ScoreBasic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70d70f81",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d9665bc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "plt.rc('font', size=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "65296a11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst1=[*range(10,30)]\n",
    "lst2=[0,0,0,0,0,0,0,0,0,1,0,1,1,1,1,1,1,1,1,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2cb3eb81",
   "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>edad</th>\n",
       "      <th>compra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   edad  compra\n",
       "0    10       0\n",
       "1    11       0\n",
       "2    12       0\n",
       "3    13       0\n",
       "4    14       0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(list(zip(lst1, lst2)), columns=['edad','compra'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8003f795",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='edad', ylabel='compra'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data=df, x='edad', y='compra')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "20c78786",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df['edad'].values.reshape(-1,1)\n",
    "y = df['compra'].values.reshape(-1,1)\n",
    "reg = LinearRegression().fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "49e271d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['compra_pred'] = reg.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "32a4edfb",
   "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>edad</th>\n",
       "      <th>compra</th>\n",
       "      <th>compra_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.126316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0.021053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0.094737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0.168421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0.242105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0.315789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.389474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0.463158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>0.536842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0.610526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>0.684211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>0.757895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>0.831579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>0.905263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0.978947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>1.052632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1.126316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>1.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    edad  compra  compra_pred\n",
       "0     10       0    -0.200000\n",
       "1     11       0    -0.126316\n",
       "2     12       0    -0.052632\n",
       "3     13       0     0.021053\n",
       "4     14       0     0.094737\n",
       "5     15       0     0.168421\n",
       "6     16       0     0.242105\n",
       "7     17       0     0.315789\n",
       "8     18       0     0.389474\n",
       "9     19       1     0.463158\n",
       "10    20       0     0.536842\n",
       "11    21       1     0.610526\n",
       "12    22       1     0.684211\n",
       "13    23       1     0.757895\n",
       "14    24       1     0.831579\n",
       "15    25       1     0.905263\n",
       "16    26       1     0.978947\n",
       "17    27       1     1.052632\n",
       "18    28       1     1.126316\n",
       "19    29       1     1.200000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "115b9c70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='edad', ylabel='compra'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(data=df, x='edad', y='compra', ci=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a66b62cc",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'statsmodels'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "Input \u001b[0;32mIn [25]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mregplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43medad\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcompra\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlogistic\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/nlp_course/lib/python3.9/site-packages/seaborn/_decorators.py:46\u001b[0m, in \u001b[0;36m_deprecate_positional_args.<locals>.inner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     36\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m     37\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPass the following variable\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m as \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124mkeyword arg\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     38\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFrom version 0.12, the only valid positional argument \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m\n\u001b[1;32m     44\u001b[0m     )\n\u001b[1;32m     45\u001b[0m kwargs\u001b[38;5;241m.\u001b[39mupdate({k: arg \u001b[38;5;28;01mfor\u001b[39;00m k, arg \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(sig\u001b[38;5;241m.\u001b[39mparameters, args)})\n\u001b[0;32m---> 46\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/nlp_course/lib/python3.9/site-packages/seaborn/regression.py:863\u001b[0m, in \u001b[0;36mregplot\u001b[0;34m(x, y, data, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, seed, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, dropna, x_jitter, y_jitter, label, color, marker, scatter_kws, line_kws, ax)\u001b[0m\n\u001b[1;32m    861\u001b[0m scatter_kws[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmarker\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m marker\n\u001b[1;32m    862\u001b[0m line_kws \u001b[38;5;241m=\u001b[39m {} \u001b[38;5;28;01mif\u001b[39;00m line_kws \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m copy\u001b[38;5;241m.\u001b[39mcopy(line_kws)\n\u001b[0;32m--> 863\u001b[0m \u001b[43mplotter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscatter_kws\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mline_kws\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    864\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ax\n",
      "File \u001b[0;32m~/anaconda3/envs/nlp_course/lib/python3.9/site-packages/seaborn/regression.py:370\u001b[0m, in \u001b[0;36m_RegressionPlotter.plot\u001b[0;34m(self, ax, scatter_kws, line_kws)\u001b[0m\n\u001b[1;32m    367\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscatterplot(ax, scatter_kws)\n\u001b[1;32m    369\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit_reg:\n\u001b[0;32m--> 370\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlineplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mline_kws\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    372\u001b[0m \u001b[38;5;66;03m# Label the axes\u001b[39;00m\n\u001b[1;32m    373\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mx, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mname\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n",
      "File \u001b[0;32m~/anaconda3/envs/nlp_course/lib/python3.9/site-packages/seaborn/regression.py:413\u001b[0m, in \u001b[0;36m_RegressionPlotter.lineplot\u001b[0;34m(self, ax, kws)\u001b[0m\n\u001b[1;32m    411\u001b[0m \u001b[38;5;124;03m\"\"\"Draw the model.\"\"\"\u001b[39;00m\n\u001b[1;32m    412\u001b[0m \u001b[38;5;66;03m# Fit the regression model\u001b[39;00m\n\u001b[0;32m--> 413\u001b[0m grid, yhat, err_bands \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_regression\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    414\u001b[0m edges \u001b[38;5;241m=\u001b[39m grid[\u001b[38;5;241m0\u001b[39m], grid[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m    416\u001b[0m \u001b[38;5;66;03m# Get set default aesthetics\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/nlp_course/lib/python3.9/site-packages/seaborn/regression.py:208\u001b[0m, in \u001b[0;36m_RegressionPlotter.fit_regression\u001b[0;34m(self, ax, x_range, grid)\u001b[0m\n\u001b[1;32m    206\u001b[0m     yhat, yhat_boots \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit_poly(grid, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morder)\n\u001b[1;32m    207\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlogistic:\n\u001b[0;32m--> 208\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mstatsmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgenmod\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgeneralized_linear_model\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m GLM\n\u001b[1;32m    209\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mstatsmodels\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mgenmod\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfamilies\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Binomial\n\u001b[1;32m    210\u001b[0m     yhat, yhat_boots \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfit_statsmodels(grid, GLM,\n\u001b[1;32m    211\u001b[0m                                             family\u001b[38;5;241m=\u001b[39mBinomial())\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'statsmodels'"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(data=df, x='edad', y='compra', logistic=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4fbca61e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}