P2.) Diabetes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes = datasets.load_boston()\n",
    "diabetes\n",
    "x = diabetes.data\n",
    "y = diabetes.target\n",
    "col = diabetes.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(x)\n",
    "df.columns = col\n",
    "df['age_age'] = df.AGE ** 2\n",
    "x2 = new_df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import model_selection as ms\n",
    "x_train , x_test , y_train , y_test = ms.train_test_split(x,y,random_state = 0)\n",
    "x2_train , x2_test , y2_train , y2_test = ms.train_test_split(x2,y,random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo1 = LinearRegression()\n",
    "algo1.fit(x_train , y_train)\n",
    "algo2 = LinearRegression()\n",
    "algo2.fit(x2_train , y2_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_predict = algo1.predict(x_test)\n",
    "train_score = algo1.score(x_train , y_train)\n",
    "test_score = algo1.score(x_test , y_test)\n",
    "\n",
    "train2_score = algo2.score(x2_train , y2_train)\n",
    "test2_score = algo2.score(x2_test , y2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train1 score :  0.7697699488741149\n",
      "test1 score :  0.6354638433202129\n",
      "\n",
      "train2 score :  0.3432572656762476\n",
      "test2 score :  0.12036779086863558\n"
     ]
    }
   ],
   "source": [
    "print(\"train1 score : \" , train_score)\n",
    "print(\"test1 score : \" ,test_score)\n",
    "print()\n",
    "print(\"train2 score : \" , train2_score)\n",
    "print(\"test2 score : \" ,test2_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = pd.DataFrame(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>NOX/NOX/NOX</th>\n",
       "      <th>RM/RM/RM</th>\n",
       "      <th>AGE/AGE/AGE</th>\n",
       "      <th>DIS/DIS/DIS</th>\n",
       "      <th>RAD/RAD/RAD</th>\n",
       "      <th>TAX/TAX/TAX</th>\n",
       "      <th>PTRATIO/PTRATIO/PTRATIO</th>\n",
       "      <th>B/B/B</th>\n",
       "      <th>LSTAT/LSTAT/LSTAT</th>\n",
       "      <th>age_age/age_age/age_age</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>...</td>\n",
       "      <td>0.013046</td>\n",
       "      <td>531214.963744</td>\n",
       "      <td>5.008794e+12</td>\n",
       "      <td>19145.343205</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.990866e+17</td>\n",
       "      <td>1.962637e+08</td>\n",
       "      <td>1.551557e+18</td>\n",
       "      <td>7.596358e+04</td>\n",
       "      <td>2.508802e+25</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>...</td>\n",
       "      <td>0.004991</td>\n",
       "      <td>450006.399017</td>\n",
       "      <td>1.903439e+13</td>\n",
       "      <td>74596.821765</td>\n",
       "      <td>128.0</td>\n",
       "      <td>4.860798e+16</td>\n",
       "      <td>5.661611e+08</td>\n",
       "      <td>1.551557e+18</td>\n",
       "      <td>5.328726e+06</td>\n",
       "      <td>3.623081e+26</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>...</td>\n",
       "      <td>0.004991</td>\n",
       "      <td>988524.431202</td>\n",
       "      <td>3.178985e+12</td>\n",
       "      <td>74596.821765</td>\n",
       "      <td>128.0</td>\n",
       "      <td>4.860798e+16</td>\n",
       "      <td>5.661611e+08</td>\n",
       "      <td>1.443552e+18</td>\n",
       "      <td>1.726376e+04</td>\n",
       "      <td>1.010595e+25</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>...</td>\n",
       "      <td>0.004227</td>\n",
       "      <td>821897.325116</td>\n",
       "      <td>4.227254e+11</td>\n",
       "      <td>300892.817875</td>\n",
       "      <td>2187.0</td>\n",
       "      <td>2.657485e+16</td>\n",
       "      <td>7.996340e+08</td>\n",
       "      <td>1.490496e+18</td>\n",
       "      <td>1.898591e+03</td>\n",
       "      <td>1.786968e+23</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>...</td>\n",
       "      <td>0.004227</td>\n",
       "      <td>952503.268683</td>\n",
