "         1.05353673e+00],\n",
              "       [ 2.24968346e+00, -5.87763531e-01,  1.67260991e+00,\n",
              "         1.05353673e+00],\n",
              "       [ 5.53333275e-01, -8.19166497e-01,  6.49027235e-01,\n",
              "         7.90590793e-01],\n",
              "       [ 1.03800476e+00,  5.69251294e-01,  1.10395287e+00,\n",
              "         1.18500970e+00],\n",
              "       [ 1.64384411e+00,  3.37848329e-01,  1.27454998e+00,\n",
              "         7.90590793e-01],\n",
              "       [ 4.32165405e-01, -5.87763531e-01,  5.92161531e-01,\n",
              "         7.90590793e-01],\n",
              "       [ 3.10997534e-01, -1.24957601e-01,  6.49027235e-01,\n",
              "         7.90590793e-01],\n",
              "       [ 6.74501145e-01, -5.87763531e-01,  1.04708716e+00,\n",
              "         1.18500970e+00],\n",
              "       [ 1.64384411e+00, -1.24957601e-01,  1.16081857e+00,\n",
              "         5.27644853e-01],\n",
              "       [ 1.88617985e+00, -5.87763531e-01,  1.33141568e+00,\n",
              "         9.22063763e-01],\n",
              "       [ 2.49201920e+00,  1.72626612e+00,  1.50201279e+00,\n",
              "         1.05353673e+00],\n",
              "       [ 6.74501145e-01, -5.87763531e-01,  1.04708716e+00,\n",
              "         1.31648267e+00],\n",
              "       [ 5.53333275e-01, -5.87763531e-01,  7.62758643e-01,\n",
              "         3.96171883e-01],\n",
              "       [ 3.10997534e-01, -1.05056946e+00,  1.04708716e+00,\n",
              "         2.64698913e-01],\n",
              "       [ 2.24968346e+00, -1.24957601e-01,  1.33141568e+00,\n",
              "         1.44795564e+00],\n",
              "       [ 5.53333275e-01,  8.00654259e-01,  1.04708716e+00,\n",
              "         1.57942861e+00],\n",
              "       [ 6.74501145e-01,  1.06445364e-01,  9.90221459e-01,\n",
              "         7.90590793e-01],\n",
              "       [ 1.89829664e-01, -1.24957601e-01,  5.92161531e-01,\n",
              "         7.90590793e-01],\n",
              "       [ 1.28034050e+00,  1.06445364e-01,  9.33355755e-01,\n",
              "         1.18500970e+00],\n",
              "       [ 1.03800476e+00,  1.06445364e-01,  1.04708716e+00,\n",
              "         1.57942861e+00],\n",
              "       [ 1.28034050e+00,  1.06445364e-01,  7.62758643e-01,\n",
              "         1.44795564e+00],\n",
              "       [-5.25060772e-02, -8.19166497e-01,  7.62758643e-01,\n",
              "         9.22063763e-01],\n",
              "       [ 1.15917263e+00,  3.37848329e-01,  1.21768427e+00,\n",
              "         1.44795564e+00],\n",
              "       [ 1.03800476e+00,  5.69251294e-01,  1.10395287e+00,\n",
              "         1.71090158e+00],\n",
              "       [ 1.03800476e+00, -1.24957601e-01,  8.19624347e-01,\n",
              "         1.44795564e+00],\n",
              "       [ 5.53333275e-01, -1.28197243e+00,  7.05892939e-01,\n",
              "         9.22063763e-01],\n",
              "       [ 7.95669016e-01, -1.24957601e-01,  8.19624347e-01,\n",
              "         1.05353673e+00],\n",
              "       [ 4.32165405e-01,  8.00654259e-01,  9.33355755e-01,\n",
              "         1.44795564e+00],\n",
              "       [ 6.86617933e-02, -1.24957601e-01,  7.62758643e-01,\n",
              "         7.90590793e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca=PCA(n_components=2)\n",
        "principalComponents=pca.fit_transform(std_data)\n",
        "print(principalComponents)\n",
        "pdf=pd.DataFrame(data=principalComponents,columns=['PC1','PC2'])\n",
        "pdf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "DZdp12QK8qVn",
        "outputId": "48336db1-48f5-496e-c38d-b24dc6223cb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-2.26454173e+00  5.05703903e-01]\n",
            " [-2.08642550e+00 -6.55404729e-01]\n",
            " [-2.36795045e+00 -3.18477311e-01]\n",
            " [-2.30419716e+00 -5.75367713e-01]\n",
            " [-2.38877749e+00  6.74767397e-01]\n",
            " [-2.07053681e+00  1.51854856e+00]\n",
            " [-2.44571134e+00  7.45626750e-02]\n",
            " [-2.23384186e+00  2.47613932e-01]\n",
            " [-2.34195768e+00 -1.09514636e+00]\n",
            " [-2.18867576e+00 -4.48629048e-01]\n",
            " [-2.16348656e+00  1.07059558e+00]\n",
            " [-2.32737775e+00  1.58587455e-01]\n",
            " [-2.22408272e+00 -7.09118158e-01]\n",
            " [-2.63971626e+00 -9.38281982e-01]\n",
            " [-2.19229151e+00  1.88997851e+00]\n",
            " [-2.25146521e+00  2.72237108e+00]\n",
            " [-2.20275048e+00  1.51375028e+00]\n",
            " [-2.19017916e+00  5.14304308e-01]\n",
            " [-1.89407429e+00  1.43111071e+00]\n",
            " [-2.33994907e+00  1.15803343e+00]\n",
            " [-1.91455639e+00  4.30465163e-01]\n",
            " [-2.20464540e+00  9.52457317e-01]\n",
            " [-2.77416979e+00  4.89517027e-01]\n",
            " [-1.82041156e+00  1.06750793e-01]\n",
            " [-2.22821750e+00  1.62186163e-01]\n",
            " [-1.95702401e+00 -6.07892567e-01]\n",
            " [-2.05206331e+00  2.66014312e-01]\n",
