Breast Cancer classification using Support Vector Machines.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# importing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer =load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Breast Cancer Wisconsin (Diagnostic) Database\n",
      "=============================================\n",
      "\n",
      "Notes\n",
      "-----\n",
      "Data Set Characteristics:\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",
      "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",
      "\n"
     ]
    }
   ],
   "source": [
    "print(cancer[\"DESCR\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Making DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df =  pd.DataFrame(cancer[\"data\"], columns=cancer['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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  \\\n",
       "0                 0.07871           ...                    25.38   \n",
       "1                 0.05667           ...                    24.99   \n",
       "2                 0.05999           ...                    23.57   \n",
       "3                 0.09744           ...                    14.91   \n",
       "4                 0.05883           ...                    22.54   \n",
       "\n",
       "   worst texture  worst perimeter  worst area  worst smoothness  \\\n",
       "0          17.33           184.60      2019.0            0.1622   \n",
       "1          23.41           158.80      1956.0            0.1238   \n",
       "2          25.53           152.50      1709.0            0.1444   \n",
       "3          26.50            98.87       567.7            0.2098   \n",
       "4          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   worst compactness  worst concavity  worst concave points  worst symmetry  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   worst fractal dimension  \n",
       "0                  0.11890  \n",
       "1                  0.08902  \n",
       "2                  0.08758  \n",
       "3                  0.17300  \n",
       "4                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean radius                0\n",
       "mean texture               0\n",
       "mean perimeter             0\n",
       "mean area                  0\n",
       "mean smoothness            0\n",
       "mean compactness           0\n",
       "mean concavity             0\n",
       "mean concave points        0\n",
       "mean symmetry              0\n",
       "mean fractal dimension     0\n",
       "radius error               0\n",
       "texture error              0\n",
       "perimeter error            0\n",
       "area error                 0\n",
       "smoothness error           0\n",
       "compactness error          0\n",
       "concavity error            0\n",
       "concave points error       0\n",
       "symmetry error             0\n",
       "fractal dimension error    0\n",
       "worst radius               0\n",
       "worst texture              0\n",
       "worst perimeter            0\n",
       "worst area                 0\n",
       "worst smoothness           0\n",
       "worst compactness          0\n",
       "worst concavity            0\n",
       "worst concave points       0\n",
       "worst symmetry             0\n",
       "worst fractal dimension    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e0d03d3dd8>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data=df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n",
       "       1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n",
       "       1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n",
       "       0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0,\n",
       "       0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0,\n",
       "       1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0,\n",
       "       0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['malignant', 'benign'], dtype='<U9')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer['target_names']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 30 columns):\n",
      "mean radius                569 non-null float64\n",
      "mean texture               569 non-null float64\n",
      "mean perimeter             569 non-null float64\n",
      "mean area                  569 non-null float64\n",
      "mean smoothness            569 non-null float64\n",
      "mean compactness           569 non-null float64\n",
      "mean concavity             569 non-null float64\n",
      "mean concave points        569 non-null float64\n",
      "mean symmetry              569 non-null float64\n",
      "mean fractal dimension     569 non-null float64\n",
      "radius error               569 non-null float64\n",
      "texture error              569 non-null float64\n",
      "perimeter error            569 non-null float64\n",
