From 50285f69b974b28551232ebe3ec413826e29c678 Mon Sep 17 00:00:00 2001
From: VARUNSHIYAM <138989960+Varunshiyam@users.noreply.github.com>
Date: Sat, 9 Nov 2024 19:34:29 +0530
Subject: [PATCH 1/2] fixes 854

---
 ...nt-predictions-using-multiple-models.ipynb | 1452 +++++++++++++++++
 1 file changed, 1452 insertions(+)
 create mode 100644 Prediction Models/College_placement_prediction/collegeplacement-predictions-using-multiple-models.ipynb

diff --git a/Prediction Models/College_placement_prediction/collegeplacement-predictions-using-multiple-models.ipynb b/Prediction Models/College_placement_prediction/collegeplacement-predictions-using-multiple-models.ipynb
new file mode 100644
index 00000000..111aea1d
--- /dev/null
+++ b/Prediction Models/College_placement_prediction/collegeplacement-predictions-using-multiple-models.ipynb	
@@ -0,0 +1,1452 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "409556bc",
+   "metadata": {
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:48:33.829634Z",
+     "iopub.status.busy": "2024-01-07T21:48:33.829162Z",
+     "iopub.status.idle": "2024-01-07T21:48:35.835265Z",
+     "shell.execute_reply": "2024-01-07T21:48:35.834212Z"
+    },
+    "papermill": {
+     "duration": 2.021466,
+     "end_time": "2024-01-07T21:48:35.838320",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:33.816854",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/kaggle/input/college-placement/placement-dataset.csv\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
+    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
+    "# For example, here's several helpful packages to load\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "import numpy as np # linear algebra\n",
+    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Input data files are available in the read-only \"../input/\" directory\n",
+    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
+    "\n",
+    "import os\n",
+    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
+    "    for filename in filenames:\n",
+    "        print(os.path.join(dirname, filename))\n",
+    "\n",
+    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
+    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a753911f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:48:35.859906Z",
+     "iopub.status.busy": "2024-01-07T21:48:35.859358Z",
+     "iopub.status.idle": "2024-01-07T21:48:35.913968Z",
+     "shell.execute_reply": "2024-01-07T21:48:35.912967Z"
+    },
+    "papermill": {
+     "duration": 0.068204,
+     "end_time": "2024-01-07T21:48:35.916475",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:35.848271",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "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>cgpa</th>\n",
+       "      <th>iq</th>\n",
+       "      <th>placement</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>6.8</td>\n",
+       "      <td>123.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5.9</td>\n",
+       "      <td>106.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.3</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.8</td>\n",
+       "      <td>142.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>95</th>\n",
+       "      <td>4.3</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96</th>\n",
+       "      <td>4.4</td>\n",
+       "      <td>42.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>97</th>\n",
+       "      <td>6.7</td>\n",
+       "      <td>182.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>103.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>113.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>100 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    cgpa     iq  placement\n",
+       "0    6.8  123.0          1\n",
+       "1    5.9  106.0          0\n",
+       "2    5.3  121.0          0\n",
+       "3    7.4  132.0          1\n",
+       "4    5.8  142.0          0\n",
+       "..   ...    ...        ...\n",
+       "95   4.3  200.0          0\n",
+       "96   4.4   42.0          0\n",
+       "97   6.7  182.0          1\n",
+       "98   6.3  103.0          1\n",
+       "99   6.2  113.0          1\n",
+       "\n",
+       "[100 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('/kaggle/input/college-placement/placement-dataset.csv')\n",
+    "df = df.drop(\"Unnamed: 0\", axis=1)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f03c9b83",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:48:35.938038Z",
+     "iopub.status.busy": "2024-01-07T21:48:35.937665Z",
+     "iopub.status.idle": "2024-01-07T21:48:35.960758Z",
+     "shell.execute_reply": "2024-01-07T21:48:35.959642Z"
+    },
+    "papermill": {
+     "duration": 0.036525,
+     "end_time": "2024-01-07T21:48:35.963043",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:35.926518",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "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>cgpa</th>\n",
+       "      <th>iq</th>\n",
+       "      <th>placement</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>100.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>5.991000</td>\n",
+       "      <td>123.580000</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.143634</td>\n",
+       "      <td>39.944198</td>\n",
+       "      <td>0.502519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>3.300000</td>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>5.075000</td>\n",
+       "      <td>101.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>127.500000</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>6.900000</td>\n",
+       "      <td>149.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>8.500000</td>\n",
+       "      <td>233.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             cgpa          iq   placement\n",
+       "count  100.000000  100.000000  100.000000\n",
+       "mean     5.991000  123.580000    0.500000\n",
+       "std      1.143634   39.944198    0.502519\n",
+       "min      3.300000   37.000000    0.000000\n",
+       "25%      5.075000  101.500000    0.000000\n",
+       "50%      6.000000  127.500000    0.500000\n",
+       "75%      6.900000  149.000000    1.000000\n",
+       "max      8.500000  233.000000    1.000000"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "80454133",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:48:35.984855Z",
+     "iopub.status.busy": "2024-01-07T21:48:35.983940Z",
+     "iopub.status.idle": "2024-01-07T21:48:36.438112Z",
+     "shell.execute_reply": "2024-01-07T21:48:36.436974Z"
+    },
+    "papermill": {
+     "duration": 0.467802,
+     "end_time": "2024-01-07T21:48:36.440787",
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+     "start_time": "2024-01-07T21:48:35.972985",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['cgpa'].hist()\n",
+    "plt.title('CGPA Distribution')\n",
+    "plt.xlabel('CGPA')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "287f0546",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:48:36.463573Z",
+     "iopub.status.busy": "2024-01-07T21:48:36.463138Z",
+     "iopub.status.idle": "2024-01-07T21:48:36.815229Z",
+     "shell.execute_reply": "2024-01-07T21:48:36.814052Z"
+    },
+    "papermill": {
+     "duration": 0.366504,
+     "end_time": "2024-01-07T21:48:36.817919",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:36.451415",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['iq'].hist()\n",
+    "plt.title('CGPA Distribution')\n",
+    "plt.xlabel('CGPA')\n",
+    "plt.ylabel('Frequency')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "138a42cf",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:48:36.842121Z",
+     "iopub.status.busy": "2024-01-07T21:48:36.841733Z",
+     "iopub.status.idle": "2024-01-07T21:48:36.849630Z",
+     "shell.execute_reply": "2024-01-07T21:48:36.848354Z"
+    },
+    "papermill": {
+     "duration": 0.022966,
+     "end_time": "2024-01-07T21:48:36.852128",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:36.829162",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The correlation coefficient between CGPA and IQ is: -0.0987906720582117\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Assuming df is already defined and has 'cgpa' and 'iq' columns\n",
+    "correlation = df['cgpa'].corr(df['iq'])\n",
+    "\n",
+    "print(\"The correlation coefficient between CGPA and IQ is:\", correlation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "d8125d0b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:48:36.876712Z",
