Support_Vector_Machine_Practice.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine practice notebook with breast cancer data set\n",
    "\n",
    "In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a **non-probabilistic binary linear classifier** (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting). An SVM model is a representation of the examples as points in space, mapped ***so that the examples of the separate categories are divided by a clear gap that is as wide as possible***. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall. This gap is also called maximum margin and the SVM classifier is called ***maximum margin clasifier***.\n",
    "\n",
    "In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.\n",
    "![SVM-1](./Images/SVM-1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries and load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the Data\n",
    "\n",
    "We'll use the built in breast cancer dataset from Scikit Learn. Note the load function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cancer = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The data set is presented in a dictionary form**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We can grab information and arrays out of this dictionary to create data frame and understand the features**\n",
    "\n",
    "**The description of features are as follows**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "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": [
    "**Show the feature names**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
       "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
       "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
       "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
       "       'smoothness error', 'compactness error', 'concavity error',\n",
       "       'concave points error', 'symmetry error', 'fractal dimension error',\n",
       "       'worst radius', 'worst texture', 'worst perimeter', 'worst area',\n",
       "       'worst smoothness', 'worst compactness', 'worst concavity',\n",
       "       'worst concave points', 'worst symmetry', 'worst fractal dimension'], \n",
       "      dtype='<U23')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer['feature_names']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "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 = pd.DataFrame(cancer['data'],columns=cancer['feature_names'])\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "<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>count</th>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
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       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>0.062798</td>\n",
       "      <td>...</td>\n",
       "      <td>16.269190</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>0.007060</td>\n",
       "      <td>...</td>\n",
       "      <td>4.833242</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.049960</td>\n",
       "      <td>...</td>\n",
       "      <td>7.930000</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>0.057700</td>\n",
       "      <td>...</td>\n",
       "      <td>13.010000</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>...</td>\n",
       "      <td>14.970000</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>0.066120</td>\n",
       "      <td>...</td>\n",
       "      <td>18.790000</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>0.097440</td>\n",
       "      <td>...</td>\n",
       "      <td>36.040000</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean radius  mean texture  mean perimeter    mean area  \\\n",
       "count   569.000000    569.000000      569.000000   569.000000   \n",
       "mean     14.127292     19.289649       91.969033   654.889104   \n",
       "std       3.524049      4.301036       24.298981   351.914129   \n",
       "min       6.981000      9.710000       43.790000   143.500000   \n",
       "25%      11.700000     16.170000       75.170000   420.300000   \n",
       "50%      13.370000     18.840000       86.240000   551.100000   \n",
       "75%      15.780000     21.800000      104.100000   782.700000   \n",
       "max      28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       mean smoothness  mean compactness  mean concavity  mean concave points  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096360          0.104341        0.088799             0.048919   \n",
       "std           0.014064          0.052813        0.079720             0.038803   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086370          0.064920        0.029560             0.020310   \n",
       "50%           0.095870          0.092630        0.061540             0.033500   \n",
       "75%           0.105300          0.130400        0.130700             0.074000   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       mean symmetry  mean fractal dimension           ...             \\\n",
       "count     569.000000              569.000000           ...              \n",
       "mean        0.181162                0.062798           ...              \n",
       "std         0.027414                0.007060           ...              \n",
       "min         0.106000                0.049960           ...              \n",
       "25%         0.161900                0.057700           ...              \n",
       "50%         0.179200                0.061540           ...              \n",
       "75%         0.195700                0.066120           ...              \n",
       "max         0.304000                0.097440           ...              \n",
       "\n",
       "       worst radius  worst texture  worst perimeter   worst area  \\\n",
       "count    569.000000     569.000000       569.000000   569.000000   \n",
       "mean      16.269190      25.677223       107.261213   880.583128   \n",
       "std        4.833242       6.146258        33.602542   569.356993   \n",
       "min        7.930000      12.020000        50.410000   185.200000   \n",
       "25%       13.010000      21.080000        84.110000   515.300000   \n",
       "50%       14.970000      25.410000        97.660000   686.500000   \n",
       "75%       18.790000      29.720000       125.400000  1084.000000   \n",
       "max       36.040000      49.540000       251.200000  4254.000000   \n",
       "\n",
       "       worst smoothness  worst compactness  worst concavity  \\\n",
       "count        569.000000         569.000000       569.000000   \n",
       "mean           0.132369           0.254265         0.272188   \n",
       "std            0.022832           0.157336         0.208624   \n",
       "min            0.071170           0.027290         0.000000   \n",
       "25%            0.116600           0.147200         0.114500   \n",
