Linear regression  .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import sklearn\n",
    "from sklearn import linear_model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"student-mat.csv\", sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "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>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob ...  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher ...   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other ...   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other ...   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services ...   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other ...   \n",
       "\n",
       "  famrel freetime  goout  Dalc  Walc health absences  G1  G2  G3  \n",
       "0      4        3      4     1     1      3        6   5   6   6  \n",
       "1      5        3      3     1     1      3        4   5   5   6  \n",
       "2      4        3      2     2     3      3       10   7   8  10  \n",
       "3      3        2      2     1     1      5        2  15  14  15  \n",
       "4      4        3      2     1     2      5        4   6  10  10  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[[\"G1\", \"G2\", \"G3\", \"studytime\", \"absences\", \"failures\", \"age\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "      <th>studytime</th>\n",
       "      <th>absences</th>\n",
       "      <th>failures</th>\n",
       "      <th>age</th>\n",
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      ],
      "text/plain": [
       "   G1  G2  G3  studytime  absences  failures  age\n",
       "0   5   6   6          2         6         0   18\n",
       "1   5   5   6          2         4         0   17\n",
       "2   7   8  10          2        10         3   15\n",
       "3  15  14  15          3         2         0   15\n",
       "4   6  10  10          2         4         0   16"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = \"G3\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(data.drop([predict],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array(data[predict])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test, x_train, y_test, y_train = sklearn.model_selection.train_test_split(x, y, test_size = 0.1 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "linear.fit(x_train, y_train)\n",
    "acc = linear.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7944248001922586\n"
     ]
    }
   ],
   "source": [
    "print (acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coe: \n",
      " [0.15750926 1.02464325 0.09856225 0.00165652 0.46529747 0.02651858]\n",
      "intercetp: \n",
      " -2.8645121804533886\n"
     ]
    }
   ],
   "source": [
    "print (\"coe: \\n\", linear.coef_)\n",