+     "iopub.status.busy": "2024-01-07T21:48:36.875906Z",
+     "iopub.status.idle": "2024-01-07T21:48:37.193922Z",
+     "shell.execute_reply": "2024-01-07T21:48:37.192457Z"
+    },
+    "papermill": {
+     "duration": 0.332828,
+     "end_time": "2024-01-07T21:48:37.196321",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:36.863493",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(df['iq'], df['cgpa'])\n",
+    "plt.title('Scatter plot of IQ vs CGPA')\n",
+    "plt.xlabel('IQ')\n",
+    "plt.ylabel('CGPA')\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "83b10309",
+   "metadata": {
+    "papermill": {
+     "duration": 0.011339,
+     "end_time": "2024-01-07T21:48:37.219569",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:37.208230",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "no correlation. very interesting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "90f63b1d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:48:37.245356Z",
+     "iopub.status.busy": "2024-01-07T21:48:37.244947Z",
+     "iopub.status.idle": "2024-01-07T21:48:37.660933Z",
+     "shell.execute_reply": "2024-01-07T21:48:37.659981Z"
+    },
+    "papermill": {
+     "duration": 0.432117,
+     "end_time": "2024-01-07T21:48:37.663449",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:37.231332",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score, confusion_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9556bc0e",
+   "metadata": {
+    "papermill": {
+     "duration": 0.011122,
+     "end_time": "2024-01-07T21:48:37.686062",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:37.674940",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "454b4c02",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:48:37.711355Z",
+     "iopub.status.busy": "2024-01-07T21:48:37.710342Z",
+     "iopub.status.idle": "2024-01-07T21:48:37.740003Z",
+     "shell.execute_reply": "2024-01-07T21:48:37.738828Z"
+    },
+    "papermill": {
+     "duration": 0.044967,
+     "end_time": "2024-01-07T21:48:37.742566",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:37.697599",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = df[['cgpa', 'iq']]  # Features\n",
+    "y = df['placement']  # Target variable\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "35f5ba9b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:48:37.768350Z",
+     "iopub.status.busy": "2024-01-07T21:48:37.767713Z",
+     "iopub.status.idle": "2024-01-07T21:48:37.780141Z",
+     "shell.execute_reply": "2024-01-07T21:48:37.778908Z"
+    },
+    "papermill": {
+     "duration": 0.028553,
+     "end_time": "2024-01-07T21:48:37.782810",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:37.754257",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.85\n",
+      "Confusion Matrix:\n",
+      " [[9 1]\n",
+      " [2 8]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = model.predict(X_test)\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
+    "print(\"Accuracy:\", accuracy)\n",
+    "print(\"Confusion Matrix:\\n\", conf_matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7b26bcf8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:48:37.808810Z",
+     "iopub.status.busy": "2024-01-07T21:48:37.808374Z",
+     "iopub.status.idle": "2024-01-07T21:48:38.139753Z",
+     "shell.execute_reply": "2024-01-07T21:48:38.138746Z"
+    },
+    "papermill": {
+     "duration": 0.347359,
+     "end_time": "2024-01-07T21:48:38.142046",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:37.794687",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Using Matplotlib\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')\n",
+    "plt.xlabel('Predicted Label')\n",
+    "plt.ylabel('True Label')\n",
+    "plt.title('Confusion Matrix')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "7911db11",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:48:38.168673Z",
+     "iopub.status.busy": "2024-01-07T21:48:38.168242Z",
+     "iopub.status.idle": "2024-01-07T21:48:38.686092Z",
+     "shell.execute_reply": "2024-01-07T21:48:38.685037Z"
+    },
+    "papermill": {
+     "duration": 0.534852,
+     "end_time": "2024-01-07T21:48:38.689390",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:38.154538",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.9\n",
+      "Confusion Matrix:\n",
+      " [[10  0]\n",
+      " [ 2  8]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from mlxtend.plotting import plot_decision_regions\n",
+    "\n",
+    "# Prepare the features and target variable\n",
+    "X = df[['cgpa']]  # Using only 'cgpa' as the feature\n",
+    "y = df['placement']  # Target variable\n",
+    "\n",
+    "# Split the data into training and test sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Create and train the logistic regression model\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Make predictions and evaluate the model\n",
+    "y_pred = model.predict(X_test)\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
+    "\n",
+    "# Print out the accuracy and confusion matrix\n",
+    "print(\"Accuracy:\", accuracy)\n",
+    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
+    "\n",
+    "# Convert X_train and y_train to NumPy arrays for the plotting function\n",
+    "X_train_np = X_train.to_numpy()\n",
+    "y_train_np = y_train.to_numpy()\n",
+    "\n",
+    "# Visualizing the decision boundary\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plot_decision_regions(X_train_np, y_train_np, clf=model, legend=2)\n",
+    "plt.xlabel('CGPA')\n",
+    "plt.ylabel('Placement')\n",
+    "plt.title('Decision Boundary for Logistic Regression')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "95e5b374",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:48:38.718491Z",
+     "iopub.status.busy": "2024-01-07T21:48:38.717404Z",
+     "iopub.status.idle": "2024-01-07T21:48:38.736758Z",
+     "shell.execute_reply": "2024-01-07T21:48:38.735507Z"
+    },
+    "papermill": {
+     "duration": 0.036547,
+     "end_time": "2024-01-07T21:48:38.739181",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:38.702634",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.4\n",
+      "Confusion Matrix:\n",
+      " [[4 6]\n",
+      " [6 4]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "X = df[['iq']]  # Features\n",
+    "y = df['placement']  # Target variable\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train, y_train)\n",
+    "y_pred = model.predict(X_test)\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
+    "print(\"Accuracy:\", accuracy)\n",
+    "print(\"Confusion Matrix:\\n\", conf_matrix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e368aaee",
+   "metadata": {
+    "papermill": {
+     "duration": 0.013063,
+     "end_time": "2024-01-07T21:48:38.765585",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:38.752522",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "IQ is a terrible predictor. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d6b31dc2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:48:38.794185Z",
+     "iopub.status.busy": "2024-01-07T21:48:38.793489Z",
+     "iopub.status.idle": "2024-01-07T21:48:38.815899Z",
+     "shell.execute_reply": "2024-01-07T21:48:38.814623Z"
+    },
+    "papermill": {
+     "duration": 0.039501,
+     "end_time": "2024-01-07T21:48:38.818455",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:38.778954",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.95\n",
+      "Confusion Matrix:\n",
+      " [[ 9  1]\n",
+      " [ 0 10]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
+    "\n",
+    "X = df[['cgpa']]  # Features\n",
+    "y = df['placement']  # Target variable\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "scaler = StandardScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train_scaled, y_train)\n",
+    "y_pred = model.predict(X_test_scaled)\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
+    "print(\"Accuracy:\", accuracy)\n",
+    "print(\"Confusion Matrix:\\n\", conf_matrix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a95f0656",
+   "metadata": {
+    "papermill": {
+     "duration": 0.013047,
+     "end_time": "2024-01-07T21:48:38.845384",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:38.832337",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "that's actually pretty good!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "7cee2091",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:48:38.873830Z",
