PCA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prinipal Component Analysis : to change dimensionality of the datasel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(cancer[\"data\"], columns=cancer[\"feature_names\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#using PCA we will reduce 30 dimension to 2 dimension.\n",
    "#First thing is we will do standard scalar\n",
    "#that is by using standard deviation=1 and mean=0, it will reduce the dataset\n",
    "#you can also use minmaxscalar\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler=StandardScaler()\n",
    "scaler.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaled_data=scaler.transform(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Now apply PCA algorithm\n",
    "\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#number of components you want to reduce the dimension to\n",
    "#create PCA Object\n",
    "pca=PCA(n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(n_components=2)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.fit(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_pca=pca.transform(scaled_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 30)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 2)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9.19283683,  1.94858307],\n",
       "       [ 2.3878018 , -3.76817174],\n",
       "       [ 5.73389628, -1.0751738 ],\n",
       "       ...,\n",
       "       [ 1.25617928, -1.90229671],\n",
       "       [10.37479406,  1.67201011],\n",
       "       [-5.4752433 , -0.67063679]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Second principle component')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "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": []
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}