diff --git a/Pcos Detection/Dataset/PCOS_infertility.csv b/Pcos Detection/Dataset/PCOS_infertility.csv
new file mode 100644
index 000000000..cc7133e1b
--- /dev/null
+++ b/Pcos Detection/Dataset/PCOS_infertility.csv	
@@ -0,0 +1,542 @@
+Sl. No,Patient File No.,PCOS (Y/N),  I   beta-HCG(mIU/mL),II    beta-HCG(mIU/mL),AMH(ng/mL)
+1,10001,0,1.99,1.99,2.07
+2,10002,0,60.8,1.99,1.53
+3,10003,1,494.08,494.08,6.63
+4,10004,0,1.99,1.99,1.22
+5,10005,0,801.45,801.45,2.26
+6,10006,0,237.97,1.99,6.74
+7,10007,0,1.99,1.99,3.05
+8,10008,0,100.51,100.51,1.54
+9,10009,0,1.99,1.99,1
+10,10010,0,1.99,1.99,1.61
+11,10011,0,158.51,158.51,4.47
+12,10012,0,1.99,1.99,1.67
+13,10013,1,1214.23,1214.23,7.94
+14,10014,0,1.99,1.99,2.38
+15,10015,0,1.99,1.99,0.88
+16,10016,0,1.99,1.99,0.69
+17,10017,0,8104.21,91.55,3.78
+18,10018,0,1.99,1.99,1.92
+19,10019,0,1.99,1.99,1
+20,10020,1,23.58,1.99,2.07
+21,10021,0,1.99,1.99,2.85
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+23,10023,0,218.65,218.65,4.13
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+25,10025,1,610.63,610.63,1.89
+26,10026,0,4490.18,4490.18,0.26
+27,10027,1,1.99,1.99,3.84
+28,10028,0,689.58,11.24,2.5
+29,10029,0,15,15,3.56
+30,10030,1,768.03,768.03,1.56
+31,10031,0,612.32,12,1.69
+32,10032,1,10,10,1.89
+33,10033,0,1.99,1.99,2.34
+34,10034,0,20,20,1.58
+35,10035,0,1277.49,30.66,2.36
+36,10036,0,1455,1455,3.64
+37,10037,0,18,1.99,2.78
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+43,10043,0,10,1.99,3.55
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+47,10047,1,141.06,141.06,3.66
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@@ -0,0 +1,1342 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "c3391a6a-957b-4f2a-9fdf-4fa5441dc14b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd \n",
+    "import seaborn as sns \n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a02a7431-c0b3-4bc8-a68a-7b416e1f153f",
+   "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>Sl. No</th>\n",
+       "      <th>Patient File No.</th>\n",
+       "      <th>PCOS (Y/N)</th>\n",
+       "      <th>I   beta-HCG(mIU/mL)</th>\n",
+       "      <th>II    beta-HCG(mIU/mL)</th>\n",
+       "      <th>AMH(ng/mL)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>2.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>10002</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60.80</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>1.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>10003</td>\n",
+       "      <td>1</td>\n",
+       "      <td>494.08</td>\n",
+       "      <td>494.08</td>\n",
+       "      <td>6.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>10004</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>1.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>10005</td>\n",
+       "      <td>0</td>\n",
+       "      <td>801.45</td>\n",
+       "      <td>801.45</td>\n",
+       "      <td>2.26</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>536</th>\n",
+       "      <td>537</td>\n",
+       "      <td>10537</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>1.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>537</th>\n",
+       "      <td>538</td>\n",
+       "      <td>10538</td>\n",
+       "      <td>0</td>\n",
+       "      <td>80.13</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>5.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>538</th>\n",
+       "      <td>539</td>\n",
+       "      <td>10539</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>3.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>539</th>\n",
+       "      <td>540</td>\n",
+       "      <td>10540</td>\n",
+       "      <td>0</td>\n",
+       "      <td>292.92</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>5.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>540</th>\n",
+       "      <td>541</td>\n",
+       "      <td>10541</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>541 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Sl. No  Patient File No.  PCOS (Y/N)    I   beta-HCG(mIU/mL)  \\\n",
+       "0         1             10001           0                    1.99   \n",
+       "1         2             10002           0                   60.80   \n",
+       "2         3             10003           1                  494.08   \n",
+       "3         4             10004           0                    1.99   \n",
+       "4         5             10005           0                  801.45   \n",
+       "..      ...               ...         ...                     ...   \n",
+       "536     537             10537           0                    1.99   \n",
+       "537     538             10538           0                   80.13   \n",
+       "538     539             10539           0                    1.99   \n",
+       "539     540             10540           0                  292.92   \n",
+       "540     541             10541           1                    1.99   \n",
+       "\n",
+       "     II    beta-HCG(mIU/mL) AMH(ng/mL)  \n",
+       "0                      1.99       2.07  \n",
+       "1                      1.99       1.53  \n",
+       "2                    494.08       6.63  \n",
+       "3                      1.99       1.22  \n",
+       "4                    801.45       2.26  \n",
+       "..                      ...        ...  \n",
+       "536                    1.99        1.7  \n",
+       "537                    1.99        5.6  \n",
+       "538                    1.99        3.7  \n",
+       "539                    1.99        5.2  \n",
+       "540                    1.99         20  \n",
+       "\n",
+       "[541 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset = pd.read_csv(r\"C:\\Users\\KIIT\\Downloads\\PCOS_infertility.csv\") \n",
