Enem/test_school.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Entendendo o propósito\n",
    "\n",
    "Esse estudo começou a partir de uma observação nos dados do Enem de 2022. Notamos que mais de 59% dos candidatos não forneceram informações sobre o tipo de escola do ensino médio que frequentaram. Diante desse cenário, foi decidido investigar a origem desses alunos.\n",
    "\n",
    "A distinção das notas entres alunos de escola pública e privada existe, ao analisarmos a notas por faixa de renda, há uma regressão linear quase perfeita: maior a renda, maior a nota.  Claro que existe uma zona de conflito onde as notas se assemelham e os alunos de escola é muito maior do que o de privados e, dessa forma, ocorreram extremos que possuem notas de elite.\n",
    "\n",
    "Mas para este trabalho, utilizou-se somente as notas das avaliações para criar um algoritmo de classificação baseadas nos rendimentos dos alunos. Se colocássemos a variável da renda seria muito mais prático, mas o objetivo é entender quanto a distinção das notas entre os tipos de escolas possibilita a utilização de algoritmo de classificação. É um caminho pouco usual para entender como a desigualdade influência a composição social no país. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pickle\n",
    "\n",
    "# from joblib import dump, load,\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import StackingClassifier\n",
    "\n",
    "\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "from sklearn.linear_model import SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loading_file():\n",
    "    # Read the CSV file with specific columns\n",
    "    columns_to_read = ['NU_NOTA_REDACAO', 'NU_NOTA_CN', 'NU_NOTA_CH', 'NU_NOTA_LC', 'NU_NOTA_LC', 'NU_NOTA_MT', 'TP_ESCOLA']\n",
    "    df2022 = pd.read_csv(\"MICRODADOS_ENEM_2022.csv\", sep=\";\", encoding=\"latin-1\", usecols=columns_to_read)\n",
    "    df2022 = df2022.dropna()\n",
    "    return df2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TP_ESCOLA</th>\n",
       "      <th>NU_NOTA_CN</th>\n",
       "      <th>NU_NOTA_CH</th>\n",
       "      <th>NU_NOTA_LC</th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "      <th>NU_NOTA_REDACAO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>421.1</td>\n",
       "      <td>546.0</td>\n",
       "      <td>498.8</td>\n",
       "      <td>565.3</td>\n",
       "      <td>760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>490.7</td>\n",
       "      <td>388.6</td>\n",
       "      <td>357.8</td>\n",
       "      <td>416.0</td>\n",
       "      <td>320.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>398.1</td>\n",
       "      <td>427.3</td>\n",
       "      <td>400.2</td>\n",
       "      <td>404.9</td>\n",
       "      <td>440.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>467.5</td>\n",
       "      <td>461.0</td>\n",
       "      <td>466.7</td>\n",
       "      <td>435.3</td>\n",
       "      <td>360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3</td>\n",
       "      <td>458.7</td>\n",
       "      <td>539.8</td>\n",
       "      <td>488.2</td>\n",
       "      <td>456.8</td>\n",
       "      <td>940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3476095</th>\n",
       "      <td>1</td>\n",
       "      <td>444.5</td>\n",
