{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d60aff11",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3aafae87",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"multivariable.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5ecbf4af",
   "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>area</th>\n",
       "      <th>age</th>\n",
       "      <th>bashroom</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>88.54</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>93.36</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98.90</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98.58</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>118.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>92.26</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>88.94</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>89.57</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>127.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    area  age  bashroom  price\n",
       "0  88.54    5       1.0  118.0\n",
       "1  93.36    8       1.0  114.0\n",
       "2  98.90   13       2.0  102.0\n",
       "3  98.58    5       2.0  118.4\n",
       "4  92.26    5       2.0   95.0\n",
       "5  88.94    3       1.0  118.0\n",
       "6  89.57   14       NaN  127.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df#数据存在NaN，需要处理"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cab6f22",
   "metadata": {},
   "source": [
    "$$price = ax_1 + bx_2 + cx_3 + m$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4087cc5f",
   "metadata": {},
   "source": [
    "## 1.数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cef313ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "median = df.bashroom.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f59d79bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median#卫生间没有小数，需要向下取整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f49989d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.fillna(np.floor(median))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ee51756b",
   "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>area</th>\n",
       "      <th>age</th>\n",
       "      <th>bashroom</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>88.54</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>93.36</td>\n",
       "      <td>8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98.90</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98.58</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>118.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>92.26</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>88.94</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>118.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>89.57</td>\n",
       "      <td>14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>127.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    area  age  bashroom  price\n",
       "0  88.54    5       1.0  118.0\n",
       "1  93.36    8       1.0  114.0\n",
       "2  98.90   13       2.0  102.0\n",
       "3  98.58    5       2.0  118.4\n",
       "4  92.26    5       2.0   95.0\n",
       "5  88.94    3       1.0  118.0\n",
       "6  89.57   14       1.0  127.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e41d72fc",
   "metadata": {},
   "source": [
    "## 2.训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "39295dee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(df[[\"area\", \"age\", \"bashroom\"]].values, df.price.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1b812cb",
   "metadata": {},
   "source": [
    "## 3.模型测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "636c57cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = model.predict(np.array([[105, 2, 1]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4928085a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([134.39931808])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d6dea269",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.03253137,   0.04233053, -20.81194367])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7cd87a3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.032531365235341, 0.042330533476314436, -20.81194367389132)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_[0], model.coef_[1], model.coef_[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a64c1cc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "price = model.coef_[0] * 105 + model.coef_[1] * 2 + model.coef_[2]* 1 + model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c02a4f36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "134.3993180794738"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "862d7fab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price == pred"
   ]
  }
 ],
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