Breast_Cancer_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    " \n",
    "import pandas as pd # Import Pandas for data manipulation using dataframes\n",
    "import numpy as np # Import Numpy for data statistical analysis \n",
    "import matplotlib.pyplot as plt # Import matplotlib for data visualisation\n",
    "import seaborn as sns # Statistical data visualization\n",
    "# %matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Cancer data drom the Sklearn library\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "cancer = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "         1.189e-01],\n",
       "        [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,\n",
       "         8.902e-02],\n",
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       "         1.240e-01],\n",
       "        [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n",
       "         7.039e-02]]),\n",
       " 'target': 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",
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       "        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",
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       "        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",
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       "        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]),\n",
       " 'target_names': array(['malignant', 'benign'], dtype='<U9'),\n",
       " 'DESCR': 'Breast Cancer Wisconsin (Diagnostic) Database\\n=============================================\\n\\nNotes\\n-----\\nData 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\\nThis is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\\nhttps://goo.gl/U2Uwz2\\n\\nFeatures are computed from a digitized image of a fine needle\\naspirate (FNA) of a breast mass.  They describe\\ncharacteristics of the cell nuclei present in the image.\\n\\nSeparating plane described above was obtained using\\nMultisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\\nConstruction Via Linear Programming.\" Proceedings of the 4th\\nMidwest Artificial Intelligence and Cognitive Science Society,\\npp. 97-101, 1992], a classification method which uses linear\\nprogramming to construct a decision tree.  Relevant features\\nwere selected using an exhaustive search in the space of 1-4\\nfeatures and 1-3 separating planes.\\n\\nThe actual linear program used to obtain the separating plane\\nin the 3-dimensional space is that described in:\\n[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\\nProgramming Discrimination of Two Linearly Inseparable Sets\",\\nOptimization Methods and Software 1, 1992, 23-34].\\n\\nThis database is also available through the UW CS ftp server:\\n\\nftp ftp.cs.wisc.edu\\ncd math-prog/cpo-dataset/machine-learn/WDBC/\\n\\nReferences\\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",
       " 'feature_names': 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',\n",
       "        'fractal dimension error', 'worst radius', 'worst texture',\n",
       "        'worst perimeter', 'worst area', 'worst smoothness',\n",
       "        'worst compactness', 'worst concavity', 'worst concave points',\n",
       "        'worst symmetry', 'worst fractal dimension'], dtype='<U23')}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['malignant' 'benign']\n"
     ]
    }
   ],
   "source": [
    "print(cancer['target_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 1 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 0 1 1 1 1 0 1 0 0 1 1 1 1 0 1 0 0\n",
      " 1 0 1 0 0 1 1 1 0 0 1 0 0 0 1 1 1 0 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1\n",
      " 1 1 1 1 1 1 0 0 0 1 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 1 1 0 1 1 0 1 1 1 1 0 1\n",
      " 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 0 1 1 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 1 0\n",
      " 1 0 1 1 1 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 1 1 0 1 0 0 0 0 1 1 0 0 1 1\n",
      " 1 0 1 1 1 1 1 0 0 1 1 0 1 1 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 1 1 1 1 1 1 0 1 0 1 1 0 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 0 0 0 1 1\n",
      " 1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0\n",
      " 0 1 0 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1\n",
      " 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 0 1 1 1 1 1 0 1 1\n",
      " 0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1\n",
      " 1 1 1 1 1 1 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 1 1 1 1 1 0 1 1 0 1 0 1 0 0\n",
      " 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 0 0 0 0 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "print(cancer['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['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"
     ]
    }
   ],
   "source": [
    "print(cancer['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.799e+01 1.038e+01 1.228e+02 ... 2.654e-01 4.601e-01 1.189e-01]\n",
      " [2.057e+01 1.777e+01 1.329e+02 ... 1.860e-01 2.750e-01 8.902e-02]\n",
      " [1.969e+01 2.125e+01 1.300e+02 ... 2.430e-01 3.613e-01 8.758e-02]\n",
      " ...\n",
      " [1.660e+01 2.808e+01 1.083e+02 ... 1.418e-01 2.218e-01 7.820e-02]\n",
      " [2.060e+01 2.933e+01 1.401e+02 ... 2.650e-01 4.087e-01 1.240e-01]\n",
      " [7.760e+00 2.454e+01 4.792e+01 ... 0.000e+00 2.871e-01 7.039e-02]]\n"
     ]
    }
   ],
   "source": [
    "print(cancer['data'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 30)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer['data'].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cancer = pd.DataFrame(np.c_[cancer['data'], cancer['target']], columns = np.append(cancer['feature_names'], ['target']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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 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>target</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>0.0</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>0.0</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>0.0</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>0.0</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>0.0</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  target  \n",
       "0          0.4601                  0.11890     0.0  \n",
       "1          0.2750                  0.08902     0.0  \n",
       "2          0.3613                  0.08758     0.0  \n",
       "3          0.6638                  0.17300     0.0  \n",
       "4          0.2364                  0.07678     0.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cancer.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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 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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.05623</td>\n",
       "      <td>...</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>...</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.05648</td>\n",
       "      <td>...</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07016</td>\n",
       "      <td>...</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>...</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "      <td>1.0</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",
       "564        21.56         22.39          142.00     1479.0          0.11100   \n",
       "565        20.13         28.25          131.20     1261.0          0.09780   \n",
       "566        16.60         28.08          108.30      858.1          0.08455   \n",
       "567        20.60         29.33          140.10     1265.0          0.11780   \n",
       "568         7.76         24.54           47.92      181.0          0.05263   \n",
       "\n",
       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "564           0.11590         0.24390              0.13890         0.1726   \n",
       "565           0.10340         0.14400              0.09791         0.1752   \n",
       "566           0.10230         0.09251              0.05302         0.1590   \n",
       "567           0.27700         0.35140              0.15200         0.2397   \n",
       "568           0.04362         0.00000              0.00000         0.1587   \n",
       "\n",
       "     mean fractal dimension   ...    worst texture  worst perimeter  \\\n",
       "564                 0.05623   ...            26.40           166.10   \n",
       "565                 0.05533   ...            38.25           155.00   \n",
       "566                 0.05648   ...            34.12           126.70   \n",
       "567                 0.07016   ...            39.42           184.60   \n",
       "568                 0.05884   ...            30.37            59.16   \n",
       "\n",
       "     worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "564      2027.0           0.14100            0.21130           0.4107   \n",
       "565      1731.0           0.11660            0.19220           0.3215   \n",
       "566      1124.0           0.11390            0.30940           0.3403   \n",
       "567      1821.0           0.16500            0.86810           0.9387   \n",
       "568       268.6           0.08996            0.06444           0.0000   \n",
       "\n",
       "     worst concave points  worst symmetry  worst fractal dimension  target  \n",
       "564                0.2216          0.2060                  0.07115     0.0  \n",
       "565                0.1628          0.2572                  0.06637     0.0  \n",
       "566                0.1418          0.2218                  0.07820     0.0  \n",
       "567                0.2650          0.4087                  0.12400     0.0  \n",
       "568                0.0000          0.2871                  0.07039     1.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cancer.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([1,2,3])\n",
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Example = np.c_[np.array([1,2,3]), np.array([4,5,6])]\n",
    "Example.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1c9b24a6be0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 951.875x900 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df_cancer, hue = 'target', vars = ['mean radius', 'mean texture', 'mean area', 'mean perimeter', 'mean smoothness'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c9b43beef0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(df_cancer['target'], label = \"Count\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'seaborn' has no attribute 'scatterplot'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-bd94723e0175>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatterplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'mean area'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'mean smoothness'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'target'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_cancer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: module 'seaborn' has no attribute 'scatterplot'"
     ]
    }
   ],
   "source": [
    "sns.scatterplot(x = 'mean area', y = 'mean smoothness', hue = 'target', data = df_cancer)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.lmplot('mean area', 'mean smoothness', hue ='target', data = df_cancer_all, fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's check the correlation between the variables \n",
    "# Strong correlation between the mean radius and mean perimeter, mean area and mean primeter\n",
    "plt.figure(figsize=(20,10)) \n",
    "sns.heatmap(df_cancer.corr(), annot=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Let's drop the target label coloumns\n",
    "X = df_cancer.drop(['target'],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_cancer['target']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC \n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "svc_model = SVC()\n",
    "svc_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = svc_model.predict(X_test)\n",
    "cm = confusion_matrix(y_test, y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.heatmap(cm, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_train = X_train.min()\n",
    "min_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "range_train = (X_train - min_train).max()\n",
    "range_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled = (X_train - min_train)/range_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sns.scatterplot(x = X_train['mean area'], y = X_train['mean smoothness'], hue = y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.scatterplot(x = X_train_scaled['mean area'], y = X_train_scaled['mean smoothness'], hue = y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_test = X_test.min()\n",
    "range_test = (X_test - min_test).max()\n",
    "X_test_scaled = (X_test - min_test)/range_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC \n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "svc_model = SVC()\n",
    "svc_model.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = svc_model.predict(X_test_scaled)\n",
    "cm = confusion_matrix(y_test, y_predict)\n",
    "\n",
    "sns.heatmap(cm,annot=True,fmt=\"d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(classification_report(y_test,y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {'C': [0.1, 1, 10, 100], 'gamma': [1, 0.1, 0.01, 0.001], 'kernel': ['rbf']} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = GridSearchCV(SVC(),param_grid,refit=True,verbose=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_predictions = grid.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y_test, grid_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sns.heatmap(cm, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(classification_report(y_test,grid_predictions))"
   ]
  }
 ],
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