       "      <td>504.4</td>\n",
       "      <td>489.5</td>\n",
       "      <td>423.6</td>\n",
       "      <td>580.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3476097</th>\n",
       "      <td>1</td>\n",
       "      <td>536.1</td>\n",
       "      <td>633.2</td>\n",
       "      <td>584.0</td>\n",
       "      <td>596.3</td>\n",
       "      <td>740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3476098</th>\n",
       "      <td>2</td>\n",
       "      <td>487.6</td>\n",
       "      <td>495.6</td>\n",
       "      <td>545.5</td>\n",
       "      <td>597.4</td>\n",
       "      <td>580.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3476099</th>\n",
       "      <td>1</td>\n",
       "      <td>512.5</td>\n",
       "      <td>524.8</td>\n",
       "      <td>546.8</td>\n",
       "      <td>432.0</td>\n",
       "      <td>520.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3476102</th>\n",
       "      <td>2</td>\n",
       "      <td>527.9</td>\n",
       "      <td>627.0</td>\n",
       "      <td>583.3</td>\n",
       "      <td>637.1</td>\n",
       "      <td>660.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2344823 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         TP_ESCOLA  NU_NOTA_CN  NU_NOTA_CH  NU_NOTA_LC  NU_NOTA_MT  \\\n",
       "2                1       421.1       546.0       498.8       565.3   \n",
       "3                1       490.7       388.6       357.8       416.0   \n",
       "7                1       398.1       427.3       400.2       404.9   \n",
       "9                1       467.5       461.0       466.7       435.3   \n",
       "11               3       458.7       539.8       488.2       456.8   \n",
       "...            ...         ...         ...         ...         ...   \n",
       "3476095          1       444.5       504.4       489.5       423.6   \n",
       "3476097          1       536.1       633.2       584.0       596.3   \n",
       "3476098          2       487.6       495.6       545.5       597.4   \n",
       "3476099          1       512.5       524.8       546.8       432.0   \n",
       "3476102          2       527.9       627.0       583.3       637.1   \n",
       "\n",
       "         NU_NOTA_REDACAO  \n",
       "2                  760.0  \n",
       "3                  320.0  \n",
       "7                  440.0  \n",
       "9                  360.0  \n",
       "11                 940.0  \n",
       "...                  ...  \n",
       "3476095            580.0  \n",
       "3476097            740.0  \n",
       "3476098            580.0  \n",
       "3476099            520.0  \n",
       "3476102            660.0  \n",
       "\n",
       "[2344823 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = loading_file()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Proporção no dados Brutos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Considerando o seguinte dicionário identificando os grupos:\n",
    "\n",
    "- 1: Não definido\n",
    "- 2: Escola Pública\n",
    "- 3: Escola Privada\n",
    "\n",
    "Pode-se ter a noção do tamanho de dados ausentes nesta categoria. As escolas privadas consistiram somente 8% do total de alunos e 32 % para escolas públicas. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>proportion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_ESCOLA</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.084</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           proportion\n",
