Álgorithm2_STD_DIABETES.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "28dd6c18",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7e11da6f",
   "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",
       "        vertical-align: top;\n",
       "    }\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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\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",
       "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the dataset\n",
    "data = pd.read_csv(\"D:\\\\Data\\diabetes.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dd9b83c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\TejaswiniAnaconda\\lib\\site-packages\\sklearn\\cluster\\_kmeans.py:881: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=3.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# Load the dataset\n",
    "data = pd.read_csv(\"D:\\\\Data\\\\diabetes.csv\")\n",
    "\n",
    "# Assuming you want to use certain columns for clustering, you can select those columns\n",
    "# For example, let's say you want to use columns \"Feature1\" and \"Feature2\"\n",
    "selected_features = data[[\"Pregnancies\",\"Glucose\",\"BloodPressure\",\"SkinThickness\",\"Insulin\",\"BMI\",\"Age\"]]\n",
    "\n",
    "# Initialize an empty list to store the within-cluster sum of squares (WCSS)\n",
    "wcss = []\n",
    "\n",
    "# Try different values of k (number of clusters) and compute WCSS for each k\n",
    "for i in range(1, 11):  # You can choose a different range if needed\n",
    "    kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)\n",
    "    kmeans.fit(selected_features)\n",
    "    wcss.append(kmeans.inertia_)  # inertia_ gives the WCSS value\n",
    "\n",
    "# Plot the Elbow Method graph\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(range(1, 11), wcss, marker='o', linestyle='--')\n",
    "plt.title('Elbow Method')\n",
    "plt.xlabel('Number of Clusters (k)')\n",
    "plt.ylabel('WCSS')\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d83376f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6d0a5e5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "means = data.mean()\n",
    "stds = data.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7b5796d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   3.369578\n",
       "Glucose                      31.972618\n",
       "BloodPressure                19.355807\n",
       "SkinThickness                15.952218\n",
       "Insulin                     115.244002\n",
       "BMI                           7.884160\n",
       "DiabetesPedigreeFunction      0.331329\n",
       "Age                          11.760232\n",
       "Outcome                       0.476951\n",
       "dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "76618b04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standardized Values:\n",
      "0     -0.692891\n",
      "1     -0.692891\n",
      "2     -0.692891\n",
      "3      0.123302\n",
      "4      0.765836\n",
      "         ...   \n",
      "763    0.870031\n",
      "764   -0.692891\n",
      "765    0.279594\n",
      "766   -0.692891\n",
      "767   -0.692891\n",
      "Name: Insulin, Length: 768, dtype: float64\n"
     ]
    }
   ],
   "source": [
    " import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "mean_pl = np.mean(data[\"Insulin\"])\n",
    "std_deviation_pl = np.std(data[\"Insulin\"])\n",
    "\n",
    "standardized_values_pl = (data[\"Insulin\"] - mean_pl) / std_deviation_pl\n",
    "\n",
    "# Print the standardized values\n",
    "print(\"Standardized Values:\")\n",
    "print(standardized_values_pl)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7a8d4838",
   "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": [
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt# Create a histogram for the standardized values\n",
    "\n",
    "median_pl = np.median(data[\"Insulin\"])\n",
    "standardized_median_pl = (median_pl - mean_pl) / std_deviation_pl\n",
    "plt.hist(standardized_values_pl, bins=20, alpha=0.6, color='lightblue', label='Standardized Values', edgecolor='black')\n",
    "\n",
    "plt.axvline(standardized_median_pl, color='blue', linestyle='dashed', linewidth=2, label='Standardized Median')\n",
    "plt.xlabel('Standardized Values')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Standardized Values with Standardized Median for Diabetes Dataset')\n",
    "\n",
    "plt.legend()\n",
    "plt.text(plt.xlim()[0] + 2.05, plt.ylim()[1] * 0.9, f'Median: {(standardized_median_pl):.2f}', color='blue', ha='left', va='top', fontsize=12)\n",
    "\n",
    "plt.grid(True, linestyle='--', alpha=0.7)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5753e56",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0b6276fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Left Selected Values:\n",
      "0      0\n",
      "1      0\n",
      "2      0\n",
      "5      0\n",
      "7      0\n",
      "      ..\n",
      "761    0\n",
      "762    0\n",
      "764    0\n",
      "766    0\n",
      "767    0\n",
      "Name: Insulin, Length: 384, dtype: int64\n",
      "Right Selected Values:\n",
      "3       94\n",
      "4      168\n",
      "6       88\n",
      "8      543\n",
      "13     846\n",
