machine_learning/train_test_split_6/train_test_split.ipynb

{
 "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": "iVBORw0KGgoAAAANSUhEUgAAAjkAAAGdCAYAAADwjmIIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA3NklEQVR4nO3df3RU9Z3/8VcSmEkQJhgwP5AAAVowhh8GSJxq2VoiASOrq92CRTYK6EIDK8SDwNaS0m4Lxd1TqCDaumvcReRHT7GFSGg2mLiWYDA0QkCoP2JDS4ZQMTMBCYHM5/sHzf0yJiiBkB93no9z5tS59507n/vptPPy3vv5fEKMMUYAAAA2E9rRDQAAALgeCDkAAMCWCDkAAMCWCDkAAMCWCDkAAMCWCDkAAMCWCDkAAMCWCDkAAMCWunV0AzqS3+/X8ePH1atXL4WEhHR0cwAAwBUwxqiurk79+vVTaOjlr9cEdcg5fvy44uPjO7oZAADgKhw7dkz9+/e/7P6gDjm9evWSdLGTXC5XB7cGAABcCZ/Pp/j4eOt3/HKCOuQ03aJyuVyEHAAAupgve9SEB48BAIAtEXIAAIAtEXIAAIAtEXIAAIAtEXIAAIAtEXIAAIAtEXIAAIAtEXIAAIAtBfVkgNdDo9+otPKUaurqFd0rXCkJUQoLZV0sAADa2zVdyVm5cqVCQkK0YMECa1t9fb2ysrLUp08f9ezZUw8++KBOnDgR8HdVVVXKyMhQjx49FB0drUWLFunChQsBNUVFRUpOTpbT6dTQoUOVm5vb7PPXrVunQYMGKTw8XKmpqSotLb2W07lm+RXVuvOnu/XQL/fqiU3leuiXe3XnT3crv6K6Q9sFAEAwuuqQs2/fPr3wwgsaOXJkwPaFCxdq+/bt2rp1q4qLi3X8+HE98MAD1v7GxkZlZGSooaFBe/bs0csvv6zc3FwtW7bMqqmsrFRGRobuuusulZeXa8GCBZo9e7Z27dpl1WzevFnZ2dnKycnR/v37NWrUKKWnp6umpuZqT+ma5FdUa+6G/ar21gds93jrNXfDfoIOAADtLMQYY1r7R6dPn1ZycrKee+45/du//ZtGjx6t1atXy+v16qabbtLGjRv1rW99S5J05MgR3XLLLSopKdHtt9+unTt36t5779Xx48cVExMjSXr++ee1ePFinTx5Ug6HQ4sXL1ZeXp4qKiqsz5w2bZpqa2uVn58vSUpNTdW4ceO0du1aSZLf71d8fLzmz5+vJUuWXNF5+Hw+RUZGyuv1XtPaVY1+ozt/urtZwGkSIik2MlxvLf4mt64AALhGV/r7fVVXcrKyspSRkaG0tLSA7WVlZTp//nzA9uHDh2vAgAEqKSmRJJWUlGjEiBFWwJGk9PR0+Xw+HTp0yKr5/LHT09OtYzQ0NKisrCygJjQ0VGlpaVZNS86dOyefzxfwagullacuG3AkyUiq9tartPJUm3weAAD4cq1+8HjTpk3av3+/9u3b12yfx+ORw+FQ7969A7bHxMTI4/FYNZcGnKb9Tfu+qMbn8+ns2bP69NNP1djY2GLNkSNHLtv2FStWaPny5Vd2oq1QU3f5gHM1dQAA4Nq16krOsWPH9MQTT+iVV15ReHj49WrTdbN06VJ5vV7rdezYsTY5bnSvK+uLK60DAADXrlUhp6ysTDU1NUpOTla3bt3UrVs3FRcX6+c//7m6deummJgYNTQ0qLa2NuDvTpw4odjYWElSbGxss9FWTe+/rMblcikiIkJ9+/ZVWFhYizVNx2iJ0+mUy+UKeLWFlIQoxUWG63JP24RIiou8OJwcAAC0j1aFnAkTJujgwYMqLy+3XmPHjtX06dOtf+7evbsKCwutvzl69