Logistic Regression_Invistico Airline.ipynb

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   "source": [
    "#Import library\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "from sklearn.metrics import roc_auc_score,roc_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61bcb0b8",
   "metadata": {},
   "source": [
    "Data"
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  {
   "cell_type": "code",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>satisfaction</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Customer Type</th>\n",
       "      <th>Age</th>\n",
       "      <th>Type of Travel</th>\n",
       "      <th>Class</th>\n",
       "      <th>Flight Distance</th>\n",
       "      <th>Seat comfort</th>\n",
       "      <th>Departure/Arrival time convenient</th>\n",
       "      <th>Food and drink</th>\n",
       "      <th>...</th>\n",
       "      <th>Online support</th>\n",
       "      <th>Ease of Online booking</th>\n",
       "      <th>On-board service</th>\n",
       "      <th>Leg room service</th>\n",
       "      <th>Baggage handling</th>\n",
       "      <th>Checkin service</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Online boarding</th>\n",
       "      <th>Departure Delay in Minutes</th>\n",
       "      <th>Arrival Delay in Minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>satisfied</td>\n",
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       "      <td>Personal Travel</td>\n",
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       "      <td>0</td>\n",
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       "      <td>satisfied</td>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>47</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>2464</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>satisfied</td>\n",
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       "      <td>Loyal Customer</td>\n",
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       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>satisfied</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>60</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>623</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>satisfied</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>70</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "<p>5 rows × 23 columns</p>\n",
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      "text/plain": [
       "  satisfaction  Gender   Customer Type  Age   Type of Travel     Class  \\\n",
       "0    satisfied  Female  Loyal Customer   65  Personal Travel       Eco   \n",
       "1    satisfied    Male  Loyal Customer   47  Personal Travel  Business   \n",
       "2    satisfied  Female  Loyal Customer   15  Personal Travel       Eco   \n",
       "3    satisfied  Female  Loyal Customer   60  Personal Travel       Eco   \n",
       "4    satisfied  Female  Loyal Customer   70  Personal Travel       Eco   \n",
       "\n",
       "   Flight Distance  Seat comfort  Departure/Arrival time convenient  \\\n",
       "0              265             0                                  0   \n",
       "1             2464             0                                  0   \n",
       "2             2138             0                                  0   \n",
       "3              623             0                                  0   \n",
       "4              354             0                                  0   \n",
       "\n",
       "   Food and drink  ...  Online support  Ease of Online booking  \\\n",
       "0               0  ...               2                       3   \n",
       "1               0  ...               2                       3   \n",
       "2               0  ...               2                       2   \n",
       "3               0  ...               3                       1   \n",
       "4               0  ...               4                       2   \n",
       "\n",
       "   On-board service  Leg room service  Baggage handling  Checkin service  \\\n",
       "0                 3                 0                 3                5   \n",
       "1                 4                 4                 4                2   \n",
       "2                 3                 3                 4                4   \n",
       "3                 1                 0                 1                4   \n",
       "4                 2                 0                 2                4   \n",
       "\n",
       "   Cleanliness  Online boarding  Departure Delay in Minutes  \\\n",
       "0            3                2                           0   \n",
       "1            3                2                         310   \n",
       "2            4                2                           0   \n",
       "3            1                3                           0   \n",
       "4            2                5                           0   \n",
       "\n",
       "   Arrival Delay in Minutes  \n",
       "0                       0.0  \n",
       "1                     305.0  \n",
       "2                       0.0  \n",
       "3                       0.0  \n",
       "4                       0.0  \n",
       "\n",
       "[5 rows x 23 columns]"
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     "metadata": {},
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   ],
   "source": [
    "#Import dataset\n",
    "df1=pd.read_csv(\"Invistico_Airline.csv\")\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3adcf4f6",
   "metadata": {},
   "source": [
    "Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cfce73dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(129880, 23)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#number of rows and columns\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5cc465f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction                           0\n",
       "Gender                                 0\n",
       "Customer Type                          0\n",
       "Age                                    0\n",
       "Type of Travel                         0\n",
       "Class                                  0\n",
       "Flight Distance                        0\n",
       "Seat comfort                           0\n",
       "Departure/Arrival time convenient      0\n",
       "Food and drink                         0\n",
       "Gate location                          0\n",
       "Inflight wifi service                  0\n",
       "Inflight entertainment                 0\n",
       "Online support                         0\n",
       "Ease of Online booking                 0\n",
       "On-board service                       0\n",
       "Leg room service                       0\n",
       "Baggage handling                       0\n",
       "Checkin service                        0\n",
       "Cleanliness                            0\n",
       "Online boarding                        0\n",
       "Departure Delay in Minutes             0\n",
       "Arrival Delay in Minutes             393\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#detect missing values\n",
    "df1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2cc02254",
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       "      <td>satisfied</td>\n",
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       "      <td>Loyal Customer</td>\n",
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       "      <td>Eco</td>\n",
       "      <td>1862</td>\n",
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       "      <td>dissatisfied</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129873</th>\n",
