{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3f0126de-6529-4876-98b4-eaf2d1686ec3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cb4d45a3-e785-4ed7-a7d6-af1b9d301f70",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../data/raw/student_performance_full.csv\",sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1806ec43-c5fd-4294-9e29-d2e6c081b019",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\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>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  ...  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher  ...   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other  ...   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other  ...   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services  ...   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other  ...   \n",
       "\n",
       "  famrel freetime  goout  Dalc  Walc health absences  G1  G2  G3  \n",
       "0      4        3      4     1     1      3        4   0  11  11  \n",
       "1      5        3      3     1     1      3        2   9  11  11  \n",
       "2      4        3      2     2     3      3        6  12  13  12  \n",
       "3      3        2      2     1     1      5        0  14  14  14  \n",
       "4      4        3      2     1     2      5        0  11  13  13  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6bc643f8-6eb2-4a0a-9b45-2b8ee4b7c929",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "object    17\n",
       "int64     16\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2c716419-0d3c-465f-af45-8bd722dc212d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(649, 33)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a0d18b04-4532-42b8-aa8a-705f4c7b3ae1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 649 entries, 0 to 648\n",
      "Data columns (total 33 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   school      649 non-null    object\n",
      " 1   sex         649 non-null    object\n",
      " 2   age         649 non-null    int64 \n",
      " 3   address     649 non-null    object\n",
      " 4   famsize     649 non-null    object\n",
      " 5   Pstatus     649 non-null    object\n",
      " 6   Medu        649 non-null    int64 \n",
      " 7   Fedu        649 non-null    int64 \n",
      " 8   Mjob        649 non-null    object\n",
      " 9   Fjob        649 non-null    object\n",
      " 10  reason      649 non-null    object\n",
      " 11  guardian    649 non-null    object\n",
      " 12  traveltime  649 non-null    int64 \n",
      " 13  studytime   649 non-null    int64 \n",
      " 14  failures    649 non-null    int64 \n",
      " 15  schoolsup   649 non-null    object\n",
      " 16  famsup      649 non-null    object\n",
      " 17  paid        649 non-null    object\n",
      " 18  activities  649 non-null    object\n",
      " 19  nursery     649 non-null    object\n",
      " 20  higher      649 non-null    object\n",
      " 21  internet    649 non-null    object\n",
      " 22  romantic    649 non-null    object\n",
      " 23  famrel      649 non-null    int64 \n",
      " 24  freetime    649 non-null    int64 \n",
      " 25  goout       649 non-null    int64 \n",
      " 26  Dalc        649 non-null    int64 \n",
      " 27  Walc        649 non-null    int64 \n",
      " 28  health      649 non-null    int64 \n",
      " 29  absences    649 non-null    int64 \n",
      " 30  G1          649 non-null    int64 \n",
      " 31  G2          649 non-null    int64 \n",
      " 32  G3          649 non-null    int64 \n",
      "dtypes: int64(16), object(17)\n",
      "memory usage: 167.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ccf853f2-d2a1-452f-b9c2-5a157d0e686f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>traveltime</th>\n",
       "      <th>studytime</th>\n",
       "      <th>failures</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "      <td>649.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.744222</td>\n",
       "      <td>2.514638</td>\n",
       "      <td>2.306626</td>\n",
       "      <td>1.568567</td>\n",
       "      <td>1.930663</td>\n",
       "      <td>0.221880</td>\n",
       "      <td>3.930663</td>\n",
       "      <td>3.180277</td>\n",
       "      <td>3.184900</td>\n",
       "      <td>1.502311</td>\n",
       "      <td>2.280431</td>\n",
       "      <td>3.536210</td>\n",
       "      <td>3.659476</td>\n",
       "      <td>11.399076</td>\n",
       "      <td>11.570108</td>\n",
       "      <td>11.906009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.218138</td>\n",
       "      <td>1.134552</td>\n",
       "      <td>1.099931</td>\n",
       "      <td>0.748660</td>\n",
       "      <td>0.829510</td>\n",
       "      <td>0.593235</td>\n",
       "      <td>0.955717</td>\n",
       "      <td>1.051093</td>\n",
       "      <td>1.175766</td>\n",