+    "dataset.head(541)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0b7230d4-fd7f-4593-8b24-61ee63e1267f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "541"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2ecbdf50-6f7a-4a31-bd3d-ca5f88122847",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sl. No                    0\n",
+       "Patient File No.          0\n",
+       "PCOS (Y/N)                0\n",
+       "  I   beta-HCG(mIU/mL)    0\n",
+       "II    beta-HCG(mIU/mL)    0\n",
+       "AMH(ng/mL)                0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1cdbe389-3d6d-4181-837b-4d2e08306ef6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sl. No                    0.0\n",
+       "Patient File No.          0.0\n",
+       "PCOS (Y/N)                0.0\n",
+       "  I   beta-HCG(mIU/mL)    0.0\n",
+       "II    beta-HCG(mIU/mL)    0.0\n",
+       "AMH(ng/mL)                0.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.isnull().sum()/dataset.shape[0]*100\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "aab2fea5-ceb7-4aa6-b42c-50c0d987b16e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.isnull().sum().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3e0d840f-0d3d-417f-a25b-131157966495",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(dataset.isnull())\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "a64d1972-89ff-4006-9953-3ac7567b20a3",
+   "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>Sl. No</th>\n",
+       "      <th>Patient File No.</th>\n",
+       "      <th>PCOS (Y/N)</th>\n",
+       "      <th>I   beta-HCG(mIU/mL)</th>\n",
+       "      <th>II    beta-HCG(mIU/mL)</th>\n",
+       "      <th>AMH(ng/mL)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>10001</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>2.07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>10002</td>\n",
+       "      <td>0</td>\n",
+       "      <td>60.80</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>1.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>10003</td>\n",
+       "      <td>1</td>\n",
+       "      <td>494.08</td>\n",
+       "      <td>494.08</td>\n",
+       "      <td>6.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>10004</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>1.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>10005</td>\n",
+       "      <td>0</td>\n",
+       "      <td>801.45</td>\n",
+       "      <td>801.45</td>\n",
+       "      <td>2.26</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>536</th>\n",
+       "      <td>537</td>\n",
+       "      <td>10537</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>1.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>537</th>\n",
+       "      <td>538</td>\n",
+       "      <td>10538</td>\n",
+       "      <td>0</td>\n",
+       "      <td>80.13</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>5.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>538</th>\n",
+       "      <td>539</td>\n",
+       "      <td>10539</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>3.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>539</th>\n",
+       "      <td>540</td>\n",
+       "      <td>10540</td>\n",
+       "      <td>0</td>\n",
+       "      <td>292.92</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>5.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>540</th>\n",
+       "      <td>541</td>\n",
+       "      <td>10541</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>1.99</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>541 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Sl. No  Patient File No.  PCOS (Y/N)    I   beta-HCG(mIU/mL)  \\\n",
+       "0         1             10001           0                    1.99   \n",
+       "1         2             10002           0                   60.80   \n",
+       "2         3             10003           1                  494.08   \n",
+       "3         4             10004           0                    1.99   \n",
+       "4         5             10005           0                  801.45   \n",
+       "..      ...               ...         ...                     ...   \n",
+       "536     537             10537           0                    1.99   \n",
+       "537     538             10538           0                   80.13   \n",
+       "538     539             10539           0                    1.99   \n",
+       "539     540             10540           0                  292.92   \n",
+       "540     541             10541           1                    1.99   \n",
+       "\n",
+       "     II    beta-HCG(mIU/mL) AMH(ng/mL)  \n",
+       "0                      1.99       2.07  \n",
+       "1                      1.99       1.53  \n",
+       "2                    494.08       6.63  \n",
+       "3                      1.99       1.22  \n",
+       "4                    801.45       2.26  \n",
+       "..                      ...        ...  \n",
+       "536                    1.99        1.7  \n",
+       "537                    1.99        5.6  \n",
+       "538                    1.99        3.7  \n",
+       "539                    1.99        5.2  \n",
+       "540                    1.99         20  \n",
+       "\n",
+       "[541 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset .drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "96c5414a-a9d8-4da1-8c9b-5565d8dd7e1b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(541, 6)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "535ed71e-a03d-475f-9f38-05f9e345d74b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(dataset.isnull())\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c4294499-796a-4957-abbb-d5c63a5dc58d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = dataset.drop('PCOS (Y/N)', axis=1)\n",
