KNN Breast Cancer Wisconsin (Diagnostic) Data Set-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics #importing modul metrik\n",
    "# Memuat dataset\n",
    "data = pd.read_csv(\"E:\\Semester 4\\data mining/bc.csv\",encoding = \"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\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>...</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\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>...</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\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>...</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\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>...</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\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>...</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>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "      ...       texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0     ...               17.33           184.60      2019.0            0.1622   \n",
       "1     ...               23.41           158.80      1956.0            0.1238   \n",
       "2     ...               25.53           152.50      1709.0            0.1444   \n",
       "3     ...               26.50            98.87       567.7            0.2098   \n",
       "4     ...               16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 33 columns):\n",
      "id                         569 non-null int64\n",
      "diagnosis                  569 non-null object\n",
      "radius_mean                569 non-null float64\n",
      "texture_mean               569 non-null float64\n",
      "perimeter_mean             569 non-null float64\n",
      "area_mean                  569 non-null float64\n",
      "smoothness_mean            569 non-null float64\n",
      "compactness_mean           569 non-null float64\n",
      "concavity_mean             569 non-null float64\n",
      "concave points_mean        569 non-null float64\n",
      "symmetry_mean              569 non-null float64\n",
      "fractal_dimension_mean     569 non-null float64\n",
      "radius_se                  569 non-null float64\n",
      "texture_se                 569 non-null float64\n",
      "perimeter_se               569 non-null float64\n",
      "area_se                    569 non-null float64\n",
      "smoothness_se              569 non-null float64\n",
      "compactness_se             569 non-null float64\n",
      "concavity_se               569 non-null float64\n",
      "concave points_se          569 non-null float64\n",
      "symmetry_se                569 non-null float64\n",
      "fractal_dimension_se       569 non-null float64\n",
      "radius_worst               569 non-null float64\n",
      "texture_worst              569 non-null float64\n",
      "perimeter_worst            569 non-null float64\n",
      "area_worst                 569 non-null float64\n",
      "smoothness_worst           569 non-null float64\n",
      "compactness_worst          569 non-null float64\n",
      "concavity_worst            569 non-null float64\n",
      "concave points_worst       569 non-null float64\n",
      "symmetry_worst             569 non-null float64\n",
      "fractal_dimension_worst    569 non-null float64\n",
      "Unnamed: 32                0 non-null float64\n",
      "dtypes: float64(31), int64(1), object(1)\n",
      "memory usage: 146.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# ringkasan dataset\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#menghapus kolom yang tidak berguna\n",
    "#menghapus kolom \"id\"\n",
    "data.drop(\"id\",axis=1,inplace=True)\n",
    "#menghapus the \"Unnamed: 32\" column\n",
    "data.drop(\"Unnamed: 32\",axis=1,inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 31 columns):\n",
      "diagnosis                  569 non-null object\n",
      "radius_mean                569 non-null float64\n",
      "texture_mean               569 non-null float64\n",
      "perimeter_mean             569 non-null float64\n",
      "area_mean                  569 non-null float64\n",
      "smoothness_mean            569 non-null float64\n",
      "compactness_mean           569 non-null float64\n",
      "concavity_mean             569 non-null float64\n",
      "concave points_mean        569 non-null float64\n",
      "symmetry_mean              569 non-null float64\n",
      "fractal_dimension_mean     569 non-null float64\n",
      "radius_se                  569 non-null float64\n",
      "texture_se                 569 non-null float64\n",
      "perimeter_se               569 non-null float64\n",
      "area_se                    569 non-null float64\n",
      "smoothness_se              569 non-null float64\n",
      "compactness_se             569 non-null float64\n",
      "concavity_se               569 non-null float64\n",
      "concave points_se          569 non-null float64\n",
