Diabetes dataset.ipynb

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       "         43., 198., 242., 232., 175.,  93., 168., 275., 293., 281.,  72.,\n",
       "        140., 189., 181., 209., 136., 261., 113., 131., 174., 257.,  55.,\n",
       "         84.,  42., 146., 212., 233.,  91., 111., 152., 120.,  67., 310.,\n",
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       "        220.,  57.]),\n",
       " 'DESCR': '.. _diabetes_dataset:\\n\\nDiabetes dataset\\n----------------\\n\\nTen baseline variables, age, sex, body mass index, average blood\\npressure, and six blood serum measurements were obtained for each of n =\\n442 diabetes patients, as well as the response of interest, a\\nquantitative measure of disease progression one year after baseline.\\n\\n**Data Set Characteristics:**\\n\\n  :Number of Instances: 442\\n\\n  :Number of Attributes: First 10 columns are numeric predictive values\\n\\n  :Target: Column 11 is a quantitative measure of disease progression one year after baseline\\n\\n  :Attribute Information:\\n      - Age\\n      - Sex\\n      - Body mass index\\n      - Average blood pressure\\n      - S1\\n      - S2\\n      - S3\\n      - S4\\n      - S5\\n      - S6\\n\\nNote: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).\\n\\nSource URL:\\nhttp://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\\n\\nFor more information see:\\nBradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\\n(http://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)',\n",
       " 'feature_names': ['age',\n",
       "  'sex',\n",
       "  'bmi',\n",
       "  'bp',\n",
       "  's1',\n",
       "  's2',\n",
       "  's3',\n",
       "  's4',\n",
       "  's5',\n",
       "  's6'],\n",
       " 'data_filename': 'C:\\\\Users\\\\Simrann\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\diabetes_data.csv.gz',\n",
       " 'target_filename': 'C:\\\\Users\\\\Simrann\\\\Anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\datasets\\\\data\\\\diabetes_target.csv.gz'}"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "diabetes= datasets.load_diabetes()\n",
    "diabetes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=diabetes.data\n",
    "Y=diabetes.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "metadata": {},
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    }
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    "X"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(442, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(442,)"
      ]
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "Y.shape"
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  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'.. _diabetes_dataset:\\n\\nDiabetes dataset\\n----------------\\n\\nTen baseline variables, age, sex, body mass index, average blood\\npressure, and six blood serum measurements were obtained for each of n =\\n442 diabetes patients, as well as the response of interest, a\\nquantitative measure of disease progression one year after baseline.\\n\\n**Data Set Characteristics:**\\n\\n  :Number of Instances: 442\\n\\n  :Number of Attributes: First 10 columns are numeric predictive values\\n\\n  :Target: Column 11 is a quantitative measure of disease progression one year after baseline\\n\\n  :Attribute Information:\\n      - Age\\n      - Sex\\n      - Body mass index\\n      - Average blood pressure\\n      - S1\\n      - S2\\n      - S3\\n      - S4\\n      - S5\\n      - S6\\n\\nNote: Each of these 10 feature variables have been mean centered and scaled by the standard deviation times `n_samples` (i.e. the sum of squares of each column totals 1).\\n\\nSource URL:\\nhttp://www4.stat.ncsu.edu/~boos/var.select/diabetes.html\\n\\nFor more information see:\\nBradley Efron, Trevor Hastie, Iain Johnstone and Robert Tibshirani (2004) \"Least Angle Regression,\" Annals of Statistics (with discussion), 407-499.\\n(http://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf)'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.DESCR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['age', 'sex', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "      <td>-0.068330</td>\n",
       "      <td>-0.092204</td>\n",
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       "      <td>0.050680</td>\n",
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       "      <td>-0.005671</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.034194</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.002864</td>\n",
       "      <td>-0.025930</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>-0.011595</td>\n",
       "      <td>-0.036656</td>\n",
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       "      <td>-0.036038</td>\n",
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       "      <td>0.022692</td>\n",
       "      <td>-0.009362</td>\n",
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       "      <td>-0.044642</td>\n",