+    "y = dataset['PCOS (Y/N)']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "38fc6f6e-fc96-4a44-9dbb-03580b7e7ec0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "d655285b-07ca-4a1a-aad0-d66da4005ea9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Non-numeric entries in 'AMH(ng/mL)':\n",
+      "     Sl. No  Patient File No.  PCOS (Y/N)    I   beta-HCG(mIU/mL)  \\\n",
+      "305     306             10306           0                    42.0   \n",
+      "\n",
+      "     II    beta-HCG(mIU/mL) AMH(ng/mL)  \n",
+      "305                    1.99          a  \n"
+     ]
+    }
+   ],
+   "source": [
+    "non_numeric_amh = dataset[~dataset['AMH(ng/mL)'].apply(lambda x: pd.to_numeric(x, errors='coerce')).notnull()]\n",
+    "print(\"Non-numeric entries in 'AMH(ng/mL)':\")\n",
+    "print(non_numeric_amh)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "296abc53-56e4-40ca-9526-44daf3764bc3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert the 'AMH(ng/mL)' column to numeric, coercing errors to NaN\n",
+    "dataset['AMH(ng/mL)'] = pd.to_numeric(dataset['AMH(ng/mL)'], errors='coerce')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "1088716d-a31a-4a55-9058-38f87831e02e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = dataset.dropna(subset=['AMH(ng/mL)'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "9b03db19-5230-4dbc-b5bb-af458d0bb419",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Selecting features (excluding target variable 'PCOS (Y/N)' and other non-numeric columns if any)\n",
+    "features = dataset.drop(columns=['Sl. No', 'Patient File No.', 'PCOS (Y/N)'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "92a39f52-ef95-4db2-bd93-7fce84d07e4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize the scaler\n",
+    "scaler = StandardScaler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "10981a8c-dd95-4430-bafe-048f1b05f259",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fit and transform the features\n",
+    "scaled_features = scaler.fit_transform(features)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "d77c2ef8-5cbe-463f-b9b5-d90ce46bf0b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert the scaled features back to a DataFrame\n",
+    "scaled_features_df = pd.DataFrame(scaled_features, columns=features.columns)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "7d6688cc-b5e5-4e41-b419-eb47d178e7e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Handle any remaining infinite values in the scaled data\n",
+    "scaled_features_df.replace([float('inf'), float('-inf')], float('nan'), inplace=True)\n",
+    "scaled_features_df.dropna(inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "55ee6e99-630b-45b2-ad8f-19d9e2883a2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add the target variable back to the DataFrame\n",
+    "scaled_data = pd.concat([scaled_features_df, dataset['PCOS (Y/N)'].reset_index(drop=True)], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "30b27b25-a550-4f41-8ac7-69fe34ef87d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scaled data:\n",
+      "     I   beta-HCG(mIU/mL)  II    beta-HCG(mIU/mL)  AMH(ng/mL)  PCOS (Y/N)\n",
+      "0               -0.198193               -0.147573   -0.604849           0\n",
+      "1               -0.180632               -0.147573   -0.696746           0\n",
+      "2               -0.051249                0.159255    0.171168           1\n",
+      "3               -0.198193               -0.147573   -0.749502           0\n",
+      "4                0.040536                0.350907   -0.572515           0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Display the first few rows of the scaled dataset\n",
+    "print(\"Scaled data:\")\n",
+    "print(scaled_data.head())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "c8986818-4fec-4875-8404-8df2617599ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x600 with 0 Axes>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x600 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot histograms of original and scaled features\n",
+    "plt.figure(figsize=(14, 6))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "af15c28a-486f-491a-97f3-ff68009efe73",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\KIIT\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "C:\\Users\\KIIT\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "C:\\Users\\KIIT\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "C:\\Users\\KIIT\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "C:\\Users\\KIIT\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n",
+      "C:\\Users\\KIIT\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
+      "  with pd.option_context('mode.use_inf_as_na', True):\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplot(1, 2, 1)\n",
+    "plt.title(\"Original Data\")\n",
+    "for column in features.columns:\n",
+    "    sns.histplot(dataset[column], kde=True, label=column, element=\"step\", stat=\"density\", common_norm=False)\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.title(\"Scaled Data\")\n",
+    "for column in scaled_features_df.columns:\n",
+    "    sns.histplot(scaled_features_df[column], kde=True, label=column, element=\"step\", stat=\"density\", common_norm=False)\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "d3ef22c3-1f68-4830-90e1-6ec8a2c1eeaa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot box plots of original and scaled features\n",
+    "plt.figure(figsize=(14, 6))\n",
+    "\n",
+    "plt.subplot(1, 2, 1)\n",
+    "plt.title(\"Original Data\")\n",
+    "sns.boxplot(data=features)\n",
+    "plt.xticks(rotation=90)\n",
+    "\n",
+    "plt.subplot(1, 2, 2)\n",
+    "plt.title(\"Scaled Data\")\n",
+    "sns.boxplot(data=scaled_features_df)\n",
+    "plt.xticks(rotation=90)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "ab2c6b97-74c6-41c6-90ee-05007b4c4146",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "c276968a-b4e6-4101-90d6-bf3cbec776ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Selecting features and target variable\n",