      "      ... \n",
      "751     74\n",
      "753    510\n",
      "755    110\n",
      "763    180\n",
      "765    112\n",
      "Name: Insulin, Length: 384, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "left_mask = standardized_values_pl <= standardized_median_pl\n",
    "left_selected_values = data[\"Insulin\"][left_mask]\n",
    "\n",
    "right_mask = standardized_values_pl > standardized_median_pl\n",
    "right_selected_values = data[\"Insulin\"][right_mask]\n",
    "\n",
    "print(\"Left Selected Values:\")\n",
    "print(left_selected_values)\n",
    "print(\"Right Selected Values:\")\n",
    "print(right_selected_values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "38b3745b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Left 75%:\n",
      "164    29\n",
      "294    25\n",
      "96     23\n",
      "30     23\n",
      "352    22\n",
      "       ..\n",
      "184     0\n",
      "183     0\n",
      "170     0\n",
      "182     0\n",
      "180     0\n",
      "Name: Insulin, Length: 326, dtype: int64\n",
      "Right 75%:\n",
      "107     32\n",
      "21      36\n",
      "47      36\n",
      "236     36\n",
      "50      37\n",
      "      ... \n",
      "9      235\n",
      "231    237\n",
      "342    240\n",
      "19     240\n",
      "14     245\n",
      "Name: Insulin, Length: 326, dtype: int64\n",
      "326\n",
      "326\n",
      "Selected Left Rows:\n",
      "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
      "164            0      131             88              0        0  31.6   \n",
      "294            0      161             50              0        0  21.9   \n",
      "96             2       92             62             28        0  31.6   \n",
      "30             5      109             75             26        0  36.0   \n",
      "352            3       61             82             28        0  34.4   \n",
      "..           ...      ...            ...            ...      ...   ...   \n",
      "184            4      141             74              0        0  27.6   \n",
      "183            5       73             60              0        0  26.8   \n",
      "170            6      102             82              0        0  30.8   \n",
      "182            1        0             74             20       23  27.7   \n",
      "180            6       87             80              0        0  23.2   \n",
      "\n",
      "     DiabetesPedigreeFunction  Age  Outcome  \n",
      "164                     0.743   32        1  \n",
      "294                     0.254   65        0  \n",
      "96                      0.130   24        0  \n",
      "30                      0.546   60        0  \n",
      "352                     0.243   46        0  \n",
      "..                        ...  ...      ...  \n",
      "184                     0.244   40        0  \n",
      "183                     0.268   27        0  \n",
      "170                     0.180   36        1  \n",
      "182                     0.299   21        0  \n",
      "180                     0.084   32        0  \n",
      "\n",
      "[326 rows x 9 columns]\n",
      "Selected Right Rows:\n",
      "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
      "107            4      144             58             28      140  29.5   \n",
      "21             8       99             84              0        0  35.4   \n",
      "47             2       71             70             27        0  28.0   \n",
      "236            7      181             84             21      192  35.9   \n",
      "50             1      103             80             11       82  19.4   \n",
      "..           ...      ...            ...            ...      ...   ...   \n",
      "9              8      125             96              0        0   0.0   \n",
      "231            6      134             80             37      370  46.2   \n",
      "342            1        0             68             35        0  32.0   \n",
      "19             1      115             70             30       96  34.6   \n",
      "14             5      166             72             19      175  25.8   \n",
      "\n",
      "     DiabetesPedigreeFunction  Age  Outcome  \n",
      "107                     0.287   37        0  \n",
      "21                      0.388   50        0  \n",
      "47                      0.586   22        0  \n",
      "236                     0.586   51        1  \n",
      "50                      0.491   22        0  \n",
      "..                        ...  ...      ...  \n",
      "9                       0.232   54        1  \n",
      "231                     0.238   46        1  \n",
      "342                     0.389   22        0  \n",
      "19                      0.529   32        1  \n",
      "14                      0.587   51        1  \n",
      "\n",
      "[326 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "left_diff = np.abs(left_selected_values - median_pl)\n",
    "right_diff = np.abs(right_selected_values - median_pl)\n",
    "\n",
    "left_sorted = left_selected_values.reset_index(drop=True)[np.argsort(left_diff)]\n",
    "right_sorted = right_selected_values.reset_index(drop=True)[np.argsort(right_diff)]\n",
