Supervised_Module2_Diabetes.ipynb

{
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  {
   "cell_type": "code",
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    {
     "data": {
      "text/html": [
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       "</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",
       "      <th>Unnamed: 9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>137.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0          6.0    148.0           72.0           35.0      0.0  33.6   \n",
       "1          1.0     85.0           66.0           29.0      0.0  26.6   \n",
       "2          8.0    183.0           64.0            0.0      0.0  23.3   \n",
       "3          1.0     89.0           66.0           23.0     94.0  28.1   \n",
       "4          0.0    137.0           40.0           35.0    168.0  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction   Age  Outcome  Unnamed: 9  \n",
       "0                     0.627  50.0      1.0         NaN  \n",
       "1                     0.351  31.0      0.0         NaN  \n",
       "2                     0.672  32.0      1.0         NaN  \n",
       "3                     0.167  21.0      0.0         NaN  \n",
       "4                     2.288  33.0      1.0         NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dataset = pd.read_csv('C:\\\\Users\\\\lenovo\\\\Downloads\\\\diabetes.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "dataset.fillna(0, inplace=True)\n",
    "#data = dataset.drop['Outc']\n",
    "data = dataset.values.tolist()\n",
    "#print(data)\n",
    "#dataset['Outcome'].fillna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "  2.600e+01 1.000e+00 0.000e+00]\n",
      " [2.000e+00 1.120e+02 6.600e+01 2.200e+01 0.000e+00 2.500e+01 3.070e-01\n",
      "  2.400e+01 0.000e+00 0.000e+00]\n",
      " [3.000e+00 1.130e+02 4.400e+01 1.300e+01 0.000e+00 2.240e+01 1.400e-01\n",
      "  2.200e+01 0.000e+00 0.000e+00]\n",
      " [2.000e+00 7.400e+01 0.000e+00 0.000e+00 0.000e+00 0.000e+00 1.020e-01\n",
      "  2.200e+01 0.000e+00 0.000e+00]\n",
      " [7.000e+00 8.300e+01 7.800e+01 2.600e+01 7.100e+01 2.930e+01 7.670e-01\n",
      "  3.600e+01 0.000e+00 0.000e+00]\n",
      " [0.000e+00 1.010e+02 6.500e+01 2.800e+01 0.000e+00 2.460e+01 2.370e-01\n",
      "  2.200e+01 0.000e+00 0.000e+00]\n",
      " [5.000e+00 1.370e+02 1.080e+02 0.000e+00 0.000e+00 4.880e+01 2.270e-01\n",
      "  3.700e+01 1.000e+00 0.000e+00]\n",
      " [2.000e+00 1.100e+02 7.400e+01 2.900e+01 1.250e+02 3.240e+01 6.980e-01\n",
      "  2.700e+01 0.000e+00 0.000e+00]\n",
      " [1.300e+01 1.060e+02 7.200e+01 5.400e+01 0.000e+00 3.660e+01 1.780e-01\n",
      "  4.500e+01 0.000e+00 0.000e+00]\n",
      " [2.000e+00 1.000e+02 6.800e+01 2.500e+01 7.100e+01 3.850e+01 3.240e-01\n",
      "  2.600e+01 0.000e+00 0.000e+00]\n",
      " [1.500e+01 1.360e+02 7.000e+01 3.200e+01 1.100e+02 3.710e+01 1.530e-01\n",
      "  4.300e+01 1.000e+00 0.000e+00]\n",
      " [1.000e+00 1.070e+02 6.800e+01 1.900e+01 0.000e+00 2.650e+01 1.650e-01\n",
      "  2.400e+01 0.000e+00 0.000e+00]\n",
      " [1.000e+00 8.000e+01 5.500e+01 0.000e+00 0.000e+00 1.910e+01 2.580e-01\n",
      "  2.100e+01 0.000e+00 0.000e+00]\n",
      " [4.000e+00 1.230e+02 8.000e+01 1.500e+01 1.760e+02 3.200e+01 4.430e-01\n",
      "  3.400e+01 0.000e+00 0.000e+00]\n",
      " [7.000e+00 8.100e+01 7.800e+01 4.000e+01 4.800e+01 4.670e+01 2.610e-01\n",
      "  4.200e+01 0.000e+00 0.000e+00]\n",
      " [4.000e+00 1.340e+02 7.200e+01 0.000e+00 0.000e+00 2.380e+01 2.770e-01\n",
      "  6.000e+01 1.000e+00 0.000e+00]\n",
      " [2.000e+00 1.420e+02 8.200e+01 1.800e+01 6.400e+01 2.470e+01 7.610e-01\n",
      "  2.100e+01 0.000e+00 0.000e+00]\n",
      " [6.000e+00 1.440e+02 7.200e+01 2.700e+01 2.280e+02 3.390e+01 2.550e-01\n",
      "  4.000e+01 0.000e+00 0.000e+00]\n",
      " [2.000e+00 9.200e+01 6.200e+01 2.800e+01 0.000e+00 3.160e+01 1.300e-01\n",
      "  2.400e+01 0.000e+00 0.000e+00]\n",
      " [1.000e+00 7.100e+01 4.800e+01 1.800e+01 7.600e+01 2.040e+01 3.230e-01\n",
