Regression v1.1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Which aspect of a flight best predicts a customers' overall rating of an airline?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet, RidgeCV, LassoCV, ElasticNetCV\n",
    "from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import numpy as np\n",
    "import pickle\n",
    "import patsy\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import warnings\n",
    "\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "%matplotlib inline\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we open the file for reading\n",
    "fileObject4 = open(\"budget.pickle\",'rb')\n",
    "\n",
    "# load the object from the file into variables\n",
    "df = pickle.load(fileObject4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "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",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>airline_name</th>\n",
       "      <th>author_country</th>\n",
       "      <th>content</th>\n",
       "      <th>cabin_flown</th>\n",
       "      <th>overall_rating</th>\n",
       "      <th>seat_comfort_rating</th>\n",
       "      <th>cabin_staff_rating</th>\n",
       "      <th>food_beverages_rating</th>\n",
       "      <th>inflight_entertainment_rating</th>\n",
       "      <th>ground_service_rating</th>\n",
       "      <th>wifi_connectivity_rating</th>\n",
       "      <th>value_money_rating</th>\n",
       "      <th>recommend</th>\n",
       "      <th>airline</th>\n",
       "      <th>cabin_class</th>\n",
       "      <th>overall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>51</td>\n",
       "      <td>7</td>\n",
       "      <td>DPS-SIN. Efficient check in and boarding. Flig...</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>airasia</td>\n",
       "      <td>Economy</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>59</td>\n",
       "      <td>149</td>\n",
       "      <td>No complaints here. My flight was to Phoenix/M...</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>allegiant-air</td>\n",
       "      <td>Economy</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>131</td>\n",
       "      <td>148</td>\n",
       "      <td>This is the 4th Easyjet flight this year flew ...</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>easyjet</td>\n",
       "      <td>Economy</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>147</td>\n",
       "      <td>77</td>\n",
       "      <td>I flew with fly540 from to Nairobi to Eldoret ...</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>fly540-com</td>\n",
       "      <td>Economy</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>146</td>\n",
       "      <td>7</td>\n",
       "      <td>10/8/12 I flew SZB to PEN. It was a short and ...</td>\n",
       "      <td>1</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>firefly</td>\n",
       "      <td>Economy</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     airline_name  author_country  \\\n",
       "109            51               7   \n",
       "118            59             149   \n",
       "130           131             148   \n",
       "172           147              77   \n",
       "173           146               7   \n",
       "\n",
       "                                               content  cabin_flown  \\\n",
       "109  DPS-SIN. Efficient check in and boarding. Flig...            1   \n",
       "118  No complaints here. My flight was to Phoenix/M...            1   \n",
       "130  This is the 4th Easyjet flight this year flew ...            1   \n",
       "172  I flew with fly540 from to Nairobi to Eldoret ...            1   \n",
       "173  10/8/12 I flew SZB to PEN. It was a short and ...            1   \n",
       "\n",
       "     overall_rating  seat_comfort_rating  cabin_staff_rating  \\\n",
       "109             8.0                  4.0                 4.0   \n",
       "118             9.0                  4.0                 5.0   \n",
       "130            10.0                  5.0                 5.0   \n",
       "172             9.0                  3.0                 3.0   \n",
       "173             8.0                  3.0                 3.0   \n",
       "\n",
       "     food_beverages_rating  inflight_entertainment_rating  \\\n",
       "109                    4.0                            3.0   \n",
       "118                    0.0                            0.0   \n",
       "130                    3.0                            3.0   \n",
       "172                    3.0                            3.0   \n",
       "173                    3.0                            3.0   \n",
       "\n",
       "     ground_service_rating  wifi_connectivity_rating  value_money_rating  \\\n",
       "109                    3.0                       3.0                 5.0   \n",
       "118                    3.0                       3.0                 5.0   \n",
       "130                    3.0                       3.0                 5.0   \n",
       "172                    3.0                       3.0                 4.0   \n",