+    "features = dataset.drop(columns=['Sl. No', 'Patient File No.', 'PCOS (Y/N)'])\n",
+    "target = dataset['PCOS (Y/N)']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "a8d7e791-857d-44d9-ae84-d488963a6657",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scale the features\n",
+    "scaler = StandardScaler()\n",
+    "scaled_features = scaler.fit_transform(features)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "635332f5-82e8-45e5-a495-770b62ec35b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split the data into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(scaled_features, target, test_size=0.2, random_state=42)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "edffdf3a-de1c-450f-ba64-10c080768406",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the classifiers\n",
+    "classifiers = {\n",
+    "    'Logistic Regression': LogisticRegression(),\n",
+    "    'Decision Tree': DecisionTreeClassifier(),\n",
+    "    'Random Forest': RandomForestClassifier(),\n",
+    "    'Gradient Boosting': GradientBoostingClassifier(),\n",
+    "    'AdaBoost': AdaBoostClassifier(),\n",
+    "    'Naive Bayes': GaussianNB(),\n",
+    "    'Support Vector Machine': SVC(),\n",
+    "    'K-Nearest Neighbors': KNeighborsClassifier()\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "16f975d8-89f2-4dfc-bf5a-3bfff7e10aa2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Train, predict and evaluate each classifier\n",
+    "results = []\n",
+    "\n",
+    "for name, clf in classifiers.items():\n",
+    "    clf.fit(X_train, y_train)\n",
+    "    y_pred = clf.predict(X_test)\n",
+    "    accuracy = accuracy_score(y_test, y_pred)\n",
+    "    precision = precision_score(y_test, y_pred)\n",
+    "    recall = recall_score(y_test, y_pred)\n",
+    "    f1 = f1_score(y_test, y_pred)\n",
+    "    \n",
+    "    results.append({\n",
+    "        'Classifier': name,\n",
+    "        'Accuracy': accuracy,\n",
+    "        'Precision': precision,\n",
+    "        'Recall': recall,\n",
+    "        'F1 Score': f1\n",
+    "    })"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "bf51115c-56f7-46d8-9bd3-bfc768f8b9a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "               Classifier  Accuracy  Precision    Recall  F1 Score\n",
+      "0     Logistic Regression  0.703704   0.555556  0.151515  0.238095\n",
+      "1           Decision Tree  0.574074   0.303030  0.303030  0.303030\n",
+      "2           Random Forest  0.638889   0.384615  0.303030  0.338983\n",
+      "3       Gradient Boosting  0.648148   0.380952  0.242424  0.296296\n",
+      "4                AdaBoost  0.648148   0.352941  0.181818  0.240000\n",
+      "5             Naive Bayes  0.703704   0.538462  0.212121  0.304348\n",
+      "6  Support Vector Machine  0.685185   0.428571  0.090909  0.150000\n",
+      "7     K-Nearest Neighbors  0.648148   0.333333  0.151515  0.208333\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert the results to a DataFrame and print\n",
+    "results_df = pd.DataFrame(results)\n",
+    "print(results_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "36a027c9-cf44-4885-92b8-4d432d7c5974",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the performance metrics\n",
+    "plt.figure(figsize=(14, 8))\n",
+    "metrics = ['Accuracy', 'Precision', 'Recall', 'F1 Score']\n",
+    "\n",
+    "for i, metric in enumerate(metrics, 1):\n",
+    "    plt.subplot(2, 2, i)\n",
+    "    sns.barplot(x='Classifier', y=metric, data=results_df)\n",
+    "    plt.xticks(rotation=45)\n",
+    "    plt.title(f'Classifier {metric}')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "d61d1c9b-c8cf-4d9e-ac54-16bac955cec9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "features = dataset.drop(columns=['Sl. No', 'Patient File No.', 'PCOS (Y/N)'])\n",
+    "target = dataset['PCOS (Y/N)']\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "dadf415b-1c73-4d96-a442-56ad084b2eb6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Combine features and target for correlation analysis\n",
+    "data_for_corr = pd.concat([features, target], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "9b1505c6-edcc-4dc4-bd05-9d09abb5280c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Compute the correlation matrix\n",
+    "corr_matrix = data_for_corr.corr()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "961b06d6-5769-4db6-a544-5c77d1890d56",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the heatmap\n",
+    "plt.figure(figsize=(10, 8))\n",
+    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', linewidths=0.5)\n",
+    "plt.title('Correlation Matrix Heatmap')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "b8062ddb-5651-454a-9ee4-cf7a058562ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from imblearn.over_sampling import SMOTE\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, AdaBoostClassifier\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "96b6cf0d-d471-499a-aff8-328038e2e08d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Separate features and target variable\n",
+    "X = dataset.drop(columns=['PCOS (Y/N)'])\n",
+    "y = dataset['PCOS (Y/N)']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "1a6dc6e7-7228-4f77-b005-c6a8f73fd698",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Split the data into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "04884804-77b8-431a-bc02-df056d4d6bd1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Apply SMOTE to balance the classes in the training data\n",
+    "smote = SMOTE(random_state=42)\n",
+    "X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "a4c97348-c73d-421b-930d-fc14278fdba5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Prepare a list to store the results\n",
