KNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f19f9a20",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing the needed libraries\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "get_ipython().run_line_magic('matplotlib', 'inline')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a23fe7fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c33ac44b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_breast_cancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2e8b1f52",
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "36e0ed91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The data set is presented in a dictionary form\n",
    "\n",
    "cancer.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8e9777ad",
   "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(cancer['DESCR'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "99414ffc",
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer['feature_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2c9f8e97",
   "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",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   mean radius              569 non-null    float64\n",
      " 1   mean texture             569 non-null    float64\n",
      " 2   mean perimeter           569 non-null    float64\n",
      " 3   mean area                569 non-null    float64\n",
      " 4   mean smoothness          569 non-null    float64\n",
      " 5   mean compactness         569 non-null    float64\n",
      " 6   mean concavity           569 non-null    float64\n",
      " 7   mean concave points      569 non-null    float64\n",
      " 8   mean symmetry            569 non-null    float64\n",
      " 9   mean fractal dimension   569 non-null    float64\n",
      " 10  radius error             569 non-null    float64\n",
      " 11  texture error            569 non-null    float64\n",
      " 12  perimeter error          569 non-null    float64\n",
      " 13  area error               569 non-null    float64\n",
      " 14  smoothness error         569 non-null    float64\n",
      " 15  compactness error        569 non-null    float64\n",
      " 16  concavity error          569 non-null    float64\n",
      " 17  concave points error     569 non-null    float64\n",
      " 18  symmetry error           569 non-null    float64\n",
      " 19  fractal dimension error  569 non-null    float64\n",
      " 20  worst radius             569 non-null    float64\n",
      " 21  worst texture            569 non-null    float64\n",
      " 22  worst perimeter          569 non-null    float64\n",
      " 23  worst area               569 non-null    float64\n",
      " 24  worst smoothness         569 non-null    float64\n",
      " 25  worst compactness        569 non-null    float64\n",
      " 26  worst concavity          569 non-null    float64\n",
      " 27  worst concave points     569 non-null    float64\n",
      " 28  worst symmetry           569 non-null    float64\n",
      " 29  worst fractal dimension  569 non-null    float64\n",
      "dtypes: float64(30)\n",
      "memory usage: 133.5 KB\n"
     ]
    }
   ],
   "source": [
    "# Set up DaraFrame\n",
    "\n",
    "df_feat = pd.DataFrame(cancer['data'], columns = cancer['feature_names'])\n",
    "df_feat.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "662c9a86",
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c64853ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_target = pd.DataFrame(cancer['target'], columns=['cancer'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "98a36da6",
   "metadata": {
    "scrolled": true
   },
   "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>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",
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       "  </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  worst texture  worst perimeter  \\\n",
       "0                 0.07871  ...         25.38          17.33           184.60   \n",
       "1                 0.05667  ...         24.99          23.41           158.80   \n",
       "2                 0.05999  ...         23.57          25.53           152.50   \n",
       "3                 0.09744  ...         14.91          26.50            98.87   \n",
       "4                 0.05883  ...         22.54          16.67           152.20   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0      2019.0            0.1622             0.6656           0.7119   \n",
       "1      1956.0            0.1238             0.1866           0.2416   \n",
       "2      1709.0            0.1444             0.4245           0.4504   \n",
       "3       567.7            0.2098             0.8663           0.6869   \n",
       "4      1575.0            0.1374             0.2050           0.4000   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  \n",
       "0                0.2654          0.4601                  0.11890  \n",
       "1                0.1860          0.2750                  0.08902  \n",
       "2                0.2430          0.3613                  0.08758  \n",
       "3                0.2575          0.6638                  0.17300  \n",
       "4                0.1625          0.2364                  0.07678  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0f25dea0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardizing the variables\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "49e657d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6ea1d88c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(df_feat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "41b316eb",
   "metadata": {},
   "outputs": [],
   "source": [
    " scaled_features = scaler.transform(df_feat)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c1707a90",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_feat_scaled = pd.DataFrame(scaled_features, columns = df_feat.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "68b66a7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\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>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": [
       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0     1.097064     -2.073335        1.269934   0.984375         1.568466   \n",
       "1     1.829821     -0.353632        1.685955   1.908708        -0.826962   \n",
       "2     1.579888      0.456187        1.566503   1.558884         0.942210   \n",
