House cost predictor using Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_floor=np.arange(1,6)\n",
    "house_floor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "l=[5000,8000,12000,20000,28000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5000, 10000, 15000, 20000, 25000])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b=np.arange(5000,30000,5000)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5000,  8000, 12000, 20000, 28000])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_price=np.array(l)\n",
    "house_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(house_floor,b)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x225654f7b38>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(house_floor,house_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_floor.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4],\n",
       "       [5]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_floor=house_floor.reshape(-1,1)\n",
    "house_floor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_floor.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(house_floor,house_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3000., 84200.])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict([[1],[15]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4],\n",
       "       [5]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_floor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3],\n",
       "       [ 6],\n",
       "       [ 9],\n",
       "       [12],\n",
       "       [15]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_floor*3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  5000,   8000,  12000,  20000,  28000],\n",
       "       [ 10000,  16000,  24000,  40000,  56000],\n",
       "       [ 15000,  24000,  36000,  60000,  84000],\n",
       "       [ 20000,  32000,  48000,  80000, 112000],\n",
       "       [ 25000,  40000,  60000, 100000, 140000]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_floor*house_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4],\n",
       "       [5]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_floor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "house_floor=house_floor.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_floor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5000,  8000, 12000, 20000, 28000])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  5000,  16000,  36000,  80000, 140000])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_floor*house_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_model(x,y):\n",
