PredicaoCVD.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Database/CVD_cleaned.csv\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import os\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "# é importante entender se seu problema é um problema de classificaçao ou de regressão => classificação\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "# acuracia é uma metrica apenas de classificação, se for regressão apenas o erro ja é a metrica\n",
    "from sklearn.metrics import accuracy_score, recall_score, precision_score, confusion_matrix, ConfusionMatrixDisplay\n",
    "import pandas as pd\n",
    "from imblearn.under_sampling import NearMiss,RandomUnderSampler\n",
    "import numpy as np\n",
    "\n",
    "drive_path = 'Database/'\n",
    "contents = os.listdir(drive_path)\n",
    "\n",
    "for dirname, _, filenames in os.walk(drive_path):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "df=pd.read_csv(drive_path + 'CVD_cleaned.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def metrics(y_test, y_pred):\n",
    "    TP = 0\n",
    "    TN = 0\n",
    "    FP = 0\n",
    "    FN = 0\n",
    "    for i in range(0, len(y_pred)):\n",
    "        pred = y_pred[i]\n",
    "        gt = y_test[i]\n",
    "        if((1 == pred) and (pred == gt)):\n",
    "            TP += 1\n",
    "        elif((0 == pred) and (pred == gt)):\n",
    "            TN += 1\n",
    "        elif((1 == gt) and (pred != gt)):\n",
    "            FN += 1\n",
    "        elif((0 == gt) and (pred != gt)):\n",
    "            FP += 1\n",
    "    return TP,TN,FP,FN\n",
    "\n",
    "def plot_confusion_matrix(TP, FP, FN, TN):\n",
    "    aux_values = np.array([np.array([TP,FP]), np.array([FN, TN])]).astype(np.int64)\n",
    "    aux_number = np.array([np.array([FN,TN]), np.array([TP, FP])]).astype(np.int64)\n",
    "    \n",
    "    #  0 1\n",
    "    #  2 3\n",
    "    \n",
    "    #  2 3\n",
    "    #  0 1\n",
    "\n",
    "    size = 2\n",
    "\n",
    "    x_start = 1.0\n",
    "    x_end = 0.0\n",
    "    y_start = 0.0\n",
    "    y_end = 1.0\n",
    "\n",
    "    extent = [x_start, x_end, y_start, y_end]\n",
    "\n",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    im = ax.imshow(aux_values, extent=extent, interpolation='None', cmap='viridis')\n",
    "\n",
    "    # Add the text\n",
    "    jump_x = (x_end - x_start) / (2.0 * size)\n",
    "    jump_y = (y_end - y_start) / (2.0 * size)\n",
    "    x_positions = np.linspace(start=x_start, stop=x_end, num=size, endpoint=False)\n",
    "    y_positions = np.linspace(start=y_start, stop=y_end, num=size, endpoint=False)\n",
    "\n",
    "    for y_index, y in enumerate(y_positions):\n",
    "        for x_index, x in enumerate(x_positions):\n",
    "            label = aux_number[y_index, x_index]\n",
    "            text_x = x + jump_x\n",
    "            text_y = y + jump_y\n",
    "            ax.text(text_x, text_y, label, color='black', ha='center', va='center')\n",
    "\n",
    "    ax.set(\n",
    "                xticks=np.arange(2),\n",
    "                yticks=np.arange(2),\n",
    "                xlabel=\"True label\",\n",
    "                ylabel=\"Predicted label\",\n",
    "            )\n",
    "\n",
    "    fig.colorbar(im)\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Vamos começar com os casos especiais\n",
    "\n",
    "# Convertendo a categoria de diabete separadamente porque possui 4 strings, mas pode ser simplificado para booleano\n",
    "def simplify_diabetes(diabetes_status):\n",
    "    if \"No\" in diabetes_status:\n",
    "        return 0\n",
    "    elif \"Yes\" in diabetes_status:\n",
    "        return 1\n",
    "\n",
    "processed_df = df.copy()\n",
    "processed_df['Diabetes'] = processed_df['Diabetes'].apply(simplify_diabetes)\n",
    "\n",
    "# Checkup e Age_Category podem ser tratados por meio de Label Encoding\n",
    "checkup_values = {\n",
    "    'Never': 0,\n",
    "    'Within the past year': 4,\n",
    "    'Within the past 2 years': 3,\n",
    "    'Within the past 5 years': 2,\n",
    "    '5 or more years ago': 1\n",
    "}\n",
    "\n",
