Regression_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "453050cd-7357-4a12-a2f0-a20aff5e3ca7",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 선형회귀\n",
    "- 회귀분석의 정의\n",
    "  - 하나 혹은 그 이상의 원인이 결과에 미치는 영향을 추정하여 식으로 표현할 수 있는 통계 기법\n",
    "  - 변수 사이의 인과관계를 분석하고 적합한 모델에 대해 관심 있는 변수를 예측하거나 추론하기 위한 방법\n",
    "\n",
    "\n",
    "\n",
    "- 단순선형회귀 모델\n",
    "  - 한 개의 독립변수에 기반한 직선 모델\n",
    "- 다중선형회귀 모델\n",
    "  - 두 개 이상의 독립변수에 기반한 직선 모델\n",
    "- 비선형회귀 모델\n",
    "  - 독립변수와 종속변수의 관계가 선형이 아닐 때\n",
    "- 로지스틱회귀 모델\n",
    "  - 종속변수의 값이 이분형(명목변수)일 때\n",
    "\n",
    "\n",
    "- 결정계수 ( R - Squared, $R^2$ )\n",
    "  - 전체 데이터 중 회귀 모델이 설명할 수 있는 데이터의 비율, 추정한 선형 모델이 주어진 데이터에 적합한 정도를 재는 척도. ( 모델 비교 시 결정계수가 높은 모델 선택 )\n",
    "  - $\\frac{SSR}{SST}$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "446cf423-d823-487f-9b24-53cc6c385b27",
   "metadata": {},
   "source": [
    "---\n",
    "***[회귀분석 가정사항 ]***\n",
    "\n",
    "- 선형성 : 예측하고자 하는 종속변수와 독립변수 간에 선형성을 만족하는 특성을 의미\n",
    "- 독립성 : 독립변수 간에 서로 상관관계가 없어야하는 특성을 의미\n",
    "- 정규성 : 잔차가 정규성을 만족하여 정규분포를 띄어야하는 특성을 의미\n",
    "- 등분산성 : 분산이 같다는 것으로, 특정한 패턴 없이 고르게 분포하여아함\n",
    "- 비상관성 : 잔차끼리 상관이 없어야 함."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c2221f3-0d2d-44ef-acec-543dc27220ed",
   "metadata": {},
   "source": [
    "---\n",
    "***[단순선형회귀 ]***\n",
    "\n",
    "- 오차가 가장 작은 가운데 선이 가장 좋은 추세선\n",
    "- 예측한 데이터와 실제 데이터의 차이 계산\n",
    "- 차이가 가장 작은 선 선택\n",
    "- 차이 = 오차(error, $\\epsilon$) / 잔차(residual) 또는 손실 / 비용"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03fed339-7fdb-4644-866f-5ff7aa81f7c0",
   "metadata": {},
   "source": [
    "***[회귀계수 ]***\n",
    "- $\\beta_0$(절편)과 $\\beta_1$(기울기)를 회귀계수라 부르며,\n",
    "- 회귀분석은 회귀계수를 찾아 독립변수와 종속변수 사이의 함수식을 생성\n",
    "- 회귀계수가 통계적으로 유의미한지 파악하고, 유의할 시에는 회귀모델을 이용해 종속변수의 값을 예측\n",
    "- SSE = 잔차 제곱의 합"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c9d970e-964c-417d-a715-be42097ad1d6",
   "metadata": {},
   "source": [
    "***[결정계수 ]***\n",
    "- 회귀 모델의 분산(SSR) / 전체 분산(SST) 값으로 계산 가능\n",
    "- = $\\frac{SSR}{SST}$\n",
    "- 회귀 모델이 전체 데이터에 대해 얼마나 정확한지에 대한 척도\n",
    "\n",
    "***[조정된 결정계수 ]***\n",
    "- 변수가 정확도에 영향을 미치는 정도에 따라, 조정된 결정계수\n",
    "- 독립변수 수가 많을수록 결정계수가 증가하기 때문에, 모델의 평가 지표가 높아지는 문제 발생\n",
    "- 이와 같은 문제를 해결하기 위해 조정된 결정계수 사용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e2eeb3bf-7a1f-411d-a084-1c4478c74c7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)   # FutureWarning 제거\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import math\n",
    "from scipy import stats\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "font_family = 'Malgun Gothic'\n",
    "\n",
    "sns.set(font=font_family, rc={'axes.unicode_minus' : False})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32fab238-4ffc-4d17-9c33-31e71a212676",
   "metadata": {},
   "source": [
    "# 회귀분석\n",
    "- [sklearn - load_diabetes]\n",
    "  - Data\n",
    "      - 당뇨병 진행도 예측 데이터\n",
    "      - 10종류의 독립변수, 독립변수 값은 모두 스케일링 되었음\n",
    "  \n",
    "  - Target\n",
    "      - 1년 뒤 측정한 당뇨병의 진행률\n",
    "  \n",
    "  - 독립변수\n",
    "      - age : 나이\n",
    "      - sex : 성별\n",
    "      - bmi : bmi 지수\n",
    "      - bp : 평균혈압\n",
    "      - s1 ~ s6 : 6종류의 혈액 검사 수치"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "28823bbb-c697-4457-b7c8-205b04c49de3",
   "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>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.038076</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.061696</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>-0.044223</td>\n",
       "      <td>-0.034821</td>\n",
       "      <td>-0.043401</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.019907</td>\n",
       "      <td>-0.017646</td>\n",
       "      <td>151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.001882</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.051474</td>\n",
       "      <td>-0.026328</td>\n",
       "      <td>-0.008449</td>\n",
       "      <td>-0.019163</td>\n",
       "      <td>0.074412</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.068332</td>\n",
       "      <td>-0.092204</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.085299</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.044451</td>\n",
       "      <td>-0.005670</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.034194</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.002861</td>\n",
       "      <td>-0.025930</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.089063</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.011595</td>\n",
       "      <td>-0.036656</td>\n",
       "      <td>0.012191</td>\n",
       "      <td>0.024991</td>\n",
       "      <td>-0.036038</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.022688</td>\n",
       "      <td>-0.009362</td>\n",
       "      <td>206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005383</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.036385</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>0.003935</td>\n",
       "      <td>0.015596</td>\n",
       "      <td>0.008142</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.031988</td>\n",
       "      <td>-0.046641</td>\n",
       "      <td>135.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age       sex       bmi        bp        s1        s2        s3  \\\n",
