answerbook-beginner.ipynb

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     "output_type": "stream",
     "text": [
      "/kaggle/input/ai-craft-hackathon-airline-passenger-satisfaction/sample_submission.csv\n",
      "/kaggle/input/ai-craft-hackathon-airline-passenger-satisfaction/train.csv\n",
      "/kaggle/input/ai-craft-hackathon-airline-passenger-satisfaction/test.csv\n"
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   "source": [
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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   "source": [
    "#### **🛩 Import libraries and dataset**\n",
    "****"
   ]
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   "source": [
    "1. Import libraries necessary and dataset"
   ]
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   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "\n",
    "from itertools import combinations \n",
    "from scipy import stats \n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder, OneHotEncoder \n",
    "from sklearn.model_selection import train_test_split, cross_val_score \n",
    "from sklearn.metrics import make_scorer, accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "\n",
    "\n"
   ]
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   "source": [
    "    1. Write the code that imports the train and test datasets (hint: use pd.read_csv) and assign it to a variables train and test"
   ]
  },
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   "source": [
    "train = pd.read_csv(\"/kaggle/input/ai-craft-hackathon-airline-passenger-satisfaction/train.csv\")\n",
    "test = pd.read_csv(\"/kaggle/input/ai-craft-hackathon-airline-passenger-satisfaction/test.csv\")\n"
   ]
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    "train = train[['id','Gender', 'Age', 'Type of Travel', 'Class',\n",
    "       'Flight Distance', 'Inflight wifi service',\n",
    "       'Departure/Arrival time convenient', 'Seat comfort',\n",
    "       'Checkin service',\n",
    "       'Cleanliness', 'Departure Delay in Minutes', 'satisfaction']]\n",
    "test = test[['id','Gender', 'Age', 'Type of Travel', 'Class',\n",
    "       'Flight Distance', 'Inflight wifi service',\n",
    "       'Departure/Arrival time convenient', 'Seat comfort',\n",
    "       'Checkin service',\n",
    "       'Cleanliness', 'Departure Delay in Minutes']]"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Type of Travel</th>\n",
       "      <th>Class</th>\n",
       "      <th>Flight Distance</th>\n",
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       "      <th>Seat comfort</th>\n",
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       "      <th>Cleanliness</th>\n",
       "      <th>Departure Delay in Minutes</th>\n",
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       "      <th>0</th>\n",
       "      <td>70172</td>\n",
       "      <td>Male</td>\n",
       "      <td>13</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco Plus</td>\n",
       "      <td>460</td>\n",
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       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
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       "      <td>5047</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>235</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
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       "      <th>2</th>\n",
       "      <td>110028</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>Business travel</td>\n",
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       "      <td>1142</td>\n",
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       "      <td>4</td>\n",
       "      <td>5</td>\n",
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       "      <th>3</th>\n",
       "      <td>24026</td>\n",
       "      <td>Female</td>\n",
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       "      <td>Business travel</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <th>4</th>\n",
       "      <td>119299</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>214</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>satisfied</td>\n",
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       "       id  Gender  Age   Type of Travel     Class  Flight Distance  \\\n",
       "0   70172    Male   13  Personal Travel  Eco Plus              460   \n",
       "1    5047    Male   25  Business travel  Business              235   \n",
       "2  110028  Female   26  Business travel  Business             1142   \n",
       "3   24026  Female   25  Business travel  Business              562   \n",
       "4  119299    Male   61  Business travel  Business              214   \n",
       "\n",
       "   Inflight wifi service  Departure/Arrival time convenient  Seat comfort  \\\n",
       "0                      3                                  4             5   \n",
       "1                      3                                  2             1   \n",
       "2                      2                                  2             5   \n",
       "3                      2                                  5             2   \n",
       "4                      3                                  3             5   \n",
       "\n",
       "   Checkin service  Cleanliness  Departure Delay in Minutes  \\\n",
       "0                4            5                          25   \n",
       "1                1            1                           1   \n",
       "2                4            5                           0   \n",
       "3                1            2                          11   \n",
       "4                3            3                           0   \n",
       "\n",
       "              satisfaction  \n",
       "0  neutral or dissatisfied  \n",
       "1  neutral or dissatisfied  \n",
       "2                satisfied  \n",
       "3  neutral or dissatisfied  \n",
       "4                satisfied  "
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     "execution_count": 5,
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   "source": [
    "train.head()"
   ]
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   "source": [
    "2. Create a table that outlines some information on our train data including: Count, unique, frequency, mean, standard deviation etc."
