log_reg_under_smote.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cb374491-533f-48db-822d-0afc69ecc24c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x32dc85cd0>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "def logistic(a, b, x):\n",
    "    return 1 / (1 + np.exp(-(a + b * x)))\n",
    "\n",
    "a = 2\n",
    "b = 0.01\n",
    "x = np.linspace(-1000, 1000, 100)\n",
    "\n",
    "plt.plot(x, logistic(a, b, x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "411cce58-2032-4d89-85b0-65208a5ef29a",
   "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>Unnamed: 0</th>\n",
       "      <th>default</th>\n",
       "      <th>student</th>\n",
       "      <th>balance</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>729.526495</td>\n",
       "      <td>44361.625074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>817.180407</td>\n",
       "      <td>12106.134700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1073.549164</td>\n",
       "      <td>31767.138947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>529.250605</td>\n",
       "      <td>35704.493935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>785.655883</td>\n",
       "      <td>38463.495879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 default student      balance        income\n",
       "0           0      No      No   729.526495  44361.625074\n",
       "1           1      No     Yes   817.180407  12106.134700\n",
       "2           2      No      No  1073.549164  31767.138947\n",
       "3           3      No      No   529.250605  35704.493935\n",
       "4           4      No      No   785.655883  38463.495879"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url_data = \"https://raw.githubusercontent.com/jealcalat/AEM-ITESO/main/datasets/default_islp.csv\"\n",
    "default = pd.read_csv(url_data)\n",
    "default.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cdc08e7f-c0a5-4102-8479-714fa4b15f06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11481ef90>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "default['default_bin'] = [0 if de_x == \"No\" else 1 for de_x in default['default']]\n",
    "\n",
    "plt.scatter(\n",
    "    default['balance'],\n",
    "    default['default_bin']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bdf3b4aa-57fd-4ddc-980e-70ee548378c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# default_bin ~ balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5df200f5-4728-48bd-bc51-1c489d992c27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>default_bin</td>   <th>  R-squared:         </th> <td>   0.123</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.122</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1397.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 14 Nov 2024</td> <th>  Prob (F-statistic):</th> <td>2.77e-286</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:54:49</td>     <th>  Log-Likelihood:    </th> <td>  3644.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 10000</td>      <th>  AIC:               </th> <td>  -7286.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  9998</td>      <th>  BIC:               </th> <td>  -7271.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>   -0.0752</td> <td>    0.003</td> <td>  -22.416</td> <td> 0.000</td> <td>   -0.082</td> <td>   -0.069</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>balance</th>   <td>    0.0001</td> <td> 3.47e-06</td> <td>   37.374</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>8547.967</td> <th>  Durbin-Watson:     </th>  <td>   2.023</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>180810.806</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 4.243</td>  <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>22.025</td>  <th>  Cond. No.          </th>  <td>1.93e+03</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.93e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &   default\\_bin   & \\textbf{  R-squared:         } &     0.123   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.122   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     1397.   \\\\\n",
