05_Regression/regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 선형 회귀 (Linear Regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: [[2.50779446]]\n",
      "Intercept: [4.64634325]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "\n",
    "# 예제 데이터 생성\n",
    "X = np.random.rand(100, 1) * 10  # 100개의 랜덤 데이터\n",
    "y = 2.5 * X + 5 + np.random.randn(100, 1) * 2  # y = 2.5x + 5 + 잡음\n",
    "\n",
    "# 데이터 분할\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# 선형 회귀 모델 학습\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# 예측\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print(\"Coefficients:\", model.coef_)\n",
    "print(\"Intercept:\", model.intercept_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Squared Error: 4061.83\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# 데이터 로드\n",
    "diabetes = datasets.load_diabetes()\n",
    "X = diabetes.data[:, np.newaxis, 2]  # BMI feature만 사용\n",
    "y = diabetes.target\n",
    "\n",
    "# 데이터 분할\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# 선형 회귀 모델 학습\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# 예측 및 평가\n",
    "y_pred = model.predict(X_test)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(f\"Mean Squared Error: {mse:.2f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 로지스틱 회귀 (Logistic Regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 예제 데이터 생성\n",
    "X = np.random.rand(100, 1) * 10  # 100개의 랜덤 데이터\n",
    "y = (X > 5).astype(int).ravel()  # X가 5보다 크면 1, 아니면 0\n",
    "\n",
    "# 데이터 분할\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "\n",
    "# 로지스틱 회귀 모델 학습\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# 예측\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print(\"Coefficients:\", model.coef_)\n",
    "print(\"Intercept:\", model.intercept_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 95.61%\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# 데이터 로드\n",
    "cancer = datasets.load_breast_cancer()\n",
    "X = cancer.data\n",
    "y = cancer.target\n",
    "\n",
    "# 데이터 분할\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# 로지스틱 회귀 모델 학습\n",
    "model = LogisticRegression(max_iter=10000)  # max_iter를 증가시켜 수렴을 도움\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# 예측 및 평가\n",
    "y_pred = model.predict(X_test)\n",
    "acc = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {acc*100:.2f}%\")\n"
   ]
  }
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