03_2_분류_타이타닉생존예측.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv('https://raw.githubusercontent.com/ralbu85/DataScience_2022S/master/data/titanic_processed.csv')"
   ]
  },
  {
   "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>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>pclass_1.0</th>\n",
       "      <th>pclass_2.0</th>\n",
       "      <th>pclass_3.0</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>embarked_C</th>\n",
       "      <th>embarked_Q</th>\n",
       "      <th>embarked_S</th>\n",
       "      <th>survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1038</th>\n",
       "      <td>45.5000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1039</th>\n",
       "      <td>14.5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.4542</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1040</th>\n",
       "      <td>26.5000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1041</th>\n",
       "      <td>27.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.2250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1042</th>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.8750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1043 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          age  sibsp  parch      fare  pclass_1.0  pclass_2.0  pclass_3.0  \\\n",
       "0     29.0000    0.0    0.0  211.3375           1           0           0   \n",
       "1      0.9167    1.0    2.0  151.5500           1           0           0   \n",
       "2      2.0000    1.0    2.0  151.5500           1           0           0   \n",
       "3     30.0000    1.0    2.0  151.5500           1           0           0   \n",
       "4     25.0000    1.0    2.0  151.5500           1           0           0   \n",
       "...       ...    ...    ...       ...         ...         ...         ...   \n",
       "1038  45.5000    0.0    0.0    7.2250           0           0           1   \n",
       "1039  14.5000    1.0    0.0   14.4542           0           0           1   \n",
       "1040  26.5000    0.0    0.0    7.2250           0           0           1   \n",
       "1041  27.0000    0.0    0.0    7.2250           0           0           1   \n",
       "1042  29.0000    0.0    0.0    7.8750           0           0           1   \n",
       "\n",
       "      sex_female  embarked_C  embarked_Q  embarked_S  survived  \n",
       "0              1           0           0           1         1  \n",
       "1              0           0           0           1         1  \n",