       "\\textbf{Date:}             & Thu, 14 Nov 2024 & \\textbf{  Prob (F-statistic):} & 2.77e-286   \\\\\n",
       "\\textbf{Time:}             &     19:54:49     & \\textbf{  Log-Likelihood:    } &    3644.8   \\\\\n",
       "\\textbf{No. Observations:} &       10000      & \\textbf{  AIC:               } &    -7286.   \\\\\n",
       "\\textbf{Df Residuals:}     &        9998      & \\textbf{  BIC:               } &    -7271.   \\\\\n",
       "\\textbf{Df Model:}         &           1      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "                   & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{Intercept} &      -0.0752  &        0.003     &   -22.416  &         0.000        &       -0.082    &       -0.069     \\\\\n",
       "\\textbf{balance}   &       0.0001  &     3.47e-06     &    37.374  &         0.000        &        0.000    &        0.000     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 8547.967 & \\textbf{  Durbin-Watson:     } &     2.023   \\\\\n",
       "\\textbf{Prob(Omnibus):} &   0.000  & \\textbf{  Jarque-Bera (JB):  } & 180810.806  \\\\\n",
       "\\textbf{Skew:}          &   4.243  & \\textbf{  Prob(JB):          } &      0.00   \\\\\n",
       "\\textbf{Kurtosis:}      &  22.025  & \\textbf{  Cond. No.          } &  1.93e+03   \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. \\newline\n",
       " [2] The condition number is large, 1.93e+03. This might indicate that there are \\newline\n",
       " strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:            default_bin   R-squared:                       0.123\n",
       "Model:                            OLS   Adj. R-squared:                  0.122\n",
       "Method:                 Least Squares   F-statistic:                     1397.\n",
       "Date:                Thu, 14 Nov 2024   Prob (F-statistic):          2.77e-286\n",
       "Time:                        19:54:49   Log-Likelihood:                 3644.8\n",
       "No. Observations:               10000   AIC:                            -7286.\n",
       "Df Residuals:                    9998   BIC:                            -7271.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     -0.0752      0.003    -22.416      0.000      -0.082      -0.069\n",
       "balance        0.0001   3.47e-06     37.374      0.000       0.000       0.000\n",
       "==============================================================================\n",
       "Omnibus:                     8547.967   Durbin-Watson:                   2.023\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           180810.806\n",
       "Skew:                           4.243   Prob(JB):                         0.00\n",
       "Kurtosis:                      22.025   Cond. No.                     1.93e+03\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.93e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1 = sm.OLS.from_formula('default_bin ~ balance', data=default)\n",
    "result = model1.fit()\n",
    "result.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f4e39df2-8272-4ea3-80fc-27b8127260d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x32e33c200>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(\n",
    "    default['balance'],\n",
    "    default['default_bin']\n",
    ")\n",
    "x = np.linspace(0, 3000, 100)\n",
    "predicted = -0.0752 + 0.0001 * x\n",
    "plt.plot(x, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2a00c853-7aa2-49a0-8cef-681979ddb376",
   "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>Unnamed: 0</th>\n",
       "      <th>default</th>\n",