       "      <td>1.374026e+12</td>\n",
       "      <td>300892.817875</td>\n",
       "      <td>2187.0</td>\n",
       "      <td>2.657485e+16</td>\n",
       "      <td>7.996340e+08</td>\n",
       "      <td>1.551557e+18</td>\n",
       "      <td>1.222054e+05</td>\n",
       "      <td>1.887948e+24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>0.06263</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.593</td>\n",
       "      <td>69.1</td>\n",
       "      <td>2.4786</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020281</td>\n",
       "      <td>541478.890302</td>\n",
       "      <td>7.522225e+12</td>\n",
       "      <td>574.705188</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.130156e+17</td>\n",
       "      <td>1.801089e+09</td>\n",
       "      <td>1.422083e+18</td>\n",
       "      <td>7.906519e+06</td>\n",
       "      <td>5.658387e+25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.04527</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.120</td>\n",
       "      <td>76.7</td>\n",
       "      <td>2.2875</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020281</td>\n",
       "      <td>321558.471059</td>\n",
       "      <td>1.561592e+13</td>\n",
       "      <td>327.738694</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.130156e+17</td>\n",
       "      <td>1.801089e+09</td>\n",
       "      <td>1.551557e+18</td>\n",
       "      <td>5.088631e+06</td>\n",
       "      <td>2.438569e+26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.06076</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.976</td>\n",
       "      <td>91.0</td>\n",
       "      <td>2.1675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020281</td>\n",
       "      <td>803980.107747</td>\n",
       "      <td>5.167610e+13</td>\n",
       "      <td>224.757267</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.130156e+17</td>\n",
       "      <td>1.801089e+09</td>\n",
       "      <td>1.551557e+18</td>\n",
       "      <td>1.815322e+05</td>\n",
       "      <td>2.670420e+27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.10959</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.794</td>\n",
       "      <td>89.3</td>\n",
       "      <td>2.3889</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020281</td>\n",
       "      <td>668157.423161</td>\n",
       "      <td>4.528561e+13</td>\n",
       "      <td>444.002886</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.130156e+17</td>\n",
       "      <td>1.801089e+09</td>\n",
       "      <td>1.459576e+18</td>\n",
       "      <td>4.797611e+05</td>\n",
       "      <td>2.050786e+27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.04741</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.573</td>\n",
       "      <td>6.030</td>\n",
       "      <td>80.8</td>\n",
       "      <td>2.5050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>273.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020281</td>\n",
       "      <td>289881.957262</td>\n",
       "      <td>2.248431e+13</td>\n",
       "      <td>618.947925</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.130156e+17</td>\n",
       "      <td>1.801089e+09</td>\n",
       "      <td>1.551557e+18</td>\n",
       "      <td>1.886616e+06</td>\n",
       "      <td>5.055441e+26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 97 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           0     1      2    3      4      5     6       7    8      9  ...  \\\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",
       "501  0.06263   0.0  11.93  0.0  0.573  6.593  69.1  2.4786  1.0  273.0  ...   \n",
       "502  0.04527   0.0  11.93  0.0  0.573  6.120  76.7  2.2875  1.0  273.0  ...   \n",
       "503  0.06076   0.0  11.93  0.0  0.573  6.976  91.0  2.1675  1.0  273.0  ...   \n",
       "504  0.10959   0.0  11.93  0.0  0.573  6.794  89.3  2.3889  1.0  273.0  ...   \n",
       "505  0.04741   0.0  11.93  0.0  0.573  6.030  80.8  2.5050  1.0  273.0  ...   \n",