       "173                    3.0                       3.0                 4.0   \n",
       "\n",
       "     recommend        airline cabin_class  overall  \n",
       "109          1        airasia     Economy      4.0  \n",
       "118          1  allegiant-air     Economy      4.5  \n",
       "130          1        easyjet     Economy      5.0  \n",
       "172          1     fly540-com     Economy      4.5  \n",
       "173          1        firefly     Economy      4.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['airline_name', 'author_country', 'content', 'cabin_flown',\n",
       "       'overall_rating', 'seat_comfort_rating', 'cabin_staff_rating',\n",
       "       'food_beverages_rating', 'inflight_entertainment_rating',\n",
       "       'ground_service_rating', 'wifi_connectivity_rating',\n",
       "       'value_money_rating', 'recommend', 'airline', 'cabin_class', 'overall'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "formula = 'value_money_rating ~ airline_name + author_country + seat_comfort_rating + cabin_staff_rating + food_beverages_rating + inflight_entertainment_rating + cabin_flown'\n",
    "y, X = patsy.dmatrices(formula, data=df, return_type='dataframe')\n",
    "y = y.values.ravel()\n",
    "\n",
    "ss = StandardScaler()\n",
    "Xs = ss.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(Xs, y, train_size=.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ****** Lasso ******"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.006031983664883445\n",
      "[0.53798729 0.512217   0.52967488 0.50904878 0.54588778]\n",
      "0.5269631480385832\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:    0.4s finished\n"
     ]
    }
   ],
   "source": [
    "###  Find best alpha using LassoCV\n",
    "optimal_lasso = LassoCV(n_alphas=500, cv=5, verbose=1)\n",
    "optimal_lasso.fit(X_train, y_train)\n",
    "print(optimal_lasso.alpha_)\n",
    "\n",
    "###  Cross-validate the Lasso regression  R2  with the optimal alpha\n",
    "lasso = Lasso(alpha=optimal_lasso.alpha_)\n",
    "lasso_scores = cross_val_score(lasso, X_test, y_test, scoring='r2', cv=5)\n",
    "\n",
    "print(lasso_scores)\n",
    "print(np.mean(lasso_scores)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "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",
       "        vertical-align: top;\n",
       "    }\n",
       "\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>variable</th>\n",
       "      <th>coef</th>\n",
       "      <th>abs_coef</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cabin_staff_rating</td>\n",
       "      <td>0.727176</td>\n",
       "      <td>0.727176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>seat_comfort_rating</td>\n",
       "      <td>0.525881</td>\n",
       "      <td>0.525881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>food_beverages_rating</td>\n",
       "      <td>0.063375</td>\n",
       "      <td>0.063375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>airline_name</td>\n",
       "      <td>-0.042143</td>\n",
       "      <td>0.042143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>author_country</td>\n",
       "      <td>-0.025875</td>\n",
       "      <td>0.025875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>inflight_entertainment_rating</td>\n",
       "      <td>0.022428</td>\n",
       "      <td>0.022428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cabin_flown</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        variable      coef  abs_coef\n",
       "4             cabin_staff_rating  0.727176  0.727176\n",
       "3            seat_comfort_rating  0.525881  0.525881\n",
       "5          food_beverages_rating  0.063375  0.063375\n",
       "1                   airline_name -0.042143  0.042143\n",
       "2                 author_country -0.025875  0.025875\n",
       "6  inflight_entertainment_rating  0.022428  0.022428\n",
       "0                      Intercept  0.000000  0.000000\n",
       "7                    cabin_flown -0.000000  0.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso.fit(X_train, y_train)\n",
    "lasso.predict(X_test)\n",
    "lasso_coefs = pd.DataFrame({'variable':X.columns,\n",
    "                            'coef':lasso.coef_,\n",
    "                            'abs_coef':np.abs(lasso.coef_)})\n",
    "lasso_coefs.sort_values('abs_coef', inplace=True, ascending=False)\n",
    "lasso_coefs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2de236009e8>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 564,
       "width": 708
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lasso_coefs.plot(kind='bar', x='variable', figsize=(12,7), y='coef')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ****  Use GridSearch on Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 144 candidates, totalling 720 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5652642626088528\n",
