Feature_extraction.ipynb

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        "<a href=\"https://colab.research.google.com/github/hussain0048/Machine-Learning/blob/master/Feature_extraction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "id": "_4Hs43AiBUwb"
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      "cell_type": "markdown",
      "source": [
        "# **The Breast Cancer Dataset**\n"
      ],
      "metadata": {
        "id": "WAst-plBB_9D"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Building the Baseline Model – Logistic Regression**"
      ],
      "metadata": {
        "id": "2TuEN61HCOGY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#import the necessary libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from time import time\n",
        "from sklearn.datasets import load_breast_cancer"
      ],
      "metadata": {
        "id": "TuTa-fdcCTQp"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cancer_dict = load_breast_cancer()\n"
      ],
      "metadata": {
        "id": "T5DNCGDACZC3"
      },
      "execution_count": 2,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "cancer_dict.keys()\n"
      ],
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            "text/plain": [
              "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
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      "cell_type": "code",
      "source": [
        "#define the feature and labels in the data\n",
        "data = cancer_dict.data\n",
        "columns = cancer_dict.feature_names\n",
        "\n",
        "X = pd.DataFrame(data, columns=columns)\n",
        "y = pd.Series(cancer_dict.target, name='target')\n",
        "\n",
        "#merge the X and y data\n",
        "df = pd.concat([X, y], axis=1)\n",
        "df.sample(10)"
      ],
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        "id": "kLNEPCT6Cisg",
        "outputId": "f3d74f4b-fc8f-4563-b1ad-a17c03515dc4"
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      "execution_count": 4,
      "outputs": [
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              "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
              "355       12.560         19.07           81.92      485.8          0.08760   \n",
              "328       16.270         20.71          106.90      813.7          0.11690   \n",
              "451       19.590         25.00          127.70     1191.0          0.10320   \n",
              "393       21.610         22.28          144.40     1407.0          0.11670   \n",
              "318        9.042         18.90           60.07      244.5          0.09968   \n",
              "160       11.750         20.18           76.10      419.8          0.10890   \n",
              "116        8.950         15.76           58.74      245.2          0.09462   \n",
              "175        8.671         14.45           54.42      227.2          0.09138   \n",
              "273        9.742         15.67           61.50      289.9          0.09037   \n",
              "485       12.450         16.41           82.85      476.7          0.09514   \n",
              "\n",
              "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
              "355           0.10380         0.10300              0.04391         0.1533   \n",
              "328           0.13190         0.14780              0.08488         0.1948   \n",
              "451           0.09871         0.16550              0.09063         0.1663   \n",
              "393           0.20870         0.28100              0.15620         0.2162   \n",
              "318           0.19720         0.19750              0.04908         0.2330   \n",
              "160           0.11410         0.06843              0.03738         0.1993   \n",
              "116           0.12430         0.09263              0.02308         0.1305   \n",
              "175           0.04276         0.00000              0.00000         0.1722   \n",
              "273           0.04689         0.01103              0.01407         0.2081   \n",
              "485           0.15110         0.15440              0.04846         0.2082   \n",
              "\n",
              "     mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
              "355                 0.06184  ...          22.43            89.02       547.4   \n",
              "328                 0.06277  ...          30.38           129.80      1121.0   \n",
              "451                 0.05391  ...          30.96           139.80      1421.0   \n",
              "393                 0.06606  ...          28.74           172.00      2081.0   \n",
              "318                 0.08743  ...          23.40            68.62       297.1   \n",
              "160                 0.06453  ...          26.21            88.91       543.9   \n",
              "116                 0.07163  ...          17.07            63.34       270.0   \n",
              "175                 0.06724  ...          17.04            58.36       259.2   \n",
              "273                 0.06312  ...          20.88            68.09       355.2   \n",
              "485                 0.07325  ...          21.03            97.82       580.6   \n",
              "\n",
              "     worst smoothness  worst compactness  worst concavity  \\\n",
              "355            0.1096            0.20020          0.23880   \n",
              "328            0.1590            0.29470          0.35970   \n",
              "451            0.1528            0.18450          0.39770   \n",
