pandas_enfermedades_corazon.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
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       "      <th></th>\n",
       "      <th>\"V1</th>\n",
       "      <th>\"\"V2\"\"</th>\n",
       "      <th>\"\"V3\"\"</th>\n",
       "      <th>\"\"V4\"\"</th>\n",
       "      <th>\"\"V5\"\"</th>\n",
       "      <th>\"\"V6\"\"</th>\n",
       "      <th>\"\"V7\"\"</th>\n",
       "      <th>\"\"V8\"\"</th>\n",
       "      <th>\"\"V9\"\"</th>\n",
       "      <th>\"\"Class\"\"\"</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>160</td>\n",
       "      <td>12.00</td>\n",
       "      <td>5.73</td>\n",
       "      <td>23.11</td>\n",
       "      <td>1</td>\n",
       "      <td>49</td>\n",
       "      <td>25.30</td>\n",
       "      <td>97.20</td>\n",
       "      <td>52</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>144</td>\n",
       "      <td>0.01</td>\n",
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       "      <td>28.61</td>\n",
       "      <td>2</td>\n",
       "      <td>55</td>\n",
       "      <td>28.87</td>\n",
       "      <td>2.06</td>\n",
       "      <td>63</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>118</td>\n",
       "      <td>0.08</td>\n",
       "      <td>3.48</td>\n",
       "      <td>32.28</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>29.14</td>\n",
       "      <td>3.81</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>170</td>\n",
       "      <td>7.50</td>\n",
       "      <td>6.41</td>\n",
       "      <td>38.03</td>\n",
       "      <td>1</td>\n",
       "      <td>51</td>\n",
       "      <td>31.99</td>\n",
       "      <td>24.26</td>\n",
       "      <td>58</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>134</td>\n",
       "      <td>13.60</td>\n",
       "      <td>3.50</td>\n",
       "      <td>27.78</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>25.99</td>\n",
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      "text/plain": [
       "   \"V1  \"\"V2\"\"  \"\"V3\"\"  \"\"V4\"\"  \"\"V5\"\"  \"\"V6\"\"  \"\"V7\"\"  \"\"V8\"\"  \"\"V9\"\"  \\\n",
       "0  160   12.00    5.73   23.11       1      49   25.30   97.20      52   \n",
       "1  144    0.01    4.41   28.61       2      55   28.87    2.06      63   \n",
       "2  118    0.08    3.48   32.28       1      52   29.14    3.81      46   \n",
       "3  170    7.50    6.41   38.03       1      51   31.99   24.26      58   \n",
       "4  134   13.60    3.50   27.78       1      60   25.99   57.34      49   \n",
       "\n",
       "   \"\"Class\"\"\"  \n",
       "0           2  \n",
       "1           2  \n",
       "2           1  \n",
       "3           2  \n",
       "4           2  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#PREDECIR ENFERMEDADES DEL CORAZON\n",
    "#basado en https://www.youtube.com/watch?v=pUS8gqRDDZw\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "data = pd.DataFrame(pd.np.empty((0, 10)))   \n",
    "data = pd.read_csv('enfermedades_corazon.csv',quotechar=\",\") #quotechar porque los nombres de columnas se separan por una ','\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sbp</th>\n",
       "      <th>Tabaco</th>\n",
       "      <th>ldl</th>\n",
       "      <th>Adiposity</th>\n",
       "      <th>Familia</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Obesidad</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Edad</th>\n",
       "      <th>chd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>160</td>\n",
       "      <td>12.00</td>\n",
       "      <td>5.73</td>\n",
       "      <td>23.11</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>25.30</td>\n",
       "      <td>97.20</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>144</td>\n",
       "      <td>0.01</td>\n",
       "      <td>4.41</td>\n",
       "      <td>28.61</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>28.87</td>\n",
       "      <td>2.06</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>118</td>\n",
       "      <td>0.08</td>\n",
       "      <td>3.48</td>\n",
       "      <td>32.28</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
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       "      <td>46</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>170</td>\n",
       "      <td>7.50</td>\n",
       "      <td>6.41</td>\n",
       "      <td>38.03</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>31.99</td>\n",
       "      <td>24.26</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>134</td>\n",
       "      <td>13.60</td>\n",
       "      <td>3.50</td>\n",
       "      <td>27.78</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>25.99</td>\n",
       "      <td>57.34</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sbp  Tabaco   ldl  Adiposity  Familia  Tipo  Obesidad  Alcohol  Edad  chd\n",
       "0  160   12.00  5.73      23.11        0    49     25.30    97.20    52    1\n",
       "1  144    0.01  4.41      28.61        1    55     28.87     2.06    63    1\n",
       "2  118    0.08  3.48      32.28        0    52     29.14     3.81    46    0\n",
       "3  170    7.50  6.41      38.03        0    51     31.99    24.26    58    1\n",
       "4  134   13.60  3.50      27.78        0    60     25.99    57.34    49    1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cambiamos el nombre de las columnas\n",
    "data.columns = ['sbp','Tabaco','ldl','Adiposity','Familia','Tipo','Obesidad','Alcohol','Edad','chd']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "spb            int64\n",
       "Tabaco       float64\n",
       "ldl          float64\n",
