ML_classification_kNN_DTC_RFC.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/iabhishekofficial/mobile-price-classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://gist.githubusercontent.com/urwinday/5402e8485328b3d34c8bda4cb8f8a959/raw/af9d179cf5644ef5086e0d6e5b9075e31660cea0/mobile-price-classification.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
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       "\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>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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842</td>\n",
       "      <td>0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.6</td>\n",
       "      <td>188</td>\n",
       "      <td>2</td>\n",
       "      <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",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1021</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>0.7</td>\n",
       "      <td>136</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>905</td>\n",
       "      <td>1988</td>\n",
       "      <td>2631</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>563</td>\n",
       "      <td>1</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>0.9</td>\n",
       "      <td>145</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>1263</td>\n",
       "      <td>1716</td>\n",
       "      <td>2603</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <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",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1821</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>0.6</td>\n",
       "      <td>141</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1208</td>\n",
       "      <td>1212</td>\n",
       "      <td>1411</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "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  \\\n",
       "0        188        2     ...              20       756  2549     9     7   \n",
       "1        136        3     ...             905      1988  2631    17     3   \n",
       "2        145        5     ...            1263      1716  2603    11     2   \n",
       "3        131        6     ...            1216      1786  2769    16     8   \n",
       "4        141        2     ...            1208      1212  1411     8     2   \n",
       "\n",
       "   talk_time  three_g  touch_screen  wifi  price_range  \n",
       "0         19        0             0     1            1  \n",
       "1          7        1             1     0            2  \n",
       "2          9        1             1     0            2  \n",
       "3         11        1             0     0            2  \n",
       "4         15        1             1     0            1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 0], dtype=int64)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['price_range'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 21)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2000, 20), (2000,))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = df['price_range'] # y  - целевой признак\n",
    "features = df.drop('price_range', 1) # X -  матрица признаков\n",
    "features.shape, labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1600, 20), (400, 20), (1600,), (400,))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "features_train, features_test, labels_train, labels_test = train_test_split(features, labels, test_size=0.2, random_state = 42)\n",
    "features_train.shape, features_test.shape, labels_train.shape, labels_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNeighborsClassifier GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_knn = KNeighborsClassifier()\n",
    "first_knn.fit(features_train, labels_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_params = {'n_neighbors': range(5,30,5)} \n",
    "knn_grid = GridSearchCV(first_knn, knn_params, cv =5)\n",
    "knn_grid.fit( features_train, labels_train );"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9225, {'n_neighbors': 10})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_grid.best_score_, knn_grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8375"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_tree = DecisionTreeClassifier()\n",
    "first_tree.fit( features_train, labels_train );\n",
    "\n",
    "y_pred_first_tree = first_tree.predict( features_test )\n",
    "accuracy_score(y_pred_first_tree, labels_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "tree_params = {'max_depth': np.arange(1,11), \n",
    "               'max_features': [0.5, 0.7, 1]}\n",
    "tree_grid = GridSearchCV(first_tree, tree_params, cv=5, n_jobs=-1)\n",
    "tree_grid.fit(features_train, labels_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.83375, {'max_depth': 7, 'max_features': 0.7})"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_grid.best_score_, tree_grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### feature_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature importances:\n",
      "[0.13345572 0.0012502  0.00874358 0.00244484 0.00821574 0.00166694\n",
      " 0.00761582 0.0075806  0.01715502 0.01359668 0.00312551 0.07616027\n",
      " 0.06956569 0.62956969 0.00194714 0.00476147 0.01060497 0.00142881\n",
      " 0.         0.00111129]\n"
     ]
    }
   ],
   "source": [
    "print(\"feature importances:\")\n",
    "print( first_tree.feature_importances_ )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature importances:\n",
      "  1. feature 'ram          ' (0.630)\n",
      "  2. feature 'battery_power' (0.133)\n",
      "  3. feature 'px_height    ' (0.076)\n",
      "  4. feature 'px_width     ' (0.070)\n",
      "  5. feature 'mobile_wt    ' (0.017)\n",
      "  6. feature 'n_cores      ' (0.014)\n",
      "  7. feature 'talk_time    ' (0.011)\n",
      "  8. feature 'clock_speed  ' (0.009)\n",
      "  9. feature 'fc           ' (0.008)\n",
      " 10. feature 'int_memory   ' (0.008)\n",
      " 11. feature 'm_dep        ' (0.008)\n",
      " 12. feature 'sc_w         ' (0.005)\n",
      " 13. feature 'pc           ' (0.003)\n",
      " 14. feature 'dual_sim     ' (0.002)\n",
      " 15. feature 'sc_h         ' (0.002)\n",
      " 16. feature 'four_g       ' (0.002)\n",
      " 17. feature 'three_g      ' (0.001)\n",
      " 18. feature 'blue         ' (0.001)\n",
      " 19. feature 'wifi         ' (0.001)\n",
      " 20. feature 'touch_screen ' (0.000)\n"
     ]
    }
   ],
   "source": [
    "feature_names = features.columns\n",
    "importances = first_tree.feature_importances_\n",
    "indices = np.argsort(importances)[::-1]\n",
    "\n",
    "print(\"Feature importances:\")\n",
    "for f, idx in enumerate(indices):\n",
    "    print(\"{:3d}. feature '{:13s}' ({:.3f})\".format(f + 1, feature_names[idx], importances[idx]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Построим столбцовую диаграмму, графически представляющую значимость первых 3 признаков:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d_first = 3\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.title(\"feature importances\")\n",
    "plt.bar(range(d_first), importances[indices[:d_first]], align='center')\n",
    "plt.xticks(range(d_first), np.array(feature_names)[indices[:d_first]], rotation=0)\n",
    "plt.xlim([-1, d_first]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
       "            oob_score=False, random_state=11, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import ensemble\n",
    "rf = ensemble.RandomForestClassifier(n_estimators=100,random_state=11)\n",
    "rf.fit(features_train, labels_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8975"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_rf = rf.predict( features_test )\n",
    "accuracy_score(y_pred_rf, labels_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  GridSearchCV "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 150 candidates, totalling 750 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    6.7s\n",
      "[Parallel(n_jobs=-1)]: Done 184 tasks      | elapsed:   13.4s\n",
      "[Parallel(n_jobs=-1)]: Done 434 tasks      | elapsed:   29.5s\n",
      "[Parallel(n_jobs=-1)]: Done 750 out of 750 | elapsed:   56.0s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
       "            oob_score=False, random_state=11, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid=True, n_jobs=-1,\n",
       "       param_grid={'max_depth': range(1, 11), 'max_features': range(4, 19)},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring=None, verbose=True)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_params = {'max_depth': range(1,11),'max_features': range(4,19)}\n",
    "forest_grid = GridSearchCV(rf, forest_params, cv = 5, n_jobs = -1, verbose = True)\n",
    "forest_grid.fit( features_train, labels_train )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'max_depth': 9, 'max_features': 14}, 0.894375)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_grid.best_params_, forest_grid.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9225, {'n_neighbors': 10})"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_grid.best_score_, knn_grid.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как ни странно, kNN с гиперпараметрами \n",
    "\n",
    "                {'n_neighbors': 10} \n",
    "                дал наилучший результат 0.9225 \n",
    "                \n",
    "для выбранного датасета. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import export_graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_graphviz(tree_grid.best_estimator_, out_file='mobile-price.dot',\n",
    "               feature_names=features.columns, filled=True, rotate=True,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "!dot -Tpng mobile-price.dot -o mobile-price.png"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='mobile-price.png'>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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