PCA.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PCA or principle component analysis is used for dimensionality reduction. We will look into some live code examples of the same here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer=load_breast_cancer()\n",
    "cancer.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "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",
      "        worst/largest values) of these features were computed for each image,\n",
      "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
      "        10 is Radius SE, field 20 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"
     ]
    }
   ],
   "source": [
    "print(cancer['DESCR'])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we can see that the number of dimensions present here is 3. With the help of PCA we can reduce it to 2 as below "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>...</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>...</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>...</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>...</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>...</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>...</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",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\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",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0           0.27760          0.3001              0.14710         0.2419   \n",
       "1           0.07864          0.0869              0.07017         0.1812   \n",
       "2           0.15990          0.1974              0.12790         0.2069   \n",
       "3           0.28390          0.2414              0.10520         0.2597   \n",
       "4           0.13280          0.1980              0.10430         0.1809   \n",
       "\n",
       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
       "0                 0.07871  ...         25.38          17.33           184.60   \n",
       "1                 0.05667  ...         24.99          23.41           158.80   \n",
       "2                 0.05999  ...         23.57          25.53           152.50   \n",
       "3                 0.09744  ...         14.91          26.50            98.87   \n",
       "4                 0.05883  ...         22.54          16.67           152.20   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(cancer['data'],columns=cancer['feature_names'])\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler=StandardScaler()\n",
    "scaler.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "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]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data=scaler.transform(df)\n",
    "scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca=PCA(n_components=2)\n",
    "pca.fit(scaled_data)\n",
    "x_pca=pca.transform(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 30)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 2)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Second principle component')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(x_pca[:,0],x_pca[:,1],c=cancer['target'])\n",
    "plt.xlabel('First principle component')\n",
    "plt.ylabel('Second principle component')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}