    "print (\"intercetp: \\n\", linear.intercept_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "8.252101518439721 [ 7  8  4  8  0 15  2  2  1  5  1  2  1  1  3] 8\n",
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      "-1.9082926214071767 [ 5  0  1  0  3 17  2  1  1  5  4  5  1  2  5] 0\n",
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      "9.654731915739314 [10  9  3  2  0 18  2  2  2  5  3  3  1  3  4] 10\n",
      "11.450825689810937 [14 11  1 18  0 16  4  4  1  3  4  4  1  4  5] 13\n",
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      "10.608095691823392 [10 10  2  4  0 17  4  3  1  5  2  3  1  1  2] 11\n",
      "13.892218248478489 [12 14  3  7  0 17  2  4  1  4  4  3  1  1  5] 14\n",
      "14.46686920069277 [14 15  2  0  0 16  4  2  1  4  2  3  1  1  3] 16\n",
      "3.935729772633074 [ 6  5  3  0  1 17  1  1  1  4  3  4  1  1  5] 0\n",
      "2.864709153495851 [ 7  8  2  2  3 19  0  1  1  3  4  2  1  1  5] 9\n",
      "11.114576920743318 [12 12  2 14  0 17  4  3  2  2  5  5  1  4  5] 12\n",
      "14.247442559462964 [15 13  2  9  0 18  4  4  1  5  4  3  1  1  2] 15\n",
      "12.552915930654386 [14 13  1  6  0 15  3  4  1  3  4  3  1  2  4] 13\n",
      "9.239210117848854 [ 7  8  2 12  0 17  1  2  2  4  4  4  4  5  5] 8\n",
      "7.88803131784084 [ 8  9  2  4  0 17  1  2  1  3  1  3  1  5  3] 10\n",
      "16.795384930653032 [16 17  3  0  0 16  4  4  1  4  4  3  1  1  4] 17\n",
      "9.661692248046101 [10 10  2  2  0 17  2  4  2  4  3  3  1  1  1] 10\n",
      "18.03594327165501 [19 18  2  2  0 15  4  4  1  4  3  3  1  1  5] 18\n",
      "18.32956876994912 [16 18  3  2  0 16  3  3  1  5  3  3  1  1  5] 18\n",
      "15.819972254799637 [16 15  4  7  0 18  3  3  1  5  3  3  1  1  1] 17\n",
      "11.316151352063418 [ 9  9  2 56  0 17  3  3  1  5  3  3  2  3  1] 8\n",
      "13.723137838154496 [14 15  2  4  0 15  4  4  1  3  2  2  1  1  5] 15\n",
      "1.022056628878003 [ 6  0  2  0  0 18  2  1  2  4  3  5  1  2  3] 0\n",
      "4.81265536801892 [ 9  8  1  6  3 18  1  1  3  5  2  5  1  5  4] 10\n",
      "12.259703127755063 [13 13  2 14  0 17  4  3  1  3  2  3  1  2  3] 14\n",
      "11.62465335976767 [11 11  3  0  0 17  3  4  1  4  3  4  2  5  5] 10\n",
      "10.221880112515032 [12 13  2 14  0 16  3  2  1  1  2  2  1  2  1] 12\n",
      "8.638270971257601 [ 7  9  2  0  0 18  3  1  1  4  3  4  1  1  1] 8\n",
      "9.69178033262789 [ 8 10  2  4  0 16  1  2  1  4  4  3  1  1  1] 9\n",
      "8.705594881160424 [10  8  2 10  0 18  4  3  2  4  4  5  1  2  2] 8\n",
      "9.804015336407764 [ 8  9  2  8  0 16  2  1  1  5  3  4  1  1  2] 10\n",
      "13.307670013239097 [13 14  2  0  0 15  4  3  1  4  4  1  1  1  1] 15\n",
      "10.435781640677865 [10  8  1  6  0 16  1  1  4  5  5  5  5  5  5] 11\n",
      "2.482059540128824 [ 7  6  1  0  2 17  2  1  2  3  3  2  2  2  5] 0\n",
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      "16.130380087552698 [14 15  3  4  0 16  4  4  1  5  4  5  1  1  4] 16\n",
