Machine Learning with Scikit-Learn - The Cancer Dataset - 4 - Process Outline.ipynb

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   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
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      "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": [
    "cancer = load_breast_cancer()\n",
    "print(cancer.DESCR)"
   ]
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      "['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",
      "['malignant' 'benign']\n"
     ]
    }
   ],
   "source": [
    "print(cancer.feature_names)\n",
    "print(cancer.target_names)"
   ]
  },
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    "<center> \n",
    "# Process Outline\n",
    "#### 1. Get the data (pre-process it)\n",
    "#### 2. Pick an algorithm (classifier)\n",
    "#### 3. Train the algorithm. Verify accuracy. Optimize.\n",
    "#### 4. Predict "
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>925291</td>\n",
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       "      <td>11.51</td>\n",
       "      <td>23.93</td>\n",
       "      <td>74.52</td>\n",
       "      <td>403.5</td>\n",
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       "      <td>0.10210</td>\n",
       "      <td>0.11120</td>\n",
       "      <td>0.04105</td>\n",
       "      <td>...</td>\n",
       "      <td>37.16</td>\n",
       "      <td>82.28</td>\n",
       "      <td>474.2</td>\n",
       "      <td>0.12980</td>\n",
       "      <td>0.25170</td>\n",
       "      <td>0.3630</td>\n",
       "      <td>0.09653</td>\n",
       "      <td>0.2112</td>\n",
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       "      <td>925292</td>\n",
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       "      <td>14.05</td>\n",
       "      <td>27.15</td>\n",
       "      <td>91.38</td>\n",
       "      <td>600.4</td>\n",
       "      <td>0.09929</td>\n",
       "      <td>0.11260</td>\n",
       "      <td>0.04462</td>\n",
       "      <td>0.04304</td>\n",
       "      <td>...</td>\n",
       "      <td>33.17</td>\n",
       "      <td>100.20</td>\n",
       "      <td>706.7</td>\n",
       "      <td>0.12410</td>\n",
       "      <td>0.22640</td>\n",
       "      <td>0.1326</td>\n",
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       "      <td>0.2250</td>\n",
       "      <td>0.08321</td>\n",
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       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>925311</td>\n",
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       "      <td>11.20</td>\n",
       "      <td>29.37</td>\n",
       "      <td>70.67</td>\n",
       "      <td>386.0</td>\n",
       "      <td>0.07449</td>\n",
       "      <td>0.03558</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>...</td>\n",
       "      <td>38.30</td>\n",
       "      <td>75.19</td>\n",
       "      <td>439.6</td>\n",
       "      <td>0.09267</td>\n",
       "      <td>0.05494</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1566</td>\n",
       "      <td>0.05905</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>925622</td>\n",
       "      <td>M</td>\n",
       "      <td>15.22</td>\n",
       "      <td>30.62</td>\n",
       "      <td>103.40</td>\n",
       "      <td>716.9</td>\n",
       "      <td>0.10480</td>\n",
       "      <td>0.20870</td>\n",
       "      <td>0.25500</td>\n",
       "      <td>0.09429</td>\n",
       "      <td>...</td>\n",
       "      <td>42.79</td>\n",
       "      <td>128.70</td>\n",
       "      <td>915.0</td>\n",
       "      <td>0.14170</td>\n",
       "      <td>0.79170</td>\n",
       "      <td>1.1700</td>\n",
       "      <td>0.23560</td>\n",
       "      <td>0.4089</td>\n",
       "      <td>0.14090</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>926125</td>\n",
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       "      <td>20.92</td>\n",
       "      <td>25.09</td>\n",
       "      <td>143.00</td>\n",
       "      <td>1347.0</td>\n",
       "      <td>0.10990</td>\n",
       "      <td>0.22360</td>\n",
       "      <td>0.31740</td>\n",
       "      <td>0.14740</td>\n",
       "      <td>...</td>\n",
       "      <td>29.41</td>\n",
       "      <td>179.10</td>\n",
       "      <td>1819.0</td>\n",
       "      <td>0.14070</td>\n",
       "      <td>0.41860</td>\n",
       "      <td>0.6599</td>\n",
       "      <td>0.25420</td>\n",
       "      <td>0.2929</td>\n",
       "      <td>0.09873</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>926424</td>\n",
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       "      <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>...</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>0.22160</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>926682</td>\n",
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       "      <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>...</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>0.16280</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>926954</td>\n",
       "      <td>M</td>\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>...</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>0.14180</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>927241</td>\n",