              "393            0.1502            0.57170          0.70530   \n",
              "318            0.1221            0.37480          0.46090   \n",
              "160            0.1358            0.18920          0.19560   \n",
              "116            0.1179            0.18790          0.15440   \n",
              "175            0.1162            0.07057          0.00000   \n",
              "273            0.1467            0.09370          0.04043   \n",
              "485            0.1175            0.40610          0.48960   \n",
              "\n",
              "     worst concave points  worst symmetry  worst fractal dimension  target  \n",
              "355               0.09265          0.2121                  0.07188       1  \n",
              "328               0.15830          0.3103                  0.08200       0  \n",
              "451               0.14660          0.2293                  0.06091       0  \n",
              "393               0.24220          0.3828                  0.10070       0  \n",
              "318               0.11450          0.3135                  0.10550       1  \n",
              "160               0.07909          0.3168                  0.07987       1  \n",
              "116               0.03846          0.1652                  0.07722       1  \n",
              "175               0.00000          0.2592                  0.07848       1  \n",
              "273               0.05159          0.2841                  0.08175       1  \n",
              "485               0.13420          0.3231                  0.10340       1  \n",
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              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
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              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
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              "      <th>worst texture</th>\n",
              "      <th>worst perimeter</th>\n",
              "      <th>worst area</th>\n",
              "      <th>worst smoothness</th>\n",
              "      <th>worst compactness</th>\n",
              "      <th>worst concavity</th>\n",
              "      <th>worst concave points</th>\n",
              "      <th>worst symmetry</th>\n",
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              "      <th>355</th>\n",
              "      <td>12.560</td>\n",
              "      <td>19.07</td>\n",
              "      <td>81.92</td>\n",
              "      <td>485.8</td>\n",
              "      <td>0.08760</td>\n",
              "      <td>0.10380</td>\n",
              "      <td>0.10300</td>\n",
              "      <td>0.04391</td>\n",
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              "      <th>328</th>\n",
              "      <td>16.270</td>\n",
              "      <td>20.71</td>\n",
              "      <td>106.90</td>\n",
              "      <td>813.7</td>\n",
              "      <td>0.11690</td>\n",
              "      <td>0.13190</td>\n",
              "      <td>0.14780</td>\n",
              "      <td>0.08488</td>\n",
              "      <td>0.1948</td>\n",
              "      <td>0.06277</td>\n",
              "      <td>...</td>\n",
              "      <td>30.38</td>\n",
              "      <td>129.80</td>\n",
              "      <td>1121.0</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.29470</td>\n",
              "      <td>0.35970</td>\n",
              "      <td>0.15830</td>\n",
              "      <td>0.3103</td>\n",
              "      <td>0.08200</td>\n",
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              "      <th>451</th>\n",
              "      <td>19.590</td>\n",
              "      <td>25.00</td>\n",
              "      <td>127.70</td>\n",
              "      <td>1191.0</td>\n",
              "      <td>0.10320</td>\n",
              "      <td>0.09871</td>\n",
              "      <td>0.16550</td>\n",
              "      <td>0.09063</td>\n",
              "      <td>0.1663</td>\n",
              "      <td>0.05391</td>\n",
              "      <td>...</td>\n",
              "      <td>30.96</td>\n",
              "      <td>139.80</td>\n",
              "      <td>1421.0</td>\n",
              "      <td>0.1528</td>\n",
              "      <td>0.18450</td>\n",
              "      <td>0.39770</td>\n",
              "      <td>0.14660</td>\n",
              "      <td>0.2293</td>\n",
              "      <td>0.06091</td>\n",
              "      <td>0</td>\n",
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              "      <th>393</th>\n",
              "      <td>21.610</td>\n",
              "      <td>22.28</td>\n",
              "      <td>144.40</td>\n",
              "      <td>1407.0</td>\n",
              "      <td>0.11670</td>\n",
              "      <td>0.20870</td>\n",
              "      <td>0.28100</td>\n",
              "      <td>0.15620</td>\n",
              "      <td>0.2162</td>\n",
              "      <td>0.06606</td>\n",
              "      <td>...</td>\n",
              "      <td>28.74</td>\n",
              "      <td>172.00</td>\n",
              "      <td>2081.0</td>\n",
              "      <td>0.1502</td>\n",
              "      <td>0.57170</td>\n",
              "      <td>0.70530</td>\n",
              "      <td>0.24220</td>\n",
              "      <td>0.3828</td>\n",
              "      <td>0.10070</td>\n",
              "      <td>0</td>\n",
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              "      <th>318</th>\n",
