Mod12 ex02 - transformações em X e Y.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://raw.githubusercontent.com/rhatiro/Curso_EBAC-Profissao_Cientista_de_Dados/main/ebac-course-utils/media/logo/newebac_logo_black_half.png\" alt=\"ebac-logo\">\n",
    "\n",
    "---\n",
    "\n",
    "# **Profissão: Cientista de Dados**\n",
    "### **Módulo 12** | Exercício 2\n",
    "\n",
    "Aluna [Lays Félix](https://www.linkedin.com/in/laysfelixbusiness)<br>\n",
    "Data: 16 de maio de 2024\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regressão 01 - tarefa 02 - transformações em X e Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Melhorando o ajuste no percentual de gorjetas\n",
    "\n",
    "a. Vamos partir do modelo feito na última tarefa, o que relaciona ```tip_pct``` e ```net_bill```. Carregue a base, os pacotes e reproduza este modelo aqui."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from seaborn import load_dataset\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>tip_pct</th>\n",
       "      <th>net_bill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.063204</td>\n",
       "      <td>15.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.191244</td>\n",
       "      <td>8.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.199886</td>\n",
       "      <td>17.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.162494</td>\n",
       "      <td>20.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>0.172069</td>\n",
       "      <td>20.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>29.03</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>0.256166</td>\n",
       "      <td>23.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>27.18</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.079428</td>\n",
       "      <td>25.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>22.67</td>\n",
       "      <td>2.00</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.096759</td>\n",
       "      <td>20.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>17.82</td>\n",
       "      <td>1.75</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sat</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.108899</td>\n",
       "      <td>16.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>18.78</td>\n",
       "      <td>3.00</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Thur</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>0.190114</td>\n",
       "      <td>15.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>244 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     total_bill   tip     sex smoker   day    time  size   tip_pct  net_bill\n",
       "0         16.99  1.01  Female     No   Sun  Dinner     2  0.063204     15.98\n",
       "1         10.34  1.66    Male     No   Sun  Dinner     3  0.191244      8.68\n",
       "2         21.01  3.50    Male     No   Sun  Dinner     3  0.199886     17.51\n",
       "3         23.68  3.31    Male     No   Sun  Dinner     2  0.162494     20.37\n",
       "4         24.59  3.61  Female     No   Sun  Dinner     4  0.172069     20.98\n",
       "..          ...   ...     ...    ...   ...     ...   ...       ...       ...\n",
       "239       29.03  5.92    Male     No   Sat  Dinner     3  0.256166     23.11\n",
       "240       27.18  2.00  Female    Yes   Sat  Dinner     2  0.079428     25.18\n",
       "241       22.67  2.00    Male    Yes   Sat  Dinner     2  0.096759     20.67\n",
       "242       17.82  1.75    Male     No   Sat  Dinner     2  0.108899     16.07\n",
       "243       18.78  3.00  Female     No  Thur  Dinner     2  0.190114     15.78\n",
       "\n",
       "[244 rows x 9 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips = sns.load_dataset(\"tips\")\n",
    "tips['tip_pct'] = tips['tip'] / (tips['total_bill'] - tips['tip'])\n",
    "tips['net_bill'] = tips['total_bill'] - tips['tip']\n",
    "tips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                tip_pct   R-squared:                       0.091\n",
      "Model:                            OLS   Adj. R-squared:                  0.088\n",
      "Method:                 Least Squares   F-statistic:                     24.31\n",
      "Date:                Thu, 16 May 2024   Prob (F-statistic):           1.52e-06\n",
      "Time:                        17:33:16   Log-Likelihood:                 108.00\n",
      "No. Observations:                 244   AIC:                            -212.0\n",
      "Df Residuals:                     242   BIC:                            -205.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.3053      0.023     13.166      0.000       0.260       0.351\n",
      "net_bill      -0.0061      0.001     -4.931      0.000      -0.009      -0.004\n",
      "==============================================================================\n",
      "Omnibus:                      468.674   Durbin-Watson:                   1.987\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           234437.604\n",
