{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 20,
     "status": "ok",
     "timestamp": 1783498625957,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "9WGTCWb0-6Wl"
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "Module 'scipy' has no attribute '_lib'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\courses\\basics of python\\.vscode\\pyAI3.6\\Lib\\site-packages\\scipy\\__init__.py:146\u001b[39m, in \u001b[36m__getattr__\u001b[39m\u001b[34m(name)\u001b[39m\n\u001b[32m    145\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m146\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mglobals\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m[\u001b[39;49m\u001b[30;43mname\u001b[39;49m\u001b[30;43m]\u001b[39;49m\n\u001b[32m    147\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n",
      "\u001b[31mKeyError\u001b[39m: '_lib'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[31mAttributeError\u001b[39m                            Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m pandas \u001b[38;5;28;01mas\u001b[39;00m pd\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m numpy \u001b[38;5;28;01mas\u001b[39;00m np\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m matplotlib.pyplot \u001b[38;5;28;01mas\u001b[39;00m plt\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m seaborn \u001b[38;5;28;01mas\u001b[39;00m sns\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\courses\\basics of python\\.vscode\\pyAI3.6\\Lib\\site-packages\\seaborn\\__init__.py:5\u001b[39m\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *  \u001b[38;5;66;03m# noqa: F401,F403\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpalettes\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *  \u001b[38;5;66;03m# noqa: F401,F403\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mrelational\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *  \u001b[38;5;66;03m# noqa: F401,F403\u001b[39;00m\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mregression\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *  \u001b[38;5;66;03m# noqa: F401,F403\u001b[39;00m\n\u001b[32m      7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcategorical\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *  \u001b[38;5;66;03m# noqa: F401,F403\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\courses\\basics of python\\.vscode\\pyAI3.6\\Lib\\site-packages\\seaborn\\relational.py:21\u001b[39m\n\u001b[32m     13\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m     14\u001b[39m     adjust_legend_subtitles,\n\u001b[32m     15\u001b[39m     _default_color,\n\u001b[32m   (...)\u001b[39m\u001b[32m     18\u001b[39m     _scatter_legend_artist,\n\u001b[32m     19\u001b[39m )\n\u001b[32m     20\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_compat\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m groupby_apply_include_groups\n\u001b[32m---> \u001b[39m\u001b[32m21\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_statistics\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m EstimateAggregator, WeightedAggregator\n\u001b[32m     22\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01maxisgrid\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m FacetGrid, _facet_docs\n\u001b[32m     23\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_docstrings\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m DocstringComponents, _core_docs\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\courses\\basics of python\\.vscode\\pyAI3.6\\Lib\\site-packages\\seaborn\\_statistics.py:32\u001b[39m\n\u001b[32m     30\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpandas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mpd\u001b[39;00m\n\u001b[32m     31\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m32\u001b[39m     \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mstats\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m gaussian_kde\n\u001b[32m     33\u001b[39m     _no_scipy = \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[32m     34\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\courses\\basics of python\\.vscode\\pyAI3.6\\Lib\\site-packages\\scipy\\stats\\__init__.py:635\u001b[39m\n\u001b[32m    633\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_binomtest\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m binomtest\n\u001b[32m    634\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_binned_statistic\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *\n\u001b[32m--> \u001b[39m\u001b[32m635\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_kde\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m gaussian_kde\n\u001b[32m    636\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m mstats\n\u001b[32m    637\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m qmc\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\courses\\basics of python\\.vscode\\pyAI3.6\\Lib\\site-packages\\scipy\\stats\\_kde.py:31\u001b[39m\n\u001b[32m     29\u001b[39m \u001b[38;5;66;03m# Local imports.