KNN -Cancer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "253d3f5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "82e8d0f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelEncoder\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f4338962",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "14c2dbdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _breast_cancer_dataset:\n",
      "\n",
      "Breast cancer wisconsin (diagnostic) dataset\n",
      "--------------------------------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      "    :Number of Instances: 569\n",
      "\n",
      "    :Number of Attributes: 30 numeric, predictive attributes and the class\n",
      "\n",
      "    :Attribute Information:\n",
      "        - radius (mean of distances from center to points on the perimeter)\n",
      "        - texture (standard deviation of gray-scale values)\n",
      "        - perimeter\n",
      "        - area\n",
      "        - smoothness (local variation in radius lengths)\n",
      "        - compactness (perimeter^2 / area - 1.0)\n",
      "        - concavity (severity of concave portions of the contour)\n",
      "        - concave points (number of concave portions of the contour)\n",
      "        - symmetry\n",
      "        - fractal dimension (\"coastline approximation\" - 1)\n",
      "\n",
      "        The mean, standard error, and \"worst\" or largest (mean of the three\n",
      "        worst/largest values) of these features were computed for each image,\n",
      "        resulting in 30 features.  For instance, field 0 is Mean Radius, field\n",
      "        10 is Radius SE, field 20 is Worst Radius.\n",
      "\n",
      "        - class:\n",
      "                - WDBC-Malignant\n",
      "                - WDBC-Benign\n",
      "\n",
      "    :Summary Statistics:\n",
      "\n",
      "    ===================================== ====== ======\n",
      "                                           Min    Max\n",
      "    ===================================== ====== ======\n",
      "    radius (mean):                        6.981  28.11\n",
      "    texture (mean):                       9.71   39.28\n",
      "    perimeter (mean):                     43.79  188.5\n",
      "    area (mean):                          143.5  2501.0\n",
      "    smoothness (mean):                    0.053  0.163\n",
      "    compactness (mean):                   0.019  0.345\n",
      "    concavity (mean):                     0.0    0.427\n",
      "    concave points (mean):                0.0    0.201\n",
      "    symmetry (mean):                      0.106  0.304\n",
      "    fractal dimension (mean):             0.05   0.097\n",
      "    radius (standard error):              0.112  2.873\n",
      "    texture (standard error):             0.36   4.885\n",
      "    perimeter (standard error):           0.757  21.98\n",
      "    area (standard error):                6.802  542.2\n",
      "    smoothness (standard error):          0.002  0.031\n",
      "    compactness (standard error):         0.002  0.135\n",
      "    concavity (standard error):           0.0    0.396\n",
      "    concave points (standard error):      0.0    0.053\n",
      "    symmetry (standard error):            0.008  0.079\n",
      "    fractal dimension (standard error):   0.001  0.03\n",
      "    radius (worst):                       7.93   36.04\n",
      "    texture (worst):                      12.02  49.54\n",
      "    perimeter (worst):                    50.41  251.2\n",
      "    area (worst):                         185.2  4254.0\n",
      "    smoothness (worst):                   0.071  0.223\n",
      "    compactness (worst):                  0.027  1.058\n",
      "    concavity (worst):                    0.0    1.252\n",
      "    concave points (worst):               0.0    0.291\n",
      "    symmetry (worst):                     0.156  0.664\n",
      "    fractal dimension (worst):            0.055  0.208\n",
      "    ===================================== ====== ======\n",
      "\n",
      "    :Missing Attribute Values: None\n",
      "\n",
      "    :Class Distribution: 212 - Malignant, 357 - Benign\n",
      "\n",
      "    :Creator:  Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
      "\n",
      "    :Donor: Nick Street\n",
      "\n",
      "    :Date: November, 1995\n",
      "\n",
      "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
      "https://goo.gl/U2Uwz2\n",
      "\n",
      "Features are computed from a digitized image of a fine needle\n",
      "aspirate (FNA) of a breast mass.  They describe\n",
      "characteristics of the cell nuclei present in the image.\n",
      "\n",
      "Separating plane described above was obtained using\n",
      "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