      "Skew:                          11.104   Prob(JB):                         0.00\n",
      "Kurtosis:                     153.221   Cond. No.                         43.3\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = smf.ols(formula='tip_pct ~ net_bill', data=tips).fit()\n",
    "print(reg.summary())\n",
    "\n",
    "tips['res'] = reg.resid\n",
    "\n",
    "sns.scatterplot(x='net_bill', y='res', data=tips, alpha=.75)\n",
    "plt.axhline(y=0, color='r', linestyle='--')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. ajuste o modelo de ```tip_pct``` em função do logaritmo de ```net_bill```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                tip_pct   R-squared:                       0.202\n",
      "Model:                            OLS   Adj. R-squared:                  0.198\n",
      "Method:                 Least Squares   F-statistic:                     61.16\n",
      "Date:                Thu, 16 May 2024   Prob (F-statistic):           1.62e-13\n",
      "Time:                        17:33:23   Log-Likelihood:                 123.81\n",
      "No. Observations:                 244   AIC:                            -243.6\n",
      "Df Residuals:                     242   BIC:                            -236.6\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "====================================================================================\n",
      "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "Intercept            0.6181      0.054     11.444      0.000       0.512       0.724\n",
      "np.log(net_bill)    -0.1534      0.020     -7.820      0.000      -0.192      -0.115\n",
      "==============================================================================\n",
      "Omnibus:                      440.899   Durbin-Watson:                   1.965\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           171509.052\n",
      "Skew:                           9.850   Prob(JB):                         0.00\n",
      "Kurtosis:                     131.381   Cond. No.                         17.9\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reg = smf.ols('tip_pct ~ np.log(net_bill)', data=tips).fit()\n",
    "print(reg.summary())\n",
    "\n",
    "tips['res'] = reg.resid\n",
    "\n",
    "sns.scatterplot(x='net_bill', y='res', data=tips, alpha=.75)\n",
    "plt.axhline(y=0, color='r', linestyle='--')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  3. ajuste o modelo de ```tip_pct``` em função de um polinômio de segundo grau em ```net_bill```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                tip_pct   R-squared:                       0.158\n",
      "Model:                            OLS   Adj. R-squared:                  0.151\n",
      "Method:                 Least Squares   F-statistic:                     22.55\n",
      "Date:                Thu, 16 May 2024   Prob (F-statistic):           1.06e-09\n",
      "Time:                        17:33:25   Log-Likelihood:                 117.25\n",
      "No. Observations:                 244   AIC:                            -228.5\n",
      "Df Residuals:                     241   BIC:                            -218.0\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=========================================================================================\n",
      "                            coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------------\n",
      "Intercept                 0.4851      0.047     10.332      0.000       0.393       0.578\n",
      "net_bill                 -0.0268      0.005     -5.477      0.000      -0.036      -0.017\n",
      "np.power(net_bill, 2)     0.0005      0.000      4.356      0.000       0.000       0.001\n",
      "==============================================================================\n",
      "Omnibus:                      447.620   Durbin-Watson:                   2.026\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           186035.672\n",
      "Skew:                          10.140   Prob(JB):                         0.00\n",
      "Kurtosis:                     136.744   Cond. No.                     2.42e+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, 2.42e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1e70308b790>"
      ]
     },
     "execution_count": 41,
     "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": [
    "reg = smf.ols('tip_pct  ~ net_bill + np.power(net_bill,2)', data = tips).fit()\n",
    "print(reg.summary())\n",
    "tips['res'] = reg.resid\n",
    "\n",
    "sns.scatterplot(x = 'net_bill', y = 'res', data = tips, alpha = .75)\n",
    "plt.axhline(y=0, color='r', linestyle='--')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  4. ajuste um modelo no log do ```tip_pct``` em função de ```net_bill```. Calcule o $R^2$ também em ```tip_pct```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:        np.log(tip_pct)   R-squared:                       0.235\n",