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       "      <td>-0.002592</td>\n",
       "      <td>-0.031991</td>\n",
       "      <td>-0.046641</td>\n",
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       "      <th>5</th>\n",
       "      <td>-0.092695</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.040696</td>\n",
       "      <td>-0.019442</td>\n",
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       "      <td>-0.079288</td>\n",
       "      <td>0.041277</td>\n",
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       "      <td>-0.041180</td>\n",
       "      <td>-0.096346</td>\n",
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       "      <td>-0.045472</td>\n",
       "      <td>0.050680</td>\n",
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       "      <td>-0.015999</td>\n",
       "      <td>-0.040096</td>\n",
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       "      <td>-0.039493</td>\n",
       "      <td>-0.062913</td>\n",
       "      <td>-0.038357</td>\n",
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       "    <tr>\n",
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       "      <td>0.050680</td>\n",
       "      <td>-0.001895</td>\n",
       "      <td>0.066630</td>\n",
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       "      <td>0.108914</td>\n",
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       "      <td>-0.035817</td>\n",
       "      <td>0.003064</td>\n",
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       "      <th>8</th>\n",
       "      <td>0.041708</td>\n",
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       "      <td>-0.040099</td>\n",
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       "      <td>-0.028674</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.014956</td>\n",
       "      <td>0.011349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.070900</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.039062</td>\n",
       "      <td>-0.033214</td>\n",
       "      <td>-0.012577</td>\n",
       "      <td>-0.034508</td>\n",
       "      <td>-0.024993</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.067736</td>\n",
       "      <td>-0.013504</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-0.096328</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.083808</td>\n",
       "      <td>0.008101</td>\n",
       "      <td>-0.103389</td>\n",
       "      <td>-0.090561</td>\n",
       "      <td>-0.013948</td>\n",
       "      <td>-0.076395</td>\n",
       "      <td>-0.062913</td>\n",
       "      <td>-0.034215</td>\n",
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       "      <th>11</th>\n",
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       "      <td>-0.033214</td>\n",
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       "      <td>-0.065491</td>\n",
       "      <td>0.071210</td>\n",
       "      <td>-0.096433</td>\n",
       "      <td>-0.059067</td>\n",
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       "      <td>-0.009113</td>\n",
       "      <td>-0.004321</td>\n",
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       "      <td>-0.039493</td>\n",
       "      <td>-0.030751</td>\n",
       "      <td>-0.042499</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
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       "      <td>-0.001895</td>\n",
       "      <td>0.008101</td>\n",
       "      <td>-0.004321</td>\n",
       "      <td>-0.015719</td>\n",
       "      <td>-0.002903</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.038393</td>\n",
       "      <td>-0.013504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.045341</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.025607</td>\n",
       "      <td>-0.012556</td>\n",
       "      <td>0.017694</td>\n",
       "      <td>-0.000061</td>\n",
       "      <td>0.081775</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.031991</td>\n",
       "      <td>-0.075636</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-0.052738</td>\n",
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       "      <td>-0.018062</td>\n",
       "      <td>0.080401</td>\n",
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       "      <td>0.107662</td>\n",
       "      <td>-0.039719</td>\n",
       "      <td>0.108111</td>\n",
       "      <td>0.036056</td>\n",
       "      <td>-0.042499</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.005515</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.042296</td>\n",
       "      <td>0.049415</td>\n",
       "      <td>0.024574</td>\n",
       "      <td>-0.023861</td>\n",
       "      <td>0.074412</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>0.052280</td>\n",
       "      <td>0.027917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.070769</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.012117</td>\n",