      "{'alpha': 0.0001, 'copy_X': True, 'fit_intercept': True, 'max_iter': 1000, 'normalize': True, 'positive': False, 'precompute': False, 'random_state': None, 'selection': 'cyclic', 'tol': 0.002, 'warm_start': False}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 720 out of 720 | elapsed:    0.9s finished\n"
     ]
    }
   ],
   "source": [
    "lasso = Lasso()\n",
    "parameters = {'alpha':[1e-04,2e-03,0.01,0.1,0.5,0.7,1,2,5], \n",
    "              'normalize':[True,False], \n",
    "              'tol':[1e-07,1e-06,2e-05,5e-05,1e-04,5e-04,2e-03,1e-02]}\n",
    "\n",
    "gridsearch_lasso = GridSearchCV(lasso, parameters, cv=5, verbose=1)\n",
    "gridsearch_lasso.fit(X_train, y_train)\n",
    "\n",
    "print(gridsearch_lasso.best_score_)\n",
    "print(gridsearch_lasso.best_estimator_.get_params())\n",
    "\n",
    "y_pred_lasso1 = gridsearch_lasso.best_estimator_.predict(X_test).reshape(-1,1)\n",
    "\n",
    "mse_lasso = mean_squared_error(y_test, y_pred_lasso1)\n",
    "rmse_lasso = np.sqrt(mse_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.1096138  0.11546373 0.10335954 0.10689544 0.10377892]\n",
      "Mean: 0.10782228664374227 Std: 0.004443538400070982\n",
      "RMSE: 1.092160804771218\n"
     ]
    }
   ],
   "source": [
    "scores_lasso = cross_val_score(lasso, X_train, y_train, scoring='explained_variance', cv=5)\n",
    "print(scores_lasso)\n",
    "print(\"Mean:\", np.mean(scores_lasso), \"Std:\", np.std(scores_lasso))\n",
    "print(\"RMSE:\", rmse_lasso)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ****** ElasticNet ******"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0322802732688067\n",
      "0.035\n",
      "[0.53694936 0.51257422 0.5296666  0.50989866 0.54452305]\n",
      "0.5267223799973502\n"
     ]
    }
   ],
   "source": [
    "###  Find best alpha and l1_ratio using ElasticNetCV\n",
    "l1_ratios = np.linspace(0.01, 0.035, 25)\n",
    "\n",
    "optimal_enet = ElasticNetCV(l1_ratio=l1_ratios, n_alphas=1, cv=5)\n",
    "optimal_enet.fit(X_train, y_train)\n",
    "print(optimal_enet.alpha_)\n",
    "print(optimal_enet.l1_ratio_)\n",
    "\n",
    "enet = ElasticNet(alpha=optimal_enet.alpha_, l1_ratio=optimal_enet.l1_ratio_)\n",
    "enet_scores = cross_val_score(enet, X_test, y_test, scoring='r2', cv=5)\n",
    "\n",
    "print(enet_scores)\n",
    "print(np.mean(enet_scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ****  Use Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           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=1000, n_jobs=None,\n",
       "           oob_score=False, random_state=5, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf = RandomForestRegressor(n_estimators = 1000, random_state = 5)\n",
    "rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5149192663117715\n"
     ]
    }
   ],
   "source": [
    "print(cross_val_score(rf,X_test,y_test,cv=10,scoring='explained_variance', verbose=False).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 8 folds for each of 3 candidates, totalling 24 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  14 out of  24 | elapsed:   25.1s remaining:   17.9s\n",
      "[Parallel(n_jobs=-1)]: Done  24 out of  24 | elapsed:   26.9s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=8, error_score='raise-deprecating',\n",
       "          estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           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='warn', n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False),\n",
       "          fit_params=None, iid='warn', n_iter=5, n_jobs=-1,\n",
       "          param_distributions={'criterion': ['mse'], 'max_depth': [80], 'max_features': ['auto'], 'n_estimators': [1100, 1200, 1300], 'min_samples_leaf': [2], 'min_samples_split': [2]},\n",
       "          pre_dispatch='2*n_jobs', random_state=5, refit=True,\n",
       "          return_train_score='warn', scoring=None, verbose=2)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_param={\n",
    "    'criterion': ['mse'],\n",
    "    'max_depth': [80],\n",
    "    'max_features': ['auto'],\n",
    "    'n_estimators': [1100,1200,1300],\n",
    "    'min_samples_leaf': [2],\n",
    "    'min_samples_split': [2]\n",
    "}\n",
    "rf = RandomForestRegressor()\n",
    "rf_random = RandomizedSearchCV(estimator=rf,param_distributions=rf_param,n_iter=5,cv=8,verbose=2,random_state=5,n_jobs=-1)\n",
    "rf_random.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'explained_variance'\n",
    "‘neg_mean_squared_error’\n",
    "‘neg_mean_absolute_error’\n",
    "‘neg_mean_squared_log_error’\n",
    "‘neg_median_absolute_error’"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5775654175119475\n",
      "{'n_estimators': 1200, 'min_samples_split': 2, 'min_samples_leaf': 2, 'max_features': 'auto', 'max_depth': 80, 'criterion': 'mse'}\n",
      "0.5349948686276844\n"
     ]
    }
   ],
   "source": [
    "print(rf_random.best_score_)\n",
    "print(rf_random.best_params_)\n",
    "model=rf_random.best_estimator_\n",
    "print(cross_val_score(model,X_test,y_test,cv=8,scoring='explained_variance',verbose=False).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ****  Use RFECV for feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting estimator with 8 features.\n",