      "Construction Via Linear Programming.\" Proceedings of the 4th\n",
      "Midwest Artificial Intelligence and Cognitive Science Society,\n",
      "pp. 97-101, 1992], a classification method which uses linear\n",
      "programming to construct a decision tree.  Relevant features\n",
      "were selected using an exhaustive search in the space of 1-4\n",
      "features and 1-3 separating planes.\n",
      "\n",
      "The actual linear program used to obtain the separating plane\n",
      "in the 3-dimensional space is that described in:\n",
      "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
      "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
      "Optimization Methods and Software 1, 1992, 23-34].\n",
      "\n",
      "This database is also available through the UW CS ftp server:\n",
      "\n",
      "ftp ftp.cs.wisc.edu\n",
      "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
      "\n",
      ".. topic:: References\n",
      "\n",
      "   - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
      "     for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
      "     Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
      "     San Jose, CA, 1993.\n",
      "   - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
      "     prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
      "     July-August 1995.\n",
      "   - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
      "     to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
      "     163-171.\n"
     ]
    }
   ],
   "source": [
    "print(c['DESCR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "617188e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "(569, 30)"
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     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "80790838",
   "metadata": {},
   "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',\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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5badd94d",
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 65,
     "metadata": {},
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    }
   ],
   "source": [
    "c_df = pd.DataFrame(c.data)\n",
    "c_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "8e7c1ad0",
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    "c_df_target = pd.DataFrame(c.target)\n",
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   "execution_count": 92,
   "id": "90092e82",
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    "sc= StandardScaler()"
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    "c_df=sc.fit_transform(c_df)"
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    {
     "ename": "AttributeError",
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     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[94], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mc_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhead\u001b[49m()\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'head'"
     ]
    }
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   "source": [
    "c_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "84fbb391",
   "metadata": {},
   "outputs": [
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       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.097064</td>\n",
       "      <td>-2.073335</td>\n",
       "      <td>1.269934</td>\n",
       "      <td>0.984375</td>\n",
       "      <td>1.568466</td>\n",
       "      <td>3.283515</td>\n",
       "      <td>2.652874</td>\n",
       "      <td>2.532475</td>\n",
       "      <td>2.217515</td>\n",
       "      <td>2.255747</td>\n",
       "      <td>...</td>\n",
       "      <td>1.886690</td>\n",
       "      <td>-1.359293</td>\n",
       "      <td>2.303601</td>\n",
       "      <td>2.001237</td>\n",
       "      <td>1.307686</td>\n",
       "      <td>2.616665</td>\n",
       "      <td>2.109526</td>\n",
       "      <td>2.296076</td>\n",
       "      <td>2.750622</td>\n",
       "      <td>1.937015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.829821</td>\n",
       "      <td>-0.353632</td>\n",
       "      <td>1.685955</td>\n",
       "      <td>1.908708</td>\n",
       "      <td>-0.826962</td>\n",
       "      <td>-0.487072</td>\n",
       "      <td>-0.023846</td>\n",
       "      <td>0.548144</td>\n",
       "      <td>0.001392</td>\n",
       "      <td>-0.868652</td>\n",
       "      <td>...</td>\n",
       "      <td>1.805927</td>\n",
       "      <td>-0.369203</td>\n",
       "      <td>1.535126</td>\n",
       "      <td>1.890489</td>\n",
       "      <td>-0.375612</td>\n",
       "      <td>-0.430444</td>\n",
       "      <td>-0.146749</td>\n",
       "      <td>1.087084</td>\n",
       "      <td>-0.243890</td>\n",
       "      <td>0.281190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.579888</td>\n",