       "TP_ESCOLA            \n",
       "1               0.592\n",
       "2               0.324\n",
       "3               0.084"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['TP_ESCOLA'].value_counts(normalize=True).round(3).to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nos box-plots, é possível observar que os dados relacionados aos candidatos que não especificaram a escola exibem distribuições com medianas superiores às das escolas públicas. Esse padrão sugere a presença de uma parcela de alunos provenientes de escolas particulares ou mesmo alunos de escolas públicas com desempenhos excepcionais."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\filip\\AppData\\Local\\Temp\\ipykernel_10576\\1335163070.py:7: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(x = 'TP_ESCOLA', y = column, data = df, palette = 'rocket')\n",
      "C:\\Users\\filip\\AppData\\Local\\Temp\\ipykernel_10576\\1335163070.py:7: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(x = 'TP_ESCOLA', y = column, data = df, palette = 'rocket')\n",
      "C:\\Users\\filip\\AppData\\Local\\Temp\\ipykernel_10576\\1335163070.py:7: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(x = 'TP_ESCOLA', y = column, data = df, palette = 'rocket')\n",
      "C:\\Users\\filip\\AppData\\Local\\Temp\\ipykernel_10576\\1335163070.py:7: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(x = 'TP_ESCOLA', y = column, data = df, palette = 'rocket')\n",
      "C:\\Users\\filip\\AppData\\Local\\Temp\\ipykernel_10576\\1335163070.py:7: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(x = 'TP_ESCOLA', y = column, data = df, palette = 'rocket')\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1500x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_list = ['NU_NOTA_CN', 'NU_NOTA_CH', 'NU_NOTA_LC', 'NU_NOTA_MT', 'NU_NOTA_REDACAO']\n",
    "fig = plt.figure(figsize = (15,10))\n",
    "\n",
    "for i in range(len(num_list)):\n",
    "    column = num_list[i]\n",
    "    sub = fig.add_subplot(2,3, i +1)\n",
    "    sns.boxplot(x = 'TP_ESCOLA', y = column, data = df, palette = 'rocket')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Proporção nos dados sem os indefinidos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para a criação dos modelos, removemos do conjunto de dados original as notas associadas aos registros não definidos, resultando em uma alteração na distribuição proporcional dos alunos: de 79,4% para escolas públicas e 20,6% para escolas privadas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>proportion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP_ESCOLA</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           proportion\n",
       "TP_ESCOLA            \n",
       "2               0.785\n",
       "3               0.215"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_N1 = df[df['TP_ESCOLA'] > 1]\n",
    "df_N1 = df_N1[df_N1['NU_NOTA_CN'] >0]  \n",
    "df_N1 = df_N1[df_N1['NU_NOTA_CH'] >0]  \n",
    "df_N1 = df_N1[df_N1['NU_NOTA_LC'] >0]  \n",
    "df_N1 = df_N1[df_N1['NU_NOTA_MT'] >0]  \n",
    "df_N1 = df_N1[df_N1['NU_NOTA_REDACAO'] >0]  \n",
