Sklearn Simple Regression Guide.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import linear_model, datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  MEDV  \n",
       "0     15.3  396.90   4.98  24.0  \n",
       "1     17.8  396.90   9.14  21.6  \n",
       "2     17.8  392.83   4.03  34.7  \n",
       "3     18.7  394.63   2.94  33.4  \n",
       "4     18.7  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the Boston dataset\n",
    "boston = datasets.load_boston()\n",
    "df = pd.DataFrame(boston.data, columns=boston.feature_names)\n",
    "# Add the target variable (label)\n",
    "df['MEDV'] = boston.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(404, 2) (102, 2) (404,) (102,)\n"
     ]
    }
   ],
   "source": [
    "# Set the features and label\n",
    "X = pd.DataFrame(np.c_[df['AGE'], df['RM']], columns = ['AGE','RM'])\n",
    "y = df['MEDV']\n",
    "\n",
    "# Split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      65.2\n",
       "1      78.9\n",
       "2      61.1\n",
       "3      45.8\n",
       "4      54.2\n",
       "       ... \n",
       "501    69.1\n",
       "502    76.7\n",
       "503    91.0\n",
       "504    89.3\n",
       "505    80.8\n",
       "Name: AGE, Length: 506, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X['AGE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1b609980fd0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the features\n",
    "plt.scatter(X['RM'], y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1b609a67040>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X['AGE'], y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the regressor\n",
    "regressor = linear_model.LinearRegression()\n",
    "model = regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient of determination: 0.529269171356878\n",
      "Intercept: -28.203538066489102\n",
      "slope: [-0.06640957  8.7957305 ]\n"
     ]
    }
   ],
   "source": [
    "print('Coefficient of determination:', model.score(X, y))\n",
    "print('Intercept:', model.intercept_)\n",
    "print('slope:', model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b609c39310>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age regression line\n",
    "plt.plot(X['AGE'], y, 'o')\n",
    "model.coef_[0], model.intercept_ = np.polyfit(X['AGE'], y, 1)\n",