       "      <td>0.056301</td>\n",
       "      <td>0.034206</td>\n",
       "      <td>0.049416</td>\n",
       "      <td>-0.039719</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.027368</td>\n",
       "      <td>-0.001078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-0.038207</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.010517</td>\n",
       "      <td>-0.036656</td>\n",
       "      <td>-0.037344</td>\n",
       "      <td>-0.019476</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.018118</td>\n",
       "      <td>-0.017646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-0.027310</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.018062</td>\n",
       "      <td>-0.040099</td>\n",
       "      <td>-0.002945</td>\n",
       "      <td>-0.011335</td>\n",
       "      <td>0.037595</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.008944</td>\n",
       "      <td>-0.054925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-0.049105</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.056863</td>\n",
       "      <td>-0.043542</td>\n",
       "      <td>-0.045599</td>\n",
       "      <td>-0.043276</td>\n",
       "      <td>0.000779</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.011901</td>\n",
       "      <td>0.015491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-0.085430</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.022373</td>\n",
       "      <td>0.001215</td>\n",
       "      <td>-0.037344</td>\n",
       "      <td>-0.026366</td>\n",
       "      <td>0.015505</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.072128</td>\n",
       "      <td>-0.017646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-0.085430</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.004050</td>\n",
       "      <td>-0.009113</td>\n",
       "      <td>-0.002945</td>\n",
       "      <td>0.007767</td>\n",
       "      <td>0.022869</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.061177</td>\n",
       "      <td>-0.013504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.045341</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.060618</td>\n",
       "      <td>0.031053</td>\n",
       "      <td>0.028702</td>\n",
       "      <td>-0.047347</td>\n",
       "      <td>-0.054446</td>\n",
       "      <td>0.071210</td>\n",
       "      <td>0.133599</td>\n",
       "      <td>0.135612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-0.063635</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.035829</td>\n",
       "      <td>-0.022885</td>\n",
       "      <td>-0.030464</td>\n",
       "      <td>-0.018850</td>\n",
       "      <td>-0.006584</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.025952</td>\n",
       "      <td>-0.054925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-0.067268</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.012673</td>\n",
       "      <td>-0.040099</td>\n",
       "      <td>-0.015328</td>\n",
       "      <td>0.004636</td>\n",
       "      <td>-0.058127</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.019199</td>\n",
       "      <td>-0.034215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-0.107226</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.077342</td>\n",
       "      <td>-0.026328</td>\n",
       "      <td>-0.089630</td>\n",
       "      <td>-0.096198</td>\n",
       "      <td>0.026550</td>\n",
       "      <td>-0.076395</td>\n",
       "      <td>-0.042572</td>\n",
       "      <td>-0.005220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-0.023677</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.059541</td>\n",
       "      <td>-0.040099</td>\n",
       "      <td>-0.042848</td>\n",
       "      <td>-0.043589</td>\n",
       "      <td>0.011824</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.015998</td>\n",
       "      <td>0.040343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.052606</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.021295</td>\n",
       "      <td>-0.074528</td>\n",
       "      <td>-0.040096</td>\n",
       "      <td>-0.037639</td>\n",
       "      <td>-0.006584</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.000609</td>\n",
       "      <td>-0.054925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.067136</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.006206</td>\n",
       "      <td>0.063187</td>\n",
       "      <td>-0.042848</td>\n",
       "      <td>-0.095885</td>\n",
       "      <td>0.052322</td>\n",
       "      <td>-0.076395</td>\n",
       "      <td>0.059424</td>\n",
       "      <td>0.052770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>0.074401</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.085408</td>\n",
       "      <td>0.063187</td>\n",
       "      <td>0.014942</td>\n",
       "      <td>0.013091</td>\n",