    "df_N1['TP_ESCOLA'].value_counts(normalize=True).round(3).to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\filip\\AppData\\Local\\Temp\\ipykernel_10576\\3484418880.py:7: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(x = 'TP_ESCOLA', y = column, data = df_N1, palette = 'rocket')\n",
      "C:\\Users\\filip\\AppData\\Local\\Temp\\ipykernel_10576\\3484418880.py:7: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(x = 'TP_ESCOLA', y = column, data = df_N1, palette = 'rocket')\n",
      "C:\\Users\\filip\\AppData\\Local\\Temp\\ipykernel_10576\\3484418880.py:7: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(x = 'TP_ESCOLA', y = column, data = df_N1, palette = 'rocket')\n",
      "C:\\Users\\filip\\AppData\\Local\\Temp\\ipykernel_10576\\3484418880.py:7: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(x = 'TP_ESCOLA', y = column, data = df_N1, palette = 'rocket')\n",
      "C:\\Users\\filip\\AppData\\Local\\Temp\\ipykernel_10576\\3484418880.py:7: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.boxplot(x = 'TP_ESCOLA', y = column, data = df_N1, palette = 'rocket')\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_list = ['NU_NOTA_CN', 'NU_NOTA_CH', 'NU_NOTA_LC', 'NU_NOTA_MT', 'NU_NOTA_REDACAO']\n",
    "fig = plt.figure(figsize = (15,10))\n",
    "\n",
    "for i in range(len(num_list)):\n",
    "    column = num_list[i]\n",
    "    sub = fig.add_subplot(2,3, i +1)\n",
    "    sns.boxplot(x = 'TP_ESCOLA', y = column, data = df_N1, palette = 'rocket')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Construção dos Modelos\n",
    "\n",
    "Entre a opções de algoritmos classificatórios, utilizamos:\n",
    "\n",
    "-\tRegressão logística\n",
    "-\tRandom Forest\n",
    "-\tNavy Bayes\n",
    "-\tGradient Boosting\n",
    "-\tAdabosting\n",
    "-   SVC\n",
    "-   K Neighbors\n",
    "\n",
    "\n",
    "Realizamos a divisão do conjunto de dados em conjuntos de teste e treino e realizamos os treinamentos mantendo os parâmetros default de cada modelo no sklearn. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_pipeline = [\n",
    "    RandomForestClassifier(),\n",
    "    AdaBoostClassifier(),\n",
    "    GradientBoostingClassifier(),\n",
    "    LogisticRegression(),\n",
    "    GaussianNB(),\n",
    "    KNeighborsClassifier(),\n",
    "    SGDClassifier()\n",
    "]\n",
    "\n",
    "models_classifier = ['Random Forest', 'AdaBoost', 'Gradient Boosting', 'Logistic Regression', 'Naive Bayes', 'K Neighbors', 'SVC']\n",
    "acc_list =[]\n",
    "auc_list = []\n",
    "cm_list = []\n",
    "\n",
    "y = df_N1['TP_ESCOLA']\n",
    "X = df_N1[['NU_NOTA_CH', 'NU_NOTA_CN' , 'NU_NOTA_MT' , 'NU_NOTA_LC', 'NU_NOTA_REDACAO']]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y , test_size=0.2, random_state=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for model, name in zip(model_pipeline, models_classifier):\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    acc_list.append(round(metrics.accuracy_score(y_test, y_pred), 3))\n",
    "    \n",
    "    lb = LabelBinarizer()\n",
    "    y_test_binary = lb.fit_transform(y_test)\n",
    "    y_pred_binary = lb.transform(y_pred)\n",
    "    \n",
    "    fpr, tpr, _ = metrics.roc_curve(y_test_binary, y_pred_binary)\n",
    "    auc_list.append(round(metrics.auc(fpr, tpr), 3))\n",
    "    \n",
    "    cm_list.append(metrics.confusion_matrix(y_test, y_pred))\n",