    "plt.plot(X['AGE'], model.coef_[0]*X['AGE']+model.intercept_, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b60a323c40>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MYsjH70bWHXnhX/jHnedlfHwld2hMXFF8IJkn7YQt+qEkAg5QHRPTdssgEdL3vBP3twc2L2mczXu31EUE/OdjrqR28hy+6LdHxsdXcouKuKL4QLpVEpN164nly6+2J62LIsDhe/dB0jM3jh+M6E+dNIEI//3cDADm7TuCAVfN5qnBo6jAynRRigMVcUXxgXTbiHmdXGMPOkK0JG1sDvZtZw5l9WfJvXkBenR1zjHv16WN6y85CYYNi6wbfsnfuPC0azFiyUVlZTa3CCXXaExcUXzAraPOqEF9HbdPp2Rs7HZOOdiXz1yadH8DbNveTqBS4jJPfvGP+7jg1cciyz86fQoL9j64w/6tbaYkmmeUC+qJK4oPuHXUcVs/afRAzyEQgaQ9Mb3MtmxtN/To2oWa6iAj1i5n9U1jIgL+4LDvUjt5jqOA25RC84xyQT1xRfEBN5ELNbfQ0Bjq4MXWDath8ZqNKfthguVJJ3rCsZktvYKBDl6243E2bWLhH78P7dbkoO3BKg67+H6aKruntEELWBUPKuKK4gPJwiNOqYYNjSHP/TAh/iaRmM7Y3NJKoELoXRWgeWsrFYkTfYzh1mduY9yb86PrFi7k6Je/osmDh60FrIoLDacoig8kK1CVmGoYm17olVhP2CmzpTXca2236iBtxkRCNd959zVW3zw2KuDXXWfN2T/88KQhEnt/LWBVfKT0xEWkO/Ay0C28/SxjzBQR6QPMBGqB1cAZxphN/pmqKKWDLXKXuQwyxgr21Nkr0pqYk1igyk18N21tjcy43PmLT1n0vz+KvPflHrX0fGcldI+GTtyeHipFuPWMISrcRYqXcMrXwLHGmC9FJAC8IiL/B5wGvGCMmSYi9UA9MNlHWxUlp2QzLd4rbjVLYgcnm1u8NWuwaWszLF6z0VPLNTHt3PfoFI5a3Rhd+eab9BzcMc970uiBcWEZsEIn6nkXNylF3Fj9274MLwbC/wxwKnBMeP19wAJUxJUSIZNp8elwbcNyx+JUNvbgZCa0Q9Jj24z/93NM/78/RJZ/+Z2LeODAMXzgIOBQ3H1HFXc8DWyKSCWwBNgHuN0Ys0hEdjHGrAcwxqwXkZ1d9p0ITATo379/bqxWlCzxUmAqXWzP3mtsO5s0vWQCvvemj3hhxsTI8r92358JE26kraIyZUcgrf1dengScWNMGzBURKqBJ0XkW15PYIyZAcwAq1FyRlYqSo5Jd1p8KhI9ey9UVwWo6trFUfR7J3nPjUBbK7Pvu5z9mlZH1o286B5CvSz/SrNKOidppRgaY5pFZAFwIvCJiPQLe+H9gA1+GKgofuA2iJdO/nNsTL1DGp8HYgceEzn52/0YvmcfJs1aljLfG+Ci12ZR/9JfI8uXnHIVc/Y7Ki6rREMjnZOUKYYi0jfsgSMiQeB44G1gNnBueLNzgaf8MlJRco2XPpbJSKw6mE3VQCdefLuJumE1TD99CFWB6M9UBEbu3Sdi+7c+fo/VN42JCPjcgSOpvepp5ux3FGCFXezrUgHvnHjxxPsB94Xj4hXAo8aYOSLyT+BRETkPWAuM99FORckpmQ7ipRv3zhQ7rLN4zUZaYtqtGQNvrN3MhP17c+kFJ9J7S3PkvQMvfZCNVb06HCvbWL9S3HjJTvk3MMxh/WfAcX4YpZQP+UjzcyPdQbx0496C5QnHNjH2SnVVwLX92hXz7uKC6xuiK+bNY+QbXdiY5MaitU46L0U/Y7OhMcTIafMZUD+XkdPmJy38o5QWiSEJO82vWL9jLzW/K0Xiy8JOOzl5KokLxnRsv3b46qVWoarXwwJ+6aXWhieckHSGKGitk85MUddO8TuXVyksfqT5+Ukqb9ZtYkw6ZWZtmltaI5OAqls+Z+kfvh99r3tPjrjoHq4/53Dqwuvsc/7q6RUdBks1K6VzU9QiXmo/ciU9cp3mlwlO4RxwjpUnE+PE7I/EqoLpUhmOwdw2+2ZOWflyZP2pZ9/Kst2iNsb+DuzwUCFDVEr+KWoRL4YfueIfuUjzywanJ71Jjy0DIZLWF/v053VaulNVwXQ5YeU/uOOpaZHl6Ueeze2Hnxm3Tai5hQH1czsItU7YKS+KWsQL/SNX/MVNFPP16J+s+l8s9tPfwvpjI/sl83K99ssMVAo9unaJE/l+nzfxzzt+HFl+5xv9GfOj37Oti7M3HzuWABpmLEeKWsQL/SNX/Kdbl4rI99u7KsCUsYPzJkTpPNHZ2ybzctNOPzQweLcdePX9jUh7Gw/OvJbD1i6PvH3c+Xfw/je8dZXXMGP5UtQirgV5Oi9O6XpfxeRD54N0BhxTPf1lMu2+td3w6vsb+X7jM9zw3P9G1l89+hIeHnpiZNmuhOhWEdFGw4zlSVGLOGh8r7NSDIPWTk96gQqJi4mDt6c/ryGUWPb+dB0v3P3TyPLCPb/N2Wf8hvaK+FTBdmOsVEVgQP1c14xFDTOWJ0Uv4krnpBgGrd2e9JzWpbqxJLO7pjrIlq+3R2LfXbe38sy9l7LPxg8j2xz203tZv2Nfx/1jxdnt6UFAw4xlStFP9lE6J25eY6G9SbvZQrrhOze7a6qDLKw/lqmnDCZQIVzy6iO8c+v3IgJ+Yd011E6e4yrgiV18nCb1CPCDEf31ibVMUU9cKQiFGrSOzaGurgrw5VfbIxkpoeaWuG7zybI+GhpDcRNrqgIVBCokLrtFgFGDLHHuvfwN3r3xlOj++x/NFWP/m8rKCnCpUtijayU3fO+ADrng4P6koDni5YeYHFdfS8bw4cPN4sWL83Y+pbjxQ3CSHTOTwUeIr33SuyrA/v12YOH7