       "      <td>satisfied</td>\n",
       "      <td>Female</td>\n",
       "      <td>disloyal Customer</td>\n",
       "      <td>63</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>1942</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>393 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        satisfaction  Gender      Customer Type  Age   Type of Travel  \\\n",
       "145        satisfied  Female     Loyal Customer   21  Personal Travel   \n",
       "246        satisfied    Male     Loyal Customer   28  Personal Travel   \n",
       "710        satisfied    Male     Loyal Customer   28  Personal Travel   \n",
       "736        satisfied    Male     Loyal Customer    9  Personal Travel   \n",
       "818     dissatisfied    Male     Loyal Customer    9  Personal Travel   \n",
       "...              ...     ...                ...  ...              ...   \n",
       "127739     satisfied    Male     Loyal Customer   44  Business travel   \n",
       "128131     satisfied    Male     Loyal Customer   39  Business travel   \n",
       "128710     satisfied  Female     Loyal Customer   45  Business travel   \n",
       "129774     satisfied    Male  disloyal Customer   49  Personal Travel   \n",
       "129873     satisfied  Female  disloyal Customer   63  Personal Travel   \n",
       "\n",
       "           Class  Flight Distance  Seat comfort  \\\n",
       "145          Eco             4804             0   \n",
       "246          Eco             1364             0   \n",
       "710          Eco             1792             0   \n",
       "736          Eco             1862             0   \n",
       "818          Eco             1668             1   \n",
       "...          ...              ...           ...   \n",
       "127739  Business             3431             2   \n",
       "128131  Business             1595             4   \n",
       "128710       Eco              871             5   \n",
       "129774       Eco             1594             5   \n",
       "129873       Eco             1942             5   \n",
       "\n",
       "        Departure/Arrival time convenient  Food and drink  ...  \\\n",
       "145                                     2               0  ...   \n",
       "246                                     3               0  ...   \n",
       "710                                     5               0  ...   \n",
       "736                                     5               0  ...   \n",
       "818                                     0               1  ...   \n",
       "...                                   ...             ...  ...   \n",
       "127739                                  2               3  ...   \n",
       "128131                                  4               4  ...   \n",
       "128710                                  1               1  ...   \n",
       "129774                                  4               5  ...   \n",
       "129873                                  5               4  ...   \n",
       "\n",
       "        Online support  Ease of Online booking  On-board service  \\\n",
       "145                  5                       5                 4   \n",
       "246                  4                       2                 1   \n",
       "710                  5                       5                 4   \n",
       "736                  3                       3                 5   \n",
       "818                  4                       4                 4   \n",
       "...                ...                     ...               ...   \n",
       "127739               4                       5                 5   \n",
       "128131               5                       5                 5   \n",
       "128710               2                       5                 5   \n",
       "129774               3                       3                 1   \n",
       "129873               3                       3                 5   \n",
       "\n",
       "        Leg room service  Baggage handling  Checkin service  Cleanliness  \\\n",
       "145                    1                 4                2            3   \n",
       "246                    1                 3                4            3   \n",
       "710                    4                 5                3            4   \n",
       "736                    3                 4                4            4   \n",
       "818                    5                 1                5            1   \n",
       "...                  ...               ...              ...          ...   \n",
       "127739                 5                 5                5            5   \n",
       "128131                 5                 5                3            5   \n",
       "128710                 5                 5                1            5   \n",
       "129774                 4                 3                3            4   \n",
       "129873                 2                 5                3            5   \n",
       "\n",
       "        Online boarding  Departure Delay in Minutes  Arrival Delay in Minutes  \n",
       "145                   5                          20                       NaN  \n",
       "246                   2                           0                       NaN  \n",
       "710                   5                          24                       NaN  \n",
       "736                   3                           0                       NaN  \n",
       "818                   4                           0                       NaN  \n",
       "...                 ...                         ...                       ...  \n",
       "127739                4                          70                       NaN  \n",
       "128131                5                           0                       NaN  \n",
       "128710                5                         100                       NaN  \n",
       "129774                3                           0                       NaN  \n",
       "129873                3                           7                       NaN  \n",
       "\n",
       "[393 rows x 23 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#missing values in \"Arrival Delay in Minutes\"\n",
    "df1.loc[df1['Arrival Delay in Minutes'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "110fd573",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(129487, 23)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop data with missing values\n",
    "df2=df1[~(df1['Arrival Delay in Minutes'].isnull())]\n",
    "df2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "189c912b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfied       70882\n",
       "dissatisfied    58605\n",
       "Name: satisfaction, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#count satisfaction\n",
    "df2['satisfaction'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bb24783d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot satisfaction\n",
    "sns.countplot(x=df2['satisfaction'],palette='hls')\n",
    "plt.show()\n",
    "plt.savefig('count_satisfaction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "12aa86ae",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percetage of Satisfied : 54.740630333547\n",
      "Percetage of Satisfied : 45.259369666453\n"
     ]
    }
   ],
   "source": [
    "#percentage of satisfaction\n",
    "count_satf=len(df2[df2['satisfaction']=='satisfied'])\n",
    "count_dissatf=len(df2[df2['satisfaction']=='dissatisfied'])\n",
    "pct_of_satf=count_satf/(count_satf+count_dissatf)\n",