    "processed_df['Checkup'] = processed_df['Checkup'].map(checkup_values)\n",
    "\n",
    "age_category_values = {\n",
    "    '18-24': 1,\n",
    "    '25-29': 2,\n",
    "    '30-34': 3,\n",
    "    '35-39': 4,\n",
    "    '40-44': 5,\n",
    "    '45-49': 6,\n",
    "    '50-54': 7,\n",
    "    '55-59': 8,\n",
    "    '60-64': 9,\n",
    "    '65-69': 10,\n",
    "    '70-74': 11,\n",
    "    '75-79': 12,\n",
    "    '80+': 13\n",
    "}\n",
    "\n",
    "processed_df['Age_Category'] = processed_df['Age_Category'].map(age_category_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def binary_encoding(df, category, str1, str2):\n",
    "  df[category].replace([str1, str2], [0, 1], inplace=True)\n",
    "  return df\n",
    "\n",
    "processed_df = binary_encoding(processed_df, \"Heart_Disease\", \"No\", \"Yes\")\n",
    "processed_df = binary_encoding(processed_df, \"Smoking_History\", \"No\", \"Yes\")\n",
    "processed_df = binary_encoding(processed_df, \"Arthritis\", \"No\", \"Yes\")\n",
    "processed_df = binary_encoding(processed_df, \"Depression\", \"No\", \"Yes\")\n",
    "processed_df = binary_encoding(processed_df, \"Exercise\", \"No\", \"Yes\")\n",
    "processed_df = binary_encoding(processed_df, \"Skin_Cancer\", \"No\", \"Yes\")\n",
    "processed_df = binary_encoding(processed_df, \"Other_Cancer\", \"No\", \"Yes\")\n",
    "processed_df = binary_encoding(processed_df, \"Smoking_History\", \"No\", \"Yes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Checkup</th>\n",
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      "text/plain": [
       "   Checkup  Exercise  Heart_Disease  Skin_Cancer  Other_Cancer  Depression  \\\n",
       "0        3         0              0            0             0           0   \n",
       "1        4         0              1            0             0           0   \n",
       "2        4         1              0            0             0           0   \n",
       "3        4         1              1            0             0           0   \n",
       "4        4         0              0            0             0           0   \n",
       "\n",
       "   Diabetes  Arthritis  Age_Category  Height_(cm)  ...  Fruit_Consumption  \\\n",
       "0         0          1            11        150.0  ...               30.0   \n",
       "1         1          0            11        165.0  ...               30.0   \n",
       "2         1          0             9        163.0  ...               12.0   \n",
       "3         1          0            12        180.0  ...               30.0   \n",
       "4         0          0            13        191.0  ...                8.0   \n",
       "\n",
       "   Green_Vegetables_Consumption  FriedPotato_Consumption  \\\n",
       "0                          16.0                     12.0   \n",
       "1                           0.0                      4.0   \n",
       "2                           3.0                     16.0   \n",
       "3                          30.0                      8.0   \n",
       "4                           4.0                      0.0   \n",
       "\n",
       "   General_Health_Excellent  General_Health_Fair  General_Health_Good  \\\n",
       "0                         0                    0                    0   \n",
       "1                         0                    0                    0   \n",
       "2                         0                    0                    0   \n",
       "3                         0                    0                    0   \n",
       "4                         0                    0                    1   \n",
       "\n",
       "   General_Health_Poor  General_Health_Very Good  Sex_Female  Sex_Male  \n",
       "0                    1                         0           1         0  \n",
       "1                    0                         1           1         0  \n",
       "2                    0                         1           1         0  \n",
       "3                    1                         0           0         1  \n",
       "4                    0                         0           0         1  \n",
       "\n",
       "[5 rows x 24 columns]"