       "2              1           0           0           1         0  \n",
       "3              0           0           0           1         0  \n",
       "4              1           0           0           1         0  \n",
       "...          ...         ...         ...         ...       ...  \n",
       "1038           0           1           0           0         0  \n",
       "1039           1           1           0           0         0  \n",
       "1040           0           1           0           0         0  \n",
       "1041           0           1           0           0         0  \n",
       "1042           0           0           0           1         0  \n",
       "\n",
       "[1043 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## X,y 분리하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[df.columns[:-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['survived']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 로지스틱회귀 학습"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "reg = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 정확도 측정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=reg.predict(X)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1        True\n",
       "2       False\n",
       "3        True\n",
       "4        True\n",
       "        ...  \n",
       "1038     True\n",
       "1039     True\n",
       "1040     True\n",
       "1041     True\n",
       "1042     True\n",
       "Name: survived, Length: 1043, dtype: bool"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred==y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "622"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(y_pred==y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5963566634707574"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(y_pred==y)/y.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 학습된 모형 예측"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=reg.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.07760797, 0.92239203],\n",
       "       [0.30774838, 0.69225162],\n",
       "       [0.03653536, 0.96346464],\n",
       "       ...,\n",
       "       [0.80269825, 0.19730175],\n",
       "       [0.80570625, 0.19429375],\n",
       "       [0.89374819, 0.10625181]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict_proba(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 학습된 계수 분석하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.82068772e-02, -3.29779991e-01,  5.74165731e-02,\n",
       "         3.29226394e-04,  1.04268288e+00, -6.71073636e-02,\n",