      "symmetry_se                569 non-null float64\n",
      "fractal_dimension_se       569 non-null float64\n",
      "radius_worst               569 non-null float64\n",
      "texture_worst              569 non-null float64\n",
      "perimeter_worst            569 non-null float64\n",
      "area_worst                 569 non-null float64\n",
      "smoothness_worst           569 non-null float64\n",
      "compactness_worst          569 non-null float64\n",
      "concavity_worst            569 non-null float64\n",
      "concave points_worst       569 non-null float64\n",
      "symmetry_worst             569 non-null float64\n",
      "fractal_dimension_worst    569 non-null float64\n",
      "dtypes: float64(30), object(1)\n",
      "memory usage: 137.9+ KB\n"
     ]
    }
   ],
   "source": [
    "#hasil\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\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>...</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>M</td>\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>...</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>M</td>\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>...</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>M</td>\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>...</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>M</td>\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>...</td>\n",
       "      <td>22.54</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0         M        17.99         10.38          122.80     1001.0   \n",
       "1         M        20.57         17.77          132.90     1326.0   \n",
       "2         M        19.69         21.25          130.00     1203.0   \n",
       "3         M        11.42         20.38           77.58      386.1   \n",
       "4         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   symmetry_mean           ...             radius_worst  texture_worst  \\\n",
       "0         0.2419           ...                    25.38          17.33   \n",
       "1         0.1812           ...                    24.99          23.41   \n",
       "2         0.2069           ...                    23.57          25.53   \n",
       "3         0.2597           ...                    14.91          26.50   \n",
       "4         0.1809           ...                    22.54          16.67   \n",
       "\n",
       "   perimeter_worst  area_worst  smoothness_worst  compactness_worst  \\\n",
       "0           184.60      2019.0            0.1622             0.6656   \n",
       "1           158.80      1956.0            0.1238             0.1866   \n",
       "2           152.50      1709.0            0.1444             0.4245   \n",
       "3            98.87       567.7            0.2098             0.8663   \n",
       "4           152.20      1575.0            0.1374             0.2050   \n",
       "\n",
       "   concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0           0.7119                0.2654          0.4601   \n",
       "1           0.2416                0.1860          0.2750   \n",
       "2           0.4504                0.2430          0.3613   \n",
       "3           0.6869                0.2575          0.6638   \n",
       "4           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  \n",
       "0                  0.11890  \n",
       "1                  0.08902  \n",
       "2                  0.08758  \n",
       "3                  0.17300  \n",
       "4                  0.07678  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5 baris pertama\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#diagnosis adalah variabel yang bertanggung jawab untuk klasifikasi\n",
    "#mengganti M dan B masing-masing dengan 1 dan 0\n",
    "data.diagnosis=data.diagnosis.map({'M':1,'B':0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    357\n",
       "1    212\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#menghitung variabel diagnosis\n",
    "data.diagnosis.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocessing datasetselesai \n",
    "#splitting dataset ke training dan testing\n",
    "train, test = train_test_split(data, test_size = 0.3,random_state=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(398, 31)\n",
      "(171, 31)\n"
     ]
    }
   ],
   "source": [
    "#mencari hasil\n",
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#membuat variabel independen untuk training\n",
    "train_X = train.iloc[:, 1:31]\n",
    "#membuat variabel responsible untuk training\n",
    "train_y=train.diagnosis\n",
    "#membuat variabel independen untuk testing\n",
    "test_X= test.iloc[:, 1:31]\n",
    "#membuat variabel responsible untuk ttesting\n",
    "test_y =test.diagnosis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(398, 30)\n",
      "(398,)\n",
      "(171, 30)\n",
      "(171,)\n"
     ]
    }
   ],
   "source": [
    "#mencari hasil\n",
    "print(train_X.shape)\n",