       "0  0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1 -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "2  0.085299  0.050680  0.044451 -0.005670 -0.045599 -0.034194 -0.032356   \n",
       "3 -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "4  0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "\n",
       "         s4        s5        s6  target  \n",
       "0 -0.002592  0.019907 -0.017646   151.0  \n",
       "1 -0.039493 -0.068332 -0.092204    75.0  \n",
       "2 -0.002592  0.002861 -0.025930   141.0  \n",
       "3  0.034309  0.022688 -0.009362   206.0  \n",
       "4 -0.002592 -0.031988 -0.046641   135.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_diabetes\n",
    "\n",
    "data = load_diabetes()\n",
    "df = pd.DataFrame(data.data, columns=data.feature_names)\n",
    "df['target'] = data.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0bd8f160-7d0d-4048-a18f-599ab8796102",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 442 entries, 0 to 441\n",
      "Data columns (total 11 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   age     442 non-null    float64\n",
      " 1   sex     442 non-null    float64\n",
      " 2   bmi     442 non-null    float64\n",
      " 3   bp      442 non-null    float64\n",
      " 4   s1      442 non-null    float64\n",
      " 5   s2      442 non-null    float64\n",
      " 6   s3      442 non-null    float64\n",
      " 7   s4      442 non-null    float64\n",
      " 8   s5      442 non-null    float64\n",
      " 9   s6      442 non-null    float64\n",
      " 10  target  442 non-null    float64\n",
      "dtypes: float64(11)\n",
      "memory usage: 38.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e50bdb3f-cef5-4544-9292-b5ba4483a3ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bmi', ylabel='target'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot('bmi', 'target', lowess=True, line_kws={'color':'red'}, data= df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1aaec76d-81d7-4acb-9b20-98859a2ab6c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'rate')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 이상값 확인\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "\n",
    "sns.boxplot('bmi', data=df, ax=ax1)\n",
    "ax1.set_title('bmi')\n",
    "\n",
    "sns.boxplot('target', data=df, ax=ax2)\n",
    "ax2.set_title('rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0cd8cb95-966c-4968-b02b-f79c431b13c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'rate')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 분포 시각화\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,4))\n",
    "\n",
    "sns.kdeplot(df['bmi'], ax=ax1)\n",
    "ax1.set_title('bmi')\n",
    "\n",
    "sns.kdeplot(df['target'], ax=ax2)\n",
    "ax2.set_title('rate')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0e7eee8a-f577-4c45-a48d-7ec8805075f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5961166556214368\n",
      "0.43906639932477265\n"
     ]
    }
   ],
   "source": [
    "# 비대칭 확인 ( 왜도 )\n",
    "print(stats.skew(df['bmi']))\n",
    "print(stats.skew(df['target']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4499387-4dc8-420b-bb13-de940f02da6f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5315e07f-4581-4c59-b179-5b6fb0cd526e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(442,)\n",
      "(442, 1)\n"
     ]
    }
   ],
   "source": [
    "# 데이터셋 생성\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "X = df.bmi.values\n",
    "y = df.target.values\n",
    "\n",
    "l_train_X1 = X.reshape(-1, 1)\n",
    "l_test_y1 = y.reshape(-1, 1)\n",
    "\n",
    "print(X.shape)\n",
    "print(l_train_X1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d6ddcdb6-909c-4862-9468-13f926047029",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 모델 학습\n",
    "lr_model = LinearRegression()\n",
    "lr_model.fit(l_train_X1, l_test_y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f45d0116-b766-406f-98fc-3126cfff6bd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[949.43526038]\n",
      "[152.13348416]\n"
     ]
    }
   ],
   "source": [
    "# 회귀계수 확인\n",
    "print(lr_model.coef_[0])\n",
    "print(lr_model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1a89a163-d8ce-4749-95a5-973614cc6c23",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mType:\u001b[0m        ndarray\n",
       "\u001b[1;31mString form:\u001b[0m [152.13348416]\n",
       "\u001b[1;31mLength:\u001b[0m      1\n",
       "\u001b[1;31mFile:\u001b[0m        c:\\users\\user\\anaconda3\\lib\\site-packages\\numpy\\__init__.py\n",
       "\u001b[1;31mDocstring:\u001b[0m  \n",
       "ndarray(shape, dtype=float, buffer=None, offset=0,\n",
       "        strides=None, order=None)\n",
       "\n",
       "An array object represents a multidimensional, homogeneous array\n",
       "of fixed-size items.  An associated data-type object describes the\n",
       "format of each element in the array (its byte-order, how many bytes it\n",
       "occupies in memory, whether it is an integer, a floating point number,\n",
       "or something else, etc.)\n",
       "\n",
       "Arrays should be constructed using `array`, `zeros` or `empty` (refer\n",
       "to the See Also section below).  The parameters given here refer to\n",
       "a low-level method (`ndarray(...)`) for instantiating an array.\n",
       "\n",