    "    global m,c\n",
    "    m=(((np.mean(x)*np.mean(y))-(np.mean(x*y)))/((np.mean(x)**2)-(np.mean(x*x))))\n",
    "    c=np.mean(y)-(np.mean(x)*m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(x):\n",
    "    y=[]\n",
    "    for p in x:\n",
    "        y.append(p*m+c)\n",
    "    return np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_model(house_floor,house_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5800.0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2800.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3000., 84200.])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PREDICT=[1,15]\n",
    "P=predict(PREDICT)\n",
    "P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EI7q39juW1DEqCJEw4pwjZWkud72bwf7CEv50Rk+uPr0nURFauirVTwUhEibW7zzA1Jlp/C97B0O7tuTeiwfQq30zv2NJHaaCEAlxxaVlPPO/tTzywUoiGjTgznH9+eXxXWigpatSw1QQIiHsm415TEoJkLl5L6P6tedvF/anQ/Mov2NJPaGCEAlBBwpLeOj9LGZ8to62zRrz1K+GMrp/B79jST2jghAJMR+t2Mqts9LZtCefXw3vyk2jexMbpaWrUvtUECIhYtu+Av72dgbvLt9Mr/ZNefP3JzC0ayu/Y0k9poIQ8ZlzjtdTN3L3u5kUFJdxw9m9+N2pPYhspEt/ir9UECI+Wr19P1NSAixeu4vh3Vpxz8UD6NG2qd+xRAAVhIgvikrKeGrRav75UTZREQ24/ycDuHRoZy1dlZCighCpZUvW72LSWwFWbdvP+cd15LYLkmjXTEtXJfSoIERqyd6CYh6Yt4KXFm+gU/Nonr0imTP6tPc7lshRqSBEasG8tC3cPieN7fsKuXJkN244pxdNGuvbT0KbvkJFatCWPQXcPieN+elb6dsxlumXJTOwcwu/Y4lUiQpCpAaUlTleWrye++dlUVxaxqRz+3DVSd2IaKilqxI+VBAi1Wzl1n1MTgmwZP1uTurZhrsv6k/X1k38jiVyzFQQItWkoLiUxz/O5qlFq2nauBEP/3QgFw2O06U/JWypIESqwRdrdjIlJcCaHQe4eHAcU8/rS+umjf2OJfKjqCBEfoQ9B4u5971MXv1qI51bRfOfq47n5MS2fscSqRYqCJEfwDnHO8s387e3M9h9sIjfndqd68/sRXSkLv0pdYcKQuQY5eblc+usND5asY3j4psz49fD6Nepud+xRKqdCkKkikrLHDM+W8dD72cBcOv5SVwxMoGGev8kqaOqvCjbzBqa2TIze8e7383MFpvZKjN7zcwivfHG3v1s7/GECs8x2RvPMrNRFcZHe2PZZjap+j49keqRsWkvFz3xKdPeyeD4bq14/8+ncNVJ3VQOUqcdy6t2rgMyK9y/H/iHcy4R2A1c5Y1fBex2zvUE/uHNw8ySgPFAP2A08IRXOg2Bx4FzgSTg595cEd/lF5Vy33sruOCf/2NTXj7/9/PBPHfFMOJbxvgdTaTGVakgzCweOA942rtvwBnAm96UGcA47/ZY7z7e42d688cCrzrnCp1za4Fs4HjvI9s5t8Y5VwS86s0V8dX/Vu1g1COf8NSi1VwyJJ4P/nIqFwzspNc1SL1R1XMQjwB/BZp591sDec65Eu9+DhDn3Y4DNgI450rMbI83Pw74osJzVtxm4xHjw4OFMLOJwESALl26VDG6yLHZdaCIu97NIGVpLt3bNOGV347ghB6t/Y4lUusqLQgzOx/Y5pxbYmanHRoOMtVV8tjRxoPtxbggYzjnpgPTAZKTk4POEfmhnHPM+jqXO9/JZG9+Mdee0ZNrTu9JVISWrkr9VJU9iBOBC81sDBAFxFK+R9HCzBp5exHxwCZvfg7QGcgxs0ZAc2BXhfFDKm5ztHGRWrFh50Gmzgrw31U7GNylBfddfBy9OzSrfEOROqzScxDOucnOuXjnXALlJ5k/cs79EvgYuMSbNgGY7d2e493He/wj55zzxsd7q5y6AYnAl8BXQKK3KirS+zvmVMtnJ1KJktIy/rVoNec8sohlG/K4c2w/3vr9SJWDCD/udRA3A6+a2V3AMuAZb/wZ4D9mlk35nsN4AOdcupm9DmQAJcA1zrlSADP7IzAfaAg865xL/xG5RKpkeU4ek94KkLF5L2cntWfa2H50bB7tdyyRkGHlv9yHn+TkZJeamup3DAlDBwpLeHjBSp77dC1tmjZm2tj+jO7fwe9YIjXOzJY455KrOl+vpJZ65eMV27hlVhq5efn8akQX/jq6D7FREX7HEglJKgipF7bvK2TaOxm8/c0merZrypu/P4HkhFZ+xxIJaSoIqdOcc7yRmsPdczPJLyrlL2f34nendqdxIy1dFamMCkLqrDXb9zNlZoAv1uzi+IRW3HPxAHq2a+p3LJGwoYKQOqeopIzpn6zmsY+yiWrUgPsuHsBPkzvTQG+sJ3JMVBBSpyxZv5spKQGytu7jvOM6cvsFSbRrFuV3LJGwpIKQOmFfQTEPzs/iP1+sp2NsFE9fnsxZSe39jiUS1lQQEvbeT9/CbbPT2bqvgCtGJnDDOb1p2lhf2iI/lr6LJGxt3VvA7bPTmZe+hT4dmvHUZUMZ1LmF37FE6gwVhISdsjLHy19u4P73VlBUWsbNo/vwm5O7EdHwWK5/JSKVUUFIWFm1dR+TUwKkrt/NiT1bc/e4ASS0aeJ3LJE6SQUhYaGwpJTHP17NkwuzadK4EQ9dOpCfDInT1d1EapAKQkLe4jU7mTwzwJrtB7hocBy3nNeX1k0b+x1LpM5TQUjI2nOwmPvmZfLKlxuJbxnNjF8fz6m92vodS6TeUEFIyHHOMTewhdvnpLP7YBG/O6U7152VSEykvlxFapO+4ySkbMrL59ZZaXy4Yhv942J5/sph9I9r7ncskXpJBSEhobTM8cLn63hofhZlDm45ry9XjEygkZauivhGBSG+y9y8l0kpAb7ZmMepvdpy17j+dG4V43cskXpPBSG+KSgu5dEPV/HvT9bQPDqCR8cP4sKBnbR0VSREqCCk2s1alsuD87PYlJdPpxbR3DSqN+MGx31rzqfZO5gyM8D6nQf5aXI8U8b0pUVMpE+JRSQYFYRUq1nLcpmcEiC/uBSA3Lx8JqcEABg3OI7dB4q4691M3lqaQ0LrGF7+7XBG9mjjZ2QROQoVhFSrB+dnHS6HQ/KLS3lg3goApr2Twd78Yq45vQfXnpFIVIQu/SkSqlQQUq025eUHH99TwPWvfc2gzi247ycD6NMhtpaTicixUkFIterUIprcICVhwB0X9uNXI7rSUJf+FAkLWmQu1eqmUb2JPuKwUQOD2y5IYsLIBJWDSBhRQUi1Oqdfe4Z3a3X4fquYSP5+6UCuPLGbj6lE5IfQISapNh9nbeOWmWnk5uXzi+FduHl0H5pHR/gdS0R+IBWE/Gg79hcy7e0M5nyziZ7tmvLG709gWEKryjcUkZCmgpAfzDnHG0tyuPvdTPKLSrn+rET+cFoPGjfS0lWRukAFIT/I2h0HmJIS4PM1OxmW0JJ7Lx5Az3bN/I4lItVIBSHHpLi0jOmfrOHRD1fRuFED7rloAOOHdaaBVieJ1DkqCKmypRt2M/mtAFlb9zFmQAfuuKAf7WKj/I4lIjVEBSGV2l9YwoPzVvDCF+vpEBvFvy9P5uyk9n7HEpEapoKQ77UgYyu3zU5jy94CJpyQwI2jetO0sb5sROoDfadLUNv2FnD7nHTeS9tCnw7NeOKXQxjcpaXfsUSkFqkg5FvKyhyvfLWB+95bQWFJGTeN6s3EU7oToUt/itQ7lX7Xm1lnM/vYzDLNLN3MrvPGW5nZAjNb5f3Z0hs3M3vMzLLNbLmZDanwXBO8+avMbEKF8aFmFvC2ecx0STFfZG/bx8+mf87UmWkMiGvO/OtP4ZrTe6ocROqpquxBlAA3OOeWmlkzYImZLQCuAD50zt1nZpOAScDNwLlAovcxHHgSGG5mrYDbgWTAec8zxzm325szEfgCmAuMBt6rvk9TjmbWslwemLeCTXsKAIiJbMiDlxzHJUPjdelPkXqu0l8NnXObnXNLvdv7gEwgDhgLzPCmzQDGebfHAi+4cl8ALcysIzAKWOCc2+WVwgJgtPdYrHPuc+ecA16o8FxSg2Yty+Wvby4/XA5QfogpomEDlYOIHNu7uZpZAjAYWAy0d85thvISAdp50+KAjRU2y/HGvm88J8i41KA9+cVMmRmgqLTsW+MFJWU8OD/Lp1QiEkqqXBBm1hR4C7jeObf3+6YGGXM/YDxYholmlmpmqdu3b68ssgThnGNuYDNnPbyIg0WlQecc7apwIlK/VKkgzCyC8nJ4yTmX4g1v9Q4P4f25zRvPATpX2Dwe2FTJeHyQ8e9wzk13ziU755Lbtm1blehSwaa8fH77whKufmkp7Zo1pm3TxkHndWoRXcvJRCQUVWUVkwHPAJnOuYcrPDQHOLQSaQIwu8L45d5qphHAHu8Q1HzgHDNr6a14OgeY7z22z8xGeH/X5RWeS6pBaZnj+U/XcvbDi/g0ewdTx/Rl9jUnMvW8vt+5+lt0RENuGtXbp6QiEkqqsorpROAyIGBmX3tjU4D7gNfN7CpgA3Cp99hcYAyQDRwErgRwzu0yszuBr7x505xzu7zbfwCeB6IpX72kFUzVZMWWvUx6K8DXG/M4pVdb7h7Xn86tYgAYN7j8VM+D87PYlJdPpxbR3DSq9+FxEanfrHzhUPhJTk52qampfscIWQXFpTz24Sqmf7KG5tER3HZBEhcO7KTVSSL1mJktcc4lV3W+XkldB32WvYMpMwOs23mQS4bGM3VMX1o2ifQ7loiEGRVEHbL7QBH3zM3kjSU5dG0dw8u/Gc7Inm38jiUiYUoFUQc455jzzSamvZ3Bnvxirj6tB386M5GoCF36U0R+OBVEmNu46yC3zEpj0crtDOzcghcvHkDfjrF+xxKROkAFEaZKSst47tN1PLxgJQ0M7rggictOSKChLv0pItVEBRGG0nL3MCllOWm5ezmrbzumje2vF7eJSLVTQYSRg0UlPPLBKp7531paNYnkiV8O4dz+HbR0VURqhAoiTCxauZ2pMwPk7M7n58d3YdLoPjSPifA7lojUYSqIELdjfyF3vpPB7K830b1tE16bOILh3Vv7HUtE6gEVRIhyzvHmkhzunpvJgcISrjszkatP70HjRlq6KiK1QwURgtbtOMCUmQE+W72T5K4tuffiASS2b8asZbl63yQRqTUqiBBSXFrGv/+7hkc/WEVkwwbcfVF/fj6sCw0aGLOW5TI5JUB+cfk1HHLz8pmcEgBQSYhIjVBBhIhlG3YzOSXAii37OLd/B+64sB/tY6MOP/7g/KzD5XBIfnEpD87PUkGISI1QQfhsf2EJD83PYsbn62jfLIrplw3lnH4dvjPvaFd509XfRKSmqCB89EHGVm6dncaWvQVcPqIrN47qTbOo4EtXO7WIJjdIGegFciJSU6p8TWqpPtv2FnDNS0v5zQupxEZF8NYfRvK3sf2PWg4AN43qrau/iUit0h5ELSorc7yWupF75mZSWFLGTaN689uTuxPZqPKe1tXfRKS2qSBqSfa2/UxJCfDlul2M6N6Key4aQPe2TY/pOcYNjlMhiEitUUHUsMKSUp5cuJonPl5NdGRDHrjkOC4dGq/3TxKRkKeCqCGzluVy17sZ7NhfBMCQLi3412XJtG3W2OdkIiJVo5PUNeCVxRu44Y1vDpcDQObmfXyavcPHVCIix0YFUY2cc8xL28zUWQFKy9y3Hjv0ojYRkXChQ0zVZPOefG6bnc6CjK1HnaMXtYlIONEexI9UWuaY8dk6zn74E/67ajtTxvShU/OooHP1ojYRCSfag/gRVmzZy+SUAMs25HFyYhvuHjeALq1jaNcs6ltvrAd6UZuIhB8VxA9QUFzKPz/K5qlFq4mNjuCRnw1i7KBOh5eu6kVtIlIXqCA8s5bl0uUn51FUUsoNVz961B/on63ewdSZaazdcYCLh8Rxy3lJtGoS+Z15elGbiIQ7FQQcvtbCcyVHv9ZC3sEi7pmbyeupOXRpFcOLVw3npMQ2vmUWEalpKgi+/1oLYwd14u3lm5n2djq7Dxbzh9N68KczEomO1KU/RaRuU0Fw9OWnuXn5XPn8VyzM2s7A+Oa88OvhJHWKreV0IiL+UEFw9GstGPDl2l3cdn4SE0Ym0LCB3j9JROoPvQ6Cb19rYW3LTofH+3aMZcFfTuXXJ3VTOYhIvVM/9yBOO+1bd8cByQdL+U+PYTw9bBwt8vdx86r3Gf/FGmxmhYkLF9ZiSBERf9XPgghiW7t4/p00jp8tX8Ck4pU0Ly30O5KIiK/qZ0EE2RMYAnw8ahxdC/O0pyAigs5BfEvXwjy/I4iIhIyQKQgzG21mWWaWbWaT/M4jIlLfhURBmFlD4HHgXCAJ+LmZJfmbSkSkfguJggCOB7Kdc2ucc0XAq8BYnzOJiNRroVIQccDGCvdzvDHDkX/YAAAFV0lEQVQREfFJqKxiCvYqNPedSWYTgYkAXbp0qf4UWr0kInJYqOxB5ACdK9yPBzYdOck5N905l+ycS27btm2thRMRqY9CpSC+AhLNrJuZRQLjgTk+ZxIRqddC4hCTc67EzP4IzAcaAs8659J9jiUiUq+FREEAOOfmAnP9ziEiIuVC5RCTiIiEGBWEiIgEpYIQEZGgVBAiIhKUCkJERIIy577zguWwYGbbgQPADr+zVKINoZ8RwiNnOGQE5axO4ZARwiNnG6CJc67KrzIO24IAMLNU51yy3zm+TzhkhPDIGQ4ZQTmrUzhkhPDI+UMy6hCTiIgEpYIQEZGgwr0gpvsdoArCISOER85wyAjKWZ3