       "      <td>0.015505</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.006209</td>\n",
       "      <td>0.085907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>-0.052738</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.000817</td>\n",
       "      <td>-0.026328</td>\n",
       "      <td>0.010815</td>\n",
       "      <td>0.007141</td>\n",
       "      <td>0.048640</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.035817</td>\n",
       "      <td>0.019633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>0.081666</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.006728</td>\n",
       "      <td>-0.004523</td>\n",
       "      <td>0.109883</td>\n",
       "      <td>0.117056</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>0.091875</td>\n",
       "      <td>0.054724</td>\n",
       "      <td>0.007207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>-0.005515</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.008883</td>\n",
       "      <td>-0.050428</td>\n",
       "      <td>0.025950</td>\n",
       "      <td>0.047224</td>\n",
       "      <td>-0.043401</td>\n",
       "      <td>0.071210</td>\n",
       "      <td>0.014823</td>\n",
       "      <td>0.003064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>-0.027310</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.080019</td>\n",
       "      <td>0.098763</td>\n",
       "      <td>-0.002945</td>\n",
       "      <td>0.018101</td>\n",
       "      <td>-0.017629</td>\n",
       "      <td>0.003312</td>\n",
       "      <td>-0.029528</td>\n",
       "      <td>0.036201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>-0.052738</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.071397</td>\n",
       "      <td>-0.074528</td>\n",
       "      <td>-0.015328</td>\n",
       "      <td>-0.001314</td>\n",
       "      <td>0.004460</td>\n",
       "      <td>-0.021412</td>\n",
       "      <td>-0.046879</td>\n",
       "      <td>0.003064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>0.009016</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.024529</td>\n",
       "      <td>-0.026328</td>\n",
       "      <td>0.098876</td>\n",
       "      <td>0.094196</td>\n",
       "      <td>0.070730</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.021394</td>\n",
       "      <td>0.007207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>-0.020045</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.054707</td>\n",
       "      <td>-0.053871</td>\n",
       "      <td>-0.066239</td>\n",
       "      <td>-0.057367</td>\n",
       "      <td>0.011824</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.074089</td>\n",
       "      <td>-0.005220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>0.023546</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.036385</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>0.001183</td>\n",
       "      <td>0.034698</td>\n",
       "      <td>-0.043401</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>-0.033249</td>\n",
       "      <td>0.061054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>0.038076</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.016428</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>0.039710</td>\n",
       "      <td>0.045032</td>\n",
       "      <td>-0.043401</td>\n",
       "      <td>0.071210</td>\n",
       "      <td>0.049769</td>\n",
       "      <td>0.015491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>-0.078165</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.077863</td>\n",
       "      <td>0.052858</td>\n",
       "      <td>0.078236</td>\n",
       "      <td>0.064447</td>\n",
       "      <td>0.026550</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.040672</td>\n",
       "      <td>-0.009362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>0.009016</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.039618</td>\n",
       "      <td>0.028758</td>\n",
       "      <td>0.038334</td>\n",
       "      <td>0.073529</td>\n",
       "      <td>-0.072854</td>\n",
       "      <td>0.108111</td>\n",
       "      <td>0.015567</td>\n",
       "      <td>-0.046641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>0.001751</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.011039</td>\n",
       "      <td>-0.019442</td>\n",
       "      <td>-0.016704</td>\n",
       "      <td>-0.003819</td>\n",
       "      <td>-0.047082</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.024053</td>\n",
       "      <td>0.023775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>-0.078165</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.040696</td>\n",
       "      <td>-0.081414</td>\n",
       "      <td>-0.100638</td>\n",
       "      <td>-0.112795</td>\n",
       "      <td>0.022869</td>\n",
       "      <td>-0.076395</td>\n",
       "      <td>-0.020289</td>\n",
