Feature Selection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "dbc15358",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47f4a1b0",
   "metadata": {},
   "source": [
    "Hi Guys, Welcome to [Tirendaz Academy](https://youtube.com/c/tirendazacademy) 😀\n",
    "</br>\n",
    "In this notebook, I'm going to talk about feature selection.\n",
    "</br>\n",
    "Happy learning 🐱‍🏍 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d00b6b56",
   "metadata": {},
   "source": [
    "## Loading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "75bff510",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "466f0b7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"wine.data\", header = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c04a5717",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['Class label', 'Alcohol', 'Malic acid', 'Ash', \n",
    "'Alcalinity of ash', 'Magnesium', 'Total phenols', 'Flavanoids', \n",
    "'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', \n",
    "'Hue', 'OD280/OD315 of diluted wines', 'Proline']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "42d673ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class label</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic acid</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity of ash</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Total phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoid phenols</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color intensity</th>\n",
       "      <th>Hue</th>\n",
       "      <th>OD280/OD315 of diluted wines</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Class label  Alcohol  Malic acid   Ash  Alcalinity of ash  Magnesium  \\\n",
       "0            1    14.23        1.71  2.43               15.6        127   \n",
       "1            1    13.20        1.78  2.14               11.2        100   \n",
       "2            1    13.16        2.36  2.67               18.6        101   \n",
       "3            1    14.37        1.95  2.50               16.8        113   \n",
       "4            1    13.24        2.59  2.87               21.0        118   \n",
       "\n",
       "   Total phenols  Flavanoids  Nonflavanoid phenols  Proanthocyanins  \\\n",
       "0           2.80        3.06                  0.28             2.29   \n",
       "1           2.65        2.76                  0.26             1.28   \n",
       "2           2.80        3.24                  0.30             2.81   \n",
       "3           3.85        3.49                  0.24             2.18   \n",
       "4           2.80        2.69                  0.39             1.82   \n",
       "\n",
       "   Color intensity   Hue  OD280/OD315 of diluted wines  Proline  \n",
       "0             5.64  1.04                          3.92     1065  \n",
       "1             4.38  1.05                          3.40     1050  \n",
       "2             5.68  1.03                          3.17     1185  \n",
       "3             7.80  0.86                          3.45     1480  \n",
       "4             4.32  1.04                          2.93      735  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6291bfa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3], dtype=int64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(df[\"Class label\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0f48548",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0649ca29",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, 1:].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "79edec6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.iloc[:,0].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7f025fd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "451560eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,\n",
    "                                                   random_state=0,\n",
    "                                                   stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13e2bc24",
   "metadata": {},
   "source": [
    "## Data Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f1d51bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4ef3b8f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "stdscaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "51076b73",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_std = stdscaler.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ef7551d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_std = stdscaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85b85e1c",
   "metadata": {},
   "source": [
    "## Building the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "638fcbd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1e762747",
   "metadata": {},
   "outputs": [],
   "source": [
    "forest = RandomForestClassifier(n_estimators=500, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "edfe42c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_labels = df.columns[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9603797a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=500, random_state=1)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c717c218",
   "metadata": {},
   "source": [
    "## Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7ce55844",
   "metadata": {},
   "outputs": [],
   "source": [
    "importances = forest.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dbe23857",
   "metadata": {},
   "outputs": [],
   "source": [
    "indices = np.argsort(importances)[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "613b0358",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12,  6,  9, 11,  0, 10,  5,  4,  1,  3,  8,  2,  7], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ccfcce16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Proline 0.19425196709852016\n",
      "2 Flavanoids 0.15967892986778753\n",
      "3 Color intensity 0.14907323774360948\n",
      "4 OD280/OD315 of diluted wines 0.12223110107587146\n",
      "5 Alcohol 0.11479722353879414\n",
      "6 Hue 0.07259491714547278\n",
      "7 Total phenols 0.050824811977803086\n",
      "8 Magnesium 0.031922184349841815\n",
      "9 Malic acid 0.028867963800009225\n",
      "10 Alcalinity of ash 0.025900122601886087\n",
      "11 Proanthocyanins 0.022483463782451273\n",
      "12 Ash 0.014381677761740612\n",
      "13 Nonflavanoid phenols 0.012992399256212534\n"
     ]
    }
   ],
   "source": [
    "for f in range(X_train.shape[1]):\n",
    "    print(f\"{f+1}\", feat_labels[indices[f]],\n",
    "         importances[indices[f]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66ba31a2",
   "metadata": {},
   "source": [
    "## Feature Selecetion with SelectFromModel class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9ca56211",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectFromModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "91518553",
   "metadata": {},
   "outputs": [],
   "source": [
    "selector = SelectFromModel(forest, threshold = 0.1, \n",
    "                          prefit=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "eca6de52",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_selected = selector.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fb9da492",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Proline 0.19425196709852016\n",
      "2 Flavanoids 0.15967892986778753\n",
      "3 Color intensity 0.14907323774360948\n",
      "4 OD280/OD315 of diluted wines 0.12223110107587146\n",
      "5 Alcohol 0.11479722353879414\n"
     ]
    }
   ],
   "source": [
    "for f in range(X_selected.shape[1]):\n",
    "    print(f\"{f+1}\", feat_labels[indices[f]],\n",
    "         importances[indices[f]])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7b742d4",
   "metadata": {},
   "source": [
    "Don't forget to follow us on [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [Twitter](http://twitter.com/tirendazacademy) | [GitHub](http://github.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) | [Kaggle](https://www.kaggle.com/tirendazacademy) 😎"
   ]
  }
 ],
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