      "6.297360834540825 [ 8  7  1  7  1 18  1  3  1  4  3  3  2  3  3] 8\n",
      "7.077504789331012 [ 7  6  1 18  0 17  2  2  1  5  3  2  1  2  3] 6\n",
      "17.244559227180527 [17 17  4  0  0 17  3  2  1  5  2  2  1  2  5] 18\n",
      "12.2566299595676 [10 13  1 12  0 17  2  2  1  3  4  4  1  3  5] 12\n",
      "5.928466564648886 [ 5  6  2  6  0 18  4  4  2  4  3  4  1  1  3] 6\n",
      "12.023724367269093 [13 12  3  6  0 17  1  1  1  4  3  3  1  1  3] 12\n",
      "9.162817111082287 [ 8  9  2  2  0 16  2  1  1  4  3  5  1  1  5] 10\n",
      "4.081494669614063 [ 7  8  2 10  3 15  1  1  1  4  3  2  2  3  3] 10\n",
      "8.774668168815978 [ 9  9  1  8  0 16  3  3  3  3  3  4  3  5  3] 10\n",
      "10.321344699259175 [ 8 11  2  0  0 15  1  1  1  4  4  2  1  2  5] 11\n",
      "5.036802831149178 [ 8  9  1  0  3 15  2  1  4  4  5  5  2  5  5] 10\n",
      "13.44810752352668 [15 14  2  4  0 18  4  4  1  3  2  4  1  4  2] 14\n",
      "14.562332932261324 [10 13  1  6  0 15  3  3  1  5  3  4  4  4  1] 13\n",
      "10.895177571858191 [11 12  2 12  0 16  3  3  1  2  3  5  1  4  3] 11\n",
      "14.717734252558792 [15 15  2  2  0 16  4  2  1  4  5  1  1  3  5] 16\n",
      "9.614496017173927 [12 11  2  0  0 17  1  2  2  3  2  2  1  2  3] 12\n",
      "7.624448943406536 [ 7 10  3  0  1 16  3  3  1  4  1  2  1  1  2] 10\n",
      "7.73347672269785 [ 6  7  2  0  0 16  2  2  1  5  1  5  1  1  4] 0\n",
      "11.522635790511355 [10 12  3  4  0 15  2  1  3  5  2  2  1  1  4] 12\n",
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      "10.786486414464234 [10 10  1  2  0 17  1  3  1  5  3  3  1  4  2] 10\n",
      "8.792370080041021 [ 9  9  2 11  2 20  2  2  1  5  5  4  4  5  4] 9\n",
      "14.968504095065796 [15 14  2  8  0 18  2  1  1  5  2  4  1  2  4] 14\n",
      "9.50251015133985 [ 8 10  1  0  0 15  4  4  1  3  3  4  2  3  5] 11\n",
      "13.899881921812543 [13 13  2 23  0 17  4  3  1  5  2  2  1  2  5] 13\n",
      "-0.9529026964366842 [12  0  3  0  2 15  3  4  2  4  2  2  2  2  5] 0\n",
      "15.462826606062702 [15 15  2  4  0 15  2  2  2  5  3  3  1  3  4] 15\n",
      "11.60769086249849 [12 12  1  2  0 15  2  2  1  4  2  2  1  2  5] 11\n",
      "6.8709746898766415 [ 9  7  2 20  1 17  1  3  3  5  2  4  1  4  5] 8\n",
      "12.051542209996995 [11 11  2  2  0 18  4  4  1  4  4  4  3  3  5] 11\n",
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      "11.681638930306423 [11 13  1 10  1 18  3  2  2  2  5  5  5  5  5] 13\n",
      "15.456986911195646 [14 16  3  0  0 15  2  2  1  4  5  2  1  1  3] 16\n",
      "3.886338161600669 [ 6  6  2 22  2 17  4  3  1  3  4  5  2  4  1] 4\n",
      "14.48355047667296 [15 15  3  0  0 17  4  3  1  4  2  2  1  2  3] 15\n",
      "10.144239490099283 [ 8 10  2  0  0 15  3  3  1  5  3  2  1  1  2] 12\n",
      "9.620990680702187 [ 8  9  2  4  0 18  1  1  2  5  4  4  1  1  4] 10\n",
