Principal_Component_Analysis.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Principal Component Analysis.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM2CZRdq0L9xkhIcqlpBBhn",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Neel7317/Machine_learning-Unsupervised-/blob/master/Principal_Component_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wpE7Mz9rfrzy"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bJf6xkwTgstH"
      },
      "source": [
        "from sklearn.datasets import load_breast_cancer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vOILJFoSgvDi"
      },
      "source": [
        "\n",
        "cancer = load_breast_cancer()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DAsiP2zXg1ZW",
        "outputId": "2c774e50-4d31-4394-d463-d89a3073e04a"
      },
      "source": [
        "cancer.keys()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hOy9eL_3g99M",
        "outputId": "df0f6c48-3a73-48f7-f0d2-b0de2483b7a8"
      },
      "source": [
        "print(cancer['DESCR'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ".. _breast_cancer_dataset:\n",
            "\n",
            "Breast cancer wisconsin (diagnostic) dataset\n",
            "--------------------------------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 569\n",
            "\n",
            "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
            "\n",
            "    :Attribute Information:\n",
            "        - radius (mean of distances from center to points on the perimeter)\n",
            "        - texture (standard deviation of gray-scale values)\n",
            "        - perimeter\n",
            "        - area\n",
            "        - smoothness (local variation in radius lengths)\n",
            "        - compactness (perimeter^2 / area - 1.0)\n",
            "        - concavity (severity of concave portions of the contour)\n",
            "        - concave points (number of concave portions of the contour)\n",
            "        - symmetry \n",
            "        - fractal dimension (\"coastline approximation\" - 1)\n",
            "\n",
            "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
            "        largest values) of these features were computed for each image,\n",
            "        resulting in 30 features.  For instance, field 3 is Mean Radius, field\n",
            "        13 is Radius SE, field 23 is Worst Radius.\n",
            "\n",
            "        - class:\n",
            "                - WDBC-Malignant\n",
            "                - WDBC-Benign\n",
            "\n",
            "    :Summary Statistics:\n",
            "\n",
            "    ===================================== ====== ======\n",
            "                                           Min    Max\n",
            "    ===================================== ====== ======\n",
            "    radius (mean):                        6.981  28.11\n",
            "    texture (mean):                       9.71   39.28\n",
            "    perimeter (mean):                     43.79  188.5\n",
            "    area (mean):                          143.5  2501.0\n",
            "    smoothness (mean):                    0.053  0.163\n",
            "    compactness (mean):                   0.019  0.345\n",
            "    concavity (mean):                     0.0    0.427\n",
            "    concave points (mean):                0.0    0.201\n",
            "    symmetry (mean):                      0.106  0.304\n",
            "    fractal dimension (mean):             0.05   0.097\n",
            "    radius (standard error):              0.112  2.873\n",
            "    texture (standard error):             0.36   4.885\n",
            "    perimeter (standard error):           0.757  21.98\n",
            "    area (standard error):                6.802  542.2\n",
            "    smoothness (standard error):          0.002  0.031\n",
            "    compactness (standard error):         0.002  0.135\n",
            "    concavity (standard error):           0.0    0.396\n",
            "    concave points (standard error):      0.0    0.053\n",
            "    symmetry (standard error):            0.008  0.079\n",
            "    fractal dimension (standard error):   0.001  0.03\n",
            "    radius (worst):                       7.93   36.04\n",
            "    texture (worst):                      12.02  49.54\n",
            "    perimeter (worst):                    50.41  251.2\n",
            "    area (worst):                         185.2  4254.0\n",
            "    smoothness (worst):                   0.071  0.223\n",
            "    compactness (worst):                  0.027  1.058\n",
            "    concavity (worst):                    0.0    1.252\n",
            "    concave points (worst):               0.0    0.291\n",
            "    symmetry (worst):                     0.156  0.664\n",
            "    fractal dimension (worst):            0.055  0.208\n",
            "    ===================================== ====== ======\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "\n",
            "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
            "\n",
            "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
