Logistics regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split,cross_val_score\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the data using read_csv\n",
    "df = pd.read_csv('Operation Reimagine_Airline Passenger Satisfaction Dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop the first column\n",
    "df.drop(columns=df.columns[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</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>Inflight wifi service</th>\n",
       "      <th>Departure/Arrival time convenient</th>\n",
       "      <th>Ease of Online booking</th>\n",
       "      <th>...</th>\n",
       "      <th>Inflight entertainment</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>Inflight service</th>\n",
       "      <th>Cleanliness</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>19556</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>52</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>160</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>50</td>\n",
       "      <td>44.0</td>\n",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90035</td>\n",
       "      <td>Female</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>36</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>2863</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12360</td>\n",
       "      <td>Male</td>\n",
       "      <td>disloyal Customer</td>\n",
       "      <td>20</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>192</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  Gender      Customer Type  Age   Type of Travel     Class  \\\n",
       "0  19556  Female     Loyal Customer   52  Business travel       Eco   \n",
       "1  90035  Female     Loyal Customer   36  Business travel  Business   \n",
       "2  12360    Male  disloyal Customer   20  Business travel       Eco   \n",
       "\n",
       "   Flight Distance  Inflight wifi service  Departure/Arrival time convenient  \\\n",
       "0              160                      5                                  4   \n",
       "1             2863                      1                                  1   \n",
       "2              192                      2                                  0   \n",
       "\n",
       "   Ease of Online booking  ...  Inflight entertainment  On-board service  \\\n",
       "0                       3  ...                       5                 5   \n",
       "1                       3  ...                       4                 4   \n",
       "2                       2  ...                       2                 4   \n",
       "\n",
       "   Leg room service  Baggage handling  Checkin service  Inflight service  \\\n",
       "0                 5                 5                2                 5   \n",
       "1                 4                 4                3                 4   \n",
       "2                 1                 3                2                 2   \n",
       "\n",
       "   Cleanliness  Departure Delay in Minutes  Arrival Delay in Minutes  \\\n",
       "0            5                          50                      44.0   \n",
       "1            5                           0                       0.0   \n",
       "2            2                           0                       0.0   \n",
       "\n",
       "              satisfaction  \n",
       "0                satisfied  \n",
       "1                satisfied  \n",
       "2  neutral or dissatisfied  \n",
       "\n",
       "[3 rows x 24 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25976 entries, 0 to 25975\n",
      "Data columns (total 24 columns):\n",
      " #   Column                             Non-Null Count  Dtype  \n",
      "---  ------                             --------------  -----  \n",
      " 0   id                                 25976 non-null  int64  \n",
      " 1   Gender                             25976 non-null  object \n",
      " 2   Customer Type                      25976 non-null  object \n",
      " 3   Age                                25976 non-null  int64  \n",
      " 4   Type of Travel                     25976 non-null  object \n",
      " 5   Class                              25976 non-null  object \n",
      " 6   Flight Distance                    25976 non-null  int64  \n",
      " 7   Inflight wifi service              25976 non-null  int64  \n",
      " 8   Departure/Arrival time convenient  25976 non-null  int64  \n",
      " 9   Ease of Online booking             25976 non-null  int64  \n",
      " 10  Gate location                      25976 non-null  int64  \n",
      " 11  Food and drink                     25976 non-null  int64  \n",
      " 12  Online boarding                    25976 non-null  int64  \n",
      " 13  Seat comfort                       25976 non-null  int64  \n",
      " 14  Inflight entertainment             25976 non-null  int64  \n",
      " 15  On-board service                   25976 non-null  int64  \n",
      " 16  Leg room service                   25976 non-null  int64  \n",
      " 17  Baggage handling                   25976 non-null  int64  \n",
      " 18  Checkin service                    25976 non-null  int64  \n",
      " 19  Inflight service                   25976 non-null  int64  \n",
      " 20  Cleanliness                        25976 non-null  int64  \n",
      " 21  Departure Delay in Minutes         25976 non-null  int64  \n",
      " 22  Arrival Delay in Minutes           25893 non-null  float64\n",
      " 23  satisfaction                       25976 non-null  object \n",
      "dtypes: float64(1), int64(18), object(5)\n",
      "memory usage: 4.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert categorical labels to numeric labels\n",
    "df['Satisfaction_lvl'] = df['satisfaction'].apply(lambda x: 1 if x == 'satisfied' else 0)\n",
    "df['Gender_type'] = df['Gender'].apply(lambda x: 1 if x == 'Male' else 0)\n",