       "      <td>-0.050783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>0.030811</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.034229</td>\n",
       "      <td>0.043677</td>\n",
       "      <td>0.057597</td>\n",
       "      <td>0.068831</td>\n",
       "      <td>-0.032356</td>\n",
       "      <td>0.057557</td>\n",
       "      <td>0.035462</td>\n",
       "      <td>0.085907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>-0.034575</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.005650</td>\n",
       "      <td>-0.005671</td>\n",
       "      <td>-0.073119</td>\n",
       "      <td>-0.062691</td>\n",
       "      <td>-0.006584</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.045421</td>\n",
       "      <td>0.032059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>0.048974</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.088642</td>\n",
       "      <td>0.087287</td>\n",
       "      <td>0.035582</td>\n",
       "      <td>0.021546</td>\n",
       "      <td>-0.024993</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>0.066048</td>\n",
       "      <td>0.131470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>-0.041840</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.033151</td>\n",
       "      <td>-0.022885</td>\n",
       "      <td>0.046589</td>\n",
       "      <td>0.041587</td>\n",
       "      <td>0.056003</td>\n",
       "      <td>-0.024733</td>\n",
       "      <td>-0.025952</td>\n",
       "      <td>-0.038357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>-0.009147</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.056863</td>\n",
       "      <td>-0.050428</td>\n",
       "      <td>0.021822</td>\n",
       "      <td>0.045345</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>-0.009919</td>\n",
       "      <td>-0.017646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>0.070769</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.030996</td>\n",
       "      <td>0.021872</td>\n",
       "      <td>-0.037344</td>\n",
       "      <td>-0.047034</td>\n",
       "      <td>0.033914</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.014956</td>\n",
       "      <td>-0.001078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>0.009016</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.055229</td>\n",
       "      <td>-0.005671</td>\n",
       "      <td>0.057597</td>\n",
       "      <td>0.044719</td>\n",
       "      <td>-0.002903</td>\n",
       "      <td>0.023239</td>\n",
       "      <td>0.055684</td>\n",
       "      <td>0.106617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>-0.027310</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.060097</td>\n",
       "      <td>-0.029771</td>\n",
       "      <td>0.046589</td>\n",
       "      <td>0.019980</td>\n",
       "      <td>0.122273</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.051401</td>\n",
       "      <td>-0.009362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>0.016281</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.001339</td>\n",
       "      <td>0.008101</td>\n",
       "      <td>0.005311</td>\n",
       "      <td>0.010899</td>\n",
       "      <td>0.030232</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.045421</td>\n",
       "      <td>0.032059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>-0.012780</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.023451</td>\n",
       "      <td>-0.040099</td>\n",
       "      <td>-0.016704</td>\n",
       "      <td>0.004636</td>\n",
       "      <td>-0.017629</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>-0.038459</td>\n",
       "      <td>-0.038357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>-0.056370</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.074108</td>\n",
       "      <td>-0.050428</td>\n",
       "      <td>-0.024960</td>\n",
       "      <td>-0.047034</td>\n",
       "      <td>0.092820</td>\n",
       "      <td>-0.076395</td>\n",
       "      <td>-0.061177</td>\n",
       "      <td>-0.046641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>0.041708</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>0.019662</td>\n",
       "      <td>0.059744</td>\n",
       "      <td>-0.005697</td>\n",
       "      <td>-0.002566</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>-0.002592</td>\n",
       "      <td>0.031193</td>\n",
       "      <td>0.007207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>-0.005515</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.015906</td>\n",
       "      <td>-0.067642</td>\n",
       "      <td>0.049341</td>\n",
       "      <td>0.079165</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>0.034309</td>\n",
       "      <td>-0.018118</td>\n",
       "      <td>0.044485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>0.041708</td>\n",
       "      <td>0.050680</td>\n",
       "      <td>-0.015906</td>\n",
       "      <td>0.017282</td>\n",