            "\n",
            "    :Donor: Nick Street\n",
            "\n",
            "    :Date: November, 1995\n",
            "\n",
            "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
            "https://goo.gl/U2Uwz2\n",
            "\n",
            "Features are computed from a digitized image of a fine needle\n",
            "aspirate (FNA) of a breast mass.  They describe\n",
            "characteristics of the cell nuclei present in the image.\n",
            "\n",
            "Separating plane described above was obtained using\n",
            "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
            "Construction Via Linear Programming.\" Proceedings of the 4th\n",
            "Midwest Artificial Intelligence and Cognitive Science Society,\n",
            "pp. 97-101, 1992], a classification method which uses linear\n",
            "programming to construct a decision tree.  Relevant features\n",
            "were selected using an exhaustive search in the space of 1-4\n",
            "features and 1-3 separating planes.\n",
            "\n",
            "The actual linear program used to obtain the separating plane\n",
            "in the 3-dimensional space is that described in:\n",
            "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
            "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
            "Optimization Methods and Software 1, 1992, 23-34].\n",
            "\n",
            "This database is also available through the UW CS ftp server:\n",
            "\n",
            "ftp ftp.cs.wisc.edu\n",
            "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
            "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
            "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
            "     San Jose, CA, 1993.\n",
            "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
            "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
            "     July-August 1995.\n",
            "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
            "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
            "     163-171.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CcGogqwfhBFn"
      },
      "source": [
        "df = pd.DataFrame(cancer['data'],columns=cancer['feature_names'])\n",
        "#(['DESCR', 'data', 'feature_names', 'target_names', 'target'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249
        },
        "id": "KV7NSEfyhJKo",
        "outputId": "2b5b867a-6956-4de2-e52f-2866224d4600"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean radius</th>\n",
              "      <th>mean texture</th>\n",
              "      <th>mean perimeter</th>\n",
              "      <th>mean area</th>\n",
              "      <th>mean smoothness</th>\n",
              "      <th>mean compactness</th>\n",
              "      <th>mean concavity</th>\n",
              "      <th>mean concave points</th>\n",
              "      <th>mean symmetry</th>\n",
              "      <th>mean fractal dimension</th>\n",
              "      <th>radius error</th>\n",
              "      <th>texture error</th>\n",
              "      <th>perimeter error</th>\n",
              "      <th>area error</th>\n",
              "      <th>smoothness error</th>\n",
              "      <th>compactness error</th>\n",
              "      <th>concavity error</th>\n",
              "      <th>concave points error</th>\n",
              "      <th>symmetry error</th>\n",
              "      <th>fractal dimension error</th>\n",
              "      <th>worst radius</th>\n",
              "      <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",
              "      <th>worst fractal dimension</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17.99</td>\n",
              "      <td>10.38</td>\n",
              "      <td>122.80</td>\n",
              "      <td>1001.0</td>\n",
              "      <td>0.11840</td>\n",
              "      <td>0.27760</td>\n",
              "      <td>0.3001</td>\n",
              "      <td>0.14710</td>\n",
              "      <td>0.2419</td>\n",
              "      <td>0.07871</td>\n",
              "      <td>1.0950</td>\n",
              "      <td>0.9053</td>\n",
              "      <td>8.589</td>\n",
              "      <td>153.40</td>\n",
              "      <td>0.006399</td>\n",
              "      <td>0.04904</td>\n",
              "      <td>0.05373</td>\n",
              "      <td>0.01587</td>\n",
              "      <td>0.03003</td>\n",
              "      <td>0.006193</td>\n",
              "      <td>25.38</td>\n",
              "      <td>17.33</td>\n",
              "      <td>184.60</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>0.1622</td>\n",
              "      <td>0.6656</td>\n",
              "      <td>0.7119</td>\n",
              "      <td>0.2654</td>\n",
              "      <td>0.4601</td>\n",
              "      <td>0.11890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>20.57</td>\n",
              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.0869</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.99</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.1238</td>\n",