       "      <th>student</th>\n",
       "      <th>balance</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>729.526495</td>\n",
       "      <td>44361.625074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>817.180407</td>\n",
       "      <td>12106.134700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>1073.549164</td>\n",
       "      <td>31767.138947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>529.250605</td>\n",
       "      <td>35704.493935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>785.655883</td>\n",
       "      <td>38463.495879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 default student      balance        income\n",
       "0           0      No      No   729.526495  44361.625074\n",
       "1           1      No     Yes   817.180407  12106.134700\n",
       "2           2      No      No  1073.549164  31767.138947\n",
       "3           3      No      No   529.250605  35704.493935\n",
       "4           4      No      No   785.655883  38463.495879"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "# performance metrics\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, roc_auc_score, roc_curve, f1_score\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "default = pd.read_csv(url_data)\n",
    "default.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "21f75049-cfb0-4228-89b6-239f703766e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(default['default']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fe7c1f87-e929-48ca-878e-319828a97687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "default\n",
       "No     9667\n",
       "Yes     333\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "default['default'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c36c4fca-13f4-4d88-9995-264aed82b257",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cambiar No a 0, Yes a 1.\n",
    "default['default'] = [0 if de_x == \"No\" else 1 for de_x in default['default']]\n",
    "\n",
    "X = default[['balance']]\n",
    "y = default['default']\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)\n",
    "y_pred = model.predict(X_test) # etiquetas\n",
    "y_pred_proba = model.predict_proba(X_test)[:, 1] # probabilidad\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7a2ed03c-f656-4328-8be8-960212ced56f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PRecision : 0.97\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'PRecision : {accuracy:.2f}') # primeros dos decimales de accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cc09a941-2910-4ee6-a8d1-618276996ca0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(50.72222222222221, 0.5, 'True')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "sns.heatmap(conf_matrix,\n",
    "            annot=True,\n",
    "            fmt=\"d\", # valores enteros\n",
    "            cmap=\"Blues\",\n",
    "            cbar=False,\n",
    "            xticklabels=[\"Default=No\", \"Default=Yes\"],\n",
    "            yticklabels=[\"Default=No\", \"Default=Yes\"]\n",
    "           )\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"True\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcd7634e",
   "metadata": {},
   "source": [
    "\n",
    "### Ecuación Logística\n",
    "\n",
    "La ecuación logística es una función sigmoide que se utiliza en modelos de regresión logística para predecir la probabilidad de una variable binaria. La ecuación logística se define como:\n",
    "\n",
    "\\[ P(Y=1|X) = \\frac{1}{1 + e^{-(a + bX)}} \\]\n",
    "\n",
    "donde:\n",
    "- \\( P(Y=1|X) \\) es la probabilidad de que el evento \\( Y \\) ocurra dado el valor de \\( X \\).\n",
    "- \\( a \\) es el intercepto.\n",
    "- \\( b \\) es el coeficiente de la variable \\( X \\).\n",
    "- \\( e \\) es la base del logaritmo natural (aproximadamente 2.71828).\n",
    "\n",