    "print(\"Percetage of Satisfied :\",pct_of_satf*100 )\n",
    "print(\"Percetage of Satisfied :\",(1-pct_of_satf)*100 )"
   ]
  },
  {
   "cell_type": "raw",
   "id": "039a43d7",
   "metadata": {},
   "source": [
    "The ratio between satisfied and dissatisfied is almost same, it is 55:45"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c56ab0a2",
   "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>Age</th>\n",
       "      <th>Flight Distance</th>\n",
       "      <th>Seat comfort</th>\n",
       "      <th>Departure/Arrival time convenient</th>\n",
       "      <th>Food and drink</th>\n",
       "      <th>Gate location</th>\n",
       "      <th>Inflight wifi service</th>\n",
       "      <th>Inflight entertainment</th>\n",
       "      <th>Online support</th>\n",
       "      <th>Ease of Online booking</th>\n",
       "      <th>On-board service</th>\n",
       "      <th>Leg room service</th>\n",
       "      <th>Baggage handling</th>\n",
       "      <th>Checkin service</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Online boarding</th>\n",
       "      <th>Departure Delay in Minutes</th>\n",
       "      <th>Arrival Delay in Minutes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>satisfaction</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>dissatisfied</th>\n",
       "      <td>37.468373</td>\n",
       "      <td>2025.202833</td>\n",
       "      <td>2.467315</td>\n",
       "      <td>3.016517</td>\n",
       "      <td>2.660609</td>\n",
       "      <td>3.008003</td>\n",
       "      <td>2.919922</td>\n",
       "      <td>2.609044</td>\n",
       "      <td>2.959833</td>\n",
       "      <td>2.851873</td>\n",
       "      <td>2.972818</td>\n",
       "      <td>3.052555</td>\n",
       "      <td>3.364269</td>\n",
       "      <td>2.971845</td>\n",
       "      <td>3.377203</td>\n",
       "      <td>2.869653</td>\n",
       "      <td>17.728777</td>\n",
       "      <td>18.504599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>satisfied</th>\n",
       "      <td>41.049604</td>\n",
       "      <td>1944.469640</td>\n",
       "      <td>3.145552</td>\n",
       "      <td>2.968582</td>\n",
       "      <td>3.010285</td>\n",
       "      <td>2.975805</td>\n",
       "      <td>3.521374</td>\n",
       "      <td>4.024266</td>\n",
       "      <td>3.983085</td>\n",
       "      <td>3.985031</td>\n",
       "      <td>3.872196</td>\n",
       "      <td>3.844587</td>\n",
       "      <td>3.969287</td>\n",
       "      <td>3.645721</td>\n",
       "      <td>3.977639</td>\n",
       "      <td>3.751799</td>\n",
       "      <td>12.092393</td>\n",
       "      <td>12.268883</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Age  Flight Distance  Seat comfort  \\\n",
       "satisfaction                                             \n",
       "dissatisfied  37.468373      2025.202833      2.467315   \n",
       "satisfied     41.049604      1944.469640      3.145552   \n",
       "\n",
       "              Departure/Arrival time convenient  Food and drink  \\\n",
       "satisfaction                                                      \n",
       "dissatisfied                           3.016517        2.660609   \n",
       "satisfied                              2.968582        3.010285   \n",
       "\n",
       "              Gate location  Inflight wifi service  Inflight entertainment  \\\n",
       "satisfaction                                                                 \n",
       "dissatisfied       3.008003               2.919922                2.609044   \n",
       "satisfied          2.975805               3.521374                4.024266   \n",
       "\n",
       "              Online support  Ease of Online booking  On-board service  \\\n",
       "satisfaction                                                             \n",
       "dissatisfied        2.959833                2.851873          2.972818   \n",
       "satisfied           3.983085                3.985031          3.872196   \n",
       "\n",
       "              Leg room service  Baggage handling  Checkin service  \\\n",
       "satisfaction                                                        \n",
       "dissatisfied          3.052555          3.364269         2.971845   \n",
       "satisfied             3.844587          3.969287         3.645721   \n",
       "\n",
       "              Cleanliness  Online boarding  Departure Delay in Minutes  \\\n",
       "satisfaction                                                             \n",
       "dissatisfied     3.377203         2.869653                   17.728777   \n",
       "satisfied        3.977639         3.751799                   12.092393   \n",
       "\n",
       "              Arrival Delay in Minutes  \n",
       "satisfaction                            \n",
       "dissatisfied                 18.504599  \n",
       "satisfied                    12.268883  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#exploration by mean\n",
    "df2.groupby('satisfaction').mean()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "8e70821d",
   "metadata": {},
   "source": [
    "1. The average age who gets satisfied is 41\n",
    "2. The average rating of each variable that gets satisfied is 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0232923f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-11-3d506256ba2f>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2['Satisfaction']=df2['satisfaction'].apply(lambda x: 1 if x=='satisfied' else 0)\n"
     ]
    },
    {
     "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>Gender</th>\n",
       "      <th>Customer Type</th>\n",
       "      <th>Age</th>\n",
       "      <th>Type of Travel</th>\n",
       "      <th>Class</th>\n",
       "      <th>Flight Distance</th>\n",
       "      <th>Seat comfort</th>\n",
       "      <th>Departure/Arrival time convenient</th>\n",
       "      <th>Food and drink</th>\n",
       "      <th>Gate location</th>\n",
       "      <th>...</th>\n",
       "      <th>Ease of Online booking</th>\n",
       "      <th>On-board service</th>\n",
       "      <th>Leg room service</th>\n",
       "      <th>Baggage handling</th>\n",
       "      <th>Checkin service</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Online boarding</th>\n",
       "      <th>Departure Delay in Minutes</th>\n",
       "      <th>Arrival Delay in Minutes</th>\n",
       "      <th>Satisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>65</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>265</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>47</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>2464</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>310</td>\n",
       "      <td>305.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>15</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>2138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>60</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>623</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>70</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender   Customer Type  Age   Type of Travel     Class  Flight Distance  \\\n",
       "0  Female  Loyal Customer   65  Personal Travel       Eco              265   \n",
       "1    Male  Loyal Customer   47  Personal Travel  Business             2464   \n",
       "2  Female  Loyal Customer   15  Personal Travel       Eco             2138   \n",
       "3  Female  Loyal Customer   60  Personal Travel       Eco              623   \n",
       "4  Female  Loyal Customer   70  Personal Travel       Eco              354   \n",