              "      <td>0.1866</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
              "    </tr>\n",
              "    <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>0.1974</td>\n",
              "      <td>0.12790</td>\n",
              "      <td>0.2069</td>\n",
              "      <td>0.05999</td>\n",
              "      <td>0.7456</td>\n",
              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
              "      <td>0.04006</td>\n",
              "      <td>0.03832</td>\n",
              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.57</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.1444</td>\n",
              "      <td>0.4245</td>\n",
              "      <td>0.4504</td>\n",
              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "    </tr>\n",
              "    <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>0.2414</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>14.91</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.2098</td>\n",
              "      <td>0.8663</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.1980</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.54</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.1374</td>\n",
              "      <td>0.2050</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   mean radius  mean texture  ...  worst symmetry  worst fractal dimension\n",
              "0        17.99         10.38  ...          0.4601                  0.11890\n",
              "1        20.57         17.77  ...          0.2750                  0.08902\n",
              "2        19.69         21.25  ...          0.3613                  0.08758\n",
              "3        11.42         20.38  ...          0.6638                  0.17300\n",
              "4        20.29         14.34  ...          0.2364                  0.07678\n",
              "\n",
              "[5 rows x 30 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LKzJGyOUhVhn"
      },
      "source": [
        "\n",
        "# ##**PCA Visualization**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m3ep339HhgUn"
      },
      "source": [
        "\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A7QkqpgahjMh",
        "outputId": "291ec67c-ae56-4daa-9df4-3da9a629e5c0"
      },
      "source": [
        "scaler = StandardScaler()\n",
        "scaler.fit(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "StandardScaler(copy=True, with_mean=True, with_std=True)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nKYWo1nVhmo7"
      },
      "source": [
        "scaled_data = scaler.transform(df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "joPn_ocJh2r5"
      },
      "source": [
        "PCA with Scikit Learn uses a very similar process to other preprocessing functions that come with SciKit Learn. We instantiate a PCA object, find the principal components using the fit method, then apply the rotation and dimensionality reduction by calling transform().\n",
        "\n",
        "We can also specify how many components we want to keep when creating the PCA object."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "apKOrVUAhrMy"
      },
      "source": [
        "from sklearn.decomposition import PCA"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dVCKWWWAh7rM"
      },
      "source": [
        "pca = PCA(n_components=3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4h0hQaTGh_8K",
        "outputId": "0ba352a3-6768-485c-fbab-aa9eaccbfb01"
      },
      "source": [
        "pca.fit(scaled_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PCA(copy=True, iterated_power='auto', n_components=3, random_state=None,\n",
              "    svd_solver='auto', tol=0.0, whiten=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8nlcZyckiDo-"
      },
      "source": [
        "x_pca = pca.transform(scaled_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_g1nb91JiMrY",
        "outputId": "a1782028-d52d-481f-899f-3c8f19765f8e"
      },
      "source": [
        "scaled_data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 30)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ibROMTJiQBh",
        "outputId": "e81afdb6-b9e8-4120-f1a4-fe0a8047c712"
      },
      "source": [
        "x_pca.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(569, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qXsRR-I_ib0-"
      },
      "source": [
        "\n",
        "# Great! We've reduced 30 dimensions to just 3! Let's plot these three dimensions out!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "e2UHtsNtiS0v",
        "outputId": "2457e606-ce06-457b-f027-ff458a6de246"
      },
      "source": [
        "plt.scatter(x_pca[:,0],x_pca[:,2],c=cancer['target'],cmap='plasma')\n",
        "plt.xlabel('First principal component')\n",
        "plt.ylabel('Second Principal Component')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Second Principal Component')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kc0zBxQZjHpZ"
      },
      "source": [
        "# **Now,after implmenting PCA we can select any ML Algorithm for our dataset like KNN,Logistic regression,Decision tree etc**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RRhJQSfsizTF"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}