       "      <td>-0.037344</td>\n",
       "      <td>-0.013840</td>\n",
       "      <td>-0.024993</td>\n",
       "      <td>-0.011080</td>\n",
       "      <td>-0.046879</td>\n",
       "      <td>0.015491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>-0.045472</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>0.039062</td>\n",
       "      <td>0.001215</td>\n",
       "      <td>0.016318</td>\n",
       "      <td>0.015283</td>\n",
       "      <td>-0.028674</td>\n",
       "      <td>0.026560</td>\n",
       "      <td>0.044528</td>\n",
       "      <td>-0.025930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>-0.045472</td>\n",
       "      <td>-0.044642</td>\n",
       "      <td>-0.073030</td>\n",
       "      <td>-0.081414</td>\n",
       "      <td>0.083740</td>\n",
       "      <td>0.027809</td>\n",
       "      <td>0.173816</td>\n",
       "      <td>-0.039493</td>\n",
       "      <td>-0.004220</td>\n",
       "      <td>0.003064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>442 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0         1         2         3         4         5         6  \\\n",
       "0    0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
       "1   -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
       "2    0.085299  0.050680  0.044451 -0.005671 -0.045599 -0.034194 -0.032356   \n",
       "3   -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
       "4    0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
       "5   -0.092695 -0.044642 -0.040696 -0.019442 -0.068991 -0.079288  0.041277   \n",
       "6   -0.045472  0.050680 -0.047163 -0.015999 -0.040096 -0.024800  0.000779   \n",
       "7    0.063504  0.050680 -0.001895  0.066630  0.090620  0.108914  0.022869   \n",
       "8    0.041708  0.050680  0.061696 -0.040099 -0.013953  0.006202 -0.028674   \n",
       "9   -0.070900 -0.044642  0.039062 -0.033214 -0.012577 -0.034508 -0.024993   \n",
       "10  -0.096328 -0.044642 -0.083808  0.008101 -0.103389 -0.090561 -0.013948   \n",
       "11   0.027178  0.050680  0.017506 -0.033214 -0.007073  0.045972 -0.065491   \n",
       "12   0.016281 -0.044642 -0.028840 -0.009113 -0.004321 -0.009769  0.044958   \n",
       "13   0.005383  0.050680 -0.001895  0.008101 -0.004321 -0.015719 -0.002903   \n",
       "14   0.045341 -0.044642 -0.025607 -0.012556  0.017694 -0.000061  0.081775   \n",
       "15  -0.052738  0.050680 -0.018062  0.080401  0.089244  0.107662 -0.039719   \n",
       "16  -0.005515 -0.044642  0.042296  0.049415  0.024574 -0.023861  0.074412   \n",
       "17   0.070769  0.050680  0.012117  0.056301  0.034206  0.049416 -0.039719   \n",
       "18  -0.038207 -0.044642 -0.010517 -0.036656 -0.037344 -0.019476 -0.028674   \n",
       "19  -0.027310 -0.044642 -0.018062 -0.040099 -0.002945 -0.011335  0.037595   \n",
       "20  -0.049105 -0.044642 -0.056863 -0.043542 -0.045599 -0.043276  0.000779   \n",
       "21  -0.085430  0.050680 -0.022373  0.001215 -0.037344 -0.026366  0.015505   \n",
       "22  -0.085430 -0.044642 -0.004050 -0.009113 -0.002945  0.007767  0.022869   \n",
       "23   0.045341  0.050680  0.060618  0.031053  0.028702 -0.047347 -0.054446   \n",
       "24  -0.063635 -0.044642  0.035829 -0.022885 -0.030464 -0.018850 -0.006584   \n",
       "25  -0.067268  0.050680 -0.012673 -0.040099 -0.015328  0.004636 -0.058127   \n",
       "26  -0.107226 -0.044642 -0.077342 -0.026328 -0.089630 -0.096198  0.026550   \n",
       "27  -0.023677 -0.044642  0.059541 -0.040099 -0.042848 -0.043589  0.011824   \n",
       "28   0.052606 -0.044642 -0.021295 -0.074528 -0.040096 -0.037639 -0.006584   \n",
       "29   0.067136  0.050680 -0.006206  0.063187 -0.042848 -0.095885  0.052322   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "412  0.074401 -0.044642  0.085408  0.063187  0.014942  0.013091  0.015505   \n",
       "413 -0.052738 -0.044642 -0.000817 -0.026328  0.010815  0.007141  0.048640   \n",
       "414  0.081666  0.050680  0.006728 -0.004523  0.109883  0.117056 -0.032356   \n",
       "415 -0.005515 -0.044642  0.008883 -0.050428  0.025950  0.047224 -0.043401   \n",
       "416 -0.027310 -0.044642  0.080019  0.098763 -0.002945  0.018101 -0.017629   \n",
       "417 -0.052738 -0.044642  0.071397 -0.074528 -0.015328 -0.001314  0.004460   \n",
       "418  0.009016 -0.044642 -0.024529 -0.026328  0.098876  0.094196  0.070730   \n",
       "419 -0.020045 -0.044642 -0.054707 -0.053871 -0.066239 -0.057367  0.011824   \n",
       "420  0.023546 -0.044642 -0.036385  0.000068  0.001183  0.034698 -0.043401   \n",
       "421  0.038076  0.050680  0.016428  0.021872  0.039710  0.045032 -0.043401   \n",
       "422 -0.078165  0.050680  0.077863  0.052858  0.078236  0.064447  0.026550   \n",
       "423  0.009016  0.050680 -0.039618  0.028758  0.038334  0.073529 -0.072854   \n",
       "424  0.001751  0.050680  0.011039 -0.019442 -0.016704 -0.003819 -0.047082   \n",