    "En el contexto de la regresión logística, esta ecuación se utiliza para modelar la relación entre una o más variables independientes \\( X \\) y una variable dependiente binaria \\( Y \\)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fc6f1d8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x3315bd280>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w27nEr1R31+AxZLCL5lUAAHpBGAmzVLeL0zEAADjAaZoLZIyhURUAgAvAV/gL0JfGVQAAcG7MjFyAsxtXaVQFAMA5ZkbCZOcSvy69yE2jKgAADjEz0k9n94qkurliBgCA/mBmpB/oFQEAIHyYGekHekUAAAgfZkYuEL0iAABcGGZGLhC9IgAAXBjCSD8M/FsLAgAQOwgjDhljdFdVne0yAACIG4QRh052BLXnaECSNDY7jcZVAAAuEGHkAmycX0C/CAAAF4gwcgHIIQAAXDjCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijDiEKuvAgAQXoQRB1h9FQCA8COMOMDqqwAAhB9hpJ9YfRUAgPAgjPQTOQQAgPAgjAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjDrD6KgAA4UcY6SNWXwUAIDIII33E6qsAAEQGYaQfWH0VAIDwIYz0AzkEAIDwIYz0Ec2rAABEBmGkD2heBQAgcggjfUDzKgAAkUMYcYjmVQAAwosw4hA5BACA8CKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsII33AHXsBAIgcwsh5cMdeAAAiizByHtyxFwCAyCKMOMAdewEACD/CiAPkEAAAwq9fYaSyslI5OTlKSUlRfn6+duzY0afj1q9fr6SkJM2YMaM/L2sFzasAAESW4zCyYcMGFRcXq6ysTLt27dKECRNUWFiob7/99pzHffnll/rTn/6kadOm9bvYaKN5FQCAyHMcRlavXq158+Zp7ty5Gjt2rKqqqpSamqp169b1ekwwGNS9996rp59+WqNGjbqggqOJ5lUAACLPURhpb29XfX29/H7/z0+QnCy/36+6ut5nEJYtW6bMzEzdf//9fXqdtrY2BQKBLg/baF4FACAyHIWR5uZmBYNBeb3eLtu9Xq8aGxt7PGbbtm169dVXtXbt2j6/Tnl5udLT00MPn8/npMyIIIcAABAZEb2a5vjx45o1a5bWrl2rjIyMPh9XUlKilpaW0OPIkSMRrLJnxhidaA9G/XUBAEg0g5zsnJGRIZfLpaampi7bm5qalJWV1W3/L774Ql9++aWmT58e2tbZ2fnTCw8apH379mn06NHdjvN4PPJ4PE5KCytjjO6sqlP9V99bqwEAgEThaGbE7XYrNzdXtbW1oW2dnZ2qra1VQUFBt/3HjBmjzz77TA0NDaHHHXfcoZtvvlkNDQ0D4vRLT052BLsEkbzLh9O8CgBAhDiaGZGk4uJizZkzR3l5eZo8ebIqKirU2tqquXPnSpJmz56tkSNHqry8XCkpKRo3blyX44cNGyZJ3bYPVDuX+HXpRW6aVwEAiBDHYaSoqEjHjh1TaWmpGhsbNXHiRNXU1ISaWg8fPqzk5PhZ2DXV7SKIAAAQQUnGDPw1RgOBgNLT09XS0qK0tLSIv96J9lMaW7pZkrRnWaFS3Y4zGwAACa+vn9/xM4UBAABiEmEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhpAcDf01aAADiB2HkLMYY3VVVZ7sMAAASBmHkLCc7gtpzNCBJGpudpiGDXZYrAgAgvhFGzmHj/ALu2AsAQIQRRs5gjNGJ9mDoZ3IIAACRN8h2AQOFMUZ3VtWp/qvvbZcCAEBCYWbkf052BLsEkbzLh9MvAgBAFDAz0oOdS/y69CI3/SIAAEQBMyM9SHW7CCIAAEQJYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWHkf4yxXQEAAImJMCLJGKO7qupslwEAQEIijEg62RHUnqMBSdLY7DQNGeyyXBEAAImDMHKWjfMLlJSUZLsMAAASBmHkLOQQAACiizAimlcBALAp4cMIzasAANiV8GGE5lUAAOxK+DByJppXAQCIPsLIGcghAABEH2EEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYFW/wkhlZaVycnKUkpKi/Px87dixo9d9165dq2nTpmn48OEaPny4/H7/OfcHAACJxXEY2bBhg4qLi1VWVqZdu3ZpwoQJKiws1Lffftvj/lu3btU999yjjz/+WHV1dfL5fLrlllv09ddfX3DxAAAg9iUZY4yTA/Lz83XDDTdozZo1kqTOzk75fD498sgjWrRo0XmPDwaDGj58uNasWaPZs2f36TUDgYDS09PV0tKitLQ0J+We14n2UxpbulmStGdZoVLdg8L6/AAAJKq+fn47mhlpb29XfX29/H7/z0+QnCy/36+6uro+PceJEyfU0dGhSy65pNd92traFAgEujwAAEB8chRGmpubFQwG5fV6u2z3er1qbGzs03M88cQTGjFiRJdAc7by8nKlp6eHHj6fz0mZAAAghkT1apqVK1dq/fr1evfdd5WSktLrfiUlJWppaQk9jhw5EsUqAQBANDlqkMjIyJDL5VJTU1OX7U1NTcrKyjrnsc8//7xWrlypDz/8UNddd9059/V4PPJ4PE5KAwAAMcrRzIjb7VZubq5qa2tD2zo7O1VbW6uCgoJej3vuuee0fPly1dTUKC8vr//VAgCAuOP40pHi4mLNmTNHeXl5mjx5sioqKtTa2qq5c+dKkmbPnq2RI0eqvLxckvR///d/Ki0t1ZtvvqmcnJxQb8nFF1+siy++OIxvBQAAxCLHYaSoqEjHjh1TaWmpGhsbNXHiRNXU1ISaWg8fPqzk5J8nXF566SW1t7frzjvv7PI8ZWVleuqppy6segAAEPMcrzNiA+uMAAAQeyKyzggAAEC4EUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGBVwocRY2xXAABAYkvoMGKM0V1VdbbLAAAgoSV0GDnZEdSeowFJ0tjsNA0Z7LJcEQAAiSehw8iZNs4vUFJSku0yAABIOISR/yGHAABgB2EEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgVb/CSGVlpXJycpSSkqL8/Hzt2LHjnPtv3LhRY8aMUUpKisaPH6/q6up+FQsAAOKP4zCyYcMGFRcXq6ysTLt27dKECRNUWFiob7/9tsf9t2/frnvuuUf333+/du/erRkzZmjGjBn617/+dcHFAwCA2JdkjDFODsjPz9cNN9ygNWvWSJI6Ozvl8/n0yCOPaNGiRd32LyoqUmtrqz744IPQtl//+teaOHGiqqqq+vSagUBA6enpamlpUVpampNye2WM0f9rbVfeMx9KkvYsK1Sqe1BYnhsAAPT989vRzEh7e7vq6+vl9/t/foLkZPn9ftXV1fV4TF1dXZf9JamwsLDX/SWpra1NgUCgyyPcTnYEQ0EEAADY4yiMNDc3KxgMyuv1dtnu9XrV2NjY4zGNjY2O9pek8vJypaenhx4+n89JmY7lXT5cQwa7IvoaAACgZwPyvERJSYmKi4tDPwcCgbAHkiGDXdqzrDD030