       "Adiposity    float64\n",
       "Familia        int64\n",
       "Tipo           int64\n",
       "Obesidad     float64\n",
       "Alcohol      float64\n",
       "Edad           int64\n",
       "chd            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#analizamos datos\n",
    "data.dtypes #son todos enteros y flotantes asi que OK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "spb          0\n",
       "Tabaco       0\n",
       "ldl          0\n",
       "Adiposity    0\n",
       "Familia      0\n",
       "Tipo         0\n",
       "Obesidad     0\n",
       "Alcohol      0\n",
       "Edad         0\n",
       "chd          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#a ver si hay datos perdidos\n",
    "data.isnull().sum() #todo 'ceros' = OK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sbp</th>\n",
       "      <th>Tabaco</th>\n",
       "      <th>ldl</th>\n",
       "      <th>Adiposity</th>\n",
       "      <th>Familia</th>\n",
       "      <th>Tipo</th>\n",
       "      <th>Obesidad</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Edad</th>\n",
       "      <th>chd</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50.427350</td>\n",
       "      <td>12.00</td>\n",
       "      <td>5.73</td>\n",
       "      <td>23.11</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>25.30</td>\n",
       "      <td>97.20</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36.752137</td>\n",
       "      <td>0.01</td>\n",
       "      <td>4.41</td>\n",
       "      <td>28.61</td>\n",
       "      <td>1</td>\n",
       "      <td>55</td>\n",
       "      <td>28.87</td>\n",
       "      <td>2.06</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14.529915</td>\n",
       "      <td>0.08</td>\n",
       "      <td>3.48</td>\n",
       "      <td>32.28</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>29.14</td>\n",
       "      <td>3.81</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>58.974359</td>\n",
       "      <td>7.50</td>\n",
       "      <td>6.41</td>\n",
       "      <td>38.03</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>31.99</td>\n",
       "      <td>24.26</td>\n",
       "      <td>58</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28.205128</td>\n",
       "      <td>13.60</td>\n",
       "      <td>3.50</td>\n",
       "      <td>27.78</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>25.99</td>\n",
       "      <td>57.34</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sbp  Tabaco   ldl  Adiposity  Familia  Tipo  Obesidad  Alcohol  Edad  \\\n",
       "0  50.427350   12.00  5.73      23.11        0    49     25.30    97.20    52   \n",
       "1  36.752137    0.01  4.41      28.61        1    55     28.87     2.06    63   \n",
       "2  14.529915    0.08  3.48      32.28        0    52     29.14     3.81    46   \n",
       "3  58.974359    7.50  6.41      38.03        0    51     31.99    24.26    58   \n",
       "4  28.205128   13.60  3.50      27.78        0    60     25.99    57.34    49   \n",
       "\n",
       "   chd  \n",
       "0    1  \n",
       "1    1  \n",
       "2    0  \n",
       "3    1  \n",
       "4    1  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#si vemos los datos veremos que hemos de modificar varias  columnas:\n",
    " #familia y chd: tiene valores de 1 o 2, lo dejaremos en 0 o 1\n",
    " #sbp: tiene una escala de datos mayor que el resto asi que hemos de escalarlos entre 0 y 100\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "data['Familia']=encoder.fit_transform(data['Familia'])\n",
    "data['chd']=encoder.fit_transform(data['chd'])\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scale = MinMaxScaler((feature_range)=(0,100))\n",
    "data['sbp']=scale.fit_transform(data['sbp'].values.reshape(-1,1))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xfe359a6dd8>"
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     "execution_count": 44,
     "metadata": {},
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizar Tabaco vs. Edad\n",
    "data.plot(x='Edad', y='Tabaco', kind='scatter', figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### CON ESTO HEMOS TERMINADO DE PREPROCESAR DATOS, AHORA PASAMOS A LOS ALGORITMOS DE ML ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[57  9]\n",
      " [17 10]]\n"
     ]
    }
   ],
   "source": [
    "#Analisis NL\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score, precision_score\n",
    "\n",
    "#definir variable dependiente e independientes\n",
    "y = data['chd']\n",
    "X = data.drop('chd', axis=1) #x es todas las columnas excepto chd\n",
    "\n",
    "#separar datos de entrenamiento y prueba\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=1)\n",
    "\n",
    "#definimos algoritmo a utilizar\n",
    "algoritmo = svm.SVC(kernel='linear')\n",
    "\n",
    "#entrenamos el modelo con los datos que separamos antes\n",
    "algoritmo.fit(X_train, y_train)\n",
    "\n",
    "#realizar prediccion con el modelo obtenido\n",
    "y_test_pred = algoritmo.predict(X_test)\n",
    "\n",
    "#calcular matriz de confusion\n",
    "print(confusion_matrix(y_test, y_test_pred)) #67datos correctos (57+10), 26 incorrectos (17+9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5263157894736842"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_test_pred) #0,72\n",
    "precision_score(y_test, y_test_pred) #0,52 \n",
    "\n",
    "# nos reafirma lo obtenido en la matriz de confusion: que nuestro modelo es malo \n",
    "# por lo que hay que probar otras caracteristicas y otros algoritmos de clasificacion"
   ]
  }
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