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     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def one_hot_encoding(df, category):\n",
    "  dummies = pd.get_dummies(df[category], prefix=category)\n",
    "  df = pd.concat([df, dummies], axis='columns')\n",
    "  df = df.drop([category], axis=1)\n",
    "  return df\n",
    "\n",
    "processed_df = one_hot_encoding(processed_df, \"General_Health\")\n",
    "processed_df = one_hot_encoding(processed_df, \"Sex\")\n",
    "processed_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Checkup</th>\n",
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      "text/plain": [
       "   Checkup  Exercise  Heart_Disease  Skin_Cancer  Other_Cancer  Depression  \\\n",
       "0        3         0              0            0             0           0   \n",
       "1        4         0              1            0             0           0   \n",
       "2        4         1              0            0             0           0   \n",
       "3        4         1              1            0             0           0   \n",
       "4        4         0              0            0             0           0   \n",
       "\n",
       "   Diabetes  Arthritis  Age_Category  Height_(cm)  Weight_(kg)    BMI  \\\n",
       "0         0          1            11        150.0        32.66  14.54   \n",
       "1         1          0            11        165.0        77.11  28.29   \n",
       "2         1          0             9        163.0        88.45  33.47   \n",
       "3         1          0            12        180.0        93.44  28.73   \n",
       "4         0          0            13        191.0        88.45  24.37   \n",
       "\n",
       "   Smoking_History  Alcohol_Consumption  Fruit_Consumption  \\\n",
       "0                1                  0.0               30.0   \n",
       "1                0                  0.0               30.0   \n",
       "2                0                  4.0               12.0   \n",
       "3                0                  0.0               30.0   \n",
       "4                1                  0.0                8.0   \n",
       "\n",
       "   Green_Vegetables_Consumption  FriedPotato_Consumption  General_Health  Sex  \n",
       "0                          16.0                     12.0               1    1  \n",
       "1                           0.0                      4.0               4    1  \n",
       "2                           3.0                     16.0               4    1  \n",
       "3                          30.0                      8.0               1    0  \n",
       "4                           4.0                      0.0               3    0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Unindo as colunas de General Health em uma só\n",
    "# poor = 1\n",
    "# fair = 2\n",
    "# good = 3\n",
    "# very_good = 4\n",
    "# excellent = 5\n",
    "\n",
    "neural_df = processed_df.copy()\n",
    "neural_df.insert(loc=17, column='General_Health', value=-1)\n",
    "neural_df.insert(loc=23, column='Sex', value=-1)\n",
    "\n",
    "neural_df.loc[neural_df[\"General_Health_Poor\"] == True, \"General_Health\"] = 1\n",
    "neural_df.loc[neural_df[\"General_Health_Fair\"] == True, \"General_Health\"] = 2\n",
    "neural_df.loc[neural_df[\"General_Health_Good\"] == True, \"General_Health\"] = 3\n",
    "neural_df.loc[neural_df[\"General_Health_Very Good\"] == True, \"General_Health\"] = 4\n",
    "neural_df.loc[neural_df[\"General_Health_Excellent\"] == True, \"General_Health\"] = 5\n",
    "\n",
    "# Unindo as colunas de sexo numa só\n",
    "# Female = 0\n",
    "# Male = 1\n",
    "\n",
    "neural_df.loc[neural_df[\"Sex_Female\"] == True, \"Sex\"] = 1\n",
    "neural_df.loc[neural_df[\"Sex_Male\"] == True, \"Sex\"] = 0\n",
    "\n",
    "del neural_df[\"General_Health_Poor\"]\n",
    "del neural_df[\"General_Health_Fair\"]\n",
    "del neural_df[\"General_Health_Good\"]\n",
    "del neural_df[\"General_Health_Very Good\"]\n",
    "del neural_df[\"General_Health_Excellent\"]\n",
    "\n",
    "del neural_df[\"Sex_Female\"]\n",
    "del neural_df[\"Sex_Male\"]\n",
    "\n",
    "\n",