lJSWF9fgAA0DeOwkhGRoZcLpeampq6bG9qalJWVlaPx2RlZTnaX5I8Ho88Ho+T0hxLSkqiRwQAgAHA0Wkat9ut3Nxc1dbWhrZ1dnaqtrZWBQUFPR5TUFDQZX9J2rJlS6/7AwCAxOJ4aqC4uFhz5sxRXl6eJk+erIqKCrW2tmru3LmSpNmzZ2vkyJEqLy+XJD366KO66aabtGrVKt1+++1av369du7cqZdffjm87wQAAMQkx2GkqKhIx44dU2lpqRobGzVx4kTV1NSEmlQPHz6s5OSfJ1ymTJmiN998U0uWLNGTTz6pX/3qV3rvvfc0bty48L0LAAAQsxyvM2JDJNYZAQAAkRWRdUYAAADCjTACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsComblt7epHYQCBguRIAANBXpz+3z7fYe0yEkePHj0uSfD6f5UoAAIBTx48fV3p6eq//HhP3puns7NQ333yjoUOHKikpKWzPGwgE5PP5dOTIEe55E0GMc/Qw1tHBOEcH4xwdkRxnY4yOHz+uESNGdLmJ7tliYmYkOTlZl112WcSePy0tjV/0KGCco4exjg7GOToY5+iI1Difa0bkNBpYAQCAVYQRAABgVUKHEY/Ho7KyMnk8HtulxDXGOXoY6+hgnKODcY6OgTDOMdHACgAA4ldCz4wAAAD7CCMAAMAqwggAALCKMAIAAKyK+zBSWVmpnJwcpaSkKD8/Xzt27Djn/hs3btSYMWOUkpKi8ePHq7q6OkqVxjYn47x27VpNmzZNw4cP1/Dhw+X3+8/7/wU/c/o7fdr69euVlJSkGTNmRLbAOOF0nH/44QctWLBA2dnZ8ng8uuqqq/j70QdOx7miokJXX321hgwZIp/Pp4ULF+rHH3+MUrWx6ZNPPtH06dM1YsQIJSUl6b333jvvMVu3btX1118vj8ejK6+8Uq+99lpkizRxbP369cbtdpt169aZf//732bevHlm2LBhpqmpqcf9P/30U+Nyucxzzz1n9uzZY5YsWWIGDx5sPvvssyhXHlucjvPMmTNNZWWl2b17t9m7d6+57777THp6uvnPf/4T5cpjj9OxPu3QoUNm5MiRZtq0aeZ3v/tddIqNYU7Hua2tzeTl5ZnbbrvNbNu2zRw6dMhs3brVNDQ0RLny2OJ0nN944w3j8XjMG2+8YQ4dOmQ2b95ssrOzzcKFC6NceWyprq42ixcvNu+8846RZN59991z7n/w4EGTmppqiouLzZ49e8wLL7xgXC6XqampiViNcR1GJk+ebBYsWBD6ORgMmhEjRpjy8vIe97/77rvN7bff3mVbfn6++cMf/hDROmOd03E+26lTp8zQoUPN66+/HqkS40Z/xvrUqVNmypQp5pVXXjFz5swhjPSB03F+6aWXzKhRo0x7e3u0SowLTsd5wYIF5je/+U2XbcXFxWbq1KkRrTOe9CWMPP744+baa6/tsq2oqMgUFhZGrK64PU3T3t6u+vp6+f3+0Lbk5GT5/X7V1dX1eExdXV2X/SWpsLCw1/3Rv3E+24kTJ9TR0aFLLrkkUmXGhf6O9bJly5SZman7778/GmXGvP6M8/vvv6+CggItWLBAXq9X48aN04oVKxQMBqNVdszpzzhPmTJF9fX1oVM5Bw8eVHV1tW677bao1JwobHwWxsSN8vqjublZwWBQXq+3y3av16vPP/+8x2MaGxt73L+xsTFidca6/ozz2Z544gmNGDGi2y8/uurPWG/btk2vvvqqGhoaolBhfOjPOB88eFAfffSR7r33XlVXV+vAgQN6+OGH1dHRobKysmiUHXP6M84zZ85Uc3OzbrzxRhljdOrUKc2fP19PPvlkNEpOGL19FgYCAZ08eVJDhgwJ+2vG7cwIYsPKlSu1fv16vfvuu0pJSbFdTlw5fvy4Zs2apbVr1yojI8N2OXGts7NTmZmZevnll5Wbm6uioiItXrxYVVVVtkuLK1u3btWKFSv04osvateuXXrnnXe0adMmLV++3HZpuEBxOzOSkZEhl8ulpqamLtubmpqUlZXV4zFZWVmO9kf/xvm0559/XitXrtSHH36o6667LpJlxgWnY/3FF1/oyy+/1PTp00PbOjs7JUmDBg3Svn37NHr06MgWHYP68zudnZ2twYMHy+VyhbZdc801amxsVHt7u9xud0RrjkX9GeelS5dq1qxZeuCBByRJ48ePV2trqx588EEtXrxYycl8vw6H3j4L09LSIjIrIsXxzIjb7VZubq5qa2tD2zo7O1VbW6uCgoIejykoKOiyvyRt2bKl1/3Rv3GWpOeee07Lly9XTU2N8vLyolFqzHM61mPGjNFnn32mhoaG0OOOO+7QzTffrIaGBvl8vmiWHzP68zs9depUHThwIBT2JGn//v3Kzs4miPSiP+N84sSJboHjdAA03GYtbKx8FkasNXYAWL9+vfF4POa1114ze/bsMQ8++KAZNmyYaWxsNMYYM2vWLLNo0aLQ/p9++qkZNGiQef75583evXtNWVkZl/b2gdNxXrlypXG73ebtt982R48eDT2OHz9u6y3EDKdjfTaupukbp+N8+PBhM3ToUPPHP/7R7Nu3z3zwwQcmMzPTPPPMM7beQkxwOs5lZWVm6NCh5u9//7s5ePCg+cc//mFGjx5t7r77bltvISYcP37c7N692+zevdtIMqtXrza7d+82X331lTHGmEWLFplZs2aF9j99ae+f//xns3fvXlNZWcmlvRfqhRdeML/85S+N2+02kydPNv/85z9D/3bTTTeZOXPmdNn/rbfeMldddZVxu93m2muvNZs2bYpyxbHJyThffvnlRlK3R1lZWfQLj0FOf6fPRBjpO6fjvH37dpOfn288Ho8ZNWqUefbZZ82pU6eiXHXscTLOHR0d5qmnnjKjR482KSkpxufzmYcffth8//330S88hnz88cc9/s09PbZz5swxN910U7djJk6caNxutxk1apT561//GtEak4xhbgsAANgTtz0jAAAgNhBGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWPX/Aet2ECCLGYYhAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)\n",
    "plt.plot(fpr, tpr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "569d9993-f270-41eb-a170-dc83c1391c49",
   "metadata": {},
   "outputs": [],
   "source": [
    "TN, FP, FN, TP = conf_matrix.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ad00101c-a729-489e-ae2a-cce6328d2af2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensibilidad: 0.25, Especificidad: 0.99\n"
     ]
    }
   ],
   "source": [
    "sensibilidad = TP/(FN+TP)\n",
    "especificidad = TN / (TN + FP)\n",
    "print(\n",
    "    f'Sensibilidad: {sensibilidad:.2f}, Especificidad: {especificidad:.2f}'\n",
    ") # SMOTE: synthetic minority over-sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2af540b2-eb9e-4a7c-aab1-40785cda4366",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "default\n",