       "425 -0.078165 -0.044642 -0.040696 -0.081414 -0.100638 -0.112795  0.022869   \n",
       "426  0.030811  0.050680 -0.034229  0.043677  0.057597  0.068831 -0.032356   \n",
       "427 -0.034575  0.050680  0.005650 -0.005671 -0.073119 -0.062691 -0.006584   \n",
       "428  0.048974  0.050680  0.088642  0.087287  0.035582  0.021546 -0.024993   \n",
       "429 -0.041840 -0.044642 -0.033151 -0.022885  0.046589  0.041587  0.056003   \n",
       "430 -0.009147 -0.044642 -0.056863 -0.050428  0.021822  0.045345 -0.028674   \n",
       "431  0.070769  0.050680 -0.030996  0.021872 -0.037344 -0.047034  0.033914   \n",
       "432  0.009016 -0.044642  0.055229 -0.005671  0.057597  0.044719 -0.002903   \n",
       "433 -0.027310 -0.044642 -0.060097 -0.029771  0.046589  0.019980  0.122273   \n",
       "434  0.016281 -0.044642  0.001339  0.008101  0.005311  0.010899  0.030232   \n",
       "435 -0.012780 -0.044642 -0.023451 -0.040099 -0.016704  0.004636 -0.017629   \n",
       "436 -0.056370 -0.044642 -0.074108 -0.050428 -0.024960 -0.047034  0.092820   \n",
       "437  0.041708  0.050680  0.019662  0.059744 -0.005697 -0.002566 -0.028674   \n",
       "438 -0.005515  0.050680 -0.015906 -0.067642  0.049341  0.079165 -0.028674   \n",
       "439  0.041708  0.050680 -0.015906  0.017282 -0.037344 -0.013840 -0.024993   \n",
       "440 -0.045472 -0.044642  0.039062  0.001215  0.016318  0.015283 -0.028674   \n",
       "441 -0.045472 -0.044642 -0.073030 -0.081414  0.083740  0.027809  0.173816   \n",
       "\n",
       "            7         8         9  \n",
       "0   -0.002592  0.019908 -0.017646  \n",
       "1   -0.039493 -0.068330 -0.092204  \n",
       "2   -0.002592  0.002864 -0.025930  \n",
       "3    0.034309  0.022692 -0.009362  \n",
       "4   -0.002592 -0.031991 -0.046641  \n",
       "5   -0.076395 -0.041180 -0.096346  \n",
       "6   -0.039493 -0.062913 -0.038357  \n",
       "7    0.017703 -0.035817  0.003064  \n",
       "8   -0.002592 -0.014956  0.011349  \n",
       "9   -0.002592  0.067736 -0.013504  \n",
       "10  -0.076395 -0.062913 -0.034215  \n",
       "11   0.071210 -0.096433 -0.059067  \n",
       "12  -0.039493 -0.030751 -0.042499  \n",
       "13  -0.002592  0.038393 -0.013504  \n",
       "14  -0.039493 -0.031991 -0.075636  \n",
       "15   0.108111  0.036056 -0.042499  \n",
       "16  -0.039493  0.052280  0.027917  \n",
       "17   0.034309  0.027368 -0.001078  \n",
       "18  -0.002592 -0.018118 -0.017646  \n",
       "19  -0.039493 -0.008944 -0.054925  \n",
       "20  -0.039493 -0.011901  0.015491  \n",
       "21  -0.039493 -0.072128 -0.017646  \n",
       "22  -0.039493 -0.061177 -0.013504  \n",
       "23   0.071210  0.133599  0.135612  \n",
       "24  -0.002592 -0.025952 -0.054925  \n",
       "25   0.034309  0.019199 -0.034215  \n",
       "26  -0.076395 -0.042572 -0.005220  \n",
       "27  -0.039493 -0.015998  0.040343  \n",
       "28  -0.039493 -0.000609 -0.054925  \n",
       "29  -0.076395  0.059424  0.052770  \n",
       "..        ...       ...       ...  \n",
       "412 -0.002592  0.006209  0.085907  \n",
       "413 -0.039493 -0.035817  0.019633  \n",
       "414  0.091875  0.054724  0.007207  \n",
       "415  0.071210  0.014823  0.003064  \n",
       "416  0.003312 -0.029528  0.036201  \n",
       "417 -0.021412 -0.046879  0.003064  \n",
       "418 -0.002592 -0.021394  0.007207  \n",
       "419 -0.039493 -0.074089 -0.005220  \n",
       "420  0.034309 -0.033249  0.061054  \n",
       "421  0.071210  0.049769  0.015491  \n",
       "422 -0.002592  0.040672 -0.009362  \n",
       "423  0.108111  0.015567 -0.046641  \n",
       "424  0.034309  0.024053  0.023775  \n",
       "425 -0.076395 -0.020289 -0.050783  \n",
       "426  0.057557  0.035462  0.085907  \n",
       "427 -0.039493 -0.045421  0.032059  \n",
       "428  0.034309  0.066048  0.131470  \n",
       "429 -0.024733 -0.025952 -0.038357  \n",
       "430  0.034309 -0.009919 -0.017646  \n",
       "431 -0.039493 -0.014956 -0.001078  \n",
       "432  0.023239  0.055684  0.106617  \n",
       "433 -0.039493 -0.051401 -0.009362  \n",
       "434 -0.039493 -0.045421  0.032059  \n",
       "435 -0.002592 -0.038459 -0.038357  \n",
       "436 -0.076395 -0.061177 -0.046641  \n",
       "437 -0.002592  0.031193  0.007207  \n",
       "438  0.034309 -0.018118  0.044485  \n",
       "439 -0.011080 -0.046879  0.015491  \n",
       "440  0.026560  0.044528 -0.025930  \n",
       "441 -0.039493 -0.004220  0.003064  \n",
       "\n",
       "[442 rows x 10 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.DataFrame(X)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>bp</th>\n",
       "      <th>s1</th>\n",
       "      <th>s2</th>\n",
       "      <th>s3</th>\n",
       "      <th>s4</th>\n",
       "      <th>s5</th>\n",
       "      <th>s6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "      <td>4.420000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-3.634285e-16</td>\n",