      "12.461078231504008 [11 13  2  2  0 15  4  4  2  4  4  3  1  1  2] 14\n",
      "6.978282958417616 [ 6  8  1  0  1 18  3  2  2  4  4  5  2  4  5] 8\n",
      "10.284663394820518 [13 11  3 40  1 19  2  3  1  4  1  2  1  1  3] 11\n",
      "12.445300262210665 [12 12  2  6  0 17  2  2  2  4  4  4  2  3  5] 12\n",
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      "11.667636554648485 [13 11  2  4  0 17  4  3  2  4  5  5  1  3  2] 11\n",
      "8.347893114055546 [13 11  2  2  0 17  2  1  3  2  1  1  1  1  3] 11\n",
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      "7.45625134064535 [ 8  9  1  0  0 16  2  1  2  3  3  2  1  3  3] 8\n",
      "14.537418682136167 [14 15  2  0  0 17  2  4  1  4  3  2  1  1  5] 15\n",
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      "9.861361586030739 [10  9  2 75  0 18  3  2  2  4  1  1  1  1  5] 9\n",
      "14.104581134054346 [15 14  3  6  0 18  2  1  1  4  2  4  1  3  2] 14\n",
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      "5.88482242221537 [ 5  9  2  6  2 15  3  2  2  4  4  4  1  4  3] 7\n",
      "11.54438073604203 [11 11  4  0  0 16  2  2  1  5  2  3  1  3  3] 11\n",
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      "12.035274544656154 [14 12  1  3  0 18  1  2  3  4  3  3  2  3  3] 12\n",
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      "10.968311184850645 [10 10  4  6  0 16  2  2  2  5  3  5  1  1  5] 11\n",
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      "10.237455938357792 [11 11  3  3  0 17  2  2  1  3  3  2  2  2  3] 11\n",
      "14.689879228520073 [13 15  3  0  0 17  4  3  1  4  4  3  1  3  4] 15\n",
      "6.461925172204582 [ 9  7  2 18  0 16  1  1  2  3  4  2  1  1  5] 6\n",
      "12.787020316297564 [14 13  3 12  1 19  3  1  1  5  4  3  1  2  5] 13\n",
      "5.3399091755725046 [ 6  5  1  7  0 18  4  4  3  4  4  3  2  2  5] 6\n",
      "6.016478875537857 [ 6  6  2  2  0 17  2  1  2  4  2  5  1  2  5] 6\n",
      "11.404118965041617 [11 10  1  8  0 17  2  2  4  4  4  5  5  5  4] 10\n",
      "5.902259847329402 [10  8  1  3  3 21  1  1  1  5  5  3  3  3  3] 7\n",
      "4.383165037803668 [ 7  6  1  0  1 17  1  1  3  5  2  1  1  2  1] 0\n",
      "18.598809865086555 [17 18  1  0  0 20  3  2  1  5  5  3  1  1  5] 18\n",
      "13.565628277248123 [14 13  3  8  0 18  3  2  2  5  4  2  1  1  4] 14\n",
      "17.01156489464677 [16 18  2  0  0 15  3  2  1  4  2  2  1  1  1] 19\n",
      "10.60183922922208 [11 12  3  4  1 19  2  1  1  4  3  4  1  3  3] 11\n",
      "8.570408477099845 [11 10  2 12  1 16  2  2  2  4  4  2  1  1  3] 10\n",
      "12.583224420770058 [12 13  2  2  0 16  2  2  3  4  3  3  2  3  4] 13\n",
      "15.416073537981752 [16 16  1  0  0 15  4  4  2  4  3  2  1  1  5] 15\n",
      "1.7384673070276513 [ 7  7  2  5  3 16  3  1  1  2  3  3  2  2  4] 7\n",
