FS7 - Feature Importance.ipynb

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    "# Feature Importance\n",
    "Feature Importance is a technique used in feature selection to determine the relevance or importance of each feature in a machine learning model. It helps to identify the features that contribute the most to the predictive power of the model and can be used to rank or select the most important features.\n",
    "\n",
    "Feature Importance is often calculated based on the concept of how much a feature contributes to the reduction in the model's error or impurity. The specific method for calculating feature importance depends on the type of model used. Here are a few common techniques for calculating feature importance:\n",
    "\n",
    "- Decision Tree-based Feature Importance\n",
    "- Coefficient Magnitude (Linear Models)\n",
    "- Permutation Importance\n",
    "- Recursive Feature Elimination (RFE)\n",
    "- LASSO Regularization (L1 Penalty)\n",
    "\n",
    "The feature importance scores obtained from these techniques provide a ranking or measure of the relative importance of each feature in the context of the specific model or problem. Based on these scores, features can be selected by setting a threshold or selecting the top-k most important features.\n",
    "\n",
    "Feature Importance is a valuable tool in feature selection as it allows for the identification and focus on the most informative features, which can lead to improved model performance, reduced dimensionality, and increased interpretability."
   ]
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   "cell_type": "code",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>battery_power</th>\n",
       "      <th>blue</th>\n",
       "      <th>clock_speed</th>\n",
       "      <th>dual_sim</th>\n",
       "      <th>fc</th>\n",
       "      <th>four_g</th>\n",
       "      <th>int_memory</th>\n",
       "      <th>m_dep</th>\n",
       "      <th>mobile_wt</th>\n",
       "      <th>n_cores</th>\n",
       "      <th>...</th>\n",
       "      <th>px_height</th>\n",
       "      <th>px_width</th>\n",
       "      <th>ram</th>\n",
       "      <th>sc_h</th>\n",
       "      <th>sc_w</th>\n",
       "      <th>talk_time</th>\n",
       "      <th>three_g</th>\n",
       "      <th>touch_screen</th>\n",
       "      <th>wifi</th>\n",
       "      <th>price_range</th>\n",
       "    </tr>\n",
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       "      <td>...</td>\n",
       "      <td>20</td>\n",
       "      <td>756</td>\n",
       "      <td>2549</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
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       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>905</td>\n",
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       "      <td>7</td>\n",
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       "      <td>0.9</td>\n",
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       "      <td>...</td>\n",
       "      <td>1263</td>\n",
       "      <td>1716</td>\n",
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       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>615</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.8</td>\n",
       "      <td>131</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>1216</td>\n",
       "      <td>1786</td>\n",
       "      <td>2769</td>\n",
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       "      <td>11</td>\n",
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       "<p>5 rows × 21 columns</p>\n",
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      "text/plain": [
       "   battery_power  blue  clock_speed  dual_sim  fc  four_g  int_memory  m_dep  \\\n",
       "0            842     0          2.2         0   1       0           7    0.6   \n",
       "1           1021     1          0.5         1   0       1          53    0.7   \n",
       "2            563     1          0.5         1   2       1          41    0.9   \n",
       "3            615     1          2.5         0   0       0          10    0.8   \n",
       "4           1821     1          1.2         0  13       1          44    0.6   \n",
       "\n",
       "   mobile_wt  n_cores  ...  px_height  px_width   ram  sc_h  sc_w  talk_time  \\\n",
       "0        188        2  ...         20       756  2549     9     7         19   \n",
       "1        136        3  ...        905      1988  2631    17     3          7   \n",
       "2        145        5  ...       1263      1716  2603    11     2          9   \n",
       "3        131        6  ...       1216      1786  2769    16     8         11   \n",
       "4        141        2  ...       1208      1212  1411     8     2         15   \n",
       "\n",
       "   three_g  touch_screen  wifi  price_range  \n",
       "0        0             0     1            1  \n",
       "1        1             1     0            2  \n",
       "2        1             1     0            2  \n",
       "3        1             0     0            2  \n",
       "4        1             1     0            1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings as wr\n",
    "wr.filterwarnings('ignore')\n",
    "\n",
    "import pandas as pd\n",
    "df = pd.read_csv('Datasets\\mobile_dataset.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "41ff9cf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:, :-1]\n",
    "y = df[['price_range']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c3bb7bd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ca99fd14",
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "ExtraTreesClassifier()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ExtraTreesClassifier()\n",
    "model.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "06e0d7a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.06236207 0.0205468  0.0334456  0.0199164  0.03164905 0.01676029\n",
      " 0.03561033 0.03377937 0.03649913 0.03248937 0.0328677  0.04703014\n",
      " 0.04896366 0.39301655 0.03413895 0.03389411 0.03506001 0.01393622\n",
      " 0.01805202 0.01998222]\n"
     ]
    }
   ],
   "source": [
    "print(model.feature_importances_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1c228750",
   "metadata": {},
   "outputs": [],
   "source": [
    "ranked_features = pd.Series(model.feature_importances_, index = X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f617743a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ram              0.393017\n",
       "battery_power    0.062362\n",
       "px_width         0.048964\n",
       "px_height        0.047030\n",
       "mobile_wt        0.036499\n",
       "int_memory       0.035610\n",
       "talk_time        0.035060\n",
       "sc_h             0.034139\n",
       "sc_w             0.033894\n",
       "m_dep            0.033779\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranked_features.nlargest(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "058c8a85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ranked_features.nlargest(10).plot.bar()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}