+    "results = []\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "1b83198a-b0b9-4297-aa55-6b68ff91ca38",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ixQoVFhaqcePGCgkJUUlJSZljK/oHs6CgoMK2k/+RrujYVq1aeRxf0efk5M9IZd7n0xkwYIDq16+vefPmKSkpSa+//rqGDBmiiIgISf/9fE6fPr3CJ1NdLpdKS0sVGBhYJv30VnW+B1X9s3Ei8dy1a5fXc5x9pWHDhnI4HPrwww/LfdiuvDagupEAWmbcuHEyxuhPf/pTuQ9NHDt2TH//+98lSb169ZKkMhOjc3NztXXrVq+e+PPWiSc/T14U+ERfyhMYGKj4+Hg988wzkqTPPvuswmN79+6tNWvWuAu+E55//nmFhYX5bLmH8847T2PGjFG/fv2UlpZW4XEOh0N16tRRYGCgu624uFgvvPBCmWOrKlUtLS3VwIED5XA43A8KTJ8+XcuWLTvl64KDg9W1a1eP7VRrt/3www/lLvVSWlqqb775RmFhYapXr56kXz8HhYWFHpPkjx49qrfffrvcc7/77rsex5aWlurll19Wy5Yty/yflhdffNHj67Vr12rHjh3u9egSEhIUGhpa5vO+a9cu9xQCb5zJ9yckJESDBg3SqlWrNGXKFB07dsxjSLR79+6qV6+etmzZUua9P7EFBwcrNDRUSUlJevXVV0+ZMlWUaFfVe+CNqv7ZSElJUWBgoGbOnFllfTxTf/jDH2SM0b///e9yv1feTLMAfI0E0DIJCQmaOXOmRo4cqS5duuiuu+7SxRdfrGPHjmnjxo2aM2eO2rdvr379+qlt27a64447NH36dAUEBKhv377avn27Hn74YTVt2rTMU29n4+qrr1Z0dLSGDx+uRx99VHXq1NGCBQv0/fffexw3a9YsrVmzRqmpqWrWrJmOHDnifprwqquuqvD8EydO1Ouvv66ePXtqwoQJio6O1osvvqh//OMfysrKUlRUVJXdy8kmT5582mNSU1M1depUDRo0SHfccYf27dunJ554otykoEOHDlqyZIlefvlltWjRQiEhIWf0D8rEiRP14YcfatWqVYqJidH999+vnJwcDR8+XJ07d1ZcXFylz1meF154QbNnz9agQYPUrVs3RUVFadeuXXr22WeVl5enCRMmKDg4WNKva8RNmDBBN998s8aMGaMjR47o6aefrvDp64YNG6pXr156+OGHFR4erhkzZuhf//pXmaVgJGnDhg26/fbbdcMNN+j777/X+PHjdd5552nkyJGSpHr16unhhx/WQw89pCFDhmjgwIHat2+fHnnkEYWEhGjixIle3W+HDh20bNkyzZw5U126dFFAQIC6du162tcNHz5czzzzjKZOnaoLL7xQiYmJ7n0RERGaPn260tLStH//fl1//fVq3Lix9uzZo88//1x79uxxFz5Tp07VFVdcofj4eP3lL39Rq1at9MMPP2jlypWaPXu2IiMj3X8JY86cOYqMjFRISIji4uLUoEGDKnkPvFWVPxsXXHCBHnroIT322GMqLi52L9OyZcsW7d2716snlKtK9+7ddccdd+i2227Thg0bdOWVVyo8PFy7d+/WRx99pA4dOuiuu+6qtv4A5fLrIyjwm02bNpm0tDTTrFkzExwcbMLDw03nzp3NhAkTPJ4eLC0tNVOmTDFt2rQxQUFBpmHDhuaWW24x33//vcf5kpKSzMUXX1zmOuU91alyngI2xphPPvnEJCYmmvDwcHPeeeeZiRMnmmeffdbjKeB169aZP/7xj6Z58+bG6XSaBg0amKSkJLNy5coy1/jtU8DGGLN582bTr18/ExUVZYKDg03Hjh09nvAz5r9Py7766qse7eU9EVie3z7peCrlPek4b94807ZtW+N0Ok2LFi1MZmamee655zzu3xhjtm/fblJSUkxkZKSR5H5/K+r7b/edeOpz1apVJiAgoMx7tG/fPtOsWTPTrVs3U1JScsp78NaWLVvM/fffb7p27WoaNWpk6tSpY+rXr2+SkpLMCy+8UOb4N954w3Tq1MmEhoaaFi1amOzs7AqfAh41apSZMWOGadmypQkKCjIXXnihefHFFz2OO/E9WbVqlbn11ltNvXr13E+6fvPNN2Wu/+yzz5pLLrnEBAcHm6ioKHPttdeavLw8j2PS0tJMeHh4ufe7f/9+c/3115t69eoZh8NR7tPtFencuXO5T5mekJOTY1JTU010dLQJCgoy5513nklNTS3zPd+yZYu54YYbTIMGDUxwcLBp1qyZGTp0qDly5Ij7mGnTppm4uDgTGBhY5rN9tu9BearjZ8MYY55//nnTrVs3ExISYiIiIkznzp097s3b31Wnegp4z5495d7byX2ZN2+eiY+PN+Hh4SY0NNS0bNnSDBkyxGzYsOGU7wFQHRzGnObxPQCogRwOh0aNGqXs7OxTHrdgwQLddtttys3N9SqJAwAbMAcQAADAMhSAAAAAlmEIGAAAwDIkgAAAAJahAAQAALAMBSAAAIBlKAABAAAsUyv/EsiRX/zdAwC+Ur/b3f7uAgAfKd546nU9fSm0s+9+t/jzvipCAggAAGCZWpkAAgAAVIrDrkyMAhAAAMDh8HcPqpVd5S4AAABIAAEAAGwbArbrbgEAAEACCAAAwBxAAAAA1GokgAAAAMwBBAAAQG1GAggAAGDZHEAKQAAAAIaAAQAAUJuRAAIAAFg2BEwCCAAAYBkSQAAAAOYAAgAAoDYjAQQAAGAOIAAAAGozEkAAAADmAAIAAFjG4fDdVgkXXHCBHA5HmW3UqFGSJGOMJk2apNjYWIWGhio5OVl5eXmVvl0KQAAAgBoiNzdXu3fvdm+rV6+WJN1www2SpKysLE2dOlXZ2dnKzc1VTEyM+vTpo0OHDlXqOhSAAAAAjgDfbZXQqFEjxcTEuLfXX39dLVu2VFJSkowxmjZtmsaPH68BAwaoffv2WrhwoQ4fPqzFixdX6joUgAAAAD5UUlKigwcPemwlJSWnfd3Ro0e1aNEiDRs2TA6HQ/n5+SooKFBKSor7GKfTqaSkJK1du7ZSfaIABAAA8GECmJmZqaioKI8tMzPztF1asWKFfvzxRw0dOlSSVFBQIElyuVwex7lcLvc+b/EUMAAAgA+NGzdOo0eP9mhzOp2nfd1zzz2nvn37KjY21qPdcdKDJcaYMm2nQwEIAAAQ4LuFoJ1Op1cF32/t2LFD77zzjpYtW+Zui4mJkfRrEtikSRN3e2FhYZlU8HQYAgYAAKhh5s+fr8aNGys1NdXdFhcXp5iYGPeTwdKv8wRzcnKUmJhYqfOTAAIAANSghaCPHz+u+fPnKy0tTXXq/LdUczgcSk9PV0ZGhlq3bq3WrVsrIyNDYWFhGjRoUKWuQQEIAABQg/4W8DvvvKOdO3dq2LBhZfaNHTtWxcXFGjlypA4cOKD4+HitWrVKkZGRlbqGwxhjqqrDNcWRX/zdAwC+Ur/b3f7uAgAfKd6Y7bdrh/bO8Nm5i999yGfnPlMkgAAAADVoCLg62HW3AAAAIAEEAACoSXMAqwMJIAAAgGVIAAEAAJgDCAAAgNqMBBAAAMCyOYAUgAAAAAwBAwAAoDYjAQQAALBsCJgEEAAAwDIkgAAAAMwBBAAAQG1GAggAAMAcQAAAANRmJIAAAACWzQGkAAQAALCsALTrbgEAAEACCAAAwEMgAAAAqNVIAAEAAJgDCAAAgNqMBBAAAIA5gAAAAKjNSAABAAAsmwNIAQgAAMAQMAAAAGozEkAAAGA9BwkgAAAAajMSQAAAYD0SQAAAANRqJIAAAAB2BYAkgAAAALYhAQQAANazbQ4gBSAAALCebQUgQ8AAAACWIQEEAADWIwEEAABArUYCCAAArEcCCAAAgFqNBBAAAMCuAJAEEAAAwDYkgAAAwHrMAQQAAECtRgIIAACsZ1sCSAEIAACsZ1sByBAwAACAZUgAAQCA9UgAAQAAUKuRAAIAANgVAJIAAgAA2IYEEAAAWI85gAAAAPCbf//737rlllvUoEEDhYWFqVOnTvr000/d+40xmjRpkmJjYxUaGqrk5GTl5eVV6hoUgAAAwHoOh8NnW2UcOHBA3bt3V1BQkN58801t2bJFf/vb31SvXj33MVlZWZo6daqys7OVm5urmJgY9enTR4cOHfL6OgwBAwAA69WUIeApU6aoadOmmj9/vrvtggsucP+3MUbTpk3T+PHjNWDAAEnSwoUL5XK5tHjxYo0YMcKr65AAAgAA+FBJSYkOHjzosZWUlJR77MqVK9W1a1fdcMMNaty4sTp37qy5c+e69+fn56ugoEApKSnuNqfTqaSkJK1du9brPlEAAgAAOHy3ZWZmKioqymPLzMwstxvbtm3TzJkz1bp1a7399tu68847dc899+j555+XJBUUFEiSXC6Xx+tcLpd7nzcYAgYAAPChcePGafTo0R5tTqez3GOPHz+url27KiMjQ5LUuXNn5eXlaebMmRoyZIj7uJOHrI0xlRrGJgEEAADW8+VDIE6nU3Xr1vXYKioAmzRponbt2nm0XXTRRdq5c6ckKSYmRpLKpH2FhYVlUsFToQAEAACoIbp3766vvvrKo+3rr79W8+bNJUlxcXGKiYnR6tWr3fuPHj2qnJwcJSYmen0dhoABAID1aspTwPfdd58SExOVkZGhG2+8UZ988onmzJmjOXPmSPq1n+np6crIyFDr1q3VunVrZWRkKCwsTIMGDfL6OhSAAAAANUS3bt20fPlyjRs3To8++qji4uI0bdo0DR482H3M2LFjVVxcrJEjR+rAgQOKj4/XqlWrFBkZ6fV1HMYY44sb8Kcjv/i7BwB8pX63u/3dBQA+Urwx22/XbnLHUp+de/ec63x27jNFAggAAKxXU4aAqwsPgQAAAFiGBBAAAMCuAJAEEAAAwDYkgAAAwHrMAQQAAECtRgIIAACsRwIIAACAWo0EEAAAWM+2BJACEAAAwK76jyFgAAAA25AAAgAA69k2BEwCCAAAYBkSQAAAYD0SQAAAANRqFIA4J326IVd/Hnmnrkq+Qh0vbqs1777j7y4BOEP/+scjKt6YXWZ78i83ljl2+vibVbwxW3cPSq7+jqJWczgcPttqIoaAcU4qLj6stm3b6to/DtD96X/2d3cAnIUrbvlfBQb89x/Jdq1i9casP2vZ6o0ex/VLvkTdOlyg/xT+WM09BGofCkCck67okaQreiT5uxsAqsDeAz97fP3Abe313c49+vDTb9xtsY2i9ORfblC/kc9o+fS7qruLsEBNTep8xa8F4K5duzRz5kytXbtWBQUFcjgccrlcSkxM1J133qmmTZv6s3sAgGoWVCdQN1/dTU8vWuNuczgceu7xIXpy4bvauq3Aj71DrWZX/ee/AvCjjz5S37591bRpU6WkpCglJUXGGBUWFmrFihWaPn263nzzTXXv3v2U5ykpKVFJSYlHmwl0yul0+rL7AAAfuKbnJaoXGapFf//Y3Xb/bX30S+lxPfPS+/7rGFDL+K0AvO+++3T77bfrySefrHB/enq6cnNzT3mezMxMPfLIIx5t4x+eqP+ZMKmqugoAqCZp/RP19j+3aPeenyRJnS9qqlEDk5U4aIqfe4bajiHgavLll19q0aJFFe4fMWKEZs2addrzjBs3TqNHj/ZoM4GkfwBwrmnWpL56xbfVzQ/Mdbd179xSjaMj9PUbj7rb6tQJ1OTRA3T34J66MHWiP7oKnPP8VgA2adJEa9euVdu2bcvdv27dOjVp0uS053E6yw73HvmlSroIAKhGt