       "50%            0.131300           0.211900         0.226700   \n",
       "75%            0.146000           0.339100         0.382900   \n",
       "max            0.222600           1.058000         1.252000   \n",
       "\n",
       "       worst concave points  worst symmetry  worst fractal dimension  \n",
       "count            569.000000      569.000000               569.000000  \n",
       "mean               0.114606        0.290076                 0.083946  \n",
       "std                0.065732        0.061867                 0.018061  \n",
       "min                0.000000        0.156500                 0.055040  \n",
       "25%                0.064930        0.250400                 0.071460  \n",
       "50%                0.099930        0.282200                 0.080040  \n",
       "75%                0.161400        0.317900                 0.092080  \n",
       "max                0.291000        0.663800                 0.207500  \n",
       "\n",
       "[8 rows x 30 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Is there any missing data?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(pd.isnull(df).sum()) # Sum of the count of null objects in all columns of data frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What are the 'target' data in the data set?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "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, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1,\n",
       "       1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0,\n",
       "       1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1,\n",
       "       0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1,\n",
       "       0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1,\n",
       "       0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
       "       0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1,\n",
       "       1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0,\n",
       "       1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
       "       1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 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, 1,\n",
       "       1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer['target']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Adding the target data to the DataFrame**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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 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",
       "      <th>Cancer</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>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",
       "      <td>0</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>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",
       "      <td>0</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>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",
       "      <td>0</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>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",
       "      <td>0</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>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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 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 texture  worst perimeter  worst area  \\\n",
       "0                 0.07871   ...            17.33           184.60      2019.0   \n",
       "1                 0.05667   ...            23.41           158.80      1956.0   \n",
       "2                 0.05999   ...            25.53           152.50      1709.0   \n",
       "3                 0.09744   ...            26.50            98.87       567.7   \n",
       "4                 0.05883   ...            16.67           152.20      1575.0   \n",
       "\n",
       "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
       "0            0.1622             0.6656           0.7119                0.2654   \n",
       "1            0.1238             0.1866           0.2416                0.1860   \n",
       "2            0.1444             0.4245           0.4504                0.2430   \n",
       "3            0.2098             0.8663           0.6869                0.2575   \n",
       "4            0.1374             0.2050           0.4000                0.1625   \n",
       "\n",
       "   worst symmetry  worst fractal dimension  Cancer  \n",
       "0          0.4601                  0.11890       0  \n",
       "1          0.2750                  0.08902       0  \n",
       "2          0.3613                  0.08758       0  \n",
       "3          0.6638                  0.17300       0  \n",
       "4          0.2364                  0.07678       0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cancer'] = pd.DataFrame(cancer['target'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the relative counts of benign (0) vs malignant (1) cases of cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2cfe818b710>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2cfe817afd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='Cancer',data=df,palette='RdBu_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run a 'for' loop to draw boxlots of all the mean features (first 10 columns) for '0' and '1' CANCER OUTCOME"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2cfe9662b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2cfe96926a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ICCoDERFBZSAiIsD/Awj1ia8Bx2NSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2cfeb728128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2cfeb7d9860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2cfeb817908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2cfeb7a4a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2cfeb8f9278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2cfeb998668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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iItIHy3oWPp+PtLQ0AJKTk6mvr++23u/3s3HjRuLi4oxlDzzwgHGMPhgM4nA4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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2cfeba299b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2cfebabb668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "l=list(df.columns[0:10])\n",
    "for i in range(len(l)-1):\n",
    "    sns.boxplot(x='Cancer',y=l[i], data=df, palette='winter')\n",
    "    plt.figure()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Not all the features seperate out the cancer predictions equally clearly\n",