    "\n",
    "\n",
    "num_values = len(left_selected_values)\n",
    "left_75_percent = left_sorted[:int(0.85 * num_values)]\n",
    "right_75_percent = right_sorted[:int(0.85 * num_values)]\n",
    "\n",
    "print(\"Left 75%:\")\n",
    "print(left_75_percent)\n",
    "print(\"Right 75%:\")\n",
    "print(right_75_percent)\n",
    "print(len(right_75_percent))\n",
    "print(len(left_75_percent))\n",
    "\n",
    "selected_left_rows = data.loc[left_75_percent.index]\n",
    "selected_right_rows = data.loc[right_75_percent.index]\n",
    "\n",
    "# Print the selected rows\n",
    "print(\"Selected Left Rows:\")\n",
    "print(selected_left_rows)\n",
    "print(\"Selected Right Rows:\")\n",
    "print(selected_right_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1257683a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combined Dataset:\n",
      "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
      "164            0      131             88              0        0  31.6   \n",
      "294            0      161             50              0        0  21.9   \n",
      "96             2       92             62             28        0  31.6   \n",
      "30             5      109             75             26        0  36.0   \n",
      "352            3       61             82             28        0  34.4   \n",
      "..           ...      ...            ...            ...      ...   ...   \n",
      "9              8      125             96              0        0   0.0   \n",
      "231            6      134             80             37      370  46.2   \n",
      "342            1        0             68             35        0  32.0   \n",
      "19             1      115             70             30       96  34.6   \n",
      "14             5      166             72             19      175  25.8   \n",
      "\n",
      "     DiabetesPedigreeFunction  Age  Outcome  \n",
      "164                     0.743   32        1  \n",
      "294                     0.254   65        0  \n",
      "96                      0.130   24        0  \n",
      "30                      0.546   60        0  \n",
      "352                     0.243   46        0  \n",
      "..                        ...  ...      ...  \n",
      "9                       0.232   54        1  \n",
      "231                     0.238   46        1  \n",
      "342                     0.389   22        0  \n",
      "19                      0.529   32        1  \n",
      "14                      0.587   51        1  \n",
      "\n",
      "[652 rows x 9 columns]\n"
     ]
    }
   ],
   "source": [
    "combined_dataset = pd.concat([selected_left_rows, selected_right_rows], axis=0)\n",
    "\n",
    "print(\"Combined Dataset:\")\n",
    "print(combined_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9b363c81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combined Dataset without 'variety' column:\n",
      "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
      "164            0      131             88              0        0  31.6   \n",
      "294            0      161             50              0        0  21.9   \n",
      "96             2       92             62             28        0  31.6   \n",
      "30             5      109             75             26        0  36.0   \n",
      "352            3       61             82             28        0  34.4   \n",
      "..           ...      ...            ...            ...      ...   ...   \n",
      "9              8      125             96              0        0   0.0   \n",
      "231            6      134             80             37      370  46.2   \n",
      "342            1        0             68             35        0  32.0   \n",
      "19             1      115             70             30       96  34.6   \n",
      "14             5      166             72             19      175  25.8   \n",
      "\n",
      "     DiabetesPedigreeFunction  Age  \n",
      "164                     0.743   32  \n",
      "294                     0.254   65  \n",
      "96                      0.130   24  \n",
      "30                      0.546   60  \n",
      "352                     0.243   46  \n",
      "..                        ...  ...  \n",
      "9                       0.232   54  \n",
      "231                     0.238   46  \n",
      "342                     0.389   22  \n",
      "19                      0.529   32  \n",
      "14                      0.587   51  \n",
      "\n",
      "[652 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "combined_dataset_without_variety = combined_dataset.drop(columns=[\"Outcome\"])\n",
    "\n",
    "# Print the modified dataset\n",
    "print(\"Combined Dataset without 'variety' column:\")\n",
    "print(combined_dataset_without_variety)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "84472518",
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_dataset_without_variety=combined_dataset_without_variety.drop(columns=[\"DiabetesPedigreeFunction\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "283c80fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Silhouette Score for 2 clusters: 0.61\n",
      "Cluster Labels: [0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0\n",
