knn_classification_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "734de881",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c32d9b67",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"C:/Users/Zeynep/Desktop/ML/data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cfc301fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b2b872c5",
   "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",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   id                       569 non-null    int64  \n",
      " 1   diagnosis                569 non-null    object \n",
      " 2   radius_mean              569 non-null    float64\n",
      " 3   texture_mean             569 non-null    float64\n",
      " 4   perimeter_mean           569 non-null    float64\n",
      " 5   area_mean                569 non-null    float64\n",
      " 6   smoothness_mean          569 non-null    float64\n",
      " 7   compactness_mean         569 non-null    float64\n",
      " 8   concavity_mean           569 non-null    float64\n",
      " 9   concave points_mean      569 non-null    float64\n",
      " 10  symmetry_mean            569 non-null    float64\n",
      " 11  fractal_dimension_mean   569 non-null    float64\n",
      " 12  radius_se                569 non-null    float64\n",
      " 13  texture_se               569 non-null    float64\n",
      " 14  perimeter_se             569 non-null    float64\n",
      " 15  area_se                  569 non-null    float64\n",
      " 16  smoothness_se            569 non-null    float64\n",
      " 17  compactness_se           569 non-null    float64\n",
      " 18  concavity_se             569 non-null    float64\n",
      " 19  concave points_se        569 non-null    float64\n",
      " 20  symmetry_se              569 non-null    float64\n",
      " 21  fractal_dimension_se     569 non-null    float64\n",
      " 22  radius_worst             569 non-null    float64\n",
      " 23  texture_worst            569 non-null    float64\n",
      " 24  perimeter_worst          569 non-null    float64\n",
      " 25  area_worst               569 non-null    float64\n",
      " 26  smoothness_worst         569 non-null    float64\n",
      " 27  compactness_worst        569 non-null    float64\n",
      " 28  concavity_worst          569 non-null    float64\n",
      " 29  concave points_worst     569 non-null    float64\n",
      " 30  symmetry_worst           569 non-null    float64\n",
      " 31  fractal_dimension_worst  569 non-null    float64\n",
      " 32  Unnamed: 32              0 non-null      float64\n",
      "dtypes: float64(31), int64(1), object(1)\n",
      "memory usage: 146.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7f186a67",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop([\"Unnamed: 32\",\"id\"],axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "85e27cb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "M = data[data.diagnosis==\"M\"]\n",
    "B = data[data.diagnosis==\"B\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e8a9cf70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 212 entries, 0 to 567\n",
      "Data columns (total 31 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   diagnosis                212 non-null    object \n",
      " 1   radius_mean              212 non-null    float64\n",
      " 2   texture_mean             212 non-null    float64\n",
      " 3   perimeter_mean           212 non-null    float64\n",
      " 4   area_mean                212 non-null    float64\n",
      " 5   smoothness_mean          212 non-null    float64\n",
      " 6   compactness_mean         212 non-null    float64\n",
      " 7   concavity_mean           212 non-null    float64\n",
      " 8   concave points_mean      212 non-null    float64\n",
      " 9   symmetry_mean            212 non-null    float64\n",
      " 10  fractal_dimension_mean   212 non-null    float64\n",
      " 11  radius_se                212 non-null    float64\n",
      " 12  texture_se               212 non-null    float64\n",
      " 13  perimeter_se             212 non-null    float64\n",
      " 14  area_se                  212 non-null    float64\n",
      " 15  smoothness_se            212 non-null    float64\n",
      " 16  compactness_se           212 non-null    float64\n",
      " 17  concavity_se             212 non-null    float64\n",
      " 18  concave points_se        212 non-null    float64\n",
      " 19  symmetry_se              212 non-null    float64\n",
      " 20  fractal_dimension_se     212 non-null    float64\n",
      " 21  radius_worst             212 non-null    float64\n",
      " 22  texture_worst            212 non-null    float64\n",
      " 23  perimeter_worst          212 non-null    float64\n",
      " 24  area_worst               212 non-null    float64\n",
      " 25  smoothness_worst         212 non-null    float64\n",
      " 26  compactness_worst        212 non-null    float64\n",
      " 27  concavity_worst          212 non-null    float64\n",
      " 28  concave points_worst     212 non-null    float64\n",
      " 29  symmetry_worst           212 non-null    float64\n",
      " 30  fractal_dimension_worst  212 non-null    float64\n",
      "dtypes: float64(30), object(1)\n",
      "memory usage: 53.0+ KB\n"
     ]
    }
   ],
   "source": [
    "M.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "99c61faf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 357 entries, 19 to 568\n",
      "Data columns (total 31 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   diagnosis                357 non-null    object \n",
      " 1   radius_mean              357 non-null    float64\n",
      " 2   texture_mean             357 non-null    float64\n",
      " 3   perimeter_mean           357 non-null    float64\n",
      " 4   area_mean                357 non-null    float64\n",