       "\n",
       "   Seat comfort  Departure/Arrival time convenient  Food and drink  \\\n",
       "0             0                                  0               0   \n",
       "1             0                                  0               0   \n",
       "2             0                                  0               0   \n",
       "3             0                                  0               0   \n",
       "4             0                                  0               0   \n",
       "\n",
       "   Gate location  ...  Ease of Online booking  On-board service  \\\n",
       "0              2  ...                       3                 3   \n",
       "1              3  ...                       3                 4   \n",
       "2              3  ...                       2                 3   \n",
       "3              3  ...                       1                 1   \n",
       "4              3  ...                       2                 2   \n",
       "\n",
       "   Leg room service  Baggage handling  Checkin service  Cleanliness  \\\n",
       "0                 0                 3                5            3   \n",
       "1                 4                 4                2            3   \n",
       "2                 3                 4                4            4   \n",
       "3                 0                 1                4            1   \n",
       "4                 0                 2                4            2   \n",
       "\n",
       "   Online boarding  Departure Delay in Minutes  Arrival Delay in Minutes  \\\n",
       "0                2                           0                       0.0   \n",
       "1                2                         310                     305.0   \n",
       "2                2                           0                       0.0   \n",
       "3                3                           0                       0.0   \n",
       "4                5                           0                       0.0   \n",
       "\n",
       "   Satisfaction  \n",
       "0             1  \n",
       "1             1  \n",
       "2             1  \n",
       "3             1  \n",
       "4             1  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change satisfaction variable to binary, \"1\" means \"satisfied\" and \"0\" means dissatisfied\n",
    "df2['Satisfaction']=df2['satisfaction'].apply(lambda x: 1 if x=='satisfied' else 0)\n",
    "df3=df2.drop('satisfaction',axis=1)\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8dc53c72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Female', 'Male'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#unique elements in the gender variable\n",
    "df3['Gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d31a582c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualization of Satisfaction Frequency by Gender\n",
    "plot1=pd.crosstab(df3['Gender'],df3['Satisfaction'])\n",
    "plot1.div(plot1.sum(1).astype(float),axis=0).plot(kind='bar',stacked=True)\n",
    "plt.title('Satisfaction Frequency by Gender')\n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Satisfaction Frequency')\n",
    "plt.savefig('satisfac_freq_gender')"
   ]
  },
  {
   "cell_type": "raw",
   "id": "7138836f",
   "metadata": {},
   "source": [
    "The gender does not seem a strong predictor of the satisfaction variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1c18e452",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Loyal Customer', 'disloyal Customer'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#unique elements in the Customer Type variable\n",
    "df3['Customer Type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b0dcb10d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualization of Satisfaction Frequency by cs type\n",
    "plot2=pd.crosstab(df3['Customer Type'],df3['Satisfaction'])\n",
    "plot2.div(plot2.sum(1).astype(float),axis=0).plot(kind='bar',stacked=True)\n",
    "plt.title('Satisfaction Frequency by Customer Type')\n",
    "plt.xlabel('Customer Type')\n",
    "plt.ylabel('Satisfaction Frequency')\n",
    "plt.savefig('satisfac_freq_cstype')"
   ]
  },
  {
   "cell_type": "raw",
   "id": "6d34cc63",
   "metadata": {},
   "source": [
    "The customer type seems a strong predictor of the satisfaction variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e18b41f2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Personal Travel', 'Business travel'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#unique elements of the travel type variable\n",
    "df3['Type of Travel'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3c971ce0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualization of Satisfaction Frequency by travel type\n",
    "plot3=pd.crosstab(df3['Type of Travel'],df3['Satisfaction'])\n",
    "plot3.div(plot3.sum(1).astype(float),axis=0).plot(kind='bar',stacked=True)\n",
    "plt.title('Satisfaction Frequency by Type of Travel')\n",
    "plt.xlabel('Type of Travel')\n",
    "plt.ylabel('Satisfaction Frequency')\n",
    "plt.savefig('satisfac_freq_traveltype')"
   ]
  },
  {
   "cell_type": "raw",
   "id": "06d1f5b3",
   "metadata": {},
   "source": [
    "The type of travel does not seem a strong predictor of the satisfaction variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "58aac27b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Eco', 'Business', 'Eco Plus'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#unique elements of class\n",
    "df3['Class'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0789e62b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualization of Satisfaction Frequency by class\n",
    "plot4=pd.crosstab(df3['Class'],df3['Satisfaction'])\n",
    "plot4.div(plot4.sum(1).astype(float),axis=0).plot(kind='bar',stacked=True)\n",
    "plt.title('Satisfaction Frequency by Class')\n",
    "plt.xlabel('Class')\n",
    "plt.ylabel('Satisfaction Frequency')\n",
    "plt.savefig('satisfac_freq_class')"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a8439700",
   "metadata": {},
   "source": [
    "The class seems a strong predictor of the satisfaction variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2ab38bb4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([65, 47, 15, 60, 70, 30, 66, 10, 56, 22, 58, 34, 62, 35, 13, 52, 55,\n",
       "       28,  9, 25, 53, 16, 64, 42, 21, 20, 26, 48, 57, 31, 17, 33, 32, 38,\n",
       "       29, 24, 37,  7, 39, 11, 49,  8, 40, 45, 67, 59, 44, 69, 51, 18, 23,\n",
       "       12, 46, 41, 54, 27, 63, 61, 36, 50, 68, 19, 14, 43, 72, 71, 80, 77,\n",
       "       85, 78, 75, 79, 74, 73, 76], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#unique elements of age\n",