       "For more information, refer to the `numpy` module and examine the\n",
       "methods and attributes of an array.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "(for the __new__ method; see Notes below)\n",
       "\n",
       "shape : tuple of ints\n",
       "    Shape of created array.\n",
       "dtype : data-type, optional\n",
       "    Any object that can be interpreted as a numpy data type.\n",
       "buffer : object exposing buffer interface, optional\n",
       "    Used to fill the array with data.\n",
       "offset : int, optional\n",
       "    Offset of array data in buffer.\n",
       "strides : tuple of ints, optional\n",
       "    Strides of data in memory.\n",
       "order : {'C', 'F'}, optional\n",
       "    Row-major (C-style) or column-major (Fortran-style) order.\n",
       "\n",
       "Attributes\n",
       "----------\n",
       "T : ndarray\n",
       "    Transpose of the array.\n",
       "data : buffer\n",
       "    The array's elements, in memory.\n",
       "dtype : dtype object\n",
       "    Describes the format of the elements in the array.\n",
       "flags : dict\n",
       "    Dictionary containing information related to memory use, e.g.,\n",
       "    'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.\n",
       "flat : numpy.flatiter object\n",
       "    Flattened version of the array as an iterator.  The iterator\n",
       "    allows assignments, e.g., ``x.flat = 3`` (See `ndarray.flat` for\n",
       "    assignment examples; TODO).\n",
       "imag : ndarray\n",
       "    Imaginary part of the array.\n",
       "real : ndarray\n",
       "    Real part of the array.\n",
       "size : int\n",
       "    Number of elements in the array.\n",
       "itemsize : int\n",
       "    The memory use of each array element in bytes.\n",
       "nbytes : int\n",
       "    The total number of bytes required to store the array data,\n",
       "    i.e., ``itemsize * size``.\n",
       "ndim : int\n",
       "    The array's number of dimensions.\n",
       "shape : tuple of ints\n",
       "    Shape of the array.\n",
       "strides : tuple of ints\n",
       "    The step-size required to move from one element to the next in\n",
       "    memory. For example, a contiguous ``(3, 4)`` array of type\n",
       "    ``int16`` in C-order has strides ``(8, 2)``.  This implies that\n",
       "    to move from element to element in memory requires jumps of 2 bytes.\n",
       "    To move from row-to-row, one needs to jump 8 bytes at a time\n",
       "    (``2 * 4``).\n",
       "ctypes : ctypes object\n",
       "    Class containing properties of the array needed for interaction\n",
       "    with ctypes.\n",
       "base : ndarray\n",
       "    If the array is a view into another array, that array is its `base`\n",
       "    (unless that array is also a view).  The `base` array is where the\n",
       "    array data is actually stored.\n",
       "\n",
       "See Also\n",
       "--------\n",
       "array : Construct an array.\n",
       "zeros : Create an array, each element of which is zero.\n",
       "empty : Create an array, but leave its allocated memory unchanged (i.e.,\n",
       "        it contains \"garbage\").\n",
       "dtype : Create a data-type.\n",
       "numpy.typing.NDArray : A :term:`generic <generic type>` version\n",
       "                       of ndarray.\n",
       "\n",
       "Notes\n",
       "-----\n",
       "There are two modes of creating an array using ``__new__``:\n",
       "\n",
       "1. If `buffer` is None, then only `shape`, `dtype`, and `order`\n",
       "   are used.\n",
       "2. If `buffer` is an object exposing the buffer interface, then\n",
       "   all keywords are interpreted.\n",
       "\n",
       "No ``__init__`` method is needed because the array is fully initialized\n",
       "after the ``__new__`` method.\n",
       "\n",
       "Examples\n",
       "--------\n",
       "These examples illustrate the low-level `ndarray` constructor.  Refer\n",
       "to the `See Also` section above for easier ways of constructing an\n",
       "ndarray.\n",
       "\n",
       "First mode, `buffer` is None:\n",
       "\n",
       ">>> np.ndarray(shape=(2,2), dtype=float, order='F')\n",
       "array([[0.0e+000, 0.0e+000], # random\n",
       "       [     nan, 2.5e-323]])\n",
       "\n",
       "Second mode:\n",
       "\n",
       ">>> np.ndarray((2,), buffer=np.array([1,2,3]),\n",
       "...            offset=np.int_().itemsize,\n",
       "...            dtype=int) # offset = 1*itemsize, i.e. skip first element\n",
       "array([2, 3])\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_model.intercept_?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "da33c6fc-3c5a-426d-8963-13a570c88e8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1746.73703661],\n",
       "       [ -797.30177622],\n",
       "       [  152.13348416],\n",
       "       [ 1101.56874455],\n",
       "       [ 2051.00400493]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 선형회귀 모델로 값 예측\n",
    "lr_model.predict([[-2], [-1], [0], [1], [2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "820c39df-eb30-4f15-856b-a4ad10da6f97",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df = pd.DataFrame(lr_model.predict(l_train_X1), columns=['l_train_X1_pred'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4f85e714-2069-4bb6-a14e-dd42976b3cc2",