       "0    9667\n",
       "1     333\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "default['default'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "016456fd-f4b0-4433-959d-07f4548111a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of minority: 333\n",
      "Size of majority: 9667\n",
      "Size of majority_downsampled: 333\n"
     ]
    }
   ],
   "source": [
    "from sklearn.utils import resample\n",
    "# filtrar clases\n",
    "majority = default[default['default'] == 0] # No\n",
    "minority = default[default['default'] == 1] # Yes\n",
    "\n",
    "majority_downsampled = resample(\n",
    "    majority,\n",
    "    replace=False,\n",
    "    # cuantas muestras quiero de majority\n",
    "    n_samples=len(minority), # len(minority)\n",
    "    random_state=42\n",
    ")\n",
    "print(f\"Size of minority: {len(minority)}\")\n",
    "print(f\"Size of majority: {len(majority)}\")\n",
    "print(f\"Size of majority_downsampled: {len(majority_downsampled)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "16a856a1-4070-4852-8518-0007220932e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "default\n",
       "1    333\n",
       "0    333\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "default_balanced = pd.concat([minority, majority_downsampled])\n",
    "default_balanced['default'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fbc28528-4bb6-4b55-8971-6cc424fa1c02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 5)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "default.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c2c646de-40f6-44a5-9698-8441942b43b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x32fb981d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(\n",
    "    default['balance'],\n",
    "    default['default']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c9d08b5a-e90f-4cd0-b42d-1e603b6bfd45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x32fcffc80>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(\n",
    "    default_balanced['balance'],\n",
    "    default_balanced['default']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59384692",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "06ed2d0e-c72c-4af8-87cf-c076e24dbfa9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sensibilidad: 0.25, Especificidad: 0.99\n"
     ]
    }
   ],
   "source": [
    "sensibilidad = TP/(FN+TP)\n",
    "especificidad = TN / (TN + FP)\n",
    "print(\n",
    "    f'Sensibilidad: {sensibilidad:.2f}, Especificidad: {especificidad:.2f}'\n",
    ") # SMOTE: synthetic minority over-sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ded20bdc-96d9-4eb0-a10d-5c9cacc26ba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = default_balanced[['balance']]\n",
    "y = default_balanced['default']\n",
    "\n",
    "X_train, X_test, y_tain, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_tain)\n",
    "\n",
    "y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "94365cec-488b-4b2d-9422-61456602aa1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sensibilidad 0.2463768115942029\n",
      "especificidad 0.9943034697048162\n"
     ]
    }
   ],
   "source": [
    "TN, FP, FN, TP = conf_matrix.ravel()\n",
    "sensibilidad = TP / (FN + TP)\n",
    "especificidad = TN / (TN + FP)\n",
    "print(f'sensibilidad {sensibilidad}')\n",
    "print(f'especificidad {especificidad}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7600c8f7-6cec-4563-964e-7dfead65beef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precisión: 0.87\n",
      "F1-Score: 0.89\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(50.72222222222221, 0.5, 'True')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "# y_pred se obtiene con X_test\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "print(f\"Precisión: {accuracy:.2f}\")\n",