\u001b[39;00m\n\u001b[32m     30\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_stats\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m gaussian_kernel_estimate, gaussian_kernel_estimate_log\n\u001b[32m---> \u001b[39m\u001b[32m31\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_multivariate\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m multivariate_normal\n\u001b[32m     33\u001b[39m __all__ = [\u001b[33m'\u001b[39m\u001b[33mgaussian_kde\u001b[39m\u001b[33m'\u001b[39m]\n\u001b[32m     36\u001b[39m \u001b[38;5;28;01mclass\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mgaussian_kde\u001b[39;00m:\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\courses\\basics of python\\.vscode\\pyAI3.6\\Lib\\site-packages\\scipy\\stats\\_multivariate.py:2067\u001b[39m\n\u001b[32m   2063\u001b[39m             t_centered = t_centered.reshape(mean.shape)\n\u001b[32m   2064\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m t_centered\n\u001b[32m-> \u001b[39m\u001b[32m2067\u001b[39m matrix_t = \u001b[30;43mmatrix_t_gen\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   2070\u001b[39m \u001b[38;5;28;01mclass\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatrix_t_frozen\u001b[39;00m:\n\u001b[32m   2071\u001b[39m     \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, mean, row_spread, col_spread, df, seed=\u001b[38;5;28;01mNone\u001b[39;00m):\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\courses\\basics of python\\.vscode\\pyAI3.6\\Lib\\site-packages\\scipy\\stats\\_multivariate.py:1730\u001b[39m, in \u001b[36mmatrix_t_gen.__init__\u001b[39m\u001b[34m(self, seed)\u001b[39m\n\u001b[32m   1728\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, seed=\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m   1729\u001b[39m     \u001b[38;5;28msuper\u001b[39m().\u001b[34m__init__\u001b[39m(seed)\n\u001b[32m-> \u001b[39m\u001b[32m1730\u001b[39m     \u001b[38;5;28mself\u001b[39m.\u001b[34m__doc__\u001b[39m = \u001b[30;43mscipy\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43m_lib\u001b[39;49m.doccer.docformat(\n\u001b[32m   1731\u001b[39m         \u001b[38;5;28mself\u001b[39m.\u001b[34m__doc__\u001b[39m, matrix_t_docdict_params\n\u001b[32m   1732\u001b[39m     )\n",
      "\u001b[36mFile \u001b[39m\u001b[32md:\\courses\\basics of python\\.vscode\\pyAI3.6\\Lib\\site-packages\\scipy\\__init__.py:148\u001b[39m, in \u001b[36m__getattr__\u001b[39m\u001b[34m(name)\u001b[39m\n\u001b[32m    146\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mglobals\u001b[39m()[name]\n\u001b[32m    147\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m148\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\n\u001b[32m    149\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mModule \u001b[39m\u001b[33m'\u001b[39m\u001b[33mscipy\u001b[39m\u001b[33m'\u001b[39m\u001b[33m has no attribute \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    150\u001b[39m     )\n",
      "\u001b[31mAttributeError\u001b[39m: Module 'scipy' has no attribute '_lib'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1783498625958,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "fkOje1oi_Nc_"
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv('/content/student_exam_pass (1).csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1783498625965,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "Omtqa4dD_TCt",
    "outputId": "d915e8fc-d6b4-493c-cd41-fd7a9d95d88f"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"df\",\n  \"rows\": 300,\n  \"fields\": [\n    {\n      \"column\": \"Hours_Studied\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9427102077469596,\n        \"min\": 0.1,\n        \"max\": 9.9,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          9.4,\n          5.1,\n          8.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Attendance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.155835041175155,\n        \"min\": 40.7,\n        \"max\": 100.0,\n        \"num_unique_values\": 237,\n        \"samples\": [\n          43.5,\n          81.8,\n          61.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Passed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "df"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-3fa3cf80-5f46-4bd0-bcbb-3111462130fb\" class=\"colab-df-container\">\n",
       "    <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>Hours_Studied</th>\n",
       "      <th>Attendance</th>\n",
       "      <th>Passed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.7</td>\n",
       "      <td>43.1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.5</td>\n",
       "      <td>71.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.3</td>\n",
       "      <td>72.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "      <td>78.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.6</td>\n",
       "      <td>83.6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3fa3cf80-5f46-4bd0-bcbb-3111462130fb')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-3fa3cf80-5f46-4bd0-bcbb-3111462130fb button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-3fa3cf80-5f46-4bd0-bcbb-3111462130fb');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   Hours_Studied  Attendance  Passed\n",
       "0            3.7        43.1       0\n",
       "1            9.5        71.9       1\n",
       "2            7.3        72.4       1\n",
       "3            6.0        78.2       0\n",
       "4            1.6        83.6       0"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 10,
     "status": "ok",
     "timestamp": 1783498625976,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "M85eC4gP_WOf",
    "outputId": "c4eb6dc9-86a2-4f08-bcdb-2b0502fd0964"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 300 entries, 0 to 299\n",
      "Data columns (total 3 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Hours_Studied  300 non-null    float64\n",