Gz3t22frZt744w/i1g392UM0B3cEoDoYoEc3q264XWvFxs5BB2/hHafr0/ZqnQMRWWKMGe74noq40hlI9Ixt7FADEOdl54OX7jqfPZs/jixPOOsG/rnnkLhtBPhg2smMnDbfMdZdHQzw9fZ2T8Lsdgw7pKOULslEXMMpSsmTzMM25F+8f/jGXK5//o7I8ts77cmJ593uuK09Jd9tYNRptqdbFk8xDBYr+UdFXClZ8lXb2yu7N3/MK3edH7fu2z9/hM+793Td5/OvWhlQPzftzkAfNbd0CB31CgYcRb/Qg8WKv6iIKzklXwNrmca3/UBMOx/cfErcup+Mu475+xyScl97HNRJwIOBSroHKhxbuPUKBjrUfXFCZzh3flTElZyRz9LBmUyu8YP/fvl+Lvnno5Hl5/c5lAvG/TLr4wow7qAahu/Zx3GwUgTX67cHSLWvZnlQNiKuqVf+43UWptt3kc53VOg47/6f/Idn/vqzuHUDr3yCr7t0zcnxDVafzevrnLNTLp+5NOm+OphZPpSFiGtzifzgZWCtoTHEpMeWxeVmT3psGYvXbOTxJSHP31EmjRZyQaCtlXdv+V7cuu/98BYaawbl/FzJim6lGgso9E1OyR9lMWMzmYeo5A4vszCnzl7Rodxra7vhwUVrk35HiW36qrrm/0/31jm3xgn4vQeNpXbyHF8EHJIPSGo7NsWmLDxxTb3KD15mYbo1SHBLzLCzMLwM4vnFYWuW8fAjv4hbt9ekpzoUqsolqQYktR2bYlMWIq7NJfKDH6WDd6sOFmwQs8fXW1nxuzPi1h17/p385xu7+37u7oEKLp+5lOnzViUdM2i87gQd7ylzymLGpk5HLp6B3WG/fs4xZa4qUIFBOoh1tUvus988+uBVHPLhW5HlG4/5EXcdenpezu00/X7cQTVxYwb2+nL6Gy5VcvHb02n3FI+IFYJiuok1NIaYNGtZXL3uQKUw/XRrOrpTeCCfjH3rJf749PTI8uZuPRjy80esAippkijGiQQqBQwdimY57ePWEEKzUIqbXP32dNo95d1cohgaMNikCrlMn7eqICK+05ZNLP7T2XHrDr74fpp69sn4mLedOZTLkqQC2jeu2M/CLd7vNptTx3WKm3z89spGxMuZYhvYTXZDzbtNxvCv289h5y2bIqsuP/kKnvxWdt5t76oAdcNqmDp7hWM4qCpQESfet505lLphNa5FrNw8cR3XKW7y8dsrixTDcsfvBgyJ6X8NjaGMj5G/4B785PWnWH3z2IiAL9t1X2onz8lawAOVwpSxgwEizSBiqQi3fws1t8R1q29oDDmmDgYDlUw4dA/H9ZqFUtzko/mJeuJlgJ8NGHIxkSrfdVD23PQRL82YGLfugMtm8kW3Hlkfu1Ks+L597U7ho9hWbTb2I7Yd33YKNw3fs0/ZjuuUKvloflI2A5vljl8Du8lqWE8aPdDTOd2OYRMMVFAhwpZt2Ym8U6Gqc8b/ipf3OijtY1VItHiVjZ1F8uLbTUmv2a3ZsV1bXOlc+J2dktITF5E9gPuBXYF2YIYx5vci0geYCdQCq4EzjDGb3I6jFBa/BnbdxNf2yL146Knigy2t7VnbefWL93Dhv56ILM8ZdCSXnDo54+P99oyhQNRj7hUMsG17m6f2bjpvobzwO6nCS0x8O3ClMWY/YARwsYjsD9QDLxhj9gVeCC8rZURDY4hkiXdeSx34KV79N61n9U1j4gT8m1c+mZWAVwUqIj/MhfXHctuZQ/l6eztbHW42TtfsFvfW+LaSCSk9cWPMemB9+P9fiMhKoAY4FTgmvNl9wAIg81+GUnJMn7cq7YHIxGJYdiGnVDnV6dKlbTuPPzCJIR+/G1k39pzbWN5v36yP/VVrOw2Nobh4d7J4fuKThh8zW5XyJa2BTRGpBYYBi4BdwgKPMWa9iOzsss9EYCJA//79s7FVKTIySZOyve7EwcxcCvh5/3qSX754d2Q5FymDsbRjFfKyRTdVLRenJ41ynreg5BbPIi4iPYHHgcuMMZ+LxxlsxpgZwAywBjYzMVIpTtItBxsbMvCjHkpije/n9zmUiaf9AiO5z6S1s0vskJLbH7aGSRS/8STiIhLAEvAHjTF2cPETEekX9sL7ARv8MlIpTpzSp9xI7DKTy8kO3Vu/YsGMiez65cbIuoMv/htNPXvn7BxOpMqq6V0VYMrYwepxK77iJTtFgLuBlcaY38a8NRs4F5gWfn3KFwuVoiU2tptKzBLre7g19U2XyQv+yk8XzYos/+j0KSzY++Csj+uFVE8hKuBKPvDiiY8EzgaWi4hdCOIaLPF+VETOA9YC4/0xUSlmYmO71zYsj0uxg/jZi7FkUE8qjkPWvcmjD0UToh4aciLXjL44qwNXVghticnfWaDdo5R84CU75RVwzSQ7LrfmKDbFUHUxXRuurzvA86zC5gyLXPVq+YI3/vgDKo2Vzrc10I1DL74/J7MtcyngULgiY7mkGP4OleTotPsipBh6gmZqg5esi4bGEBUuBZ1cMYZbn7mNcW/Oj6w67QfTeWP3/bwfowCUcpXBYvg7VFKjBbCKkGLoCeqXDbYwpCPg33n3NVbfPDYi4L8//CxqJ88pegEH5/TCXBQMywfF8HeopEY98SKkGErH+mVDOqmFu3zxKYv+90eR5TXVu3LCT27