{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7d1db536",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "76b100fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"carprices.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "806166f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(\"Car Model\", axis=\"columns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1dfdbd5a",
   "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>Mileage</th>\n",
       "      <th>Sell Price($)</th>\n",
       "      <th>Age(yrs)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>69000</td>\n",
       "      <td>18000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>35000</td>\n",
       "      <td>34000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57000</td>\n",
       "      <td>26100</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22500</td>\n",
       "      <td>40000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46000</td>\n",
       "      <td>31500</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>59000</td>\n",
       "      <td>29400</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>52000</td>\n",
       "      <td>32000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>72000</td>\n",
       "      <td>19300</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>91000</td>\n",
       "      <td>12000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>67000</td>\n",
       "      <td>22000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>83000</td>\n",
       "      <td>20000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>79000</td>\n",
       "      <td>21000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>59000</td>\n",
       "      <td>33000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Mileage  Sell Price($)  Age(yrs)\n",
       "0     69000          18000         6\n",
       "1     35000          34000         3\n",
       "2     57000          26100         5\n",
       "3     22500          40000         2\n",
       "4     46000          31500         4\n",
       "5     59000          29400         5\n",
       "6     52000          32000         5\n",
       "7     72000          19300         6\n",
       "8     91000          12000         8\n",
       "9     67000          22000         6\n",
       "10    83000          20000         7\n",
       "11    79000          21000         7\n",
       "12    59000          33000         5"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d76d64b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "acdf9d06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1bb29b183c8>"
      ]
     },
     "execution_count": 9,
     "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": [
    "plt.scatter(df[\"Mileage\"], df[\"Sell Price($)\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1627f829",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1bb28350d08>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df[\"Age(yrs)\"], df[\"Sell Price($)\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "932ff8d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop(\"Sell Price($)\", axis=\"columns\")\n",
    "y = df[\"Sell Price($)\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a3714dd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f7263cf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=10)#random_state可以保证每次运行结果都一样，否则每次都是随机划分。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "48944228",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 3)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_train), len(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "524b6f13",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "29f0bfaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e60d2dd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dcefda82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([41842.49106079, 22531.68057211, 18423.93325387])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ecefdb9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9224816911971742"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(x_test, y_test)"
   ]
  }
 ],
 "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}