      " 1   Attendance     300 non-null    float64\n",
      " 2   Passed         300 non-null    int64  \n",
      "dtypes: float64(2), int64(1)\n",
      "memory usage: 7.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1783498625987,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "_PHK3k74_cmK",
    "outputId": "9eca8774-d837-45c8-a0f2-dd19fd0bef33"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 178
    },
    "executionInfo": {
     "elapsed": 26,
     "status": "ok",
     "timestamp": 1783498626014,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "lxKMvIES_hA_",
    "outputId": "78b67a5c-8075-4d3c-ac68-228892562336"
   },
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Passed</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div><br><label><b>dtype:</b> int64</label>"
      ],
      "text/plain": [
       "Passed\n",
       "1    167\n",
       "0    133\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Passed'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 467
    },
    "executionInfo": {
     "elapsed": 180,
     "status": "ok",
     "timestamp": 1783498626191,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "IWO-QXrr_oVZ",
    "outputId": "333d07c9-329e-4cad-f2ca-b759f772b932"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Passed', ylabel='count'>"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df,x='Passed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 468
    },
    "executionInfo": {
     "elapsed": 582,
     "status": "ok",
     "timestamp": 1783498626773,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "uJi6ZMQX_0RI",
    "outputId": "39588e1b-6516-42ae-f2b2-d9a3c84442f2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Hours_Studied', ylabel='Attendance'>"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df,x='Hours_Studied',y='Attendance',hue='Passed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 468
    },
    "executionInfo": {
     "elapsed": 4278,
     "status": "ok",
     "timestamp": 1783498631052,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "z5XCKaU9AUKE",
    "outputId": "3a4ea9cd-e05b-4f3c-c4fc-e8b2d6dc1d4c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Hours_Studied', ylabel='Passed'>"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=df,x='Hours_Studied',y='Passed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1783498631056,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "vHcSQSgnALgB"
   },
   "outputs": [],
   "source": [
    "x=df.drop('Passed',axis=1)\n",
    "y=df['Passed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1783498631065,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "6aqGwA9XBFhq",
    "outputId": "6a11a43a-0c65-4710-b482-0d5dfa93fbf4"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"x\",\n  \"rows\": 300,\n  \"fields\": [\n    {\n      \"column\": \"Hours_Studied\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9427102077469596,\n        \"min\": 0.1,\n        \"max\": 9.9,\n        \"num_unique_values\": 94,\n        \"samples\": [\n          9.4,\n          5.1,\n          8.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Attendance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.155835041175155,\n        \"min\": 40.7,\n        \"max\": 100.0,\n        \"num_unique_values\": 237,\n        \"samples\": [\n          43.5,\n          81.8,\n          61.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "x"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-f52ecfb7-7954-48af-9ced-d97a651ec0ca\" class=\"colab-df-container\">\n",
       "    <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>Hours_Studied</th>\n",
       "      <th>Attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.7</td>\n",
       "      <td>43.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.5</td>\n",
       "      <td>71.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.3</td>\n",
       "      <td>72.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.0</td>\n",
       "      <td>78.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.6</td>\n",
       "      <td>83.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f52ecfb7-7954-48af-9ced-d97a651ec0ca')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-f52ecfb7-7954-48af-9ced-d97a651ec0ca button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-f52ecfb7-7954-48af-9ced-d97a651ec0ca');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   Hours_Studied  Attendance\n",
       "0            3.7        43.1\n",
       "1            9.5        71.9\n",
       "2            7.3        72.4\n",
       "3            6.0        78.2\n",
       "4            1.6        83.6"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1783498631068,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "nvOzuOIABIZb"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 53,
     "status": "ok",
     "timestamp": 1783498631136,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "3N7ddTJXBPzr"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "sc=StandardScaler()\n",
    "x_train=sc.fit_transform(x_train)\n",
    "x_test=sc.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "executionInfo": {
     "elapsed": 8,
     "status": "ok",
     "timestamp": 1783498631137,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "WcduvSd6BYC2",