+     "iopub.status.busy": "2024-01-07T21:48:38.872806Z",
+     "iopub.status.idle": "2024-01-07T21:48:39.266156Z",
+     "shell.execute_reply": "2024-01-07T21:48:39.265005Z"
+    },
+    "papermill": {
+     "duration": 0.410494,
+     "end_time": "2024-01-07T21:48:39.268902",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:38.858408",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from mlxtend.plotting import plot_decision_regions\n",
+    "\n",
+    "# Assuming you've scaled your features as X_train_scaled and you have y_train from before\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train_scaled, y_train)\n",
+    "\n",
+    "# No need to convert to NumPy arrays since they already are\n",
+    "X_train_np = X_train_scaled  # Already a NumPy array\n",
+    "y_train_np = y_train.to_numpy()  # Convert y_train to a NumPy array if it's not already\n",
+    "\n",
+    "# Plotting\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plot_decision_regions(X_train_np, y_train_np, clf=model, legend=2)\n",
+    "plt.xlabel('Standardized CGPA')\n",
+    "plt.ylabel('Standardized IQ')\n",
+    "plt.title('Decision Boundary for Logistic Regression')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "1777547d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:48:39.297947Z",
+     "iopub.status.busy": "2024-01-07T21:48:39.297532Z",
+     "iopub.status.idle": "2024-01-07T21:48:39.862509Z",
+     "shell.execute_reply": "2024-01-07T21:48:39.861366Z"
+    },
+    "papermill": {
+     "duration": 0.582669,
+     "end_time": "2024-01-07T21:48:39.865299",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:39.282630",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.85\n",
+      "Confusion Matrix:\n",
+      " [[9 1]\n",
+      " [2 8]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X = df[['cgpa','iq']]  # Features\n",
+    "y = df['placement']  # Target variable\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "scaler = StandardScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train_scaled, y_train)\n",
+    "y_pred = model.predict(X_test_scaled)\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
+    "print(\"Accuracy:\", accuracy)\n",
+    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
+    "\n",
+    "# Assuming you've scaled your features as X_train_scaled and you have y_train from before\n",
+    "model = LogisticRegression()\n",
+    "model.fit(X_train_scaled, y_train)\n",
+    "\n",
+    "# No need to convert to NumPy arrays since they already are\n",
+    "X_train_np = X_train_scaled  # Already a NumPy array\n",
+    "y_train_np = y_train.to_numpy()  # Convert y_train to a NumPy array if it's not already\n",
+    "\n",
+    "# Plotting\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plot_decision_regions(X_train_np, y_train_np, clf=model, legend=2)\n",
+    "plt.xlabel('Standardized CGPA')\n",
+    "plt.ylabel('Standardized IQ')\n",
+    "plt.title('Decision Boundary for Logistic Regression')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "690d3474",
+   "metadata": {
+    "papermill": {
+     "duration": 0.015157,
+     "end_time": "2024-01-07T21:48:39.896256",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:39.881099",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "OBVIOUSLY it is a good idea to completely ignore IQ as it isnt good for our prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "f40f9934",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:48:39.928490Z",
+     "iopub.status.busy": "2024-01-07T21:48:39.927892Z",
+     "iopub.status.idle": "2024-01-07T21:48:40.494705Z",
+     "shell.execute_reply": "2024-01-07T21:48:40.493582Z"
+    },
+    "papermill": {
+     "duration": 0.585635,
+     "end_time": "2024-01-07T21:48:40.497521",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:39.911886",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Assume X and y are already defined and split into train and test sets\n",
+    "model = RandomForestClassifier()\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Get and visualize feature importances\n",
+    "importances = model.feature_importances_\n",
+    "indices = np.argsort(importances)\n",
+    "features = X_train.columns\n",
+    "\n",
+    "plt.title('Feature Importances')\n",
+    "plt.barh(range(len(indices)), importances[indices], color='b', align='center')\n",
+    "plt.yticks(range(len(indices)), [features[i] for i in indices])\n",
+    "plt.xlabel('Relative Importance')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "1cbc5a03",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:48:40.532172Z",
+     "iopub.status.busy": "2024-01-07T21:48:40.531384Z",
+     "iopub.status.idle": "2024-01-07T21:48:40.538564Z",
+     "shell.execute_reply": "2024-01-07T21:48:40.537269Z"
+    },
+    "papermill": {
+     "duration": 0.026474,
+     "end_time": "2024-01-07T21:48:40.541180",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:40.514706",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.82750366, 0.17249634])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "importances"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7d57355d",
+   "metadata": {
+    "papermill": {
+     "duration": 0.015415,
+     "end_time": "2024-01-07T21:48:40.571999",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:40.556584",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "IQ doesnt matter much"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "53082a72",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:48:40.604193Z",
+     "iopub.status.busy": "2024-01-07T21:48:40.603757Z",
+     "iopub.status.idle": "2024-01-07T21:48:40.625686Z",
+     "shell.execute_reply": "2024-01-07T21:48:40.624128Z"
+    },
+    "papermill": {
+     "duration": 0.040988,
+     "end_time": "2024-01-07T21:48:40.628318",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:40.587330",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.95\n",
+      "Confusion Matrix:\n",
+      " [[ 9  1]\n",
+      " [ 0 10]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "X = df[['cgpa','iq']]  # Using only 'cgpa' as the feature for simplicity\n",
+    "y = df['placement']  # Target variable\n",
+    "scaler = StandardScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)\n",
+    "model = SVC(kernel='linear')  # You can try other kernels like 'rbf'\n",
+    "model.fit(X_train_scaled, y_train)\n",
+    "y_pred = model.predict(X_test_scaled)\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
+    "\n",
+    "# Print out the accuracy and confusion matrix\n",
+    "print(\"Accuracy:\", accuracy)\n",
+    "print(\"Confusion Matrix:\\n\", conf_matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e1afbc47",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:48:40.660697Z",
+     "iopub.status.busy": "2024-01-07T21:48:40.660263Z",
+     "iopub.status.idle": "2024-01-07T21:48:41.339410Z",
+     "shell.execute_reply": "2024-01-07T21:48:41.338165Z"
+    },
+    "papermill": {
+     "duration": 0.698453,
+     "end_time": "2024-01-07T21:48:41.342170",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:40.643717",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plot_decision_regions(X_train_scaled, y_train.values, clf=model, legend=2)\n",
+    "plt.xlabel('Standardized CGPA')\n",
+    "plt.ylabel('Placement')\n",
+    "plt.title('Decision Boundary for SVM')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "f602665a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:48:41.376604Z",
+     "iopub.status.busy": "2024-01-07T21:48:41.375902Z",
+     "iopub.status.idle": "2024-01-07T21:48:41.396698Z",
+     "shell.execute_reply": "2024-01-07T21:48:41.395167Z"
+    },
+    "papermill": {
+     "duration": 0.041079,
+     "end_time": "2024-01-07T21:48:41.399097",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:41.358018",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.9\n",
+      "Confusion Matrix:\n",
+      " [[10  0]\n",
+      " [ 2  8]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "scaler = StandardScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)\n",
+    "model = KNeighborsClassifier(n_neighbors=5)  # You can experiment with the number of neighbors\n",
+    "model.fit(X_train_scaled, y_train)\n",
+    "y_pred = model.predict(X_test_scaled)\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
+    "\n",
+    "# Print out the accuracy and confusion matrix\n",
+    "print(\"Accuracy:\", accuracy)\n",