      " 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 1 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 0\n",
      " 1 0 0 0 0 0 0 0 0 1 1 1 0 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 1 0 0 1 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 0 1 0 0 0 1\n",
      " 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0\n",
      " 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0\n",
      " 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 1\n",
      " 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 1 0 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 1 1 1 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 1 1 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 1]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "\n",
    "k = 2\n",
    "\n",
    "kmeans = KMeans(n_clusters=k, random_state=0)\n",
    "\n",
    "kmeans.fit(combined_dataset_without_variety)\n",
    "clusters = kmeans.predict(combined_dataset_without_variety)\n",
    "silhouette_avg = silhouette_score(combined_dataset_without_variety, clusters)\n",
    "print(\"Silhouette Score for {} clusters: {:.2f}\".format(k, silhouette_avg))\n",
    "print(\"Cluster Labels:\", clusters)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "6d4eb8f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d3c9c0bd",
   "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": [
    "# Access columns by their names\n",
    "feature1 = combined_dataset_without_variety['Insulin']\n",
    "feature2 = combined_dataset_without_variety['Glucose']\n",
    "\n",
    "# Create a scatter plot\n",
    "plt.scatter(feature1, feature2, c=clusters, cmap='viridis')\n",
    "plt.title('K-Means Clustering')\n",
    "plt.xlabel('Insulin')\n",
    "plt.ylabel('Glucose')\n",
    "plt.show()\n",
    "\n",
    "# Create a scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bcb3c3d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# K MEANS DIABETES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0c654c2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8541021b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers deleted for k=2: 116\n",
      "Number of outliers deleted for k=3: 116\n",
      "Number of outliers deleted for k=4: 116\n",
      "Number of outliers deleted for k=5: 116\n",
      "Number of outliers deleted for k=6: 116\n",
      "Number of outliers deleted for k=7: 116\n",
      "Number of outliers deleted for k=8: 116\n",
      "Number of outliers deleted for k=9: 116\n",
      "Number of outliers deleted for k=10: 116\n",
      "Silhouette score for k=2: 0.1956540901138544\n",
      "Silhouette score for k=3: 0.17954310409084448\n",
      "Silhouette score for k=4: 0.19445228353012212\n",
      "Silhouette score for k=5: 0.16409270998945927\n",
      "Silhouette score for k=6: 0.1688296160416177\n",
      "Silhouette score for k=7: 0.1696266555507008\n",
      "Silhouette score for k=8: 0.1617625301829036\n",
      "Silhouette score for k=9: 0.1655890769386893\n",
      "Silhouette score for k=10: 0.1630492369096289\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.metrics import silhouette_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load the diabetes dataset\n",
    "data = pd.read_csv(\"D:\\\\Data\\\\diabetes.csv\")\n",
    "\n",
    "# Drop the \"Outcome\" column\n",
    "data = data.drop(columns=[\"Outcome\"])\n",
    "\n",
    "# Standardize the data\n",
    "scaler = StandardScaler()\n",
    "scaled_data = scaler.fit_transform(data)\n",
    "\n",
    "# Define a range of k values for K-Means\n",
    "k_values = range(2, 11)\n",
    "\n",
    "# Initialize variables to store outlier counts and silhouette scores\n",
    "outlier_counts = []\n",
    "silhouette_scores = []\n",
    "\n",
    "# Perform K-Means clustering for each k value\n",
    "for k in k_values:\n",
    "    kmeans = KMeans(n_clusters=k, random_state=42)\n",
    "    cluster_labels = kmeans.fit_predict(scaled_data)\n",
    "    \n",
    "    # Detect and count outliers using the Mahalanobis distance method\n",
    "    cluster_centers = kmeans.cluster_centers_\n",
    "    mahalanobis_distances = np.sqrt(np.sum(((scaled_data - cluster_centers[cluster_labels]) ** 2), axis=1))\n",
    "    threshold = np.percentile(mahalanobis_distances, 85)  # Define a threshold for outliers (e.g., 95th percentile)\n",
    "    outliers = np.where(mahalanobis_distances > threshold)[0]\n",
    "    outlier_counts.append(len(outliers))\n",
    "    \n",
    "    # Calculate silhouette score\n",
    "    silhouette_avg = silhouette_score(scaled_data, cluster_labels)\n",
    "    silhouette_scores.append(silhouette_avg)\n",
    "\n",
    "# Print the number of outliers deleted for each k\n",
    "for k, outliers_deleted in zip(k_values, outlier_counts):\n",
    "    print(f'Number of outliers deleted for k={k}: {outliers_deleted}')\n",
    "\n",
    "# Print the silhouette scores for each k\n",
    "for k, silhouette_avg in zip(k_values, silhouette_scores):\n",
    "    print(f'Silhouette score for k={k}: {silhouette_avg}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4660152",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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