       "      <td>0.456187</td>\n",
       "      <td>1.566503</td>\n",
       "      <td>1.558884</td>\n",
       "      <td>0.942210</td>\n",
       "      <td>1.052926</td>\n",
       "      <td>1.363478</td>\n",
       "      <td>2.037231</td>\n",
       "      <td>0.939685</td>\n",
       "      <td>-0.398008</td>\n",
       "      <td>...</td>\n",
       "      <td>1.511870</td>\n",
       "      <td>-0.023974</td>\n",
       "      <td>1.347475</td>\n",
       "      <td>1.456285</td>\n",
       "      <td>0.527407</td>\n",
       "      <td>1.082932</td>\n",
       "      <td>0.854974</td>\n",
       "      <td>1.955000</td>\n",
       "      <td>1.152255</td>\n",
       "      <td>0.201391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.768909</td>\n",
       "      <td>0.253732</td>\n",
       "      <td>-0.592687</td>\n",
       "      <td>-0.764464</td>\n",
       "      <td>3.283553</td>\n",
       "      <td>3.402909</td>\n",
       "      <td>1.915897</td>\n",
       "      <td>1.451707</td>\n",
       "      <td>2.867383</td>\n",
       "      <td>4.910919</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.281464</td>\n",
       "      <td>0.133984</td>\n",
       "      <td>-0.249939</td>\n",
       "      <td>-0.550021</td>\n",
       "      <td>3.394275</td>\n",
       "      <td>3.893397</td>\n",
       "      <td>1.989588</td>\n",
       "      <td>2.175786</td>\n",
       "      <td>6.046041</td>\n",
       "      <td>4.935010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.750297</td>\n",
       "      <td>-1.151816</td>\n",
       "      <td>1.776573</td>\n",
       "      <td>1.826229</td>\n",
       "      <td>0.280372</td>\n",
       "      <td>0.539340</td>\n",
       "      <td>1.371011</td>\n",
       "      <td>1.428493</td>\n",
       "      <td>-0.009560</td>\n",
       "      <td>-0.562450</td>\n",
       "      <td>...</td>\n",
       "      <td>1.298575</td>\n",
       "      <td>-1.466770</td>\n",
       "      <td>1.338539</td>\n",
       "      <td>1.220724</td>\n",
       "      <td>0.220556</td>\n",
       "      <td>-0.313395</td>\n",
       "      <td>0.613179</td>\n",
       "      <td>0.729259</td>\n",
       "      <td>-0.868353</td>\n",
       "      <td>-0.397100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0         1         2         3         4         5         6   \\\n",
       "0  1.097064 -2.073335  1.269934  0.984375  1.568466  3.283515  2.652874   \n",
       "1  1.829821 -0.353632  1.685955  1.908708 -0.826962 -0.487072 -0.023846   \n",
       "2  1.579888  0.456187  1.566503  1.558884  0.942210  1.052926  1.363478   \n",
       "3 -0.768909  0.253732 -0.592687 -0.764464  3.283553  3.402909  1.915897   \n",
       "4  1.750297 -1.151816  1.776573  1.826229  0.280372  0.539340  1.371011   \n",
       "\n",
       "         7         8         9   ...        20        21        22        23  \\\n",
       "0  2.532475  2.217515  2.255747  ...  1.886690 -1.359293  2.303601  2.001237   \n",
       "1  0.548144  0.001392 -0.868652  ...  1.805927 -0.369203  1.535126  1.890489   \n",
       "2  2.037231  0.939685 -0.398008  ...  1.511870 -0.023974  1.347475  1.456285   \n",
       "3  1.451707  2.867383  4.910919  ... -0.281464  0.133984 -0.249939 -0.550021   \n",
       "4  1.428493 -0.009560 -0.562450  ...  1.298575 -1.466770  1.338539  1.220724   \n",
       "\n",
       "         24        25        26        27        28        29  \n",
       "0  1.307686  2.616665  2.109526  2.296076  2.750622  1.937015  \n",
       "1 -0.375612 -0.430444 -0.146749  1.087084 -0.243890  0.281190  \n",
       "2  0.527407  1.082932  0.854974  1.955000  1.152255  0.201391  \n",
       "3  3.394275  3.893397  1.989588  2.175786  6.046041  4.935010  \n",
       "4  0.220556 -0.313395  0.613179  0.729259 -0.868353 -0.397100  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf_data = pd.DataFrame(c_df)\n",
    "cdf_data.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "00e84373",
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   "source": [
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   "id": "6e2e42e9",
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   "id": "3bcb618e",
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   "outputs": [
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   "id": "072c0a32",
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   "execution_count": 102,
   "id": "5d69f440",
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      "C:\\Users\\user\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:215: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
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   "execution_count": 107,
   "id": "835a9de2",
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     "execution_count": 107,
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