      "10.5653831882495 [10 11  2  2  0 15  1  2  1  4  3  2  1  1  5] 11\n",
      "8.97728835339946 [10 10  2 14  1 16  2  2  2  4  3  3  2  2  5] 9\n",
      "16.085795677317698 [15 15  2  2  0 16  4  4  1  5  4  4  1  2  5] 16\n",
      "5.531195055372592 [ 6  5  2  4  0 17  3  3  1  4  3  4  1  4  4] 6\n",
      "7.019432650024239 [ 7  7  3  0  0 16  3  3  1  4  3  3  1  3  4] 8\n",
      "13.741424293617264 [14 14  3  4  0 17  4  4  2  4  3  3  1  2  4] 14\n",
      "13.819985153574958 [16 16  2  2  0 16  2  3  1  2  3  1  1  1  3] 17\n",
      "11.789850223112165 [12 12  3  2  0 15  3  4  1  4  3  2  1  1  5] 11\n",
      "12.550103885113671 [12 13  1  6  1 16  2  1  1  4  4  4  3  5  5] 14\n",
      "7.7384206830868845 [ 8  8  2  2  0 18  4  3  1  4  3  2  1  1  3] 8\n",
      "9.165896856753466 [12 12  2  2  0 17  3  2  2  1  2  3  1  2  5] 11\n",
      "6.932248417802654 [ 8  8  3  2  0 18  1  1  4  4  3  2  1  2  4] 10\n",
      "10.416846994632994 [11 11  1  0  0 17  4  4  2  4  1  1  2  2  5] 10\n",
      "9.48580991361323 [ 9  9  2  6  0 15  4  3  1  4  3  3  2  3  5] 10\n",
      "9.583311453561791 [ 9 10  2  2  0 15  1  2  1  4  3  2  1  1  5] 9\n",
      "10.520047727271223 [ 8  9  2  4  0 18  4  4  1  5  4  3  3  4  2] 10\n",
      "16.26798907211184 [18 18  4  6  0 17  3  1  2  3  1  2  1  1  3] 18\n",
      "15.075794079182995 [13 13  1  0  0 15  3  4  1  5  5  5  3  2  5] 12\n",
      "8.245432127384701 [10  9  3  4  0 17  3  3  1  3  2  3  1  1  4] 9\n",
      "9.93903081305064 [11  9  3 10  0 16  3  2  2  5  3  3  1  3  2] 10\n",
      "7.799326281555013 [ 9  8  2 16  1 18  3  1  1  5  3  3  1  1  4] 7\n",
      "12.884909593914287 [15 14  3  2  0 15  4  2  1  3  2  2  1  1  5] 15\n",
      "14.518396707722793 [15 15  2  0  0 18  3  3  1  4  1  3  1  2  1] 15\n",
      "10.2464180332294 [13 11  2  3  1 19  1  2  1  4  5  2  2  2  4] 11\n",
      "12.436099332201003 [13 12  1 20  0 16  2  1  1  4  5  2  1  1  5] 12\n",
      "12.578955437465371 [11 13  2  2  0 15  4  2  1  4  3  3  1  1  5] 13\n",
      "3.4884218832382894 [ 8  7  2  0  3 16  1  3  1  4  3  5  1  1  3] 0\n",
      "9.773297538429237 [ 8 10  2  4  0 16  3  3  3  5  3  2  1  1  4] 10\n",
      "11.44230862445341 [16 12  1  8  0 17  1  2  1  2  2  2  3  3  5] 13\n",
      "14.425245297971916 [12 14  1  0  0 16  3  2  1  5  4  3  1  1  5] 15\n",
      "7.3637960231639745 [ 7  9  1  2  1 17  1  1  2  4  4  4  1  2  5] 8\n",
      "14.198707506805619 [13 14  1  0  0 16  2  3  2  5  3  3  1  1  3] 14\n",
      "18.44817887007984 [19 19  4  4  0 16  4  3  1  4  2  2  1  1  2] 20\n",
      "13.962650770539451 [14 14  1  2  0 16  1  1  1  3  4  4  3  3  1] 13\n",
      "10.183858658106573 [ 9 10  3  9  0 18  4  3  1  4  3  4  1  1  5] 9\n",
      "8.994729670812976 [10  9  3  2  0 15  2  4  1  4  3  2  1  1  5] 8\n",
      "14.498434496950253 [13 14  3  4  0 15  4  2  1  5  3  3  1  3  1] 14\n",