n60C3rGxIl95VgYxqnDulF5aSd1sMf4dKatQTL0LyUb6yUDZ4EQAx7dw/85dxAn7iT/7Edy+9N+8CXlMdZMrYwUk7yzshAuMO6hhaKiXvthj+DpXUqIgXIcVQW8MvG1IJwPh/P8cHN5/CUasbAbju+AupnTyHtbvt1aFqYKZ4zV+xr7duWA03nnYA1cGA53MYAw++tpbahJCJ200s1NxSdGGVYvg7VFKj4ZQixK/aGl4zDeztWlrbIh1lEifrZIrbBKEBG0O8+OcLI8uv1+zPWd+/kbYKS0SciktlSrJ7QU11MO7zAWtSj71uzJB+HdIoU50nNmSSbJZrsYVVtMZLaaD1xMsErw1b89FUuaExFGmIHGhrZfZ9l7Nf0+rI+0dcdDcf9tolJ+dKh8Smw26fRablAuwbYbJZrtr4WHEiWT1xDaeUCV5jsbmM2bplYdQNq6HxuhOY27KQd2/5XkTALx07idrJc/Ii4IlhAgFGDeobt87ts8h0PtFHzS2R0EyybRQlHVTEywSvmQa5ykiwvVinPpIsWQIiDP7DjQDMHTiS2que5un9j07rHDa9qwJpDTxWBwOMO6gmLjZugMeXhOLi0m7XnOnDqz0eUDeshhodNFRyhIp4meAmDhUiccKVq4wEJy9WtjSW8pAAABRlSURBVHzJ0UcOhuHRp8IDL32Qi+uuzni6fDBQiTEdG1C4EagQpp4ymBffbuoQG0984nC75prqIL2rvA9y2nbaMfaGxhBbvt6edBtF8YqKeJnglGkA0GZM1EN22S4TcUkcvLv2hT/z1m3j6b1lMwCX//gmaifPYWNVr7SOayNYYnrjaQewOUUhrdhm8z26dXG0z8nuSaMHduhUD5aHvmlrq+csF9vOumE1kSeUxOJfFRK9iRQqS6VUJiEp8Wh2SicnNiOlVzDA19vbOqTqxdb4yFVGgp3VcvjqpTw089rI+vsPHMM5S56moX5uxtdUHQywdMoJkeVkVRQDlQIG2sMxkOaWVq5+Yjki7mGR2K49TkptYl5TdSQSiBuodJvsZH8nhZr8U0qTkJR4VMQ7MYk/zGSlX2Pjv5l2nYm9YVRv3UzjH38QeW9ztx4c/tN72dKtinNIv6FELJu/amVA/dzIDcYt46N3VQBjOl53qtCLLV7T562itS15ANwQvWE5kRiS8TK2UIjCWckGtFXEixsNp3Ri0pniXp1mjDeRyEDmpq38fvbNcQJ+6tm3MuSymWzpVhUZ0HML73jBGOIGSwFuPO0AaqqDkTDL784cypSxgzOqWW6Ll9fB3GR1YLZu2+5pzCGRj8KTf/IV3tAp9qWLeuIeKcWSnOl4utlOF5g+bxXHLH+JO56aFll3y5E/5E+HnxVZjo2t1w2rYfGaja4TZ7w2TrYFd2H9sY757pkSam6hJounBZtNW1vjwhKjBvXlwdfWpry26qpAXsMbbk9Gmi1T/KiIe6AU44Wpej8mkmpwMCnr1rHw6mhp+ff67M53f/xHtnWJevexrcoaGkNMnb0iqZdssGLfXjxp21u0b7TZCq/NqEF9eXxJKOteoLFZL48vCXX4ThKbUbhl3PgZ3nAKSWm2TGmg4RQPlFLRIpvp81al1UE+I4+rrQ1GjYL+/SOrjjvvDo6/4M44AQeo6tolaXZGIjXVQZZOOYHfnTk0EiapdElDrBDh2oblTJq1LGcCDvDi202RME22fNTcwtTZKxxvCDt06xKXsigY18/Hr/CGPQkpNiSVy1m6in+oJ+6BUowXJrMtcep4Oh6X7e0eveAJ/mfe7ZH1S6+ZxnhzAK0uVapse7zE6QOVwpavt8cNXsbeABL3bzPGcz2TdLBnWCY7t1eqk5SzbW5pjRsfSFYnxs/wRqYD2kphUU/cA6VYkjPZRJVMPa6GxhB/njGXhVcfFxHw1/YcQsPraxh6w2R6dnf3CWx7vNz42tosT9QevLx85lKubVge8RbdPPJcE/sZ2udOd5IPRMMjblSKeLo5aHhDcUJF3AOlWJIzmc11w2pYWH8sH0w7ucOAoCtff83Q0Ycz985opcHDfnovZ511A9P//h6QvG+m/Vl5ufEl+qEGq6yrnb/dnsEobLq6L1g3kMSaL1Vd03t49TIhyWuXIw1vKE6oiHugFOOFObX5+uuhe3dqm6yQxU9Prad28hzW72gVjLK9azeBtutwj5w2n1Bzi+eZjrEYiIxBZPIE1Kt7IC0vOrGMbKp64E7YE33qhtW42ty7KuAp5l5THSzqvzelcKiId2Iy8rhjWbTIcmF/+UsAnhtyHLVXPc3/DToibjO7/oqb9z9mSL9IMSyIznRMF1tAM8kxtwcKHWbRx+H0duwgdjo3kNht3T6bKWMHp7yeYn/qUwqLirgHklbkK1Kymijy+efQqxeMGBFd9+mnbL33PoIO4QS7/gp0nHRz42kH8OLbTR1ivranm058O7YKYCax8U1bW6msEKqDgYh9PxzRP2Jv76qAa0ZP7A3EC4nCm+zJKPG93lWBOBuL/alPKSzaFMIDdhggkWIt4J9VY4dLLoHbo1knvPACHBvfKOHKR5c5xnF7VwWo6tqlw4SoAfVz00p3TMybdrPf6ToDFQJC0unyTt9bquyT2H32unpu0lZxAvxgRH+ur3OvG64o6aBNIbKk1FIMM8prnzfPCp3YAn755dY0zmPjxS7ZwOKmra2OTyteQhCVIhHP89bxQ+Lyw928USfvdvr4IUw/fUjSOLPT95Ys9THRq07V69Ng5ZgrSj7QPHEPlNqU5LRuOhs2wC4xnXR23hnefx969nQ9vtfiVfaNY9LogUyatSypd9xuDB9MOzlunZcQgltuc92wGtcnKKfvLdn1JN5AvEzHtzNbSqlMg1KaqCfugVJLMfSU124MjBsXL+CLF8MnnyQVcEhvYNGeMNMjRWpeujdELzF/r9/btQ3uNVacskK8XL+dolgqYyhK6aKeuAcK2fU7k8JbKetgPPwwfP/70R2mTYPJkz3bZBevenjROtqMoVKErl2EFoeZhrY4J8uTTnfGaGLdFbdaNl6/t4cXrXM9n5Ndsce1UyZjnzGcatb4UfekFIuyKbknpYiLyD3AGGCDMeZb4XV9gJlALbAaOMMYs8k/MwtPIaYkZ1N4q3ugIrJfdTDA1FMGU9e7NX7Wy7e/Da+/Dl27pm3X40tCkcHNNmPY3m4NKrYmFHKKneTjFIKoFElrxqjb4KObSHr53pJNtnHbN/a4iWLqFmrJ5RhKKRZlU/zBSzjlr8CJCevqgReMMfsCL4SXlRyTyQCl/eOOrdOxfVsrR/64DgYMiG64ahUsW5a2gLvZ1dpm6Nm9i+tgpFto49YzhngWnVR1VzIVyWSpil5CIIn5+PloglyKRdkUf0jpiRtjXhaR2oTVpwLHhP9/H7AA8P48rngik6yYxB/3OUue5td/vyu6wd13w09+YnmPGQ68uZ2/eWsrjded4PheLkJSqUQ6U5GccOgergW0nLz7RM971KC+vPh2U9xyYgnbXI+hlFrGlOIfmcbEdzHGrAcwxqwXkZ1zaJMSJpOsGPtH/M2m1Tx3zyWR9S8NOJCj33sdKiqyfhTPNFsn25BUqqyYTEXy+roDXEU8URSdPrvYfUPNLTy+JMS4g2rihD3X8epSy5hS/MP37BQRmSgii0VkcVOT5s6mQyZZMbU9Knj5zvPiBPzQ//or10ycDhXW153to3ihsnUmjR7oOl2/OhjISiS9hkC8lNJtaW3jxbebsit5kIJSy5hS/CNTT/wTEekX9sL7ARvcNjTGzABmgDVjM8PzlSTZZg+kHYK47jpe/M1vIosXnHYtz+87wprtGPPjzvZRvFDZOnZWTGJ7s2CgkqmnDAbS+8zjGjtXBZIOzNp4/Yz8DmsUMmNKKS4yFfHZwLnAtPDrUzmzqJOQq+wBTyGIhQvhiGhRqjVjz+D7h03ko81fUePw487Fo3gmoZFMbmpO+wzfs4/jcdL5zBO33bS1lUClVVdlc0urq31eJzrlI6yhTRwU8FA7RUQexhrE3An4BJgCNACPAv2BtcB4Y8zGVCcr1dopmZCXeiubN8Ouu8JXX1nLlZXQ1AS9eyfdLavaKg7H8iLMmZzTyz6x5wfnnqJOn3mm34+XDj+ZfpaJ51EvW7HJqnaKMWaCMaafMSZgjNndGHO3MeYzY8xxxph9w68pBbzc8DV7wBiYOBGqq6MCvmABbN+eUsDBuebIuINqmD5vVVpVD9Op7phJHD7VPonnT1WBMNW6ZOttnD672EqIuag6WIpVM5XCoTM2fcK37IG5c2HMmOjy5MnWjMs0SZyskknoJ5nIJu6XiWim2sfLICM4f+bZfD9+hzHS+VwVRWun+ETOswc+/tiabWkL+O67w5YtaQu4U82RTLNV0hHmTPqUptrH61ON02derNkdDY2hvMz4VDoP6on7RM6yB9rb4dRTYc6c6LrGRhg6NG2b3DxuN2/WSTRiY7UVIo5T1p3EN2U9FwdS7eNlkLF3lXPqYbrfj1uMOpexa/v7cUNzwBUntClEgUkqAn/7G5xzTnTjW2+FK67I+Fxug3mVLmKcOMjnZVAvUCH07N6F5q0dMzxylZ3iFgZKJBcDjG7nCQYqGXdQjePMzEzP6fb9ZHtcpfRJNrCpIl5A3MTh9wfvwAmnxvSxHD4cXn0VAt4b/TqRrMNOMFCZUoyS3QTajaFXMMCWbdvj6ob7LT6xIt8rGEAExxtINmR78/NKsu/nd2cOVQEvY5KJuIZTCkhiLLpL23Yeue9yhlz/bnSj996DvffOyfncwg92LnkqL9ktJms3dBg5bX5ciVjwf0AuH7nSbtftVv0w09h1su9HBVxxQ0W8gMT+2M/715P88sW7o2/efz+cfXZOz5csxuxFDFNldOSzKFM+86iTldH1OibghUzGDRRFs1MKyG7VQfb/5D+svmlMRMCf3+cQjrjh+ZwLOCTvuO6FVBkdmWSgZEK+86jdrnvCoXvkNMMl2+9HKU80Jl4otm6lZcDeBDd8HFl18MV/48veOxX1DzfdgUY/YuJeZlvm2lPPR3aKorihA5vFRn093HRTZPG/z72Bx3cd0ilEIB+i5jYAKMAH007O281EUfKFDmwWCy+/DEcfHV2eOBHuvJNbRLilcFblFKe2ZZfPXJqTlEKbVLF5nfGolBMq4vlg0ybYaSdr4g5AMAjr10OvXoW1y0cymcrvdZ9UA4Da9UYpJ3Rg00+MgXPPhT59ogL+yiuwdWunFnDwp+CVTaoBwHwNsCpKMaCeuF889RTU1UWXr70WYho2dHb8KHgVS7KUSE3VU8oJFfFcEwpZxals9toL3nzTCqGUEZlUCcxV5UfteqOUEyriuaK9HU46CZ57Lrpu+XL41rcKZ1MB8aPgVTpo1xulXNCYeC645x6rq44t4H/4gxUPL1MBh8wmruhkF0VJH80Tz4Z33oGBMV7i4YfDSy9BF33AURQld2ieeK7Ztg0OPhj+/e/oug8+gNragpmkKEp5ouGUdLnpJujWLSrgDz1khU5UwBVFKQDqiXtlyRKrrrfNuHHw2GNWyzRFUZQCoSKeii+/tNIEm5qi6z75BHbeuXA2KYqihNFwSjKuvBJ22CEq4M8+a4VOVMAVRSkS1BN34oUX4Pjjo8sXXwx/+lPh7FEURXFBRTyWzz6zClXZVFfDmjWw446Fs0lRFCUJGk4BK0QyYUK8gL/2mlV9UAVcUZQiRkV81iyoqIBHHrGWf/1rS9QPPbSwdimKonigfMMp69ZB//7R5UGDYOlSKwdcURSlRCg/T7ytDUaNihfwt96ClStVwBVFKTmyEnEROVFEVonIeyJSnyujfOOuu6y6JgsWWMt33GGFTvbbr6BmKYqiZErG4RQRqQRuB74DfAi8LiKzjTFv5cq4nLFyJey/f3T5mGPg73+3Kg8qiqKUMNnExA8B3jPG/AdARB4BTgWKR8S//hqGDoW3346uW7MmPpSiKIpSwmQTTqkB1sUsfxheF4eITBSRxSKyuCl26rrfXH89dO8eFfDHHrNCJyrgiqJ0IrLxxJ0qP3UoTm6MmQHMAKueeBbn88aiRTBiRHR5wgR48EEtVKUoSqckGxH/ENgjZnl34KPszMmCL76welt+/nl0XVNT/AQeRVGUTkY24ZTXgX1FZICIdAXOAmbnxqw0ufRSa2alLeB//7sVOlEBVxSlk5OxJ26M2S4ilwDzgErgHmPMipxZ5oV58+DEE6PLl18Ov/1tXk1QFEUpJFnN2DTGPAM8kyNbvLNhA+yyS3R5553h/fehZ8+8m6IoilJISmvGpjFWR51YAX/9datJgwq4oihlSOmI+MMPW4WqnnjCWr7xRkvUhzs2gFYURSkLSqMA1tlnwwMPWP//9rct77tr18LapCiKUgSUhidul4VdtQqWLVMBVxRFCVMaIn7JJVbo5JvfLLQliqIoRUVpiLiiKIriiIq4oihKCaMiriiKUsKoiCuKopQwKuKKoigljIq4oihKCaMiriiKUsKoiCuKopQwYoz/zXYiJxNpAtbk7YSZsRPwaaGNyAN6nZ2PcrnWcrzOPY0xfZ02yquIlwIistgY0+mraul1dj7K5Vr1OuPRcIqiKEoJoyKuKIpSwqiId2RGoQ3IE3qdnY9yuVa9zhg0Jq4oilLCqCeuKIpSwqiIK4qilDAq4jGISKWINIrInELb4icislpElovIUhFZXGh7/EJEqkVkloi8LSIrReSwQtuUa0RkYPh7tP99LiKXFdouPxCRy0VkhYi8KSIPi0j3QtvkFyLy8/B1rkj1fZZGj8388XNgJbBjoQ3JA6OMMZ19wsTvgWeNMaeLSFegqtAG5RpjzCpgKFhOCBACniyoUT4gIjXAz4D9jTEtIvIocBbw14Ia5gMi8i3gAuAQYBvwrIjMNca867S9euJhRGR34GTgL4W2RckeEdkROAq4G8AYs80Y01xYq3znOOB9Y0yxz4rOlC5AUES6YN2QPyqwPX6xH/CaMWarMWY78BLwPbeNVcSj/A64CmgvtCF5wADPicgSEZlYaGN8Yi+gCbg3HCL7i4j0KLRRPnMW8HChjfADY0wIuAVYC6wHNhtjniusVb7xJnCUiHxDRKqA7wJ7uG2sIg6IyBhggzFmSaFtyRMjjTEHAicBF4vIUYU2yAe6AAcCdxhjhgFbgPrCmuQf4XDRKcBjhbbFD0SkN3AqMADYDeghIj8srFX+YIxZCdwEPA88CywDtrttryJuMRI4RURWA48Ax4rIA4U1yT+MMR+FXzdgxU8PKaxFvvAh8KExZlF4eRaWqHdWTgLeMMZ8UmhDfOJ44ANjTJMxphV4Aji8wDb5hjHmbmPMgcaYo4CNgGM8HFTEATDGXG2M2d0YU4v1SDrfGNMp7/Ii0kNEdrD/D5yA9fjWqTDGfAysE5GB4VXHAW8V0CS/mUAnDaWEWQuMEJEqERGs73NlgW3yDRHZOfzaHziNJN+tZqeUH7sAT1q/A7oADxljni2sSb5xKfBgONTwH+DHBbbHF8Jx0+8AFxbaFr8wxiwSkVnAG1ihhUY69/T7x0XkG0ArcLExZpPbhjrtXlEUpYTRcIqiKEoJoyKuKIpSwqiIK4qilDAq4oqiKCWMiriiKEoJoyKuKIpSwqiIK4qilDD/DzmDR5LMH1tsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Room number regression line\n",
    "plt.plot(X['RM'], y, 'o')\n",
    "model.coef_[0], model.intercept_ = np.polyfit(X['RM'], y, 1)\n",
    "plt.plot(X['RM'], model.coef_[0]*X['RM']+model.intercept_, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted response:\n",
      "[787.25015146 324.23031145 917.24260217  89.67193479 856.28969868\n",