    "outputId": "f1c42829-8fe6-4d90-d3e6-9682b40d412f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-13 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-13 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-13 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-13 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-13 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-13 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-13 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-13 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-13 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-13 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-13 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-13 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-13 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-13 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-13 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-13 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-13\" 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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model=LogisticRegression()\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1783498631138,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "Uq9MGcZzBs7_"
   },
   "outputs": [],
   "source": [
    "y_pred=model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1783498631141,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "8_B7F5EZ_fcb"
   },
   "outputs": [],
   "source": [
    "y_proba=model.predict_proba(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 61,
     "status": "ok",
     "timestamp": 1783498631204,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "CU_1lReuCAQ-",
    "outputId": "a4d04f01-31b9-4b54-a579-08d8d8aee550"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.69988623 0.30011377]\n",
      " [0.42527002 0.57472998]\n",
      " [0.58919907 0.41080093]\n",
      " [0.76480918 0.23519082]\n",
      " [0.57367346 0.42632654]\n",
      " [0.04528664 0.95471336]\n",
      " [0.14046494 0.85953506]\n",
      " [0.40757147 0.59242853]\n",
      " [0.216246   0.783754  ]\n",
      " [0.10971106 0.89028894]\n",
      " [0.5346803  0.4653197 ]\n",
      " [0.0602411  0.9397589 ]\n",
      " [0.47365534 0.52634466]\n",
      " [0.06789187 0.93210813]\n",
      " [0.77753448 0.22246552]\n",
      " [0.12179708 0.87820292]\n",
      " [0.62154847 0.37845153]\n",
      " [0.82918584 0.17081416]\n",
      " [0.60310359 0.39689641]\n",
      " [0.09007259 0.90992741]\n",
      " [0.25676755 0.74323245]\n",
      " [0.74533137 0.25466863]\n",
      " [0.86988241 0.13011759]\n",
      " [0.22019552 0.77980448]\n",
      " [0.24020626 0.75979374]\n",
      " [0.37456508 0.62543492]\n",
      " [0.0756127  0.9243873 ]\n",
      " [0.43189683 0.56810317]\n",
      " [0.15485319 0.84514681]\n",
      " [0.57174774 0.42825226]\n",
      " [0.24370124 0.75629876]\n",
      " [0.11101371 0.88898629]\n",
      " [0.10304791 0.89695209]\n",
      " [0.80237581 0.19762419]\n",
      " [0.64002624 0.35997376]\n",
      " [0.04378673 0.95621327]\n",
      " [0.07970165 0.92029835]\n",
      " [0.04180235 0.95819765]\n",
      " [0.24289757 0.75710243]\n",
      " [0.96499995 0.03500005]\n",
      " [0.35068464 0.64931536]\n",
      " [0.70938823 0.29061177]\n",
      " [0.92569296 0.07430704]\n",
      " [0.09967697 0.90032303]\n",
      " [0.12601839 0.87398161]\n",
      " [0.69674884 0.30325116]\n",
      " [0.84608506 0.15391494]\n",
      " [0.37067227 0.62932773]\n",
      " [0.5949172  0.4050828 ]\n",
      " [0.0697759  0.9302241 ]\n",
      " [0.04344225 0.95655775]\n",
      " [0.67376528 0.32623472]\n",
      " [0.83413022 0.16586978]\n",
      " [0.1101565  0.8898435 ]\n",
      " [0.75168639 0.24831361]\n",
      " [0.20777106 0.79222894]\n",
      " [0.85814339 0.14185661]\n",
      " [0.39120563 0.60879437]\n",
      " [0.08069528 0.91930472]\n",
      " [0.16533579 0.83466421]]\n"
     ]
    }
   ],
   "source": [
    "print(y_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 14,
     "status": "ok",
     "timestamp": 1783498631205,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "BnOVBZRDiRMU"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 13,
     "status": "ok",
     "timestamp": 1783498631206,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "xoMsYIx0jcxT"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1783498631207,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "cSo5TE6PCD_3",
    "outputId": "df8943f8-b9d8-44f1-f1e5-6c283272c81d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6\n"
     ]
    }
   ],
   "source": [
    "accuracy=accuracy_score(y_test,y_pred)\n",
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1783498631208,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "MvRHXzlPiX_6"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1783498631240,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "XyrWcGtlihB3",
    "outputId": "966d1847-9c11-41ec-ee66-b51fa6f072b9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.50      0.50      0.50        24\n",
      "           1       0.67      0.67      0.67        36\n",
      "\n",
      "    accuracy                           0.60        60\n",
      "   macro avg       0.58      0.58      0.58        60\n",
      "weighted avg       0.60      0.60      0.60        60\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "executionInfo": {
     "elapsed": 164,
     "status": "ok",
     "timestamp": 1783498631403,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "HldZy_YQjQFb",