    "print(train_y.shape)\n",
    "print(test_X.shape)\n",
    "print(test_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "neighbors=np.arange(1,31)\n",
    "accuracy_train=[]\n",
    "accuracy_test=[]\n",
    "for i,k in enumerate(neighbors):\n",
    "    knn=KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(train_X,train_y)\n",
    "    accuracy_train.append(knn.score(train_X,train_y))\n",
    "    accuracy_test.append(knn.score(test_X,test_y))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Akurasi ideal: 0.9415204678362573\n",
      "K ideal: 7\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(13,8))\n",
    "plt.plot(neighbors,accuracy_train,label=\"Akurasi training\")\n",
    "plt.plot(neighbors,accuracy_test,label=\"Akurasi testing\")\n",
    "acideal=np.max(accuracy_test)\n",
    "kideal=1+accuracy_test.index(np.max(accuracy_test))\n",
    "plt.title('Tingkat Akurasi')\n",
    "plt.xlabel(\"K yang dipilih\")\n",
    "plt.ylabel(\"Tingkat Akurasi\")\n",
    "plt.xticks(neighbors)\n",
    "plt.legend()\n",
    "plt.show()\n",
    "print(\"Akurasi ideal:\",acideal)\n",
    "print(\"K ideal:\",kideal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Masukan jumlah k:7\n",
      "Akurasi: 0.9415204678362573\n",
      "Confusion Metrix:\n",
      " [[102   7]\n",
      " [  3  59]]\n"
     ]
    }
   ],
   "source": [
    "#membuat instance\n",
    "model = KNeighborsClassifier(n_neighbors=int(input(\"Masukan jumlah k:\")))\n",
    "#learning\n",
    "model.fit(train_X,train_y)\n",
    "#Prediksi\n",
    "prediction=model.predict(test_X)\n",
    "\n",
    "#evaluation (Akurasi)\n",
    "print(\"Akurasi:\",metrics.accuracy_score(prediction,test_y))\n",
    "#evaluation(Confusion Metrix)\n",
    "print(\"Confusion Metrix:\\n\",metrics.confusion_matrix(prediction,test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n",
      "531       11.670         20.02           75.21      416.2          0.10160   \n",
      "166       10.800          9.71           68.77      357.6          0.09594   \n",
      "485       12.450         16.41           82.85      476.7          0.09514   \n",
      "66         9.465         21.01           60.11      269.4          0.10440   \n",
      "220       13.650         13.16           87.88      568.9          0.09646   \n",
      "356       13.050         18.59           85.09      512.0          0.10820   \n",
      "414       15.130         29.81           96.71      719.5          0.08320   \n",
      "525        8.571         13.10           54.53      221.3          0.10360   \n",
      "77        18.050         16.15          120.20     1006.0          0.10650   \n",
      "239       17.460         39.28          113.40      920.6          0.09812   \n",
      "254       19.450         19.33          126.50     1169.0          0.10350   \n",
      "447       14.800         17.66           95.88      674.8          0.09179   \n",
      "301       12.460         19.89           80.43      471.3          0.08451   \n",
      "133       15.710         13.93          102.00      761.7          0.09462   \n",
      "187       11.710         17.19           74.68      420.3          0.09774   \n",
      "78        20.180         23.97          143.70     1245.0          0.12860   \n",
      "319       12.430         17.00           78.60      477.3          0.07557   \n",
      "412        9.397         21.68           59.75      268.8          0.07969   \n",
      "349       11.950         14.96           77.23      426.7          0.11580   \n",
      "11        15.780         17.89          103.60      781.0          0.09710   \n",
      "240       13.640         15.60           87.38      575.3          0.09423   \n",
      "29        17.570         15.05          115.00      955.1          0.09847   \n",
      "302       20.090         23.86          134.70     1247.0          0.10800   \n",
      "521       24.630         21.60          165.50     1841.0          0.10300   \n",
      "373       20.640         17.35          134.80     1335.0          0.09446   \n",
      "481       13.900         19.24           88.73      602.9          0.07991   \n",
      "100       13.610         24.98           88.05      582.7          0.09488   \n",
      "304       11.460         18.16           73.59      403.1          0.08853   \n",
      "159       10.900         12.96           68.69      366.8          0.07515   \n",
      "360       12.540         18.07           79.42      491.9          0.07436   \n",
      "..           ...           ...             ...        ...              ...   \n",
      "202       23.290         26.67          158.90     1685.0          0.11410   \n",
      "435       13.980         19.62           91.12      599.5          0.10600   \n",
      "375       16.170         16.07          106.30      788.5          0.09880   \n",
      "47        13.170         18.66           85.98      534.6          0.11580   \n",