    "df['Customer_type_satis'] = df['Customer Type'].apply(lambda x: 1 if x == 'Loyal Customer' else 0)  \n",
    "df['Travel_type'] = df['Type of Travel'].apply(lambda x: 1 if x == 'Personal Travel' else 0)\n",
    "\n",
    "\n",
    "def class_transform(x):\n",
    "    if x == 'Business':\n",
    "        return 0\n",
    "    elif x == 'Eco':\n",
    "        return 1\n",
    "    else:\n",
    "        return 2\n",
    "\n",
    "df['Class_type'] = df['Class'].apply(class_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop na\n",
    "sub_df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define x and y\n",
    "features = ['Age', 'Flight Distance', 'Inflight wifi service',\n",
    "             'Departure/Arrival time convenient', 'Ease of Online booking',\n",
    "             'Gate location', 'Food and drink', 'Online boarding', 'Seat comfort',\n",
    "             'Inflight entertainment', 'On-board service', 'Leg room service',\n",
    "             'Baggage handling', 'Checkin service', 'Inflight service',\n",
    "             'Cleanliness', 'Departure Delay in Minutes', 'Arrival Delay in Minutes',\n",
    "             'Gender_type', 'Customer_type_satis', 'Travel_type', 'Class_type']\n",
    "\n",
    "y = sub_df['Satisfaction_lvl']\n",
    "X = sub_df[features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initiate LogisticRegression method\n",
    "model = LogisticRegression(max_iter=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\winnie\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# train the model using X and y training data\n",
    "log_model = model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-7.54669582])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a dataframe displaying the features and its coefficient\n",
    "coef = pd.DataFrame(log_model.coef_).transpose() \n",
    "coef.columns =['coefficient']\n",
    "coef['feature'] = features\n",
    "coef = coef[['feature', 'coefficient']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>-0.010304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Flight Distance</td>\n",
       "      <td>-0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Inflight wifi service</td>\n",
       "      <td>0.428614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Departure/Arrival time convenient</td>\n",
       "      <td>-0.164617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ease of Online booking</td>\n",
       "      <td>-0.190673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gate location</td>\n",
       "      <td>-0.001693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Food and drink</td>\n",
       "      <td>-0.024978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Online boarding</td>\n",
       "      <td>0.598607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Seat comfort</td>\n",
       "      <td>0.036203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Inflight entertainment</td>\n",
       "      <td>0.057425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>On-board service</td>\n",
       "      <td>0.288690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Leg room service</td>\n",
       "      <td>0.237586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Baggage handling</td>\n",
       "      <td>0.137625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Checkin service</td>\n",
       "      <td>0.343676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Inflight service</td>\n",
       "      <td>0.120502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Cleanliness</td>\n",
       "      <td>0.241931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Departure Delay in Minutes</td>\n",
       "      <td>0.000465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Arrival Delay in Minutes</td>\n",
       "      <td>-0.006341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Gender_type</td>\n",
       "      <td>0.114924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Customer_type_satis</td>\n",
       "      <td>2.068499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Travel_type</td>\n",
       "      <td>-2.854305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Class_type</td>\n",
       "      <td>-0.465559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              feature  coefficient\n",
       "0                                 Age    -0.010304\n",
       "1                     Flight Distance    -0.000004\n",
       "2               Inflight wifi service     0.428614\n",
       "3   Departure/Arrival time convenient    -0.164617\n",
       "4              Ease of Online booking    -0.190673\n",
       "5                       Gate location    -0.001693\n",
       "6                      Food and drink    -0.024978\n",
       "7                     Online boarding     0.598607\n",
       "8                        Seat comfort     0.036203\n",
       "9              Inflight entertainment     0.057425\n",
       "10                   On-board service     0.288690\n",
       "11                   Leg room service     0.237586\n",
       "12                   Baggage handling     0.137625\n",
       "13                    Checkin service     0.343676\n",
       "14                   Inflight service     0.120502\n",
       "15                        Cleanliness     0.241931\n",
       "16         Departure Delay in Minutes     0.000465\n",
       "17           Arrival Delay in Minutes    -0.006341\n",
       "18                        Gender_type     0.114924\n",
       "19                Customer_type_satis     2.068499\n",
       "20                        Travel_type    -2.854305\n",
       "21                         Class_type    -0.465559"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict the test data - label\n",
    "prediction_label = log_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         precision    recall  f1-score   support\n",
      "\n",