   ]
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       "      <td>103904.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1189.448375</td>\n",
       "      <td>997.147281</td>\n",
       "      <td>31.0</td>\n",
       "      <td>414.0</td>\n",
       "      <td>843.0</td>\n",
       "      <td>1743.0</td>\n",
       "      <td>4983.0</td>\n",
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       "    <tr>\n",
       "      <th>Inflight wifi service</th>\n",
       "      <td>103904.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>Departure/Arrival time convenient</th>\n",
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       "      <td>103904.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <th>Checkin service</th>\n",
       "      <td>103904.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>4.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>1.312273</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Departure Delay in Minutes</th>\n",
       "      <td>103904.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.815618</td>\n",
       "      <td>38.230901</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1592.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>satisfaction</th>\n",
       "      <td>103904</td>\n",
       "      <td>2</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
       "      <td>58879</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      count unique                      top  \\\n",
       "id                                 103904.0    NaN                      NaN   \n",
       "Gender                               103904      2                   Female   \n",
       "Age                                103904.0    NaN                      NaN   \n",
       "Type of Travel                       103904      2          Business travel   \n",
       "Class                                103904      3                 Business   \n",
       "Flight Distance                    103904.0    NaN                      NaN   \n",
       "Inflight wifi service              103904.0    NaN                      NaN   \n",
       "Departure/Arrival time convenient  103904.0    NaN                      NaN   \n",
       "Seat comfort                       103904.0    NaN                      NaN   \n",
       "Checkin service                    103904.0    NaN                      NaN   \n",
       "Cleanliness                        103904.0    NaN                      NaN   \n",
       "Departure Delay in Minutes         103904.0    NaN                      NaN   \n",
       "satisfaction                         103904      2  neutral or dissatisfied   \n",
       "\n",
       "                                    freq          mean           std   min  \\\n",
       "id                                   NaN  64924.210502  37463.812252   1.0   \n",
       "Gender                             52727           NaN           NaN   NaN   \n",
       "Age                                  NaN     39.379706     15.114964   7.0   \n",
       "Type of Travel                     71655           NaN           NaN   NaN   \n",
       "Class                              49665           NaN           NaN   NaN   \n",
       "Flight Distance                      NaN   1189.448375    997.147281  31.0   \n",
       "Inflight wifi service                NaN      2.729683      1.327829   0.0   \n",
       "Departure/Arrival time convenient    NaN      3.060296      1.525075   0.0   \n",
       "Seat comfort                         NaN      3.439396      1.319088   0.0   \n",
       "Checkin service                      NaN       3.30429      1.265396   0.0   \n",
       "Cleanliness                          NaN      3.286351      1.312273   0.0   \n",
       "Departure Delay in Minutes           NaN     14.815618     38.230901   0.0   \n",
       "satisfaction                       58879           NaN           NaN   NaN   \n",
       "\n",
       "                                        25%      50%       75%       max  \n",
       "id                                 32533.75  64856.5  97368.25  129880.0  \n",
       "Gender                                  NaN      NaN       NaN       NaN  \n",
       "Age                                    27.0     40.0      51.0      85.0  \n",
       "Type of Travel                          NaN      NaN       NaN       NaN  \n",
       "Class                                   NaN      NaN       NaN       NaN  \n",
       "Flight Distance                       414.0    843.0    1743.0    4983.0  \n",
       "Inflight wifi service                   2.0      3.0       4.0       5.0  \n",
       "Departure/Arrival time convenient       2.0      3.0       4.0       5.0  \n",
       "Seat comfort                            2.0      4.0       5.0       5.0  \n",
       "Checkin service                         3.0      3.0       4.0       5.0  \n",
       "Cleanliness                             2.0      3.0       4.0       5.0  \n",
       "Departure Delay in Minutes              0.0      0.0      12.0    1592.0  \n",
       "satisfaction                            NaN      NaN       NaN       NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "desc = pd.DataFrame(train.describe(include = 'all').transpose())\n",
    "desc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f89198b7",
   "metadata": {
    "papermill": {
     "duration": 0.029554,
     "end_time": "2023-07-12T15:12:26.915159",
     "exception": false,
     "start_time": "2023-07-12T15:12:26.885605",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "4. Use the summary stats function to view important information such as number of missing values, mean value, max value, data type etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1e1cde88",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:26.977293Z",
     "iopub.status.busy": "2023-07-12T15:12:26.976826Z",
     "iopub.status.idle": "2023-07-12T15:12:26.985331Z",
     "shell.execute_reply": "2023-07-12T15:12:26.983922Z"
    },
    "papermill": {
     "duration": 0.043347,
     "end_time": "2023-07-12T15:12:26.988158",
     "exception": false,
     "start_time": "2023-07-12T15:12:26.944811",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def summary_stats(df): \n",
    "    print(f'The shape of the data is: {df.shape}')\n",
    "    summary = pd.DataFrame(df.dtypes, columns = ['data type'])\n",
    "    summary['Number of missing values'] = df.isnull().sum().values \n",
    "    summary['% of missing values'] = df.isnull().sum().values / len(df) * 100 \n",
    "    summary['min value'] = desc['min'].values\n",
    "    summary['mean value'] = desc['mean'].values\n",
    "    summary['max value'] = desc['max'].values\n",
    "    summary['mode value'] = df.mode().values[0]\n",
    "    return summary\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "918bf5c8",
   "metadata": {
    "papermill": {
     "duration": 0.029173,
     "end_time": "2023-07-12T15:12:27.048327",
     "exception": false,
     "start_time": "2023-07-12T15:12:27.019154",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    1. Run the above function of the train dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a2499c79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:27.110842Z",