      "Fitting estimator with 7 features.\n",
      "Number of features selected by RFE: 6 \n",
      "\n",
      "Index(['airline_name', 'author_country', 'seat_comfort_rating',\n",
      "       'cabin_staff_rating', 'food_beverages_rating',\n",
      "       'inflight_entertainment_rating'],\n",
      "      dtype='object') \n",
      "\n",
      "[3 1 1 1 1 1 1 2]\n"
     ]
    }
   ],
   "source": [
    "# Use feature selection to identify relevant features\n",
    "from sklearn.feature_selection import RFECV\n",
    "\n",
    "rfe_lr = LinearRegression()\n",
    "rfe1 = RFECV(rfe_lr, step=1, cv=10, n_jobs=-1, verbose=1)\n",
    "rfe1.fit(X_train, y_train)\n",
    "\n",
    "print('Number of features selected by RFE: {}'.format(rfe1.n_features_),'\\n')\n",
    "rfe1_list = X.columns[rfe1.support_]\n",
    "\n",
    "print(rfe1_list,'\\n')\n",
    "print(rfe1.ranking_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### ****  PCA with Stochastic Gradient Descent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "n_components=9 must be between 0 and min(n_samples, n_features)=8 with svd_solver='full'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-28-31c44c149041>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m11\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m     \u001b[0mpca\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPCA\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_components\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0mpca\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m     \u001b[0mX_train2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpca\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mX_test2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpca\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\pca.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    338\u001b[0m             \u001b[0mReturns\u001b[0m \u001b[0mthe\u001b[0m \u001b[0minstance\u001b[0m \u001b[0mitself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    339\u001b[0m         \"\"\"\n\u001b[1;32m--> 340\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    341\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    342\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\pca.py\u001b[0m in \u001b[0;36m_fit\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    404\u001b[0m         \u001b[1;31m# Call different fits for either full or truncated SVD\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    405\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fit_svd_solver\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'full'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 406\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fit_full\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_components\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    407\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fit_svd_solver\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'arpack'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'randomized'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    408\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fit_truncated\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_components\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fit_svd_solver\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\decomposition\\pca.py\u001b[0m in \u001b[0;36m_fit_full\u001b[1;34m(self, X, n_components)\u001b[0m\n\u001b[0;32m    423\u001b[0m                              \u001b[1;34m\"min(n_samples, n_features)=%r with \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    424\u001b[0m                              \u001b[1;34m\"svd_solver='full'\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 425\u001b[1;33m                              % (n_components, min(n_samples, n_features)))\n\u001b[0m\u001b[0;32m    426\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mn_components\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    427\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_components\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIntegral\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minteger\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: n_components=9 must be between 0 and min(n_samples, n_features)=8 with svd_solver='full'"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "\n",
    "n_list=[x for x in range(1,11,1)]\n",
    "score_list=[]\n",
    "for n in range(1,11,1):\n",
    "    pca = PCA(n_components=n)\n",
    "    pca.fit(X_train)\n",
    "    X_train2 = pca.transform(X_train)\n",
    "    X_test2 = pca.transform(X_test)\n",
    "    \n",
    "    sgd_reg = SGDRegressor()\n",
    "    selector = RFECV(sgd_reg, step=1, cv=5,scoring='r2')\n",
    "    selector = selector.fit(X_train2, y_train)\n",
    "    score_list.append(selector.score(X_test2,y_test))\n",
    "    \n",
    "print(np.array(score_list).max())\n",
    "score_list.index(np.array(score_list).max())+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}