       "      <td>0.924834</td>\n",
       "      <td>1.284380</td>\n",
       "      <td>1.446259</td>\n",
       "      <td>4.640759</td>\n",
       "      <td>2.745265</td>\n",
       "      <td>2.913639</td>\n",
       "      <td>3.230656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age        Medu        Fedu  traveltime   studytime    failures  \\\n",
       "count  649.000000  649.000000  649.000000  649.000000  649.000000  649.000000   \n",
       "mean    16.744222    2.514638    2.306626    1.568567    1.930663    0.221880   \n",
       "std      1.218138    1.134552    1.099931    0.748660    0.829510    0.593235   \n",
       "min     15.000000    0.000000    0.000000    1.000000    1.000000    0.000000   \n",
       "25%     16.000000    2.000000    1.000000    1.000000    1.000000    0.000000   \n",
       "50%     17.000000    2.000000    2.000000    1.000000    2.000000    0.000000   \n",
       "75%     18.000000    4.000000    3.000000    2.000000    2.000000    0.000000   \n",
       "max     22.000000    4.000000    4.000000    4.000000    4.000000    3.000000   \n",
       "\n",
       "           famrel    freetime       goout        Dalc        Walc      health  \\\n",
       "count  649.000000  649.000000  649.000000  649.000000  649.000000  649.000000   \n",
       "mean     3.930663    3.180277    3.184900    1.502311    2.280431    3.536210   \n",
       "std      0.955717    1.051093    1.175766    0.924834    1.284380    1.446259   \n",
       "min      1.000000    1.000000    1.000000    1.000000    1.000000    1.000000   \n",
       "25%      4.000000    3.000000    2.000000    1.000000    1.000000    2.000000   \n",
       "50%      4.000000    3.000000    3.000000    1.000000    2.000000    4.000000   \n",
       "75%      5.000000    4.000000    4.000000    2.000000    3.000000    5.000000   \n",
       "max      5.000000    5.000000    5.000000    5.000000    5.000000    5.000000   \n",
       "\n",
       "         absences          G1          G2          G3  \n",
       "count  649.000000  649.000000  649.000000  649.000000  \n",
       "mean     3.659476   11.399076   11.570108   11.906009  \n",
       "std      4.640759    2.745265    2.913639    3.230656  \n",
       "min      0.000000    0.000000    0.000000    0.000000  \n",
       "25%      0.000000   10.000000   10.000000   10.000000  \n",
       "50%      2.000000   11.000000   11.000000   12.000000  \n",
       "75%      6.000000   13.000000   13.000000   14.000000  \n",
       "max     32.000000   19.000000   19.000000   19.000000  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7374316b-2f06-4fec-b564-8ee3baeebe7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school        0\n",
       "sex           0\n",
       "age           0\n",
       "address       0\n",
       "famsize       0\n",
       "Pstatus       0\n",
       "Medu          0\n",
       "Fedu          0\n",
       "Mjob          0\n",
       "Fjob          0\n",
       "reason        0\n",
       "guardian      0\n",
       "traveltime    0\n",
       "studytime     0\n",
       "failures      0\n",
       "schoolsup     0\n",
       "famsup        0\n",
       "paid          0\n",
       "activities    0\n",
       "nursery       0\n",
       "higher        0\n",
       "internet      0\n",
       "romantic      0\n",
       "famrel        0\n",
       "freetime      0\n",
       "goout         0\n",
       "Dalc          0\n",
       "Walc          0\n",
       "health        0\n",
       "absences      0\n",
       "G1            0\n",
       "G2            0\n",
       "G3            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f326b764-d55d-4ce9-b883-80fde68c05f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['G3'],bins=20)\n",
    "plt.title(\"Distribution de la note finale G3\")\n",
    "plt.xlabel(\"Note\")\n",
    "plt.ylabel(\"Nombre d'étudiants\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7b415072-3eb3-4e76-8678-006a348759be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "numeric_df = df.select_dtypes(include=['int64', 'float64'])\n",
    "correlation = numeric_df.corr()\n",
    "\n",
    "\n",
    "sns.heatmap(correlation[['G3']].sort_values(by='G3', ascending=False), \n",
    "            annot=True, \n",
    "            cmap='coolwarm', \n",
    "            vmin=-1, vmax=1)\n",
    "plt.title('Corrélation des variables avec la Note Finale (G3)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dac64e74-4571-4ee5-a23f-d90648e4225d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='G1', ylabel='G3'>"
      ]
     },
     "execution_count": 30,
     "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=\"G1\", y=\"G3\")\n",
    "sns.scatterplot(data=df, x=\"G2\", y=\"G3\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cd65798-aee5-4004-bc2e-b498e0079b4a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}