      "Model:                            OLS   Adj. R-squared:                  0.232\n",
      "Method:                 Least Squares   F-statistic:                     74.48\n",
      "Date:                Thu, 16 May 2024   Prob (F-statistic):           8.27e-16\n",
      "Time:                        17:33:30   Log-Likelihood:                -106.00\n",
      "No. Observations:                 244   AIC:                             216.0\n",
      "Df Residuals:                     242   BIC:                             223.0\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -1.2749      0.056    -22.872      0.000      -1.385      -1.165\n",
      "net_bill      -0.0259      0.003     -8.630      0.000      -0.032      -0.020\n",
      "==============================================================================\n",
      "Omnibus:                       49.929   Durbin-Watson:                   1.938\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              258.392\n",
      "Skew:                           0.664   Prob(JB):                     7.78e-57\n",
      "Kurtosis:                       7.864   Cond. No.                         43.3\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1e702fb55d0>"
      ]
     },
     "execution_count": 42,
     "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": [
    "reg = smf.ols('np.log(tip_pct) ~ net_bill', data = tips).fit()\n",
    "print(reg.summary())\n",
    "tips['res'] = reg.resid\n",
    "\n",
    "sns.scatterplot(x = 'net_bill', y = 'res', data = tips, alpha = .75)\n",
    "plt.axhline(y=0, color='r', linestyle='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# outra forma de calcular o R^2\n",
    "tips['pred'] = reg.fittedvalues\n",
    "\n",
    "r = tips[['pred', 'tip_pct']].corr().iloc[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.09129084920662969\n"
     ]
    }
   ],
   "source": [
    "print('R-squared:', \n",
    "      tips[['pred', 'tip_pct']].corr().iloc[0,1]**2\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  5. ajuste um modelo no log do ```tip_pct``` em função do log de ```net_bill```. Calcule o $R^2$ também em ```tip_pct```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:        np.log(tip_pct)   R-squared:                       0.293\n",
      "Model:                            OLS   Adj. R-squared:                  0.291\n",
      "Method:                 Least Squares   F-statistic:                     100.5\n",
      "Date:                Thu, 16 May 2024   Prob (F-statistic):           5.20e-20\n",
      "Time:                        17:33:42   Log-Likelihood:                -96.357\n",
      "No. Observations:                 244   AIC:                             196.7\n",
      "Df Residuals:                     242   BIC:                             203.7\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "====================================================================================\n",
      "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------\n",
      "Intercept           -0.3942      0.133     -2.961      0.003      -0.657      -0.132\n",
      "np.log(net_bill)    -0.4849      0.048    -10.026      0.000      -0.580      -0.390\n",
      "==============================================================================\n",
      "Omnibus:                       12.633   Durbin-Watson:                   1.976\n",
      "Prob(Omnibus):                  0.002   Jarque-Bera (JB):               28.054\n",
      "Skew:                           0.141   Prob(JB):                     8.10e-07\n",
      "Kurtosis:                       4.637   Cond. No.                         17.9\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x1e77ef46510>"
      ]
     },
     "execution_count": 45,
     "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": [
    "reg = smf.ols('np.log(tip_pct) ~ np.log(net_bill)', data = tips).fit()\n",
    "print(reg.summary())\n",
    "tips['res'] = reg.resid\n",
    "\n",
    "sns.scatterplot(x = 'tip_pct', y = 'res', data = tips, alpha = .75)\n",
    "plt.axhline(y=0, color='r', linestyle='--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# outra forma de calcular o R^2\n",
    "tips['pred'] = reg.fittedvalues\n",
    "\n",
    "r = tips[['pred', 'tip_pct']].corr().iloc[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.20172884237605476\n"
     ]
    }
   ],
   "source": [
    "print('R-squared:', \n",
    "      tips[['pred', 'tip_pct']].corr().iloc[0,1]**2\n",
    "     )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  6. qual modelo resultou no melhor ajuste?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O modelo no log do tip_pct em função do log de net_bill porque o R² (20%) é maior que o modelo log do tip_pct em função de net_bill (9%)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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