            " [-2.16819365e+00  5.52016495e-01]\n",
            " [-2.14030596e+00  3.36640409e-01]\n",
            " [-2.26879019e+00 -3.14878603e-01]\n",
            " [-2.14455443e+00 -4.83942097e-01]\n",
            " [-1.83193810e+00  4.45266836e-01]\n",
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            " [ 4.96802558e-03 -1.02940111e+00]\n",
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            " [ 1.56941176e-01 -1.30310327e+00]\n",
            " [ 7.38256104e-01  4.02470382e-01]\n",
            " [ 4.72369682e-01 -4.16608222e-01]\n",
            " [ 1.22798821e+00 -9.40914793e-01]\n",
            " [ 6.29381045e-01 -4.16811643e-01]\n",
            " [ 7.00472799e-01 -6.34939277e-02]\n",
            " [ 8.73536987e-01  2.50708611e-01]\n",
            " [ 1.25422219e+00 -8.26200998e-02]\n",
            " [ 1.35823985e+00  3.28820266e-01]\n",
            " [ 6.62126138e-01 -2.24346071e-01]\n",
            " [-4.72815133e-02 -1.05721241e+00]\n",
            " [ 1.21534209e-01 -1.56359238e+00]\n",
            " [ 1.41182261e-02 -1.57339235e+00]\n",
            " [ 2.36010837e-01 -7.75923784e-01]\n",
            " [ 1.05669143e+00 -6.36901284e-01]\n",
            " [ 2.21417088e-01 -2.80847693e-01]\n",
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            " [ 1.04941336e+00  5.22197265e-01]\n",
            " [ 1.03587821e+00 -1.39246648e+00]\n",
            " [ 6.70675999e-02 -2.12620735e-01]\n",
            " [ 2.75425066e-01 -1.32981591e+00]\n",
            " [ 2.72335066e-01 -1.11944152e+00]\n",
            " [ 6.23170540e-01  2.75426333e-02]\n",
            " [ 3.30005364e-01 -9.88900732e-01]\n",
            " [-3.73627623e-01 -2.01793227e+00]\n",
            " [ 2.82944343e-01 -8.53950717e-01]\n",
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            " [ 2.52244473e-01 -5.95860746e-01]\n",
            " [ 1.84767259e+00  8.71696662e-01]\n",
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            " [ 2.20634950e+00  5.54470105e-01]\n",
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            " [ 1.25308651e+00 -1.16739134e+00]\n",
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            " [ 1.59180930e+00  6.77035372e-01]\n",
            " [ 1.47128019e+00  2.53192472e-01]\n",
            " [ 2.43737848e+00  2.55675734e+00]\n",
            " [ 3.30914118e+00 -2.36132010e-03]\n",
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            " [ 2.04049626e+00  9.07398765e-01]\n",
            " [ 9.73915114e-01 -5.71174376e-01]\n",
            " [ 2.89806444e+00  3.97791359e-01]\n",
            " [ 1.32919369e+00 -4.86760542e-01]\n",
            " [ 1.70424071e+00  1.01414842e+00]\n",
            " [ 1.95772766e+00  1.00333452e+00]\n",
            " [ 1.17190451e+00 -3.18896617e-01]\n",
            " [ 1.01978105e+00  6.55429631e-02]\n",
            " [ 1.78600886e+00 -1.93272800e-01]\n",
            " [ 1.86477791e+00  5.55381532e-01]\n",
            " [ 2.43549739e+00  2.46654468e-01]\n",
            " [ 2.31608241e+00  2.62618387e+00]\n",
            " [ 1.86037143e+00 -1.84672394e-01]\n",
            " [ 1.11127173e+00 -2.95986102e-01]\n",
            " [ 1.19746916e+00 -8.17167742e-01]\n",
            " [ 2.80094940e+00  8.44748194e-01]\n",
            " [ 1.58015525e+00  1.07247450e+00]\n",
            " [ 1.34704442e+00  4.22255966e-01]\n",
            " [ 9.23432978e-01  1.92303705e-02]\n",
            " [ 1.85355198e+00  6.72422729e-01]\n",
            " [ 2.01615720e+00  6.10397038e-01]\n",
            " [ 1.90311686e+00  6.86024832e-01]\n",
            " [ 1.15318981e+00 -7.01326114e-01]\n",
            " [ 2.04330844e+00  8.64684880e-01]\n",
            " [ 2.00169097e+00  1.04855005e+00]\n",
            " [ 1.87052207e+00  3.82821838e-01]\n",
            " [ 1.55849189e+00 -9.05313601e-01]\n",
            " [ 1.52084506e+00  2.66794575e-01]\n",
            " [ 1.37639119e+00  1.01636193e+00]\n",
            " [ 9.59298576e-01 -2.22839447e-02]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          PC1       PC2\n",
              "0   -2.264542  0.505704\n",
              "1   -2.086426 -0.655405\n",
              "2   -2.367950 -0.318477\n",
              "3   -2.304197 -0.575368\n",
              "4   -2.388777  0.674767\n",
              "..        ...       ...\n",
              "145  1.870522  0.382822\n",
              "146  1.558492 -0.905314\n",
              "147  1.520845  0.266795\n",
              "148  1.376391  1.016362\n",
              "149  0.959299 -0.022284\n",
              "\n",
              "[150 rows x 2 columns]"
            ],
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              "      <th>PC2</th>\n",
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              "  <tbody>\n",
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              "      <td>0.505704</td>\n",
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              "</table>\n",
              "<p>150 rows × 2 columns</p>\n",
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              "        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",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
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              "\n",