   "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>l_train_X1_pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>210.710038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>103.262195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>194.337033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>141.124769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>117.588574</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   l_train_X1_pred\n",
       "0       210.710038\n",
       "1       103.262195\n",
       "2       194.337033\n",
       "3       141.124769\n",
       "4       117.588574"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4452d9b0-f49a-4e2b-9412-97768e7b6e41",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df['target'] = l_test_y1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "af930012-b643-4fc1-9955-348ee82b41b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_df['pred - target ( over value )'] = pred_df['l_train_X1_pred'] - pred_df['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "46767b52-d5ce-4f58-aaf7-22a2783fef1e",
   "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>l_train_X1_pred</th>\n",
       "      <th>target</th>\n",
       "      <th>pred - target ( over value )</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>210.710038</td>\n",
       "      <td>151.0</td>\n",
       "      <td>59.710038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>103.262195</td>\n",
       "      <td>75.0</td>\n",
       "      <td>28.262195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>194.337033</td>\n",
       "      <td>141.0</td>\n",
       "      <td>53.337033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>141.124769</td>\n",
       "      <td>206.0</td>\n",
       "      <td>-64.875231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>117.588574</td>\n",
       "      <td>135.0</td>\n",
       "      <td>-17.411426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   l_train_X1_pred  target  pred - target ( over value )\n",
       "0       210.710038   151.0                     59.710038\n",
       "1       103.262195    75.0                     28.262195\n",
       "2       194.337033   141.0                     53.337033\n",
       "3       141.124769   206.0                    -64.875231\n",
       "4       117.588574   135.0                    -17.411426"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d6e831f8-6d46-4638-9474-90ebfb02726c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 잔차 계산\n",
    "lr_prediction = lr_model.predict(l_train_X1)\n",
    "lr_residuals = l_test_y1 - lr_prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8e398dc5-0da2-47c2-8d32-f3131560112c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_squared : 0.3439237602253802\n"
     ]
    }
   ],
   "source": [
    "# R_squared 계산\n",
    "SSE = (lr_residuals**2).sum()\n",
    "SST = ((l_test_y1 - l_test_y1.mean())**2).sum()\n",
    "R_squared = 1 - (SSE/SST)\n",
    "print('R_squared :', R_squared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5dca7210-a1d3-4dbd-a835-ffee2faa41c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 단순회귀 모델 시각화\n",
    "sns.regplot(l_train_X1, l_test_y1, lowess=True, line_kws={'color':'red'}, data = df)\n",
    "plt.title('y = {}*x + {}'.format(lr_model.coef_[0], lr_model.intercept_))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c2cbd74f-5c45-48e0-968f-224a4d141ab6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>target</td>      <th>  R-squared:         </th> <td>   0.344</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.342</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   230.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 20 Jul 2022</td> <th>  Prob (F-statistic):</th> <td>3.47e-42</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:10:15</td>     <th>  Log-Likelihood:    </th> <td> -2454.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   442</td>      <th>  AIC:               </th> <td>   4912.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   440</td>      <th>  BIC:               </th> <td>   4920.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>  152.1335</td> <td>    2.974</td> <td>   51.162</td> <td> 0.000</td> <td>  146.289</td> <td>  157.978</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bmi</th>       <td>  949.4353</td> <td>   62.515</td> <td>   15.187</td> <td> 0.000</td> <td>  826.570</td> <td> 1072.301</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>11.674</td> <th>  Durbin-Watson:     </th> <td>   1.848</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.003</td> <th>  Jarque-Bera (JB):  </th> <td>   7.310</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.156</td> <th>  Prob(JB):          </th> <td>  0.0259</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.453</td> <th>  Cond. No.          </th> <td>    21.0</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 target   R-squared:                       0.344\n",
       "Model:                            OLS   Adj. R-squared:                  0.342\n",
       "Method:                 Least Squares   F-statistic:                     230.7\n",
       "Date:                Wed, 20 Jul 2022   Prob (F-statistic):           3.47e-42\n",
       "Time:                        11:10:15   Log-Likelihood:                -2454.0\n",
