데이터모형구축작업2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 데이터 모형 구축 작업\n",
    "\n",
    "## - 분류 : LogisticRegresion(class 2개), SVC(2개 이상 class)\n",
    "\n",
    "## - 예측 : Linear_regression, SVM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Wine 품질 분류(0,1,2 class)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.datasets import load_wine\n",
    "wine = load_wine()\n",
    "\n",
    "data = pd.DataFrame(wine.data, columns=wine.feature_names)\n",
    "target = pd.Series(wine.target, dtype=\"category\") #class 0,1,2 분류 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
      "0    14.23        1.71  2.43               15.6      127.0           2.80   \n",
      "1    13.20        1.78  2.14               11.2      100.0           2.65   \n",
      "2    13.16        2.36  2.67               18.6      101.0           2.80   \n",
      "3    14.37        1.95  2.50               16.8      113.0           3.85   \n",
      "4    13.24        2.59  2.87               21.0      118.0           2.80   \n",
      "\n",
      "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
      "0        3.06                  0.28             2.29             5.64  1.04   \n",
      "1        2.76                  0.26             1.28             4.38  1.05   \n",
      "2        3.24                  0.30             2.81             5.68  1.03   \n",
      "3        3.49                  0.24             2.18             7.80  0.86   \n",
      "4        2.69                  0.39             1.82             4.32  1.04   \n",
      "\n",
      "   od280/od315_of_diluted_wines  proline  \n",
      "0                          3.92   1065.0  \n",
      "1                          3.40   1050.0  \n",
      "2                          3.17   1185.0  \n",
      "3                          3.45   1480.0  \n",
      "4                          2.93    735.0  \n"
     ]
    }
   ],
   "source": [
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          alcohol  malic_acid         ash  alcalinity_of_ash   magnesium  \\\n",
      "count  178.000000  178.000000  178.000000         178.000000  178.000000   \n",
      "mean    13.000618    2.336348    2.366517          19.494944   99.741573   \n",
      "std      0.811827    1.117146    0.274344           3.339564   14.282484   \n",
      "min     11.030000    0.740000    1.360000          10.600000   70.000000   \n",
      "25%     12.362500    1.602500    2.210000          17.200000   88.000000   \n",
      "50%     13.050000    1.865000    2.360000          19.500000   98.000000   \n",
      "75%     13.677500    3.082500    2.557500          21.500000  107.000000   \n",
      "max     14.830000    5.800000    3.230000          30.000000  162.000000   \n",
      "\n",
      "       total_phenols  flavanoids  nonflavanoid_phenols  proanthocyanins  \\\n",
      "count     178.000000  178.000000            178.000000       178.000000   \n",
      "mean        2.295112    2.029270              0.361854         1.590899   \n",
      "std         0.625851    0.998859              0.124453         0.572359   \n",
      "min         0.980000    0.340000              0.130000         0.410000   \n",
      "25%         1.742500    1.205000              0.270000         1.250000   \n",
      "50%         2.355000    2.135000              0.340000         1.555000   \n",
      "75%         2.800000    2.875000              0.437500         1.950000   \n",
      "max         3.880000    5.080000              0.660000         3.580000   \n",
      "\n",
      "       color_intensity         hue  od280/od315_of_diluted_wines      proline  \n",
      "count       178.000000  178.000000                    178.000000   178.000000  \n",
      "mean          5.058090    0.957449                      2.611685   746.893258  \n",
      "std           2.318286    0.228572                      0.709990   314.907474  \n",
      "min           1.280000    0.480000                      1.270000   278.000000  \n",
      "25%           3.220000    0.782500                      1.937500   500.500000  \n",
      "50%           4.690000    0.965000                      2.780000   673.500000  \n",
      "75%           6.200000    1.120000                      3.170000   985.000000  \n",
      "max          13.000000    1.710000                      4.000000  1680.000000  \n"
     ]
    }
   ],
   "source": [
    "print(data.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(106, 13)\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(data,target,test_size = 0.4,stratify = target)\n",
    "print(x_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler() # scaler 정의\n",
    "x_train_scaled = scaler.fit_transform(x_train) #train에 fit\n",
    "\n",
    "model = svm.SVC(probability = True)\n",
    "model.fit(x_train_scaled, y_train)\n",
    "#model.score(x_train_scaled,y_train)\n",
    "x_test_scaled = scaler.transform(x_test)\n",
    "pred_proba = model.predict_proba(x_test_scaled)\n",
    "pred = model.predict(x_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[24,  0,  0],\n",
       "       [ 0, 29,  0],\n",
       "       [ 0,  0, 19]], dtype=int64)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(pred,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.39974679, 0.01512413, 0.01227044, 0.96925024, 0.00252343,\n",
       "       0.00400841, 0.01015575, 0.96235571, 0.97823541, 0.00737945,\n",
       "       0.01265324, 0.96568623, 0.01772114, 0.86579474, 0.01918525,\n",
       "       0.00921557, 0.87842415, 0.01352545, 0.95431782, 0.0026294 ,\n",
       "       0.01367915, 0.0033606 , 0.0060989 , 0.00625828, 0.00605029,\n",
       "       0.05519792, 0.01585747, 0.96304984, 0.98513338, 0.01104218,\n",
       "       0.98597037, 0.00201797, 0.79626992, 0.00258323, 0.00869755,\n",
       "       0.00641541, 0.00852012, 0.92719305, 0.06411737, 0.00521329,\n",
       "       0.92022489, 0.0020956 , 0.00368589, 0.84752421, 0.01327669,\n",
       "       0.04025488, 0.04699491, 0.84086793, 0.00727426, 0.00626041,\n",
       "       0.00683394, 0.95690181, 0.01759252, 0.01117591, 0.00784658,\n",
       "       0.14464131, 0.96461039, 0.967115  , 0.00726545, 0.98328781,\n",
       "       0.01095922, 0.01634274, 0.01107624, 0.34668501, 0.01229022,\n",
       "       0.00313681, 0.01666694, 0.00885212, 0.04226703, 0.04908874,\n",
       "       0.02101747, 0.00404757])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_proba[:,2]"
   ]
  }
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