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              "        border-radius: 50%;\n",
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              "        display: none;\n",
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              "        width: 32px;\n",
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              "\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('pdf')\"\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_21ec59b0-3fb2-42f7-95b5-02583fbceaf8 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('pdf');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "pdf",
              "summary": "{\n  \"name\": \"pdf\",\n  \"rows\": 150,\n  \"fields\": [\n    {\n      \"column\": \"PC1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.711827612696242,\n        \"min\": -2.7741697937051564,\n        \"max\": 3.309141182676078,\n        \"num_unique_values\": 147,\n        \"samples\": [\n          1.0197810528162627,\n          0.3973072828233272,\n          2.0161572006777586\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PC2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9630179756979308,\n        \"min\": -2.6580626779617607,\n        \"max\": 2.722371076574469,\n        \"num_unique_values\": 147,\n        \"samples\": [\n          0.06554296309664809,\n          -1.7581689474435256,\n          0.6103970375551129\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(pdf['PC1'],pdf['PC2'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "qUZgMaIk-LVR",
        "outputId": "71d07da0-dad5-4817-f41e-4aa096b1ecde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7dc4ee848700>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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gYrCh3ZhA1BRbQdQJL8fwS/k+Sq2Y5Pq6Gnu/a3c7k7DAnzMEIgCMVKoZZF6P4Yd5JlwxxdUyv5urtyiI5d6t8oGsBDEAdRs5IgCMlK8QlhvrkpRiDL8U78MvxRQ+y/xuzOJ3Yzl+11Qs8FcYAhEARnKzumc2pUoi9fp9+K2Y5PqwJeazwF9hGJoBYCyrQlh1LgydlDKJ1Mv3YYJikuvDlJjPAn+FIRABYDSvblSlTiL14n2YVCirmOT6sCTm2x1mGT+uQnd+pinwAahbCERKoFQXC5MuSoCbvLhR+ZFE6ub7cCPJ1u1rxtD9nT2uUopJx97qi8z1KF/yriTVjhujnas+werpQxCIeKxUVfWo3gc4U8yMD7+5UanV7WtGtv0NFYXrkZ3K2P/4mQ8ThIzA0fBQqarqUb0PcC6oSaRuJNm6fc2w2p8b+w6asCXgloKni94VK8iL3mUWr7M6Md1avK5UrwOEVdB6E3ceOK5FG3bl3c5qsTi3rxn59lfMvoMs6kPlxix6F2WlysgPc/looBRMnLWR6yZWbJKt29cMJ1NWR+47zDfrsCTglgKBiEdKlZEf5vLRQCmYdjPM10NTbJKt29eMQq4tR0+cDFxPFLxDIOKRUmXkh7l8NOA1026GdpJQ5zfWFZVk6/Y1o5Bry8FjvbrnyT8UlWyL8CBZ1SOlKusc5vLRgJdMS/K2m4QqqagkW7evGfn2l23fj+w+5HlFWwQHgYhH3M7IT6bS2nnguLa0HtbOA8cHT9KgZv4DfipVeXcnnORuFDMzw+1rRq79Zdv35y+eoq6ePsvthr5PK1bXQwQTQzMecqusc77u47CXjwbcZmKSt9OcjGLLqrt5zcjs79Z/e0lvvn0q6zaZffe9m7K1T6vjYdpwGopHIOKxYjPyn3ixU//z4T2jvj9yLNXEzH/AVKVK8naSCFtITkYxMzO8uGZYBSGSdPvVH9KCpnrtPHDc1r6yHQ83CrnBPAQiJWD3YjHyonX8RJ/+12O/y7ptWgNdnc1b2zW/sU7lZTHL1zFtVgDgt0Ju+k7PI6dP7n5UenVrimlmqMtKTNKax/fqU031Bb/PfMNpI6+HCI5IBiIm3pjzlUfOxk73Md2YwGhOb4ZOz6NCntztlAc3Nd/L6VBXIe/TxOE0uCNyyaotbZ26fN12LdqwS3/3aKsWbdily9dt97Xs8BMvHtFNecoj55JrLNWkWQGAKZwkbDo9j4pJhA1qefBC8lucvk9qJoVXpHpETBxffOLFTq14JPvwi13ZupnpxgRys5OwWch5VOyTexDzvQoZ6nL6PqmZFF6RCURMvDG3tGVPRHXCar5/IRdDE4esAC/luxkWch658eQetPLgheZ9OHmfQV4tGblFJhAxbXwxX3KXXVZjxnYvhl09A9uRS4KoynUzLCSoKMWTu2kPDaXIbwlyDg1yi0wgYtr4opOForIpi0n3LbIeSrJ7kVvz7y9rX2dC39nRYdSQFVAquW7qhQQVXj+5m/rQYDXUVTuuQmuuaXKlbXaH00wK0pBfZAIR08YXiw147lv0UV31EesTO9/FMKO795Qe3NGR9WfkkiDs8t3UCwkqvHxyNzHPbagFTfVKpdL631va1N07UFPkeG+/1jzerrIyuRaMWA2nmRqk5ULgFKFZM6atyVJowFMfr9IDi2fqqo9Myrmd3bLL+dgptwwEkZ3ZMIWWQ/di9ouJZelHGsh7+91gEJLh9ky9zHDaNTPO1exp4wua3WQCE2dx+iGWTqeNLdLf09OjeDyuRCKhmpqaoveX+UOVsj+llPJpIplK6/J123P2WJTFpP/7uRkaX11VcLTc0tap2za3qbu3v6j2fvvzM3TNjHOL2gdgisz5ZzU8munpeO6WK4p60nbzaXfngeNatGFX3u0eWXqJL4muyVRaF31jm2V11ZHH1O3XdvJ5msCqd8uP+5EXnNy/IzM0I7m/vkIxcnXfZty3aOao4ZfMYk92L2wLmur1Tn9SX/3RfxbVXqbEIUycJq8XOqXWzdkvpuW5jXTf9v05S7x7OSHAtMkI+Zg4i9NPkQpEJLPm6FsFRlZPWoU+ldXFTy+4jUyJQxgVclP3e0qtaXluQyVTaW18Pnuu2UheBEqmB2kjBS1w8lrkAhHJ/wvKUHYDo2KS1OwmrjIlDlFh8k3dip3z+KyxY3x5aNjd0a0337HuDRnKi2MatM8zaIGT1yKTrGqybIlXQyVTaf3Dz14uOEktX8JdTNKNH28IXFlpoFCmJa/bkTmPcz1M/PntU9rW3lWyNmXYvWGeebo3gVLQPs+gBU5eIxAJgPu271dXT5/lz+3MbMmXxb/qqkY9d8sVemTpJfr252fokaWX6LlbriAIQSgVOhvGb/Mb63Tm2DGWP8/kFpR65ozdG+YNl0315JgG7fMMWuDktUgOzQRJS1unvvXk721t+/z+YzmHd/INA5k0ZAV4zaTkdbt2d3QXnBDqZb2KWQ21OnPsmJxtG1dZrhVXvM+V18smSJ8nVWKHIxAxUOaC0ZV4R2se32v79+57ev/g/62SWAk2gPc4SV43ofCUk9yCoe09eOxtPbL70OCSDlLpC32NKfe+Az5In2eQAievRaqOSBBkmxlTiLDMRQdMYErFTru1RL467/169DeHcl5H3LxGmF7jZCRTPk/J/4DIK07u3+SIGMSqMmAhTKm0CASdSRU77eQWnDl2jO558vd5ryNuXiOCNAvEpM9Tyj9ZIQoIRAyRq8BNoSjPDhTHtLLq+ZIyM62w2xq3rhFBmQVi2ueJAQQihih2Nd5cTHgKAYLISeGpUsk1A+6r896XM2HUSrHXiKDMAjHx8wTJqsZweiGwKgufjd9PIUBQmTrkYJWU+e8vHilof8VeI4IyC8TUzzPq6BHxSWbNmC2th7XzwHGdPa7S0e/Xxav0//77zEA8hQBBZfKQQ7bcAqftcPMa4cWKw24z+fOMMnpEfJAtY7uupkpjK8r1dn/S8vfGVZbrG9d+WHU172VWl5XJ+KcQIKjylVU3bS0mu8s5SN5cI0xayyuboH2eUUGPSIlZZmz3nMwZhEhSRXmZPj190rDM6iA8hQBBFbSKnbnaO5JX1wiTZ4EE7fOMCuqIlFAyldbl67YXlZRqNQ/fjbnoYZ3PDhTLpLoTdmTvda3UollTNPXscZE/v4P2eQaRk/s3gUgJ2S36k8u3Pz9D18w416UWvYcTE8gtaIF60Npbahwfbzm5f5Mj4gGrP3A3MrG9SKLKDBeNjEgzBX4Y4gGcLY9gwk2O5Rxy4/iYg0DEZbl6FooJIrxKospX4Cezmuf8xjqeFgAb6F0EnCFZ1UX5Sgf/ubcv53TbjGw/T0u6/eoPuR4MUOAHcI9p5cOBICAQcYmd0sFrHt+r26+2ztiOSbrx4w2jZsBkrHl8r+sXMgr8IOxG1uxxq3x3/7spfe+Xf9QdW9r0vV/+Ue/0JykfDhSAoRmX2O1ZOGtcRd6ln6efd5b+58N7Ru3Di5wNCvwgzLwaJln7RLs2/LJDQ2OKbzyxV7lS/4f2LpKbALynJIHI/fffr7vuuktdXV2aPn267r33Xs2aNasUL10yTnoWrplxrmXRn2QqrTWPt2f9XS9yNijwg7DyKgl77RPtenBHx6jv251/SO8iTGFCUrVUgkDkscce08qVK/XAAw/oYx/7mO655x596lOf0iuvvKKJEyd6/fIl47RnwSpj20nOhhtPVUFZIwJwwqsk7P53U9rwy9FBiBP0LsIEJiVVe54j8s///M9aunSpbrjhBjU2NuqBBx7Q2LFj9f3vf9/rly4pt1af9CNng+qsCBuvkrD/ZedBFZriwdpPMIVpSdWe9oj09/frhRde0KpVqwa/V1ZWpnnz5mnnzp2jtu/r61NfX9/g1z09PV42z1Vu9Sz4lbNh+hoRgJNuZLuB+s//csG1+7f+avfb9hs8RCl7F03pboeZTCzZ4GkgcuzYMSWTSZ1zzjnDvn/OOedo3759o7Zfu3atmpubvWySpzI9C7kSUfPxM2eDAj8wldNuZLuB+g93vqof7nzVdpf0+bVjnTX8L5xcA4phUnc7zFTq4X87jJq+u2rVKiUSicF/r732mt9NcmxBU72eu+UKPbL0En378zP0yNJL9NwtV9i+CLAoEzBcId3I+YZKR7LbJf2F2VPl9NS7/eoPOboGZDiddmxadzvMZGLJBk8DkbPPPlvl5eX605/+NOz7f/rTn1RXVzdq+8rKStXU1Az7F0TFrj5JzgYwwE59nmy1OZysQptvX0NVnFampX/TYGOP7zm7utLxNaClrVOXr9uuRRt26e8ebdWiDbt0+brtlsFEoccJ0WNiyQZPA5GKigpddNFFeuqppwa/l0ql9NRTT2n27NlevnTgFduzAoRBMUmnVgF9IfsaatVVjbrx4w22e1ucXtAL6dmgQjLscmtihZs8H5pZuXKlNmzYoB/84Afau3evli1bpt7eXt1www1ev3TgFduzAgRdsd3IQwP6L84+37XXXHVVo9r/zwJVV1mn2RVyQS+0Z8PE7naYycThf88Dkc997nO6++67dccdd2jGjBlqbW1VS0vLqARWABipkG7kkbkVkjR72nhdabM30e5rnl5Rrrs++5HB5RmGKvSCXmjPhond7TCXacP/JamsumLFCq1YsaIULwWgxLycLup0FlmuWSPzG+tcn5Hmxky5oQrt2aBCMpwyqWQDa80YjHoAMJ3b00Wz/c3brc9jp6S71b6kzArXzruk3bygF9qzYUqFZK5ZwWJKyYZYOm13hYTS6+npUTweVyKRCOwMmkJRDwCms7rxZ247Trt4c/3NS8p5PiRTaV2+brvlsEamR+C5W67QtvauUfvKtk8/ZN5Hvp6N5265IusNvpTXjZFBx597+7Xmca5ZGODk/k0gYiC3L/CA25KptC775nZ19eS/8dt5IrbzN5+r12HngeNatGFX3te5/eoP6ezqSnW88ZbueWp/1nZnXs/Nc8xJT0HmWEjZezbyta0UvRLZAp5suGZFl5P7N0MzhjGx/C4w0n3b/2AZhEjOqjM6+Zu32pfd3Io1j+/N+XMvzjGnvRTF5p143d1uFTRmwzULdhCIGMbE8rvAUC1tnfrWk3+wta2dAMGNv3k3Z4O4eY7ZyVuxCkZMSSQcKlfQaIVrFvIhEDEM9QBgssyNyC47AYIbf/P5Zo0UothzrNjeTVMSCYfKFzTmwjULVoxaawbUA4DZnNyIRhbzslo7xY2/eacl3Yt9PTvCWO20mGCCaxas0CNiGOoBwGRObkRDp4uWor6HVW6FU26dY3aPVVfinaJep5QKCSa4ZiEfApESypXNPvRnn794iu558ve+1gMAsrF7I/rqvPcP5j4UU9/D6d/8yNyKYyf68iaoDuXmOWb3WK15fK8Odb+jqWePNSYXxIrTITCuWbCD6bsl4rRGwpljx0iS3nz71KjtmQYHv+SrcyFJdTWVev7WT6i8LFZ0fY9i/+bttHcoN88xp6/tRRu8YDW9OBvT3wu8Qx0Rw+SqkWB18DM/++q892nq2eOMf1JCdDipc2G3vscjSy/R7GnjPamBka+9N897v2e9EU6muo5sl8m1N6werG6/+kM6a1ylUTN94A8CEYMMFH56Sl09fY5/12lRKKBU7NbG2NJ6WH/3aGve/X378zN0zYxzvWiqJH8rFbe0deq2zS+pu/dU/o3/IgjnPuXckQsFzQxy3/b9BQUhEvPvYS67dS5MmQXmZ12OBU31eudUSl99rNX27wTh3DdxejGCiUDEQwOFn35f9H6Yfw8T2bkRZZIbc81iGTnN1yt+3jjragoLtDj3EQXUEfGI08JPuTD/HkFVXhbTp6fnHvr49PT60HfpZwIyp++Scx9RQCDikWIqEGbEVLqnRcALyVRaP/vPzpzb/Ow/OweLm9ndZ7bCaG5t7wWnBddKee6bcHwQbQzNeMRplyo1Q2CiYhMS7QTkTnIhnCad+pmkOpLdgmtunft2PjuTjg+ii0CkAHZOcPuFn96nD9RVW660+V/eP1G3//QlHTz+tqaOH6vbrmrU6RXlrr4fIBs3blJurp3kdAG5Qhec89LIpNmDx3r1yO5DwxLa7a6ym4udz87E44NoYvquQ3YvznaKGdUPmZ6XLbi5adNvta396Kjfm984URu+eLHbbw0YlKv2jWS/xoXTOiJWnBRGc1pIze8eR7enwdr57OY31gXm+CCYnNy/yRFxIHOCjzx5M08QLW3vjYXnGhOO/eXf0K7XTEb/NTPO1exp4y2DEEna1n5U/3X984zlwhP5Vo2VBioB2/n7y5ekaTcXwukCckFacG7kuZ/vxp8rp8PuZ7frwPHAHB+EH4GITYVcnDNjwnXx4cM0dfGqnE+U7/QnLYOQjBdefVOz//HJYcEP4AY3b+L5AnLJXi6E0yEeN4eETNLS1qnL123Xog279HePtmrRhl26fN32weuA3c9u5x+P2Xq9oB0fBBM5IjY5uTgP7WIupJDSPz5hb9rv0bf6GcuF69y+iVslaTrJhXBaGM2UQmpuspPT0fduyube7A23BOn4ILgIRGwq5uLstJDSweNv295WGuiJmd9Yx1guXOHFTbzYyqb5Vn0dudS80+1Nl69HNqaB68Ddn51ua3+zp43XT/a8Hprjg2BjaMamUj5hTR0/1va2jOXCbW7ldYzkNBdi5O86GeJxa0jIFHZ7ZBWTrc/ukgvGh+r4INgIRGzy6uKcLfHstqsaHbePsVy4xdSbuNOcq0JztArlZWEwu+f3sbf6bH92pT4+gBWGZmzKXJyXbdrjWvGxXFOB5zdOzJuwOhRjuXCTG3kdxco2rdXpEE+pFrvzujCYkx7Z2dPG2/7s/FwMEMigjohDbl1w7Mz1/8me1/MGI3bm+7NcNwrlxd9O2Cp+ulVzJZd8dYmyXQc47+EnJ/dvApECFHuCOym21P9uSjf+y2+14w+jp9vZudAF6YKO8Cum4qebN3a3lLJwWua4SNl7ZE06LgAFzTxWTNKd5Gwq8OkV5frhlz+mBxbPVL3DsVwnBdgAr9n5e3SzmFop2D2XH3q+o+jcEXI6EFbkiPigkKnAVmO50kAZ7ZG9M3an+zHtF6Vg9++xunJMQfV6/GL3XF7z+N7B/xfTI0lOB8KIQMQHhU4FHlmPJFc3d/z0ikBd0BFuYa34WUiSeLGLyjmtSwSYjqEZH7gxFThfN/eT7V222mLKBR3hZv/vLFgVP/Ody9mYOMQE+IlAxAfF1mnI182dlrS59bCttphyQUe42f07mz1tvCf1eryS61zOJdMD9Ks/HNP3fvlH3bGlTd/75R/Vb7tEOxAeBCI+yZd4Nr+xzrI4Ur5ubknq7j2l2nEVgbmgI9zs9gIGseKn1blsxxc37taax/fqhztf1ZrH9+qDt/9ca22uNQWEBTkiPrJKPNvW3jVqSuDQBDe73dwzJsf19L43XCvABhTKSUFAE4qpOTXyXD52om9YgqqVkb2aqbT04I4OSdKqAiosBwl1TpBBHRHD2KmhED+9Qos27Mq7r/HjKrTmmr/Smsf3UkcERnBS1ybIN6p8BcjyKYtJ+9ZcqYrTwtlpTX2j8KOgWUDZLY707Nfn6pK1T6q791TefT6y9BLNaqgN7AUd4RPkAMMJqwJkdt1+9Yf05b+5wN1GGSBIBetQOAqaBZTdKY4vvPpnfWbGubb2efTEyaILsAFuiUoQIlnnjowdU27r91/tftuLZvkqaAXrUBrkiBjESaGzeY11+t7zB/Nuy6wY+CFbwLGtvSty3fHZ8sDaDid05xP580fOrx1bghaWlpOq0tRKiQ4CEYM4KXSWmYWQbxEsZsWg1LKN/585dozefHv0UGKxxb2CYGQBsovOP0trf75XuR76y2LSF2ZP9b5xJVZIVWmEH0MzBnFS6KzYWiSAF6wK7WULQqRodsdXnFampX/TkHObqz5cH8pzt9Cq0gg3AhGDOA0uWAQLJsk1/p/L0O74qFh1VaNu/HiDrGKNf3+xU5ev2x66hSndqCqN8GHWjIGcTm2LUgIgzLXzwHFb08qtfOu/TddnZp7nYovM1/9uSrf920v68Z7XR/0srLNIrGYThfX9RpWT+zc5IgZyusImi2DBBMWO6695fK9OryiP1E2ovCym5w9kX+gvrKtkB7FgHbxFIOKRYnspCC4QNMWO6/+5tz/0iasjRXUWidOHLYQbgYgHqBqIKMo3kyufsPYA5OLXLBIThnN52EIGgYjLrKoGRmGaIqIt33oyaUnjKsvV25e03EdYewCs+DGLhAclmIZZMzkkU2nLFXCttqdqIKIs10yuBxbP1Deu/bCt/USljkS+WSSSu7NIrKZXZx6UwjZLB8FAj4iFQp4aojreCwyVa/x/54HjtvYRlToSuXqRMt45ldS29q6ieyvyPShFbVgM5qBHJItCnxqoGggMsFrfiDoSo2V6keJjx2T9eeLtU670Vjh5UAJKiUBkhGKGV6gaCOSWr2hfWtKVTQO9KVEawpzfWKeq07Jfjt0a1uVBCaYiEBmhmKcGnvaA/KzySGJ/OXG+//xBLdqwK5SVRa3s7uhWV0+f5c/d6K3gQQmm8iwQufPOO3XppZdq7NixOvPMM716GdcV89TA+i+APQua6vXcLVfokaWX6MuXTZWkUYvARSmBshS9FTwowVSeBSL9/f267rrrtGzZMq9ewhPFPjWw/gvCyukssnzKy2Ka1VCrJ9q6sv48SjPNStFbwYMSTOXZrJnm5mZJ0kMPPeTVS3giX1GmmAaCilxPDVQNRNh4VXuCmWYD3Lju2EF5dZjIqOm7fX196ut7b5y0p6en5G3IV5RJsvfUQNVABF2m+uaT7V