       "No. Observations:                 442   AIC:                             4912.\n",
       "Df Residuals:                     440   BIC:                             4920.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept    152.1335      2.974     51.162      0.000     146.289     157.978\n",
       "bmi          949.4353     62.515     15.187      0.000     826.570    1072.301\n",
       "==============================================================================\n",
       "Omnibus:                       11.674   Durbin-Watson:                   1.848\n",
       "Prob(Omnibus):                  0.003   Jarque-Bera (JB):                7.310\n",
       "Skew:                           0.156   Prob(JB):                       0.0259\n",
       "Kurtosis:                       2.453   Cond. No.                         21.0\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 단순선형회귀 통계적 분석\n",
    "from statsmodels.formula.api import ols\n",
    "\n",
    "res_sr = ols('target ~ bmi', data=df).fit()   # 종속변수 : target, 독립변수 : bmi\n",
    "\n",
    "# 결과확인\n",
    "res_sr.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "1cbdbf27-f39b-48ab-af57-8e752c0d9b23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(442, 11)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "eef4b38f-39d3-45fd-96cf-60f83407ee8a",
   "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>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.173737</td>\n",
       "      <td>0.185085</td>\n",
       "      <td>0.335428</td>\n",
       "      <td>0.260061</td>\n",
       "      <td>0.219243</td>\n",
       "      <td>-0.075181</td>\n",
       "      <td>0.203841</td>\n",
       "      <td>0.270774</td>\n",
       "      <td>0.301731</td>\n",
       "      <td>0.187889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>0.173737</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.088161</td>\n",
       "      <td>0.241010</td>\n",
       "      <td>0.035277</td>\n",
       "      <td>0.142637</td>\n",
       "      <td>-0.379090</td>\n",
       "      <td>0.332115</td>\n",
       "      <td>0.149916</td>\n",
       "      <td>0.208133</td>\n",
       "      <td>0.043062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi</th>\n",
       "      <td>0.185085</td>\n",
       "      <td>0.088161</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.395411</td>\n",
       "      <td>0.249777</td>\n",
       "      <td>0.261170</td>\n",
       "      <td>-0.366811</td>\n",
       "      <td>0.413807</td>\n",
       "      <td>0.446157</td>\n",
       "      <td>0.388680</td>\n",
       "      <td>0.586450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bp</th>\n",
       "      <td>0.335428</td>\n",
       "      <td>0.241010</td>\n",
       "      <td>0.395411</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.242464</td>\n",
       "      <td>0.185548</td>\n",
       "      <td>-0.178762</td>\n",
       "      <td>0.257650</td>\n",
       "      <td>0.393480</td>\n",
       "      <td>0.390430</td>\n",
       "      <td>0.441482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s1</th>\n",
       "      <td>0.260061</td>\n",
       "      <td>0.035277</td>\n",
       "      <td>0.249777</td>\n",
       "      <td>0.242464</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.896663</td>\n",
       "      <td>0.051519</td>\n",
       "      <td>0.542207</td>\n",
       "      <td>0.515503</td>\n",
       "      <td>0.325717</td>\n",
       "      <td>0.212022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s2</th>\n",
       "      <td>0.219243</td>\n",
       "      <td>0.142637</td>\n",
       "      <td>0.261170</td>\n",
       "      <td>0.185548</td>\n",
       "      <td>0.896663</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.196455</td>\n",
       "      <td>0.659817</td>\n",
       "      <td>0.318357</td>\n",
       "      <td>0.290600</td>\n",
       "      <td>0.174054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s3</th>\n",
       "      <td>-0.075181</td>\n",
       "      <td>-0.379090</td>\n",
       "      <td>-0.366811</td>\n",
       "      <td>-0.178762</td>\n",
       "      <td>0.051519</td>\n",
       "      <td>-0.196455</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.738493</td>\n",
       "      <td>-0.398577</td>\n",
       "      <td>-0.273697</td>\n",
       "      <td>-0.394789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s4</th>\n",
       "      <td>0.203841</td>\n",
       "      <td>0.332115</td>\n",
       "      <td>0.413807</td>\n",
       "      <td>0.257650</td>\n",
       "      <td>0.542207</td>\n",
       "      <td>0.659817</td>\n",
       "      <td>-0.738493</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.617859</td>\n",
       "      <td>0.417212</td>\n",
       "      <td>0.430453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s5</th>\n",
       "      <td>0.270774</td>\n",
       "      <td>0.149916</td>\n",
       "      <td>0.446157</td>\n",
       "      <td>0.393480</td>\n",
       "      <td>0.515503</td>\n",
       "      <td>0.318357</td>\n",
       "      <td>-0.398577</td>\n",
       "      <td>0.617859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.464669</td>\n",
       "      <td>0.565883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s6</th>\n",
       "      <td>0.301731</td>\n",
       "      <td>0.208133</td>\n",