    "    \n",
    "    # Save model\n",
    "    with open(f'{name}_classifier.pkl', \"wb\") as pickle_out:\n",
    "        pickle.dump(model, pickle_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Avaliação dos modelos\n",
    "\n",
    "\n",
    "Para avaliar os modelos construídos, utilizamos a matriz de confusão, a acurácia e a AUC como métricas. Observamos uma acurácia em torno de 80% em todos os modelos, enquanto a AUC fica próxima de 60%. No entanto, a análise mais relevadora é da matriz de confusão, mostrando a dificuldade dos modelos em identificar corretamente os alunos de escolas privadas. Os erros nessa categoria chegam a 70%, enquanto para escolas públicas ficam em 5%.\n",
    "\n",
    "O modelo Naive Bayes apresenta uma acurácia de 56,69% para escolas privadas e AUC de 70%, superando os demais. No entanto, esse modelo também aumenta os erros na identificação de alunos de escolas públicas para 17%. Apesar disso, optamos por esse modelo, pois oferece uma previsão razoável, embora não seja perfeito. Ainda há espaço para testes e aprimoramentos futuros, já que as possibilidades de construção são quase infinitas.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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CBQoY27Zts3t84vP/8ssvtmVXr141ihYtahQpUsSIi4szDMMwxo0bZ0gy5syZY1vv1q1bRq1atQxfX19bp3r16mVkz57diI2NTfZ9WbhwoSHJ+PHHH+/y7gFA+rh8+bIhyWjVqlWS+y5evGicO3fOdrvzcznxs7dv375JHufo8zsiIsJwcXExTpw4YVuW0v0Mw0jYfxg4cKDt51deecXIly+fERUVZbdeu3btjBw5ctjG8OOPPxqSjDJlytjtJ40fP96QZOzatcu2rEWLFkme926CgoIc9qdOnTpJxpXS/ZBWrVoZHh4expEjR2zL/vnnHyNbtmxG/fr1bcsqVapktGjR4q7j69Gjh92+0J3++34m7v/cuc9oGIbRunVrI3fu3Laft23bZkgy3nzzTbv1OnXqlGSbAB5uid/B/P7778muk9LPtDfeeMNwcXExduzYYVt2/vx5I1euXEn2ERo0aGA0aNDA9vPTTz9tlCtX7q5jHTVqVLLfKwUFBdl9LzNgwABDkrFkyZIk68bHx9/1eYKCgoymTZvaurlr1y7j5ZdfNiQZPXr0sK33yy+/GJKMuXPn2j1+1apVdsvPnj1ruLu7J2n0oEGDDEl2407886hbt67dvsnVq1cNPz8/o2vXrnbbOHv2rJEjRw7b8osXLxqSjFGjRiX7+r755pt7/pkbRtLGpPTvQeJraNy4sd173bt3b8PNzc3u3wsAp+GCGjduLH9/fxUqVEjPPvusfHx8tGzZMrvfvHJzc7OdvzE+Pl4XLlxQbGysqlWrpu3btyfZZtu2be1mtxNP7XX06FFJCRej2rlzpzp27KgcOXLY1mvSpInKli1rt60VK1bIzc1NPXv2tFv+1ltvyTAMrVy50m55o0aN7I7kSJxJbtOmjbJly5ZkeeKY7mXSpElau3at3e3O19KpUyflypXLtn7FihXVpEkTrVixIsm2Xn31VbuflyxZovj4eD3//POKioqy3fLmzasSJUrYTneW+F6tXr06yWHrAPCwmjt3rgIDA/XYY49JSjj8um3btpo/f77t1FkrVqxQzZo1Vb16ddvj/P39baduudOdR5tcvXpVUVFRqlevnq5fv679+/fbrevl5WX7zF+9erWmTJkiX19fNW/eXAcPHrStt2LFClWvXt3u1Iq+vr7q1q2bjh8/rr1799rWy5s3r9q3b29bL0uWLOrZs6euXbumDRs2SJL8/PwUHR1taw0AZDZXrlyR5PgoioYNG8rf3992Szyt051ee+21JMvu/PyOjo5WVFSUateuLcMwtGPHDknO7Wf8l2EYWrx4sVq2bCnDMOz+XR4SEqLLly8n2fcJDQ21O8/9f/d77leNGjVs/fn+++81fPhw7dmzR0899ZTttGMp3Q+Ji4vTmjVr1KpVKxUrVsy2Xr58+fTCCy/o119/tf15+fn5ac+ePTp06FCqxv9f/93/qVevns6fP2973lWrVklK+M3lO73xxhumjgNA5ufMZ9qqVatUq1Ytu7Oh5MqVy+E+wn/5+fnp77//dnhK+fuxePFiVapUyXbE+Z3uPJ1hctasWWPrZoUKFTR79myFhobaHRm/cOFC5ciRQ02aNLFrWNWqVeXr62v7bmn9+vWKjY116jO3a9eucnNzs/28du1aXbp0Se3bt7d7Ljc3N9WoUcP2XFmzZpWHh4d++umnJKcCS+Tn5ycp4YjU27dv3/O9kJz7e5CoW7dudu91vXr1FBcXpxMnTqToOWENTJbANgmwaNEiNW/eXFFRUQ4PSZw1a5YqVqxoO3etv7+/li9fbrtWxp0KFy5s93PixEniB2PiB5GjCxOWKlXK7ucTJ04of/78dhMd0v9ONfLfD7X/PnfiTtKd50K+c3lyH9b/Vb16dTVu3Njudufz/3fciWOMioqyHaKY6M5D56WE65AYhqESJUrY7Tj6+/tr3759+vfff22PCw8P1/Tp05UnTx6FhIRo0qRJDv8MAOBhEBcXp/nz5+uxxx7TsWPHdPjwYR0+fFg1atRQZGSk1q9fLynhszglTZESDtdu3bq1cuTIoezZs8vf3992scT/fp66ubnZPvObNm2qbt26