    "neural_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(random_state=42)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#define qual a coluna alvo e a coluna de dados\n",
    "y = neural_df[\"Heart_Disease\"] #output esperado\n",
    "neural_df.drop(\"Heart_Disease\", inplace=True, axis=1) #todo o restante\n",
    "X = neural_df\n",
    "\n",
    "# divide o dataset em treino e teste\n",
    "# o random size é referente a seed inicial, para fazer outros testes sempre com a mesma base (sempre vai dar o mesmo resultado com o dataframe usado)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "model = DecisionTreeClassifier(random_state=42)\n",
    "\n",
    "model.fit(X_train, y_train) #treina o modelo\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1159 52015 4759 3838\n",
      "Accuracy:  0.8608246588204821\n",
      "Recall:  0.23193916349809887\n",
      "Precision:  0.19584319026698208\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted = model.predict(X_test) #predicao (f(x))\n",
    "\n",
    "TP,TN,FP,FN = metrics(y_test.to_numpy(), predicted)\n",
    "recall = TP/(TP+FN)\n",
    "precision = TP/(TP+FP)\n",
    "accuracy = (TN+TP)/(TP+FP+FN+TN)\n",
    "print(TP,TN,FP,FN)\n",
    "print(\"Accuracy: \", accuracy)\n",
    "print(\"Recall: \", recall)\n",
    "print(\"Precision: \", precision)\n",
    "plot_confusion_matrix(TP,FP,FN,TN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Podemos perceber que há um desequilíbrio no dataset: há muito mais pessoas que não possuem/ já possuíram doenças cardiovasculares do que pessoas que possuem/já possuíram. Comprovando o desequilíbrio:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def graficoDiseaseCVD(tabela):\n",
    "    '''Funcao que retorna um grafico de barras com a\n",
    "    quantidade de pessoas em cada categoria de comorbidade a partir de uma tabela'''\n",
    "    keys = [\"Heart_Disease\"]\n",
    "    values = {}\n",
    "    for key in keys:\n",
    "        values[key] = len(tabela.query(f'{key} == \"Yes\"'))\n",
    "        values['No_'+key] = len(tabela.query(f'{key} == \"No\"'))\n",
    "    fig = plt.figure(figsize=(7,5))\n",
    "    a,b = (zip(*values.items()))\n",
    "    return list(a), list(b)\n",
    "\n",
    "CVDDisease, countCVDDisease = graficoDiseaseCVD(df)\n",
    "ax=sns.barplot(x=CVDDisease,y=countCVDDisease,color = 'blue', alpha = 0.5, width=0.9)\n",
    "plt.title(\"Number of people with CVD and other diseases\")\n",
    "ax.set(xlabel=\"Diseases\", ylabel=\"Count people with CVD and other diseases\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Vamos reselecionar os dados para treino:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "rus = RandomUnderSampler(random_state=0)\n",
    "underX, undery =rus.fit_resample(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=42)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(random_state=42)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_model = DecisionTreeClassifier(random_state=42)\n",
    "_model.fit(underX, undery) #treina o modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.663709507697787\n",
      "Recall:  0.6539923954372624\n",
      "Precision:  0.1464682681964862\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted = _model.predict(X_test) #predicao (f(x))\n",
    "\n",
    "TP,TN,FP,FN = metrics(y_test.to_numpy(), predicted)\n",
    "recall = TP/(TP+FN)\n",
    "precision = TP/(TP+FP)\n",
    "accuracy = (TN+TP)/(TP+FP+FN+TN)\n",
    "print(\"Accuracy: \", accuracy)\n",
    "print(\"Recall: \", recall)\n",
    "print(\"Precision: \", precision)\n",
    "plot_confusion_matrix(TP,FP,FN,TN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Podemos perceber que, mesmo com o dataset equilibrado, a árvore de decisão parece afetada de forma negativa. Portanto, não é o melhor modelo para a predição."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Redes Neurais de predição de cada risco de CVD em relação aos hábitos dos pacientes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "# renomeando os dados de treino e de teste para o dados reselecionados\n",
    "x_train = underX.to_numpy()\n",
    "x_train = x_train.astype(np.float32)\n",
