praticaa.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Idade</th>\n",
       "      <th>RendimentoTotal</th>\n",
       "      <th>Salario</th>\n",
       "      <th>LimitedeCreditoImediato</th>\n",
       "      <th>LimitedoChequeEspecial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70</td>\n",
       "      <td>10736</td>\n",
       "      <td>5214</td>\n",
       "      <td>400</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>380</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44</td>\n",
       "      <td>5800</td>\n",
       "      <td>5800</td>\n",
       "      <td>500</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72</td>\n",
       "      <td>4300</td>\n",
       "      <td>4300</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75</td>\n",
       "      <td>4400</td>\n",
       "      <td>4400</td>\n",
       "      <td>3000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Idade  RendimentoTotal  Salario  LimitedeCreditoImediato  \\\n",
       "0     70            10736     5214                      400   \n",
       "1     68             5000     5000                      380   \n",
       "2     44             5800     5800                      500   \n",
       "3     72             4300     4300                     2000   \n",
       "4     75             4400     4400                     3000   \n",
       "\n",
       "   LimitedoChequeEspecial  \n",
       "0                     500  \n",
       "1                     600  \n",
       "2                     800  \n",
       "3                    1000  \n",
       "4                    1000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('limite_crédito.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Idade</th>\n",
       "      <th>RendimentoTotal</th>\n",
       "      <th>Salario</th>\n",
       "      <th>LimitedeCreditoImediato</th>\n",
       "      <th>LimitedoChequeEspecial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Idade</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066396</td>\n",
       "      <td>0.063227</td>\n",
       "      <td>0.064149</td>\n",
       "      <td>0.075883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RendimentoTotal</th>\n",
       "      <td>0.066396</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.960551</td>\n",
       "      <td>0.031041</td>\n",
       "      <td>0.858120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salario</th>\n",
       "      <td>0.063227</td>\n",
       "      <td>0.960551</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.081792</td>\n",
       "      <td>0.888839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LimitedeCreditoImediato</th>\n",
       "      <td>0.064149</td>\n",
       "      <td>0.031041</td>\n",
       "      <td>0.081792</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.226990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LimitedoChequeEspecial</th>\n",
       "      <td>0.075883</td>\n",
       "      <td>0.858120</td>\n",
       "      <td>0.888839</td>\n",
       "      <td>0.226990</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Idade  RendimentoTotal   Salario  \\\n",
       "Idade                    1.000000         0.066396  0.063227   \n",
       "RendimentoTotal          0.066396         1.000000  0.960551   \n",
       "Salario                  0.063227         0.960551  1.000000   \n",
       "LimitedeCreditoImediato  0.064149         0.031041  0.081792   \n",
       "LimitedoChequeEspecial   0.075883         0.858120  0.888839   \n",
       "\n",
       "                         LimitedeCreditoImediato  LimitedoChequeEspecial  \n",
       "Idade                                   0.064149                0.075883  \n",
       "RendimentoTotal                         0.031041                0.858120  \n",
       "Salario                                 0.081792                0.888839  \n",
       "LimitedeCreditoImediato                 1.000000                0.226990  \n",
       "LimitedoChequeEspecial                  0.226990                1.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1250x1250 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "function = 'LimitedoChequeEspecial~Idade+RendimentoTotal+Salario+LimitedeCreditoImediato'\n",
    "model = smf.ols(formula=function, data=df).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              OLS Regression Results                              \n",
      "==================================================================================\n",
      "Dep. Variable:     LimitedoChequeEspecial   R-squared:                       0.816\n",
      "Model:                                OLS   Adj. R-squared:                  0.799\n",
      "Method:                     Least Squares   F-statistic:                     47.65\n",
      "Date:                    Sat, 08 Jun 2024   Prob (F-statistic):           2.92e-15\n",
      "Time:                            10:50:04   Log-Likelihood:                -445.61\n",
      "No. Observations:                      48   AIC:                             901.2\n",
      "Df Residuals:                          43   BIC:                             910.6\n",
      "Df Model:                               4                                         \n",
      "Covariance Type:                nonrobust                                         \n",
      "===========================================================================================\n",
      "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------\n",
      "Intercept               -2851.7932   1889.470     -1.509      0.139   -6662.272     958.685\n",
      "Idade                       3.7798     26.772      0.141      0.888     -50.212      57.772\n",
      "RendimentoTotal             0.0753      0.116      0.649      0.519      -0.158       0.309\n",
      "Salario                     0.4770      0.157      3.031      0.004       0.160       0.794\n",
      "LimitedeCreditoImediato     1.0602      0.436      2.429      0.019       0.180       1.940\n",
      "==============================================================================\n",
      "Omnibus:                        2.177   Durbin-Watson:                   1.791\n",
      "Prob(Omnibus):                  0.337   Jarque-Bera (JB):                1.346\n",
      "Skew:                          -0.098   Prob(JB):                        0.510\n",
      "Kurtosis:                       3.797   Cond. No.                     1.06e+05\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, 1.06e+05. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "function = 'LimitedoChequeEspecial~RendimentoTotal+Salario+LimitedeCreditoImediato'\n",