363vMHR/3cjSJ9JFAOcOu6YwcPSjCNUcmqa9euVTweH/w3efJkX9rB8AqirqVtYBn6RRt2ZQ1CJHeGTkigfE8prztW06sBPzjqEbn11lu1bt26nNvs3btXH/zgBwtqzKpVq7Ry5crBr3t6enwNRrx6ajBhnQfAitUyBdkUO3RSqiGJoKC3AlHkKBD52te+puuvvz7nNhdccEHBjamsrFRlZWXBv+82L4ZXWOcBJstVRyeXQoZOMgH5lU11+v7zBz0fkggKhnURNY4CkQkTJmjChAletSX0WBAPpsuXPGrF6dBJtoA8FpPSQ04OEiiBaPAsWfXQoUPq7u7WoUOHlEwm1draKkm68MILdcYZZ3j1ssZinQcEgdOejUKGTqwC8kyayZcvm6p5jXUMSQAR4Vkgcscdd+gHP/jB4Ncf/ehHJUlPP/205syZ49XLGotpiggCJz0bhQyd5Bv6iUl6oq1Lt10dreGYbMglQ1R4Fog89NBDgash4iWmKSII8iWPDlXI0AkBuT3kkiFKjJq+G2ZMU0QQ2Km++aXLpuqRpZfouVuucHxTJCDPr9DVv4GgIhApEdZ5QFDkqmfxwOKZumPhXxVce4KAPLdiVv8GgsqoyqphVsrKiUCxvKpnQd2Q3Bi6QhTRI1JCVGxFkHhRfZOF13Jj6ApRRI9IiVE5EVHHwmvWGLpCFBGI+IDKiYg6AvLsGLpCFBGIAPAFAflo5JIhisgRAeC6ZCqtnQeOa0vrYe08cJxZHg6QS4aooUcEgKsoxlU8hq4QJbF0Om3so0pPT4/i8bgSiYRqamr8bg6APKzWkcncPoc+0VPCHAgvJ/dvekQAuMLJwo7b2rvoNQEgiRwRAC6xW4zrvu37KWEOYBCBCABX2C2ytfH5jkiUMCdhF7CHoRkArrBbZOvNd05Z/iwsJcxJ2AXso0cEgCvsLOx45uljbO0ryCXMWT0XcIZABIAr7Kwjc8NlU23tK6glzFk9F3COQASAa/IV41pxxfvy9prUB7iEuZPVcwEMIEcEgKvyFeMKcwlzVs8FnCMQAeC6XOvIhHn1XVbPBZwjEAFQcmEtYc7quYBzBCIAfBHG1XdZPRdwjmRVAHARq+cCztAjAhiOxeGCJ6xDT4AXCEQAgwWpQqdbAVNYAq8wDj0BXiAQAQyVqdA5MukxU6Gz1N38uQIEtwKmIAVeANwRS6fTxpb46+npUTweVyKRUE1Njd/NAUommUrr8nXbLYtjZWZfPHfLFSXpLcgVIEjKGjBlWmU3YLIKvJzuB4D/nNy/SVYFDGRShc58a6fc+m8vFV3SnNLoQHQRiAAGMqVCZ74AIS3pzbftraabi0mBl5+SqbR2HjiuLa2HtfPAcQIvRAI5IoCBTKnQmS9AsCtfwGRK4OUn8mMQVfSIAAbKVOj0e3E4t278+QImUwIvv+Qb/mpp6/SpZYD3CEQAA2UqdEoaFYyUskJnsTd+uwGTKYGXH8iPQdQRiACGMqFCp50A4ayxYwb/P/Jnkr2AyZTAyw/kxyDqyBEBDOZ3hU47a6es/dsPS1LRq+mGeVXeXMiPQdQRiACG87tCp90AwY2Aye/Ayw92h7/OHlfpcUsAf1DQDIAtYSm9bppM8bquxMmseSIZdTVV+odPh7dnCOHi5P5NIALAWFEJfjKzZiRZBiNUmEWQOLl/MzQDwEhO62oEOWjJDH/9w89eVldPX9Zt0hoIRpq3tmt+Y11g3huQD4EIAOM4XfAvDMXAFjTVq7pqjP7Hd39tuc3QGTSs7IuwYPouAKM4rasRpmJgx97K3hsyEjNoECYEIgCM4qSuRtiKgUW9wiyiiUAEgFGc1NUIWzGwKFeYRXQRiACGifoKrE56BcJWDCzKFWYRXSSrAgYJQ9JlsTK9AlZ1NWIaKKY2q6HWdk9HkIYyolphFtFFIAIYwulMkbCyU1Y+0yvgJGgJkihWmEV0MTQDGCBsSZfFsrvgX5iHMjKl/a+Zca5mTxsfyPcA2EGPCGAAJ0mXUakfYbdXgKEMINgIRAADhC3p0i12F/xjKAMILgIRwADUjyie36sUAygMOSKAAagfASCqCEQAA4Q56RIAciEQAQxhd6YIAISJZzkiBw8e1Jo1a7R9+3Z1dXVp0qRJWrx4sf7+7/9eFRUVXr0sEGgkXQKIGs8CkX379imVSunBBx/UhRdeqLa2Ni1dulS9vb26++67vXpZIPDsJl0mU2kCFgCBF0un0yWrkHTXXXdp/fr1+uMf/2hr+56eHsXjcSUSCdXU1HjcOiA4KAUPwGRO7t8lzRFJJBKqrbXO+u/r61NPT8+wfwCGy5SCH1kALVMKvqWt06eWAYBzJQtE9u/fr3vvvVc33nij5TZr165VPB4f/Dd58uRSNQ8IBErBAwgbx4HIrbfeqlgslvPfvn37hv3O4cOHtWDBAl133XVaunSp5b5XrVqlRCIx+O+1115z/o6AEHNSCh4AgsBxsurXvvY1XX/99Tm3ueCCCwb/f+TIEc2dO1eXXnqpvvOd7+T8vcrKSlVWVjptEhAZlIIHEDaOA5EJEyZowoQJtrY9fPiw5s6dq4suukgbN25UWRllS4BiUAoeQNh4Nn338OHDmjNnjs4//3zdfffdeuONNwZ/VldX59XLAqGWKQXflTiZNU8kpoECaJSCBxAUngUi27Zt0/79+7V//36dd955w35WwhnDQKhkSsEv27RHMWlYMEIpeABB5NlYyfXXX690Op31H4DCUQoeQJh41iMCwDuUggcQFgQiQEDZLQUPACZjGgsAAPANgQgAAPANgQgAAPANgQgAAPANgQgAAPANgQgAAPANgQgAAPANdUQiLplKUxQLAOAbApEIa2nrVPPWdnUm3lsyvj5epdULGykTDgAoCYZmIqqlrVPLNu0ZFoRIUlfipJZt2qOWtk6fWgYAiBICkQhKptJq3tqedRn5zPeat7YrmWKBQgCAtwhEImh3R/eonpCh0pI6Eye1u6O7dI0CAEQSgUgEHT1hHYQUsh0AAIUiEImgidVVrm4HAEChCEQiaFZDrerjVbKapBvTwOyZWQ21pWwWACCCCEQiqLwsptULGyVpVDCS+Xr1wkbqiQAAPEcgElELmuq1fvFM1cWHD7/Uxau0fvFM6ogAAEqCgmYRtqCpXvMb66isCgDwDYFIxJWXxTR72ni/mwEAiCiGZgAAgG8IRAAAgG8IRAAAgG8IRAAAgG8IRAAAgG8IRAAAgG8IRAAAgG8IRAAAgG8oaAZEXDKVprouAN8QiAAR1tLWqeat7epMnBz8Xn28SqsXNrLeEICSYGgGiKiWtk4t27RnWBAiSV2Jk1q2aY9a2jp9ahmAKCEQASIomUqreWu70ll+lvle89Z2JVPZtgAA9xCIABG0u6N7VE/IUGlJnYmT2t3RXbpGAYgkAhEggo6esA5CCtkOAApFIAJE0MTqKle3A4BCEYgAETSroVb18SpZTdKNaWD2zKyG2lI2C0AEEYgAEVReFtPqhY2SNCoYyXy9emEj9UQAeI5ABIioBU31Wr94puriw4df6uJVWr94JnVEAJQEBc2ACFvQVK/5jXVUVgXgGwIRIOLKy2KaPW28380AEFEMzQAAAN8QiAAAAN8QiAAAAN8QiAAAAN8QiAAAAN8QiAAAAN8QiAAAAN8QiAAAAN8QiAAAAN8YXVk1nU5Lknp6enxuCQAAsCtz387cx3MxOhA5ceKEJGny5Mk+twQAADh14sQJxePxnNvE0nbCFZ+kUikdOXJE1dXVisXeW4Srp6dHkydP1muvvaaamhofW2g2jpM9HCd7OE72cazs4TjZE8TjlE6ndeLECU2aNEllZbmzQIzuESkrK9N5551n+fOamprAfCh+4jjZw3Gyh+NkH8fKHo6TPUE7Tvl6QjJIVgUAAL4hEAEAAL4JZCBSWVmp1atXq7Ky0u+mGI3jZA/HyR6Ok30cK3s4TvaE/TgZnawKAADCLZA9IgAAIBwIRAAAgG8IRAAAgG8IRAAAgG8CH4h8+tOf1pQpU1RVVaX6+np94Qtf0JEjR/xullEOHjyoL3/5y2poaNDpp5+uadOmafXq1erv7/e7aca58847demll2rs2LE688wz/W6OUe6//35NnTpVVVVV+tjHPqbdu3f73STj7NixQwsXLtSkSZMUi8X005/+1O8mGWft2rW6+OKLVV1drYkTJ+raa6/VK6+84nezjLN+/Xp95CMfGSxiNnv2bP385z/3u1meCHwgMnfuXP3oRz/SK6+8op/85Cc6cOCAPvvZz/rdLKPs27dPqVRKDz74oF5++WV961vf0gMPPKDbbrvN76YZp7+/X9ddd52WLVvmd1OM8thjj2nlypVavXq19uzZo+nTp+tTn/qUjh496nfTjNLb26vp06fr/vvv97spxnr22We1fPly7dq1S9u2bdOpU6f0yU9+Ur29vX43zSjnnXeevvnNb+qFF17Qb3/7W11xxRW65ppr9PLLL/vdNPelQ2bLli3pWCyW7u/v97spRvunf/qndENDg9/NMNbGjRvT8Xjc72YYY9asWenly5cPfp1MJtOTJk1Kr1271sdWmU1SevPmzX43w3hHjx5NS0o/++yzfjfFeGeddVb6u9/9rt/NcF3ge0SG6u7u1r/+67/q0ksv1ZgxY/xujtESiYRqa2v9bgYCoL+/Xy+88ILmzZs3+L2ysjLNmzdPO3fu9LFlCINEIiFJXI9ySCaTevTRR9Xb26vZs2f73RzXhSIQueWWWzRu3DiNHz9ehw4d0pYtW/xuktH279+ve++9VzfeeKPfTUEAHDt2TMlkUuecc86w759zzjnq6uryqVUIg1QqpZtvvlmXXXaZmpqa/G6OcV566SWdccYZqqys1E033aTNmzersbHR72a5zshA5NZbb1UsFsv5b9++fYPbf/3rX9fvfvc7/eIXv1B5ebm++MUvKh2BgrFOj5MkHT58WAsWLNB1112npUuX+tTy0irkOAHw3vLly9XW1qZHH33U76YY6QMf+IBaW1v161//WsuWLdOSJUvU3t7ud7NcZ2SJ9zfeeEPHjx/Puc0FF1ygioqKUd9//fXXNXnyZP3qV78KZRfWUE6P05EjRzRnzhxdcskleuihh1RWZmQc6rpC/p4eeugh3XzzzXrzzTc9bp35+vv7NXbsWP34xz/WtddeO/j9JUuW6M0336QH0kIsFtPmzZuHHTO8Z8WKFdqyZYt27NihhoYGv5sTCPPmzdO0adP04IMP+t0UV53mdwOymTBhgiZMmFDQ76ZSKUlSX1+fm00ykpPjdPjwYc2dO1cXXXSRNm7cGJkgRCru7wlSRUWFLrroIj311FODN9VUKqWnnnpKK1as8LdxCJx0Oq2vfOUr2rx5s5555hmCEAdSqVQo721GBiJ2/frXv9ZvfvMbXX755TrrrLN04MAB3X777Zo2bVroe0OcOHz4sObMmaPzzz9fd999t954443Bn9XV1fnYMvMcOnRI3d3dOnTokJLJpFpbWyVJF154oc444wx/G+ejlStXasmSJfrrv/5rzZo1S/fcc496e3t1ww03+N00o7z11lvav3//4NcdHR1qbW1VbW2tpkyZ4mPLzLF8+XI9/PDD2rJli6qrqwfzjOLxuE4//XSfW2eOVatW6corr9SUKVN04sQJPfzww3rmmWf0H//xH343zX3+TtopzosvvpieO3duura2Nl1ZWZmeOnVq+qabbkq//vrrfjfNKBs3bkxLyvoPwy1ZsiTrcXr66af9bprv7r333vSUKVPSFRUV6VmzZqV37drld5OM8/TTT2f9+1myZInfTTOG1bVo48aNfjfNKF/60pfS559/frqioiI9YcKE9Cc+8Yn0L37xC7+b5Qkjc0QAAEA0RCdRAAAAGIdABAAA+IZABAAA+IZABAAA+IZABAAA+IZABAAA+IZABAAA+IZABAAA+IZABAAA+IZABAAA+IZABAAA+IZABAAA+Ob/Ayr0YAxe85ldAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#RANDOM DATASET"