    "y_train = undery.to_numpy()\n",
    "y_train = y_train.astype(np.float32)\n",
    "\n",
    "x_test = X_test.to_numpy()\n",
    "x_test = x_test.astype(np.float32)\n",
    "y_test = y_test.to_numpy()\n",
    "y_test = y_test.astype(np.float32)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_8\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_9 (InputLayer)        [(None, 18)]              0         \n",
      "                                                                 \n",
      " dense_17 (Dense)            (None, 64)                1216      \n",
      "                                                                 \n",
      " dropout_9 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_18 (Dense)            (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1281 (5.00 KB)\n",
      "Trainable params: 1281 (5.00 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "input_layer = tf.keras.layers.Input(shape=(18))\n",
    "\n",
    "x = input_layer\n",
    "x = tf.keras.layers.Dense(64, kernel_initializer='uniform', activation='relu')(x)\n",
    "x = tf.keras.layers.Dropout(0.3)(x)\n",
    "\n",
    "out_layer = tf.keras.layers.Dense(1, kernel_initializer='uniform', activation='sigmoid')(x)\n",
    "\n",
    "model = tf.keras.Model(input_layer, out_layer)\n",
    "optimizer=tf.keras.optimizers.Adam(0.0001)\n",
    "loss = tf.keras.losses.BinaryFocalCrossentropy(apply_class_balancing=True)\n",
    "\n",
    "model.compile(optimizer=optimizer,\n",
    "              loss=loss,\n",
    "              metrics=[\"accuracy\"])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "250/250 [==============================] - 2s 3ms/step - loss: 0.0845 - accuracy: 0.6060 - val_loss: 0.1128 - val_accuracy: 0.0000e+00\n",
      "Epoch 2/100\n",
      "250/250 [==============================] - 1s 3ms/step - loss: 0.0675 - accuracy: 0.6263 - val_loss: 0.0992 - val_accuracy: 0.0000e+00\n",
      "Epoch 3/100\n",
      "250/250 [==============================] - 1s 3ms/step - loss: 0.0622 - accuracy: 0.6270 - val_loss: 0.0943 - val_accuracy: 0.0000e+00\n",
      "Epoch 4/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0589 - accuracy: 0.6300 - val_loss: 0.0908 - val_accuracy: 5.0063e-04\n",
      "Epoch 5/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0565 - accuracy: 0.6380 - val_loss: 0.0843 - val_accuracy: 0.0329\n",
      "Epoch 6/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0549 - accuracy: 0.6491 - val_loss: 0.0857 - val_accuracy: 0.0574\n",
      "Epoch 7/100\n",
      "250/250 [==============================] - 1s 3ms/step - loss: 0.0539 - accuracy: 0.6585 - val_loss: 0.0795 - val_accuracy: 0.1229\n",
      "Epoch 8/100\n",
      "250/250 [==============================] - 1s 3ms/step - loss: 0.0532 - accuracy: 0.6688 - val_loss: 0.0811 - val_accuracy: 0.1369\n",
      "Epoch 9/100\n",
      "250/250 [==============================] - 1s 3ms/step - loss: 0.0529 - accuracy: 0.6705 - val_loss: 0.0790 - val_accuracy: 0.1655\n",
      "Epoch 10/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.0525 - accuracy: 0.6794 - val_loss: 0.0785 - val_accuracy: 0.1876\n",
      "Epoch 11/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0523 - accuracy: 0.6788 - val_loss: 0.0794 - val_accuracy: 0.1902\n",
      "Epoch 12/100\n",
      "250/250 [==============================] - 1s 3ms/step - loss: 0.0523 - accuracy: 0.6806 - val_loss: 0.0799 - val_accuracy: 0.1984\n",
      "Epoch 13/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0520 - accuracy: 0.6818 - val_loss: 0.0774 - val_accuracy: 0.2441\n",
      "Epoch 14/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0518 - accuracy: 0.6847 - val_loss: 0.0808 - val_accuracy: 0.2055\n",
      "Epoch 15/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0517 - accuracy: 0.6857 - val_loss: 0.0814 - val_accuracy: 0.1856\n",
      "Epoch 16/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0516 - accuracy: 0.6882 - val_loss: 0.0764 - val_accuracy: 0.2196\n",
      "Epoch 17/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0516 - accuracy: 0.6862 - val_loss: 0.0824 - val_accuracy: 0.1937\n",
      "Epoch 18/100\n",
      "250/250 [==============================] - 1s 3ms/step - loss: 0.0515 - accuracy: 0.6864 - val_loss: 0.0894 - val_accuracy: 0.1449\n",
      "Epoch 19/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0514 - accuracy: 0.6862 - val_loss: 0.0732 - val_accuracy: 0.2860\n",
      "Epoch 20/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0512 - accuracy: 0.6900 - val_loss: 0.0815 - val_accuracy: 0.2095\n",
      "Epoch 21/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0512 - accuracy: 0.6875 - val_loss: 0.0772 - val_accuracy: 0.2370\n",
      "Epoch 22/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0512 - accuracy: 0.6887 - val_loss: 0.0825 - val_accuracy: 0.2045\n",
      "Epoch 23/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0511 - accuracy: 0.6907 - val_loss: 0.0824 - val_accuracy: 0.2054\n",
      "Epoch 24/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0512 - accuracy: 0.6855 - val_loss: 0.0811 - val_accuracy: 0.2212\n",
      "Epoch 25/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0510 - accuracy: 0.6897 - val_loss: 0.0809 - val_accuracy: 0.2066\n",
      "Epoch 26/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0511 - accuracy: 0.6880 - val_loss: 0.0818 - val_accuracy: 0.2094\n",
      "Epoch 27/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0510 - accuracy: 0.6864 - val_loss: 0.0792 - val_accuracy: 0.1962\n",
      "Epoch 28/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0510 - accuracy: 0.6879 - val_loss: 0.0784 - val_accuracy: 0.2368\n",
      "Epoch 29/100\n",
      "250/250 [==============================] - 1s 3ms/step - loss: 0.0509 - accuracy: 0.6883 - val_loss: 0.0803 - val_accuracy: 0.2178\n",
      "Epoch 30/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0509 - accuracy: 0.6910 - val_loss: 0.0827 - val_accuracy: 0.2154\n",
      "Epoch 31/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0509 - accuracy: 0.6890 - val_loss: 0.0802 - val_accuracy: 0.2295\n",
      "Epoch 32/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0509 - accuracy: 0.6880 - val_loss: 0.0742 - val_accuracy: 0.3030\n",
      "Epoch 33/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0510 - accuracy: 0.6892 - val_loss: 0.0792 - val_accuracy: 0.2437\n",
      "Epoch 34/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0509 - accuracy: 0.6871 - val_loss: 0.0817 - val_accuracy: 0.2145\n",
      "Epoch 35/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0509 - accuracy: 0.6931 - val_loss: 0.0770 - val_accuracy: 0.2369\n",
      "Epoch 36/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0509 - accuracy: 0.6887 - val_loss: 0.0800 - val_accuracy: 0.2327\n",
      "Epoch 37/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0509 - accuracy: 0.6905 - val_loss: 0.0837 - val_accuracy: 0.2323\n",
      "Epoch 38/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0507 - accuracy: 0.6945 - val_loss: 0.0820 - val_accuracy: 0.1819\n",
      "Epoch 39/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0509 - accuracy: 0.6892 - val_loss: 0.0806 - val_accuracy: 0.2070\n",
      "Epoch 40/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0508 - accuracy: 0.6906 - val_loss: 0.0847 - val_accuracy: 0.1718\n",
      "Epoch 41/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0508 - accuracy: 0.6871 - val_loss: 0.0835 - val_accuracy: 0.1867\n",
      "Epoch 42/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0507 - accuracy: 0.6915 - val_loss: 0.0778 - val_accuracy: 0.2610\n",
      "Epoch 43/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0508 - accuracy: 0.6898 - val_loss: 0.0782 - val_accuracy: 0.2437\n",
      "Epoch 44/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0507 - accuracy: 0.6914 - val_loss: 0.0800 - val_accuracy: 0.1994\n",
      "Epoch 45/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0508 - accuracy: 0.6913 - val_loss: 0.0835 - val_accuracy: 0.1890\n",
      "Epoch 46/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0507 - accuracy: 0.6904 - val_loss: 0.0889 - val_accuracy: 0.1914\n",
      "Epoch 47/100\n",
      "250/250 [==============================] - 1s 3ms/step - loss: 0.0508 - accuracy: 0.6928 - val_loss: 0.0824 - val_accuracy: 0.1946\n",