+    "print(\"Confusion Matrix:\\n\", conf_matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "a6af45e4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:48:41.432336Z",
+     "iopub.status.busy": "2024-01-07T21:48:41.431934Z",
+     "iopub.status.idle": "2024-01-07T21:49:21.583714Z",
+     "shell.execute_reply": "2024-01-07T21:49:21.582513Z"
+    },
+    "papermill": {
+     "duration": 40.188281,
+     "end_time": "2024-01-07T21:49:21.603184",
+     "exception": false,
+     "start_time": "2024-01-07T21:48:41.414903",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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OO+5Qo0aNJJ0LTgMGDNA777yj48ePq0OHDvrpp580d+5c9e7dW506dcrZ3wMPPKCHHnpIffr00XXXXadNmzZp6dKlRerwnC86OlqjRo3S1KlTdeONN6pHjx7auHGjlixZkmefN954oyZPnqx7771X8fHx+u233zR//vxcnanC6t+/v9544w2tXLlSL774oke1Zxs9erQ++ugjde/eXY888ogiIyM1d+5c7dmzR1988YVXp/pFR0dr+fLluvrqq9W7d2+tWLFCV155pdvxd999t5599lklJiYWav9Wq1Xjxo3Tvffe66WKAaBoCEwAcImeeeYZSecuwBoZGam4uDi99tpruvfee/OEjcsuu0w///yzJk2apDlz5iglJUWVKlVSixYtcvYjnVvRLCEhQRMmTNDChQs1d+5cVapUSZ07d1a1atXyraN69eo5n/LPmzdPoaGhatSokT799FP16dPHbf3h4eFatWqVRo8erblz5+qvv/5Sw4YNNXv27FwXfvWWN954I+f/7Xa76tSpo+eff15PPPFErnHvvfee6tSpozlz5mjhwoWKjY3VmDFjchbIyDZo0CDt2bNH77//vr799lu1a9dOy5cvV+fOnT2u8bnnnlNYWJjefvttrVy5Um3atNGyZctydbYkaezYsTp9+rQWLFigTz75RC1bttQ333yj0aNHF/mYrVq1UpMmTbR169acrqSnYmJi9MMPP+ipp57SjBkzlJaWpqZNm+pf//pXnufgDdWrV9eyZcvUrl07de/eXWvWrFHp0qXzHRsaGqqnn366SAEoeyrfrl27vFUyABSaxeAsSgAASoQWLVooMjLyolPbAADFh3OYAAAoAX7++WclJiaqf//+ZpcCADgPHSYAAEy0efNmbdiwQa+88oqOHTum3bt35zrvDQBgLjpMAACY6PPPP9e9996rzMxMffTRR4QlAChh6DABAAAAgBt0mAAAAADADQITAAAAALhBYAIAAAAAN4LuwrXvrtltdgkAgAD3v51bte3TKfr46dsUEsJnkwBQIkXESk37XnQYf8UBAPAil9OpH798VwOub05YAoAAwF9yAAC8xOV06ovpT+iOluV1w1WNzC4HAOAFQTclDwAAX3A5nfrilSd0Z8sKurdbS7PLAQB4CR0mAAC8IPH7/6h9dRGWACDAEJgAALhEp078qY3/mq3bOsSZXQoAwMuYkpeHIYski9ll+Ijx/7fAfYYAULxOnfhTHz/3kN55uLMa1KhkdjkAAC8jMJ3HIkOlQ5wqZZeslsBsvjkNl85kSKdd1v+PhgCAS/HvmRP09pBOala/qtmlAAB8gMCUw1B5W5bKhdslqz1gw4RFhkrZM2Q7m6E/M0NFpwkALk3m2dO6vE4Vs8sAAPgIgen/hUhyWENkWB0yArS7JJ2bjmexOuSwZikkU3KZXRAAAABQggVuMvCAxRI83ZZgeq4AAACApwhMAAAAAOAGgQkAAAAA3CAwAQAAAIAbBKYAsfij2erftbVualVLj97VQ9t/22h2SQAAAIDfY5U8LzlyaL/Sz551e78jPFyVKlfzybFXf7tI706bqOHjX1TDpi301bx3NW7wnXrvX/9V+aiKPjkmAAAAEAwITF5w5NB+PT3sXmU43S/SbbeG6Lk3Z/skNH35z1nq1uduXX/zHZKk4c+8pJ8SVmjpwo90+wPDvX48AAAAIFgQmLwg/exZZThdimrfT2GRlfPcn5Z6SClr5hXYgfJUZmaGdmz5Vbff/3cwCgkJUYur2mnrpg1ePx4AAAAQTAhMXhQWWVmlKtUo1mP+9WeqXE6nykdF59pePipa/9uzs1hrAQAAAAINiz4AAAAAgBsEJj9XtkKkQqxWHU85mmv78ZSjqhBVyaSqAAAAgMBAYPJzNptd9S9rqsR1/83Z5nK5lPjjf9W4WSsTKwMAAAD8H+cwBYBb+g/Wy+MeVf0mzdQwrrkWzntXaWfP6Pred5hdGgAAAODXCExelJZ6qEjbvaVDt146kZqieW+9pD+PHVWdRk303NsLVKFi9MUfDAAAAMAtApMXOMLDZbeGKGXNPLdj7NYQOcLDfVZDz7vuU8+77vPZ/gEAAIBgRGDygkqVq+m5N2cXeJ0lR3i4Ty5aCwAAAMB3CExeQhgCAAAAAg+r5AEAAACAGwQmAAAAAHCDwAQAAAAAbhCYAAAAAMANAhMAAAAAuEFgAgAAAAA3CEwAAAAA4AaBCQAAD508nqq/Uo8qxGIxuxQAgI8QmALAbz+v1YRh/XXXtc3VLa6yflixxOySACAobPzP55p4V7ysVv45BYBAxV94HzAMQ/t2bZdhGMVyvLSzZ1S7wWUaOm5KsRwPAPD/XC6VLxNudhUAAB8KNbuAQLQ+4TvNf2Oy7n7kGV3ZvrPPj9e6XWe1buf74wAAAADBhg6TlzmdTi37bLbCzhzSss9my+l0ml0SAAAAAA8RmLxsw/er9GfSVo26rrL+TNqqDd+vMrskAAAAAB4iMHlRdnepQw2LbmoapfY1LHSZAAAAAD9GYPKi7O7SwLbRkqSBV1WkywQAAAD4MQKTl5zfXWoQc27FpIaxpegyAQAAAH6MwOQlF3aXshVHl+nsmdPatW2zdm3bLElKPpCkXds268ih/T47JgAAABAMWFbcC7K7S1dXk2pFOZSR5cq5r3bFMF1d7VyXqdXVHWW1Wr1+/D9+36Sn7uuT8/U70yZKkrr0vE2jnn/d68cDAAAAgoXfBKaZM2dq5syZ2rt3rySpSZMmeuaZZ9S9e3dzC5O0e9tmHTu4Vz9kOnXtW3vzHZNl26vd2zarfpNmXj9+s9bx+va3Q17fLwAAABDs/CYwVatWTS+88ILq168vwzA0d+5c9erVSxs3blSTJk1Mra1Wg8bqP2qKsjIz3Y4JtdlUq0HjYqwKAAAAwKXym8B000035fr6+eef18yZM/Xjjz+aHphsNrtatm1vag0AAAAAvM9vAtP5nE6nPvvsM50+fVpt27Z1Oy49PV3p6em5tmVmpMtmd/i6RAAAAAABwK9Wyfvtt98UEREhh8Ohhx56SAsXLtRll13mdvzUqVNVrly5XLcl898uxooBAAAA+DO/CkwNGzZUYmKi1q1bpyFDhmjAgAHasmWL2/FjxozRiRMnct263/2Q2/GGYfii7BIpmJ4rAAAA4Cm/mpJnt9tVr149SVKrVq20fv16vf7665o1a1a+4x0OhxyO3NPvbPZj+Y51SXIZhixyyfCvHFlkFrnkMgy5Lj4UAAAACGp+FZgu5HK58pyj5DmLTmZKtox02ewK2NBkkUuZGek6mXnuKwAAAADu+U1gGjNmjLp3764aNWro5MmTWrBggVatWqWlS5d67RhnXValnnWqTNYZhVgsslgCK1AYhiGXYehk5rnnCgAAAKBgfhOYjhw5ov79++vQoUMqV66cmjZtqqVLl+q6667z4lEsOusK1dn0QO4vnfsvAAAAgIvzm8D0/vvvF+PRLJzfAwAAACBAGykAAAAA4AUEJgAAAABwg8AEAAAAAG4QmAAAAADADQITAAAAALjhN6vkAb6QcvigMtLOur3fHhauqJgqxVgRAAAAShICE4JWyuGDmjF2sDKdhtsxNqtFw6fMIjQBAAAEKQITglZG2lllOg1FtusnR2RsnvvTU5OVmjCvwA4UAAAAAhuBCUHPERmr8OgaZpcBwM+4XC4d2LFZ5Vo3NbsUAIAPsegDAAAe2Lf9NzWukKVm9auaXQoAwIcITAAAeMDlzFJsZBmzywAA+BiBCQAAAADcIDABAAAAgBss+oCgl56aXKTtAAAACB4EJgQte1i4bFaLUhPmuR1js1pkDwsvxqoAAABQkhCYELSiYqpo+JRZBV5nyR4WzkVrAQAAghiBCUGNMAQAAICCsOgDAAAAALhBYAIAAAAANwhMAAAAAOAG5zABgJ9LOXyQxUsAAPARAhMA+LGUwwc1Y+xgZToNt2NsVouGT5lFaAIAwAMEJgDwYxlpZ5XpNBTZrp8ckbF57k9PTVZqwrwCO1DwzI6fVqhXrXJmlwEA8DECEwAEAEdkrMKja5hdRtBY88k/1Cj0kPpf38XsUgAAPsaiDwAAFEFmRrq2rV2u5+8jLAFAMCAwAQBQSJkZ6fpkynCNv+tqs0sBABQTAhMAAIXgcjr18ZRhGtWjgXrGNza7HABAMSEwAQBQCMlJu1S3nKGbCEsAEFRY9AEAAkB6anKRtqNo/jxySP+eMUYLnrzJ7FIAAMWMwAQAfsweFi6b1aLUhHlux9isFtnDwouxqsCz/P0pmjvyBtWtFm12KQCAYkZgAgA/FhVTRcOnzCrwOkv2sHAuWnsJ/khcK8upw2pQnbAEAMGIwAQAfo4w5Dsphw/q549e0dfP3aWQEE77BYBgxF9/AADcSPzPF7rr2stVOtxhdikAAJMQmAAAyMfqT2cp9vQ2PdDjCrNLAQCYiMAEAMAFfvnPl4pK3aRXh3SXxWIxuxwAgIkITAAAnCcrK1OJKxbqidviCUsAAAITAADZXC6XPnvxcY24oYmqVapgdjkAgBKAwAQAwP87djBJle1n1LdjnNmlAABKCAITAACS/ko9psWvPqGn77za7FIAACUIgQkAAEnLP5iqWUO7qHGtWLNLAQCUIAQmAEDQ2/37L8o4tk9xdbkIMAAgNwITACCo7f79F62bN0VfTbpDViv/LAIAcgs1uwAAAMy07O2JWvXyAJUpHWZ2KQCAEoiP0gAAQevHf81Tl5a1CUsAALcITACAoPTr6q9l3ZOgFx64zuxSAAAlGFPyAABBx+V06pflX+jDRzrJYrGYXQ4AoASjwwQACCoup1NfvPKEHuhYR7WrRJldDgCghKPDBAAIKl+/NV53XxGpAde3MLsUAIAfoMMEAAgap/86ruP7/yAsAQAKjQ4TACAonP7ruD56drBmDWGRBwBA4dFhAgAEhQ0rFunxG5qoeYNqZpcCAPAjBCYAQMD73x+btW/tYnVr09jsUgAAfoYpeQCAgPfNm+O0/IW7Vb5MKbNLAQD4GTpMAICAVy4iTJFlS5tdBgDADxGYAAABr1RMHb3z9XqzywAA+CG/CUxTp05V69atVaZMGVWqVEm9e/fW9u3bzS4LAOAHbhr+vD7+JVU/bdlndikAAD/jN4Fp9erVGjp0qH788UctX75cmZmZuv7663X69GmzSwMAlHAhISFq0fU2zVj0kwzDMLscAIAf8ZtFH7799ttcX8+ZM0eVKlXShg0b1L59+3wfk56ervT09FzbMjPSZbM7fFYnAKBkatTqGiUuWaD9R46rekwFs8sBAPgJv+kwXejEiROSpMjISLdjpk6dqnLlyuW6LZn/dnGVCAAoYeL7DlG/Fxfq+MkzZpcCAPATFsMP5ya4XC717NlTx48f13//+1+34/LrMH340wE6TAAQxHb99rNSVr6jd0f0MrsUAICZImKlpn0vOsxvpuSdb+jQodq8eXOBYUmSHA6HHI7c4chmP+bL0oCglnL4oDLSzrq93x4WrqiYKsVYEZBXzcbN9N3c4/oj6Yga1KhkdjkAgBLO7wLTsGHD9PXXX2vNmjWqVq2a2eUA+H8phw9qxtjBynS6b1rbrBYNnzKL0ARThYbadNWtD2vBd4s0cWAXs8sBAJRwfhOYDMPQ8OHDtXDhQq1atUq1a9c2uyQA58lIO6tMp6HIdv3kiIzNc396arJSE+YV2IECistlV1yjz777QgtWbNJdnZuZXQ48ZBiGtu5NVuNasbJYLGaXAyBA+c2iD0OHDtWHH36oBQsWqEyZMkpOTlZycrLOnuXNF1CSOCJjFR5dI88tvxAFmMUaGqq+T76qV75czzLjfmzJ2i3qP2m2lqzdYnYpAAKY3wSmmTNn6sSJE+rYsaMqV66cc/vkk0/MLg0A4IesoaFq3rm3xs/5zuxS4AGn06X3FiVIaSf03qIEOZ0us0sCEKD8JjAZhpHvbeDAgWaXBgDwU1f1HKAl63fq9Nn0iw9GibJ03VYlHTiocZ0jlXTgkJau22p2SQAClN8EJgAAfKHNrUP0ymc/mF0GiiC7u9S+hlU3x5VR+xohdJkA+AyBCQAQ1Bq0iNc3P+/Rzv1HzS4FhZTdXXqwTVlJ0qA2ZekyAfAZAhMAr0pPTdbZo0l5bumpyWaXBuSrdJlyumX0DPUYPVcuFx2Kku787lKjmHPXWmwc46DLBMBn/GZZcQAlmz0sXDarRakJ89yOsVktsoeFF2NVwMW5XC6t/Od0PXVXR4WE8DliSZfdXZrSt0Ku7YPalFW/z851mXrENzGpOgCBiMAEwCuiYqpo+JRZBV5nyR4WzkVrUeL8b+cW1Q47pft7dDK7FFxEdncpvmqI6kTZlZH195LwdaPsiq9q0XuLEtS1TWNZrYRfAN5BYALgNYQh+JvTJ09o+bvP6e0hhCV/kLhjv5KSjykpy6n4mW6m+YYeU+KO/WrVqEbxFgcgYBGYAABBa/HrY/SPhzqqRYNqZpeCQoirW0UvDOurjMwst2PstlDF1eXDGwDeQ2ACAASlg3v+kHHyiFo1rG52KSgkuy1UXVo3NLsMAEGGCb4AgKC0eeWXeqRnS2VmOc0uBQBQghGYAABBqWX3e/TBhlPq+fR8ffPD7zIM4+IPAgAEHYsRZP9CvLtmt9klAABKkP/t3KpfVy1SZlKiqkaX1/CerTgHBgCCQUSs1LTvRYdxDhMAIKhVr9dY1es11vGUIzp76qQefmeyytsy9e7jNykmsowsFovZJQIATESHCQCAC/xv51atmT9dsY4MjezTRq0b1zS7JACAtxWyw8Q5TAAAXKB6vca6e8K7qn79g3pkznqt37rP7JIAACYhMAEA4EbNxi1kZKapYrkIs0sBAJiEwAQAgBs7N29Q16aVVbtKlNmlAABMQmACAMCNNfOmaWivK80uAwBgIgITAAD52LT6G13VqKoqVShjdikAABMRmAAAuMDWdSt05IdP9MbD3c0uBQBgMq7DBADAeQzD0IZvP9XshzorNNRqdjkAAJPRYQIA4Dxbfv6vWlW2qk7VimaXAgAoAQhMAAD8P2dWltZ/9b76dWlqdikAgBKCwAQAwP87tG+XGlQMVYsG1c0uBQBQQnAOE/xGyuGDykg76/Z+e1i4omKqFGNFAALNslkT9OlTN5pdBgCgBCEwwS+kHD6oGWMHK9NpuB1js1o0fMosQhMAj2z7aZVqVwxX9ZgKZpcCAChBCEzwCxlpZ5XpNBTZrp8ckbF57k9PTVZqwrwCO1AA4M7u33/R9m/e1ufP3GF2KX7HMAxt3ZusxrViZbFYzC4HALyOc5jgVxyRsQqPrpHnll+IAoDC2vSfL/RU33iFOWxml+J3lqzdov6TZmvJ2i1mlwIAPkFgAgAEtYN7d6hc2n61a17X7FL8jtPp0nuLEqS0E3pvUYKcTpfZJQGA1xGYAABBbdWH0/VAt5Zml+GXlq7bqqQDBzWuc6SSDhzS0nVbzS4JALyOwAQACFrHjx1WeMZxdW5Vz+xS/E52d6l9Datujiuj9jVC6DIBCEgEJgBAUPrzaLI+n/KwXh9yvdml+KXs7tKDbcpKkga1KUuXCUBAYpU8+JX01OQibQcAd9Z99YFevq+D6levZHYpfuf87lKjGIckqXGMI6fL1LVNY1mtfCYLIDAQmOAX7GHhslktSk2Y53aMzWqRPSy8GKsC4K+Sk3br+K5f1Pq+e8wuxS9ld5em9M19zapBbcqq32fnukw94puYVB0AeBeBCX4hKqaKhk+ZVeB1luxh4Vy0FkChbFq1SA90a84y4h7I7i7FVw1RnSi7MrL+vqB43Si74qta6DIBCCgEJvgNwhAAbzhz6qRSf/+v7rj3XrNL8UuJO/YrKfmYkrKcip/pZjp06DEl7tivVo1qFG9xAOADBCYAKKFSDh+kq+oDhsupSlHlZLFYzC7FL8XVraIXhvVVRmaW2zF2W6ji6vKzCSAwEJgAoARKOXxQM8YOVqbTcDvGZrVo+JRZhKYisoaG6n/JKcrKcio