       "        -9.92266410e-01,  2.50297787e+00,  6.18952202e-01,\n",
       "        -6.23530838e-01, -1.21122600e-02]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.82068772e-02, -3.29779991e-01,  5.74165731e-02,\n",
       "         3.29226394e-04,  1.04268288e+00, -6.71073636e-02,\n",
       "        -9.92266410e-01,  2.50297787e+00,  6.18952202e-01,\n",
       "        -6.23530838e-01, -1.21122600e-02]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 11 artists>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6oAAAHTCAYAAAAuzuU+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAilElEQVR4nO3de7RmZ10f8O+PTLgoiGBGjUmGQYkKyE2GAAtbUwUFogQLFqgXQDFqRbCtq8ZiA8UuG++Wmxi5CIpil1xMJRAjEFEUyAAhtyESMEpCWiNYMAXF4K9/7H3Im8k5mTM575zznDOfz1rvOvvyvPt59vPe9nffTnV3AAAAYBS32+oGAAAAwCJBFQAAgKEIqgAAAAxFUAUAAGAogioAAABDEVQBAAAYyq6tbsBajjvuuN67d+9WNwMAAIAj4L3vfe/fdvfu1eYNG1T37t2b/fv3b3UzAAAAOAKq6q/WmufUXwAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGIqgCgAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQ9lwUK2qk6rq7VV1RVVdXlXPXqXMqVX1yaq6eH6ctdF6AQAA2Jl2LWEZNyb5j939vqq6S5L3VtUF3X3FQeX+pLu/bQn1AQAAsINt+Ihqd1/X3e+bh/8+yYEkJ2x0uQAAABydlnqNalXtTfKgJO9eZfbDq+oDVfXmqrrvMusFAABg51jGqb9Jkqq6c5LXJfmx7v7UQbPfl+Qe3X1DVT02yRuTnLzKMs5IckaS7NmzZ1lNAwDYcnvPfNNWN2FTXX32aVvdBGAbW8oR1ao6NlNIfU13v/7g+d39qe6+YR4+L8mxVXXcKuXO6e593b1v9+7dy2gaAAAA28wy7vpbSV6e5EB3/9IaZb58LpeqOmWu9+MbrRsAAICdZxmn/j4iyfckubSqLp6n/ecke5Kku1+a5IlJfriqbkzymSRP7u5eQt0AAADsMBsOqt39p0nqEGVelORFG60LAACAnW+pd/0FAACAjRJUAQAAGIqgCgAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGIqgCgAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGIqgCgAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGIqgCgAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGIqgCgAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGIqgCgAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKBsOqlV1UlW9vaquqKrLq+rZq5SpqnpBVV1VVZdU1ddvtF4AAAB2pl1LWMaNSf5jd7+vqu6S5L1VdUF3X7FQ5jFJTp4fD03yq/NfAAAAuJkNH1Ht7uu6+33z8N8nOZDkhIOKnZ7k1T15V5IvrqrjN1o3AAAAO89Sr1Gtqr1JHpTk3QfNOiHJRxfGr8ktwywAAAAsL6hW1Z2TvC7Jj3X3p27jMs6oqv1Vtf/6669fVtMAAADYRpYSVKvq2Ewh9TXd/fpVilyb5KSF8RPnaTfT3ed0977u3rd79+5lNA0AAIBtZhl3/a0kL09yoLt/aY1i5yb53vnuvw9L8snuvm6jdQMAALDzLOOuv49I8j1JLq2qi+dp/znJniTp7pcmOS/JY5NcleTTSZ6+hHoBAADYgTYcVLv7T5PUIcp0kh/ZaF0AAADsfEu96y8AAABslKAKAADAUARVAAAAhiKoAgAAMBRBFQAAgKEIqgAAAAxFUAUAAGAogioAAABDEVQBAAAYiqAKAADAUARVAAAAhiKoAgAAMBRBFQAAgKEIqgAAAAxFUAUAAGAogioAAABDEVQBAAAYiqAKAADAUARVAAAAhiKoAgAAMBRBFQAAgKEIqgAAAAxFUAUAAGAogioAAABDEVQBAAAYiqAKAADAUARVAAAAhiKoAgAAMBRBFQAAgKEIqgAAAAxFUAUAAGAogioAAABDEVQBAAAYiqAKAADAUARVAAAAhiKoAgAAMBRBFQAAgKEIqgAAAAxFUAUAAGAogioAAABDEVQBAAAYiqAKAADAUARVAAAAhiKoAgAAMBRBFQAAgKEIqgAAAAxFUAUAAGAoSwmqVfWKqvqbqrpsjfmnVtUnq+ri+XHWMuoFAABg59m1pOX8RpIXJXn1rZT5k+7+tiXVBwAAwA61lCOq3f2OJJ9YxrIAAAA4um3mNaoPr6oPVNWbq+q+m1gvAAAA28iyTv09lPcluUd331BVj03yxiQnH1yoqs5IckaS7NmzZ5OaBgAAwEg25Yhqd3+qu2+Yh89LcmxVHbdKuXO6e19379u9e/dmNA0AAIDBbEpQraovr6qah0+Z6/34ZtQNAADA9rKUU3+r6neSnJrkuKq6JslzkxybJN390iRPTPLDVXVjks8keXJ39zLqBgAAYGdZSlDt7qccYv6LMv37GgAAALhVm3nXXwAAADgkQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGIqgCgAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGIqgCgAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGIqgCgAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGIqgCgAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGIqgCgAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGIqgCgAAwFB2bXUDANgce89801Y3YVNdffZpW90EAOA2WsoR1ap6RVX9TVVdtsb8qqoXVNVVVXVJVX39MuoFAABg51nWqb+/keTRtzL/MUlOnh9nJPnVJdULAADADrOUoNrd70jyiVspcnqSV/fkXUm+uKqOX0bdAAAA7CybdTOlE5J8dGH8mnkaAAAA3MxQd/2tqjOqan9V7b/++uu3ujkAAABsgc0KqtcmOWlh/MR52s109zndva+79+3evXuTmgYAAMBINiuonpvke+e7/z4sySe7+7pNqhsAAIBtZCn/R7WqfifJqUmOq6prkjw3ybFJ0t0vTXJekscmuSrJp5M8fRn1AgAAsPMsJah291MOMb+T/Mgy6gIAAGBnG+pmSgAAACCoAgAAMBRBFQAAgKEIqgAAAAxFUAUAAGAogioAAABDEVQBAAAYiqAKAADAUARVAAAAhiKoAgAAMBRBFQAAgKEIqgAAAAxFUAUAAGAogioAAABDEVQBAAAYiqAKAADAUARVAAAAhiKoAgAAMBRBFQAAgKEIqgAAAAxFUAUAAGAogioAAABDEVQBAAAYiqAKAADAUARVAAAAhiKoAgAAMBRBFQAAgKEIqgAAAAxFUAUAAGAogioAAABDEVQBAAAYiqAKAADAUARVAAAAhiKoAgAAMBRBFQAAgKEIqgAAAAxFUAUAAGAogioAAABD2bXVDQAAgEV7z3zTVjdhU1199mlb3QQYjiOqAAAADEVQBQAAYCiCKgAAAEMRVAEAABiKoAoAAMBQBFUAAACGIqgCAAAwFEEVAACAoQiqAAAADGUpQbWqHl1VV1bVVVV15irzn1ZV11fVxfPjGcuoFwAAgJ1n10YXUFXHJHlxkkcluSbJRVV1bndfcVDR3+3uZ260PgAAAHa2ZRxRPSXJVd39ke7+bJLXJjl9CcsFAADgKLSMoHpCko8ujF8zTzvYE6rqkqr6vao6abUFVdUZVbW/qvZff/31S2gaAAAA281m3UzpfyXZ2933T3JBkletVqi7z+nufd29b/fu3ZvUNAAAAEayjKB6bZLFI6QnztM+r7s/3t3/OI++LMmDl1AvAAAAO9AygupFSU6uqntW1e2TPDnJuYsFqur4hdHHJTmwhHoBAADYgTZ819/uvrGqnpnk/CTHJHlFd19eVc9Psr+7z03yrKp6XJIbk3wiydM2Wi8AAAA704aDapJ093lJzjto2lkLwz+Z5CeXURcAAAA722bdTAkAAADWRVAFAABgKIIqAAAAQxFUAQAAGIqgCgAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGIqgCgAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGIqgCgAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGIqgCgAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGIqgCgAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGMqurW4AcHN7z3zTVjdhU1199mlb3QQAAAbjiCoAAABDEVQBAAAYylKCalU9uqqurKqrqurMVebfoap+d57/7qrau4x6AQAA2Hk2HFSr6pgkL07ymCT3SfKUqrrPQcW+P8nfdfe9kvxykp/daL0AAADsTMs4onpKkqu6+yPd/dkkr01y+kFlTk/yqnn495J8c1XVEuoGAABgh1lGUD0hyUcXxq+Zp61aprtvTPLJJF+yhLoBAADYYYb69zRVdUaSM5Jkz549W9yaQ/NvRNZPX23Oc+HWeG+tn++s9dNXm/Pco42+Wr+j7TOY+M46HNv5s7SMoHptkpMWxk+cp61W5pqq2pXkrkk+fvCCuvucJOckyb59+3oJbQMAAEiyvYPb0WYZp/5elOTkqrpnVd0+yZOTnHtQmXOTPHUefmKSt3W3IAoAAMAtbPiIanffWFXPTHJ+kmOSvKK7L6+q5yfZ393nJnl5kt+sqquSfCJTmAUAAIBbWMo1qt19XpLzDpp21sLwPyT5zmXUNRKnDgAAACzfMk79BQAAgKURVAEAABjKUP+eBgBG4NIOANhajqgCAAAwFEEVAACAoQiqAAAADEVQBQAAYCiCKgAAAEMRVAEAABiKoAoAAMBQBFUAAACGIqgCAAAwFEEVAACAoQiqAAAADEVQBQAAYCiCKgAAAEMRVAEAABiKoAoAAMBQBFUAAACGIqgCAAAwFEEVAACAoQiqAAAADEVQBQAAYCiCKgAAAEMRVAEAABiKoAoAAMBQBFUAAACGIqgCAAAwFEEVAACAoQiqAAAADEVQBQAAYCiCKgAAAEPZtdUN4Ohw9dmnbXUTAACAbcIRVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGIqgCgAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGMqGgmpV3b2qLqiqD81/77ZGuc9V1cXz49yN1AkAAMDOttEjqmcmeWt3n5zkrfP4aj7T3Q+cH4/bYJ0AAADsYBsNqqcnedU8/Kokj9/g8gAAADjKbTSofll3XzcP/+8kX7ZGuTtW1f6qeldVPX6thVXVGXO5/ddff/0GmwYAAMB2tOtQBarqj5J8+SqznrM40t1dVb3GYu7R3ddW1VcmeVtVXdrdHz64UHefk+ScJNm3b99aywIAAGAHO2RQ7e5HrjWvqv5PVR3f3ddV1fFJ/maNZVw7//1IVV2Y5EFJbhFUAQAAYKOn/p6b5Knz8FOT/P7BBarqblV1h3n4uCSPSHLFBusFAABgh9poUD07yaOq6kNJHjmPp6r2VdXL5jL3TrK/qj6Q5O1Jzu5uQRUAAIBVHfLU31vT3R9P8s2rTN+f5Bnz8J8lud9G6gEAAODosdEjqgAAALBUgioAAABDEVQBAAAYiqAKAADAUARVAAAAhiKoAgAAMBRBFQAAgKEIqgAAAAxl11Y3AAAAuG2uPvu0rW4CHBGOqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGIqgCgAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGIqgCgAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGIqgCgAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGIqgCgAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAMRVAFAABgKIIqAAAAQxFUAQAAGMqGgmpVfWdVXV5V/1xV+26l3KOr6sqquqqqztxInQAAAOxsGz2ielmSf53kHWsVqKpjkrw4yWOS3CfJU6rqPhusFwAAgB1q10ae3N0HkqSqbq3YKUmu6u6PzGVfm+T0JFdspG4AAAB2pg0F1XU6IclHF8avSfLQTagXADjCrj77tK1uAgA70CGDalX9UZIvX2XWc7r795fZmKo6I8kZSbJnz55lLhoAAIBt4pBBtbsfucE6rk1y0sL4ifO01eo6J8k5SbJv377eYL0AAABsQ5vx72kuSnJyVd2zqm6f5MlJzt2EegEAANiGNvrvab6jqq5J8vAkb6qq8+fpX1FV5yVJd9+Y5JlJzk9yIMn/7O7LN9ZsAAAAdqqN3vX3DUnesMr0jyV57ML4eUnO20hdAAAAHB0249RfAAAAWDdBFQAAgKEIqgAAAAxFUAUAAGAogioAAABDEVQBAAAYiqAKAADAUARVAAAAhiKoAgAAMBRBFQAAgKEIqgAAAAxFUAUAAGAogioAAABDEVQBAAAYSnX3VrdhVVV1fZK/2up2DOq4JH+71Y3YJvTV+umrw6O/1k9frZ++Wj99tX76av301frpq8Ojv1Z3j+7evdqMYYMqa6uq/d29b6vbsR3oq/XTV4dHf62fvlo/fbV++mr99NX66av101eHR38dPqf+AgAAMBRBFQAAgKEIqtvTOVvdgG1EX62fvjo8+mv99NX66av101frp6/WT1+tn746PPrrMLlGFQAAgKE4ogoAAMBQBFW2vap6WVXdZx6+Yavbs1NV1dOq6kVb3Y7NUFXPqqoDVfWarW4LAMDRSFBl2+vuZ3T3FVvdjp2iqnZtdRsG8O+SPKq7v+tQBY+m/qqqU6vqD45wHc+sqquqqqvquFsp99Sq+tD8eOqRbNNtsUl99ZqqurKqLquqV1TVsWuU01dVL6+qD1TVJVX1e1V15zXK/eT8/ruyqr71SLbpSKiq75x3sr39CNYx/E7LZbSxqq6+te+gQzz3wqpa89+QVNWdq+rXqurDVfXeufxDb3trb7tt0Fd3rapXz5/LD8/fe3e77a297bZBX31fVV06f89dVlWn3/aWjkFQHVBVvXH+4rq8qs6Yp31/Vf1FVb2nqn595YNSVbur6nVVddH8eMTWtv7IqqovrKo3zRscl1XVkw7+4FbVL89999aq2j1Pe1ZVXTF/eF87T3teVf1mVf35vAH3A1u1XstWVXur6oPzF/qBecPsC6rqrPl9cllVnVNVNZe/sKp+par2J3l2VT2kqv5s7uf3VNVd5kV/RVW9Ze6vn9u6NTxyquqlSb4yyZur6ifm98f75/74mrnM06rq3Kp6W5K3zu/LV8x99f6d8OOwhd6Z5JFJ/mqtAlV19yTPTfLQJKckee5Wbbhssdck+dok90typyTPOLiAvvq8f9/dD+ju+yf56yTPPLhATWfmPDnJfZM8OslLquqYzW3mhn1/kh/o7n+11Q3ZzjbhdX9Zkk8kObm7H5zk6UluU3jZapvQVy9P8pHuvld3f1WSq5L8xhGu84g4kn1VVScmeU6Sb5i/5x6W5JIjVd9mEVTH9H3zF9e+JM+qqhOS/JdMb7pHZNowWfE/kvxydz8kyRMyffntZI9O8rF5g+PrkrzloPlfmGR/d983yR9n2kBLkjOTPGj+8P7QQvn7J/mmJA9PclZVfcURbf3m+pokL+nueyf5VKajhC/q7ofMfXenJN+2UP728z+ifmGS303y7O5+QKbQ8Jm5zAOTPCnThvGTquqkTVmTTdTdP5TkY0n+VZJfTfIvuvtBSc5K8jMLRb8+yRO7+xsz/Ti8rbtPmZ/381X1hZvb8tvmVnZqrLWzYuV5p6wR4u87l7943jF08mo7mNZqT3e/v7uvPkSzvzXJBd39ie7+uyQXZPpuOKIG7KvzepbkPUlOXKWYvkrS3Z+al1OZvvtWu5Pk6Ule293/2N1/mWmD+JRl9cmi1dpeVQ+uqj+uaUf1+VV1fE1Hk65c6IffqTV2qlbVWUm+IcnLq+rnq+qY+e9Fc5/94Fzu1Lme36+qj1TV2VX1XXP/XlpVXzWX+/aqevf8WvxRVX3ZKnUe0Z3lVfXdC6/7r83rdMO8XpfP7Tqlpp2tH6mqxy08/aR5+oeq6rkLy7zFwYB5+g1V9YtV9YFM2wQr0+9UVW+uqh+oNXZKzmVeO7/X35DpPbbWOn1Vph1HP9Xd/5wk3f2X3f0mfXWLdbpXkgcn+emFyc9P8oCVz4S++rwvTfL3SW5Iku6+Yf4e296622OwR5LnJfnA/PhkppD1qoX5z8oUOJLkb5JcvPC4Nsmdt3odjmDffHWSq5P8bKYAkSQXJtk3D38uya55+CuTXDwPvyXJ7yX57pX+mfv5+QvLfnWSx2/1Oi6pn/Ym+euF8W9K8sZMOzPeneTS+b1y5kIffuM8fL8k71xlmU9L8usL42/OtOduy9f3CPTf1Zn2bp+U5A1JLpv77IMLffHKhfL75zIrn8O/TnLvrV6Pw3ivdJJHzOOvSPKfknwkyUPmaV+UZFeSU5P8weK0efiRSV43D78wyXfNw7fP9MP6hIPeO3dd72uwxrwfz7SRtzL+X5L8+FHcV8cmeV/m70R9tWabXpnk/yR5e5IvWGX+i5J898L4yzPtjDoS/XOLtif5syS75/EnJXnFPPyoJH+e6WjvWw6x3Atz0+/hGSuvfZI7ZPqeuufc3/83yfHz9GuT/Ne53LOT/Mo8fLfc9N8hnpHkF+fhp+WmbZDfzvw7kGRPkgNL7KN7J/lfSY6dx1+S5Hvn99Vj5mlvSPKH82fgAbnpN/9pSa5L8iXze+WyhX65+/x3ZfqXzOOd5N8s1H91pvfxHyX53nnaz6y8R5J8cZK/yLSD/D8svF73T3LjSn2rrNfjkrxhye+no6qv5nV5vL662Xodk+T8TNsfr0zy7ct8j23V46i5tmq7qKpTM/04P7y7P11VFyb5YKYP1mpul+Rh3f0Pm9LALdbdf1FVX5/ksUn+W1W99VBPmf+eluRfJvn2JM+pqvsdNP/g8jvBauv2kkxfch+tqucluePC/P+3jmX+48Lw55Id/x3y00ne3t3fUVV7M20Erljsr0ryhO6+chPbtkwf7e53zsO/lekI8XXdfVFys6NRi8+5a5JXVdXJmd5bK9dH/nmmz9iJSV7f3R+qqkuT/GJV/WymQPInR3yNjpwR++olSd4xYL8O1Vfd/fSaTr17YaYg+MplrORtdLO2J/m7JF+X5IK5P47JtEGc7r6gqr4zyYszbTSv17ckuX9VPXEev2uSk5N8NslF3X1dklTVhzNtlK+0a+W04ROT/G5VHZ9p58BfrlLHI5PcZ+E1/KKqunN3L+PGht+c6WjaRfPy75Rp5/xnc9PZVJcm+cfu/qf5/bB34fkXdPfHk6SqXp/paPP+TGeqfcdc5qRMffLxTL9przuoDb+f5Oe6e+XGet+S5HFV9ePz+B0zBfR/meQFSdLdl1TVZp9yqa/Wb0f2VXd/rqoeneQh8zr+clU9uLuft55OGZVTf8dz1yR/N4fUr810uu8XJvnGqrpbTTduecJC+T9M8qMrI1X1wM1s7Gar6dTcT3f3byX5+UynXy66XZKVH+V/m+RPq+p2SU7q7rcn+YlMfbxyI43Tq+qOVfUlmfYyX3SEV2Ez7amqldNM/m2SP52H/7amG4k8cfWn5cokx1fVQ5Kkqu5SR9ENgw5y10xHG5JpT+pazk/yo1Wfv+b3QUe4Xct28E6NT63jOSsh/usy7QC6Y5J0929n2gv+mSTnVdU3dfdfZPqsXpppB9NZG2zvtZk2BFacmJtepyNtqL6aTz3bnWnP+2r01WKDuj+X5LW5+e/oik3rq4PbPrfn8u5+4Py4X3d/S5LMv2H3TvLpTEc516uS/OjCMu/Z3SuBdHGn4z8vjP9zbtoB+cJMR07vl+QHc/MdmytWdpav1HHCkkLqSvtftbDsr5k3uv+p50NIi23v6TTaxd+qW+ysPehgwAOSvH9hvf5hfn8semeSR698t+emnZIrbdrT3QcOc70uz3Tq6jKvV9ypfXVFkgfOn4FpodPwAzKdRXJb7NS+Sk/e093/PdMZGKt9z20rgup43pJkV1UdSHJ2kndl+qH8mUzXIL0z02kDn5zLPyvJvpquP7kiN7/+cie6X5L3VNXFma4//W8Hzf9/SU6pqssyne76/Ex7pn9r3iv2/iQv6O7/O5e/JNNpYO9K8tPd/bEjvgab58okPzK/l+6W6XrLX890Ssr5WSOUd/dnMx1teOF8TcUFWX0D5Wjwc0n+e1W9P7d+9PinMx35uaSqLs/Nr6fZDg7eqfGuHHpnxaohvqq+MtONL16Qaa/x/dexg+lwnZ/kW+add3fLtDf6/A0uc72G6auqekama1CfMm9Mreao76ua3GtlOFPg/eAqRc9N8uSqukNV3TPTEZH3HOY6r8sqbX9okt0r/VVVx1bVfefi/z7JgUx9+Mpa4+7Oqzg/yQ+vlK+qr67Du3Z+8bV46hpljuTO8rcmeWJVfem87LtX1T0O4/mPmp9zpySPz7T9tNrBgFtzVqaj3S+ex9faKfmOTK9PqurrMp2muaru/nCmI3D/dWE5e6vqtMNYt4Pt1L66KtN2208tTP6pJG/t7r9e/+rdzI7sq6r6iprOOFzxwNzKTQm3jR7g/GOPQz9y03WVuzKdW/8dW92m7f7IdI3qEb9Wa4vWbW+Sy7a6HR7jP+b3ygcznZp5INMpSl+Q6fShd2W6Vv5dmc5CODU3XUv48EzX0bw/0w6jq+fpZ2Y6YnBxph1vd88Upi6Zp12UNa6xmZ//rCTXZLoW52NJXjZP37cyPI9/X6ab3VyV5OlHaV/dmOTDuena6LP01aptuV2mjclLM+2oe02SL5rnPS43v1fBc+Y+vTLz9WpHqH9u0fZMG5bvmPvm8iQ/kOmmeAeS3GV+3i9lvp50jeVemJuumbtdpp3cK+v99kwb1J/v71Wes/hanJ7pmuL3ZgrTF87Tn5abrlE9LtPN9y7JdPTrpUvupyfNfXTJ3I6HJblhYf7zsvA7vjJvbuMb53X+UJLnztPvkOn+Cgfm+RcmOXXxuQvLunpev8p0mvjPZTpN9NfmPr18oa/ulOlI/YEkr890L4hb++x+UaYdxx+eX5sLM1+7ra9usV5fnOQ35766Psl5SY7RV7dYp3skeVum792LMx1k+Kplfh634rFykTyDq6pfyHRawR0z7cF8dnvxNqSmazRv6O5f2Oq2LFtN11P+QU+nz8GavFfWT1+tn74Clq2mO/2+Kcmzuvu8rW4PR97Ret3ZttPdP37oUhyO3uYXmN+anv69hw1EAGBH6OmGhffa6naweRxRBWDT1fQ/4e550OSf6O7Nun5y29BX63e09FVVvTvT6YaLvqe7L92K9rA6r9P66av1O5r6SlAFAABgKO76CwAAwFAEVQAAAIYiqAIAADAUQRUAAIChCKoAAAAM5f8D5kIsEAeeT24AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.bar(X.columns, reg.coef_[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터표준화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_std = (X-X.mean())/X.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.fit(X_std,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 11 artists>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.bar(X.columns, reg.coef_[0])"
   ]
  }
 ],
 "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}