ad26dbp8+bL69etnW+/EiRPJdiDx/jvH+d+LGf93vddff10lS5ZUs2bNVLBgQXXu3Nn2RRIAZAaJ/3a/du1akvumTJmitWvXas6cOQ4f6+7u7vCUWSdPnrRNDPj6+srf318NGjSQ9L/Pb2f2M/7r3LlzunTpkqZOnZrk3+ShoaGSZPt3eaJ77ffcrzx58tj606JFC7333nuaPn26Nm7cqOnTp0tK+X7IuXPndP369WTXi4+Pt10ncciQIbp06ZJKliypChUqqE+fPvrrr79S9VqklO0furq6JtlPKl68eKqfG8DDxZnPtBMnTjj8HEnJZ8u7774rX19fVa9eXSVKlFCPHj3022+/3fe4jxw5ovLly9/34xMn0VetWqXRo0fLz89PFy9etJuwP3TokC5fvqyAgIAkHbt27ZqtYYn9+O/7kCtXriSn9Urk6HssSXr88ceTPNeaNWtsz+Xp6amPPvpIK1euVGBgoOrXr6+RI0fq7Nmztm01aNBAbdq00eDBg5UnTx49/fTT+uKLLxQTE5Ps++HM34NEadVsPFy4ZglUvXp1VatWTZLUqlUr1a1bVy+88IIOHDhg+02wOXPmqFOnTmrVqpX69OmjgIAAubm5KSIiIsnFpCTZzTbfyfjPBdnTQnLPnZ5j+q//nkM/Pj5eLi4uWrlypcNx3vkbeR9//LE6deqkb7/9VmvWrFHPnj0VERGhzZs3p+g8zACQmfzwww86c+aM5s+fr/nz5ye5f+7cuWratGmKt3fp0iU1aNBA2bNn15AhQxQcHCwvLy9t375d7777rsMLwv9XwYIFVapUKf38889OvRZnBAQEaOfOnVq9erVWrlyplStX6osvvlCHDh0cXgweADKaHDlyKF++fNq9e3eS+xKP8L7zwq538vT0TDKpHBcXpyZNmujChQt69913Vbp0afn4+Oj06dPq1KlTij6/7yVxGy+99JI6duzocJ2KFSva/fwg9zEaNWokSfr555/T7IiL+vXr68iRI7Z9jenTp2vs2LGaPHmyunTpct/bzUj7YgCQEmXKlNGBAwf0/fffa9WqVVq8eLE+/fRTDRgwQIMHD37g40mcRJekkJAQlS5dWk8++aTGjx+v8PBwSQkdCwgI0Ny5cx1uI7nrqaSEo++xpITrluTNmzfJ+u7u//vK+c0331TLli21dOlSrV69Wh988IEiIiL0ww8/qEqVKnJxcdGiRYu0efNmfffdd1q9erU6d+6sjz/+WJs3b07Rtb5SghYhJZgsgZ3ECZDHHntMEydOVN++fSVJixYtUrFixbRkyRK7Q9YGDhx4X88TFBQkSQ4P7z5w4ECSddetW6erV6/aHV2SeKqUxG2ll8Tn/++4pYQx5smTRz4+PnfdRnBwsAzDUNGiRVWyZMl7PmeFChVUoUIFvf/++9q4caPq1KmjyZMna9iwYZJSdggnAGQGc+fOVUBAgMNTtCxZskTffPONJk+erKCgoBQ15aefftL58+e1ZMkS1a9f37b82LFjTo0rNjbW7relg4KCku1A4v2J//3XX38pPj7e7otAR03z8PBQy5Yt1bJlS8XHx+v111/XlClT9MEHH6h48eJ81gPI8Fq0aKHp06dr69atdqdJvB+7du3SwYMHNWvWLHXo0MG2/L+nK3RmP+O//P39lS1bNsXFxdm+kDKDWZ/XsbGxkv53tE5K90O8vLzk7e2d7Hqurq52R+HnypVLoaGhCg0N1bVr11S/fn0NGjTINlmSFv0JCgpSfHy8jh07ZndU0OHDh01/LgCZm7+/f4o/04KCghx+jqT0s8XHx0dt27ZV27ZtdevWLT3zzDMaPny4+vXrJy8vL6c+D4ODgx3+AsH9atGihRo0aKARI0aoe/fu8vHxUXBwsNatW6c6deokmdy4U2I/Dh8+bHfEyPnz51N8lEVwcLCkhF/ySkkzg4OD9dZbb+mtt97SoUOHVLlyZX388cd2R5nWrFlTNWvW1PDhwzVv3jy9+OKLmj9/vsPJemf+HgDO4DRcSKJhw4aqXr26xo0bp5s3b0r63+zrnbOtW7Zs0aZNm+7rOfLly6fKlStr1qxZdqc8Wbt2re287omaN2+uuLg4TZw40W752LFj5eLiombNmt3XGMxy52u5dOmSbfnu3bu1Zs0aNW/e/J7beOaZZ+Tm5qbBgwcnmdE2DEPnz5+XlHDu58SdpEQVKlSQq6ur3eGJPj4+dmMBgMzoxo0bWrJkiZ588kk9++yzSW5hYWG6evWqli1bpubNm2vz5s3aunWr7fHnzp1L8ltVjnp269Ytffrppyke18GDB3XgwAFVqlTJtqx58+baunWrXRejo6M1depUFSlSxHae/ObNm+vs2bNasGCBbb3Y2FhNmDBBvr6+ttPJJH7uJ3J1dbX9NnPi533iRDyf9wAyqnfeeUfe3t7q3LmzIiMjk9zvzG9yOvr8NgxD48ePt1vPmf0MR8/Rpk0bLV682OEXWufOnUvxeO/k4+Njymlzv/vuO0my9Sel+yFubm5q2rSpvv32W7ujeSIjIzVv3jzVrVtX2bNnl5S0P76+vipevHiSfQ3J3P6EhIRIUpIeT5gwwbTnAPBwcOYzLSQkRJs2bdLOnTtt6124cCHZIy/u9N/PQw8PD5UtW1aGYdiuq+HM52GbNm30559/6ptvvkly3/0e2fDuu+/q/PnzmjZtmiTp+eefV1xcnIYOHZpk3djYWNs4GzVqJHd3d3322Wd26/z3e7e7CQkJUfbs2TVixAiH1xlJbOb169dt3y0mCg4OVrZs2WxtuXjxYpL3IPE6M8mdisuZvweAMziyBA716dNHzz33nGbOnKlXX31VTz75pJYsWaLWrVurRYsWOnbsmCZPnqyyZcs6PA9xSkRERKhFixaqW7euOnfurAsXLmjChAkqV66c3TZ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",
      "text/plain": [
       "<Figure size 2000x1000 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (20,10))\n",
    "for i in range(len(cm_list)):\n",
    "    cm = cm_list[i]\n",
    "    name = models_classifier[i]\n",
    "\n",
    "    row_sums = cm.sum(axis=1)\n",
    "    cm_percentages = cm / row_sums[:, np.newaxis]\n",
    "\n",
    "\n",