       "      <td>0.388680</td>\n",
       "      <td>0.390430</td>\n",
       "      <td>0.325717</td>\n",
       "      <td>0.290600</td>\n",
       "      <td>-0.273697</td>\n",
       "      <td>0.417212</td>\n",
       "      <td>0.464669</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.382483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>0.187889</td>\n",
       "      <td>0.043062</td>\n",
       "      <td>0.586450</td>\n",
       "      <td>0.441482</td>\n",
       "      <td>0.212022</td>\n",
       "      <td>0.174054</td>\n",
       "      <td>-0.394789</td>\n",
       "      <td>0.430453</td>\n",
       "      <td>0.565883</td>\n",
       "      <td>0.382483</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             age       sex       bmi        bp        s1        s2        s3  \\\n",
       "age     1.000000  0.173737  0.185085  0.335428  0.260061  0.219243 -0.075181   \n",
       "sex     0.173737  1.000000  0.088161  0.241010  0.035277  0.142637 -0.379090   \n",
       "bmi     0.185085  0.088161  1.000000  0.395411  0.249777  0.261170 -0.366811   \n",
       "bp      0.335428  0.241010  0.395411  1.000000  0.242464  0.185548 -0.178762   \n",
       "s1      0.260061  0.035277  0.249777  0.242464  1.000000  0.896663  0.051519   \n",
       "s2      0.219243  0.142637  0.261170  0.185548  0.896663  1.000000 -0.196455   \n",
       "s3     -0.075181 -0.379090 -0.366811 -0.178762  0.051519 -0.196455  1.000000   \n",
       "s4      0.203841  0.332115  0.413807  0.257650  0.542207  0.659817 -0.738493   \n",
       "s5      0.270774  0.149916  0.446157  0.393480  0.515503  0.318357 -0.398577   \n",
       "s6      0.301731  0.208133  0.388680  0.390430  0.325717  0.290600 -0.273697   \n",
       "target  0.187889  0.043062  0.586450  0.441482  0.212022  0.174054 -0.394789   \n",
       "\n",
       "              s4        s5        s6    target  \n",
       "age     0.203841  0.270774  0.301731  0.187889  \n",
       "sex     0.332115  0.149916  0.208133  0.043062  \n",
       "bmi     0.413807  0.446157  0.388680  0.586450  \n",
       "bp      0.257650  0.393480  0.390430  0.441482  \n",
       "s1      0.542207  0.515503  0.325717  0.212022  \n",
       "s2      0.659817  0.318357  0.290600  0.174054  \n",
       "s3     -0.738493 -0.398577 -0.273697 -0.394789  \n",
       "s4      1.000000  0.617859  0.417212  0.430453  \n",
       "s5      0.617859  1.000000  0.464669  0.565883  \n",
       "s6      0.417212  0.464669  1.000000  0.382483  \n",
       "target  0.430453  0.565883  0.382483  1.000000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cff7dae5-836e-4f6b-8901-d148845708d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = sns.light_palette('darkgray', as_cmap=True)\n",
    "sns.heatmap(df.corr(), annot=True, cmap=cmap)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "9e798788-f110-461b-8811-b4baf4f4937d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "절편: [152.13348416]\n",
      "회귀계수: [ -10.0098663  -239.81564367  519.84592005  324.3846455  -792.17563855\n",
      "  476.73902101  101.04326794  177.06323767  751.27369956   67.62669218]\n"
     ]
    }
   ],
   "source": [
    "# 다중선형회귀 모델\n",
    "dlr_train_X_df = df.copy()\n",
    "dlr_train_X_df = dlr_train_X_df.drop('target', 1)\n",
    "\n",
    "dlr_test_y = df.target.values\n",
    "dlr_test_y = dlr_test_y.reshape(-1, 1)\n",
    "\n",
    "dlr_model = LinearRegression()\n",
    "dlr_model.fit(dlr_train_X_df, dlr_test_y)\n",
    "\n",
    "print('절편:', dlr_model.intercept_)\n",
    "print('회귀계수:', dlr_model.coef_[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "71e3239b-7bc2-4eec-a9b8-d2f5a49455bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 잔차\n",
    "prediction = dlr_model.predict(dlr_train_X_df)\n",
    "residuals = dlr_test_y - prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "24963479-a6ec-4470-a343-3c394be30140",
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.420000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-3.665243e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.353672e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.558268e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-3.853589e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-2.278216e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.780588e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.513525e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0\n",
       "count  4.420000e+02\n",
       "mean  -3.665243e-15\n",
       "std    5.353672e+01\n",
       "min   -1.558268e+02\n",
       "25%   -3.853589e+01\n",
       "50%   -2.278216e-01\n",
       "75%    3.780588e+01\n",
       "max    1.513525e+02"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(residuals).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "6914211f-7430-4da9-915b-21bb09b08053",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_squared:  0.5177484222203499\n"
     ]
    }
   ],
   "source": [
    "# R_squared 계산\n",
    "SSE = (residuals**2).sum()\n",
    "SST = ((dlr_test_y - dlr_test_y.mean())**2).sum()\n",
    "R_squared = 1 - (SSE/SST)\n",
    "print('R_squared: ', R_squared)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d1640e3f-9e1a-4605-b568-ad86898f5687",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2859.6963475867497\n",
      "53.47612876402657\n",
      "score =  0.5177484222203499\n",