      "6.430927083295945 [ 7  9  4  0  0 15  4  3  2  2  2  2  1  1  3] 0\n",
      "17.986969163096962 [18 19  1 10  0 15  4  2  1  3  5  2  1  1  3] 19\n",
      "3.409169433542351 [ 8  8  1  2  3 17  2  1  2  4  5  1  1  1  3] 10\n",
      "14.720281456303717 [15 14  4  4  0 18  2  3  1  4  5  5  1  3  2] 14\n",
      "1.5202152084111376 [ 9  0  2  0  0 15  3  4  4  5  3  3  1  1  5] 0\n",
      "12.069607931799812 [12 13  2  0  0 16  2  2  1  3  3  4  1  1  4] 14\n",
      "14.025098811118347 [14 14  1  2  0 15  4  4  1  4  3  3  1  3  5] 14\n",
      "12.117864290902268 [12 12  2  0  0 16  2  2  1  4  4  4  1  1  3] 11\n",
      "11.539830222899054 [ 8 12  1  0  0 17  2  1  1  4  4  2  2  4  5] 12\n",
      "14.277218731621147 [14 14  1  4  0 16  4  4  1  4  4  4  1  2  2] 14\n",
      "17.79270128639471 [18 18  3  5  0 18  2  2  1  4  3  3  1  2  2] 19\n",
      "7.74945325673033 [ 9  9  2 15  2 19  3  3  1  4  3  5  3  3  5] 9\n",
      "9.800882769966227 [ 7  9  3  0  0 16  4  3  1  5  3  5  1  1  3] 8\n",
      "3.6268559926570365 [ 3  5  2  8  1 18  3  3  1  4  3  3  1  3  5] 5\n",
      "12.93651465953085 [13 14  3  6  0 16  4  4  1  3  2  3  1  2  2] 14\n",
      "10.609991978421629 [10 10  2  2  0 15  4  3  2  5  4  3  1  2  3] 11\n",
      "10.571876050453954 [10 13  4  6  0 15  4  4  4  1  3  5  3  5  1] 13\n",
      "15.999609520977419 [16 15  3 16  0 17  3  4  1  4  4  5  1  3  5] 15\n",
      "10.49707125200475 [11 10  1  0  0 18  2  3  2  5  2  3  1  2  4] 10\n",
      "14.276151314305263 [17 15  1  2  0 16  2  2  3  4  2  2  1  2  3] 15\n",
      "10.453566696044529 [10  9  3  7  0 17  4  4  1  5  4  4  1  3  4] 9\n",
      "16.858017919160417 [17 18  2 21  0 18  4  3  1  3  1  2  1  3  2] 18\n",
      "12.117149798182933 [12 12  1  4  0 17  2  3  2  5  2  2  1  1  2] 13\n",
      "12.044119484551537 [13 13  4  4  0 15  4  4  1  1  3  3  5  5  3] 12\n",
      "2.266021813931922 [ 6  0  1  0  0 16  4  3  1  5  4  5  1  1  3] 0\n",
      "12.24688145941997 [13 13  2  2  0 16  2  2  2  3  4  4  1  4  5] 11\n",
      "9.030685557009939 [10 10  4  0  0 15  4  2  1  3  3  3  1  1  3] 10\n",
      "12.429455732130293 [13 13  2  2  0 16  2  4  2  4  2  2  1  2  5] 13\n",
      "19.8173075812457 [18 19  1  6  0 15  4  4  1  5  5  3  1  1  4] 19\n",
      "10.020721665454703 [ 7 10  2  0  0 15  3  4  1  5  3  2  1  1  1] 11\n",
      "13.925896702452636 [13 15  2  2  0 16  1  0  2  4  3  2  1  1  3] 16\n",
      "10.737654382355586 [12 10  2  8  0 18  4  4  2  4  3  4  2  2  4] 11\n",
      "9.88715578844219 [ 7 10  2  6  0 16  3  3  3  5  3  3  1  3  2] 10\n",
      "10.764785937204286 [11 11  2  2  0 17  2  3  2  4  4  3  1  1  3] 10\n",
      "14.171773928168026 [13 13  3  7  0 18  3  2  1  5  4  3  2  3  1] 14\n",
      "7.868773037276853 [10  9  4  0  0 17  2  2  1  3  4  1  1  1  2] 0\n",
