Diabetes Classification using KNN Algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "287f60dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9afa2720",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"C:/diabetes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "00e6154f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
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       "      <td>168</td>\n",
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       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
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       "      <td>112</td>\n",
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       "      <td>0</td>\n",
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       "      <td>70</td>\n",
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       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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       "<p>768 rows × 9 columns</p>\n",
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      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0              6      148             72             35        0  33.6   \n",
       "1              1       85             66             29        0  26.6   \n",
       "2              8      183             64              0        0  23.3   \n",
       "3              1       89             66             23       94  28.1   \n",
       "4              0      137             40             35      168  43.1   \n",
       "..           ...      ...            ...            ...      ...   ...   \n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                       0.627   50        1  \n",
       "1                       0.351   31        0  \n",
       "2                       0.672   32        1  \n",
       "3                       0.167   21        0  \n",
       "4                       2.288   33        1  \n",
       "..                        ...  ...      ...  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8c7ce3b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>DiabetesPedigreeFunction</th>\n",
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       "      <td>43.1</td>\n",
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       "      <td>33</td>\n",
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      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  \n",
       "0                     0.627   50  \n",
       "1                     0.351   31  \n",
       "2                     0.672   32  \n",
       "3                     0.167   21  \n",
       "4                     2.288   33  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=data.drop(['Outcome'],axis=1)\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "833e7050",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
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       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
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       "      <td>23</td>\n",
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      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  \n",
       "763                     0.171   63  \n",
       "764                     0.340   27  \n",
       "765                     0.245   30  \n",
       "766                     0.349   47  \n",
       "767                     0.315   23  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "515dd0ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#x is input hence, we drop outcome column and put rest all in x."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "65638356",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=data['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6b04f24d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      0\n",
       "2      1\n",
       "3      0\n",
       "4      1\n",
       "      ..\n",
       "763    0\n",
       "764    0\n",
       "765    0\n",
       "766    1\n",
       "767    0\n",
       "Name: Outcome, Length: 768, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "45da75bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "41d14889",
   "metadata": {},
   "outputs": [],
   "source": [
    "#formula of minmaxscaler:\n",
    "# {value-x(min)}/{x(max)-x(min)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6d5c7a80",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler=MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "83d3babd",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=scaler.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b53453b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.35294118, 0.74371859, 0.59016393, ..., 0.50074516, 0.23441503,\n",
       "        0.48333333],\n",
       "       [0.05882353, 0.42713568, 0.54098361, ..., 0.39642325, 0.11656704,\n",
       "        0.16666667],\n",
       "       [0.47058824, 0.91959799, 0.52459016, ..., 0.34724292, 0.25362938,\n",
       "        0.18333333],\n",
       "       ...,\n",
       "       [0.29411765, 0.6080402 , 0.59016393, ..., 0.390462  , 0.07130658,\n",
       "        0.15      ],\n",
       "       [0.05882353, 0.63316583, 0.49180328, ..., 0.4485842 , 0.11571307,\n",
       "        0.43333333],\n",
       "       [0.05882353, 0.46733668, 0.57377049, ..., 0.45305514, 0.10119556,\n",
       "        0.03333333]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "157ab085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      0\n",
       "2      1\n",
       "3      0\n",
       "4      1\n",
       "      ..\n",
       "763    0\n",
       "764    0\n",
       "765    0\n",
       "766    1\n",
       "767    0\n",
       "Name: Outcome, Length: 768, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ffc31bfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f03b5c42",
   "metadata": {},
   "outputs": [],
   "source": [
    "xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.3,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4b10b109",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b3c82419",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn=KNeighborsClassifier(n_neighbors=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "96fc905d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.fit(xtrain,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "30a07d7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\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"
     ]
    }
   ],
   "source": [
    "ypred=knn.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f2277edd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1,\n",
       "       0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1,\n",
       "       0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n",
       "       1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
       "       0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0,\n",
       "       1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,\n",
       "       0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ypred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d4e4d1e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "285    0\n",
       "101    0\n",
       "581    0\n",
       "352    0\n",
       "726    0\n",
       "      ..\n",
       "241    0\n",
       "599    0\n",
       "650    0\n",
       "11     1\n",
       "214    1\n",
       "Name: Outcome, Length: 231, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a880fdc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4f6a6b57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[119  27]\n",
      " [ 40  45]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(ytest,ypred))\n",
    "#in confusion matrix see diagonals left top to right bottom shows , number of correct predictions\n",
    "#whereas other diagonal shows number of wrong predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "84dce595",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.82      0.78       146\n",
      "           1       0.62      0.53      0.57        85\n",
      "\n",
      "    accuracy                           0.71       231\n",
      "   macro avg       0.69      0.67      0.68       231\n",
      "weighted avg       0.70      0.71      0.70       231\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(ytest,ypred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "53742a5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "718ec1d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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:\\ProgramData\\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"
     ]
    }
   ],
   "source": [
    "error_rate=[]\n",
    "\n",
    "for i in range(1,40):\n",
    "    knn=KNeighborsClassifier(n_neighbors=i)\n",
    "    knn.fit(xtrain,ytrain)\n",
    "    pred_i=knn.predict(xtest)\n",
    "    \n",
    "    error_rate.append(np.mean(pred_i != ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "19747c35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Error rate')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,40), error_rate,color='blue', linestyle='--', markersize=10, markerfacecolor='red', marker='o')\n",
    "plt.title('K versus Error rate')\n",
    "# plt.ylim(0.290,0.30)\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Error rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "047c5a79",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assuming lowest error rate at 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4432c7f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[119  27]\n",
      " [ 40  45]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\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"
     ]
    }
   ],
   "source": [
    "knn=KNeighborsClassifier(n_neighbors=11)\n",
    "knn.fit(xtrain,ytrain)\n",
    "predictions= knn.predict(xtest)\n",
    "\n",
    "print(confusion_matrix(ytest,ypred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d822f680",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.75      0.82      0.78       146\n",
      "           1       0.62      0.53      0.57        85\n",
      "\n",
      "    accuracy                           0.71       231\n",
      "   macro avg       0.69      0.67      0.68       231\n",
      "weighted avg       0.70      0.71      0.70       231\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(ytest,ypred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3a140dcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Error rate')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(range(1,40), error_rate,color='blue', linestyle='--', markersize=10, markerfacecolor='red', marker='o')\n",
    "plt.title('K versus Error rate')\n",
    "# plt.ylim(0.290,0.30)\n",
    "plt.xlabel('K')\n",
    "plt.ylabel('Error rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f3754ba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf8a415d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fd3f1cc",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}