Lab4-1(KNN).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "69c9a8ba-ee9d-43e7-8b65-b8cee7c10b20",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "34bf9790-c59a-466a-9116-e15bfdbea0bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"C:/Users/baraa/PythonCodes/archive/data.csv\")      # Read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f72be0e1-44c6-48b4-bf87-899da4ff66e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a1f8dffc-0ad1-45e1-ae63-1572573c1f14",
   "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": 7,
   "id": "08eb8ba3-ffc9-4d1e-b01a-25383a42e4d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop([\"Unnamed: 32\",\"id\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "89d0ab86-6844-4075-bad2-75e2f7e765f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "M =data[data.diagnosis==\"M\"]\n",
    "B =data[data.diagnosis==\"B\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "59efb01f-c350-447f-8095-67f76f7bd696",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 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": 10,
   "id": "e80e4bb0-ce88-440e-88e3-4021451cc0d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 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": 11,
   "id": "213196fb-3c15-41c2-bc1f-a6997a226ab6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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": 12,
   "id": "38716e99-fbf3-4acd-afec-74022b829182",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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.legend()\n",
    "plt.xlabel(\"radius_mean\")\n",
    "plt.ylabel(\"texture_mean\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8dd756ff-2033-41cf-b3aa-399026b6c1b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.diagnosis=[1 if each==\"M\" else 0 for each in data.diagnosis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "40912a0d-9a9c-407a-ba2f-ada4dd15090e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "564    1\n",
       "565    1\n",
       "566    1\n",
       "567    1\n",
       "568    0\n",
       "Name: diagnosis, Length: 569, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.diagnosis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0cae8d35-5f1f-4eb8-8ba3-71c3c3adc96f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=data.diagnosis.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8f18c8b1-9508-45ab-9459-8c34d59b383d",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_data=data.iloc[:,1:3].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a3ccf10e-f505-4c51-b0b2-3e8d18c5e35e",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=(x_data - np.min(x_data))/(np.max(x_data)-np.min(x_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "76267a91-da74-40b2-a5e4-e26f056447ec",
   "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": 19,
   "id": "eaf89ed5-2d7f-4ee7-bfde-d99f6613a9f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=3)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=3)"
      ]
     },
     "execution_count": 19,
     "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": 20,
   "id": "d6a9641e-983f-487d-9b0d-5a7623a1114d",
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_head=knn.predict(x_test)\n",
    "y_head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c0067f21-6a32-4091-a22a-de11837d6906",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "when k is 3, accuracy of KNN classification 0.8421052631578947 \n"
     ]
    }
   ],
   "source": [
    "print(\"when k is {}, accuracy of KNN classification {} \" .format(3,knn.score(x_test,y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f075bf66-6018-4372-9e14-87830e1b99c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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 labels\")\n",
    "plt.xlabel(\"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": null,
   "id": "cbcbdc99-29e5-425a-a41f-ea036f4a5e43",
   "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",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}