KiqqqrkdrslSW63WwcPHgwYBVVQUCCXy6XExESr5tJjNNU0HcPhcGjMmDEBNX6/X4WFhVZNewoLDVHOlItt/3zQaXqfMyWRh44BAGhHrXomp1evXkpKSgrYdsMNN6hPnz7W9lmzZik7O1tRUVFyuVyaP3++3G63br/9dknSxIkTlZiYqBkzZmjVqlXyeDx6+umnlZWVJafTKUmaM2eO1q5dq6eeekozZ87U7t27tWXLFuXl5Vmfm52drczMTI0dO1YpKSlavXq1zpw5o0cfffSaOuRqTUqK0/qHk7V8++GAh5BjI8OVMyVRk5LiOqRdAAAEqzaf8fhnP/uZQkND9eCDD+rcuXNKT0/Xc889Z+0PCwvTjh07NHfuXLndbt1www3KzMzUD3/4Q6smISFBeXl5WrhwodasWaP+/fvrxRdfVHp6ulUzdepUnTx5UsuWLZPH49Ho0aOVn5/f7GHk9jQpKU53J8Yy4zEAAJ3AVc2TYxdtNU8OAABoP9d1nhwAAIDOjpADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsiZADAABsqVUhZ/369Ro5cqRcLpdcLpfcbrd27txp7f/GN76hkJCQgNecOXMCjlFVVaWMjAz16NFD0dHRWrRokS5cuBBQU1RUpOTkZDmdTg0dOlS5ubnN2rJu3ToNGjRI4eHhSk1NVWlpaWtOBQAA2FyrQk7//v21cuVKlZWV6Z133tE3v/lN3XfffTp06JBV89hjj6m6utp6rVq1ytrX2NiojIwMNTQ0aM+ePXr55ZeVm5urZcuWWTWVlZXKyMjQXXfdpfLyci1YsECzZ8/Wrl27rJrNmzcrOztbOTk52r9/v0aNGqX09HTV1NRcS18AAAAbCTHGmGs5QFRUlJ555hnNmjVL3/jGNzR69GitXr26xdqdO3fq3nvv1fHjxxUTEyNJev7557V48WKdPHlSDodDixcvVl5enioqKqy/mzZtmmpra5Wfny9JSk1N1bhx47R27VpJkt/vV3x8vObPn68lS5Zccdt9Pp8iIyPl9XrlcrmusgcAAEB7utLf76t+JqexsVGbNm3SmTNn5Ha7re2vvPKK+vbtq6SkJC1dulSfffaZta+kpEQjRoywAo4kpaeny+fzWVeDSkpKlJaWFvBZ6enpKikpkSQ1NDSorKwsoCY0NFRpaWlWzeWcO3dOPp8v4AUAAOypW2v/4ODBg3K73aqvr1fPnj21bds2JSYmSpK+853vaODAgerXr58OHDigxYsX6+jRo/r1r38tSfJ4PAEBR5L13uPxfGGNz+fT2bNn9emnn6qxsbHFmiNHjnxh21esWKHly5e39pQBAEAX1OqQM2zYMJWXl8vr9epXv/qVMjMzVVxcrMTERD3++ONW3YgRIxQXF6cJEyboww8/1JAhQ9q04Vdj6dKlys7Ott77fD7Fx8d3YIsAAMD10uqQ43A4NHToUEnSmDFjtG/fPq1Zs0YvvPBCs9rU1FRJ0gcffKAhQ4YoNja22SioEydOSJJiY2Ot/2zadmmNy+VSRESEwsLCFBYW1mJN0zEux+l0yul0tuJsAQBAV3XN8+T4/X6dO3euxX3l5eWSpLi4OEmS2+3WwYMHA0ZBFRQUyOVyWbe83G63CgsLA45TUFBgPffjcDg0ZsyYgBq/36/CwsKAZ4MAAEBwa9WVnKVLl2ry5MkaMGCA6urqtHHjRhUVFWnXrl368MMPtXHjRt1zzz3q06ePDhw4oIULF2r8+PEaOXKkJGnixIlKTEzUjBkztGrVKnk8Hj399NPKysqyrrDMmTNHa9eu1VNPPaWZM2dq9+7d2rJli/Ly8qx2ZGdnKzMzU2PHjlVKSopWr16tM2fO6NFHH23DrgEAAF2aaYW