      "area error                 569 non-null float64\n",
      "smoothness error           569 non-null float64\n",
      "compactness error          569 non-null float64\n",
      "concavity error            569 non-null float64\n",
      "concave points error       569 non-null float64\n",
      "symmetry error             569 non-null float64\n",
      "fractal dimension error    569 non-null float64\n",
      "worst radius               569 non-null float64\n",
      "worst texture              569 non-null float64\n",
      "worst perimeter            569 non-null float64\n",
      "worst area                 569 non-null float64\n",
      "worst smoothness           569 non-null float64\n",
      "worst compactness          569 non-null float64\n",
      "worst concavity            569 non-null float64\n",
      "worst concave points       569 non-null float64\n",
      "worst symmetry             569 non-null float64\n",
      "worst fractal dimension    569 non-null float64\n",
      "dtypes: float64(30)\n",
      "memory usage: 133.4 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df\n",
    "y=cancer['target']\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(  X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support vactor classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    " prediction = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.00      0.00      0.00        66\n",
      "          1       0.61      1.00      0.76       105\n",
      "\n",
      "avg / total       0.38      0.61      0.47       171\n",
      "\n",
      "[[  0  66]\n",
      " [  0 105]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(classification_report(y_test, prediction))\n",
    "print(confusion_matrix(y_test, prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# here we see everything is predicted in class one\n",
    "** so we used grid search to find the most suitable parameters for the svm object **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Anaconda3\\lib\\site-packages\\sklearn\\grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.grid_search import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    " param_grid = {\"C\": [0.1, 1, 10, 100, 1000], 'gamma':[1, 0.1, 0.01, 0.001, 0.0001]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = GridSearchCV(SVC(), param_grid, verbose=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 25 candidates, totalling 75 fits\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ......................... C=0.1, gamma=1, score=0.631579 -   0.0s\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ......................... C=0.1, gamma=1, score=0.631579 -   0.0s\n",
      "[CV] C=0.1, gamma=1 ..................................................\n",
      "[CV] ......................... C=0.1, gamma=1, score=0.636364 -   0.0s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ....................... C=0.1, gamma=0.1, score=0.631579 -   0.0s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ....................... C=0.1, gamma=0.1, score=0.631579 -   0.0s\n",
      "[CV] C=0.1, gamma=0.1 ................................................\n",
      "[CV] ....................... C=0.1, gamma=0.1, score=0.636364 -   0.0s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ...................... C=0.1, gamma=0.01, score=0.631579 -   0.0s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ...................... C=0.1, gamma=0.01, score=0.631579 -   0.0s\n",
      "[CV] C=0.1, gamma=0.01 ...............................................\n",
      "[CV] ...................... C=0.1, gamma=0.01, score=0.636364 -   0.0s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ..................... C=0.1, gamma=0.001, score=0.631579 -   0.0s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ..................... C=0.1, gamma=0.001, score=0.631579 -   0.0s\n",
      "[CV] C=0.1, gamma=0.001 ..............................................\n",
      "[CV] ..................... C=0.1, gamma=0.001, score=0.636364 -   0.0s\n",
      "[CV] C=0.1, gamma=0.0001 .............................................\n",
      "[CV] .................... C=0.1, gamma=0.0001, score=0.902256 -   0.0s\n",
      "[CV] C=0.1, gamma=0.0001 .............................................\n",
      "[CV] .................... C=0.1, gamma=0.0001, score=0.962406 -   0.0s\n",
      "[CV] C=0.1, gamma=0.0001 .............................................\n",
      "[CV] .................... C=0.1, gamma=0.0001, score=0.916667 -   0.0s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ........................... C=1, gamma=1, score=0.631579 -   0.0s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ........................... C=1, gamma=1, score=0.631579 -   0.0s\n",
      "[CV] C=1, gamma=1 ....................................................\n",
      "[CV] ........................... C=1, gamma=1, score=0.636364 -   0.0s\n",