      "neutral or dissatisfied       0.86      0.91      0.88      2879\n",
      "              satisfied       0.87      0.82      0.85      2300\n",
      "\n",
      "               accuracy                           0.87      5179\n",
      "              macro avg       0.87      0.86      0.87      5179\n",
      "           weighted avg       0.87      0.87      0.87      5179\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, prediction_label, target_names=['neutral or dissatisfied', 'satisfied']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt; \n",
    "plt.rcdefaults()\n",
    "import numpy as np\n",
    "from matplotlib import rcParams\n",
    "# figure size in inches\n",
    "rcParams['figure.figsize'] = 8,3\n",
    " \n",
    "objects = ('Inflight wifi service', 'Class', 'Online boarding', 'Customer type', 'Type of travel')\n",
    "x_pos = np.arange(len(objects))\n",
    "performance = [0.429, 0.466, 0.599, 2.068, 2.854]\n",
    "\n",
    "plt.barh(x_pos, performance, align='center', alpha=0.5, color='dimgrey') \n",
    "plt.yticks(x_pos, objects)\n",
    "plt.xticks([0, 1, 2, 3, 4], labels=\"\") \n",
    "plt.title('Top 5 Drivers of Satisfaction (Feature Importance)', fontweight=\"bold\")\n",
    "\n",
    "for index, value in enumerate(performance):\n",
    "    plt.text(value, index, str(value))  \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt; \n",
    "plt.rcdefaults()\n",
    "import numpy as np\n",
    "from matplotlib import rcParams\n",
    "# figure size in inches\n",
    "rcParams['figure.figsize'] = 8,3\n",
    " \n",
    "objects = ('Personal travel', 'Business travel')\n",
    "x_pos = np.arange(len(objects))\n",
    "performance = [10, 59]\n",
    "\n",
    "plt.barh(x_pos, performance, align='center', alpha=0.5, color='green') \n",
    "plt.yticks(x_pos, objects)\n",
    "plt.xticks([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100], labels=\"\") \n",
    "plt.title('Business Travellers are More Satisfied than Personal Travellers \\n \\n To Focus on Strengthening Satisfaction of Business Travellers', fontweight=\"bold\")\n",
    "\n",
    "for index, value in enumerate(performance):\n",
    "    plt.text(value, index, str(value))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt; \n",
    "plt.rcdefaults()\n",
    "import numpy as np\n",
    "from matplotlib import rcParams\n",
    "# figure size in inches\n",
    "rcParams['figure.figsize'] = 8,3\n",
    " \n",
    "objects = ('Disloyal customer', 'Loyal customer')\n",
    "x_pos = np.arange(len(objects))\n",
    "performance = [25, 48]\n",
    "\n",
    "plt.barh(x_pos, performance, align='center', alpha=0.5, color='green') \n",
    "plt.yticks(x_pos, objects)\n",
    "plt.xticks([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100], labels=\"\") \n",
    "plt.title('Loyal Customers are More Satisfied than Disloyal Customers \\n \\n To Focus on Tailoring Incentives for Loyalty Program', fontweight=\"bold\")\n",
    "\n",
    "for index, value in enumerate(performance):\n",
    "    plt.text(value, index, str(value))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt; \n",
    "plt.rcdefaults()\n",
    "import numpy as np\n",
    "from matplotlib import rcParams\n",
    "# figure size in inches\n",
    "rcParams['figure.figsize'] = 8,3\n",
    " \n",
    "objects = ('Neutral/dissatisfied with online boarding', 'Satisfied with online boarding') \n",
    "x_pos = np.arange(len(objects))\n",
    "performance = [15, 72]\n",
    "\n",
    "plt.barh(x_pos, performance, align='center', alpha=0.5, color='green') \n",
    "plt.yticks(x_pos, objects)\n",
    "plt.xticks([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100], labels=\"\") \n",
    "plt.title('Customers Satisfied with Online Boarding Gave Higher Overall Satisfaction Ratings \\n \\n To Focus on Strengthening Online Boarding Experience', fontweight=\"bold\", fontsize=10)\n",
    "\n",
    "for index, value in enumerate(performance):\n",
    "    plt.text(value, index, str(value))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt; \n",
    "plt.rcdefaults()\n",
    "import numpy as np\n",
    "from matplotlib import rcParams\n",
    "# figure size in inches\n",
    "rcParams['figure.figsize'] = 8,3\n",
    " \n",
    "objects = ('Eco class', 'Eco Plus class', 'Business class')\n",
    "x_pos = np.arange(len(objects))\n",
    "performance = [19, 25, 70]\n",
    "\n",
    "plt.barh(x_pos, performance, align='center', alpha=0.5, color='green') \n",
    "plt.yticks(x_pos, objects)\n",
    "plt.xticks([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100], labels=\"\") \n",
    "plt.title('Business Class Travellers are More Satisfied than Other Classes \\n \\n To Focus on Strengthening Satisfaction of Business Class Travellers', fontweight=\"bold\")\n",
    "\n",
    "for index, value in enumerate(performance):\n",
    "    plt.text(value, index, str(value))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt; \n",
    "plt.rcdefaults()\n",
    "import numpy as np\n",
    "from matplotlib import rcParams\n",
    "# figure size in inches\n",
    "rcParams['figure.figsize'] = 8,3\n",
    " \n",
    "objects = ('Neutral/dissatisfied with inflight wifi service', 'Satisfied with inflight wifi service') \n",
    "x_pos = np.arange(len(objects))\n",
    "performance = [31, 75]\n",
    "\n",
    "plt.barh(x_pos, performance, align='center', alpha=0.5, color='green') \n",
    "plt.yticks(x_pos, objects)\n",
    "plt.xticks([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100], labels=\"\") \n",
    "# plt.xlabel('Satisfaction')\n",
    "plt.title(\"Customers Satisfied with Inflight Wifi Gave Higher Overall Satisfaction Ratings \\n \\n To Focus on Managing Customer's Expectations on Inflight Wifi\", fontweight=\"bold\", fontsize=11)\n",
    "\n",
    "for index, value in enumerate(performance):\n",
    "    plt.text(value, index, str(value))\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}