       "      <td>1.308343e-16</td>\n",
       "      <td>-8.045349e-16</td>\n",
       "      <td>1.281655e-16</td>\n",
       "      <td>-8.835316e-17</td>\n",
       "      <td>1.327024e-16</td>\n",
       "      <td>-4.574646e-16</td>\n",
       "      <td>3.777301e-16</td>\n",
       "      <td>-3.830854e-16</td>\n",
       "      <td>-3.412882e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "      <td>4.761905e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.072256e-01</td>\n",
       "      <td>-4.464164e-02</td>\n",
       "      <td>-9.027530e-02</td>\n",
       "      <td>-1.123996e-01</td>\n",
       "      <td>-1.267807e-01</td>\n",
       "      <td>-1.156131e-01</td>\n",
       "      <td>-1.023071e-01</td>\n",
       "      <td>-7.639450e-02</td>\n",
       "      <td>-1.260974e-01</td>\n",
       "      <td>-1.377672e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-3.729927e-02</td>\n",
       "      <td>-4.464164e-02</td>\n",
       "      <td>-3.422907e-02</td>\n",
       "      <td>-3.665645e-02</td>\n",
       "      <td>-3.424784e-02</td>\n",
       "      <td>-3.035840e-02</td>\n",
       "      <td>-3.511716e-02</td>\n",
       "      <td>-3.949338e-02</td>\n",
       "      <td>-3.324879e-02</td>\n",
       "      <td>-3.317903e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.383060e-03</td>\n",
       "      <td>-4.464164e-02</td>\n",
       "      <td>-7.283766e-03</td>\n",
       "      <td>-5.670611e-03</td>\n",
       "      <td>-4.320866e-03</td>\n",
       "      <td>-3.819065e-03</td>\n",
       "      <td>-6.584468e-03</td>\n",
       "      <td>-2.592262e-03</td>\n",
       "      <td>-1.947634e-03</td>\n",
       "      <td>-1.077698e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.807591e-02</td>\n",
       "      <td>5.068012e-02</td>\n",
       "      <td>3.124802e-02</td>\n",
       "      <td>3.564384e-02</td>\n",
       "      <td>2.835801e-02</td>\n",
       "      <td>2.984439e-02</td>\n",
       "      <td>2.931150e-02</td>\n",
       "      <td>3.430886e-02</td>\n",
       "      <td>3.243323e-02</td>\n",
       "      <td>2.791705e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.107267e-01</td>\n",
       "      <td>5.068012e-02</td>\n",
       "      <td>1.705552e-01</td>\n",
       "      <td>1.320442e-01</td>\n",
       "      <td>1.539137e-01</td>\n",
       "      <td>1.987880e-01</td>\n",
       "      <td>1.811791e-01</td>\n",
       "      <td>1.852344e-01</td>\n",
       "      <td>1.335990e-01</td>\n",
       "      <td>1.356118e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age           sex           bmi            bp            s1  \\\n",
       "count  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02   \n",
       "mean  -3.634285e-16  1.308343e-16 -8.045349e-16  1.281655e-16 -8.835316e-17   \n",
       "std    4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02   \n",
       "min   -1.072256e-01 -4.464164e-02 -9.027530e-02 -1.123996e-01 -1.267807e-01   \n",
       "25%   -3.729927e-02 -4.464164e-02 -3.422907e-02 -3.665645e-02 -3.424784e-02   \n",
       "50%    5.383060e-03 -4.464164e-02 -7.283766e-03 -5.670611e-03 -4.320866e-03   \n",
       "75%    3.807591e-02  5.068012e-02  3.124802e-02  3.564384e-02  2.835801e-02   \n",
       "max    1.107267e-01  5.068012e-02  1.705552e-01  1.320442e-01  1.539137e-01   \n",
       "\n",
       "                 s2            s3            s4            s5            s6  \n",
       "count  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02  4.420000e+02  \n",
       "mean   1.327024e-16 -4.574646e-16  3.777301e-16 -3.830854e-16 -3.412882e-16  \n",
       "std    4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02  4.761905e-02  \n",
       "min   -1.156131e-01 -1.023071e-01 -7.639450e-02 -1.260974e-01 -1.377672e-01  \n",
       "25%   -3.035840e-02 -3.511716e-02 -3.949338e-02 -3.324879e-02 -3.317903e-02  \n",
       "50%   -3.819065e-03 -6.584468e-03 -2.592262e-03 -1.947634e-03 -1.077698e-03  \n",
       "75%    2.984439e-02  2.931150e-02  3.430886e-02  3.243323e-02  2.791705e-02  \n",
       "max    1.987880e-01  1.811791e-01  1.852344e-01  1.335990e-01  1.356118e-01  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns=diabetes.feature_names\n",
    "df.head()\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(331, 10)\n",
      "(111, 10)\n",
      "(331,)\n",
      "(111,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import model_selection\n",
    "X_train,X_test,Y_train,Y_test=model_selection.train_test_split(X,Y)\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(Y_train.shape)\n",
    "print(Y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "alg=LinearRegression()\n",
    "alg.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_pred=alg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(Y_test,Y_pred)\n",
    "plt.axis([0,450,0,450])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}