    "    sub = fig.add_subplot(2,4, i + 1).set_title(name)\n",
    "    cm_plot = sns.heatmap(cm_percentages, annot = True, fmt=\".2%\", cmap = 'Blues_r')\n",
    "    cm_plot.set_xlabel('Valores preditos')\n",
    "    cm_plot.set_ylabel('Valores atuais')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Acurácia</th>\n",
       "      <th>AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.804</td>\n",
       "      <td>0.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.811</td>\n",
       "      <td>0.623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.811</td>\n",
       "      <td>0.621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>0.774</td>\n",
       "      <td>0.699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>K Neighbors</td>\n",
       "      <td>0.786</td>\n",
       "      <td>0.619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.806</td>\n",
       "      <td>0.608</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Acurácia    AUC\n",
       "0        Random Forest     0.804  0.620\n",
       "1             AdaBoost     0.810  0.623\n",
       "2    Gradient Boosting     0.811  0.623\n",
       "3  Logistic Regression     0.811  0.621\n",
       "4          Naive Bayes     0.774  0.699\n",
       "5          K Neighbors     0.786  0.619\n",
       "6                  SVC     0.806  0.608"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_df = pd.DataFrame({'Model':models_classifier, 'Acurácia':acc_list, 'AUC':auc_list} )\n",
    "result_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utilizando o modelo escolhido, conseguimos classificar os estudantes que não identificaram a escola de ensino médio. Com isso, constatamos que 72,16% desses estudantes provêm de escolas públicas e 27,83% de escolas privadas. Considerando o erro apresentado, podemos afirmar que a proporção está na faixa de 70/30 em relação à escolha pública e privada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\filip\\AppData\\Local\\Temp\\ipykernel_10576\\2301197206.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_S1['Previsto Modelo'] = predict_values(\"Naive Bayes_classifier.pkl\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Previsto Modelo\n",
       "2    0.72168\n",
       "3    0.27832\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def predict_values(model_file_path):\n",
    "    \n",
    "    X_S1 = df_S1[['NU_NOTA_CH', 'NU_NOTA_CN', 'NU_NOTA_MT', 'NU_NOTA_LC', 'NU_NOTA_REDACAO']]\n",
    "\n",
    "    with open(model_file_path, \"rb\") as pickle_in:\n",
    "        model = pickle.load(pickle_in)\n",
    "\n",
    "    value_counts = model.predict(X_S1)\n",
    "    return value_counts\n",
    "\n",
    "df_S1 = df[df['TP_ESCOLA'] == 1]\n",
    "df_S1['Previsto Modelo'] = predict_values(\"Naive Bayes_classifier.pkl\")\n",
    "df_S1['Previsto Modelo'].value_counts(normalize=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metodos Essemble\n",
    "\n",
    "Na busca por um modelo mais eficiente, exploramos dois modelos Ensemble, que essencialmente combinam vários modelos em uma única solução. Utilizamos o Weighted Average e o Stacking. O Weighted Average é uma média ponderada das previsões dos modelos, enquanto o Stacking divide o conjunto de dados entre algoritmos para realizar as predições.\n",
    "\n",