      "mean_squared_error = 2859.6963475867497\n",
      "RMSE = 53.47612876402657\n"
     ]
    }
   ],
   "source": [
    "# 모델 평가 MSE & RMSE\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "\n",
    "mse = mean_squared_error(dlr_test_y, prediction)\n",
    "print(mse)\n",
    "rmse = sqrt(mse)\n",
    "print(rmse)\n",
    "\n",
    "print('score = ', dlr_model.score(X=dlr_train_X_df, y=dlr_test_y))\n",
    "print('mean_squared_error =', mse)\n",
    "print('RMSE =', rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "64893abe-83f7-4702-bcae-9a1d2d300642",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 다중선형회귀 모델 - 통계적 데이터 분석\n",
    "\n",
    "s_d_model = ols('target ~ age+sex+bmi+bp+s1+s2+s3+s4+s5+s6', df)\n",
    "m_result = s_d_model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "bbfe6f95-faf8-41f0-8a4a-4807924659e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>target</td>      <th>  R-squared:         </th> <td>   0.518</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.507</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   46.27</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 20 Jul 2022</td> <th>  Prob (F-statistic):</th> <td>3.83e-62</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>12:52:54</td>     <th>  Log-Likelihood:    </th> <td> -2386.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   442</td>      <th>  AIC:               </th> <td>   4794.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   431</td>      <th>  BIC:               </th> <td>   4839.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    10</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>  152.1335</td> <td>    2.576</td> <td>   59.061</td> <td> 0.000</td> <td>  147.071</td> <td>  157.196</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>       <td>  -10.0099</td> <td>   59.749</td> <td>   -0.168</td> <td> 0.867</td> <td> -127.446</td> <td>  107.426</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sex</th>       <td> -239.8156</td> <td>   61.222</td> <td>   -3.917</td> <td> 0.000</td> <td> -360.147</td> <td> -119.484</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bmi</th>       <td>  519.8459</td> <td>   66.533</td> <td>    7.813</td> <td> 0.000</td> <td>  389.076</td> <td>  650.616</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bp</th>        <td>  324.3846</td> <td>   65.422</td> <td>    4.958</td> <td> 0.000</td> <td>  195.799</td> <td>  452.970</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>s1</th>        <td> -792.1756</td> <td>  416.680</td> <td>   -1.901</td> <td> 0.058</td> <td>-1611.153</td> <td>   26.802</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>s2</th>        <td>  476.7390</td> <td>  339.030</td> <td>    1.406</td> <td> 0.160</td> <td> -189.620</td> <td> 1143.098</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>s3</th>        <td>  101.0433</td> <td>  212.531</td> <td>    0.475</td> <td> 0.635</td> <td> -316.684</td> <td>  518.770</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>s4</th>        <td>  177.0632</td> <td>  161.476</td> <td>    1.097</td> <td> 0.273</td> <td> -140.315</td> <td>  494.441</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>s5</th>        <td>  751.2737</td> <td>  171.900</td> <td>    4.370</td> <td> 0.000</td> <td>  413.407</td> <td> 1089.140</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>s6</th>        <td>   67.6267</td> <td>   65.984</td> <td>    1.025</td> <td> 0.306</td> <td>  -62.064</td> <td>  197.318</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.506</td> <th>  Durbin-Watson:     </th> <td>   2.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.471</td> <th>  Jarque-Bera (JB):  </th> <td>   1.404</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.017</td> <th>  Prob(JB):          </th> <td>   0.496</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.726</td> <th>  Cond. No.          </th> <td>    227.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                 target   R-squared:                       0.518\n",
       "Model:                            OLS   Adj. R-squared:                  0.507\n",
       "Method:                 Least Squares   F-statistic:                     46.27\n",
       "Date:                Wed, 20 Jul 2022   Prob (F-statistic):           3.83e-62\n",
       "Time:                        12:52:54   Log-Likelihood:                -2386.0\n",
       "No. Observations:                 442   AIC:                             4794.\n",
       "Df Residuals:                     431   BIC:                             4839.\n",
       "Df Model:                          10                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept    152.1335      2.576     59.061      0.000     147.071     157.196\n",
       "age          -10.0099     59.749     -0.168      0.867    -127.446     107.426\n",
       "sex         -239.8156     61.222     -3.917      0.000    -360.147    -119.484\n",
       "bmi          519.8459     66.533      7.813      0.000     389.076     650.616\n",
       "bp           324.3846     65.422      4.958      0.000     195.799     452.970\n",
       "s1          -792.1756    416.680     -1.901      0.058   -1611.153      26.802\n",
       "s2           476.7390    339.030      1.406      0.160    -189.620    1143.098\n",
       "s3           101.0433    212.531      0.475      0.635    -316.684     518.770\n",