      "14.705686098324854 [14 15  2  2  0 17  3  2  2  4  4  4  1  3  1] 15\n",
      "-2.433746492140193 [ 4  0  1  0  2 16  3  3  2  4  3  2  1  1  5] 0\n",
      "11.254213868936748 [15 12  2  0  1 19  3  3  1  4  5  3  1  2  5] 12\n",
      "3.3217721050759197 [ 6  5  1  0  1 19  1  1  2  4  3  2  1  3  5] 0\n",
      "14.205617783990323 [16 15  3  0  0 17  0  2  2  3  3  3  2  3  2] 15\n",
      "12.71526086006969 [13 13  2  0  0 18  1  4  3  4  3  4  1  4  5] 13\n",
      "10.764571157143891 [ 9  9  2  2  0 17  3  1  1  5  4  4  3  4  5] 10\n",
      "11.906811183276677 [12 11  2  2  0 17  2  3  1  5  3  3  1  3  3] 12\n",
      "14.515094314685111 [12 12  2 13  0 17  4  4  1  5  3  5  4  5  3] 13\n",
      "9.32017456436449 [12 10  2 18  1 19  1  1  1  4  4  3  1  3  3] 10\n",
      "-2.810871773322835 [ 5  0  1  0  3 19  3  2  1  4  5  4  1  1  4] 0\n",
      "17.358133754109023 [16 17  1  4  0 15  4  3  1  4  5  5  1  3  1] 18\n",
      "9.438082384198365 [ 7  7  1 14  0 16  4  4  1  4  5  5  5  5  4] 5\n",
      "7.951192434796232 [ 9  9  2  0  1 19  1  2  1  4  2  4  2  2  3] 0\n",
      "12.13445239570932 [ 9 11  2  0  0 15  4  4  1  5  4  2  3  4  5] 12\n",
      "14.65614288846735 [15 15  2  0  0 16  2  1  1  4  2  3  1  2  5] 15\n",
      "15.981691868577531 [17 15  1  4  0 17  4  4  1  5  2  3  1  2  5] 16\n",
      "13.422680734078565 [11 12  2 54  0 16  3  3  1  4  3  3  2  4  5] 11\n",
      "10.944597773783425 [10 10  2 17  0 17  3  1  1  4  5  4  2  3  1] 10\n",
      "11.636093201886771 [12 12  2 12  1 18  2  1  1  5  4  3  1  1  5] 13\n",
      "8.64011101267607 [10 10  1  4  0 18  1  1  2  3  3  2  1  2  3] 10\n",
      "9.329184641913182 [10  9  3 28  1 17  3  4  1  4  4  3  3  4  5] 9\n",
      "14.527238958551482 [11 14  1  6  0 16  4  4  1  5  3  4  1  2  1] 14\n",
      "16.17202742278773 [16 15  2 10  0 17  3  2  1  5  3  4  1  3  3] 15\n",
      "12.743581144968775 [12 12  2 12  0 17  3  3  1  4  3  4  2  3  4] 11\n",
      "15.966538476601109 [14 15  1  3  0 17  3  3  1  4  3  5  3  5  5] 16\n",
      "14.379206150001984 [14 15  2  0  0 15  4  3  1  4  3  2  1  1  1] 15\n",
      "14.860010402987673 [14 16  1  3  0 17  3  1  2  2  4  5  3  4  2] 16\n",
      "10.6093975277512 [13 11  3  8  0 17  2  2  1  3  4  3  1  1  3] 11\n",
      "12.461928394596338 [14 12  1  4  0 17  3  3  1  4  4  3  1  3  5] 11\n",
      "12.710950838228587 [13 12  1  0  0 16  4  4  1  5  3  2  1  2  5] 12\n",
      "15.993703541032954 [16 16  4 12  0 15  3  1  2  4  4  2  2  3  3] 16\n",
      "1.7878307059760647 [11  0  3  0  0 15  4  4  1  4  3  3  1  1  5] 0\n",
      "9.257784711448014 [ 6 10  2  4  0 16  3  3  1  4  3  2  1  2  5] 10\n",
      "9.668824049193441 [ 9 10  3  0  0 18  2  2  1  4  3  3  1  1  3] 0\n",
      "10.25528508131127 [12 11  1 16  0 16  3  4  1  3  2  1  1  4  5] 11\n",
      "10.314602523251219 [10  9  2  4  0 17  4  3  2  4  4  4  4  4  4] 9\n",