01Gp2OX7HbgtVl9YNzS5DkmQYhrbuTVbjWrEEYAA+Q2ACgBIoI+2sMp2GItv1kyMyNs/96anJSk2YV2AHCvkLKxWh2vE99eHyjRrY/Qqzy8ElWLJ2i555Z5EmP9iLRSYA+AyBCQBKMEdkrMKjOQ/E2ypUraMzqXvNLgOXIHvxCaWdYJEJAD7FXxYAAOB3spc2H9c5kgvmAvApAhMAIOhElKug/25OkmG4P0cMJdf5F869Oa5MzgVznU6X2aUBCEAEJgBA0KlzWXMdt8do8+5DZpcCD2R3lx5sU1bSuQvm0mUC4CsEJgBAUGrR7S498d4Ks8tAEZ3fXWoU45AkNY5x0GUC4DMEJgAowdJTk3X2aFKeW3qqmwuGotBqNW6uNGsZbd932OxSUAQXdpey0WUC4CuskgcAJZA9LFw2q0WpCfPcjrFZLbKHhRdjVYHnxuFT1H/KEM0d0UONasaYXQ4uIru7FF81RHWi7MrI+vsctLpRdsVXtbBiHgCvsxhBdsbru2t2m10CABRKyuGDBV5nyR4WzkVrveBEylF9NmWIPh/bW9VjKphdDgqwYVuSBk/9p5SV7n5QqEOzxvRXq0Ysxw/gIiJipaZ9LzqMwAQAbhBYzGHG933tvz9Wy6xfNeyWq726X3hXRmaW1iTuUkZmltsxdluo2jevK7uNSTQALqKQgYm/JgCQj5TDBzVj7GBlOt1/pmSzWjR8yixCkxeZ9X1vfX0fffrCGlWuuFl92l/utf3Cu+y2UHVp3dDsMgAEGQITAOQjI+2sMp2GItv1kyMyNs/96anJSk2YV2AnBEVn1vc9NNSm20a/rpefvF23tGsii8Xi1f0DAPwXgQkACuCIjFV4NOdCFDczvu+hoTbVb9tVj7z5jR6+6Qo1rpU3sAEAgg9LyAAA8P/a9R0so8lNevCdHzTy7W+1ZQ8XtgWAYEeHCQCA8zRr30OXx1+vPVsTde8bL6tWpEMzH71BkWVLm10aAMAEdJgAAAHBMAwd3LtD3lj81RoaqnpxV+iBaR+r4a1P6aZnF6v/C1/I6XR5oVIAFzIMQ1v2HPLK7y/gbQQmAEBA2Lxulf757HBtXrfKq/utXv8yDZz6ocLiemjivJVe3TeAc5as3aL+k2ZrydotZpcC5EFgAoACpKcm6+zRpDy39NRks0sLaEX9vrucTv2w+ENFpB3SD4s/lMvp9HpNce26aenPu5Ry4rTX9w0EM6fTpfcWJUhpJ/TeogQ6uShx/OocpjVr1mjatGnasGGDDh06pIULF6p3795mlwUgANnDwmWzWpSaMM/tGJvVIntYeDFWFfg8/b7/vn6NTh/Ypqc6V9aLq7fr9/VrFHdVJ6/WFlYqQpd3H6jPV/+swT3jvbpvIJgtXbdVSQcOalznSD2/+pCWrtuqHvFNzC4LyOFXgen06dNq1qyZ7rvvPt1yyy1mlwMggEXFVNHwKbMKvN6PPSyci9Z6mSff9+zuUocaIbohLlI/7jmtHxZ/qCat2yvEavVqfc3iO+v9CfN1a4dmiirHIhDApcruLrWvYdXNcWWUsCdN7y1KUNc2jWW1MhEKJYNfBabu3bure/fuhR6fnp6u9PT0XNsyM9Jlszu8XRoAwEuKGkKzu0v9b6skSerXpqJWf+abLlOpMmVV/8pr9emq3zSk11Ve3TcQjLK7S1P6VpAkDWpTVv0+o8uEksWvAlNRTZ06VZMmTcq17caBj6jnvY+aVBEAf5Fy+KBmjB2sTKf7FZtsVouGT5lFl8lE53eX6lc6N02vQUy4OtSw+KzL1L7vYH356pMqXSpR/a9r7tV9A8Hk/O5So5hzH2Y3jnGofY0QukwoUQI6MI0ZM0YjRozIte3Dnw6YVA0Af5KRdlaZTkOR7frJERmb5/701GSlJswrcOoYfO/C7lI2X3aZQqxW3fL4S/rHyJt1T+emCgnhDR3giQu7S9noMqGkCejA5HA45HDknn5nsx8zqRoA/sgRGavw6Bpml1GglMMHg/Jcq+zu0tXVLKoZ6VBG1t8ra9WKcujqavJZlynEalXd+Bv0ztfr9VDPNl7dNxAMsrtL8VVDVCfKroysv7v5daPsiq9qocuEEiOgAxMABLpgnjr4v51bdCJ5n9ZmOXX9zH35jskI3af/7dyimg3jvH78uA43acErw3VPl+aKKMW5sUBRJO7Yr6TkY0rKcip+ppvLNIQeU+KO/WrVqGR/aIXAR2ACAD8WzFMHq9ZpqBsfniRnVobbMdZQu6rWaeiT40fFVFHV5p20OnGnbvCTaUOGYWjr3mQ1rhUri8VidjkIYnF1q+iFYX2VkZnldozdFqq4uoH1QQ/8k18FplOnTmnnzp05X+/Zs0eJiYmKjIxUjRp8+gAgePnD1EFvC7XZddkVV5tawxXd7tAzE+9TNz+ZNrRk7RY9884iTX6wF+eGwFR2W6i6tPbNhxmAt5X8v+7n+fnnn9WiRQu1aNFCkjRixAi1aNFCzzzzjMmVAQCCUdnIiqrWqKW++n6L2aVcVPY5I0o7ofcWJcjpdF38QQAA/wpMHTt2lGEYeW5z5swxuzQAASo9NVlnjybluaWnuplzj6DT7cFxen3RBrPLuKjsFcnGdY5U0oFzK5Ch5DEMQ1v2HJJhuD8vEUDx8qspeQBQXOxh4bJZLUpNmOd2jM1qkT0svBirQkkUGmqTzVay/zk9/3o3N8eVUcKeNFYgK6GYNgmUPCX7LzwAmCQqpoqGT5kVlMt1o2j279quUiX8X9MLr3fDdW5KpgunTRJogZKhhP+JBwDz+FMYcjdFkKmDvpN9/aslsyZp+sCrtG3f4Vz3l3LYVCM20qTq/nZ+d6lRzLnlzxvHONS+RghvykuY86dNPr+aQAuUFAQmAPBjTB00x/nXvzp5+H/q92JynmW6w60uLXnxPtND04XdpWx0mUoWpk0CJReBCQD8GFMHzXH+9a+yfvhMNbr3U4jVmnP/mT+P6Oia+TqTnmlilX+/CY+vGqI6UXZlZP29kEDdKLviq1p4U15CMG0SKLkITADg5whD5nFExsrqKKWISlUVYi15/6Qm7tivpORjSspyKn6mm+mZoceUuGO/WjUKrut4lSRMmwRKtpL31x0AAD/izEgzuwS34upW0QvD+iojM8vtGLstVHF1PQ/dhmFo695kNa4Vm2daIgqHaZNAyUZgAgDAA1npaUpO+FSR1WqXyO6SdC4MdWnd0KfHYBnsS8O0SaDkK5l/4QEAKKGOHNinlR++qrTjh1X1ii6q2aar2SWZhmWwLx3TJoGSj8AEAEAhZGVm6ItpI5R1IlmP9mypEX9Eq2Kdy4N6GhrLYF+64pg2CeDSeBSY6tSpo/Xr1ysqKirX9uPHj6tly5bavXu3V4oDAKCkOHk8VdVLZWj2M/dq277DslgsOvPnkXzHutseSFgG2zuKY9okgEvjUWDau3evnE5nnu3p6ek6cODAJRcFAP4m+yKm7rC0t/+zh4Xrj6QjSkvPVCmHTeFWl46ume92fLjVpVIOWzFWWLz8bRlsFqcA4KkiBabFixfn/P/SpUtVrly5nK+dTqdWrFihWrVqea04APAH51/E1B2b1aLhU2YRmvxY6TLldNVdI3TLxLf0xcQ7tOTF+wq8zlIph830i9b6ij8ug83iFAA8VaTA1Lt3b0mSxWLRgAEDct1ns9lUq1YtvfLKK14rDgD8wfkXMXVExua5Pz01WakJ8wrsQME/NLqivSwWi26Z8Ka+mHiHSoXZzS7JFP62DDaLUwC4FEUKTC6XS5JUu3ZtrV+/XhUrVvRJUQACW6BOX3NExio8mlWsAl3DVu0ki0W9J7yhW69plLM93B6qu7q0CPg34v64DDaLU5R8TJlESebROUx79uzxdh0AggTT1xAIGra8RmUiY7TpwL6cbX8e2KNZI95X9ZhI2UND9NyAjqoeU6GAvfgnf1sGm8Up/ANTJlGSebys+IoVK7RixQodOXIkp/OU7YMPPrjkwgAEJqavIVBUqVVfVWrVz7Xt7A136Mypk/pk6iM6lPpXQAYmf1sG298WpwhGTJlESedRYJo0aZImT56sK664QpUrV6Z1CqDImL6GQBQSYtXi10dr+v3tdGXjmmaX4xP+tAy2Py5OEYyYMomSzqPA9Pbbb2vOnDnq16+ft+sBAMBv/bjkE7WrW0adWtQzuxTI/xanCEZMmYQ/8OgnMSMjQ/Hx8d6uBQD8Wnpqss4eTcpzS091c54HAk67Xv21McWhOUt/MbuUoJff4hTZt/MXp3A6XRffGXwmO9Q+2KaspHNhNunAuTALlBQedZgeeOABLViwQOPHj/d2PQDgd+xh4bJZLUpNmOd2jM1qkT0svBirghlCrFb1GTlNC6Y/IZexQfd1a2V2SUHL3xanCEZMmYS/8CgwpaWl6Z133tF//vMfNW3aVDZb7iuZT58+3SvFAYA/iIqpouFTZgXkUukouhCrVX1GTNMnrz6pzXuWaMB1TdWsXlWzywo6/rY4RTBiyiT8hUeB6ddff1Xz5s0lSZs3b851HwtAACgMd9PU/HX6GmEI5wuxWnXLiGna9dsGDf7HdL01uINaNaxudllBxZ8WpwhG/ng9LwQvjwLTypUrvV0HgCDB9DUEi5CQENVv1lo//6uC/jyVZnY5QInClEn4E4+vwyRJO3fu1K5du9S+fXuFh4fLMAw6TAAKxPQ1XEzK4YMB9fMRkvanurSqf/GBQBBhyiT8iUeBKSUlRbfddptWrlwpi8WiHTt2qE6dOrr//vtVoUIFvfLKK96uE0AA8ac3uyheKYcPasbYwcp0Gm7H2KwWDZ8yi58jwI8xZRL+xKPA9Pjjj8tmsykpKUmNGzfO2X777bdrxIgRBCYAgEcy0s4q02kosl0/OSJj89yfnpqs1IR5BXagAADwJo8C07Jly7R06VJVq1Yt1/b69etr3759XikMABC8HJGxCo/2//MWTp34Uy6n0+wyAACXwKPAdPr0aZUqVSrP9tTUVDkcjksuCgBKgkA7lwbF6+TxVH3y/BC9O7SLz49lGIa27k1W41qxJfZcYn+oEQDy41Fgateunf75z3/q2WeflXRuKXGXy6WXXnpJnTp18mqBAGAGzqUxjzMrU2mph/K9Ly31kJxZmcVcUdGdPJ6qT557SO8O66KmxXANpiVrt+iZdxZp8oO9Sux1a/yhRgDIj0eB6aWXXlLnzp31888/KyMjQ08++aR+//13paam6vvvv/d2jQBQ7DiXxhzHjx3RXymHdWbpe7KE5P0nynBlKevkMR0/dkSVa9b1WR2H9u1SRvq51zasVGnFVKtd6MeePJ6iT54boveGX1csK3xlX89GaSdK7HVr/KFGAHDHo8B0+eWX648//tCbb76pMmXK6NSpU7rllls0dOhQVa5c2ds1AoBpAuVcGn+RmZEmWe0qf83dskVVy3t/yn4dWzbz3DgfSNr+mzZ9t1Chx7arcc1KkqR1u5IVWr2ZylasrKYdblDZChXzfWxWVqbWL/1cW1Z+qfcfuV6X1ymezuPSdVuVdOCgxnWO1POrD2npuq0lroNT1BoLO32PaX4AioPH12EqV66cxo0b581aAACQJIVERMlWLibPdme6bzp6KYcPasUHUxVy+ojG3RGvds3uzHkD7nK5tHrjTp04fVDPTbhf5Srl/8Hgn8eO6v7OjTRpTC9Vj6ngkzovlN25aV/DqpvjyihhT1qJ6+B4UmNhp+8xzQ9AcfA4MKWlpenXX3/VkSNH5HK5ct3Xs2fPSy4MABB8bI4wWSQ5zxxXxvHkPPc7zxyX5f/HFYZhGFrxz1d0ZPsGWUMKCBCuTL01tJvqV49WyAXjQkJC1KlVA0nSDfFN3F5o0xoSojCHrVB1eUt252ZK33MBbVCbsur3mXe6TN7q3hS1xuyA5TpzvMBgxTQ/AMXFo8D07bffqn///jp27Fie+ywWi5wsoQoA8ED5qEoqUyFKUZUqK6xi9Tz3p9ktyqwQpfJRlS66L8MwtPSDF9W8dIrGv3SPV+qzhVplC7V6ZV+X6vzOTaOYcyvUNo5xqH2NEK8ECG90bzypcem6rfpjd5IebBaidzYluQ1WJWEqIlMCgeDg0V/S4cOHq2/fvjp06JBcLleuG2EJAHAprFarQm122eyOPLdQm11Wa+ECy7fvTVWrMqkaf09H3xZskuzA8GCbsrm2D2pTVkkHzgUIT13YvXE6XRd/kBdqdDpdendRgq6IyVKXOiG6IiZL7+Zz/Aun+WUHME/r9NSStVvUf9JsLVm7pViPC6B4eRSYDh8+rBEjRigmJu/8cgAIJOmpyTp7NCnPLT0173QxlByn/zqu0/s2aexdHcwuxSeyA0N81RDVibIrI8vIudWNsiu+qsUrQWdc50iPw5cnNS5dt1U7difp1sZWVS4Tqj6NrNqxOynP8S8MYt4IiZ4+v0sNlQBKPo+m5N16661atWqV6tb13ZKuAGAme1i4bFaLUhPmuR1js1pkDwsvxqqCh7tAWpigmp52Vp+/9Lgm9Y33dlklRuKO/UpKPqakLKfiZ7r5noQeU+KO/WrVqGirPHprIYmi1nh+d6lZrEMVwq1qXtnQFTHpeve84/t6KmJhlYQpgQCKh0eB6c0331Tfvn2VkJCguLg42Wy5T3J95JFHvFIcAJglKqaKhk+ZVeB1luxh4QF70dqUwwdNee6XGlTT087q4+ce1sS+zdS5VT2v11dSxNWtoheG9XW7AIUk2W2hHl0HylsLSRS1xuzu0gsdrapY+lzgqVg6RH0aWTVm9d/nMl1YXzZvLnhxMf6wOiEA7/EoMH300UdatmyZwsLCtGrVqlwnOlosFgITgIBQlEBgVsDwhZTDBzVj7GBlOg23Y2xWi4ZPmeX153QpQTU97aw+enaIJt/WIqDDknQuaHRp3dDr+/Vm96YoNWZ3l1pUylLjaLussigjy5BVFl1WyaoW0Rl6d1GCulzRMM80v2znT/PzdXDx5eqEAEoejwLTuHHjNGnSJI0ePTrP8qsAEGzMDBi+kJF2VplOQ5Ht+skRGZvn/vTUZKUmzCsw1FwKT75H6WfP6KNnH9Kzd7TStS0DOyz5klndm8Qd+7VtzwGlnc5Qn+QshYb8/UFslsvQiTRDYaUP6NPvfvHZVMTCKilTAgEUH48CU0ZGhm6//XbCEgDI/IDhK47IWIVH++ZNpzelnz2jBc8+pOfvbKVOLQhLnspvkYZsvu7eXFYrVlEVyimynFP3tymX5/73151Qakg53XT15YqNKueTqYiFVRKmBAIoXh4FpgEDBuiTTz7R2LFjvV0PAPgtfwkYgSTtzGl9/NwQTbnrCnVszkJEl8KXC0lczJa9yUpLS9PBLKueXXkqnxFWKTRNOw8c88lUxMIyM1QCMI9HgcnpdOqll17S0qVL1bRp0zyLPkyfPt0rxQEALi6Qzp+6kGEYOrRvpyrXrJfnwqAulysnLHUwOSwFwgVMfbmQREk+dlF4GioD4ecDCGYeBabffvtNLVq0kCRt3rw51338IQCA4hNo509daPO6Vfrm3Rd1w6CnFHdVp1z3HU7arWplQ0wPS9K5C5g+884iTX6wl99Ox/LVQhIl/dhF4WmwC4SfDyCYeRSYVq5c6e06AAAeCNTzpyTJ5XTqh8UfKiLtkH5Y/KGatG6vEKtVknR4/x4teeNJfT6+j8lV5r2AKdOxApcnwY6fD8D/XdJv7M6dO7V06VKdPXvuH2LDcP8JJwDAd7LPn7rwll+IKqz01GSdPZqU51aYi8d6w+/r1+j0gW16vHNlnT6wXb+vXyNJOnZov76ZPlJfjL9VVaPLF0stBTn/AqZJB86d9F9SGYahLXsO8e91MfKnnw8A+fOow5SSkqLbbrtNK1eulMVi0Y4dO1SnTh3df//9qlChgl555RVv1wkAJZ67IFFcAcNbLvXisd6Q3V3qUCNEN8RF6sc9p3O6TLt+W6/HerZQlei8q6kVN3+7gClTw4qXv/18AMifR4Hp8ccfl81mU1JSkho3bpyz/fbbb9eIESMITACCSkkIGN50KReP9Zbs7lL/2ypJkvq1qajVn/3dZQoJKRnny/rTBUyZGlb8/OnnA4B7HgWmZcuWaenSpapWrVqu7fXr19e+ffu8UhgA+IuSEDC8zcxaz+8u1a90LmQ2iAlXhxoWfb/onwq3SkPva2Nafdn87QKm508Ne341b9p9zd9+PgC451FgOn36tEqVKpVne2pqqhwOxyUXBQD+xhcBozDLhQeiC7tL2fq1qailH25Ro7rV1KJBdZOq+5uvL2DqzaWomRpW/LjALRA4PApM7dq10z//+U89++yzks4tJe5yufTSSy+pU6dOF3k0AOBiCrtc+O0Pn7uAeKCcP5XdXbq6mkU1Ix3KyHLl3Ge3ShlnTsh5trycTpepb/SL4wKm3jzfiKlhxYsL3AKBxaPA9NJLL6lz5876+eeflZGRoSeffFK///67UlNT9f3333u7RgAIOoVdLlwWBdT5U//buUUnkvdpbZZT1888N8XbZRg6dvyUzqZnqnbFcKWe+CvPhUGLm6cXMC0sb55vxNSw4ufrnw8AxcujwHT55