       "      <td>M</td>\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>...</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>0.26500</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>92751</td>\n",
       "      <td>B</td>\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>...</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>0.00000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "559  925291         B        11.51         23.93           74.52      403.5   \n",
       "560  925292         B        14.05         27.15           91.38      600.4   \n",
       "561  925311         B        11.20         29.37           70.67      386.0   \n",
       "562  925622         M        15.22         30.62          103.40      716.9   \n",
       "563  926125         M        20.92         25.09          143.00     1347.0   \n",
       "564  926424         M        21.56         22.39          142.00     1479.0   \n",
       "565  926682         M        20.13         28.25          131.20     1261.0   \n",
       "566  926954         M        16.60         28.08          108.30      858.1   \n",
       "567  927241         M        20.60         29.33          140.10     1265.0   \n",
       "568   92751         B         7.76         24.54           47.92      181.0   \n",
       "\n",
       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "559          0.09261           0.10210         0.11120              0.04105   \n",
       "560          0.09929           0.11260         0.04462              0.04304   \n",
       "561          0.07449           0.03558         0.00000              0.00000   \n",
       "562          0.10480           0.20870         0.25500              0.09429   \n",
       "563          0.10990           0.22360         0.31740              0.14740   \n",
       "564          0.11100           0.11590         0.24390              0.13890   \n",
       "565          0.09780           0.10340         0.14400              0.09791   \n",
       "566          0.08455           0.10230         0.09251              0.05302   \n",
       "567          0.11780           0.27700         0.35140              0.15200   \n",
       "568          0.05263           0.04362         0.00000              0.00000   \n",
       "\n",
       "        ...       texture_worst  perimeter_worst  area_worst  \\\n",
       "559     ...               37.16            82.28       474.2   \n",
       "560     ...               33.17           100.20       706.7   \n",
       "561     ...               38.30            75.19       439.6   \n",
       "562     ...               42.79           128.70       915.0   \n",
       "563     ...               29.41           179.10      1819.0   \n",
       "564     ...               26.40           166.10      2027.0   \n",
       "565     ...               38.25           155.00      1731.0   \n",
       "566     ...               34.12           126.70      1124.0   \n",
       "567     ...               39.42           184.60      1821.0   \n",
       "568     ...               30.37            59.16       268.6   \n",
       "\n",
       "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "559           0.12980            0.25170           0.3630   \n",
       "560           0.12410            0.22640           0.1326   \n",
       "561           0.09267            0.05494           0.0000   \n",
       "562           0.14170            0.79170           1.1700   \n",
       "563           0.14070            0.41860           0.6599   \n",
       "564           0.14100            0.21130           0.4107   \n",
       "565           0.11660            0.19220           0.3215   \n",
       "566           0.11390            0.30940           0.3403   \n",
       "567           0.16500            0.86810           0.9387   \n",
       "568           0.08996            0.06444           0.0000   \n",
       "\n",
       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \\\n",
       "559               0.09653          0.2112                  0.08732   \n",
       "560               0.10480          0.2250                  0.08321   \n",
       "561               0.00000          0.1566                  0.05905   \n",
       "562               0.23560          0.4089                  0.14090   \n",
       "563               0.25420          0.2929                  0.09873   \n",
       "564               0.22160          0.2060                  0.07115   \n",
       "565               0.16280          0.2572                  0.06637   \n",
       "566               0.14180          0.2218                  0.07820   \n",
       "567               0.26500          0.4087                  0.12400   \n",
       "568               0.00000          0.2871                  0.07039   \n",
       "\n",
       "     Unnamed: 32  \n",
       "559          NaN  \n",
       "560          NaN  \n",
       "561          NaN  \n",
       "562          NaN  \n",
       "563          NaN  \n",
       "564          NaN  \n",
       "565          NaN  \n",
       "566          NaN  \n",
       "567          NaN  \n",
       "568          NaN  \n",
       "\n",
       "[10 rows x 33 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "raw_data=pd.read_csv('breast-cancer-wisconsin-data.csv', delimiter=',')\n",
    "raw_data.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
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