      "Epoch 48/100\n",
      "250/250 [==============================] - 1s 3ms/step - loss: 0.0509 - accuracy: 0.6896 - val_loss: 0.0834 - val_accuracy: 0.2208\n",
      "Epoch 49/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0507 - accuracy: 0.6915 - val_loss: 0.0848 - val_accuracy: 0.1877\n",
      "Epoch 50/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0507 - accuracy: 0.6889 - val_loss: 0.0832 - val_accuracy: 0.2227\n",
      "Epoch 51/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0507 - accuracy: 0.6925 - val_loss: 0.0843 - val_accuracy: 0.2054\n",
      "Epoch 52/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0507 - accuracy: 0.6906 - val_loss: 0.0757 - val_accuracy: 0.2673\n",
      "Epoch 53/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0507 - accuracy: 0.6906 - val_loss: 0.0821 - val_accuracy: 0.2288\n",
      "Epoch 54/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0507 - accuracy: 0.6935 - val_loss: 0.0780 - val_accuracy: 0.2736\n",
      "Epoch 55/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0507 - accuracy: 0.6897 - val_loss: 0.0873 - val_accuracy: 0.1543\n",
      "Epoch 56/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0506 - accuracy: 0.6905 - val_loss: 0.0818 - val_accuracy: 0.2473\n",
      "Epoch 57/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0508 - accuracy: 0.6913 - val_loss: 0.0767 - val_accuracy: 0.2426\n",
      "Epoch 58/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0507 - accuracy: 0.6899 - val_loss: 0.0808 - val_accuracy: 0.1886\n",
      "Epoch 59/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0506 - accuracy: 0.6927 - val_loss: 0.0769 - val_accuracy: 0.2453\n",
      "Epoch 60/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0506 - accuracy: 0.6926 - val_loss: 0.0790 - val_accuracy: 0.2123\n",
      "Epoch 61/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0507 - accuracy: 0.6922 - val_loss: 0.0738 - val_accuracy: 0.2756\n",
      "Epoch 62/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0506 - accuracy: 0.6921 - val_loss: 0.0817 - val_accuracy: 0.1997\n",
      "Epoch 63/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0506 - accuracy: 0.6903 - val_loss: 0.0851 - val_accuracy: 0.2129\n",
      "Epoch 64/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.6919 - val_loss: 0.0772 - val_accuracy: 0.2441\n",
      "Epoch 65/100\n",
      "250/250 [==============================] - 1s 3ms/step - loss: 0.0507 - accuracy: 0.6918 - val_loss: 0.0797 - val_accuracy: 0.2377\n",
      "Epoch 66/100\n",
      "250/250 [==============================] - 1s 4ms/step - loss: 0.0506 - accuracy: 0.6900 - val_loss: 0.0830 - val_accuracy: 0.1695\n",
      "Epoch 67/100\n",
      "250/250 [==============================] - 1s 3ms/step - loss: 0.0507 - accuracy: 0.6920 - val_loss: 0.0781 - val_accuracy: 0.2338\n",
      "Epoch 68/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0506 - accuracy: 0.6937 - val_loss: 0.0781 - val_accuracy: 0.2572\n",
      "Epoch 69/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0504 - accuracy: 0.6925 - val_loss: 0.0767 - val_accuracy: 0.2847\n",
      "Epoch 70/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.6938 - val_loss: 0.0785 - val_accuracy: 0.2541\n",
      "Epoch 71/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0506 - accuracy: 0.6905 - val_loss: 0.0824 - val_accuracy: 0.1969\n",
      "Epoch 72/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.6927 - val_loss: 0.0816 - val_accuracy: 0.2397\n",
      "Epoch 73/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0507 - accuracy: 0.6893 - val_loss: 0.0766 - val_accuracy: 0.2509\n",
      "Epoch 74/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.6902 - val_loss: 0.0835 - val_accuracy: 0.1785\n",
      "Epoch 75/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0506 - accuracy: 0.6907 - val_loss: 0.0739 - val_accuracy: 0.3282\n",
      "Epoch 76/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.6912 - val_loss: 0.0840 - val_accuracy: 0.1991\n",
      "Epoch 77/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.6903 - val_loss: 0.0805 - val_accuracy: 0.2317\n",