    "model = smf.ols(formula=function, data=df).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              OLS Regression Results                              \n",
      "==================================================================================\n",
      "Dep. Variable:     LimitedoChequeEspecial   R-squared:                       0.816\n",
      "Model:                                OLS   Adj. R-squared:                  0.803\n",
      "Method:                     Least Squares   F-statistic:                     64.97\n",
      "Date:                    Sat, 08 Jun 2024   Prob (F-statistic):           3.34e-16\n",
      "Time:                            10:51:30   Log-Likelihood:                -445.62\n",
      "No. Observations:                      48   AIC:                             899.2\n",
      "Df Residuals:                          44   BIC:                             906.7\n",
      "Df Model:                               3                                         \n",
      "Covariance Type:                nonrobust                                         \n",
      "===========================================================================================\n",
      "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------\n",
      "Intercept               -2632.8485   1067.330     -2.467      0.018   -4783.910    -481.787\n",
      "RendimentoTotal             0.0758      0.115      0.662      0.512      -0.155       0.307\n",
      "Salario                     0.4767      0.156      3.063      0.004       0.163       0.790\n",
      "LimitedeCreditoImediato     1.0642      0.431      2.471      0.017       0.196       1.932\n",
      "==============================================================================\n",
      "Omnibus:                        2.073   Durbin-Watson:                   1.776\n",
      "Prob(Omnibus):                  0.355   Jarque-Bera (JB):                1.245\n",
      "Skew:                          -0.075   Prob(JB):                        0.537\n",
      "Kurtosis:                       3.775   Cond. No.                     6.08e+04\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, 6.08e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "function = 'LimitedoChequeEspecial~Salario+LimitedeCreditoImediato'\n",
    "model = smf.ols(formula=function, data=df).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              OLS Regression Results                              \n",
      "==================================================================================\n",
      "Dep. Variable:     LimitedoChequeEspecial   R-squared:                       0.814\n",
      "Model:                                OLS   Adj. R-squared:                  0.806\n",
      "Method:                     Least Squares   F-statistic:                     98.47\n",
      "Date:                    Sat, 08 Jun 2024   Prob (F-statistic):           3.66e-17\n",
      "Time:                            10:52:11   Log-Likelihood:                -445.86\n",
      "No. Observations:                      48   AIC:                             897.7\n",
      "Df Residuals:                          45   BIC:                             903.3\n",
      "Df Model:                               2                                         \n",
      "Covariance Type:                nonrobust                                         \n",
      "===========================================================================================\n",
      "                              coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------\n",
      "Intercept               -2636.5605   1060.626     -2.486      0.017   -4772.770    -500.351\n",
      "Salario                     0.5757      0.042     13.582      0.000       0.490       0.661\n",
      "LimitedeCreditoImediato     1.0153      0.422      2.408      0.020       0.166       1.865\n",
      "==============================================================================\n",
      "Omnibus:                        1.010   Durbin-Watson:                   1.779\n",
      "Prob(Omnibus):                  0.604   Jarque-Bera (JB):                0.330\n",
      "Skew:                           0.014   Prob(JB):                        0.848\n",
      "Kurtosis:                       3.405   Cond. No.                     3.83e+04\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, 3.83e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['predict'] = model.predict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Idade</th>\n",
       "      <th>RendimentoTotal</th>\n",
       "      <th>Salario</th>\n",
       "      <th>LimitedeCreditoImediato</th>\n",
       "      <th>LimitedoChequeEspecial</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70</td>\n",
       "      <td>10736</td>\n",
       "      <td>5214</td>\n",
       "      <td>400</td>\n",
       "      <td>500</td>\n",
       "      <td>771.172966</td>\n",
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       "      <td>68</td>\n",
       "      <td>5000</td>\n",
       "      <td>5000</td>\n",
       "      <td>380</td>\n",
       "      <td>600</td>\n",
       "      <td>627.670650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44</td>\n",
       "      <td>5800</td>\n",
       "      <td>5800</td>\n",
       "      <td>500</td>\n",
       "      <td>800</td>\n",
       "      <td>1210.054377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72</td>\n",
       "      <td>4300</td>\n",
       "      <td>4300</td>\n",
       "      <td>2000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1869.506077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>75</td>\n",
       "      <td>4400</td>\n",
       "      <td>4400</td>\n",
       "      <td>3000</td>\n",
       "      <td>1000</td>\n",
       "      <td>2942.390924</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   Idade  RendimentoTotal  Salario  LimitedeCreditoImediato  \\\n",
       "0     70            10736     5214                      400   \n",
       "1     68             5000     5000                      380   \n",
       "2     44             5800     5800                      500   \n",
       "3     72             4300     4300                     2000   \n",
       "4     75             4400     4400                     3000   \n",
       "\n",
       "   LimitedoChequeEspecial      predict  \n",
       "0                     500   771.172966  \n",
       "1                     600   627.670650  \n",
       "2                     800  1210.054377  \n",
       "3                    1000  1869.506077  \n",
       "4                    1000  2942.390924  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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