              "      <td>9.042</td>\n",
              "      <td>18.90</td>\n",
              "      <td>60.07</td>\n",
              "      <td>244.5</td>\n",
              "      <td>0.09968</td>\n",
              "      <td>0.19720</td>\n",
              "      <td>0.19750</td>\n",
              "      <td>0.04908</td>\n",
              "      <td>0.2330</td>\n",
              "      <td>0.08743</td>\n",
              "      <td>...</td>\n",
              "      <td>23.40</td>\n",
              "      <td>68.62</td>\n",
              "      <td>297.1</td>\n",
              "      <td>0.1221</td>\n",
              "      <td>0.37480</td>\n",
              "      <td>0.46090</td>\n",
              "      <td>0.11450</td>\n",
              "      <td>0.3135</td>\n",
              "      <td>0.10550</td>\n",
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              "      <th>160</th>\n",
              "      <td>11.750</td>\n",
              "      <td>20.18</td>\n",
              "      <td>76.10</td>\n",
              "      <td>419.8</td>\n",
              "      <td>0.10890</td>\n",
              "      <td>0.11410</td>\n",
              "      <td>0.06843</td>\n",
              "      <td>0.03738</td>\n",
              "      <td>0.1993</td>\n",
              "      <td>0.06453</td>\n",
              "      <td>...</td>\n",
              "      <td>26.21</td>\n",
              "      <td>88.91</td>\n",
              "      <td>543.9</td>\n",
              "      <td>0.1358</td>\n",
              "      <td>0.18920</td>\n",
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              "      <td>0.3168</td>\n",
              "      <td>0.07987</td>\n",
              "      <td>1</td>\n",
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              "      <th>116</th>\n",
              "      <td>8.950</td>\n",
              "      <td>15.76</td>\n",
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              "      <td>0.09462</td>\n",
              "      <td>0.12430</td>\n",
              "      <td>0.09263</td>\n",
              "      <td>0.02308</td>\n",
              "      <td>0.1305</td>\n",
              "      <td>0.07163</td>\n",
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              "      <td>17.07</td>\n",
              "      <td>63.34</td>\n",
              "      <td>270.0</td>\n",
              "      <td>0.1179</td>\n",
              "      <td>0.18790</td>\n",
              "      <td>0.15440</td>\n",
              "      <td>0.03846</td>\n",
              "      <td>0.1652</td>\n",
              "      <td>0.07722</td>\n",
              "      <td>1</td>\n",
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              "      <th>175</th>\n",
              "      <td>8.671</td>\n",
              "      <td>14.45</td>\n",
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              "      <td>227.2</td>\n",
              "      <td>0.09138</td>\n",
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              "      <td>0.2592</td>\n",
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              "      <th>273</th>\n",
              "      <td>9.742</td>\n",
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              "      <td>0.01407</td>\n",
              "      <td>0.2081</td>\n",
              "      <td>0.06312</td>\n",
              "      <td>...</td>\n",
              "      <td>20.88</td>\n",
              "      <td>68.09</td>\n",
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              "      <td>0.09370</td>\n",
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              "      <td>0.2841</td>\n",
              "      <td>0.08175</td>\n",
              "      <td>1</td>\n",
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              "      <th>485</th>\n",
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              "      <td>16.41</td>\n",
              "      <td>82.85</td>\n",
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              "      <td>0.2082</td>\n",
              "      <td>0.07325</td>\n",
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              "      <td>21.03</td>\n",
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              "      <td>580.6</td>\n",
              "      <td>0.1175</td>\n",
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              "      <td>0.13420</td>\n",
              "      <td>0.3231</td>\n",
              "      <td>0.10340</td>\n",
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-dbe7bb60-b231-4d33-9231-e4d968c72d6d button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-a107c6a9-c089-4148-8fdd-846dfa193fdd 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-a107c6a9-c089-4148-8fdd-846dfa193fdd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score, recall_score, precision_score\n",
        "\n",
        "#scale the data\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)"
      ],
      "metadata": {
        "id": "SIdhq7StCqdU"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def apply_model(X, y):\n",
        "    '''\n",
        "    This function receives the X and y dataset set, splits into train and test,\n",
        "    applies a Logistic Regression algorithm on the train data, make prediction after training,\n",
        "    and returns the recall\n",
        "    '''\n",
        "\n",
        "    #split the data\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1, test_size=0.3)\n",
        "\n",
        "    #apply the Logistic Regression algorithm on the data\n",