    "Os modelos não apresentaram resultados satifatórios."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Método Essemble: Weighted Average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the pickled models\n",
    "with open(\"Naive Bayes_classifier.pkl\", \"rb\") as file:\n",
    "    classifier_NB = pickle.load(file)\n",
    "\n",
    "with open(\"K Neighbors_classifier.pkl\", \"rb\") as file:\n",
    "    classifier_NK = pickle.load(file)\n",
    "\n",
    "predictions_NB = classifier_NB.predict(X_test)\n",
    "predictions_NK = classifier_NK.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign weights to the predictions of each model\n",
    "weight_NB = 0.8  # Weight for Naive Bayes\n",
    "weight_NK = 0.2# Weight for K Neighbors\n",
    "\n",
    "y_pred = (weight_NB * predictions_NB) + (weight_NK * predictions_NK)\n",
    "y_pred = np.round(y_pred).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(45.722222222222214, 0.5, 'Valores atuais')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cm_emseble1 = metrics.confusion_matrix(y_test, y_pred)\n",
    "row_sums = cm_emseble1.sum(axis=1)\n",
    "cm_percentages = cm_emseble1 / row_sums[:, np.newaxis]\n",
    "\n",
    "fig = plt.figure(figsize = (6,6))\n",
    "cm_plot = sns.heatmap(cm_percentages, annot = True, fmt=\".2%\", cmap = 'Blues_r')\n",
    "cm_plot.set_xlabel('Valores preditos')\n",
    "cm_plot.set_ylabel('Valores atuais')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Método Ensemble: Stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_models = [\n",
    "    ('LogisticRegression', LogisticRegression()),\n",
    "    ('GaussianNB', GaussianNB()),\n",
    "    ('KNeighbors', KNeighborsClassifier())\n",
    "]\n",
    "\n",
    "meta_classifier = LogisticRegression() \n",
    "\n",
    "stacking_model = StackingClassifier(estimators=base_models, final_estimator=meta_classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Stacking Classifier: 0.810\n"
     ]
    }
   ],
   "source": [
    "stacking_model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred_stacking = stacking_model.predict(X_test)\n",
    "\n",
    "# Evaluate the stacking model\n",
    "accuracy_stacking = np.mean(y_pred_stacking == y_test)\n",
    "print(f\"Accuracy of Stacking Classifier: {accuracy_stacking:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(45.722222222222214, 0.5, 'Valores atuais')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cm_emseble2 = metrics.confusion_matrix(y_test, y_pred_stacking)\n",
    "row_sums = cm_emseble1.sum(axis=1)\n",
    "cm_percentages = cm_emseble2/ row_sums[:, np.newaxis]\n",
    "\n",
    "fig = plt.figure(figsize = (6,6))\n",
    "cm_plot = sns.heatmap(cm_percentages, annot = True, fmt=\".2%\", cmap = 'Blues_r')\n",
    "cm_plot.set_xlabel('Valores preditos')\n",
    "cm_plot.set_ylabel('Valores atuais')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusão"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os resultados obtidos indicam a viabilidade de prever o tipo de escola com base nas notas, conforme previsto. No entanto, há desafios na identificação das escolas privadas. Recomenda-se a exploração de modelos mais complexos que possam oferecer uma eficácia aprimorada. Atos para o próximo capítulo."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.11.6"
  }
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