Árboles de decisión.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PREPARACIÓN AMBIENTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "import numpy as np "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Escriba el PATH directorio / carpeta de su archivo de analisisC:\\Users\\lc.mojica639\\Documents\\Cientificas de Datos\\Bootcamp\\\n"
     ]
    }
   ],
   "source": [
    "pathArchivos = input(\"Escriba el PATH directorio / carpeta de su archivo de analisis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\lc.mojica639\\\\Documents\\\\Cientificas de Datos\\\\Bootcamp\\\\'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pathArchivos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ÁRBOLES DE DECISIÓN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CLASIFICACIÓN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predeciremos si un pagaré de banco es auténtico o falso dependiendo de los cuatro atributos diferentes de la imagen de la nota. Los atributos son la varianza de la imagen transformada en ondas, la curtosis de la imagen, la entropía y el sesgo de la imagen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importar archivo\n",
    "dataset = pd.read_csv(pathArchivos + \"bill_authentication.csv\",sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para obtener información más detallada sobre este conjunto de datos, consulte el repositorio de UCI ML para este conjunto de datos. https://archive.ics.uci.edu/ml/datasets/banknote+authentication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1372, 5)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Variance</th>\n",
       "      <th>Skewness</th>\n",
       "      <th>Curtosis</th>\n",
       "      <th>Entropy</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.62160</td>\n",
       "      <td>8.6661</td>\n",
       "      <td>-2.8073</td>\n",
       "      <td>-0.44699</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.54590</td>\n",
       "      <td>8.1674</td>\n",
       "      <td>-2.4586</td>\n",
       "      <td>-1.46210</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.86600</td>\n",
       "      <td>-2.6383</td>\n",
       "      <td>1.9242</td>\n",
       "      <td>0.10645</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.45660</td>\n",
       "      <td>9.5228</td>\n",
       "      <td>-4.0112</td>\n",
       "      <td>-3.59440</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.32924</td>\n",
       "      <td>-4.4552</td>\n",
       "      <td>4.5718</td>\n",
       "      <td>-0.98880</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Variance  Skewness  Curtosis  Entropy  Class\n",
       "0   3.62160    8.6661   -2.8073 -0.44699      0\n",
       "1   4.54590    8.1674   -2.4586 -1.46210      0\n",
       "2   3.86600   -2.6383    1.9242  0.10645      0\n",
       "3   3.45660    9.5228   -4.0112 -3.59440      0\n",
       "4   0.32924   -4.4552    4.5718 -0.98880      0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dataset.drop('Class', axis=1)  \n",
    "y = dataset['Class'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split  \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier  \n",
    "classifier = DecisionTreeClassifier()  \n",
    "classifier.fit(X_train, y_train)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[227   3]\n",
      " [  2 180]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.99      0.99      0.99       230\n",
      "          1       0.98      0.99      0.99       182\n",
      "\n",
      "avg / total       0.99      0.99      0.99       412\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix  \n",
    "print(confusion_matrix(y_test, y_pred))  \n",
    "print(classification_report(y_test, y_pred)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## REGRESIÓN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predecir los consumos de gas (en millones de galones) en 48 estados de EE. UU. Según el impuesto a la gasolina (en centavos), el ingreso per cápita (dólares), las carreteras pavimentadas (en millas) y la proporción de población con una licencia de conducir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importar archivo\n",
    "dataset2 = pd.read_csv(pathArchivos + \"petrol_consumption.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset original: http://people.sc.fsu.edu/~jburkardt/datasets/regression/x16.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Petrol_tax</th>\n",
       "      <th>Average_income</th>\n",
       "      <th>Paved_Highways</th>\n",
       "      <th>Population_Driver_licence(%)</th>\n",
       "      <th>Petrol_Consumption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.0</td>\n",
       "      <td>3571</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.525</td>\n",
       "      <td>541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.0</td>\n",
       "      <td>4092</td>\n",
       "      <td>1250</td>\n",
       "      <td>0.572</td>\n",
       "      <td>524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.0</td>\n",
       "      <td>3865</td>\n",
       "      <td>1586</td>\n",
       "      <td>0.580</td>\n",
       "      <td>561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.5</td>\n",
       "      <td>4870</td>\n",
       "      <td>2351</td>\n",
       "      <td>0.529</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>4399</td>\n",
       "      <td>431</td>\n",
       "      <td>0.544</td>\n",
       "      <td>410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Petrol_tax  Average_income  Paved_Highways  Population_Driver_licence(%)  \\\n",
       "0         9.0            3571            1976                         0.525   \n",
       "1         9.0            4092            1250                         0.572   \n",
       "2         9.0            3865            1586                         0.580   \n",
       "3         7.5            4870            2351                         0.529   \n",
       "4         8.0            4399             431                         0.544   \n",
       "\n",
       "   Petrol_Consumption  \n",
       "0                 541  \n",
       "1                 524  \n",