16ToML9h/Tmh3nutsX/yNWaj7/yOO7vM0Zp8T8+0fP/t766u4hajASwmjzwwAO688479emnn6pPnz5yuVxyOBwqKCjQ6tWr9eyzz2ratGn+6h5quMNFRdq5c6f763/v2qV/bd2qqKgoNYmN9WPPAJwJh8OhIdderhdf/1ilpcfd7ft/KtL+n4o8jj32S6l+2HtQ3+worO5uArWG3wrAkSNHqkGDBnryySc1e/ZslZaWSpICAwPVpUsXPf/887rxxrKLgAKSlJf3pW6/bYj76yeyMiVJ11z7Rz2WMdlf3QJwhnrFt1WzJtFauIJUD/5hWQAohzHG+LsTx44d0969eyVJDRs2VFBQ0FmdjyFgoPaq3+1uf3cBgI8Ub8z227VbPfCmz8797RN9fXbuM1UjFoIOCgryar4fAACALzAHEAAAwDKW1X8K8HcHAAAAUL1IAAEAgPVsGwImAQQAALAMCSAAALCeZQEgCSAAAIBtSAABAID1AgLsigBJAAEAACxDAggAAKxn2xxACkAAAGA9loEBAABArUYCCAAArGdZAEgCCAAAYBsSQAAAYD3mAAIAAKBWIwEEAADWIwEEAABArUYCCAAArGdZAEgBCAAAwBAwAAAAajUSQAAAYD3LAkASQAAAANuQAAIAAOsxBxAAAAC1GgkgAACwnmUBIAkgAACAbSgAAQCA9RwOh8+2ypg0aVKZ18fExLj3G2M0adIkxcbGKjQ0VMnJycrLy6v0/VIAAgAA1CAXX3yxdu/e7d42b97s3peVlaWpU6cqOztbubm5iomJUZ8+fXTo0KFKXYM5gAAAwHo1aQ5gnTp1PFK/E4wxmjZtmsaPH68BAwZIkhYuXCiXy6XFixdrxIgRXl+DBBAAAFjPl0PAJSUlOnjwoMdWUlJSYV+++eYbxcbGKi4uTjfffLO2bdsmScrPz1dBQYFSUlLcxzqdTiUlJWnt2rWVul8KQAAAAB/KzMxUVFSUx5aZmVnusfHx8Xr++ef19ttva+7cuSooKFBiYqL27dungoICSZLL5fJ4jcvlcu/zFkPAAADAer4cAh43bpxGjx7t0eZ0Oss9tm/fvu7/7tChgxISEtSyZUstXLhQl19++f/vq2dnjTGVftiEBBAAAMCHnE6n6tat67FVVACeLDw8XB06dNA333zjnhd4ctpXWFhYJhU8HQpAAABgvZqyDMzJSkpKtHXrVjVp0kRxcXGKiYnR6tWr3fuPHj2qnJwcJSYmVuq8DAEDAADUEA888ID69eunZs2aqbCwUI8//rgOHjyotLQ0ORwOpaenKyMjQ61bt1br1q2VkZGhsLAwDRo0qFLXoQAEAADWqynLwOzatUsDBw7U3r171ahRI11++eVav369mjdvLkkaO3asiouLNXLkSB04cEDx8fFatWqVIiMjK3UdhzHG+OIG/OnIL/7uAQBfqd/tbn93AYCPFG/M9tu1E7M+8Nm514690mfnPlMkgAAAwHpnO1fvXEMBCAAArGdZ/cdTwAAAALYhAQQAANazbQiYBBAAAMAyJIAAAMB6JIAAAACo1UgAAQCA9SwLAEkAAQAAbEMCCAAArGfbHEAKQAAAYD3L6j+GgAEAAGxDAggAAKxn2xAwCSAAAIBlSAABAID1LAsASQABAABsQwIIAACsF2BZBEgCCAAAYBkSQAAAYD3LAkAKQAAAAJaBAQAAQK1GAggAAKwXYFcASAIIAABgGxJAAABgPeYAAgAAoFYjAQQAANazLAAkAQQAALANCSAAALCeQ3ZFgBSAAADAeiwDAwAAgFqNBBAAAFiPZWAAAABQq5EAAgAA61kWAJIAAgAA2IYEEAAAWC/AsgiQBBAAAMAyJIAAAMB6lgWAFIAAAAAsAwMAAIBajQQQAABYz7IAkAQQAADANiSAAADAeiwDAwAAgFqNBBAAAFjPrvyPBBAAAMA6JIAAAMB6tq0DSAEIAACsF2BX/ccQMAAAgG1IAAEAgPVsGwImAQQAALAMCSAAALCeZQEgCSAAAIBtKAABAID1HA6Hz7azkZmZKYfDofT0dHebMUaTJk1SbGysQkNDlZycrLy8vEqd16sh4JUrV3p9wmuuuaZSHQAAAEBZubm5mjNnji655BKP9qysLE2dOlULFixQmzZt9Pjjj6tPnz766quvFBkZ6dW5vSoA+/fv79XJHA6HSktLvToWAACgpqhp6wD+/PPPGjx4sObOnavHH3/c3W6M0bRp0zR+/HgNGDBAkrRw4UK5XC4tXrxYI0aM8Or8Xg0BHz9+3KuN4g8AAJyLfDkEXFJSooMHD3psJSUlp+zPqFGjlJqaqquuusqjPT8/XwUFBUpJSXG3OZ1OJSUlae3atV7fL3MAAQAAfCgzM1NRUVEeW2ZmZoXHL1myRJ999lm5xxQUFEiSXC6XR7vL5XLv88YZLQNTVFSknJwc7dy5U0ePHvXYd88995zJKQEAAPzGlyPA48aN0+jRoz3anE5nucd+//33uvfee7Vq1SqFhIRUeM6THy4xxlTqgZNKF4AbN27U1VdfrcOHD6uoqEjR0dHau3evwsLC1LhxYwpAAACA33A6nRUWfCf79NNPVVhYqC5durjbSktL9cEHHyg7O1tfffWVpF+TwCZNmriPKSwsLJMKnkqlh4Dvu+8+9evXT/v371doaKjWr1+vHTt2qEuXLnriiScqezoAAAC/C3A4fLZVRu/evbV582Zt2rTJvXXt2lWDBw/Wpk2b1KJFC8XExGj16tXu1xw9elQ5OTlKTEz0+jqVTgA3bdqk2bNnKzAwUIGBgSopKVGLFi2UlZWltLQ09xMpAAAAqJzIyEi1b9/eoy08PFwNGjRwt6enpysjI0OtW7dW69atlZGRobCwMA0aNMjr61S6AAwKCnKPMbtcLu3cuVMXXXSRoqKitHPnzsqeDgAAwO/OpT8FN3bsWBUXF2vkyJE6cOCA4uPjtWrVKq/XAJTOoADs3LmzNmzYoDZt2qhnz56aMGGC9u7dqxdeeEEdOnSo7OkAAABwCu+//77H1w6HQ5MmTdKkSZPO+JyVngOYkZHhnnT42GOPqUGDBrrrrrtUWFioOXPmnHFHAAAA/KWm/ik4X6l0Ati1a1f3fzdq1EhvvPFGlXYIAAAAvnVG6wACAADUJjU0qPOZSheAcXFxp4wzt23bdlYdAgAAqG6VXa7lXFfpAjA9Pd3j62PHjmnjxo166623NGbMmKrqFwAAAHyk0gXgvffeW277M888ow0bNpx1hwAAAKqbZQFg5Z8Crkjfvn21dOnSqjodAAAAfKTKHgJ57bXXFB0dXVWnAwAAqDY1dbkWXzmjhaB/+yYZY1RQUKA9e/ZoxowZVdo5AAAAVL1KF4DXXnutRwEYEBCgRo0aKTk5WRdeeGGVdg4ATvbok6P93QUAtVCVzYk7R1S6ADybPzsCAAAA/6t0wRsYGKjCwsIy7fv27VNgYGCVdAoAAKA68afgTsMYU257SUmJgoODz7pDAAAA1S2gZtZpPuN1Afj0009L+rVCfvbZZxUREeHeV1paqg8++IA5gAAAAOcArwvAJ598UtKvCeCsWbM8hnuDg4N1wQUXaNasWVXfQwAAAB8jAaxAfn6+JKlnz55atmyZ6tev77NOAQAAwHcqPQfwvffe80U/AAAA/KamPqzhK5V+Cvj666/X5MmTy7T/7//+r2644YYq6RQAAAB8p9IFYE5OjlJTU8u0//73v9cHH3xQJZ0CAACoTgEO3201UaULwJ9//rnc5V6CgoJ08ODBKukUAAAAfKfSBWD79u318ssvl2lfsmSJ2rVrVyWdAgAAqE4Oh++2mqjSD4E8/PDDuu666/Tdd9+pV69ekqR3331Xixcv1muvvVblHQQAAPC1gJpaqflIpQvAa665RitWrFBGRoZee+01hYaGqmPHjlqzZo3q1q3riz4CAACgClW6AJSk1NRU94MgP/74o1588UWlp6fr888/V2lpaZV2EAAAwNcqPSfuHHfG97tmzRrdcsstio2NVXZ2tq6++mpt2LChKvsGAAAAH6hUArhr1y4tWLBA8+bNU1FRkW688UYdO3ZMS5cu5QEQAABwzrJsCqD3CeDVV1+tdu3aacuWLZo+fbr+85//aPr06b7sGwAAAHzA6wRw1apVuueee3TXXXepdevWvuwTAABAtbLtKWCvE8APP/xQhw4dUteuXRUfH6/s7Gzt2bPHl30DAACAD3hdACYkJGju3LnavXu3RowYoSVLlui8887T8ePHtXr1ah06dMiX/QQAAPAZ2xaCrvRTwGFhYRo2bJg++ugjbd68Wffff78mT56sxo0b65prrvFFHwEAAHyKvwVcCW3btlVWVpZ27dqll156qar6BAAAAB86o4WgTxYYGKj+/furf//+VXE6AACAasVDIAAAAKjVqiQBBAAAOJdZFgCSAAIAANiGBBAAAFivpj6t6yskgAAAAJYhAQQAANZzyK4IkAIQAABYjyFgAAAA1GokgAAAwHokgAAAAKjVSAABAID1HJatBE0CCAAAYBkSQAAAYD3mAAIAAKBWIwEEAADWs2wKIAUgAABAgGUVIEPAAAAAliEBBAAA1uMhEAAAANRqFIAAAMB6DofvtsqYOXOmLrnkEtWtW1d169ZVQkKC3nzzTfd+Y4wmTZqk2NhYhYaGKjk5WXl5eZW+XwpAAACAGuL888/X5MmTtWHDBm3YsEG9evXStdde6y7ysrKyNHXqVGVnZys3N1cxMTHq06ePDh06VKnrUAACAADrBcjhs60y+vXrp6uvvlpt2rRRmzZt9Ne//lURERFav369jDGaNm2axo8frwEDBqh9+/ZauHChDh8+rMWLF1fyfgEAAOAzJSUlOnjwoMdWUlJy2teVlpZqyZIlKioqUkJCgvLz81VQUKCUlBT3MU6nU0lJSVq7dm2l+kQBCAAArOfLOYCZmZmKiory2DIzMyvsy+bNmxURESGn06k777xTy5cvV7t27VRQUCBJcrlcHse7XC73Pm+xDAwAALCeL5eBGTdunEaPHu3R5nQ6Kzy+bdu22rRpk3788UctXbpUaWlpysnJce93nPRkiTGmTNvpUAACAAD4kNPpPGXBd7Lg4GC1atVKktS1a1fl5ubqqaee0oMPPihJKigoUJMmTdzHFxYWlkkFT4chYAAAYL0Ah8Nn29kyxqikpERxcXGKiYnR6tWr3fuOHj2qnJwcJSYmVuqcJIAAAAA1xEMPPaS+ffuqadOmOnTokJYsWaL3339fb731lhwOh9LT05WRkaHWrVurdevWysjIUFhYmAYNGlSp61AAAgAA61VBUFclfvjhB916663avXu3oqKidMkll+itt95Snz59JEljx45VcXGxRo4cqQMHDig+Pl6rVq1SZGRkpa7jMMYYX9yAPx35xd89AOAr0z/a5u8uAPCRMckt/HbtuR/v8Nm5/xTf3GfnPlMkgAAAwHpVMVfvXMJDIAAAAJYhAQQAANazLACkAAQAALBtSNS2+wUAALAeCSAAALBeZf+U2rmOBBAAAMAyJIAAAMB6duV/JIAAAADWIQEEAADWYyFoAAAA1GokgAAAwHp25X8UgAAAANb9JRCGgAEAACxDAggAAKzHQtAAAACo1UgAAQCA9WxLxGy7XwAAAOuRAAIAAOsxBxAAAAC1GgkgAACwnl35HwkgAACAdUgAAQCA9WybA0gBCAAArGfbkKht9wsAAGA9EkAAAGA924aASQABAAAsQwIIAACsZ1f+RwIIAABgHRJAAABgPcumAJIAAgAA2IYEEAAAWC/AslmAFIAAAMB6DAEDAACgViMBBAAA1nNYNgRMAggAAGAZEkAAAGA95gACAACgViMBBAAA1rNtGRgSQAAAAMuQAAIAAOvZNgeQAhAAAFjPtgKQIWAAAADLkAACAADrsRA0AAAAajUSQAAAYL0AuwJAEkAAAADbkAACAADrMQcQAAAAtRoJIAAAsJ5t6wBSAAIAAOsxBAwAAIBajQQQAABYj2VgAAAA4BeZmZnq1q2bIiMj1bhxY/Xv319fffWVxzHGGE2aNEmxsbEKDQ1VcnKy8vLyKnUdCkAAAGA9hw//Vxk5OTkaNWqU1q9fr9WrV+uXX35RSkqKioqK3MdkZWVp6tSpys7OVm5urmJiYtSnTx8dOnTI+/s1xphK9ewccOQXf/cAgK9M/2ibv7sAwEfGJLfw27U//PqAz859WfMwlZSUeLQ5nU45nc7TvnbPnj1q3LixcnJydOWVV8oYo9jYWKWnp+vBBx+UJJWUlMjlcmnKlCkaMWKEV30iAQQAANZzOHy3ZWZmKioqymPLzMz0ql8//fSTJCk6OlqSlJ+fr4KCAqWkpLiPcTqdSkpK0tq1a72+Xx4CAQAA8KFx48Zp9OjRHm3epH/GGI0ePVpXXHGF2rdvL0kqKCiQJLlcLo9jXS6XduzY4XWfKAABAID1fPkQsLfDvSe7++679cUXX+ijjz4qs89x0srVxpgybadCAQgAAKwXUMP+FMif//xnrVy5Uh988IHOP/98d3tMTIykX5PAJk2auNsLCwvLpIKnUqPnAH7//fcaNmzYKY8pKSnRwYMHPbaTJ1oCAACcC4wxuvvuu7Vs2TKtWbNGcXFxHvvj4uIUExOj1atXu9uOHj2qnJwcJSYmen2dGl0A7t+/XwsXLjzlMeVNrPzfKd5NrAQAAJB+HQL21VYZo0aN0qJFi7R48WJFRkaqoKBABQUFKi4u/rWfDofS09OVkZGh5cuX68svv9TQoUMVFhamQYMGeX0dvw4Br1y58pT7t207/XIP5U2sNIGVH2cHAADwt5kzZ0qSkpOTPdrnz5+voUOHSpLGjh2r4uJijRw5UgcOHFB8fLxWrVqlyMhIr6/j13UAAwIC5HA4dKouOBwOlZaWVuq8rAMI1F6sAwjUXv5cB3D9dz/67NyXt6zns3OfKb8OATdp0kRLly7V8ePHy90+++wzf3YPAACgVvJrAdilS5dTFnmnSwcBAACqQk35U3DVxa9zAMeMGePxt+1O1qpVK7333nvV2CMAAIDaz68FYI8ePU65Pzw8XElJSdXUGwAAYKsatgygz7EQNAAAsJ5l9V/NXgcQAAAAVY8EEAAAwLIIkAQQAADAMiSAAADAejV1uRZfIQEEAACwDAkgAACwnm3LwJAAAgAAWIYEEAAAWM+yAJACEAAAwLYKkCFgAAAAy5AAAgAA67EMDAAAAGo1EkAAAGA9loEBAABArUYCCAAArGdZAEgCCAAAYBsSQAAAAMsiQApAAABgPZaBAQAAQK1GAggAAKzHMjAAAACo1UgAAQCA9SwLAEkAAQAAbEMCCAAAYFkESAIIAABgGRJAAABgPdYBBAAAQK1GAggAAKxn2zqAFIAAAMB6ltV/DAEDAADYhgQQAADAsgiQBBAAAMAyJIAAAMB6LAMDAACAWo0EEAAAWM+2ZWBIAAEAACxDAggAAKxnWQBIAQgAAGBbBcgQMAAAgGVIAAEAgPVYBgYAAAC1GgkgAACwHsvAAAAAoFYjAQQAANazLAAkAQQAALANCSAAAIBlESAFIAAAsB7LwAAAAMBvPvjgA/Xr10+xsbFyOBxasWKFx35jjCZNmqTY2FiFhoYqOTlZeXl5lboGBSAAALCew+G7rbKKiorUsWNHZWdnl7s/KytLU6dOVXZ2tnJzcxUTE6M+ffro0KFDXl+DIWAAAIAapG/fvurbt2+5+4wxmjZtmsaPH68BAwZIkhYuXCiXy6XFixdrxIgRXl2DBBAAAFjP4cOtpKREBw8e9NhKSkrOqJ/5+fkqKChQSkqKu83pdCopKUlr1671+jwUgAAAAD6UmZmpqKgojy0zM/OMzlVQUCBJcrlcHu0ul8u9zxsMAQMAAPjwIeBx48Zp9OjRHm1Op/Oszuk4aXKhMaZM26lQAAIAAPiQ0+k864LvhJiYGEm/JoFNmjRxtxcWFpZJBU+FIWAAAGA9hw//V5Xi4uIUExOj1atXu9uOHj2qnJwcJSYmen0eEkAAAGC9M1muxVd+/vlnffvtt+6v8/PztWnTJkVHR6tZs2ZKT09XRkaGWrdurdatWysjI0NhYWEaNGiQ19egAAQAAKhBNmzYoJ49e7q/PjF/MC0tTQsWLNDYsWNVXFyskSNH6sCBA4qPj9eqVasUGRnp9TUcxhhT5T33syO/+LsHAHxl+kfb/N0FAD4yJrmF3679/f4zW5bFG02jq2b+X1ViDiAAAIBlGAIGAADWq0lzAKsDCSAAAIBlSAABAAB8uRJ0DUQCCAAAYBkSQAAAYD3b5gBSAAIAAOtZVv8xBAwAAGAbEkAAAGA924aASQABAAAsQwIIAACs57BsFiAJIAAAgGVIAAEAAOwKAEkAAQAAbEMCCAAArGdZAEgBCAAAwDIwAAAAqNVIAAEAgPVYBgYAAAC1GgkgAACAXQEgCSAAAIBtSAABAID1LAsASQABAABsQwIIAACsZ9s6gBSAAADAeiwDAwAAgFqNBBAAAFjPtiFgEkAAAADLUAACAABYhgIQAADAMswBBAAA1mMOIAAAAGo1EkAAAGA929YBpAAEAADWYwgYAAAAtRoJIAAAsJ5lASAJIAAAgG1IAAEAACyLAEkAAQAALEMCCAAArGfbMjAkgAAAAJYhAQQAANZjHUAAAADUaiSAAADAepYFgBSAAAAAtlWADAEDAABYhgQQAABYj2VgAAAAUKuRAAIAAOuxDAwAAABqNYcxxvi7E8CZKikpUWZmpsaNGyen0+nv7gCoQvx8A75DAYhz2sGDBxUVFaWffvpJdevW9Xd3AFQhfr4B32EIGAAAwDIUgAAAAJahAAQAALAMBSDOaU6nUxMnTmSCOFAL8fMN+A4PgQAAAFiGBBAAAMAyFIAAAACWoQAEAACwDAUgAACAZSgAcU6bMWOG4uLiFBISoi5duujDDz/0d5cAnKUPPvhA/fr1U2xsrBwOh1asWOHvLgG1DgUgzlkvv/yy0tPTNX78eG3cuFE9evRQ3759tXPnTn93DcBZKCoqUseOHZWdne3vrgC1FsvA4JwVHx+vSy+9VDNnznS3XXTRRerfv78yMzP92DMAVcXhcGj58uXq37+/v7sC1CokgDgnHT16VJ9++qlSUlI82lNSUrR27Vo/9QoAgHMDBSDOSXv37lVpaalcLpdHu8vlUkFBgZ96BQDAuYECEOc0h8Ph8bUxpkwbAADwRAGIc1LDhg0VGBhYJu0rLCwskwoCAABPFIA4JwUHB6tLly5avXq1R/vq1auVmJjop14BAHBuqOPvDgBnavTo0br11lvVtWtXJSQkaM6cOdq5c6fuvPNOf3cNwFn4+eef9e2337q/zs/P16ZNmxQdHa1mzZr5sWdA7cEyMDinzZgxQ1lZWdq9e7fat2+vJ598UldeeaW/uwXgLLz//vvq2bNnmfa0tDQtWLCg+jsE1EIUgAAAAJZhDiAAAIBlKAABAAAsQwEIAABgGQpAAAAAy1AAAgAAWIYCEAAAwDIUgAAAAJahAAQAALAMBSCAGmvSpEnq1KmT++uhQ4eqf//+1d6P7du3y+FwaNOmTdV+bQDwBQpAAJU2dOhQORwOORwOBQUFqUWLFnrggQdUVFTk0+s+9dRTXv8pMIo2AKhYHX93AMC56fe//73mz5+vY8eO6cMPP9Ttt9+uoqIizZw50+O4Y8eOKSgoqEquGRUVVSXnAQDbkQACOCNOp1MxMTFq2rSpBg0apMGDB2vFihXuYdt58+apRYsWcjqdMsbop59+0h133KHGjRurbt266tWrlz7//HOPc06ePFkul0uRkZEaPny4jhw54rH/5CHg48ePa8qUKWrVqpWcTqeaNWumv/71r5KkuLg4SVLnzp3lcDiUnJzsft38+fN10UUXKSQkRBdeeKFmzJjhcZ1PPvlEnTt3VkhIiLp27aqNGzdW4TsHAP5HAgigSoSGhurYsWOSpG+//VavvPKKli5dqsDAQElSamqqoqOj9cYbbygqKkqzZ89W79699fXXXys6OlqvvPKKJk6cqGeeeUY9evTQCy+8oKefflotWrSo8Jrjxo3T3Llz9eSTT+qKK67Q7t279a9//UvSr0XcZZddpnfeeUcXX3yxgoODJUlz587VxIkTlZ2drc6dO2vjxo3605/+pPDwcKWlpamoqEh/+MMf1KtXLy1atEj5+fm69957ffzuAUA1MwBQSWlpaebaa691f/3xxx+bBg0amBtvvNFMnDjRBAUFmcLCQvf+d99919StW9ccOXLE4zwtW7Y0s2fPNsYYk5CQYO68806P/fHx8aZjx47lXvfgwYPG6XSauXPnltvH/Px8I8ls3LjRo71p06Zm8eLFHm2PPfaYSUhIMMYYM3v2bBMdHW2Kiorc+2fOnFnuuQDgXMUQMIAz8vrrrysiIkIhISFKSEjQlVdeqenTp0uSmjdvrkaNGrmP/fTTT/Xzzz+rQYMGioiIcG/5+fn67rvvJElbt25VQkKCxzVO/vq3tm7dqpKSEvXu3dvrPu/Zs0fff/+9hg8f7tGPxx9/3KMfHTt2VFhYmFf9AIBzEUPAAM5Iz549NXPmTAUFBSk2NtbjQY/w8HCPY48fP64mTZro/fffL3OeevXqndH1Q0NDK/2a48ePS/p1GDg+Pt5j34mhamPMGfUHAM4lFIAAzkh4eLhatWrl1bGXXnqpCgoKVKdOHV1wwQXlHnPRRRdp/fr1GjJkiLtt/fr1FZ6zdevWCg0N1bvvvqvbb7+9zP4Tc/5KS0vdbS6XS+edd562bdumwYMHl3vedu3a6YUXXlBxcbG7yDxVPwDgXMQQMACfu+qqq5SQkKD+/fvr7bff1vbt27V27Vr9z//8jzZs2CBJuvfeezVv3jzNmzdPX3/9tSZOnKi8vLwKzxkSEqIHH3xQY8eO1fPPP6/vvvtO69ev13PPPSdJaty4sUJDQ/XWW2/phx9+0E8//STp18WlMzMz9dRTT+nrr7/W5s2bNX/+fE2dOlWSNGjQIAUEBGj48OHasmWL3njjDT3xxBM+focAoHpRAALwOYfDoTfeeENXXnmlhg0bpjZt2ujmm2/W9u3b5XK5JEk33XSTJkyYoAcffFBdunTRjh07dNddd53yvA8//LDuv/9+TZgwQRdddJFuuukmFRYWSpLq1Kmjp59+WrNnz1ZsbKyuvfZaSdLtt9+uZ599VgsWLFCHDh2UlJSkBQsWuJeNiYiI0N///ndt2bJFnTt31vjx4zVlyhQfvjsAUP0chgkvAAAAViEBBAAAsAwFIAAAgGUoAAEAACxDAQgAAGAZCkAAAADLUAACAABYhgIQAADAMhSAAAAAlqEABAAAsAwFIAAAgGUoAAEAACzz/wAyJYbXaJOG5QAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Train and evaluate each classifier\n",
+    "for name, clf in classifiers.items():\n",
+    "    clf.fit(X_train_balanced, y_train_balanced)\n",
+    "    y_pred = clf.predict(X_test)\n",
+    "    \n",
+    "    accuracy = accuracy_score(y_test, y_pred)\n",
+    "    precision = precision_score(y_test, y_pred)\n",
+    "    recall = recall_score(y_test, y_pred)\n",