       "\n",
       "     NOX/NOX/NOX       RM/RM/RM   AGE/AGE/AGE    DIS/DIS/DIS  RAD/RAD/RAD  \\\n",
       "0       0.013046  531214.963744  5.008794e+12   19145.343205          1.0   \n",
       "1       0.004991  450006.399017  1.903439e+13   74596.821765        128.0   \n",
       "2       0.004991  988524.431202  3.178985e+12   74596.821765        128.0   \n",
       "3       0.004227  821897.325116  4.227254e+11  300892.817875       2187.0   \n",
       "4       0.004227  952503.268683  1.374026e+12  300892.817875       2187.0   \n",
       "..           ...            ...           ...            ...          ...   \n",
       "501     0.020281  541478.890302  7.522225e+12     574.705188          1.0   \n",
       "502     0.020281  321558.471059  1.561592e+13     327.738694          1.0   \n",
       "503     0.020281  803980.107747  5.167610e+13     224.757267          1.0   \n",
       "504     0.020281  668157.423161  4.528561e+13     444.002886          1.0   \n",
       "505     0.020281  289881.957262  2.248431e+13     618.947925          1.0   \n",
       "\n",
       "      TAX/TAX/TAX  PTRATIO/PTRATIO/PTRATIO         B/B/B  LSTAT/LSTAT/LSTAT  \\\n",
       "0    1.990866e+17             1.962637e+08  1.551557e+18       7.596358e+04   \n",
       "1    4.860798e+16             5.661611e+08  1.551557e+18       5.328726e+06   \n",
       "2    4.860798e+16             5.661611e+08  1.443552e+18       1.726376e+04   \n",
       "3    2.657485e+16             7.996340e+08  1.490496e+18       1.898591e+03   \n",
       "4    2.657485e+16             7.996340e+08  1.551557e+18       1.222054e+05   \n",
       "..            ...                      ...           ...                ...   \n",
       "501  1.130156e+17             1.801089e+09  1.422083e+18       7.906519e+06   \n",
       "502  1.130156e+17             1.801089e+09  1.551557e+18       5.088631e+06   \n",
       "503  1.130156e+17             1.801089e+09  1.551557e+18       1.815322e+05   \n",
       "504  1.130156e+17             1.801089e+09  1.459576e+18       4.797611e+05   \n",
       "505  1.130156e+17             1.801089e+09  1.551557e+18       1.886616e+06   \n",
       "\n",
       "     age_age/age_age/age_age  \n",
       "0               2.508802e+25  \n",
       "1               3.623081e+26  \n",
       "2               1.010595e+25  \n",
       "3               1.786968e+23  \n",
       "4               1.887948e+24  \n",
       "..                       ...  \n",
       "501             5.658387e+25  \n",
       "502             2.438569e+26  \n",
       "503             2.670420e+27  \n",
       "504             2.050786e+27  \n",
       "505             5.055441e+26  \n",
       "\n",
       "[506 rows x 97 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in df:\n",
    "    new_df[i+'/'+i+'/'+i] = df[i] ** 7\n",
    "new_df"
   ]
  },
  {
   "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": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "array([[-0.07090025, -0.04464164,  0.09295276, ...,  0.00035983,\n",
       "        -0.05454415, -0.0010777 ],\n",
       "       [ 0.0090156 , -0.04464164, -0.01267283, ...,  0.03430886,\n",
       "         0.02337484, -0.0052198 ],\n",
       "       [-0.04183994,  0.05068012,  0.01427248, ...,  0.07120998,\n",
       "         0.03546194, -0.01350402],\n",
       "       ...,\n",
       "       [-0.02367725, -0.04464164, -0.046085  , ..., -0.00259226,\n",
       "        -0.03324879,  0.01134862],\n",
       "       [ 0.03444337, -0.04464164, -0.00728377, ..., -0.03949338,\n",
       "        -0.02139368,  0.00720652],\n",
       "       [ 0.03081083, -0.04464164, -0.02021751, ..., -0.03949338,\n",
       "        -0.01090444, -0.0010777 ]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(y_predict , y_test)\n",
    "plt.axis([0,350,0,350])\n",
    "plt.show()\n",
    "x_train"
   ]
  }
 ],
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