      "497       12.470         17.31           80.45      480.1          0.08928   \n",
      "13        15.850         23.95          103.70      782.7          0.08401   \n",
      "221       13.560         13.90           88.59      561.3          0.10510   \n",
      "22        15.340         14.26          102.50      704.4          0.10730   \n",
      "255       13.960         17.05           91.43      602.4          0.10960   \n",
      "109       11.340         21.26           72.48      396.5          0.08759   \n",
      "348       11.470         16.03           73.02      402.7          0.09076   \n",
      "129       19.790         25.12          130.40     1192.0          0.10150   \n",
      "152        9.731         15.34           63.78      300.2          0.10720   \n",
      "67        11.310         19.04           71.80      394.1          0.08139   \n",
      "213       17.420         25.56          114.50      948.0          0.10060   \n",
      "495       14.870         20.21           96.12      680.9          0.09587   \n",
      "517       19.890         20.26          130.50     1214.0          0.10370   \n",
      "219       19.530         32.47          128.00     1223.0          0.08420   \n",
      "290       14.410         19.73           96.03      651.0          0.08757   \n",
      "488       11.680         16.17           75.49      420.5          0.11280   \n",
      "309       13.050         13.84           82.71      530.6          0.08352   \n",
      "6         18.250         19.98          119.60     1040.0          0.09463   \n",
      "405       10.940         18.59           70.39      370.0          0.10040   \n",
      "452       12.000         28.23           76.77      442.5          0.08437   \n",
      "54        15.100         22.02           97.26      712.8          0.09056   \n",
      "305       11.600         24.49           74.23      417.2          0.07474   \n",
      "560       14.050         27.15           91.38      600.4          0.09929   \n",
      "285       12.580         18.40           79.83      489.0          0.08393   \n",
      "355       12.560         19.07           81.92      485.8          0.08760   \n",
      "329       16.260         21.88          107.50      826.8          0.11650   \n",
      "\n",
      "     compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n",
      "531           0.09453        0.042000             0.021570         0.1859   \n",
      "166           0.05736        0.025310             0.016980         0.1381   \n",
      "485           0.15110        0.154400             0.048460         0.2082   \n",
      "66            0.07773        0.021720             0.015040         0.1717   \n",
      "220           0.08711        0.038880             0.025630         0.1360   \n",
      "356           0.13040        0.096030             0.056030         0.2035   \n",
      "414           0.04605        0.046860             0.027390         0.1852   \n",
      "525           0.07632        0.025650             0.015100         0.1678   \n",
      "77            0.21460        0.168400             0.108000         0.2152   \n",
      "239           0.12980        0.141700             0.088110         0.1809   \n",
      "254           0.11880        0.137900             0.085910         0.1776   \n",
      "447           0.08890        0.040690             0.022600         0.1893   \n",
      "301           0.10140        0.068300             0.030990         0.1781   \n",
      "133           0.09462        0.071350             0.059330         0.1816   \n",
      "187           0.06141        0.038090             0.032390         0.1516   \n",
      "78            0.34540        0.375400             0.160400         0.2906   \n",
      "319           0.03454        0.013420             0.016990         0.1472   \n",
      "412           0.06053        0.037350             0.005128         0.1274   \n",
      "349           0.12060        0.011710             0.017870         0.2459   \n",
      "11            0.12920        0.099540             0.066060         0.1842   \n",
      "240           0.06630        0.047050             0.037310         0.1717   \n",
      "29            0.11570        0.098750             0.079530         0.1739   \n",
      "302           0.18380        0.228300             0.128000         0.2249   \n",
      "521           0.21060        0.231000             0.147100         0.1991   \n",
      "373           0.10760        0.152700             0.089410         0.1571   \n",
      "481           0.05326        0.029950             0.020700         0.1579   \n",
      "100           0.08511        0.086250             0.044890         0.1609   \n",
      "304           0.07694        0.033440             0.015020         0.1411   \n",
      "159           0.03718        0.003090             0.006588         0.1442   \n",