      "15.550088537173874 [15 16  2  2  0 16  4  2  1  4  2  3  1  1  5] 16\n",
      "7.608404466157431 [ 7 10  2 25  1 16  2  2  2  3  3  3  1  2  3] 11\n",
      "4.653575839715131 [ 6  6  2 13  1 18  3  3  1  3  2  4  2  4  4] 8\n",
      "9.733835445867863 [12 11  1 16  1 16  3  2  2  4  5  2  1  1  2] 12\n",
      "10.970796079630334 [10 11  4 10  0 15  3  3  1  4  3  3  1  1  4] 11\n",
      "4.956443946107649 [ 6  5  2  6  0 17  4  4  2  4  1  4  1  1  1] 6\n",
      "10.795157351465754 [11 10  3  4  0 15  2  3  1  5  3  2  1  2  5] 11\n",
      "12.933609672396297 [16 15  1  5  0 18  4  4  1  1  4  2  2  2  1] 16\n",
      "11.850485164563437 [11 11  2  4  0 16  3  4  1  5  3  3  1  1  5] 11\n",
      "1.0467193099217225 [ 7  0  3  0  0 18  2  1  2  4  4  4  1  1  3] 0\n",
      "6.482947665765099 [ 8  7  2  6  0 17  3  3  1  3  3  3  1  3  3] 9\n",
      "14.54323946592011 [15 16  1  4  0 15  4  2  1  2  2  4  2  4  1] 15\n",
      "15.804387431946267 [15 15  2 10  0 16  4  3  1  5  4  2  1  2  5] 15\n",
      "13.219916209983367 [11 15  1  2  0 16  4  3  2  3  3  3  1  1  4] 15\n",
      "11.965672311380928 [15 12  3  0  0 17  4  2  2  4  3  3  1  1  3] 14\n",
      "18.723306320110996 [19 18  3  0  0 18  4  4  2  5  4  4  1  1  1] 19\n",
      "17.197087823745438 [18 18  1 24  1 17  4  4  2  4  2  4  2  3  2] 18\n",
      "7.116989588429016 [ 6  9  1  4  1 18  2  1  1  3  2  5  2  5  5] 8\n",
      "11.638333983009932 [11 12  1  0  0 18  3  2  3  4  4  1  3  4  5] 10\n",
      "8.312329849566416 [ 8  8  1  0  0 15  2  2  1  5  4  1  1  1  1] 11\n",
      "9.089214385190584 [11  9  2 12  0 17  2  2  1  4  2  2  1  1  3] 9\n",
      "10.790190419977652 [10 10  2  4  0 16  2  2  1  4  3  5  2  4  4] 10\n",
      "2.90822388451471 [ 7  7  2  4  2 19  3  2  2  3  2  2  1  1  3] 9\n",
      "10.139956390160844 [11 10  3  4  0 18  2  3  1  4  3  3  1  2  3] 10\n",
      "3.974771825871331 [ 5  8  2  0  3 17  1  1  4  5  3  5  1  5  5] 7\n",
      "10.902911640680795 [13 12  2  2  1 18  1  1  2  4  4  3  2  3  5] 12\n",
      "13.375153512020969 [10 13  1  4  0 15  4  4  1  5  3  3  1  1  5] 14\n",
      "10.211185179173034 [15 13  3 14  2 19  1  1  1  4  1  2  1  1  3] 13\n",
      "13.198468337837735 [12 14  3  1  0 17  3  1  1  3  4  3  2  3  5] 15\n"
     ]
    }
   ],
   "source": [
    "predictions = linear.predict(x_test)\n",
    "for x in range (len(predictions)):\n",
    "    print (predictions[x], x_test[x], y_test[x])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "language": "python",
   "name": "python3"
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  "language_info": {
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    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
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 },
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}