      " 5   smoothness_mean          357 non-null    float64\n",
      " 6   compactness_mean         357 non-null    float64\n",
      " 7   concavity_mean           357 non-null    float64\n",
      " 8   concave points_mean      357 non-null    float64\n",
      " 9   symmetry_mean            357 non-null    float64\n",
      " 10  fractal_dimension_mean   357 non-null    float64\n",
      " 11  radius_se                357 non-null    float64\n",
      " 12  texture_se               357 non-null    float64\n",
      " 13  perimeter_se             357 non-null    float64\n",
      " 14  area_se                  357 non-null    float64\n",
      " 15  smoothness_se            357 non-null    float64\n",
      " 16  compactness_se           357 non-null    float64\n",
      " 17  concavity_se             357 non-null    float64\n",
      " 18  concave points_se        357 non-null    float64\n",
      " 19  symmetry_se              357 non-null    float64\n",
      " 20  fractal_dimension_se     357 non-null    float64\n",
      " 21  radius_worst             357 non-null    float64\n",
      " 22  texture_worst            357 non-null    float64\n",
      " 23  perimeter_worst          357 non-null    float64\n",
      " 24  area_worst               357 non-null    float64\n",
      " 25  smoothness_worst         357 non-null    float64\n",
      " 26  compactness_worst        357 non-null    float64\n",
      " 27  concavity_worst          357 non-null    float64\n",
      " 28  concave points_worst     357 non-null    float64\n",
      " 29  symmetry_worst           357 non-null    float64\n",
      " 30  fractal_dimension_worst  357 non-null    float64\n",
      "dtypes: float64(30), object(1)\n",
      "memory usage: 89.2+ KB\n"
     ]
    }
   ],
   "source": [
    "B.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "51bce3cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(M.radius_mean,M.area_mean,color=\"red\",label=\"malignant\")\n",
    "plt.scatter(B.radius_mean,B.area_mean,color=\"green\",label=\"benign\")\n",
    "plt.legend() \n",
    "plt.xlabel(\"radius_mean\")\n",
    "plt.ylabel(\"area_mean\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f2284968",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(M.radius_mean,M.texture_mean,color=\"red\",label=\"malignant\") \n",
    "plt.scatter(B.radius_mean,B.texture_mean,color=\"green\",label=\"benign\")\n",
    "plt.xlabel(\"radius_mean\")\n",
    "plt.ylabel(\"texture_mean\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "78e49336",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.diagnosis = [1 if each==\"M\" else 0 for each in data.diagnosis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8d460aac",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = data.diagnosis.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "730e0c93",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data= data.iloc[:,1:3].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8ca29b81",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = (x_data - np.min(x_data))/(np.max(x_data) - np.min(x_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c33f8850",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.3,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "95227813",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knn = KNeighborsClassifier(n_neighbors = 3) \n",
    "knn.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "25e1f446",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
       "       1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1,\n",
       "       1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0,\n",
       "       0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b63b82c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best accuracy is 0.8596491228070176 with K = 5\n"
     ]
    }
   ],
   "source": [
    "test_accuracy = []\n",
    "for each in range(1,15):\n",
    "    knn2 = KNeighborsClassifier(n_neighbors = each)\n",
    "    knn2.fit(x_train,y_train)\n",
    "    test_accuracy.append(knn2.score(x_test,y_test))\n",
    "    \n",
    "plt.figure(figsize=(5,5))\n",
    "plt.plot(range(1,15),test_accuracy)\n",
    "plt.title('K Values VS Accuracy')\n",
    "plt.xlabel(\"k values\")\n",
    "plt.ylabel(\"accuracy\")\n",
    "plt.grid()\n",
    "plt.show()\n",
    "print(\"Best accuracy is {} with K = {}\".format(np.max(test_accuracy),1+test_accuracy.index(np.max(test_accuracy))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "20fc2514",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n",
      "C:\\Users\\Zeynep\\anaconda3\\lib\\site-packages\\sklearn\\neighbors\\_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.\n",
      "  mode, _ = stats.mode(_y[neigh_ind, k], axis=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "error = []\n",
    "for i in range (1,40):\n",
    "  knn= KNeighborsClassifier(n_neighbors=i)\n",
    "  knn.fit(x_train,y_train)\n",
    "  pred_i = knn.predict(x_test)\n",
    "  error.append(np.mean(pred_i != y_test))\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(range(1,40), error, color='red',linestyle='dashed',marker='o',markerfacecolor='blue',markersize=10)\n",
    "plt.title('Error Rate K Value')\n",
    "plt.xlabel('K Value')\n",
    "plt.ylabel('Mean Error')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1936b6e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_head)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "289cf863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(figsize=(5, 5))\n",
    "sns.heatmap(cm,annot=True,fmt= '.0f',linewidths=0.5,linecolor=\"red\",ax=ax)\n",
    "plt.xlabel(\"y_pred\")\n",
    "plt.ylabel(\"y_test\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c09fa4a9",
   "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",
   "pygments_lexer": "ipython3",
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