+    "    f1 = f1_score(y_test, y_pred)\n",
+    "     # Append the results to the list\n",
+    "    results.append({\n",
+    "        'Classifier': name,\n",
+    "        'Accuracy': accuracy,\n",
+    "        'Precision': precision,\n",
+    "        'Recall': recall,\n",
+    "        'F1 Score': f1\n",
+    "    })\n",
+    "    # Plot the confusion matrix\n",
+    "    conf_matrix = confusion_matrix(y_test, y_pred)\n",
+    "    plt.figure(figsize=(8, 6))\n",
+    "    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=[0, 1], yticklabels=[0, 1])\n",
+    "    plt.xlabel('Predicted')\n",
+    "    plt.ylabel('Actual')\n",
+    "    plt.title(f'Confusion Matrix - {name}')\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "810eca5d-9373-4952-8865-831f9a011748",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "               Classifier  Accuracy  Precision    Recall  F1 Score\n",
+      "0     Logistic Regression  0.703704   0.514286  0.545455  0.529412\n",
+      "1           Decision Tree  0.629630   0.405405  0.454545  0.428571\n",
+      "2           Random Forest  0.685185   0.487805  0.606061  0.540541\n",
+      "3       Gradient Boosting  0.731481   0.547619  0.696970  0.613333\n",
+      "4                AdaBoost  0.685185   0.487805  0.606061  0.540541\n",
+      "5             Naive Bayes  0.518519   0.373333  0.848485  0.518519\n",
+      "6  Support Vector Machine  0.305556   0.301887  0.969697  0.460432\n",
+      "7     K-Nearest Neighbors  0.694444   0.500000  0.606061  0.547945\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert the results list to a DataFrame\n",
+    "results_df = pd.DataFrame(results)\n",
+    "\n",
+    "# Display the results\n",
+    "print(results_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "6457affd-e215-4ee0-a98e-b80646a677e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check the balance of the classes after SMOTE\n",
+    "original_class_distribution = y_train.value_counts()\n",
+    "balanced_class_distribution = pd.Series(y_train_balanced).value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "f92923dd-e379-4aca-b214-a058f1d4f6a3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotting the class distributions before and after SMOTE\n",
+    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(14, 5))\n",
+    "sns.countplot(x=y_train, ax=axes[0])\n",
+    "axes[0].set_title('Class Distribution Before SMOTE')\n",
+    "sns.countplot(x=y_train_balanced, ax=axes[1])\n",
+    "axes[1].set_title('Class Distribution After SMOTE')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "c76ed6ec-844f-4904-9718-dbe6c30911c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Class distribution in original training set: PCOS (Y/N)\n",
+      "0    288\n",
+      "1    144\n",
+      "Name: count, dtype: int64\n",
+      "Class distribution in balanced training set: PCOS (Y/N)\n",
+      "0    288\n",
+      "1    288\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Class distribution in original training set:\", original_class_distribution)\n",
+    "print(\"Class distribution in balanced training set:\", balanced_class_distribution)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31f79ecf-9930-4ed6-b409-7bcd793ac386",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Pcos Detection/Model/README.md b/Pcos Detection/Model/README.md
new file mode 100644
index 000000000..98b3e28af
--- /dev/null
+++ b/Pcos Detection/Model/README.md	
@@ -0,0 +1,71 @@
+# PCOS Detection
+
+## 🎯 GOAL
+
+The aim of the project is to build a model which would predict whether a person has PCOS (Polycystic Ovary Syndrome).
+
+## 🧵 DATASET
+
+The dataset was taken from Kaggle.
+
+## 🧾 DESCRIPTION
+
+In this project, we compare the accuracies of different models to predict PCOS in a person.
+
+## 🧮 WHAT I HAD DONE
+
+Steps followed:
+- Downloaded the dataset from Kaggle.
+- Checked for null values and performed exploratory data analysis.
+- Built models using various algorithms.
+- Compared the accuracies.
+- Performed cross-validation on all the algorithms to see if the accuracies improve.
+
+## 🚀 Models Implemented
+
+1. Logistic Regression
+2. Decision Tree
+3. Random Forest
+4. Gradient Boosting
+5. Adaboost
+6. Naive Bayes
+7. Support Vector Machine
+8. K-Nearest Neighbors
+
+## 📚 LIBRARIES NEEDED
+
+- Pandas
+- Matplotlib
+- Seaborn
+- Numpy
+- Sklearn
+
+## 📊 Exploratory Data Analysis Results
+
+![Confusion Matrix](https://raw.githubusercontent.com/abhisheks008/ML-Crate/1292b54868a3ea93e7520149b56450057ff92197/Pcos%20Detection/Images/Screenshot%202024-06-07%20114101.png)
+
+
+![Class Distribution With SMOTE](https://raw.githubusercontent.com/abhisheks008/ML-Crate/1292b54868a3ea93e7520149b56450057ff92197/Pcos%20Detection/Images/Screenshot%202024-06-07%20114118.png)
+
+
+## 📈 Performance of the Models based on the Accuracy Scores
+
+| Classifier              | Accuracy   |
+|-------------------------|------------|
+| Logistic Regression     | 0.703704   |
+| Decision Tree           | 0.629630   |
+| Random Forest           | 0.685185   |
+| Gradient Boosting       | 0.731481   |
+| AdaBoost                | 0.685185   |
+| Naive Bayes             | 0.518519   |
+| Support Vector Machine  | 0.305556   |
+| K-Nearest Neighbors     | 0.694444   |
+
+## 📢 CONCLUSION
+
+From this project, we conclude that Gradient Boosting has the highest accuracy among all the algorithms, followed by Logistic Regression, Random Forest, AdaBoost, and KNN.
+
+## ✒️ Contribution by
+
+Payal Sinha  
+GitHub: [payalsinha12](https://github.com/payalsinha12)
diff --git a/Pcos Detection/requirements.txt b/Pcos Detection/requirements.txt
new file mode 100644
index 000000000..a5dedd27c
--- /dev/null
+++ b/Pcos Detection/requirements.txt	
@@ -0,0 +1,6 @@
+pandas==1.3.4
+numpy==1.21.4
+pandas==1.3.5
+matplotlib==3.5.1
+scikit-learn==1.0.1
+seaborn==0.11.2