      "360           0.02650        0.001194             0.005449         0.1528   \n",
      "..                ...             ...                  ...            ...   \n",
      "202           0.20840        0.352300             0.162000         0.2200   \n",
      "435           0.11330        0.112600             0.064630         0.1669   \n",
      "375           0.14380        0.066510             0.053970         0.1990   \n",
      "47            0.12310        0.122600             0.073400         0.2128   \n",
      "497           0.07630        0.036090             0.023690         0.1526   \n",
      "13            0.10020        0.099380             0.053640         0.1847   \n",
      "221           0.11920        0.078600             0.044510         0.1962   \n",
      "22            0.21350        0.207700             0.097560         0.2521   \n",
      "255           0.12790        0.097890             0.052460         0.1908   \n",
      "109           0.06575        0.051330             0.018990         0.1487   \n",
      "348           0.05886        0.025870             0.023220         0.1634   \n",
      "129           0.15890        0.254500             0.114900         0.2202   \n",
      "152           0.15990        0.410800             0.078570         0.2548   \n",
      "67            0.04701        0.037090             0.022300         0.1516   \n",
      "213           0.11460        0.168200             0.065970         0.1308   \n",
      "495           0.08345        0.068240             0.049510         0.1487   \n",
      "517           0.13100        0.141100             0.094310         0.1802   \n",
      "219           0.11300        0.114500             0.066370         0.1428   \n",
      "290           0.16760        0.136200             0.066020         0.1714   \n",
      "488           0.09263        0.042790             0.031320         0.1853   \n",
      "309           0.03735        0.004559             0.008829         0.1453   \n",
      "6             0.10900        0.112700             0.074000         0.1794   \n",
      "405           0.07460        0.049440             0.029320         0.1486   \n",
      "452           0.06450        0.040550             0.019450         0.1615   \n",
      "54            0.07081        0.052530             0.033340         0.1616   \n",
      "305           0.05688        0.019740             0.013130         0.1935   \n",
      "560           0.11260        0.044620             0.043040         0.1537   \n",
      "285           0.04216        0.001860             0.002924         0.1697   \n",
      "355           0.10380        0.103000             0.043910         0.1533   \n",
      "329           0.12830        0.179900             0.079810         0.1869   \n",
      "\n",
      "     fractal_dimension_mean    ...     perimeter_worst  area_worst  \\\n",
      "531                 0.06461    ...               87.00       550.6   \n",
      "166                 0.06400    ...               73.66       414.0   \n",
      "485                 0.07325    ...               97.82       580.6   \n",
      "66                  0.06899    ...               67.03       330.7   \n",
      "220                 0.06344    ...               99.71       706.2   \n",
      "356                 0.06501    ...               94.22       591.2   \n",
      "414                 0.05294    ...              110.10       931.4   \n",
      "525                 0.07126    ...               63.30       275.6   \n",
      "77                  0.06673    ...              150.10      1610.0   \n",
      "239                 0.05966    ...              141.20      1408.0   \n",
      "254                 0.05647    ...              163.10      1972.0   \n",
      "447                 0.05886    ...              105.90       829.5   \n",
      "301                 0.06249    ...               88.13       551.3   \n",
      "133                 0.05723    ...              114.30       922.8   \n",
      "187                 0.06095    ...               84.42       521.5   \n",
      "78                  0.08142    ...              170.30      1623.0   \n",
      "319                 0.05561    ...               81.76       515.9   \n",
      "412                 0.06724    ...               66.61       301.0   \n",
      "349                 0.06581    ...               83.09       496.2   \n",
      "11                  0.06082    ...              136.50      1299.0   \n",
      "240                 0.05660    ...               94.11       683.4   \n",
      "29                  0.06149    ...              134.90      1227.0   \n",
      "302                 0.07469    ...              158.80      1696.0   \n",
      "521                 0.06739    ...              205.70      2642.0   \n",
      "373                 0.05478    ...              166.80      1946.0   \n",
      "481                 0.05594    ...              104.40       830.5   \n",
