00.14-apredizagem-regressão-logistica-classsificação.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importando o módulo de aprendizado de máquina de Regressão Logística\n",
    "\n",
    "import numpy as np                                     # importando o módulo para trabalhar calculos matemáticos\n",
    "import pickle as pkl                                   # importando o módulo para salvar o modelo tratado em uma variavel\n",
    "from sklearn.linear_model import LogisticRegression    # importando o módulo de aprendizado de máquina de Regressão Logística"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# leitura da base de dados que ja foram tratadas e salvas\n",
    "\n",
    "with open('./data/risc_credit.pkl', 'rb') as f:\n",
    "    X_risc_credit, y_risc_credit = pkl.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((14, 4), (14,))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualizando tamnho da base de dados\n",
    "\n",
    "X_risc_credit.shape, y_risc_credit.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# criando o modelo de regressão logística e treinamento do modelo\n",
    "\n",
    "rl_risc_credit = LogisticRegression()\n",
    "rl_risc_credit.fit(X_risc_credit, y_risc_credit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.61513831, -0.34196498,  0.49067887, -0.79369827],\n",
       "       [-0.72760403,  0.135437  , -0.29167359,  0.96796844],\n",
       "       [ 0.11246572,  0.20652798, -0.19900528, -0.17427017]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualizando os coeficientes do modelo\n",
    "\n",
    "rl_risc_credit.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.55733877, -0.59215165,  0.03481289])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualizando os interceptos do modelo\n",
    "\n",
    "rl_risc_credit.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['baixo', 'alto'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# previsão do modelo\n",
    "\n",
    "previsoes_rl = rl_risc_credit.predict([[0, 0, 1, 2], [2, 0, 0, 0]])\n",
    "previsoes_rl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 0, 1, 0],\n",
       "       [1, 0, 1, 1],\n",
       "       [1, 1, 1, 1],\n",
       "       [1, 1, 1, 2],\n",
       "       [1, 1, 1, 2],\n",
       "       [1, 1, 0, 2],\n",
       "       [2, 1, 1, 0],\n",
       "       [2, 1, 0, 2],\n",
       "       [0, 1, 1, 2],\n",
       "       [0, 0, 0, 2],\n",
       "       [0, 0, 1, 0],\n",
       "       [0, 0, 1, 1],\n",
       "       [0, 0, 1, 2],\n",
       "       [2, 0, 1, 1]], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualização da base de dados\n",
    "\n",
    "X_risc_credit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['alto', 'alto', 'moderado', 'alto', 'baixo', 'baixo', 'alto',\n",
       "       'moderado', 'baixo', 'baixo', 'alto', 'moderado', 'baixo', 'alto'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualização das classes da base de dados\n",
    "\n",
    "y_risc_credit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# modificação da base de dados para ter apenas 2 classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tirando a classe moderado dos registros\n",
    "\n",
    "X_risc_credit = np.delete(X_risc_credit, [2, 7, 11], axis=0)\n",
    "y_risc_credit = np.delete(y_risc_credit, [2, 7, 11], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['alto', 'alto', 'alto', 'baixo', 'baixo', 'alto', 'baixo', 'baixo',\n",
       "       'alto', 'baixo', 'alto'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualização da base de dados com as classes modificadas\n",
    "\n",
    "y_risc_credit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# criação do modelo de regressão logística e treinamento do modelo\n",
    "\n",
    "lr_risc_credit = LogisticRegression( random_state = 1 )\n",
    "lr_risc_credit.fit( X_risc_credit, y_risc_credit )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.80828993])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualização do intercept do modelo\n",
    "\n",
    "lr_risc_credit.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.76704533,  0.23906678, -0.47976059,  1.12186218]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualização dos coeficientes do modelo\n",
    "\n",
    "lr_risc_credit.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['baixo', 'alto'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# previsão do modelo\n",
    "\n",
    "\n",
    "previsoes_lr = rl_risc_credit.predict([[0, 0, 1, 2], [2, 0, 0, 0]])\n",
    "previsoes_lr"
   ]
  }
 ],
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