DAY 28-31.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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  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  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r'diabetes.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
       "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "Pregnancies                 768 non-null int64\n",
      "Glucose                     768 non-null int64\n",
      "BloodPressure               768 non-null int64\n",
      "SkinThickness               768 non-null int64\n",
      "Insulin                     768 non-null int64\n",
      "BMI                         768 non-null float64\n",
      "DiabetesPedigreeFunction    768 non-null float64\n",
      "Age                         768 non-null int64\n",
      "Outcome                     768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0\n",
       "Glucose                     0\n",
       "BloodPressure               0\n",
       "SkinThickness               0\n",
       "Insulin                     0\n",
       "BMI                         0\n",
       "DiabetesPedigreeFunction    0\n",
       "Age                         0\n",
       "Outcome                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The precentage of 1 class is : 0.35\n",
      "The precentage of 0 class is: 0.65\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.countplot(df['Outcome'])\n",
    "print('The precentage of 1 class is : %.2f' % (df['Outcome'].value_counts()[1]/len(df)))\n",
    "print('The precentage of 0 class is: %.2f' % (df['Outcome'].value_counts()[0]/len(df)))\n",
    "         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    500\n",
       "1    268\n",
       "Name: Outcome, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Outcome'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['Outcome'], df['Pregnancies']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "'', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
    "       'BMI', 'DiabetesPedigreeFunction', 'Age'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = df.corr() \n",
    "  \n",
    "f, ax = plt.subplots(figsize =(9, 8)) \n",
    "sns.heatmap(corrmat, annot=True, ax = ax, cmap =\"YlGnBu\", linewidths = 0.1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop('Outcome',axis=1).values\n",
    "y = df['Outcome'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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=0.4,random_state=42, stratify=y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "#Setup arrays to store training and test accuracies\n",
    "neighbors = np.arange(1,9)\n",
    "train_accuracy =np.empty(len(neighbors))\n",
    "test_accuracy = np.empty(len(neighbors))\n",
    "\n",
    "for i,k in enumerate(neighbors):\n",
    "    #Setup a knn classifier with k neighbors\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    \n",
    "    #Fit the model\n",
    "    knn.fit(X_train, y_train)\n",
    "    \n",
    "    #Compute accuracy on the training set\n",
    "    train_accuracy[i] = knn.score(X_train, y_train)\n",
    "    \n",
    "    #Compute accuracy on the test set\n",
    "    test_accuracy[i] = knn.score(X_test, y_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('k-NN Varying number of neighbors')\n",
    "plt.plot(neighbors, test_accuracy, label='Testing Accuracy')\n",
    "plt.plot(neighbors, train_accuracy, label='Training accuracy')\n",
    "plt.legend()\n",
    "plt.xlabel('Number of neighbors')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# maximum testing accuracy for k=7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=7, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=7)\n",
    "knn.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7305194805194806"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "y_pred = knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0,\n",
       "       1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0,\n",
       "       1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
       "       0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
       "       1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1,\n",
       "       0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[165,  36],\n",
       "       [ 47,  60]], dtype=int64)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.78      0.82      0.80       201\n",
      "           1       0.62      0.56      0.59       107\n",
      "\n",
      "    accuracy                           0.73       308\n",
      "   macro avg       0.70      0.69      0.70       308\n",
      "weighted avg       0.73      0.73      0.73       308\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_proba = knn.predict_proba(X_test)[:,1]\n",
    "from sklearn.metrics import roc_curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([0,1],[0,1],'k--')\n",
    "plt.plot(fpr,tpr, label='Knn')\n",
    "plt.xlabel('fpr')\n",
    "plt.ylabel('tpr')\n",
    "plt.title('Knn(n_neighbors=7) ROC curve')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7345050448691124"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "roc_auc_score(y_test,y_pred_proba)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## In k-fold cross-validation, the original sample is randomly partitioned into k equal size subsamples. Of the k subsamples, a single subsample is retained as the validation data for testing the model, and the remaining k-1 subsamples are used as training data. The cross-validation process is then repeated k times (the folds), with each of the k subsamples used exactly once as the validation data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "             estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30,\n",
       "                                            metric='minkowski',\n",
       "                                            metric_params=None, n_jobs=None,\n",
       "                                            n_neighbors=5, p=2,\n",
       "                                            weights='uniform'),\n",
       "             iid='warn', n_jobs=None,\n",
       "             param_grid={'n_neighbors': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
       "       35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "#In case of classifier like knn the parameter to be tuned is n_neighbors\n",
    "param_grid = {'n_neighbors':np.arange(1,50)}\n",
    "knn = KNeighborsClassifier()\n",
    "knn_cv= GridSearchCV(knn,param_grid,cv=5)\n",
    "knn_cv.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7578125"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_cv.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_neighbors': 14}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_cv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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