    "df3['Age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9f0f6fb3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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A+C5wODAaOD5tC3B+OtaOwDPAKQViMjOzflQkibwcEas6lb3a5ZY56WbElQXjOBKYHhEvRcSjwEKyxLUPsDAiHomIvwPTgSPTs94PAX6Z9p9GdimymZnVUZGO9QWSPgoMkLQT8FngD32o83RJJwF3AZPSqMAjgLm5bRanMoAnOpXvC2wOPBsRa7vY/g0knUoa76utrY2Ojo4+hF/cmjVr6lZXbxWNbdKYtT1u09/aBjWm3iIcWzndxdYMn483w+e0kYokkc+QDcL4EnA5MAs4t2R930v7Rvp5AfDxkscqLCKmAFMAxo0bF+3t7bWuEsg+IPWqq7eKxtbVc7FrbdKYtVxwb9EReerLsZXTXWyLTmivfzCdvBk+p41U5OqsF8iSyFf6WllELK/MS/ohMDMtLgG2yW06MpXRTfnTwFBJA9PZSH57MzOrkyJjZ91EF88PiYhDeluZpOERsTQtfgioXLk1A/i5pAuBrYGdgDvIhp7fSdL2ZEniOOCjEREprqPJ+kkmANf0Nh4zM+ubIue+X8jNbwh8BOix4VXS5UA7sIWkxcDZQLukPcmS0iLgXwAiYoGkK4D707FPi4hX0nFOJ2tCGwBMjYgFqYqzgOmSvgH8CfhxgddiZmb9qEhz1rxORbdJuqPAfsd3UdztF31EfBP4Zhfl1wHXdVH+CNnVW2Zm1iBFmrOG5RbXA8YCm9YsIjMzaxlFmrPmkTU/iayp6VF8Y5+ZmVGsOWv7egRiZmatp0hz1oerrY+Iq/svHDMzayVFmrNOAQ4AbkzLB5Pdsf4kWTOXk4iZ2TqqSBJZHxhdub9D0nDgkog4uaaRmZlZ0ysyAOM2uRsEAZYD29YoHjMzayFFzkTmSJpFNm4WwLHA72oXkpmZtYoiV2edLulDwLtT0ZSI+FVtwzIzs1ZQdMjPu4HnIuJ3kjaSNCQinqtlYGZm1vyKPB73k2QPf/pBKhoB/LqGMZmZWYso0rF+GnAgsBogIh4GtqplUGZm1hqKJJGX0qNpAZA0kC6Ghjczs3VPkSRys6QvA4MkvRe4EvhNbcMyM7NWUCSJnEV2d/q9ZM//uA74ai2DMjOz1lD16ixJA4AFEbEr8MP6hGRmZq2i6plIerrgQ5J8h7qZmb1BkftENgMWpKcZPl8pjIgP1iwqMzNrCd0mEUkDI2It8O91jMfMzFpItTORO4C9I+JmSd+JiM/UKyj7R6MmX9vvx5w0Zi0Ta3BcM1u3VOsTUW7+wFoHYmZmradaEvENhWZmVlW15qxdJd1DdkayQ5onLUdEvKPm0ZmZWVOrlkTeXrcozMysJXWbRCLisXoGYmZmrafIsCdmZmZdchIxM7PSnETMzKy0Hoc9kXQgcA6wXdq+cnXW22obmpmZNbsiY2f9GPgcMA94pbbhmJlZKymSRFZFxG9rHomZmbWcIknkJkn/CVwNvFQpjIi7axaVmZm1hCJJZN/0c1yuLIBD+j8cMzNrJT0mkYg4uB6BmJlZ6+nxEl9Jm0q6UNJdabpA0qb1CM7MzJpbkftEpgLPAcekaTXwk1oGZWZmraFIn8gOEfGR3PLXJM2vUTxmZtZCipyJvCjpoMpCuvnwxdqFZGZmraJIEvkU8F1JiyQ9BvwP8K897SRpqqQVku7LlQ2TNFvSw+nnZqlcki6WtFDSPZL2zu0zIW3/sKQJufKxku5N+1wsSZiZWV31mEQiYn5E7AG8AxgTEXtFxJ8LHPsSYHynssnAnIjYCZiTlgEOB3ZK06nA9yBLOsDZZJcZ7wOcXUk8aZtP5vbrXJeZmdVYt30ikj4WET+T9PlO5QBExIXVDhwRt0ga1an4SKA9zU8DOoCzUvmlERHAXElDJQ1P286OiJWp7tnAeEkdwCYRMTeVXwocBfjOejOzOqrWsT44/RzSxbqyz19vi4ilaX4Z0JbmRwBP5LZbnMqqlS/uorxLkk4lO8Ohra2Njo6OkuH3zpo1a/qlrklj1vY9mE7aBtXmuP3BsZXTirHV67NYTX99TmuhmWOrqPZkwx+k2d9FxG35dalzvU8iIiSVTUa9rWsKMAVg3Lhx0d7eXo9q6ejooD/qmjj52r4H08mkMWu54N4iF+fVn2MrpxVjW3RCe/2D6aS/Pqe10MyxVRTpWP9OwbIilqdmKtLPFal8CbBNbruRqaxa+cguys3MrI6q9YnsDxwAbNmpX2QTYEDJ+mYAE4Dz0s9rcuWnS5pO1om+KiKWSpoFfCvXmX4Y8KWIWClptaT9gNuBkyif2MzMrKRq575vATZO2+T7RVYDR/d0YEmXk3WMbyFpMdlVVucBV0g6BXiM7A54gOuAI4CFwAvAyQApWZwL3Jm2+3qlkx34NNkVYIPIOtTdqW5mVmfV+kRuBm6WdElEPNbbA0fE8d2sek8X2wZwWjfHmUo29Ern8ruA3Xsbl5mZ9Z8ifSI/kjS0siBps9TMZGZm67giSWSLiHi2shARzwBb1SwiMzNrGUWSyKuStq0sSNqO8veJmJnZm0iRi8q/Atwq6WZAwLtIN+6Zmdm6rciTDa9PAyLul4rOjIinahuWmZm1gqK3t75CdmPghsBoSUTELbULy8zMWkGPSUTSJ4AzyO4Kn092RvJH4JCaRmZmZk2vSMf6GcA7gcci4mBgL+DZWgZlZmatoUgS+VtE/A1A0gYR8SCwS23DMjOzVlCkT2Rxutnw18BsSc+QDVliZmbruGoDMG4fEY9GxIdS0TmSbgI2Ba6vS3RmZtbUqjVn/RJA0pxKQUTcHBEzIuLvNY/MzMyaXrXmrPUkfRnYufMjcqHnx+OamdmbX7UzkePI7g+pDAXfeTIzs3VctaHgHwLOl3RPRPhZHWZm9gZFLvHdWdImyvxY0t2SDqt5ZGZm1vSKJJGPR8RqskfTDgNOJHtCoZmZreOKJBGln0cAl0bEglyZmZmtw4okkXmSbiBLIrMkDQFerW1YZmbWCorcsX4KsCfwSES8IGlz4OSaRmVmZi2h2h3ru6ZxsvZMRW+T3IplZmavq3Ym8nmyJxhe0MW6wEPBm1kfjZp8bUPqXXTe+xtS75tRtftEKo/APbwyim+FpA1rGpWZmbWEIh3rfyhYZmZm65hqfSJvBUYAgyTtxeuX9W4CbFSH2MzMrMlV6xN5HzCR7LG4+cEWnwO+XMOYzMysRVTrE5kGTJP0kYi4qo4xmZlZi+jxPpGIuErS+4HdgA1z5V+vZWBmZtb8ekwikr5P1gdyMPAj4GjgjhrH1ZR6eznipDFrmdigSxjNzOqhyNVZB0TEScAzEfE1YH9g59qGZWZmraBIEnkx/XxB0tbAy8Dw2oVkZmatosjYWTMlDQX+E7ib7G71H9YyKDMzaw1FOtbPTbNXSZoJbBgRq2oblpmZtYJum7MkvTPdcFhZPgm4AjhX0rB6BGdmZs2tWp/ID4C/A0h6N9nTDC8FVgFTah+amZk1u2rNWQMiYmWaPxaYkm46vErS/JpHZmZmTa/amcgASZUk8x7gxty6Ih3yZmb2JlctGVwO3CzpKbLLfH8PIGlHsiYtMzNbx3V7JhIR3wQmAZcAB0VE5Pb5TF8qlbRI0r2S5ku6K5UNkzRb0sPp52apXJIulrRQ0j2S9s4dZ0La/mFJE/oSk5mZ9V7VZqmImNtF2f/1U90HR8RTueXJwJyIOE/S5LR8FnA4sFOa9gW+B+ybrhA7GxhHdu/KPEkzIuKZforPzMx6UOSO9Xo5EpiW5qcBR+XKL43MXGCopOFkQ9XPjoiVKXHMBsbXOWYzs3Vao5JIADdImiep8hjetohYmuaXAW1pfgTwRG7fxamsu3IzM6uTRl1ldVBELJG0FTBb0oP5lRERkqKbfXstJapTAdra2ujo6Ch1nElj1vZq+7ZBvd+nXhxbOY6tnGaLLf8dsGbNmtLfCbXWzLFVNCSJRMSS9HOFpF8B+wDLJQ2PiKWpuWpF2nwJsE1u95GpbAnQ3qm8o5v6ppBukBw3bly0t7d3tVmPejus+6Qxa7ng3ua8GtqxlePYymm22Bad0P7afEdHB2W/E2qtmWOrqHtzlqTBkoZU5oHDgPuAGUDlCqsJwDVpfgZwUrpKaz9gVWr2mgUcJmmzdCXXYanMzMzqpBH/GrQBv5JUqf/nEXG9pDuBKySdAjwGHJO2vw44AlgIvACcDBARKyWdC9yZtvt67g57MzOrg7onkYh4BNiji/Knye6M71wewGndHGsqMLW/YzQzs2Ka6RJfMzNrMU4iZmZWmpOImZmV5iRiZmalOYmYmVlpTiJmZlaak4iZmZXmJGJmZqU5iZiZWWlOImZmVpqTiJmZleYkYmZmpTmJmJlZaU4iZmZWmpOImZmV5iRiZmalOYmYmVlpTiJmZlaak4iZmZXmJGJmZqU5iZiZWWlOImZmVpqTiJmZleYkYmZmpTmJmJlZaU4iZmZWmpOImZmV5iRiZmalDWx0AGZm9TZq8rWvzU8as5aJueVaW3Te++tWVz34TMTMzEpzEjEzs9KcRMzMrDQnETMzK81JxMzMSnMSMTOz0pxEzMysNCcRMzMrzUnEzMxKcxIxM7PSWj6JSBov6SFJCyVNbnQ8ZmbrkpYeO0vSAOC7wHuBxcCdkmZExP2NjczMrGujejFOV3+O61WrMbta/UxkH2BhRDwSEX8HpgNHNjgmM7N1hiKi0TGUJuloYHxEfCItnwjsGxGnd9ruVODUtLgL8FCdQtwCeKpOdfWWYyvHsZXj2Mpppti2i4gtOxe2dHNWURExBZhS73ol3RUR4+pdbxGOrRzHVo5jK6eZY6to9easJcA2ueWRqczMzOqg1ZPIncBOkraX9BbgOGBGg2MyM1tntHRzVkSslXQ6MAsYAEyNiAUNDiuv7k1oveDYynFs5Ti2cpo5NqDFO9bNzKyxWr05y8zMGshJxMzMSnMS6QeSpkpaIem+XNkwSbMlPZx+btag2LaRdJOk+yUtkHRGs8QnaUNJd0j6c4rta6l8e0m3p6FsfpEummgISQMk/UnSzGaKTdIiSfdKmi/prlTW8N9pimOopF9KelDSA5L2b4bYJO2S3q/KtFrSmc0QW4rvc+lzcJ+ky9Pnoyn+3qpxEukflwDjO5VNBuZExE7AnLTcCGuBSRExGtgPOE3S6CaJ7yXgkIjYA9gTGC9pP+B84KKI2BF4BjilAbFVnAE8kFtuptgOjog9c/cRNMPvFOC/gesjYldgD7L3r+GxRcRD6f3aExgLvAD8qhlikzQC+CwwLiJ2J7tQ6Dia6++taxHhqR8mYBRwX275IWB4mh8OPNToGFMs15CNNdZU8QEbAXcD+5LdoTswle8PzGpQTCPJvlQOAWYCaqLYFgFbdCpr+O8U2BR4lHTRTjPF1imew4DbmiU2YATwBDCM7KrZmcD7muXvrdrkM5HaaYuIpWl+GdDWyGAAJI0C9gJup0niS81F84EVwGzgL8CzEbE2bbKY7APWCN8Gvgi8mpY3p3liC+AGSfPSsD7QHL/T7YEngZ+kZsAfSRrcJLHlHQdcnuYbHltELAH+C3gcWAqsAubRPH9v3XISqYPI/o1o6LXUkjYGrgLOjIjV+XWNjC8iXomseWEk2YCauzYijs4kfQBYERHzGh1LNw6KiL2Bw8maKN+dX9nA3+lAYG/gexGxF/A8nZqHGv15SP0KHwSu7LyuUbGlfpgjyZLw1sBg3thE3pScRGpnuaThAOnnikYFIml9sgRyWURc3WzxAUTEs8BNZKfsQyVVboRt1FA2BwIflLSIbHToQ8ja+pshtsp/rkTECrJ2/X1ojt/pYmBxRNyeln9JllSaIbaKw4G7I2J5Wm6G2A4FHo2IJyPiZeBqsr/Bpvh7q8ZJpHZmABPS/ASyvoi6kyTgx8ADEXFhblXD45O0paShaX4QWV/NA2TJ5OhGxhYRX4qIkRExiqzp48aIOKEZYpM0WNKQyjxZ+/59NMHvNCKWAU9I2iUVvQe4vxliyzme15uyoDliexzYT9JG6TNbed8a/vfWo0Z3yrwZJrI/yKXAy2T/iZ1C1n4+B3gY+B0wrEGxHUR2en4PMD9NRzRDfMA7gD+l2O4D/l8qfxtwB7CQrMlhgwb/ftuBmc0SW4rhz2laAHwllTf8d5ri2BO4K/1efw1s1kSxDQaeBjbNlTVLbF8DHkyfhZ8CGzTD31tPk4c9MTOz0tycZWZmpTmJmJlZaU4iZmZWmpOImZmV5iRiZmalOYmY1YmkoySFpKa4K9+sPziJmNXP8cCt6afZm4KTiFkdpLHLDiK7EfW4VLaepP9Nz92YLek6SUendWMl3ZwGWJxVGZbDrNk4iZjVx5Fkz9j4P+BpSWOBD5M9QmA0cCLZuGGVsc6+AxwdEWOBqcA3GxG0WU8G9ryJmfWD48kGcIRsQMfjyT5/V0bEq8AySTel9bsAuwOzs2GUGEA2rI5Z03ESMasxScPIRgEeIynIkkKQjb7b5S7AgojYv04hmpXm5iyz2jsa+GlEbBcRoyJiG7Kn/60EPpL6RtrIBnqE7El7W0p6rXlL0m6NCNysJ04iZrV3PG8867gKeCvZqM/3Az8jezzwqoj4O1niOV/Sn8lGXj6gbtGa9YJH8TVrIEkbR8QaSZuTDfl9YGTP5DBrCe4TMWusmenBXG8BznUCsVbjMxEzMyvNfSJmZlaak4iZmZXmJGJmZqU5iZiZWWlOImZmVtr/B5I1Jgp1ND7oAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualization of age\n",
    "df3['Age'].hist()\n",
    "plt.title('Satisfaction Frequency by Age')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Satisfaction Frequency')\n",
    "plt.savefig('satisfac_freq_age')"
   ]
  },
  {
   "cell_type": "raw",
   "id": "5a095cfb",
   "metadata": {},
   "source": [
    "Most of customers are in the age range of 15-60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "18ab98dd",
   "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>Customer Type</th>\n",
       "      <th>Age</th>\n",
       "      <th>Class</th>\n",
       "      <th>Flight Distance</th>\n",
       "      <th>Seat comfort</th>\n",
       "      <th>Departure/Arrival time convenient</th>\n",
       "      <th>Food and drink</th>\n",
       "      <th>Gate location</th>\n",
       "      <th>Inflight wifi service</th>\n",
       "      <th>Inflight entertainment</th>\n",
       "      <th>...</th>\n",
       "      <th>Ease of Online booking</th>\n",
       "      <th>On-board service</th>\n",
       "      <th>Leg room service</th>\n",
       "      <th>Baggage handling</th>\n",
       "      <th>Checkin service</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Online boarding</th>\n",
       "      <th>Departure Delay in Minutes</th>\n",
       "      <th>Arrival Delay in Minutes</th>\n",
       "      <th>Satisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>65</td>\n",
       "      <td>Eco</td>\n",
       "      <td>265</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>47</td>\n",
       "      <td>Business</td>\n",
       "      <td>2464</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>310</td>\n",
       "      <td>305.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>15</td>\n",