      "100                 0.05871    ...              108.60       906.5   \n",
      "304                 0.06243    ...               82.69       489.8   \n",
      "159                 0.05743    ...               78.07       470.0   \n",
      "360                 0.05185    ...               86.82       585.7   \n",
      "..                      ...    ...                 ...         ...   \n",
      "202                 0.06229    ...              177.00      1986.0   \n",
      "435                 0.06544    ...              113.90       869.3   \n",
      "375                 0.06572    ...              113.10       861.5   \n",
      "47                  0.06777    ...              102.80       759.4   \n",
      "497                 0.06046    ...               92.82       607.3   \n",
      "13                  0.05338    ...              112.00       876.5   \n",
      "221                 0.06303    ...              101.10       686.6   \n",
      "22                  0.07032    ...              125.10       980.9   \n",
      "255                 0.06130    ...              108.10       826.0   \n",
      "109                 0.06529    ...               83.99       518.1   \n",
      "348                 0.06372    ...               79.67       475.8   \n",
      "129                 0.06113    ...              148.70      1589.0   \n",
      "152                 0.09296    ...               71.04       380.5   \n",
      "67                  0.05667    ...               78.00       466.7   \n",
      "213                 0.05866    ...              120.40      1021.0   \n",
      "495                 0.05748    ...              103.90       783.6   \n",
      "517                 0.06188    ...              160.50      1646.0   \n",
      "219                 0.05313    ...              180.20      2477.0   \n",
      "290                 0.07192    ...              101.70       767.3   \n",
      "488                 0.06401    ...               86.57       549.8   \n",
      "309                 0.05518    ...               93.96       672.4   \n",
      "6                   0.05742    ...              153.20      1606.0   \n",
      "405                 0.06615    ...               82.76       472.4   \n",
      "452                 0.06104    ...               85.07       523.7   \n",
      "54                  0.05684    ...              117.70      1030.0   \n",
      "305                 0.05878    ...               81.39       476.5   \n",
      "560                 0.06171    ...              100.20       706.7   \n",
      "285                 0.05855    ...               85.56       564.1   \n",
      "355                 0.06184    ...               89.02       547.4   \n",
      "329                 0.06532    ...              113.70       975.2   \n",
      "\n",
      "     smoothness_worst  compactness_worst  concavity_worst  \\\n",
      "531           0.15500            0.29640         0.275800   \n",
      "166           0.14360            0.12570         0.104700   \n",
      "485           0.11750            0.40610         0.489600   \n",
      "66            0.15480            0.16640         0.094120   \n",
      "220           0.13110            0.24740         0.175900   \n",
      "356           0.13430            0.26580         0.257300   \n",
      "414           0.11480            0.09866         0.154700   \n",
      "525           0.16410            0.22350         0.175400   \n",
      "77            0.14780            0.56340         0.378600   \n",
      "239           0.13650            0.37350         0.324100   \n",
      "254           0.14970            0.31610         0.431700   \n",
      "447           0.12260            0.18810         0.206000   \n",
      "301           0.10500            0.21580         0.190400   \n",
      "133           0.12230            0.19490         0.170900   \n",
      "187           0.13230            0.10400         0.152100   \n",
      "78            0.16390            0.61640         0.768100   \n",
      "319           0.08409            0.04712         0.022370   \n",
      "412           0.10860            0.18870         0.186800   \n",
      "349           0.12930            0.18850         0.031220   \n",
      "11            0.13960            0.56090         0.396500   \n",
      "240           0.12780            0.12910         0.153300   \n",
      "29            0.12550            0.28120         0.248900   \n",
      "302           0.13470            0.33910         0.493200   \n",
      "521           0.13420            0.41880         0.465800   \n",
      "373           0.15620            0.30550         0.415900   \n",
      "481           0.10640            0.14150         0.167300   \n",
      "100           0.12650            0.19430         0.316900   \n",
      "304           0.11440            0.17890         0.122600   \n",
      "159           0.11710            0.08294         0.018540   \n",