      "  2.200e+01 0.000e+00 0.000e+00]\n",
      " [6.000e+00 9.300e+01 5.000e+01 3.000e+01 6.400e+01 2.870e+01 3.560e-01\n",
      "  2.300e+01 0.000e+00 0.000e+00]\n",
      " [0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00 0.000e+00\n",
      "  0.000e+00 0.000e+00 0.000e+00]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "df = np.asarray(data)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 10) (70,) (30,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "df_train, df_test, dftarget_train, dftarget_test = train_test_split(df, dataset.Outcome, test_size=0.3)\n",
    "print(df_train.shape, dftarget_train.shape, dftarget_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57    0.0\n",
      "98    0.0\n",
      "52    0.0\n",
      "67    0.0\n",
      "21    0.0\n",
      "56    1.0\n",
      "19    1.0\n",
      "82    0.0\n",
      "78    1.0\n",
      "6     1.0\n",
      "8     1.0\n",
      "93    1.0\n",
      "77    0.0\n",
      "17    1.0\n",
      "86    0.0\n",
      "50    0.0\n",
      "33    0.0\n",
      "18    0.0\n",
      "41    0.0\n",
      "35    0.0\n",
      "96    0.0\n",
      "91    0.0\n",
      "45    1.0\n",
      "92    0.0\n",
      "0     1.0\n",
      "38    1.0\n",
      "53    1.0\n",
      "62    0.0\n",
      "88    1.0\n",
      "25    1.0\n",
      "Name: Outcome, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "import numpy as np\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "scaler.fit(df_train)\n",
    "df_train =scaler.transform(df_train)\n",
    "df_test =scaler.transform(df_test)\n",
    "#dftarget_train = scaler.transform(dftarget_train)\n",
    "#print(df_train)\n",
    "\n",
    "#dftarget_train= dftarget_train[np.logical_not(np.isnan(dftarget_train))]\n",
    "print(dftarget_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier  \n",
    "\n",
    "error = []\n",
    "\n",
    "for i in range(1,70):\n",
    "    KNNnmodel_1 = KNeighborsClassifier(n_neighbors = i)\n",
    "    KNNfitted_1 = KNNnmodel_1.fit(df_train, dftarget_train)\n",
    "    KNN_predictions_1 = KNNfitted_1.predict((df_test))\n",
    "    error.append(np.mean(KNN_predictions_1 != dftarget_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 80.0 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "print(\"Accuracy:\",round(metrics.accuracy_score(dftarget_test, KNN_predictions_1),2) * 100, \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[24  0]\n",
      " [ 6  0]]\n",
      "Classification report\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "     class 0       0.80      1.00      0.89        24\n",
      "     class 1       0.00      0.00      0.00         6\n",
      "\n",
      "    accuracy                           0.80        30\n",
      "   macro avg       0.40      0.50      0.44        30\n",
      "weighted avg       0.64      0.80      0.71        30\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\lenovo\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "target_names = ['class 0', 'class 1']\n",
    "\n",
    "print(metrics.confusion_matrix(dftarget_test, KNN_predictions_1))\n",
    "print(\"Classification report\\n\", metrics.classification_report(dftarget_test, KNN_predictions_1,target_names =target_names))    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mean Error')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(range(1,70), error, color='red', linestyle='dashed', marker='o',\n",
    "         markerfacecolor='blue', markersize=10)\n",
    "plt.title('Error Rate K Value')\n",
    "plt.xlabel('K Value')\n",
    "plt.ylabel('Mean Error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True  True  True  True  True  True  True  True  True False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False]\n",
      "0.0\n"
     ]
    }
   ],
   "source": [
    "#for the best range of K\n",
    "print(error == min(error))\n",
    "z= min(error)\n",
    "print(z)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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