      "[CV] C=1, gamma=0.1 ..................................................\n",
      "[CV] ......................... C=1, gamma=0.1, score=0.631579 -   0.0s\n",
      "[CV] C=1, gamma=0.1 ..................................................\n",
      "[CV] ......................... C=1, gamma=0.1, score=0.631579 -   0.0s\n",
      "[CV] C=1, gamma=0.1 ..................................................\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] ......................... C=1, gamma=0.1, score=0.636364 -   0.0s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] ........................ C=1, gamma=0.01, score=0.631579 -   0.0s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] ........................ C=1, gamma=0.01, score=0.631579 -   0.0s\n",
      "[CV] C=1, gamma=0.01 .................................................\n",
      "[CV] ........................ C=1, gamma=0.01, score=0.636364 -   0.0s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] ....................... C=1, gamma=0.001, score=0.902256 -   0.0s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] ....................... C=1, gamma=0.001, score=0.939850 -   0.0s\n",
      "[CV] C=1, gamma=0.001 ................................................\n",
      "[CV] ....................... C=1, gamma=0.001, score=0.954545 -   0.0s\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n",
      "[CV] ...................... C=1, gamma=0.0001, score=0.939850 -   0.0s\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n",
      "[CV] ...................... C=1, gamma=0.0001, score=0.969925 -   0.0s\n",
      "[CV] C=1, gamma=0.0001 ...............................................\n",
      "[CV] ...................... C=1, gamma=0.0001, score=0.946970 -   0.0s\n",
      "[CV] C=10, gamma=1 ...................................................\n",
      "[CV] .......................... C=10, gamma=1, score=0.631579 -   0.0s\n",
      "[CV] C=10, gamma=1 ...................................................\n",
      "[CV] .......................... C=10, gamma=1, score=0.631579 -   0.0s\n",
      "[CV] C=10, gamma=1 ...................................................\n",
      "[CV] .......................... C=10, gamma=1, score=0.636364 -   0.0s\n",
      "[CV] C=10, gamma=0.1 .................................................\n",
      "[CV] ........................ C=10, gamma=0.1, score=0.631579 -   0.0s\n",
      "[CV] C=10, gamma=0.1 .................................................\n",
      "[CV] ........................ C=10, gamma=0.1, score=0.631579 -   0.0s\n",
      "[CV] C=10, gamma=0.1 .................................................\n",
      "[CV] ........................ C=10, gamma=0.1, score=0.636364 -   0.0s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] ....................... C=10, gamma=0.01, score=0.631579 -   0.0s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] ....................... C=10, gamma=0.01, score=0.631579 -   0.0s\n",
      "[CV] C=10, gamma=0.01 ................................................\n",
      "[CV] ....................... C=10, gamma=0.01, score=0.636364 -   0.0s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ...................... C=10, gamma=0.001, score=0.894737 -   0.0s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ...................... C=10, gamma=0.001, score=0.932331 -   0.0s\n",
      "[CV] C=10, gamma=0.001 ...............................................\n",
      "[CV] ...................... C=10, gamma=0.001, score=0.916667 -   0.0s\n",
      "[CV] C=10, gamma=0.0001 ..............................................\n",
      "[CV] ..................... C=10, gamma=0.0001, score=0.932331 -   0.0s\n",
      "[CV] C=10, gamma=0.0001 ..............................................\n",
      "[CV] ..................... C=10, gamma=0.0001, score=0.969925 -   0.0s\n",
      "[CV] C=10, gamma=0.0001 ..............................................\n",
      "[CV] ..................... C=10, gamma=0.0001, score=0.962121 -   0.0s\n",
      "[CV] C=100, gamma=1 ..................................................\n",
      "[CV] ......................... C=100, gamma=1, score=0.631579 -   0.0s\n",
      "[CV] C=100, gamma=1 ..................................................\n",
      "[CV] ......................... C=100, gamma=1, score=0.631579 -   0.0s\n",
      "[CV] C=100, gamma=1 ..................................................\n",
      "[CV] ......................... C=100, gamma=1, score=0.636364 -   0.0s\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] ....................... C=100, gamma=0.1, score=0.631579 -   0.0s\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] ....................... C=100, gamma=0.1, score=0.631579 -   0.0s\n",
      "[CV] C=100, gamma=0.1 ................................................\n",