    "**For example, from the following two plots it is clear that smaller area generally is indicative of positive cancer detection, while nothing concrete can be said from the plot of mean smoothness**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2cfed42c390>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PP/xQfn5+uuGGG/T0008X2XbDhg3l5eWlBQsW6MEHH3St/+CDD+r222/X3Llzi6yzefNm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\n+mt98MEH8vHxUVhYmGbNmiV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TT3THHXforrvu0vfff1/FVZfejh07NG7cOEkqU9tsNpse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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2cfed3a6c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,(ax1, ax2) = plt.subplots(1, 2, sharey=True,figsize=(12,6))\n",
    "ax1.scatter(df['mean area'],df['Cancer'])\n",
    "ax1.set_title(\"Cancer cases as a function of mean area\", fontsize=15)\n",
    "ax2.scatter(df['mean smoothness'],df['Cancer'])\n",
    "ax2.set_title(\"Cancer cases as a function of mean smoothness\", fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training and prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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  \\\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": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feat = df.drop('Cancer',axis=1) # Define a dataframe with only features\n",
    "df_feat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: Cancer, dtype: int32"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_target = df['Cancer'] # Define a dataframe with only target results i.e. cancer detections\n",
    "df_target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df_feat, df_target, test_size=0.30, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "178    1\n",
       "421    1\n",
       "57     0\n",
       "514    0\n",
       "548    1\n",
       "Name: Cancer, dtype: int32"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the Support Vector Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, 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": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predictions and Evaluations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Notice that we are classifying everything into a single class! This means our model needs to have it parameters adjusted (it may also help to normalize the data)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0  66]\n",
      " [  0 105]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**As expected, the classification report card is bad**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Tirtha\\Python\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1113: 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": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gridsearch\n",
    "\n",
    "Finding the right parameters (like what C or gamma values to use) is a tricky task! But luckily, Scikit-learn has the functionality of trying a bunch of combinations and see what works best, built in with GridSearchCV! The CV stands for cross-validation.\n",
    "\n",
    "**GridSearchCV takes a dictionary that describes the parameters that should be tried and a model to train. The grid of parameters is defined as a dictionary, where the keys are the parameters and the values are the settings to be tested.** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "param_grid = {'C': [0.1,1, 10, 100, 1000], 'gamma': [1,0.1,0.01,0.001,0.0001], 'kernel': ['rbf']} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the great things about GridSearchCV is that it is a meta-estimator. It takes an estimator like SVC, and creates a new estimator, that behaves exactly the same - in this case, like a classifier. You should add refit=True and choose verbose to whatever number you want, higher the number, the more verbose (verbose just means the text output describing the process)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grid = GridSearchCV(SVC(),param_grid,refit=True,verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, it runs the same loop with cross-validation, to find the best parameter combination. Once it has the best combination, it runs fit again on all data passed to fit (without cross-validation), to built a single new model using the best parameter setting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 25 candidates, totalling 75 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  75 out of  75 | elapsed:    1.5s 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=None, 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], 'kernel': ['rbf']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring=None, verbose=1)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# May take awhile!\n",
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**You can inspect the best parameters found by GridSearchCV in the best\\_params\\_ attribute, and the best estimator in the best\\_estimator\\_ attribute**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, 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": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Then you can re-run predictions on this grid object just like you would with a normal model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grid_predictions = grid.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now print the confusion matrix to see improved predictions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 60   6]\n",
      " [  3 102]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,grid_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Classification report shows improved F1-score**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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_predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Another set of parameters for GridSearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 25 candidates, totalling 75 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  75 out of  75 | elapsed:    0.7s 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=None, degree=3, gamma='auto', kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=1e-05, verbose=False),\n",
       "       fit_params={}, iid=True, n_jobs=1,\n",
       "       param_grid={'C': [50, 75, 100, 125, 150], 'gamma': [0.01, 0.001, 0.0001, 1e-05, 1e-06], 'kernel': ['rbf']},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring=None, verbose=1)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid = {'C': [50,75,100,125,150], 'gamma': [1e-2,1e-3,1e-4,1e-5,1e-6], 'kernel': ['rbf']} \n",
    "grid = GridSearchCV(SVC(tol=1e-5),param_grid,refit=True,verbose=1)\n",
    "grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=125, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma=1e-05, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=1e-05, verbose=False)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 60   6]\n",
      " [  3 102]]\n"
     ]
    }
   ],
   "source": [
    "grid_predictions = grid.predict(X_test)\n",
    "print(confusion_matrix(y_test,grid_predictions))"
   ]
  }
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