      "Epoch 78/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0506 - accuracy: 0.6909 - val_loss: 0.0806 - val_accuracy: 0.1662\n",
      "Epoch 79/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0506 - accuracy: 0.6891 - val_loss: 0.0797 - val_accuracy: 0.2421\n",
      "Epoch 80/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.6917 - val_loss: 0.0792 - val_accuracy: 0.2347\n",
      "Epoch 81/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.6935 - val_loss: 0.0797 - val_accuracy: 0.2150\n",
      "Epoch 82/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.6920 - val_loss: 0.0826 - val_accuracy: 0.2048\n",
      "Epoch 83/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.6910 - val_loss: 0.0824 - val_accuracy: 0.2403\n",
      "Epoch 84/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.6933 - val_loss: 0.0822 - val_accuracy: 0.1900\n",
      "Epoch 85/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0506 - accuracy: 0.6906 - val_loss: 0.0802 - val_accuracy: 0.2427\n",
      "Epoch 86/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.6935 - val_loss: 0.0840 - val_accuracy: 0.1783\n",
      "Epoch 87/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.6906 - val_loss: 0.0760 - val_accuracy: 0.2573\n",
      "Epoch 88/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.6904 - val_loss: 0.0811 - val_accuracy: 0.2280\n",
      "Epoch 89/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.6901 - val_loss: 0.0799 - val_accuracy: 0.2682\n",
      "Epoch 90/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0506 - accuracy: 0.6892 - val_loss: 0.0812 - val_accuracy: 0.2106\n",
      "Epoch 91/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.6928 - val_loss: 0.0802 - val_accuracy: 0.2454\n",
      "Epoch 92/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0506 - accuracy: 0.6899 - val_loss: 0.0812 - val_accuracy: 0.1874\n",
      "Epoch 93/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0506 - accuracy: 0.6882 - val_loss: 0.0755 - val_accuracy: 0.2584\n",
      "Epoch 94/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.6892 - val_loss: 0.0786 - val_accuracy: 0.2048\n",
      "Epoch 95/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0504 - accuracy: 0.6908 - val_loss: 0.0791 - val_accuracy: 0.2478\n",
      "Epoch 96/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0504 - accuracy: 0.6908 - val_loss: 0.0832 - val_accuracy: 0.2641\n",
      "Epoch 97/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.6925 - val_loss: 0.0817 - val_accuracy: 0.2509\n",
      "Epoch 98/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0506 - accuracy: 0.6871 - val_loss: 0.0807 - val_accuracy: 0.2483\n",
      "Epoch 99/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.6933 - val_loss: 0.0897 - val_accuracy: 0.1792\n",
      "Epoch 100/100\n",
      "250/250 [==============================] - 1s 2ms/step - loss: 0.0505 - accuracy: 0.6915 - val_loss: 0.0754 - val_accuracy: 0.3090\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x1e7ba927310>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, validation_split=0.20, epochs=100, batch_size=128, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1931/1931 [==============================] - 2s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(x_test)\n",
    "y_pred[y_pred >= 0.5] = 1.\n",
    "y_pred[y_pred < 0.5] = 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.9007624937268297\n",
      "Recall:  0.3087852711626976\n",
      "Precision:  0.3657264754681204\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "TP,TN,FP,FN = metrics(y_test, y_pred)\n",
    "recall = TP/(TP+FN)\n",
    "precision = TP/(TP+FP)\n",
    "accuracy = (TN+TP)/(TP+FP+FN+TN)\n",
    "print(\"Accuracy: \", accuracy)\n",
    "print(\"Recall: \", recall)\n",
    "print(\"Precision: \", precision)\n",
    "plot_confusion_matrix(TP,FP,FN,TN)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.9 ('base')",
   "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": "3.10.9"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "fa80cb635cf16ce7a1110c89dac6316a5da85e90d3d39e7356cab8886cf69d98"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}