    "outputId": "76ab7402-0425-44cc-bdc8-49c439191abe"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 4,
     "status": "ok",
     "timestamp": 1783498631404,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "T2XFwo0rkiJN"
   },
   "outputs": [],
   "source": [
    "import gradio as gr\n",
    "\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 650
    },
    "executionInfo": {
     "elapsed": 1521,
     "status": "ok",
     "timestamp": 1783498632961,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "Q_dpthCXkFUl",
    "outputId": "f0381bb9-9a71-4716-ade6-8c2857c669db"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It looks like you are running Gradio on a hosted Jupyter notebook, which requires `share=True`. Automatically setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
      "\n",
      "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
      "* Running on public URL: https://d3237c823f67cac9a9.gradio.live\n",
      "\n",
      "This share link is temporary and will last for up to 1 week (best effort). For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"https://d3237c823f67cac9a9.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def predict_pass_fail(hours_studied, attendance):\n",
    "    input_df = pd.DataFrame(\n",
    "        [[hours_studied, attendance]],\n",
    "        columns=[\"Hours_Studied\", \"Attendance\"]\n",
    "    )\n",
    "\n",
    "    input_scaled = sc.transform(input_df)\n",
    "    prediction = model.predict(input_scaled)[0]\n",
    "    probability = model.predict_proba(input_scaled)[0][1]\n",
    "\n",
    "    label = \"Pass\" if prediction == 1 else \"Fail\"\n",
    "    return f\"{label} (probability: {probability:.2%})\"\n",
    "demo = gr.Interface(\n",
    "    fn=predict_pass_fail,\n",
    "    inputs=[\n",
    "        gr.Slider(0, 10, value=5, label=\"Hours_Studied\"),\n",
    "        gr.Slider(40, 100, value=70, label=\"Attendance\"),\n",
    "    ],\n",
    "    outputs=gr.Textbox(label=\"Prediction\"),\n",
    "    title=\"Predicting Student Exam Pass/Fail with Logistic Regression\",\n",
    "    description=\"Enter values below and the Logistic Regression model will predict the outcome live.\"\n",
    ")\n",
    "\n",
    "demo.launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1783498632965,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "T74t8IeXw-hl"
   },
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 16,
     "status": "ok",
     "timestamp": 1783498632984,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "AHvZvsbOxIkV"
   },
   "outputs": [],
   "source": [
    "model=SVC(kernel='rbf',C=20,gamma=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 80
    },
    "executionInfo": {
     "elapsed": 17,
     "status": "ok",
     "timestamp": 1783498633006,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "FyWmEKEbxWCp",
    "outputId": "3dffd544-65ce-4951-eaff-d241dc108978"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-14 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-14 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-14 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-14 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-14 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-14 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: start;\n",
       "  justify-content: space-between;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-14 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-14 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-14 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-14 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-14 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-14 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-14 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-14 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-14 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-14 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=20, gamma=5)</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SVC</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(C=20, gamma=5)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=20, gamma=5)"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 27,
     "status": "ok",
     "timestamp": 1783498633047,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "ff9nuN-Oxa-0"
   },
   "outputs": [],
   "source": [
    "y_pred_train=model.predict(x_train)\n",
    "y_pred_test=model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 1,
     "status": "ok",
     "timestamp": 1783498633049,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "OAVtX_4Axm7f"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1783498633057,
     "user": {
      "displayName": "Mooaz Ebrahiem",
      "userId": "06463406314184077705"
     },
     "user_tz": -180
    },
    "id": "Cd8Xg4eexsuV",
    "outputId": "2aaf11c6-22a2-4996-d1f1-eb226e0c6cd2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8625\n",
      "0.6\n"
     ]
    }
   ],
   "source": [
    "accuracy_train=accuracy_score(y_train,y_pred_train)\n",
    "accuracy_test=accuracy_score(y_test,y_pred_test)\n",
    "print(accuracy_train)\n",
    "print(accuracy_test)\n"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyMpt0Jic7ZT+qPdy6Riq+KO",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "pyAI3.6",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}