      " 698.66489016 774.64279666 871.59162    845.67926913 743.77933277\n",
      " 871.45059389 800.01010665 915.28818349 508.92762402 691.82210624\n",
      " 642.02614527 889.02151926 918.4634422  325.86324434 918.10912884\n",
      " 168.57911396 314.52100265 872.08784272 652.27536104 885.58752254\n",
      " 881.40500548 350.13268892 881.01550919 856.0561702  658.90073584\n",
      " 819.87583443 497.89104316 920.74902566 888.11497018 878.27944816\n",
      " 912.10193932 584.66556165 438.37916738 556.60132979  76.69179191\n",
      " 676.28211137 665.44959882 326.35155459 349.49030617 623.42098553\n",
      " 898.48395877 880.17419204  81.84550115 784.14980366 616.61881286\n",
      " 772.74775836 365.21648348 854.01614956 673.95359813 509.8044185\n",
      " 493.24748629 930.24092535 279.62572679 458.42653251 653.59749918\n",
      " 419.83763868 228.42581634 217.74413769 346.45607357 349.96220264\n",
      " 922.54466116 921.53491866 313.84798206 221.76319769 817.96804416\n",
      " 319.32170499 706.44852282 896.08331318 195.03196063 436.21175586\n",
      " 923.50339578 838.76245755 336.59226905 924.91643541 907.68677702\n",
      " 317.46184962 905.83146672 190.86337321 892.52924922 841.13671595\n",
      " 629.77970984 843.45937764 386.74856055 283.01061094 917.74454765\n",
      " 447.89922077 822.97984404 910.24721786 493.24689745 796.726355\n",
      " 912.47224325 930.21453816 916.24691812 871.38536196 934.09345531\n",
      " 428.74081326 430.21424533]\n"
     ]
    }
   ],
   "source": [
    "y_test_predict = regressor.predict(X_test)\n",
    "print('predicted response:', y_test_predict, sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "676.5903849174067\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "rmse = (np.sqrt(mean_squared_error(y_test, y_test_predict)))\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Task 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do our features mean?\n",
    "\n",
    "CRIM: Per capita crime rate by town\n",
    "ZN: Proportion of residential land zoned for lots over 25,000 sq. ft\n",
    "INDUS: Proportion of non-retail business acres per town\n",
    "CHAS: Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n",
    "NOX: Nitric oxide concentration (parts per 10 million)\n",
    "RM: Average number of rooms per dwelling\n",
    "AGE: Proportion of owner-occupied units built prior to 1940\n",
    "DIS: Weighted distances to five Boston employment centers\n",
    "RAD: Index of accessibility to radial highways\n",
    "TAX: Full-value property tax rate per $10,000\n",
    "PTRATIO: Pupil-teacher ratio by town\n",