CISOER85jzhjW5yBERKTmhPsehIiI1JCwLAgzG21mWWaWbWaT/M4TjJl1NrOPzSzTzNLN7Dq/Mx2NmTU0s2Vm9o7fWY7GzFqY2ZtmtsL7Nz3B70xHMrM/e//XaWb2iplF+Z0JwMyeNbNtZpZWYayVmS0ws1Xeny39zOhlCpbzQe//fLmZzTSzFqGWscJjN5qZM7M2fmQ7IkvQnGZ2rfezM93MHqjsecKuIMysIfA4cC6QBPzczJL8TRVUCXCDc64vMAK4JkRzAlwHZPodohKPAvOcc32AgYRYXjOLA/4EJDvn+lP+tvXj/U112PPA6CPGJgEfOucSgQ+9+357nu/mXAD0d84dB6wEJtd2qCM8z3czYmadgbOBDbUd6Cie54icZnY6MBY4zjnXD3iosicJu4IAjgeynXNrnHNFwKuUf9IhxTm32Tm31Lu9j/IfaCF3nW0ziwfOA572O8vRmFkscArwDIBzrsg5l+dvqqAaAdFm1giIIchVEf3gnPsE2HXE8Fhghnd7BjCuVkMFESync+5951yJd/cLyq826Zuj/FsC/AP4K0EuleyHo+T8A3Cfc67Qm7OtsucJx4KIAzZWuJ9DCP7grcjMEoDBwGJ/kwT1COVf2GV+B/ke3YHtwHPeobCnzayJ36Eqcs7lUv4b2QZgM7DHOfe+v6m+V3vn3GYo/2UGaOdznqr4NfCe3yGOZGYXArnOuW/8zlKJXsDJZrbYzBaZ2bDKNgjHgrAgYyHR2sGYWVPgLeB659xev/NUZGbnA9ucc0v8zlKJRsAQ4Enn3GDK32IlFA6JHOYdwx8LdAM6AU3M7Ff+pqo7zGwq5YdtX/I7S0VmFgNMBW7zO0sVNAJaUn7I+ybgdTML9vP0sHAsiBygc4X78YTIrvyRzCyC8nJ4yTmX4neeIE4ELjSzdZQfqjvDzF70N1JQOUCOc+7QHtiblBdGKDkLWOuc2+6cKwZSgJE+Z/o+W82sI4D3Z6WHG/xiZhOA84FfutBbl9+D8l8KvvG+j+KBpWbWwddUweUAKa7cl5QfNfjeE+rhWBBfAYlm1s3MIik/ETjH50zf4TXzM0Cmc+5hv/ME45yb7JyLd84lUP7v+JFzLuR+63XObQE2mllvb+hMIMPHSMFsAEaYWYz3f38mIXYi/QhzgAne7QnAbB+zHJWZjQZuBi50zh30O8+RnHMB51w751yC932UAwzxvmZDzSzgDAAz6wVEUskbDIZdQXgnrP4IzKf8G/B151y6v6mCOhG4jPLfyr/2Psb4HSqMXQu8ZGbLgUHAPT7n+RZv7+ZNYCkQoPx7KyReXWtmrwCfA73NLMfMrgLuA842s1WUr765z8+McNSc/wSaAQu876GnQjBjyDlKzmeB7t7S11eBCZXtkemV1CIiElTY7UGIiEjtUEGIiEhQKggREQlKBSEiIkGpIEREJCgVhIiIBKWCEBGRoFQQIiIS1P8DBH8cC1yVapYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(house_floor,house_price)\n",
    "plt.plot(PREDICT,P)\n",
    "plt.scatter(PREDICT,P,c=\"red\",s=200,marker=\"+\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_floor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5000,  8000, 12000, 20000, 28000])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from linear_regression import MyLinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr=MyLinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'train the model first.'"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict([7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr.fit(house_floor,house_price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3000., 90000.])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict([1,16])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5800.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2800.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}