      "360           0.09293            0.04327         0.003581   \n",
      "..                ...                ...              ...   \n",
      "202           0.15360            0.41670         0.789200   \n",
      "435           0.16130            0.35680         0.406900   \n",
      "375           0.12350            0.25500         0.211400   \n",
      "47            0.17860            0.41660         0.500600   \n",
      "497           0.12760            0.25060         0.202800   \n",
      "13            0.11310            0.19240         0.232200   \n",
      "221           0.13760            0.26980         0.257700   \n",
      "22            0.13900            0.59540         0.630500   \n",
      "255           0.15120            0.32620         0.320900   \n",
      "109           0.16990            0.21960         0.312000   \n",
      "348           0.15310            0.11200         0.098230   \n",
      "129           0.12750            0.38610         0.567300   \n",
      "152           0.12920            0.27720         0.821600   \n",
      "67            0.12900            0.09148         0.144400   \n",
      "213           0.12430            0.17930         0.280300   \n",
      "495           0.12160            0.13880         0.170000   \n",
      "517           0.14170            0.33090         0.418500   \n",
      "219           0.14080            0.40970         0.399500   \n",
      "290           0.09983            0.24720         0.222000   \n",
      "488           0.15260            0.14770         0.149000   \n",
      "309           0.10160            0.05847         0.018240   \n",
      "6             0.14420            0.25760         0.378400   \n",
      "405           0.13630            0.16440         0.141200   \n",
      "452           0.12080            0.18560         0.181100   \n",
      "54            0.13890            0.20570         0.271200   \n",
      "305           0.09545            0.13610         0.072390   \n",
      "560           0.12410            0.22640         0.132600   \n",
      "285           0.10380            0.06624         0.005579   \n",
      "355           0.10960            0.20020         0.238800   \n",
      "329           0.14260            0.21160         0.334400   \n",
      "\n",
      "     concave points_worst  symmetry_worst  fractal_dimension_worst  diagnosis  \\\n",
      "531              0.081200          0.3206                  0.08950          0   \n",
      "166              0.046030          0.2090                  0.07699          0   \n",
      "485              0.134200          0.3231                  0.10340          0   \n",
      "66               0.065170          0.2878                  0.09211          0   \n",
      "220              0.080560          0.2380                  0.08718          0   \n",
      "356              0.125800          0.3113                  0.08317          0   \n",
      "414              0.065750          0.3233                  0.06165          1   \n",
      "525              0.085120          0.2983                  0.10490          0   \n",
      "77               0.210200          0.3751                  0.11080          1   \n",
      "239              0.206600          0.2853                  0.08496          1   \n",
      "254              0.199900          0.3379                  0.08950          1   \n",
      "447              0.083080          0.3600                  0.07285          0   \n",
      "301              0.076250          0.2685                  0.07764          0   \n",
      "133              0.137400          0.2723                  0.07071          0   \n",
      "187              0.109900          0.2572                  0.07097          0   \n",
      "78               0.250800          0.5440                  0.09964          1   \n",
      "319              0.028320          0.1901                  0.05932          0   \n",
      "412              0.025640          0.2376                  0.09206          0   \n",
      "349              0.047660          0.3124                  0.07590          0   \n",
      "11               0.181000          0.3792                  0.10480          1   \n",
      "240              0.092220          0.2530                  0.06510          0   \n",
      "29               0.145600          0.2756                  0.07919          1   \n",
      "302              0.192300          0.3294                  0.09469          1   \n",
      "521              0.247500          0.3157                  0.09671          1   \n",
      "373              0.211200          0.2689                  0.07055          1   \n",
      "481              0.081500          0.2356                  0.07603          0   \n",
      "100              0.118400          0.2651                  0.07397          1   \n",
      "304              0.055090          0.2208                  0.07638          0   \n",