       "2                 561  \n",
       "3                 414  \n",
       "4                 410  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset2.head()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Petrol_tax</th>\n",
       "      <th>Average_income</th>\n",
       "      <th>Paved_Highways</th>\n",
       "      <th>Population_Driver_licence(%)</th>\n",
       "      <th>Petrol_Consumption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.668333</td>\n",
       "      <td>4241.833333</td>\n",
       "      <td>5565.416667</td>\n",
       "      <td>0.570333</td>\n",
       "      <td>576.770833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.950770</td>\n",
       "      <td>573.623768</td>\n",
       "      <td>3491.507166</td>\n",
       "      <td>0.055470</td>\n",
       "      <td>111.885816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3063.000000</td>\n",
       "      <td>431.000000</td>\n",
       "      <td>0.451000</td>\n",
       "      <td>344.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.000000</td>\n",
       "      <td>3739.000000</td>\n",
       "      <td>3110.250000</td>\n",
       "      <td>0.529750</td>\n",
       "      <td>509.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.500000</td>\n",
       "      <td>4298.000000</td>\n",
       "      <td>4735.500000</td>\n",
       "      <td>0.564500</td>\n",
       "      <td>568.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.125000</td>\n",
       "      <td>4578.750000</td>\n",
       "      <td>7156.000000</td>\n",
       "      <td>0.595250</td>\n",
       "      <td>632.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>5342.000000</td>\n",
       "      <td>17782.000000</td>\n",
       "      <td>0.724000</td>\n",
       "      <td>968.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Petrol_tax  Average_income  Paved_Highways  \\\n",
       "count   48.000000       48.000000       48.000000   \n",
       "mean     7.668333     4241.833333     5565.416667   \n",
       "std      0.950770      573.623768     3491.507166   \n",
       "min      5.000000     3063.000000      431.000000   \n",
       "25%      7.000000     3739.000000     3110.250000   \n",
       "50%      7.500000     4298.000000     4735.500000   \n",
       "75%      8.125000     4578.750000     7156.000000   \n",
       "max     10.000000     5342.000000    17782.000000   \n",
       "\n",
       "       Population_Driver_licence(%)  Petrol_Consumption  \n",
       "count                     48.000000           48.000000  \n",
       "mean                       0.570333          576.770833  \n",
       "std                        0.055470          111.885816  \n",
       "min                        0.451000          344.000000  \n",
       "25%                        0.529750          509.500000  \n",
       "50%                        0.564500          568.500000  \n",
       "75%                        0.595250          632.750000  \n",
       "max                        0.724000          968.000000  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "XR = dataset2.drop('Petrol_Consumption', axis=1)  \n",
    "yR = dataset2['Petrol_Consumption'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split  \n",
    "XR_train, XR_test, yR_train, yR_test = train_test_split(XR, yR, test_size=0.3, random_state=0)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor  \n",
    "regressor = DecisionTreeRegressor()  \n",
    "regressor.fit(XR_train, yR_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "yR_pred = regressor.predict(XR_test)"
   ]
  },
  {
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>534</td>\n",
       "      <td>541.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>410</td>\n",
       "      <td>414.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>577</td>\n",
       "      <td>648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>571</td>\n",
       "      <td>648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>577</td>\n",
       "      <td>648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>704</td>\n",
       "      <td>644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>487</td>\n",
       "      <td>648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>587</td>\n",
       "      <td>649.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>467</td>\n",
       "      <td>414.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>580</td>\n",
       "      <td>464.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>471</td>\n",
       "      <td>344.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>554</td>\n",
       "      <td>648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>628</td>\n",
       "      <td>648.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>631</td>\n",
       "      <td>644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>524</td>\n",
       "      <td>540.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Actual  Predicted\n",
       "29     534      541.0\n",
       "4      410      414.0\n",
       "26     577      648.0\n",
       "30     571      648.0\n",
       "32     577      648.0\n",
       "37     704      644.0\n",
       "34     487      648.0\n",
       "40     587      649.0\n",
       "7      467      414.0\n",
       "10     580      464.0\n",
       "11     471      344.0\n",
       "31     554      648.0\n",
       "33     628      648.0\n",
       "27     631      644.0\n",
       "47     524      540.0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame({'Actual':yR_test, 'Predicted':yR_pred})  \n",
    "df  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 63.46666666666667\n",
      "Mean Squared Error: 6099.733333333334\n",
      "Root Mean Squared Error: 78.10078958200957\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics  \n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(yR_test, yR_pred))  \n",
    "print('Mean Squared Error:', metrics.mean_squared_error(yR_test, yR_pred))  \n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(yR_test, yR_pred)))  "
   ]
  }
 ],
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