      "[CV] ....................... C=100, gamma=0.1, score=0.636364 -   0.0s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ...................... C=100, gamma=0.01, score=0.631579 -   0.0s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ...................... C=100, gamma=0.01, score=0.631579 -   0.0s\n",
      "[CV] C=100, gamma=0.01 ...............................................\n",
      "[CV] ...................... C=100, gamma=0.01, score=0.636364 -   0.0s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] ..................... C=100, gamma=0.001, score=0.894737 -   0.0s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] ..................... C=100, gamma=0.001, score=0.932331 -   0.0s\n",
      "[CV] C=100, gamma=0.001 ..............................................\n",
      "[CV] ..................... C=100, gamma=0.001, score=0.916667 -   0.0s\n",
      "[CV] C=100, gamma=0.0001 .............................................\n",
      "[CV] .................... C=100, gamma=0.0001, score=0.917293 -   0.0s\n",
      "[CV] C=100, gamma=0.0001 .............................................\n",
      "[CV] .................... C=100, gamma=0.0001, score=0.977444 -   0.0s\n",
      "[CV] C=100, gamma=0.0001 .............................................\n",
      "[CV] .................... C=100, gamma=0.0001, score=0.939394 -   0.0s\n",
      "[CV] C=1000, gamma=1 .................................................\n",
      "[CV] ........................ C=1000, gamma=1, score=0.631579 -   0.0s\n",
      "[CV] C=1000, gamma=1 .................................................\n",
      "[CV] ........................ C=1000, gamma=1, score=0.631579 -   0.0s\n",
      "[CV] C=1000, gamma=1 .................................................\n",
      "[CV] ........................ C=1000, gamma=1, score=0.636364 -   0.0s\n",
      "[CV] C=1000, gamma=0.1 ...............................................\n",
      "[CV] ...................... C=1000, gamma=0.1, score=0.631579 -   0.0s\n",
      "[CV] C=1000, gamma=0.1 ...............................................\n",
      "[CV] ...................... C=1000, gamma=0.1, score=0.631579 -   0.0s\n",
      "[CV] C=1000, gamma=0.1 ...............................................\n",
      "[CV] ...................... C=1000, gamma=0.1, score=0.636364 -   0.0s\n",
      "[CV] C=1000, gamma=0.01 ..............................................\n",
      "[CV] ..................... C=1000, gamma=0.01, score=0.631579 -   0.0s\n",
      "[CV] C=1000, gamma=0.01 ..............................................\n",
      "[CV] ..................... C=1000, gamma=0.01, score=0.631579 -   0.0s\n",
      "[CV] C=1000, gamma=0.01 ..............................................\n",
      "[CV] ..................... C=1000, gamma=0.01, score=0.636364 -   0.0s\n",
      "[CV] C=1000, gamma=0.001 .............................................\n",
      "[CV] .................... C=1000, gamma=0.001, score=0.894737 -   0.0s\n",
      "[CV] C=1000, gamma=0.001 .............................................\n",
      "[CV] .................... C=1000, gamma=0.001, score=0.932331 -   0.0s\n",
      "[CV] C=1000, gamma=0.001 .............................................\n",
      "[CV] .................... C=1000, gamma=0.001, score=0.916667 -   0.0s\n",
      "[CV] C=1000, gamma=0.0001 ............................................\n",
      "[CV] ................... C=1000, gamma=0.0001, score=0.909774 -   0.0s\n",
      "[CV] C=1000, gamma=0.0001 ............................................\n",
      "[CV] ................... C=1000, gamma=0.0001, score=0.969925 -   0.0s\n",
      "[CV] C=1000, gamma=0.0001 ............................................\n",
      "[CV] ................... C=1000, gamma=0.0001, score=0.931818 -   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  75 out of  75 | elapsed:    0.6s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'C': [0.1, 1, 10, 100, 1000], 'gamma': [1, 0.1, 0.01, 0.001, 0.0001]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=3)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 10, 'gamma': 0.0001}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=0.0001, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9547738693467337"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_prediction = grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 60   6]\n",
      " [  3 102]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, grid_prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.95      0.91      0.93        66\n",
      "          1       0.94      0.97      0.96       105\n",
      "\n",
      "avg / total       0.95      0.95      0.95       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, grid_prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9473684210526315"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, grid_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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