    "B: 1000(Bk — 0.63)², where Bk is the proportion of [people of African American descent] by town\n",
    "LSTAT: Percentage of lower status of the population\n",
    "MEDV: Median value of owner-occupied homes in $1000s\n",
    "\n",
    "This can be obtain via the boston.DESCR command"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#boston.DESCR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(404, 2) (102, 2) (404,) (102,)\n",
      "Coefficient of determination: 0.6379572481839603\n",
      "Intercept: -3.841177079004897\n",
      "slope: [-0.63249856  5.46509877]\n"
     ]
    }
   ],
   "source": [
    "# Set the features and label\n",
    "X = pd.DataFrame(np.c_[df['LSTAT'], df['RM']], columns = ['LSTAT','RM'])\n",
    "y = df['MEDV']\n",
    "\n",
    "# Split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n",
    "\n",
    "# Fit the regressor\n",
    "regressor = linear_model.LinearRegression()\n",
    "model = regressor.fit(X_train, y_train)\n",
    "\n",
    "print('Coefficient of determination:', model.score(X, y))\n",
    "print('Intercept:', model.intercept_)\n",
    "print('slope:', model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.589569804715174\n"
     ]
    }
   ],
   "source": [
    "y_test_predict = regressor.predict(X_test)\n",
    "rmse = (np.sqrt(mean_squared_error(y_test, y_test_predict)))\n",
    "print(rmse)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Task 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(404, 4) (102, 4) (404,) (102,)\n",
      "Coefficient of determination: 0.6495569651575734\n",
      "Intercept: -4.149810167133371\n",
      "slope: [-5.20585862e-01  5.63360387e+00 -8.77267717e-02 -4.53712818e-03]\n"
     ]
    }
   ],
   "source": [
    "# Set the features and label\n",
    "X = pd.DataFrame(np.c_[df['LSTAT'], df['RM'], df['CRIM'], df['TAX']], columns = ['LSTAT','RM','CRIM','TAX'])\n",
    "y = df['MEDV']\n",
    "\n",
    "# Split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
    "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)\n",
    "\n",
    "# Fit the regressor\n",
    "regressor = linear_model.LinearRegression()\n",
    "model = regressor.fit(X_train, y_train)\n",
    "\n",
    "print('Coefficient of determination:', model.score(X, y))\n",
    "print('Intercept:', model.intercept_)\n",
    "print('slope:', model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.6217921875770935\n"
     ]
    }
   ],
   "source": [
    "y_test_predict = regressor.predict(X_test)\n",
    "rmse = (np.sqrt(mean_squared_error(y_test, y_test_predict)))\n",
    "print(rmse)"
   ]
  }
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