      "159              0.039530          0.2738                  0.07685          0   \n",
      "360              0.016350          0.2233                  0.05521          0   \n",
      "..                    ...             ...                      ...        ...   \n",
      "202              0.273300          0.3198                  0.08762          1   \n",
      "435              0.182700          0.3179                  0.10550          1   \n",
      "375              0.125100          0.3153                  0.08960          0   \n",
      "47               0.208800          0.3900                  0.11790          1   \n",
      "497              0.105300          0.3035                  0.07661          0   \n",
      "13               0.111900          0.2809                  0.06287          1   \n",
      "221              0.090900          0.3065                  0.08177          0   \n",
      "22               0.239300          0.4667                  0.09946          1   \n",
      "255              0.137400          0.3068                  0.07957          1   \n",
      "109              0.082780          0.2829                  0.08832          0   \n",
      "348              0.065480          0.2851                  0.08763          0   \n",
      "129              0.173200          0.3305                  0.08465          1   \n",
      "152              0.157100          0.3108                  0.12590          0   \n",
      "67               0.069610          0.2400                  0.06641          0   \n",
      "213              0.109900          0.1603                  0.06818          1   \n",
      "495              0.101700          0.2369                  0.06599          0   \n",
      "517              0.161300          0.2549                  0.09136          1   \n",
      "219              0.162500          0.2713                  0.07568          1   \n",
      "290              0.102100          0.2272                  0.08799          0   \n",
      "488              0.098150          0.2804                  0.08024          0   \n",
      "309              0.035320          0.2107                  0.06580          0   \n",
      "6                0.193200          0.3063                  0.08368          1   \n",
      "405              0.078870          0.2251                  0.07732          0   \n",
      "452              0.071160          0.2447                  0.08194          0   \n",
      "54               0.153000          0.2675                  0.07873          1   \n",
      "305              0.048150          0.3244                  0.06745          0   \n",
      "560              0.104800          0.2250                  0.08321          0   \n",
      "285              0.008772          0.2505                  0.06431          0   \n",
      "355              0.092650          0.2121                  0.07188          0   \n",
      "329              0.104700          0.2736                  0.07953          1   \n",
      "\n",
      "     prediksi  \n",
      "531         0  \n",
      "166         0  \n",
      "485         0  \n",
      "66          0  \n",
      "220         0  \n",
      "356         0  \n",
      "414         1  \n",
      "525         0  \n",
      "77          1  \n",
      "239         1  \n",
      "254         1  \n",
      "447         0  \n",
      "301         0  \n",
      "133         1  \n",
      "187         0  \n",
      "78          1  \n",
      "319         0  \n",
      "412         0  \n",
      "349         0  \n",
      "11          1  \n",
      "240         0  \n",
      "29          1  \n",
      "302         1  \n",
      "521         1  \n",
      "373         1  \n",
      "481         1  \n",
      "100         1  \n",
      "304         0  \n",
      "159         0  \n",
      "360         0  \n",
      "..        ...  \n",
      "202         1  \n",
      "435         0  \n",
      "375         0  \n",
      "47          1  \n",
      "497         0  \n",
      "13          0  \n",
      "221         0  \n",
      "22          1  \n",
      "255         1  \n",
      "109         0  \n",
      "348         0  \n",
      "129         1  \n",
      "152         0  \n",
      "67          0  \n",
      "213         1  \n",
      "495         0  \n",
      "517         1  \n",
      "219         1  \n",
      "290         0  \n",
      "488         0  \n",
      "309         0  \n",
      "6           1  \n",
      "405         0  \n",
      "452         0  \n",
      "54          1  \n",
      "305         0  \n",
      "560         0  \n",
      "285         0  \n",
      "355         0  \n",
      "329         1  \n",
      "\n",
      "[171 rows x 32 columns]\n"
     ]
    }
   ],
   "source": [
    "datatest=pd.DataFrame(test_X)\n",
    "datatest['diagnosis']=test_y\n",
    "datatest['prediksi']=prediction\n",
    "print(datatest)"
   ]
  }
 ],
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