       "      <td>Eco</td>\n",
       "      <td>2138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>60</td>\n",
       "      <td>Eco</td>\n",
       "      <td>623</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>70</td>\n",
       "      <td>Eco</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Customer Type  Age     Class  Flight Distance  Seat comfort  \\\n",
       "0  Loyal Customer   65       Eco              265             0   \n",
       "1  Loyal Customer   47  Business             2464             0   \n",
       "2  Loyal Customer   15       Eco             2138             0   \n",
       "3  Loyal Customer   60       Eco              623             0   \n",
       "4  Loyal Customer   70       Eco              354             0   \n",
       "\n",
       "   Departure/Arrival time convenient  Food and drink  Gate location  \\\n",
       "0                                  0               0              2   \n",
       "1                                  0               0              3   \n",
       "2                                  0               0              3   \n",
       "3                                  0               0              3   \n",
       "4                                  0               0              3   \n",
       "\n",
       "   Inflight wifi service  Inflight entertainment  ...  Ease of Online booking  \\\n",
       "0                      2                       4  ...                       3   \n",
       "1                      0                       2  ...                       3   \n",
       "2                      2                       0  ...                       2   \n",
       "3                      3                       4  ...                       1   \n",
       "4                      4                       3  ...                       2   \n",
       "\n",
       "   On-board service  Leg room service  Baggage handling  Checkin service  \\\n",
       "0                 3                 0                 3                5   \n",
       "1                 4                 4                 4                2   \n",
       "2                 3                 3                 4                4   \n",
       "3                 1                 0                 1                4   \n",
       "4                 2                 0                 2                4   \n",
       "\n",
       "   Cleanliness  Online boarding  Departure Delay in Minutes  \\\n",
       "0            3                2                           0   \n",
       "1            3                2                         310   \n",
       "2            4                2                           0   \n",
       "3            1                3                           0   \n",
       "4            2                5                           0   \n",
       "\n",
       "   Arrival Delay in Minutes  Satisfaction  \n",
       "0                       0.0             1  \n",
       "1                     305.0             1  \n",
       "2                       0.0             1  \n",
       "3                       0.0             1  \n",
       "4                       0.0             1  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#drop not good predictor\n",
    "df4=df3.drop(['Gender','Type of Travel'],axis=1)\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f157eeae",
   "metadata": {},
   "source": [
    "Create dummy variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "306b81e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Class_Business</th>\n",
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       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Customer Type_Loyal Customer  Customer Type_disloyal Customer  \\\n",
       "0                             1                                0   \n",
       "1                             1                                0   \n",
       "2                             1                                0   \n",
       "3                             1                                0   \n",
       "4                             1                                0   \n",
       "\n",
       "   Class_Business  Class_Eco  Class_Eco Plus  \n",
       "0               0          1               0  \n",
       "1               1          0               0  \n",
       "2               0          1               0  \n",
       "3               0          1               0  \n",
       "4               0          1               0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies1=pd.get_dummies(df4[['Customer Type','Class']])\n",
    "dummies1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "643ffe23",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   Age  Flight Distance  Seat comfort  Departure/Arrival time convenient  \\\n",
       "0   65              265             0                                  0   \n",
       "1   47             2464             0                                  0   \n",
       "2   15             2138             0                                  0   \n",
       "3   60              623             0                                  0   \n",
       "4   70              354             0                                  0   \n",
       "\n",
       "   Food and drink  Gate location  Inflight wifi service  \\\n",
       "0               0              2                      2   \n",
       "1               0              3                      0   \n",
       "2               0              3                      2   \n",
       "3               0              3                      3   \n",
       "4               0              3                      4   \n",
       "\n",
       "   Inflight entertainment  Online support  Ease of Online booking  ...  \\\n",
       "0                       4               2                       3  ...   \n",
       "1                       2               2                       3  ...   \n",
       "2                       0               2                       2  ...   \n",
       "3                       4               3                       1  ...   \n",
       "4                       3               4                       2  ...   \n",
       "\n",
       "   Cleanliness  Online boarding  Departure Delay in Minutes  \\\n",
       "0            3                2                           0   \n",
       "1            3                2                         310   \n",
       "2            4                2                           0   \n",
       "3            1                3                           0   \n",
       "4            2                5                           0   \n",
       "\n",
       "   Arrival Delay in Minutes  Satisfaction  Customer Type_Loyal Customer  \\\n",
       "0                       0.0             1                             1   \n",
       "1                     305.0             1                             1   \n",
       "2                       0.0             1                             1   \n",
       "3                       0.0             1                             1   \n",
       "4                       0.0             1                             1   \n",
       "\n",
       "   Customer Type_disloyal Customer  Class_Business  Class_Eco  Class_Eco Plus  \n",
       "0                                0               0          1               0  \n",
       "1                                0               1          0               0  \n",
       "2                                0               0          1               0  \n",