      ],
      "metadata": {
        "id": "2z__qh9FBTgk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bcd=pd.read_csv('/content/breast_cancer_data.csv')\n",
        "bcd\n",
        "bcd.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U4ixCrokBXCg",
        "outputId": "0e54cad5-ae58-494d-b016-51f390b5a43b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 33)"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X1=bcd.drop(['id','diagnosis','Unnamed: 32'],axis=1)\n",
        "X1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "YJpgvk96BnWF",
        "outputId": "0b4c01a3-1f7c-41b3-a255-e0d0c013dd4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
              "0          17.99         10.38          122.80     1001.0          0.11840   \n",
              "1          20.57         17.77          132.90     1326.0          0.08474   \n",
              "2          19.69         21.25          130.00     1203.0          0.10960   \n",
              "3          11.42         20.38           77.58      386.1          0.14250   \n",
              "4          20.29         14.34          135.10     1297.0          0.10030   \n",
              "..           ...           ...             ...        ...              ...   \n",
              "564        21.56         22.39          142.00     1479.0          0.11100   \n",
              "565        20.13         28.25          131.20     1261.0          0.09780   \n",
              "566        16.60         28.08          108.30      858.1          0.08455   \n",
              "567        20.60         29.33          140.10     1265.0          0.11780   \n",
              "568         7.76         24.54           47.92      181.0          0.05263   \n",
              "\n",
              "     compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
              "0             0.27760         0.30010              0.14710         0.2419   \n",
              "1             0.07864         0.08690              0.07017         0.1812   \n",
              "2             0.15990         0.19740              0.12790         0.2069   \n",
              "3             0.28390         0.24140              0.10520         0.2597   \n",
              "4             0.13280         0.19800              0.10430         0.1809   \n",
              "..                ...             ...                  ...            ...   \n",
              "564           0.11590         0.24390              0.13890         0.1726   \n",
              "565           0.10340         0.14400              0.09791         0.1752   \n",
              "566           0.10230         0.09251              0.05302         0.1590   \n",
              "567           0.27700         0.35140              0.15200         0.2397   \n",
              "568           0.04362         0.00000              0.00000         0.1587   \n",
              "\n",
              "     fractal_dimension_mean  ...  radius_worst  texture_worst  \\\n",
              "0                   0.07871  ...        25.380          17.33   \n",
              "1                   0.05667  ...        24.990          23.41   \n",
              "2                   0.05999  ...        23.570          25.53   \n",
              "3                   0.09744  ...        14.910          26.50   \n",
              "4                   0.05883  ...        22.540          16.67   \n",
              "..                      ...  ...           ...            ...   \n",
              "564                 0.05623  ...        25.450          26.40   \n",
              "565                 0.05533  ...        23.690          38.25   \n",
              "566                 0.05648  ...        18.980          34.12   \n",
              "567                 0.07016  ...        25.740          39.42   \n",
              "568                 0.05884  ...         9.456          30.37   \n",
              "\n",
              "     perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\n",
              "0             184.60      2019.0           0.16220            0.66560   \n",
              "1             158.80      1956.0           0.12380            0.18660   \n",
              "2             152.50      1709.0           0.14440            0.42450   \n",
              "3              98.87       567.7           0.20980            0.86630   \n",
              "4             152.20      1575.0           0.13740            0.20500   \n",
              "..               ...         ...               ...                ...   \n",