Zfrjz/+0JtvvqkyZcro1KlTuuWWWzR06FBVrlzZ2zUCQNDKXi7cnfJRlQLq/KmqdRrqxocnyZmVIUnKyszQ6o9nakjnamper7LC7LZ8Lwxa3Dy9gGlhefN8I6aGFT9f/3wAKF4eBSZJKleunMaNG+fNWgrlrbfe0rRp05ScnKxmzZppxowZuvLKK4u9DgAoKfwlDBVGqM2uy664Oufrz196TK8+0EHXt25gYlV5eXIB08Ly5vlGTA0zhy9/PgAUP48C0+zZsxUREaG+ffvm2v7ZZ5/pzJkzGjBggFeKu9Ann3yiESNG6O2331abNm302muvqWvXrtq+fbsqVap08R0AAPxK+l8pur71dWaXUay8eb4RU8MA4NJ5FJimTp2qWbNm5dleqVIlPfjggz4LTNOnT9egQYN07733SpLefvttffPNN/rggw80evRonxwTAGCesjE19OHyRN1zXXNTju/NleoKw9vnGzE1DAAunUeBKSkpSbVr186zvWbNmkpKSrrkovKTkZGhDRs2aMyYMTnbQkJC1KVLF61duzbfx6Snpys9PT3XtsyMdNnsLH0OAP7ghocna860EapY7g91u7L4p+V5c6W6wvD2+UZMDQOAS+fRhOVKlSrp119/zbN906ZNioqKuuSi8nPs2DE5nU7FxMTk2h4TE6Pk5PynGUydOlXlypXLdVsy/22f1AcAvpCemqyzR5Py3PxtuXBPhVitir/1Ic1eulGG4X6JdV+4cKU6p9N18Qd54Xjnn2+UfTv/fCNf1wEAyM2jDtOdd96pRx55RGXKlFH79u0lSatXr9ajjz6qO+64w6sFXooxY8ZoxIgRubZ9+NMBk6oBgMKzh4UH1HLhl6J6vcZKatxJw2Z8rbceuanYjuvNleoKg/ONAKBk8igwPfvss9q7d686d+6s0NBzu3C5XOrfv7+mTJni1QKzVaxYUVarVYcPH861/fDhw4qNzXuNEklyOBxyOHJPv7PZj/mkPgDwpqiYKgG1XPilurr3vZr52NdyuVwKCfH9am7eXKmusDjfCABKJo8Ck91u1yeffKJnn31WmzZtUnh4uOLi4lSzZk1v15frmK1atdKKFSvUu3dvSedC2ooVKzRs2DCfHRdA4Ek5fNAvgoi3a/CX552fX1Z8pS5xVYolLEneXamusDjfCABKJo+vwyRJDRo0UIMGxXcS7ogRIzRgwABdccUVuvLKK/Xaa6/p9OnTOavmAcDFpBw+qBljByvT6f58GJvVouFTZpXY8OAJf3/efx3Zr3vaFs+/N95eqQ4A4N88Dkz79+/X4sWLlZSUpIyMjFz3TZ8+/ZILy8/tt9+uo0eP6plnnlFycrKaN2+ub7/9Ns9CEADgTkbaWWU6DUW26ydHZN7pvOmpyUpNmFdgJ8Yf+f/ztigjM7NYjuTtler8VXEvqQ74A34vgpNHgWnFihXq2bOn6tSpo23btunyyy/X3r17ZRiGWrZs6e0acxk2bBhT8ABcMkdkrMKjg+/EeX993pe1v1Hjpj+uCVlOlSsdpqsuz3tpC2/Ib6W6bOevVBcMXabiXlId8Af8XgQnjwLTmDFjNGrUKE2aNEllypTRF198oUqVKunuu+9Wt27dvF0jACDIVapaUzeOfE0frl2qY//brcjFP+vers3VpVV9rx6HlerOuXBJ9WAIiMDF8HsRvDwKTFu3btVHH310bgehoTp79qwiIiI0efJk9erVS0OGDPFqkQAQKPx54QWzRVepoeg+gyRJ+3dt1/MLXtXp9Cz1im/stWOwUt05xb2kOuAP+L0IXh4FptKlS+ect1S5cmXt2rVLTZqc+4E5doxluwEgP/6+8EJJUq1uQ90y8mV98MoQrwYmVqozZ0l1oKTj9yK4eRSYrrrqKv33v/9V48aN1aNHD40cOVK//fabvvzyS1111VXerhEAAoL/L7xQsoRYL2mhV7hhxpLqQEnH70Vw8+hfm+nTp+vUqVOSpEmTJunUqVP65JNPVL9+fZ+tkAcA3pSemv/5Ke62e5OZCy+Y+by97bfVX6th1UizywgoLKkO5MXvBTwKTHXq1Mn5/9KlS+vtt9/2WkEA4Ev2sHDZrBalJsxzO8ZmtcgeFl6MVfleID7vzd99odUv3mV2GSWCt5Y6Zkn1omGJ6eDA7wWYzwAgqETFVNHwKbOCbuGFQHzetlCrR5/q7j2Uoo1/7M/3vpYNq6tmrP91rbyx1DFLqhcdS0wHPn4vIBUhMFWoUKHQn56kpqZ6XBAA+Jo/hQJvCtbnLUnzV2zSuu2HZBiGftpxRJd16pPn3zTDMPTC1M91ZYMYVa8YoVF9r1ZISMl/A+StpY5ZUr1oWGI6OPB7AakIgem1117zYRkAAF8rypLm/rD8eVaWUy6XKyf4nDh1VvdP/5dOnM3MNc7pdKlMtUa6sucISVK/ijFyhJfKd58tO92oEylHtHXjfxX/6Pu6r2tzPXhja98+kUvkraWOWVK9aAJxiWmmGObF7wWkIgSmAQMGyOl06uWXX9bixYuVkZGhzp07a8KECQoP95857wBgNjMWXijKkuaS/GL588s69laHsZ/nfG0JsardnSNVq3Ezj/cZVqq0wkrVVkz12rrqxru1aMZ4pf61Snd0ilOtylHeKNurvLnUMUuqF16gLjHNFMO8+L2AVMRzmKZMmaKJEyeqS5cuCg8P1+uvv64jR47ogw8+8FV9ABAwzFx4oahLmvvD8udXdLtdV3S73Wf7DwkJUa9HntNPSz7WnS//S12bxmpQj1aqGl3+kvbrzU/xWerYHIH4fWeKIeBekQLTP//5T/3jH//Q4MGDJUn/+c9/dMMNN+i9997zi3neAFAU3p6WVhIWXijKkuZmLn9eUlgsFrXpcaeaX9tbO39dr1uffU2fPX2LqlWqcPEHu+GtT/H9baljX0z3MmMKmb993wsrEKcYAt5SpMCUlJSkHj165HzdpUsXWSwWHTx4UNWqVfN6cQBglqJMYStqaIL/cYSFq8mV7RVdtabunD5e0WGGpj3QWXWqVizSG3Vvforvb0sd+2K6lxlTyPzt+14YgTrFEPCWIv0WZGVlKSwsLNc2m82mzMxMN48AAP90/hS2yr2eyHOLbNdPmU7D9GlpKF6VqtZU/+f+qXo3PqzBs3/R4Olf6fvfdhf68ed/ip904Nyba0/kt9Rx9u38pY6dTpdH+/e2C4OiN+ryxT4Le0x/+b4XVvbP5YNtyko6F/4u5ecTCDRF6jAZhqGBAwfK4XDkbEtLS9NDDz2k0qVL52z78ssvvVchAJiIaWnIT/3mbVW/eVtt3/ijxn0+V5X/9Yvsoef+Se3UrKYGdm2R5zHe/BTf35Y69sV0LzOmkPnb970wAnWKIeBNRQpMAwYMyLPtnnvu8VoxAICiK8y5VvCNhi2uUv1mV+rU8b+vPzhr6pB8A5M3Fwrwp6WOfTHdy6wpZP70fS+sQJxiCHhbkQLT7NmzfVUHAMADhT3X6vaHx0oq2pLmZix/7o9CQkJUNrLi319bbUr967Qiy/4988Lbn+L701LHvlhRzqxV6vzp+14Y+U0xzHb+FEO6TAh2RQpMAICSpbDLhcuiIi1pbtby54Gg8/1P68anx6lXfAONvPVqhYZag/ZTfF9M92IKmfcE4hRDwBcITAAQAC52rlX5qEpFWtLc7OXP/Vn1+pepz/h39fsPyxX/6Hu669o4rfnp16D8FN8XQTFYw6cvBOIUQ8AXCEwAUIBAmpbG8ufFp2yFimp7w526qscdWjDlUe3clqSkCGtQfYrvi+leTCHzrkCbYgj4CoEJAPJhDwsP6mlp3r5ob7CyWCy67Ylpmj/1MTUofUZ92+ff+QjET/F9Md2LKWQAzEBgAoB8RMVUCdppab66aG+wstkdGvDMP7Tk3ee1aW+KxtzV3uySioUvpnsxhQyAGQhMAOBGsIaBwi4k4U8X7TW7Y2axWNR90Dh9+95UTVmwWmPv6uCzY5UUvpjuxRQyAGYgMAFAAPDFuVaBctHektIxs1gs6vbAGC19/wU9N3+1nr478EMTAAQCAhMA+LFgP9eqMEpSx8xisajr/aO19IMX9eyHqzT+no4+PyYA4NIQmADAjwXzuVZFVVI6ZhaLRV3ve0rLZk/T5H+u1DP9O5ldEgCgAAQmAPBzhCH/Y7FYdP29T+g/c1/WpH9+pwn9rzW7JACAG1ykAAAAD6QcPqhD+3a5vaUcPljg4y0Wi64b+IR+z6isCXNW5LnfMAxt2XNIhuH+3CsAgO/RYQIA5CuQLtpbFIVZUU+S1xaSuG7gKC2fO13jZ/9Hz97bJWf7krVb9Mw7izT5wV7qEZ//9ZsAAL5HYAIA5BLMC0kUdkW924eO9epCEtcNGKH//HO6nv5guZ677zo5nS69tyhBSjuh9xYlqGubxrJamRQCAGYgMAEAcgnUhSQK0zEr7Ip6melpkry7kESX/iP073en6rOVv6q0w6qkAwc1rnOknl99SEvXbaXLBAAmITABAPLwtzBUkKJ0zLJDolkr6jXv0kfvv/e0HK7Tal/DqpvjyihhTxpdJgAwEYEJABDQitIxO7RvVzFWlleV2g0U3uBqrfz4bb04rKokaVCbsur3GV0mADALgQkAEPD8pWPmzMrS+uUL1aSSVY1iHJKkxjEOta8RQpcJAEzCX10AAEoAwzA0b8qjMk4d0/u35T5/alCbsko6cK7LBAAoXnSYAADwkLeWXv81YYnWLXxfp1IOqU+TcNWJsisj6++V+upG2RVf1UKXCQBMQGACAOACFwtCNnuY15Ze37T6GyX/92PNeLCjhk6brx/2OxU/003gCj2mxB371apR8S9IAQDBisAEAMD/K+yKepWq1fTK0utnTp3Uv2ZN0bZ5o2QNCdELw/oqIzPL/T5toYqr6x/nYwFAoLAYhuH+6nwB6N01u80uAQBQgqUcPlis16DatObfSk74SB+N6ytbqNVr+/VnhmFo695kNa4VK4vFYnY5AAJVRKzUtO9Fh9FhAgDgPMW9ol6z9j0UEhKia0a+r5uurK2n7+5QrMcviZas3aJn3lmkyQ/2Yil1AKbjrFEAAEwWd0033f/yp0o8XVGT/vmd2eWYyul06b1FCVLaCb23KEFOp8vskgAEOQITAAAlgMVi0fX3PqHfM2I1cW7whqal67Yq6cBBjescyVLqAEoEpuQBAFBCWCwWXTfwCX334evq+OSHCgk597lmZkam+l17mfpd10zhDrvJVfpOdnepfQ2rbo4ro4Q9aSylDsB0BCYAAEqYa+95VNKjOV+7XC599+FrevvxufrH0Ot0VZNaptXmS9ndpSl9K0g6d8Hefp+d6zJxLhMAs/BxDQAAJVxISIg69x+hAVPmadhby8wuxyfO7y41inFIkhrHONS+RgjnMgEwFYEJAAA/USqijGrGtdH7/95gdilel91derBN2VzbB7Upy7lMAExFYAIAwI9cf/9ovfPNz3K5Aqfjkt1diq8aojpRdmVkGTm3ulF2xVe10GUCYBrOYQKAYlTcF0VF4AkJCVGjjjdrxsIf9WifeLPL8YrEHfuVlHxMSVlOxc9Mzn9Q6DEl7tivVo1qFG9xAIIegQkAiknK4YOaMXawMp2G2zE2q0XDp8wiNMEtwzD056F9im5S2uxSvCaubhW9MKyvMjKz3I6x20IVV5ffCwDFj8AEAMUkI+2sMp2GItv1kyMyNs/96anJSk2YV2AHKj90rYLLd/Omq31suu7q3LrIjzUMQ1v3JqtxrVhZLBYfVOfZse22UHVp3bBY6wGAwiIwAUAxc0TGKjzaO9OK6FoFl/SzZ7Tj5zX6+B8PevT4JWu36Jl3Fmnyg72KfZluM48diMwMv0CwITABgB/zVdcKJU/62TP66NmH9PKgaz16fPbCCko7UewXgzXz2IGKAAoUH/5aAUAAyO5aXXjLL0TB/zizsvTRsw/p+buuUKcW9TzaR/ay3eM6Rxb7Mt1mHjsQXRhAWT0Q8C0CEwAAJdyhfTtVr6JNHZvX9ejx518U9ua4MsV6MVgzjx2oCKBA8SIwAQg6KYcP6tC+XW5vKYcPml0ikOPw/j1a9tZYTbnXs6l4Ut6LwhbnxWDNPHYgIoACxc9vzmF6/vnn9c033ygxMVF2u13Hjx83uyQAfqgkLJKQnpr/dWbcbUfwOnowSd9MH6nPx/dR1ejyHu3j/DfYjWIckqTGMY6cN9q+PJ/IzGMHquwAOqVvBUnnAmi/z84FUM5lAnzDbwJTRkaG+vbtq7Zt2+r99983uxwAfsrMRRLsYeGyWS1KTZjndozNapE9LNzrx4Z/2rRyscbd3sbjsCTlfYOdrTjeaJt57EBEAAXM4TeBadKkSZKkOXPmmFsIgIDgzaW9CysqpoqGT5nlk2sm0bUKPL//sEzpu9bq+rtu83gf2W+w46uGqE6UXRlZf3dW60bZFV/V4rM32mYeO1ARQAFz+E1g8kR6errS09NzbcvMSJfN7jCpIgDBztvT/OhaBabf1y7XvhVz9en422S3ef5PdeKO/UpKPqakLKfiZ7oJz6HHlLhjv1o18u4HCGYeOxARQAHzBHRgmjp1ak5nKtuNAx9Rz3sfNakiAPAuX3atYA7DMPT95+/o+5fvuaSwJElxdavohWF9lZGZ5XaM3RaquLre//kw89iBiAAKmMfUwDR69Gi9+OKLBY7ZunWrGjVq5NH+x4wZoxEjRuTa9uFPBzzaFwCUVIShwGEYhpZ98JJ6tq51yWFJOhdIurRu6IXK/OvYgYgACpjH1MA0cuRIDRw4sMAxderU8Xj/DodDDkfu6Xc2+zGP9wcAgC/t+HW9KqXt1vjBPc0uBSUMARQwj6mBKTo6WtHR0WaWACBIsUgCShqXy6Ufv3hb0+5uZXYpAIDz+M05TElJSUpNTVVSUpKcTqcSExMlSfXq1VNERIS5xQHwGyySgJLq6IF9qh5h6KomtcwuBQBwHothGO6v3liCDBw4UHPnzs2zfeXKlerYsWOh9/Pumt1erAqAP0o5fJBFEnDJvPlzlJJ8QF+99Ig+HtNLtSpHeatEAEBBImKlpn0vOsxvApO3EJgAAJcq5fBBzRg7WJlO9/+E2qwWDZ8y66Kh6c8jh/TlC0P18ZjehCUAKE6FDEx+MyUPAICSIiPtrDKdhiLb9ZMjMjbP/empyUpNmFdgBypb4upv9OQtVxCWAKCEIjABAOAhR2SswqM9v+bNtp/X6Pivy9X9lju8WBVQchiGoa17k9W4VqwsFovZ5QAe4VLQAACYYOv61dq2+B/6cuKdCnfYzS4H8Ikla7eo/6TZWrJ2i9mlAB4jMAEAUMx2//aTtv9rpr6YcIfCHDazywF8wul06b1FCVLaCb23KEFOp8vskgCPEJgAAChmexP/qwn3tCcsIaAtXbdVSQcOalznSCUdOKSl67aaXRLgEQITAAAm4HQOBLLs7lL7GlbdHFdG7WuE0GWC3yIwAQDgofTUZJ09mpTnlp6abHZpgKmyu0sPtikrSRrUpixdJvgtVskDAKCI7GHhslktSk2Y53aMzWqRPSw8/ztDQnX0z1M+qg4w1/ndpUYxDklS4xhHTpepa5vGslr5zB7+g8AEBJCUwwcLvO6LPSz8ohfRBHBxUTFVNHzKLI9/39rc1F9Pj+unHvFNfFUiYJrs7tKUvhVybR/Upqz6fXauy8TPPvwJgQkIECmHD2rG2MHKdBpux9isFg2fMovQBHjBpfwelS5bXqXLVrj4QMDPZHeX4quGqE6UXRlZf/+bVDfKrviqFrpM8DsEJiBAZKSdVabTUGS7fnJExua5Pz01WakJ8wr8RBwAgEuRuGO/kpKPKSnLqfiZbs7lCz2mxB371aqR5xd9BooTgQkIMI7IWIVH848QAKD4xdWtoheG9VVGZpbbMXZbqOLqMtMB/oPABACACcpGV9HXa7fpxraNzC4F8Bq7LVRdWjc0uwzAq5g8CgCACW54eJKmL/zJ7DIAABdBYAIAoJg5s7K0Y9N6Wbl4LQCUeEzJAwCgGG38bpE2rVioTg3La8bDXc0uBwBwEQQmIMCkp+a/KpG77QCKx/YNCUr4+C1d3aCi5j3SSXWqVjS7JABAIRCYgABhDwuXzWpRasI8t2NsVovsYeHFWBUQ3DLT07Vn2yatmvOimlSvoFUv3K5wh93ssgAARWAxDMP9VS4D0LtrdptdAuAzKYcPFnidJXtYOBetBYpJ2pnT+vi5Ibq6Xnk90vtKVavEhWoBoESJiJWa9r3oMDpMQAAhDAHmMwxDqxa8oW3rVurVB69Vh+Z1zS4JAHAJCEwAAHjB2dMn9evqr/Xb8k/Vv/Pl+uDVgQpz2MwuCwBwiQhMAABcgpPHU/XbDyu0dfl89WhdT++8fp9CQrhqBwAECgITAAAe+iv1mD59fogGX99Ir029R+UiWFQFAAINgQkASigW8SjZTqQc1WdThmjO493VuFas2eUAAHyEwAQAJVDK4YOaMXawMp3uFzK1WS0aPmUWockEhmHok+eHaN7IHmpUM8bscgAAPkRgAoASKCPtrDKdhiLb9ZMjMm/3Ij01WakJ8wrsQMF39vz+i6qVP3c9pW37Duc7ppTDphqxkcVZFgDABwhMAFCCOSJjFR5dw+wycJ69WxO15oPJSvnzuHqMm+92XLjVpSUv3kdo8iHDMLR1b7Ia14qVxWIxuxwAAYrABABAEWxZ+aXG3XG1hv1jqaLb361SFSrlGXPmzyM6uma+zqRnmlBh8FiydoueeWeRJj/YSz3im5hdDoAARWACgGLEQg6BwWo9t2x4qQqVFFGpmsnVBCen06X3FiVIaSf03qIEdW3TOOd1AQBvIjABQDEpykIOAAq2dN1WJR04qHGdI/X86kNaum4rXSYAPkFgAoBiwkIOgHdkd5fa17Dq5rgyStiTRpcJgM8QmACgmBVlIYf01OQibQeCQXZ3aUrfCpKkQW3Kqt9ndJkA+AaBCQBKIHtYuGxWi1IT5rkdY7NaZA8LL8aqAPOd311qFOOQJDWOcah9jRC6TAB8gsAEACVQVEwVDZ8yiwUiSrgzfx4p0nZcugu7S9noMgHwFQITAJRQhKGSK9weqnCrS0fXFHwdplIOWzFWFfiyu0vxVUNUJ8qujKy/F1CpG2VXfFULXSYAXkdgAgCgiKpEl9eSF+8r8DpLpRw2LlrrZYk79isp+ZiSspyKn+nmPL7QY0rcsV+tGnHBZwDeQWACgGLGQg6BgTBU/OLqVtELw/oqIzPL7Ri7LVRxdenOAvAeAhMAFBMWcgAujd0Wqi6tG5pdBoAgQ2ACgGLCQg6BITSslLYmHVG9atFmlwIAKAYEJgAoRoQh/9f+juF64bkhKlMqTB2b1zW7HACAj7GEDAAARRBWqrTuHP+2xi34WSs37jS7HACAjxGYAAAoIkd4qXOhaf567dp/1OxyAAA+RGACAMADjvBSiru2tz5c8avZpQAAfIjABACAh1pd10cbT1bQq1/8YHYpAAAfITABAOAhi8WiHoOf1uIfd5ldCgDARwhMAABcgpTk/bJZDbPLAAD4CMuKAwBwCZa9+5xmP3qD2WUAAHyEwAQg6KQcPsjFY+EVuzb/rNLOv1QzNtLsUgAAPkJgAhBUUg4f1Iyxg5XpdD+Fyma1aPiUWYQmXNSv//lCE+9uJ4vFYnYpAAAfITABCCoZaWeV6TQU2a6fHJGxee5PT01WasK8AjtQgCT9b8cWRWUmq3Xjq80uBQDgQwQmAEHJERmr8OgaZpcBP2UYhhI+fkPjezWnuwQAAY5V8gAA8IAt/U9d27Ke2WUAAHyMwAQAQBHt/v0XVSpX2uwyAADFgMAEAEARrZn3smY+wlLi/sQwDG3Zc0iGwTWzABQNgQkAgCJIXLlYLWuVV5nSYWaXgiJYsnaL+k+arSVrt5hdCgA/4xeBae/evbr//vtVu3ZthYeHq27dupowYYIyMjLMLg2An0pPTdbZo0l5bumpyWaXhhLM5XRq4/LPNeaOa8wuBUXgdLr03qIEKe2E3luUIKfTZXZJAPyIX6ySt23bNrlcLs2aNUv16tXT5s2bNWjQIJ0+fVovv/yy2eUB8CP2sHDZrBalJsxzO8ZmtcgeFl6MVcFf/PrjSnW9LEpVo8ubXQqKYOm6rUo6cFDjOkfq+dWHtHTdVvWIb2J2WQD8hF8Epm7duqlbt245X9epU0fbt2/XzJkzCUwAiiQqpoqGT5lV4HWW7GHhXLQW+Tp76qRqx5Y1uwwUQXZ3qX0Nq26OK6OEPWl6b1GCurZpLKvVLybaADCZXwSm/Jw4cUKRkZEFjklPT1d6enqubZkZ6bLZHb4sDUAJRxiCJ9LOnNLmJXM07fm7zS4FRZDdXZrSt4IkaVCbsur3GV0mAIXnlx+t7Ny5UzNmzNDgwYMLHDd16lSVK1cu123J/LeLqUoAQCA5e/qULq8doyiWE/cb53eXGsWc+7C0cYxD7WuEcC4TgEIzNTCNHj1aFoulwNu2bdtyPebAgQPq1q2b+vbtq0GDBhW4/zFjxujEiRO5bt3vfsiXTwkAAJQQ2d2lB9vknkY5qE1ZJR0412UCgIsxdUreyJEjNXDgwALH1KlTJ+f/Dx48qE6dOik+Pl7vvPPORffvcDjkcOSefmezH/OoVgAA4D+yu0vxVUNUJ8qujKy/r79UN8qu+KoWzmUCUCimBqbo6GhFR0cXauyBAwfUqVMntWrVSrNnz1ZICH/cAABA/hJ37FdS8jElZTkVP9PN5QJCjylxx361alSjeIsD4Ff8YtGHAwcOqGPHjqpZs6ZefvllHT16NOe+2NhYEysD4ImUwwdZpQ6AT8XVraIXhvVVRmaW2zF2W6ji6vK3BkDB/CIwLV++XDt37tTOnTtVrVq1XPcZhuHmUQBKopTDBzVj7GBlOt3/7tqsFg2fMovQBMBjdluourRuaHYZAAKAXwSmgQMHXvRcJwD+ISPtrDKdhiLb9ZMjMm+HOD01WakJ8wrsQAEAABQXvwhMAAKPIzJW4dGcNwAAAEo2Vk4AAAAAADcITAAAAADgBoEJAAAAANwgMAEAUAjr/zVHbRtVNbsMAEAxY9EHAKZIT83/QpLutgNmO7ojUYMe7Gd2GQCAYkZgAlCs7GHhslktSk2Y53aMzWqRPSy8GKsCLs5q9f9JGYZhaOveZDWuFSuLxWJ2OQDgFwhMAIpVVEwVDZ8yq8DrLNnDwrloLeADS9Zu0TPvLNLkB3upR3wTs8sBAL9AYAJQ7AhDQPFzOl16b1GClHZC7y1KUNc2jQOiawYAvsZfSgAALuKvP4/JmZlhdhmXZOm6rUo6cFDjOkcq6cAhLV231eySAMAvEJgAACjAX38e02fPD9FbQ7uZXYrHsrtL7WtYdXNcGbWvEaL3FiXI6XSZXRoAlHgEJgAACrBp5SKNvqWlmtSpbHYpHsvuLj3YpqwkaVCbsnSZAKCQCEwAABTEMFSmlMPsKjx2fnepUcy559E4xkGXCQAKicAEAEAAu7C7lI0uEwAUDoEJAIAAld1diq8aojpRdmVkGTm3ulF2xVe10GUCgItgWXEAAAJU4o79Sko+pqQsp+JnJuc/KPSYEnfsV6tGNYq3OADwEwQmAAACVFzdKnphWF9lZGa5HWO3hSquLtdGAwB3CEwAAAQouy1UXVo3NLsMAPBrnMMEAAAAAG4QmAAAAADADQITAAAAALhBYAIAAAAANwhMAAAAAOAGgQkAAAAA3CAwAQAAAIAbBCYAAAAAcIPABAAAAABuEJgAAAAAwA0CEwAAF5GWkWV2CQAAkxCYAAAowOXtbtCkBT8oK8tpdikAABMQmAAAKECFSpVVJipGGQQmAAhKBCYAAAAAcIPABAAAAABuEJgAAChAetpZpR45rFAr/2QCQDDirz8AAAVYv/RTPdaruey2ULNLAQCYgMAEAEABjKxMVatY1uwyAAAmITABAAAAgBsEJgAAAABwg8AEAAAAAG4QmAAAAADADQITAAAAALhBYAIA4CKcTpfZJQAATEJgAgCgAPWu6KRnP/6B0AQAQYrABABAASrXrCt72WilZ2aZXQoAwAQEJgAAAABwg8AEAAAAAG4QmAAAAADADQITAAAAALhBYAIAAAAANwhMAAAAAOAGgQkAAAAA3CAwAQAAAIAbBCYAAAAAcIPABAAAAABuEJgAAAAAwA2/CUw9e/ZUjRo1FBYWpsqVK6tfv346ePCg2WUBAAAACGB+E5g6deqkTz/9VNu3b9cXX3yhXbt26dZbbzW7LAAAAAABLNTsAgrr8ccfz/n/mjVravTo0erdu7cyMzNls9lMrAwAAABAoPKbwHS+1NRUzZ8/X/Hx8QWGpfT0dKWnp+falpmRLpvd4esSAQAAAAQAv5mSJ0lPPfWUSpcuraioKCUlJWnRokUFjp86darKlSuX67Zk/tvFVC0AAAAAf2dqYBo9erQsFkuBt23btuWMf+KJJ7Rx40YtW7ZMVqtV/fv3l2EYbvc/ZswYnThxItet+90PFcdTAwAEEEfpctqw/X9mlwEAMIHFKChx+NjRo0eVkpJS4Jg6derIbrfn2b5//35Vr15dP/zwg9q2bVvoY767ZneR6wQABLezp0/q80n3as30e80uBQDgLRGxUtO+Fx1m6jlM0dHRio6O9uixLpdLkvKcowQAgLeFly4jWz4f3gEAAp9fLPqwbt06rV+/Xtdcc40qVKigXbt2afz48apbt26RuksAAAAAUBR+sehDqVKl9OWXX6pz585q2LCh7r//fjVt2lSrV6+Ww8GKdwAAAAB8wy86THFxcfruu+/MLgMAEMQyM7NkGIYsFovZpQAAipFfdJgAADBbjZadNP3zH8wuAwBQzAhMAAAUQtNrb9bvScfMLgMAUMwITAAAFEKpMuW0ee8x7T/yp9mlAACKEYEJAIBCcISFq0XPB/T56s1mlwIAKEYEJgAACqlp/LVa8P0eHU79y+xSAADFhMAEAEAh2R1hurzDDZr19QazSwEAFBMCEwAARXDVjf30beL/zC4DAFBMCEwAABSBxWJRSKhdySlMywOAYEBgAgCgiK5/cIIenvFvs8sAABQDAhMAAEUUW7Oukk9LG7YzNQ8AAl2o2QUUt2oVws0uAQAQAPqNfE5b9y5Tq/I1zC4FAOCJ8MhCDbMYhmH4uBR4WXp6uqZOnaoxY8bI4XCYXQ4uEa9n4OC1DCy8noGD1zKw8HoGDn95LQlMfuivv/5SuXLldOLECZUtW9bscnCJeD0DB69lYOH1DBy8loGF1zNw+MtryTlMAAAAAOAGgQkAAAAA3CAwAQAAAIAbBCY/5HA4NGHChBJ9chwKj9czcPBaBhZez8DBaxlYeD0Dh7+8liz6AAAAAABu0GECAAAAADcITAAAAADgBoEJAAAAANwgMAEAAACAGwSmANCzZ0/VqFFDYWFhqly5svr166eDBw+aXRaKaO/evbr//vtVu3ZthYeHq27dupowYYIyMjLMLg0eeP755xUfH69SpUqpfPnyZpeDInrrrbdUq1YthYWFqU2bNvrpp5/MLgkeWLNmjW666SZVqVJFFotFX331ldklwUNTp05V69atVaZMGVWqVEm9e/fW9u3bzS4LHpo5c6aaNm2qsmXLqmzZsmrbtq2WLFlidlluEZgCQKdOnfTpp59q+/bt+uKLL7Rr1y7deuutZpeFItq2bZtcLpdmzZql33//Xa+++qrefvttjR071uzS4IGMjAz17dtXQ4YMMbsUFNEnn3yiESNGaMKECfrll1/UrFkzde3aVUeOHDG7NBTR6dOn1axZM7311ltml4JLtHr1ag0dOlQ//vijli9frszMTF1//fU6ffq02aXBA9WqVdMLL7ygDRs26Oeff9a1116rXr166ffffze7tHyxrHgAWrx4sXr37q309HTZbDazy8ElmDZtmmbOnKndu3ebXQo8NGfOHD322GM6fvy42aWgkNq0aaPWrVvrzTfflCS5XC5Vr15dw4cP1+jRo02uDp6yWCxauHChevfubXYp8IKjR4+qUqVKWr16tdq3b292OfCCyMhITZs2Tffff7/ZpeRBhynApKamav78+YqPjycsBYATJ04oMjLS7DKAoJGRkaENGzaoS5cuOdtCQkLUpUsXrV271sTKAJzvxIkTksS/kQHA6XTq448/1unTp9W2bVuzy8kXgSlAPPXUUypdurSioqKUlJSkRYsWmV0SLtHOnTs1Y8YMDR482OxSgKBx7NgxOZ1OxcTE5NoeExOj5ORkk6oCcD6Xy6XHHntMV199tS6//HKzy4GHfvvtN0VERMjhcOihhx7SwoULddlll5ldVr4ITCXU6NGjZbFYCrxt27YtZ/wTTzyhjRs3atmyZbJarerfv7+YbVkyFPW1lKQDBw6oW7du6tu3rwYNGmRS5biQJ68lAMC7hg4dqs2bN+vjjz82uxRcgoYNGyoxMVHr1q3TkCFDNGDAAG3ZssXssvLFOUwl1NGjR5WSklLgmDp16shut+fZvn//flWvXl0//PBDiW1tBpOivpYHDx5Ux44dddVVV2nOnDkKCeFzjZLCk99LzmHyLxkZGSpVqpQ+//zzXOe6DBgwQMePH6d778c4hykwDBs2TIsWLdKaNWtUu3Zts8uBF3Xp0kV169bVrFmzzC4lj1CzC0D+oqOjFR0d7dFjXS6XJCk9Pd2bJcFDRXktDxw4oE6dOqlVq1aaPXs2YamEuZTfS/gHu92uVq1aacWKFTlvrF0ul1asWKFhw4aZWxwQxAzD0PDhw7Vw4UKtWrWKsBSAXC5XiX3vSmDyc+vWrdP69et1zTXXqEKFCtq1a5fGjx+vunXr0l3yMwcOHFDHjh1Vs2ZNvfzyyzp69GjOfbGxsSZWBk8kJSUpNTVVSUlJcjqdSkxMlCTVq1dPERER5haHAo0YMUIDBgzQFVdcoSuvvFKvvfaaTp8+rXvvvdfs0lBEp06d0s6dO3O+3rNnjxITExUZGakaNWqYWBmKaujQoVqwYIEWLVqkMmXK5JxTWK5cOYWHh5tcHYpqzJgx6t69u2rUqKGTJ09qwYIFWrVqlZYuXWp2afliSp6f++233/Too49q06ZNOn36tCpXrqxu3brp6aefVtWqVc0uD0UwZ84ct2/I+DX1PwMHDtTcuXPzbF+5cqU6duxY/AWhSN58801NmzZNycnJat68ud544w21adPG7LJQRKtWrVKnTp3ybB8wYIDmzJlT/AXBYxaLJd/ts2fP1sCBA4u3GFyy+++/XytWrNChQ4dUrlw5NW3aVE899ZSuu+46s0vLF4EJAAAAANzgBAkAAAAAcIPABAAAAABuEJgAAAAAwA0CEwAAAAC4QWACAAAAADcITAAAAADgBoEJAAAAANwgMAEAAACAGwQmAIBp9u7dK4vFosTERJ/ve9WqVbJYLDp+/LjXj5Vt4sSJat68uc/2DwAofgQmAAgiR48e1ZAhQ1SjRg05HA7Fxsaqa9eu+v7773PGWCwWffXVV+YV6SPx8fE6dOiQypUrZ3Yp+uKLL9SxY0eVK1dOERERatq0qSZPnqzU1NScMRkZGZo2bZpatmyp0qVLq1y5cmrWrJmefvppHTx4MGfcwIEDZbFYZLFYZLfbVa9ePU2ePFlZWVm5jtm1a1dZrVatX7++2J4nAAQCAhMABJE+ffpo48aNmjt3rv744w8tXrxYHTt2VEpKitmleSwjI6NQ4+x2u2JjY2WxWHxcUcHGjRun22+/Xa1bt9aSJUu0efNmvfLKK9q0aZPmzZsnSUpPT9d1112nKVOmaODAgVqzZo1+++03vfHGGzp27JhmzJiRa5/dunXToUOHtGPHDo0cOVITJ07UtGnTcu5PSkrSDz/8oGHDhumDDz4o1ucLAH7PAAAEhT///NOQZKxatcrtmJo1axqScm41a9Y0DMMwdu7cafTs2dOoVKmSUbp0aeOKK64wli9fnuexzz//vHHvvfcaERERRvXq1Y1Zs2blGrNu3TqjefPmhsPhMFq1amV8+eWXhiRj48aNhmEYRlZWlnHfffcZtWrVMsLCwowGDRoYr732Wq59DBgwwOjVq5fx3HPPGZUrVzZq1apVqH2vXLnSkGT8+eefhmEYRocOHXI91+zbnj17cr5f999/v1GxYkWjTJkyRqdOnYzExMRctUydOtWoVKmSERERYdx3333GU089ZTRr1szt93fdunWGpDzPKVt2bVOnTjVCQkKMX375Jd9xLpcrz/fjfNddd51x1VVX5Xw9ceJE44477jC2bt1qlCtXzjhz5ozbGgEAudFhAoAgERERoYiICH311VdKT0/Pd0z2dK3Zs2fr0KFDOV+fOnVKPXr00IoVK7Rx40Z169ZNN910k5KSknI9/pVXXtEVV1yhjRs36uGHH9aQIUO0ffv2nH3ceOONuuyyy7RhwwZNnDhRo0aNyvV4l8ulatWq6bPPPtOWLVv0zDPPaOzYsfr0009zjVuxYoW2b9+u5cuX6+uvvy7Uvi/05Zdf6tChQzm3W265RQ0bNlRMTIwkqW/fvjpy5IiWLFmiDRs2qGXLlurcuXPOtLlPP/1UEydO1JQpU/Tzzz+rcuXK+sc//lHgMefPn6+IiAg9/PDD+d5fvnx5SdJHH32k6667Ti1atMh33MW6ZOHh4TmdN8MwNHv2bN1zzz1q1KiR6tWrp88//7zAxwMAzmN2YgMAFJ/PP//cqFChghEWFmbEx8cbY8aMMTZt2pRrjCRj4cKFF91XkyZNjBkzZuR8XbNmTeOee+7J+drlchmVKlUyZs6caRiGYcyaNcuIiooyzp49mzNm5syZubpA+Rk6dKjRp0+fnK8HDBhgxMTEGOnp6TnbCrPvCztM55s+fbpRvnx5Y/v27YZhGEZCQoJRtmxZIy0tLde4unXr5nTN2rZtazz88MO57m/Tpk2BHabu3bsbTZs2dXt/trCwMOORRx7Jta13795G6dKljdKlSxtt27bN2X5+h8nlchnLly83HA6HMWrUKMMwDGPZsmVGdHS0kZmZaRiGYbz66qtGhw4dLloDAOAcOkwAEET69OmjgwcPavHixerWrZtWrVqlli1bas6cOQU+7tSpUxo1apQaN26s8uXLKyIiQlu3bs3TYWratGnO/1ssFsXGxurIkSOSpK1bt6pp06YKCwvLGdO2bds8x3rrrbfUqlUrRUdHKyIiQu+8806e48TFxclut+d8Xdh952fJkiUaPXq0PvnkEzVo0ECStGnTJp06dUpRUVE5nbmIiAjt2bNHu3btyjlmmzZtcu3rYsc0DKNQNeXnH//4hxITE3XffffpzJkzue77+uuvFRERobCwMHXv3l233367Jk6cKEn64IMPdPvttys0NFSSdOedd+r777/PeR4AgIKFml0AAKB4hYWF6brrrtN1112n8ePH64EHHtCECRM0cOBAt48ZNWqUli9frpdffln16tVTeHi4br311jwLLthstlxfWywWuVyuQtf28ccfa9SoUXrllVfUtm1blSlTRtOmTdO6detyjStdunSh91mQLVu26I477tALL7yg66+/Pmf7qVOnVLlyZa1atSrPY7KnzXmiQYMG+u9//6vMzMw836vz1a9fP2cqY7bKlStLkiIjI/OM79Spk2bOnCm73a4qVarkhKPU1FQtXLhQmZmZmjlzZs54p9OpDz74QM8//7zHzwUAggUdJgAIcpdddplOnz6d87XNZpPT6cw15vvvv9fAgQN18803Ky4uTrGxsdq7d2+RjtO4cWP9+uuvSktLy9n2448/5jlOfHy8Hn74YbVo0UL16tUrVCekMPu+0LFjx3TTTTepT58+evzxx3Pd17JlSyUnJys0NFT16tXLdatYsWLOMS8Mchc75l133aVTp065Pdcp+xpRd955p5YvX66NGzcWuL9spUuXVr169VSjRo2csCSdO2eqWrVq2rRpkxITE3Nur7zyiubMmZPndQYA5EVgAoAgkZKSomuvvVYffvihfv31V+3Zs0efffaZXnrpJfXq1StnXK1atbRixQolJyfrzz//lHSu4/Hll18qMTFRmzZt0l133VWkzpF0LixYLBYNGjRIW7Zs0b///W+9/PLLucbUr19fP//8s5YuXao//vhD48ePL9R1gwqz7wv16dNHpUqV0sSJE5WcnJxzczqd6tKli9q2bavevXtr2bJl2rt3r3744QeNGzdOP//8syTp0Ucf1QcffKDZs2frjz/+0IQJE/T7778XeMw2bdroySef1MiRI/Xkk09q7dq12rdvn1asWKG+fftq7ty5kqTHH39cbdu2VefOnfX666/rl19+0Z49e7R06VItWbJEVqv1ot8TSXr//fd166236vLLL891u//++3Xs2DF9++23hdoPAAQzAhMABImIiAi1adNGr776qtq3b6/LL79c48eP16BBg/Tmm2/mjHvllVe0fPlyVa9ePWeVtunTp6tChQqKj4/XTTfdpK5du6ply5ZFPv6//vUv/fbbb2rRooXGjRunF198MdeYwYMH65ZbbtHtt9+uNm3aKCUlxe2KckXd94XWrFmjzZs3q2bNmqpcuXLO7X//+58sFov+/e9/q3379rr33nvVoEED3XHHHdq3b1/OKnq33367xo8fryeffFKtWrXSvn37NGTIkIvW+uKLL2rBggVat26dunbtqiZNmmjEiBFq2rSpBgwYIOnctMkVK1boqaee0uzZs3XNNdeocePGeuyxx3T11VcX6sLCGzZs0KZNm9SnT58895UrV06dO3fW+++/f9H9AECwsxiXcgYqAAAAAAQwOkwAAAAA4AaBCQAAAADcIDABAAAAgBsEJgAAAABwg8AEAAAAAG4QmAAAAADADQITAAAAALhBYAIAAAAANwhMAAAAAOAGgQkAAAAA3CAwAQAAAIAb/wdeTsGLVpv4XgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plot_decision_regions(X_train_scaled, y_train.values, clf=model, legend=2)\n",
+    "plt.xlabel('Standardized CGPA')\n",
+    "plt.ylabel('Placement')\n",
+    "plt.title('Decision Boundary for KNN')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "64246131",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-01-07T21:49:21.638544Z",
+     "iopub.status.busy": "2024-01-07T21:49:21.638096Z",
+     "iopub.status.idle": "2024-01-07T21:49:22.333668Z",
+     "shell.execute_reply": "2024-01-07T21:49:22.332582Z"
+    },
+    "papermill": {
+     "duration": 0.71665,
+     "end_time": "2024-01-07T21:49:22.336271",
+     "exception": false,
+     "start_time": "2024-01-07T21:49:21.619621",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.9\n",
+      "Confusion Matrix:\n",
+      " [[10  0]\n",
+      " [ 2  8]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import xgboost as xgb\n",
+    "\n",
+    "X = df[['cgpa', 'iq']]  # Using both 'cgpa' and 'iq' as features\n",
+    "y = df['placement']  # Target variable\n",
+    "\n",
+    "# Split the data into training and test sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "\n",
+    "# Create and train the XGBoost model\n",
+    "model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='logloss')\n",
+    "model.fit(X_train, y_train)\n",
+    "\n",
+    "# Make predictions and evaluate the model\n",
+    "y_pred = model.predict(X_test)\n",
+    "accuracy = accuracy_score(y_test, y_pred)\n",
+    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
+    "\n",
+    "# Print out the accuracy and confusion matrix\n",
+    "print(\"Accuracy:\", accuracy)\n",
+    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
+    "\n",
+    "# Visualization\n",
+    "# Choose a representative value for 'iq' (e.g., the mean)\n",
+    "mean_iq = X_train['iq'].mean()\n",
+    "\n",
+    "# Generate a sequence of CGPA values from the min to max range\n",
+    "cgpa_range = np.linspace(X_train['cgpa'].min(), X_train['cgpa'].max(), 100).reshape(-1, 1)\n",
+    "\n",
+    "# Create a 2D array combining 'cgpa_range' and the constant 'iq' value\n",
+    "combined_input = np.hstack((cgpa_range, np.full_like(cgpa_range, mean_iq)))\n",
+    "\n",
+    "# Predict placement for these CGPA and constant IQ values\n",
+    "predicted_placement = model.predict(combined_input)\n",
+    "\n",
+    "# Plotting\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(X_train['cgpa'], y_train, color='blue', label='Training data')\n",
+    "plt.plot(cgpa_range, predicted_placement, color='red', label='XGBoost Decision Boundary')\n",
+    "plt.xlabel('CGPA')\n",
+    "plt.ylabel('Placement')\n",
+    "plt.title('CGPA vs. Placement (XGBoost with constant IQ)')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kaggle": {
+   "accelerator": "none",
+   "dataSources": [
+    {
+     "datasetId": 4233528,
+     "sourceId": 7298157,
+     "sourceType": "datasetVersion"
+    }
+   ],
+   "dockerImageVersionId": 30626,
+   "isGpuEnabled": false,
+   "isInternetEnabled": true,
+   "language": "python",
+   "sourceType": "notebook"
+  },
+  "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"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 52.769819,
+   "end_time": "2024-01-07T21:49:23.076299",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "__notebook__.ipynb",
+   "output_path": "__notebook__.ipynb",
+   "parameters": {},
+   "start_time": "2024-01-07T21:48:30.306480",
+   "version": "2.4.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From cee997b14ef272bb66d541f44ab4c114026b9c28 Mon Sep 17 00:00:00 2001
From: VARUNSHIYAM <138989960+Varunshiyam@users.noreply.github.com>
Date: Sat, 9 Nov 2024 19:34:58 +0530
Subject: [PATCH 2/2] Create Readme.md

---
 .../College_placement_prediction/Readme.md    | 50 +++++++++++++++++++
 1 file changed, 50 insertions(+)
 create mode 100644 Prediction Models/College_placement_prediction/Readme.md

diff --git a/Prediction Models/College_placement_prediction/Readme.md b/Prediction Models/College_placement_prediction/Readme.md
new file mode 100644
index 00000000..9b9486d9
--- /dev/null
+++ b/Prediction Models/College_placement_prediction/Readme.md	
@@ -0,0 +1,50 @@
+# College Placement Prediction Model
+
+This repository contains a machine learning model designed to predict college placement outcomes based on student profiles. By analyzing historical placement data and various student attributes, the model aims to estimate the likelihood of a student's placement, helping institutions and students make informed decisions.
+
+## Table of Contents
+- [Introduction](#introduction)
+- [Problem Statement](#problem-statement)
+- [Solution Overview](#solution-overview)
+- [Data](#data)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Model Evaluation](#model-evaluation)
+- [Contributing](#contributing)
+- [License](#license)
+
+## Introduction
+
+In an increasingly competitive job market, predicting college placement success is valuable for students, colleges, and recruiters. This model leverages multiple machine learning algorithms to forecast placement outcomes based on academic performance, demographic data, and extracurricular involvement.
+
+## Problem Statement
+
+Predicting college placements accurately is challenging due to:
+- **Variety of Influencing Factors**: Academic scores, previous experience, and other personal characteristics affect placement outcomes.
+- **Data Quality**: Differences in available data for each student can affect model accuracy.
+- **Feature Engineering**: Identifying key predictive factors is crucial for improving the model’s performance.
+
+This project addresses these challenges by building a model that leverages various attributes of student profiles to predict their placement likelihood.
+
+## Solution Overview
+
+The model is built using several machine learning algorithms, including:
+- **Logistic Regression**
+- **Decision Trees**
+- **Random Forest**
+- **Support Vector Machine (SVM)**
+
+Key steps in the project include:
+1. **Data Cleaning and Preparation**: Handling missing values, scaling numerical features, and encoding categorical data.
+2. **Feature Engineering**: Selecting and refining features to enhance model accuracy.
+3. **Model Training and Evaluation**: Training multiple models and comparing their performance to select the best one for predicting placements.
+
+## Data
+
+The dataset includes features like:
+- **Academic Performance**: Grades, test scores, and other academic achievements.
+- **Demographic Information**: Age, gender, and location.
+- **Extracurriculars and Skills**: Involvement in projects, certifications, and relevant skills.
+
+Data should be placed in the `data/` directory in CSV format.
+