feature_selection.ipynb

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    "# The Recursive Feature Elimination (or RFE) works by recursively removing attributes \n",
    "# and building a model on those attributes that remain.\n",
    "# It uses the model to identify which attributes \n",
    "# contribute the most to predicting the target attribute.\n",
    "\n",
    "from pandas import read_csv\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.svm import SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6d164d27",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>preg</th>\n",
       "      <th>plas</th>\n",
       "      <th>pres</th>\n",
       "      <th>skin</th>\n",
       "      <th>test</th>\n",
       "      <th>mass</th>\n",
       "      <th>pedi</th>\n",
       "      <th>age</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
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       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
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       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
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       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>766</th>\n",
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       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
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       "      <td>1</td>\n",
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       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
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       "<p>768 rows × 9 columns</p>\n",
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      "text/plain": [
       "     preg  plas  pres  skin  test  mass   pedi  age  class\n",
       "0       6   148    72    35     0  33.6  0.627   50      1\n",
       "1       1    85    66    29     0  26.6  0.351   31      0\n",
       "2       8   183    64     0     0  23.3  0.672   32      1\n",
       "3       1    89    66    23    94  28.1  0.167   21      0\n",
       "4       0   137    40    35   168  43.1  2.288   33      1\n",
       "..    ...   ...   ...   ...   ...   ...    ...  ...    ...\n",
       "763    10   101    76    48   180  32.9  0.171   63      0\n",
       "764     2   122    70    27     0  36.8  0.340   27      0\n",
       "765     5   121    72    23   112  26.2  0.245   30      0\n",
       "766     1   126    60     0     0  30.1  0.349   47      1\n",
       "767     1    93    70    31     0  30.4  0.315   23      0\n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data\n",
    "# The datasets consist of several medical predictor (independent) variables and \n",
    "# one target (dependent) variable, Outcome. \n",
    "# Independent variables include the number of pregnancies the patient has had, \n",
    "# their BMI, insulin level, age, and so on.\n",
    "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.csv\"\n",
    "names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']\n",
    "dataframe = read_csv(url, names=names)\n",
    "dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "81a53834",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Put data instances to arrays\n",
    "# X: training features\n",
    "# Y: classes\n",
    "array = dataframe.values\n",
    "X = array[:,0:8]\n",
    "Y = array[:,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0de9bef8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num Features: 3\n",
      "Selected Features: [ True False False False False  True  True False]\n",
      "Feature Ranking: [1 2 3 5 6 1 1 4]\n"
     ]
    }
   ],
   "source": [
    "# feature extraction\n",
    "model = SVR(kernel=\"linear\")\n",
    "# Select best 3 attributes\n",
    "rfe = RFE(model, n_features_to_select=3)\n",
    "fit = rfe.fit(X, Y)\n",
    "print(\"Num Features: %d\" % fit.n_features_)\n",
    "print(\"Selected Features: %s\" % fit.support_)\n",
    "print(\"Feature Ranking: %s\" % fit.ranking_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "52401fb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected features:\n",
      "preg\n",
      "mass\n",
      "pedi\n"
     ]
    }
   ],
   "source": [
    "# Show the selected features\n",
    "print('Selected features:')\n",
    "feats = list(dataframe.columns.values)\n",
    "for i, rank in enumerate(fit.ranking_):\n",
    "    if rank == 1:\n",
    "        print(feats[i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c29bd86e",
   "metadata": {},
   "outputs": [],
   "source": []
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