      "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": "iVBORw0KGgoAAAANSUhEUgAAAjkAAAGdCAYAAADwjmIIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAw00lEQVR4nO3df1RU953/8deADhBlJkFFsKAS3dUg/lhUcJrUjZGIBj3xxHY1P1zij+TooomSNWibSuwvjTm7Na0/kjR7xB5j1OypySoRy2Igm4pFMUTRapOULHZlxEadISaAztzvHx7m6wgkgMjA5fk4Z04z977n8p570s6r997P52MxDMMQAACAyQQFugEAAIDbgZADAABMiZADAABMiZADAABMiZADAABMiZADAABMiZADAABMiZADAABMqUegGwgkr9erc+fOKTw8XBaLJdDtAACAFjAMQzU1NRowYICCgpq/XtOtQ865c+cUGxsb6DYAAEAbnD17VjExMc3u79YhJzw8XNL1k2Sz2QLcDQAAaAm3263Y2Fjf73hzunXIabhFZbPZCDkAAHQx3/aoCQ8eAwAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAUyLkAAAAU+rWkwHeDh6voZKKi6quqVVkeKiS4iIUHMS6WAAAdLRbupKzbt06WSwWLVu2zLettrZWGRkZ6tOnj3r37q1Zs2bp/Pnzfp+rrKxUWlqa7rjjDkVGRmrFihW6du2aX01hYaESExMVEhKioUOHKicnp9Hf37RpkwYPHqzQ0FAlJyerpKTkVr7OLcsrr9J9Lx3Uo785rGd3lunR3xzWfS8dVF55VUD7AgCgO2pzyDly5Ihee+01jRo1ym/78uXLtXfvXr399tsqKirSuXPn9Mgjj/j2ezwepaWlqb6+XocOHdK2bduUk5Oj1atX+2oqKiqUlpamSZMmqaysTMuWLdPChQt14MABX82uXbuUmZmp7OxsHTt2TKNHj1Zqaqqqq6vb+pVuSV55lRZvP6YqV63fdqerVou3HyPoAADQwSyGYRit/dCXX36pxMREbd68WT/72c80ZswYbdiwQS6XS/369dOOHTv0/e9/X5J0+vRp3XPPPSouLtaECRO0f/9+TZ8+XefOnVP//v0lSa+++qqysrJ04cIFWa1WZWVlKTc3V+Xl5b6/OWfOHF2+fFl5eXmSpOTkZI0fP14bN26UJHm9XsXGxmrp0qVauXJli76H2+2W3W6Xy+W6pbWrPF5D9710sFHAaWCRFGUP1YdZD3DrCgCAW9TS3+82XcnJyMhQWlqaUlJS/LaXlpbq6tWrftuHDx+ugQMHqri4WJJUXFyskSNH+gKOJKWmpsrtduvkyZO+mpuPnZqa6jtGfX29SktL/WqCgoKUkpLiq2lKXV2d3G6336s9lFRcbDbgSJIhqcpVq5KKi+3y9wAAwLdr9YPHO3fu1LFjx3TkyJFG+5xOp6xWq+68806/7f3795fT6fTV3BhwGvY37PumGrfbra+//lqXLl2Sx+Npsub06dPN9r527VqtWbOmZV+0Faprmg84bakDAAC3rlVXcs6ePatnn31Wb775pkJDQ29XT7fNqlWr5HK5fK+zZ8+2y3Ejw1t2LlpaBwAAbl2rQk5paamqq6uVmJioHj16qEePHioqKtKvfvUr9ejRQ/3791d9fb0uX77s97nz588rKipKkhQVFdVotFXD+2+rsdlsCgsLU9++fRUcHNxkTcMxmhISEiKbzeb3ag9JcRGKtoequadtLJKi7deHkwMAgI7RqpAzefJknThxQmVlZb7XuHHj9Pjjj/v+uWfPniooKPB95syZM6qsrJTD4ZAkORwOnThxwm8UVH5+vmw2m+Lj4301Nx6joabhGFarVWPHjvWr8Xq9Kigo8NV0pOAgi7JnXO/95qDT8D57RjwPHQMA0IFa9UxOeHi4EhIS/Lb16tVLffr08W1fsGCBMjMzFRERIZvNpqVLl8rhcGjChAmSpClTpig+Pl5z587V+vXr5XQ69cILLygjI0MhISGSpEWLFmnjxo16/vnnNX/+fB08eFC7d+9Wbm6u7+9mZmYqPT1d48aNU1JSkjZs2KArV65o3rx5t3RC2mpqQrS2PJGoNXtP+T2EHGUPVfaMeE1NiA5IXwAAdFftPuPxL3/5SwUFBWnWrFmqq6tTamqqNm/e7NsfHBysffv2afHixXI4HOrVq5fS09P1k5/8xFcTFxen3NxcLV++XK+88opiYmL0xhtvKDU11Vcze/ZsXbhwQatXr5bT6dSYMWOUl5fX6GHkjjQ1IVoPxkcx4zEAAJ1Am+bJMYv2micHAAB0nNs6Tw4AAEBnR8gBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACmRMgBAACm1KqQs2XLFo0aNUo2m002m00Oh0P79+/37b///vtlsVj8XosWLfI7RmVlpdLS0nTHHXcoMjJSK1as0LVr1/xqCgsLlZiYqJCQEA0dOlQ5OTmNetm0aZMGDx6s0NBQJScnq6SkpDVfBQAAmFyrQk5MTIzWrVun0tJSHT16VA888IAefvhhnTx50lfz1FNPqaqqyvdav369b5/H41FaWprq6+t16NAhbdu2TTk5OVq9erWvpqKiQmlpaZo0aZLKysq0bNkyLVy4UAcOHPDV7Nq1S5mZmcrOztaxY8c0evRopaamqrq6+lbOBQAAMBGLYRjGrRwgIiJCL7/8shYsWKD7779fY8aM0YYNG5qs3b9/v6ZPn65z586pf//+kqRXX31VWVlZunDhgqxWq7KyspSbm6vy8nLf5+bMmaPLly8rLy9PkpScnKzx48dr48aNkiSv16vY2FgtXbpUK1eubHHvbrdbdrtdLpdLNputjWcAAAB0pJb+frf5mRyPx6OdO3fqypUrcjgcvu1vvvmm+vbtq4SEBK1atUpfffWVb19xcbFGjhzpCziSlJqaKrfb7bsaVFxcrJSUFL+/lZqaquLiYklSfX29SktL/WqCgoKUkpLiq2lOXV2d3G633wsAAJhTj9Z+4MSJE3I4HKqtrVXv3r21Z88excfHS5Iee+wxDRo0SAMGDNDx48eVlZWlM2fO6He/+50kyel0+gUcSb73TqfzG2vcbre+/vprXbp0SR6Pp8ma06dPf2Pva9eu1Zo1a1r7lQEAQBfU6pAzbNgwlZWVyeVy6T//8z+Vnp6uoqIixcfH6+mnn/bVjRw5UtHR0Zo8ebI+++wzDRkypF0bb4tVq1YpMzPT997tdis2NjaAHQEAgNul1SHHarVq6NChkqSxY8fqyJEjeuWVV/Taa681qk1OTpYkffrppxoyZIiioqIajYI6f/68JCkqKsr3nw3bbqyx2WwKCwtTcHCwgoODm6xpOEZzQkJCFBIS0opvCwAAuqpbnifH6/Wqrq6uyX1lZWWSpOjoaEmSw+HQiRMn/EZB5efny2az+W55ORwOFRQU+B0nPz/f99yP1WrV2LFj/Wq8Xq8KCgr8ng0CAADdW6uu5KxatUrTpk3TwIEDVVNTox07dqiwsFAHDhzQZ599ph07duihhx5Snz59dPz4cS1fvlwTJ07UqFGjJElTpkxRfHy85s6dq/Xr18vpdOqFF15QRkaG7wrLokWLtHHjRj3//POaP3++Dh48qN27dys3N9fXR2ZmptLT0zVu3DglJSVpw4YNunLliubNm9eOpwYAAHRpRivMnz/fGDRokGG
      "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
}