       "s4           177.0632    161.476      1.097      0.273    -140.315     494.441\n",
       "s5           751.2737    171.900      4.370      0.000     413.407    1089.140\n",
       "s6            67.6267     65.984      1.025      0.306     -62.064     197.318\n",
       "==============================================================================\n",
       "Omnibus:                        1.506   Durbin-Watson:                   2.029\n",
       "Prob(Omnibus):                  0.471   Jarque-Bera (JB):                1.404\n",
       "Skew:                           0.017   Prob(JB):                        0.496\n",
       "Kurtosis:                       2.726   Cond. No.                         227.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m_result.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "7e02c706-a05c-4f93-89a9-8291305687a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Intercept', 'age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']\n",
      "1.2173065138070078\n"
     ]
    },
    {
     "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>컬럼</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>1.217307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sex</td>\n",
       "      <td>1.278071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bmi</td>\n",
       "      <td>1.509437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bp</td>\n",
       "      <td>1.459428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>s1</td>\n",
       "      <td>59.202510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>s2</td>\n",
       "      <td>39.193370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>s3</td>\n",
       "      <td>15.402156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>s4</td>\n",
       "      <td>8.890986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>s5</td>\n",
       "      <td>10.075967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>s6</td>\n",
       "      <td>1.484623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    컬럼        VIF\n",
       "0  age   1.217307\n",
       "1  sex   1.278071\n",
       "2  bmi   1.509437\n",
       "3   bp   1.459428\n",
       "4   s1  59.202510\n",
       "5   s2  39.193370\n",
       "6   s3  15.402156\n",
       "7   s4   8.890986\n",
       "8   s5  10.075967\n",
       "9   s6   1.484623"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 다중공선성 확인\n",
    "## VIF ( 분산 팽창 요인 ) 계산 - 10이 넘어가면 다중공선성 존재함으로 판단\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "# 회귀분석 모델 변수\n",
    "print(s_d_model.exog_names)\n",
    "\n",
    "# bmi의 VIF\n",
    "print(variance_inflation_factor(s_d_model.exog, 1))\n",
    "\n",
    "# 독립변수 VIF\n",
    "pd.DataFrame({'컬럼': column, 'VIF':variance_inflation_factor(s_d_model.exog, i)}\n",
    "             for i, column in enumerate(s_d_model.exog_names)\n",
    "             if column != 'Intercept')    # 절편 제외"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1255255-05a9-4b29-a957-7bfbcf12bb76",
   "metadata": {},
   "source": [
    "계수가 통계적으로 유의미하지 않다면 대처\n",
    "\n",
    "계수가 통계적으로 유의미하다면 VIF가 크더라도 특별히 대처할 필요 없음\n",
    "\n",
    "변수들을 더하거나 빼서 새로운 변수를 만든다\n",
    "- (개념적으로나 이론적으로) 두 예측변수를 더하거나 빼더라도 문제가 없는 경우\n",
    "- 예) 남편의 수입과 아내의 수입이 서로 상관이 높다면, 두 개를 더해 가족 수입이라는 하나의 변수로 투입한다. \n",
    "- 더하거나 빼기 어려운 경우는 변수를 모델에서 제거한다. -- 단, 변수를 제거하는 것은 자료의 다양성을 해치고, 분석하려던 가설이나 이론에 영향을 미칠 수 있기 때문에 가급적 자제"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b2b90301-6669-46ed-b16a-89703331d9ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23c526e6940>]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 잔차 분석\n",
    "\n",
    "## 선형성\n",
    "s_d_res_fit = m_result.predict(df)\n",
    "s_d_residuals = df['target'] - s_d_res_fit\n",
    "\n",
    "# 선형성 시각화 ( red : 잔차 )\n",
    "sns.regplot(s_d_res_fit, s_d_resiuals, lowess=True, line_kws={'color':'red'})\n",
    "plt.plot([s_d_res_fit.min(), s_d_res_fit.max()], [0, 0], '--', color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "61da6d3f-dd4a-46f4-8b70-8cfb9f87c493",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23c527b06a0>]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 정규성 확인 (QQplot, 점 값이 선을 따라 배치되는 것이 이상적 )\n",
    "s_d_sr = stats.zscore(s_d_residuals)\n",
    "(s_d_x, s_d_y), _ = stats.probplot(s_d_sr)\n",
    "\n",
    "# 정규성 시각화\n",
    "sns.scatterplot(s_d_x, s_d_y)\n",
    "plt.plot([-3, 3], [-3, 3], '--', color='grey')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "7ab13d3b-ec93-459f-920f-74c71db2cb70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 잔차의 등분산성\n",
    "sns.regplot(s_d_res_fit, np.sqrt(np.abs(s_d_sr)), lowess=True, line_kws={'color':'red'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "b598ff0c-4e1f-4659-bab1-57ba1883eab5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "382    0.024475\n",
       "123    0.021348\n",
       "169    0.019559\n",
       "304    0.018944\n",
       "92     0.018133\n",
       "dtype: float64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 극단값 확인\n",
    "from statsmodels.stats.outliers_influence import OLSInfluence\n",
    "cd, _ = OLSInfluence(m_result).cooks_distance    # Cook's Distance\n",
    "cd.sort_values(ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "665a2c41-67ee-4c6a-82f7-4b9d5a5769b2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}