              "564           166.10      2027.0           0.14100            0.21130   \n",
              "565           155.00      1731.0           0.11660            0.19220   \n",
              "566           126.70      1124.0           0.11390            0.30940   \n",
              "567           184.60      1821.0           0.16500            0.86810   \n",
              "568            59.16       268.6           0.08996            0.06444   \n",
              "\n",
              "     concavity_worst  concave points_worst  symmetry_worst  \\\n",
              "0             0.7119                0.2654          0.4601   \n",
              "1             0.2416                0.1860          0.2750   \n",
              "2             0.4504                0.2430          0.3613   \n",
              "3             0.6869                0.2575          0.6638   \n",
              "4             0.4000                0.1625          0.2364   \n",
              "..               ...                   ...             ...   \n",
              "564           0.4107                0.2216          0.2060   \n",
              "565           0.3215                0.1628          0.2572   \n",
              "566           0.3403                0.1418          0.2218   \n",
              "567           0.9387                0.2650          0.4087   \n",
              "568           0.0000                0.0000          0.2871   \n",
              "\n",
              "     fractal_dimension_worst  \n",
              "0                    0.11890  \n",
              "1                    0.08902  \n",
              "2                    0.08758  \n",
              "3                    0.17300  \n",
              "4                    0.07678  \n",
              "..                       ...  \n",
              "564                  0.07115  \n",
              "565                  0.06637  \n",
              "566                  0.07820  \n",
              "567                  0.12400  \n",
              "568                  0.07039  \n",
              "\n",
              "[569 rows x 30 columns]"
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              "      <th></th>\n",
              "      <th>radius_mean</th>\n",
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              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
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              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
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              "      <td>0.05667</td>\n",
              "      <td>...</td>\n",
              "      <td>24.990</td>\n",
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              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19.69</td>\n",
              "      <td>21.25</td>\n",
              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
              "      <td>0.10960</td>\n",
              "      <td>0.15990</td>\n",
              "      <td>0.19740</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>...</td>\n",
              "      <td>23.570</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
              "      <td>0.42450</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.42</td>\n",
              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>...</td>\n",
              "      <td>14.910</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
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              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
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              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
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              "      <td>0.05623</td>\n",
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              "      <td>25.450</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
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              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
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              "      <td>23.690</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
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              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
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              "      <td>25.740</td>\n",
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              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
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              "  .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-1fe0f541-0e16-4731-b88f-dbfed5078ba0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_c94e5990-9c42-47df-969d-d49d8997a263\">\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('X1')\"\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_c94e5990-9c42-47df-969d-d49d8997a263 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('X1');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X1"
            }
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "std_x=S.fit_transform(X1)\n",
        "std_x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b2ACkUQ7CI7G",