       "3    -0.768909      0.253732       -0.592687  -0.764464         3.283553   \n",
       "4     1.750297     -1.151816        1.776573   1.826229         0.280372   \n",
       "\n",
       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0          3.283515        2.652874             2.532475       2.217515   \n",
       "1         -0.487072       -0.023846             0.548144       0.001392   \n",
       "2          1.052926        1.363478             2.037231       0.939685   \n",
       "3          3.402909        1.915897             1.451707       2.867383   \n",
       "4          0.539340        1.371011             1.428493      -0.009560   \n",
       "\n",
       "   mean fractal dimension  ...  worst radius  worst texture  worst perimeter  \\\n",
       "0                2.255747  ...      1.886690      -1.359293         2.303601   \n",
       "1               -0.868652  ...      1.805927      -0.369203         1.535126   \n",
       "2               -0.398008  ...      1.511870      -0.023974         1.347475   \n",
       "3                4.910919  ...     -0.281464       0.133984        -0.249939   \n",
       "4               -0.562450  ...      1.298575      -1.466770         1.338539   \n",
       "\n",
       "   worst area  worst smoothness  worst compactness  worst concavity  \\\n",
       "0    2.001237          1.307686           2.616665         2.109526   \n",
       "1    1.890489         -0.375612          -0.430444        -0.146749   \n",
       "2    1.456285          0.527407           1.082932         0.854974   \n",
       "3   -0.550021          3.394275           3.893397         1.989588   \n",
       "4    1.220724          0.220556          -0.313395         0.613179   \n",
       "\n",
       "   worst concave points  worst symmetry  worst fractal dimension  \n",
       "0              2.296076        2.750622                 1.937015  \n",
       "1              1.087084       -0.243890                 0.281190  \n",
       "2              1.955000        1.152255                 0.201391  \n",
       "3              2.175786        6.046041                 4.935010  \n",
       "4              0.729259       -0.868353                -0.397100  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feat_scaled.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "82a25485",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(scaled_features,np.ravel(df_target), test_size = 0.30, random_state = 105)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7276d3b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using KNN\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "02d9984d",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9fd8c12e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=1)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "db65d9d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "dbec7fe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predictions and Evaluations\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9ca8c8c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 55   4]\n",
      " [  5 107]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "525f985c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.93      0.92        59\n",
      "           1       0.96      0.96      0.96       112\n",
      "\n",
      "    accuracy                           0.95       171\n",
      "   macro avg       0.94      0.94      0.94       171\n",
      "weighted avg       0.95      0.95      0.95       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5b37d425",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choosing a K value\n",
    "\n",
    "error_rate = []\n",
    "\n",
    "for i in range(1,40):\n",
    "    \n",
    "    knn = KNeighborsClassifier(n_neighbors = i)\n",
    "    knn.fit(X_train, y_train)\n",
    "    pred_i = knn.predict(X_test)\n",
    "    error_rate.append(np.mean(pred_i != y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "221b27fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Error Rate')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "plt.plot(range(1,40),error_rate,color = 'blue', linestyle = 'dashed', marker = 'o', markerfacecolor = 'red', markersize = 10)\n",
    "plt.title('Error Rate vs K value')\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Error Rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d8cb2246",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With K=1\n",
      "\n",
      "\n",
      "[[ 55   4]\n",
      " [  5 107]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.93      0.92        59\n",
      "           1       0.96      0.96      0.96       112\n",
      "\n",
      "    accuracy                           0.95       171\n",
      "   macro avg       0.94      0.94      0.94       171\n",
      "weighted avg       0.95      0.95      0.95       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Comparing the new result to K=1\n",
    "\n",
    "# With K=1\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors =1)\n",
    "\n",
    "knn.fit(X_train, y_train)\n",
    "pred = knn.predict(X_test)\n",
    "\n",
    "print('With K=1')\n",
    "print('\\n')\n",
    "print(confusion_matrix(y_test,pred))\n",
    "print('\\n')\n",
    "print(classification_report(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8dd02915",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "With K=21\n",
      "\n",
      "\n",
      "[[ 57   2]\n",
      " [  0 112]]\n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.97      0.98        59\n",
      "           1       0.98      1.00      0.99       112\n",
      "\n",
      "    accuracy                           0.99       171\n",
      "   macro avg       0.99      0.98      0.99       171\n",
      "weighted avg       0.99      0.99      0.99       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# With K=21\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors = 21)\n",
    "\n",
    "knn.fit(X_train, y_train)\n",
    "pred = knn.predict(X_test)\n",
    "\n",
    "print('With K=21')\n",
    "print('\\n')\n",
    "print(confusion_matrix(y_test, pred))\n",
    "print('\\n')\n",
    "print(classification_report(y_test, pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04891384",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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