        "    model = LogisticRegression()\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    #make prediction\n",
        "    y_pred = model.predict(X_test)\n",
        "\n",
        "    #compute the metrics\n",
        "    accuracy = accuracy_score(y_test, y_pred)\n",
        "    recall = recall_score(y_test, y_pred)\n",
        "    precision = precision_score(y_test, y_pred)\n",
        "\n",
        "    return(f\"Accuracy score: {accuracy}\",\n",
        "           f\"Recall score: {recall}\",\n",
        "           f\"Precision score: {precision}\"\n",
        "          )"
      ],
      "metadata": {
        "id": "8hUsaXobCv3Q"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%time apply_model(X_scaled, y)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ifXOg3ucC1JW",
        "outputId": "46dca784-a44b-4318-ddc5-56790b283941"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 26.9 ms, sys: 17.4 ms, total: 44.3 ms\n",
            "Wall time: 74.1 ms\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('Accuracy score: 0.9707602339181286',\n",
              " 'Recall score: 0.9722222222222222',\n",
              " 'Precision score: 0.9813084112149533')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **1-Feature Extraction with Principal Component Analysis (PCA)**"
      ],
      "metadata": {
        "id": "df7snfsGC5jL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(n_components=2)\n",
        "\n",
        "#apply PCA to transform the features to 2\n",
        "X_pca = pca.fit_transform(X_scaled)\n",
        "\n",
        "#create the new dataframe\n",
        "df_pca = pd.concat([pd.DataFrame(X_pca), y], axis=1)"
      ],
      "metadata": {
        "id": "dX3Lsdq9DAT2"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_pca.head()\n"
      ],
      "metadata": {
        "id": "2B5Ij9yeDDg_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#visualize the dataset\n",
        "sns.scatterplot(df_pca.iloc[:, 0], df_pca.iloc[:, 1], hue=df_pca['target'])"
      ],
      "metadata": {
        "id": "HDzWrOCmDJEG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%time apply_model(X_scaled, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xU95Gnw6DS1p",
        "outputId": "f018a4b6-e6bd-4804-9318-2ffdd6ca25d9"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 28 ms, sys: 15.3 ms, total: 43.2 ms\n",
            "Wall time: 86.2 ms\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('Accuracy score: 0.9707602339181286',\n",
              " 'Recall score: 0.9722222222222222',\n",
              " 'Precision score: 0.9813084112149533')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2-Feature Extraction with Linear Discriminant Analysis (LDA)**"
      ],
      "metadata": {
        "id": "lbl37QKfDgyP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#import the LDA class\n",
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "\n",
        "#instantiate the LDA class and setting the number of components to 1\n",
        "lda = LinearDiscriminantAnalysis(n_components=1)\n",
        "\n",
        "#apply the LDA class\n",
        "lda.fit(X_scaled, y)\n",
        "X_lda = lda.transform(X_scaled)\n",
        "\n",
        "#get the resulting data\n",
        "df_lda = pd.concat([pd.DataFrame(X_lda), y], axis=1)"
      ],
      "metadata": {
        "id": "bFMChzc5DttG"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%time apply_model(X_scaled, y)"
      ],
      "metadata": {
        "id": "l0PHIW0QDzCO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **3-Feature Extraction with Isomap**\n"
      ],
      "metadata": {
        "id": "swtU3DjBD50W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#import Isomap\n",
        "from sklearn.manifold import Isomap\n",
        "\n",
        "#apply Isomap on the data\n",
        "iso = Isomap(n_components=2)\n",
        "X_iso = iso.fit_transform(X_scaled)\n",
        "\n",
        "#create the new dataset\n",
        "df_iso = pd.concat([pd.DataFrame(X_iso), y], axis=1)"
      ],
      "metadata": {
        "id": "qSpioc9hECin"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(df_iso.iloc[:, 0], df_iso.iloc[:, 1], hue=df_iso['target'])\n"
      ],
      "metadata": {
        "id": "yIZXEIATEFfv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%time apply_model(X_scaled, y)\n"
      ],
      "metadata": {
        "id": "lTwUeldtEKw9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **3-Feature Extraction with Locally Linear Embedding (LLE)**"
      ],
      "metadata": {
        "id": "ph0D5smAERt_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#import LLE\n",
        "from sklearn.manifold import LocallyLinearEmbedding\n",
        "\n",
        "#create an instance of the class\n",
        "lle = LocallyLinearEmbedding(n_components=2)\n",
        "X_lle = lle.fit_transform(X_scaled)\n",
        "\n",
        "#create the new dataset\n",
        "df_lle = pd.concat([pd.DataFrame(X_lle), y], axis=1)"
      ],
      "metadata": {
        "id": "fdwT-ztfEZK9"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%time apply_model(X_scaled, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NvnKudYuEdU9",
        "outputId": "3ca6751c-da2d-4363-9464-02586de419c4"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 21.6 ms, sys: 13.5 ms, total: 35.2 ms\n",
            "Wall time: 25.2 ms\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('Accuracy score: 0.9707602339181286',\n",
              " 'Recall score: 0.9722222222222222',\n",
              " 'Precision score: 0.9813084112149533')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    }
  ]
}