        "outputId": "e8bfba64-a446-457b-8b1b-6d14fc6398da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.09706398, -2.07333501,  1.26993369, ...,  2.29607613,\n",
              "         2.75062224,  1.93701461],\n",
              "       [ 1.82982061, -0.35363241,  1.68595471, ...,  1.0870843 ,\n",
              "        -0.24388967,  0.28118999],\n",
              "       [ 1.57988811,  0.45618695,  1.56650313, ...,  1.95500035,\n",
              "         1.152255  ,  0.20139121],\n",
              "       ...,\n",
              "       [ 0.70228425,  2.0455738 ,  0.67267578, ...,  0.41406869,\n",
              "        -1.10454895, -0.31840916],\n",
              "       [ 1.83834103,  2.33645719,  1.98252415, ...,  2.28998549,\n",
              "         1.91908301,  2.21963528],\n",
              "       [-1.80840125,  1.22179204, -1.81438851, ..., -1.74506282,\n",
              "        -0.04813821, -0.75120669]])"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pca1=PCA(n_components=2)\n",
        "pca_x=pca1.fit_transform(std_x)\n",
        "pca_x\n",
        "pdf1=pd.DataFrame(data=pca_x,columns=['PC1','PC2'])\n",
        "pdf1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "m15haNvDDJmy",
        "outputId": "8d7e984a-9d19-43e5-8d41-07202783f0c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           PC1        PC2\n",
              "0     9.192837   1.948583\n",
              "1     2.387802  -3.768172\n",
              "2     5.733896  -1.075174\n",
              "3     7.122953  10.275589\n",
              "4     3.935302  -1.948072\n",
              "..         ...        ...\n",
              "564   6.439315  -3.576817\n",
              "565   3.793382  -3.584048\n",
              "566   1.256179  -1.902297\n",
              "567  10.374794   1.672010\n",
              "568  -5.475243  -0.670637\n",
              "\n",
              "[569 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aa19030b-e8f9-485b-938f-db9be0895e66\" 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",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PC1</th>\n",
              "      <th>PC2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9.192837</td>\n",
              "      <td>1.948583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.387802</td>\n",
              "      <td>-3.768172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5.733896</td>\n",
              "      <td>-1.075174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7.122953</td>\n",
              "      <td>10.275589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.935302</td>\n",
              "      <td>-1.948072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>6.439315</td>\n",
              "      <td>-3.576817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>3.793382</td>\n",
              "      <td>-3.584048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>1.256179</td>\n",
              "      <td>-1.902297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>10.374794</td>\n",
              "      <td>1.672010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>-5.475243</td>\n",
              "      <td>-0.670637</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 2 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-aa19030b-e8f9-485b-938f-db9be0895e66')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\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-aa19030b-e8f9-485b-938f-db9be0895e66 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-aa19030b-e8f9-485b-938f-db9be0895e66');\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",
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              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --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-7497f315-32cb-457b-8f72-0ae6c90969ec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_9d5ed523-aca0-4221-9a82-3fdb09b5d130\">\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('pdf1')\"\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_9d5ed523-aca0-4221-9a82-3fdb09b5d130 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('pdf1');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "pdf1",
              "summary": "{\n  \"name\": \"pdf1\",\n  \"rows\": 569,\n  \"fields\": [\n    {\n      \"column\": \"PC1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.6476006900940443,\n        \"min\": -5.565735898982114,\n        \"max\": 16.319233231032356,\n        \"num_unique_values\": 569,\n        \"samples\": [\n          -0.7811899668983195,\n          2.70459617262997,\n          1.4320694267981482\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"PC2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.3877551389801543,\n        \"min\": -7.775852804303549,\n        \"max\": 12.57319422874726,\n        \"num_unique_values\": 569,\n        \"samples\": [\n          0.6528492587341029,\n          -4.4371417695684805,\n          -1.0496039491801836\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(pdf1['PC1'],pdf1['PC2'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "wOOU8H_MDHrU",
        "outputId": "ecbd5955-8b28-408b-9ffd-0aae586eb2cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7dc4e245d420>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}