       "3                                0               0          1               0  \n",
       "4                                0               0          1               0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5=pd.concat([df4.drop(['Customer Type','Class'],axis=1),dummies1],axis=1)\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00fb7661",
   "metadata": {},
   "source": [
    "Over-sampling using SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "88c5bc5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create independen and dependen variable\n",
    "X=df5.drop(['Satisfaction'],axis=1)\n",
    "y=df5['Satisfaction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3dff536d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "length of oversampled data is  113366\n",
      "length of satisfied data is  56683\n",
      "length of dissatisfied data is  56683\n"
     ]
    }
   ],
   "source": [
    "smo=SMOTE(random_state=0)\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=10)\n",
    "column=X_train.columns\n",
    "\n",
    "smo_data_X,smo_data_y=smo.fit_resample(X_train,y_train)\n",
    "smo_data_X=pd.DataFrame(data=smo_data_X,columns=column)\n",
    "smo_data_y=pd.DataFrame(data=smo_data_y,columns=['Satisfaction'])\n",
    "\n",
    "print(\"length of oversampled data is \", len(smo_data_X))\n",
    "print(\"length of satisfied data is \", len(smo_data_y[smo_data_y['Satisfaction']==1]))\n",
    "print(\"length of dissatisfied data is \", len(smo_data_X[smo_data_y['Satisfaction']==0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05a3633a",
   "metadata": {},
   "source": [
    "Recursive Feature Elimination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "87ced5a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df5_vars=df5.columns.values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f5cde2f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_2=['Satisfaction']\n",
    "X_2=[i for i in df5_vars if i not in 'Satisfaction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1073d26a",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\utils\\validation.py:70: FutureWarning: Pass n_features_to_select=20 as keyword args. From version 1.0 (renaming of 0.25) passing these as positional arguments will result in an error\n",
      "  warnings.warn(f\"Pass {args_msg} as keyword args. From version \"\n",
      "c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "logreg=LogisticRegression()\n",
    "rfe=RFE(logreg,20)\n",
    "rfe=rfe.fit(smo_data_X,smo_data_y.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b2cf7c87",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True False  True  True  True  True  True  True  True  True  True  True\n",
      "  True  True  True  True False False  True  True  True  True  True]\n",
      "[1 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 2 1 1 1 1 1]\n",
      "23\n"
     ]
    }
   ],
   "source": [
    "print(rfe.support_)\n",
    "print(rfe.ranking_)\n",
    "print(len(rfe.ranking_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fead9838",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Age',\n",
       " 'Flight Distance',\n",
       " 'Seat comfort',\n",
       " 'Departure/Arrival time convenient',\n",
       " 'Food and drink',\n",
       " 'Gate location',\n",
       " 'Inflight wifi service',\n",
       " 'Inflight entertainment',\n",
       " 'Online support',\n",
       " 'Ease of Online booking',\n",
       " 'On-board service',\n",
       " 'Leg room service',\n",
       " 'Baggage handling',\n",
       " 'Checkin service',\n",
       " 'Cleanliness',\n",
       " 'Online boarding',\n",
       " 'Departure Delay in Minutes',\n",
       " 'Arrival Delay in Minutes',\n",
       " 'Customer Type_Loyal Customer',\n",
       " 'Customer Type_disloyal Customer',\n",
       " 'Class_Business',\n",
       " 'Class_Eco',\n",
       " 'Class_Eco Plus']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1659dcd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#RFE has helped to select the features\n",
    "cols=['Age','Seat comfort','Departure/Arrival time convenient','Food and drink', 'Gate location',\n",
    " 'Inflight wifi service','Inflight entertainment', 'Online support', 'Ease of Online booking',\n",
    " 'On-board service', 'Leg room service', 'Baggage handling', 'Checkin service',\n",
    " 'Cleanliness', 'Online boarding','Customer Type_Loyal Customer',\n",
    " 'Customer Type_disloyal Customer', 'Class_Business', 'Class_Eco', 'Class_Eco Plus']\n",
    "X_3=smo_data_X[cols]\n",
    "y_3=smo_data_y['Satisfaction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e79a84b8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.461573\n",
      "         Iterations 6\n",
      "                                  Results: Logit\n",
      "==================================================================================\n",
      "Model:                    Logit                 Pseudo R-squared:      0.334      \n",
      "Dependent Variable:       Satisfaction          AIC:                   104693.4348\n",
      "Date:                     2021-11-29 11:26      BIC:                   104886.2023\n",
      "No. Observations:         113366                Log-Likelihood:        -52327.    \n",
      "Df Model:                 19                    LL-Null:               -78579.    \n",
      "Df Residuals:             113346                LLR p-value:           0.0000     \n",
      "Converged:                1.0000                Scale:                 1.0000     \n",
      "No. Iterations:           6.0000                                                  \n",
      "----------------------------------------------------------------------------------\n",
      "                                   Coef.  Std.Err.    z     P>|z|   [0.025  0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "Age                               -0.0237   0.0005 -46.1129 0.0000 -0.0247 -0.0226\n",
      "Seat comfort                       0.3078   0.0092  33.5304 0.0000  0.2898  0.3258\n",
      "Departure/Arrival time convenient -0.2361   0.0067 -35.4527 0.0000 -0.2492 -0.2231\n",
      "Food and drink                    -0.2051   0.0093 -22.0117 0.0000 -0.2234 -0.1869\n",
      "Gate location                     -0.0566   0.0075  -7.5916 0.0000 -0.0712 -0.0420\n",
      "Inflight wifi service             -0.2540   0.0086 -29.6779 0.0000 -0.2708 -0.2372\n",
      "Inflight entertainment             0.6235   0.0082  76.1578 0.0000  0.6074  0.6395\n",
      "Online support                     0.0270   0.0089   3.0261 0.0025  0.0095  0.0444\n",
      "Ease of Online booking             0.5125   0.0114  44.9931 0.0000  0.4902  0.5348\n",
      "On-board service                   0.2398   0.0081  29.5064 0.0000  0.2238  0.2557\n",
      "Leg room service                   0.1194   0.0069  17.2269 0.0000  0.1059  0.1330\n",
      "Baggage handling                  -0.1127   0.0092 -12.3117 0.0000 -0.1307 -0.0948\n",
      "Checkin service                    0.1640   0.0068  24.2922 0.0000  0.1507  0.1772\n",
      "Cleanliness                       -0.1443   0.0094 -15.2892 0.0000 -0.1628 -0.1258\n",
      "Online boarding                   -0.0071   0.0097  -0.7345 0.4626 -0.0260  0.0118\n",
      "Customer Type_Loyal Customer      -1.6112   0.0423 -38.1023 0.0000 -1.6941 -1.5284\n",
      "Customer Type_disloyal Customer   -3.1886   0.0450 -70.8217 0.0000 -3.2769 -3.1004\n",
      "Class_Business                     0.2064   0.0401   5.1476 0.0000  0.1278  0.2849\n",
      "Class_Eco                         -1.0101   0.0388 -26.0330 0.0000 -1.0862 -0.9341\n",
      "Class_Eco Plus                    -0.8591   0.0475 -18.0738 0.0000 -0.9523 -0.7659\n",
