{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ae02d6c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18cfa351",
   "metadata": {},
   "source": [
    "<img src='linear_regression.png' width=50%>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7ac8c6d",
   "metadata": {},
   "source": [
    "$$最小化\\sum^n_{i=1}(d_i)^2$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6729b5ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"housing_price.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "717fda9f",
   "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>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>86.45</td>\n",
       "      <td>117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91.57</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85.52</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>103.60</td>\n",
       "      <td>146.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>105.25</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>99.00</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>87.95</td>\n",
       "      <td>91.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     area  price\n",
       "0   86.45  117.0\n",
       "1   91.57   98.0\n",
       "2   85.52  114.0\n",
       "3  103.60  146.0\n",
       "4  105.25  106.0\n",
       "5   99.00  109.0\n",
       "6   87.95   91.5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8bae038f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x129771424c8>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.xlabel(\"area\")\n",
    "plt.ylabel(\"price\")\n",
    "plt.scatter(df.area, df.price, c='r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1dbdd68d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7, 1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"area\"]].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c147b635",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = linear_model.LinearRegression()\n",
    "model.fit(df[[\"area\"]].values, df.price.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cd1a7008",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.83809367]), 32.70161672527138)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_, model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d7e74d63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([115.93269948])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(np.array([[99.31]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4fffcc21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([115.93269948])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price = model.coef_ * 99.31 + model.intercept_\n",
    "price"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "410fa831",
   "metadata": {},
   "source": [
    "画出这条预测线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a5fe689e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\envs\\stark-lin\\lib\\site-packages\\numpy\\core\\shape_base.py:65: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  ary = asanyarray(ary)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12978a40ec8>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.xlabel(\"area\")\n",
    "plt.ylabel(\"price\")\n",
    "plt.scatter(df.area, df.price, c='r')\n",
    "plt.plot([0, 111],[model.intercept_, model.predict(np.array([[111]]))])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f7253975",
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = pd.read_csv(\"predict.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "a27fe359",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area\n",
       "0   150\n",
       "1   128\n",
       "2   111\n",
       "3   138"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a7ac643a",
   "metadata": {},
   "outputs": [],
   "source": [
    "preds[\"predicted\"] = model.predict(preds[[\"area\"]].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "bef47aea",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "   area   predicted\n",
       "0   150  158.415668\n",
       "1   128  139.977607\n",
       "2   111  125.730015\n",
       "3   138  148.358544"
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     "execution_count": 51,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e29158e1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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