    "print(f\"F1-Score: {f1:.2f}\")\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "sns.heatmap(conf_matrix,\n",
    "            annot=True,\n",
    "            fmt=\"d\", # valores enteros\n",
    "            cmap=\"Blues\",\n",
    "            cbar=False,\n",
    "            xticklabels=[\"Default=No\", \"Default=Yes\"],\n",
    "            yticklabels=[\"Default=No\", \"Default=Yes\"]\n",
    "           )\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"True\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8043c4d7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "57e801e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 1)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_new = np.linspace(0, 1500, 1000).reshape(-1, 1)\n",
    "x_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fb083d88-66cf-4560-8eb6-c29ad05a3b29",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jealcalat/miniconda3/lib/python3.12/site-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x32fe43290>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predicted = model.predict_proba(x_new)\n",
    "predicted[:10]\n",
    "\n",
    "plt.plot(x_new, predicted[:, 1], label=\"Default=Yes\")\n",
    "plt.plot(x_new, predicted[:, 0], label=\"Default=No\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "44791a53-104e-45d4-a2a3-ab5693a41f3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precisión: 0.87\n",
      "F1-Score: 0.89\n",
      "Matriz de Confusión:\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "# y_pred se obtiene con X_test\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "print(f\"Precisión: {accuracy:.2f}\")\n",
    "print(f\"F1-Score: {f1:.2f}\")\n",
    "print(\"Matriz de Confusión:\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c1fc23c-f0fc-45f5-8099-d7656d19c23d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "eb81bf8a-8551-4953-90d4-c56441f42375",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precisión: 0.87\n",
      "F1-Score: 0.32\n",
      "Matriz de Confusión:\n",
      "[[1683  248]\n",
      " [   9   60]]\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, f1_score, confusion_matrix\n",
    "\n",
    "# Dividir en variables predictoras y variable respuesta sin balancear aún\n",
    "X = default[['balance']]  # Incluye todas las variables predictoras deseadas\n",
    "y = default['default']\n",
    "\n",
    "# Dividir en conjuntos de entrenamiento y prueba\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Aplicar SMOTE al conjunto de entrenamiento para balancear las clases\n",
    "smote = SMOTE(random_state=42)\n",
    "X_train_smote, y_train_smote = smote.fit_resample(X_train, y_train)\n",
    "\n",
    "# Ajustar el modelo de regresión logística con los datos de entrenamiento balanceados\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train_smote, y_train_smote)\n",
    "\n",
    "# Hacer predicciones en el conjunto de prueba\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Evaluar el modelo\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "print(f\"Precisión: {accuracy:.2f}\")\n",
    "print(f\"F1-Score: {f1:.2f}\")\n",
    "print(\"Matriz de Confusión:\")\n",
    "print(conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e766442b-e909-4733-96e4-aa1b84524686",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(50.72222222222221, 0.5, 'True')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(conf_matrix,\n",
    "            annot=True,\n",
    "            fmt=\"d\", # valores enteros\n",
    "            cmap=\"Blues\",\n",
    "            cbar=False,\n",
    "            xticklabels=[\"Default=No\", \"Default=Yes\"],\n",
    "            yticklabels=[\"Default=No\", \"Default=Yes\"]\n",
    "           )\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"True\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}