      "==================================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Implementing the model\n",
    "import statsmodels.api as sm\n",
    "logit_model=sm.Logit(y_3,X_3)\n",
    "result=logit_model.fit()\n",
    "print(result.summary2())"
   ]
  },
  {
   "cell_type": "raw",
   "id": "30229e3e",
   "metadata": {},
   "source": [
    "The p-values for most of the variables are smaller than 0.05, except one variable. We will remove it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "bc4bd231",
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove variable \"Online boarding\"\n",
    "cols=['Age','Seat comfort','Departure/Arrival time convenient','Food and drink', 'Gate location',\n",
    " 'Inflight wifi service','Inflight entertainment', 'Online support', 'Ease of Online booking',\n",
    " 'On-board service', 'Leg room service', 'Baggage handling', 'Checkin service',\n",
    " 'Cleanliness','Customer Type_Loyal Customer',\n",
    " 'Customer Type_disloyal Customer', 'Class_Business', 'Class_Eco', 'Class_Eco Plus']\n",
    "X_4=smo_data_X[cols]\n",
    "y_4=smo_data_y['Satisfaction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1c04e945",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.461576\n",
      "         Iterations 6\n",
      "                                  Results: Logit\n",
      "==================================================================================\n",
      "Model:                    Logit                 Pseudo R-squared:      0.334      \n",
      "Dependent Variable:       Satisfaction          AIC:                   104691.9744\n",
      "Date:                     2021-11-29 11:26      BIC:                   104875.1035\n",
      "No. Observations:         113366                Log-Likelihood:        -52327.    \n",
      "Df Model:                 18                    LL-Null:               -78579.    \n",
      "Df Residuals:             113347                LLR p-value:           0.0000     \n",
      "Converged:                1.0000                Scale:                 1.0000     \n",
      "No. Iterations:           6.0000                                                  \n",
      "----------------------------------------------------------------------------------\n",
      "                                   Coef.  Std.Err.    z     P>|z|   [0.025  0.975]\n",
      "----------------------------------------------------------------------------------\n",
      "Age                               -0.0236   0.0005 -46.1097 0.0000 -0.0246 -0.0226\n",
      "Seat comfort                       0.3080   0.0092  33.5726 0.0000  0.2900  0.3260\n",
      "Departure/Arrival time convenient -0.2364   0.0067 -35.5431 0.0000 -0.2494 -0.2233\n",
      "Food and drink                    -0.2048   0.0093 -22.0058 0.0000 -0.2230 -0.1865\n",
      "Gate location                     -0.0568   0.0074  -7.6209 0.0000 -0.0714 -0.0422\n",
      "Inflight wifi service             -0.2554   0.0083 -30.6309 0.0000 -0.2718 -0.2391\n",
      "Inflight entertainment             0.6227   0.0081  76.7106 0.0000  0.6068  0.6386\n",
      "Online support                     0.0251   0.0086   2.9380 0.0033  0.0084  0.0419\n",
      "Ease of Online booking             0.5092   0.0105  48.5725 0.0000  0.4887  0.5298\n",
      "On-board service                   0.2403   0.0081  29.6938 0.0000  0.2244  0.2562\n",
      "Leg room service                   0.1196   0.0069  17.2664 0.0000  0.1061  0.1332\n",
      "Baggage handling                  -0.1125   0.0092 -12.2935 0.0000 -0.1304 -0.0946\n",
      "Checkin service                    0.1633   0.0067  24.4314 0.0000  0.1502  0.1764\n",
      "Cleanliness                       -0.1438   0.0094 -15.2736 0.0000 -0.1623 -0.1254\n",
      "Customer Type_Loyal Customer      -1.6123   0.0423 -38.1489 0.0000 -1.6952 -1.5295\n",
      "Customer Type_disloyal Customer   -3.1910   0.0449 -71.0511 0.0000 -3.2790 -3.1030\n",
      "Class_Business                     0.2056   0.0401   5.1300 0.0000  0.1270  0.2841\n",
      "Class_Eco                         -1.0115   0.0388 -26.0962 0.0000 -1.0875 -0.9355\n",
      "Class_Eco Plus                    -0.8604   0.0475 -18.1128 0.0000 -0.9535 -0.7673\n",
      "==================================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Implementing the model\n",
    "logit_model2=sm.Logit(y_4,X_4)\n",
    "result2=logit_model2.fit()\n",
    "print(result2.summary2())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "410515de",
   "metadata": {},
   "source": [
    "Logistic Regression Model Fitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b61365cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\asus\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_final,X_test_final,y_train_final,y_test_final=train_test_split(X_4,y_4,test_size=0.2,random_state=0)\n",
    "logreg.fit(X_train_final,y_train_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "846bb586",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression on test set:0.84\n"
     ]
    }
   ],
   "source": [
    "y_pred=logreg.predict(X_test_final)\n",
    "print('Accuracy of logistic regression on test set:{:.2f}'.format(logreg.score(X_test_final,y_test_final)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "643be3c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 257.44, 'Predicted')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test_final,y_pred)\n",
    "class_name=[0,1]\n",
    "fig,ax=plt.subplots()\n",
    "tick_marks=np.arange(len(class_name))\n",
    "plt.xticks(tick_marks,class_name)\n",
    "plt.yticks(tick_marks,class_name)\n",
    "\n",
    "sns.heatmap(pd.DataFrame(cm),annot=True,cmap='BrBG_r',fmt='g')\n",
    "ax.xaxis.set_label_position('top')\n",
    "plt.tight_layout()\n",
    "plt.title('Confusion Matrix 2',y=1.1)\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')"
   ]
  },
  {
   "cell_type": "raw",
   "id": "de6cf9be",
   "metadata": {},
   "source": [
    "We have 9636+9342 correct predictions and 1884+1812 incorrect predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11031843",
   "metadata": {},
   "source": [
    "Compute precision, recal, F-measure and support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6ea585f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.84      0.84     11448\n",
      "           1       0.84      0.83      0.83     11226\n",
      "\n",
      "    accuracy                           0.84     22674\n",
      "   macro avg       0.84      0.84      0.84     22674\n",
      "weighted avg       0.84      0.84      0.84     22674\n",
      "\n"
     ]
    }
   ],
   "source": [
    "clas_report=classification_report(y_test_final,y_pred)\n",
    "print(clas_report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7835274",
   "metadata": {},
   "source": [
    "ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "408e6f01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logit_roc_auc=roc_auc_score(y_test_final,y_pred)\n",
    "fpr,tpr,thresholds=roc_curve(y_test_final,logreg.predict_proba(X_test_final)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr,tpr,label=\"Logistic Regression(area=%0.2f)\"% logit_roc_auc)\n",
    "plt.plot([0,1],[0,1],'r--')\n",
    "plt.xlim([0.0,1.0])\n",
    "plt.ylim([0.0,1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "e69dead8",
   "metadata": {},
   "source": [
    "The dotted line represents the ROC curve of a purely random classifier. It is a good classifier, beacause it stays as far away from that line as possible"
   ]
  }
 ],
 "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}