     "iopub.status.busy": "2023-07-12T15:12:27.110342Z",
     "iopub.status.idle": "2023-07-12T15:12:27.642279Z",
     "shell.execute_reply": "2023-07-12T15:12:27.640989Z"
    },
    "papermill": {
     "duration": 0.565637,
     "end_time": "2023-07-12T15:12:27.644768",
     "exception": false,
     "start_time": "2023-07-12T15:12:27.079131",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The shape of the data is: (103904, 13)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data type</th>\n",
       "      <th>Number of missing values</th>\n",
       "      <th>% of missing values</th>\n",
       "      <th>min value</th>\n",
       "      <th>mean value</th>\n",
       "      <th>max value</th>\n",
       "      <th>mode value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64924.210502</td>\n",
       "      <td>129880.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>39.379706</td>\n",
       "      <td>85.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type of Travel</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Business travel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Business</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flight Distance</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1189.448375</td>\n",
       "      <td>4983.0</td>\n",
       "      <td>337.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inflight wifi service</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.729683</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Departure/Arrival time convenient</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.060296</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seat comfort</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.439396</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Checkin service</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.30429</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cleanliness</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.286351</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Departure Delay in Minutes</th>\n",
       "      <td>int64</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.815618</td>\n",
       "      <td>1592.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>satisfaction</th>\n",
       "      <td>object</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  data type  Number of missing values  \\\n",
       "id                                    int64                         0   \n",
       "Gender                               object                         0   \n",
       "Age                                   int64                         0   \n",
       "Type of Travel                       object                         0   \n",
       "Class                                object                         0   \n",
       "Flight Distance                       int64                         0   \n",
       "Inflight wifi service                 int64                         0   \n",
       "Departure/Arrival time convenient     int64                         0   \n",
       "Seat comfort                          int64                         0   \n",
       "Checkin service                       int64                         0   \n",
       "Cleanliness                           int64                         0   \n",
       "Departure Delay in Minutes            int64                         0   \n",
       "satisfaction                         object                         0   \n",
       "\n",
       "                                   % of missing values min value  \\\n",
       "id                                                 0.0       1.0   \n",
       "Gender                                             0.0       NaN   \n",
       "Age                                                0.0       7.0   \n",
       "Type of Travel                                     0.0       NaN   \n",
       "Class                                              0.0       NaN   \n",
       "Flight Distance                                    0.0      31.0   \n",
       "Inflight wifi service                              0.0       0.0   \n",
       "Departure/Arrival time convenient                  0.0       0.0   \n",
       "Seat comfort                                       0.0       0.0   \n",
       "Checkin service                                    0.0       0.0   \n",
       "Cleanliness                                        0.0       0.0   \n",
       "Departure Delay in Minutes                         0.0       0.0   \n",
       "satisfaction                                       0.0       NaN   \n",
       "\n",
       "                                     mean value max value  \\\n",
       "id                                 64924.210502  129880.0   \n",
       "Gender                                      NaN       NaN   \n",
       "Age                                   39.379706      85.0   \n",
       "Type of Travel                              NaN       NaN   \n",
       "Class                                       NaN       NaN   \n",
       "Flight Distance                     1189.448375    4983.0   \n",
       "Inflight wifi service                  2.729683       5.0   \n",
       "Departure/Arrival time convenient      3.060296       5.0   \n",
       "Seat comfort                           3.439396       5.0   \n",
       "Checkin service                         3.30429       5.0   \n",
       "Cleanliness                            3.286351       5.0   \n",
       "Departure Delay in Minutes            14.815618    1592.0   \n",
       "satisfaction                                NaN       NaN   \n",
       "\n",
       "                                                mode value  \n",
       "id                                                       1  \n",
       "Gender                                              Female  \n",
       "Age                                                   39.0  \n",
       "Type of Travel                             Business travel  \n",
       "Class                                             Business  \n",
       "Flight Distance                                      337.0  \n",
       "Inflight wifi service                                  3.0  \n",
       "Departure/Arrival time convenient                      4.0  \n",
       "Seat comfort                                           4.0  \n",
       "Checkin service                                        4.0  \n",
       "Cleanliness                                            4.0  \n",
       "Departure Delay in Minutes                             0.0  \n",
       "satisfaction                       neutral or dissatisfied  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_stats(train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5b0bfa5",
   "metadata": {
    "papermill": {
     "duration": 0.029216,
     "end_time": "2023-07-12T15:12:27.704787",
     "exception": false,
     "start_time": "2023-07-12T15:12:27.675571",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "5. Look at those variables that are most corrleated with whether someone is satisfied or not"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96e76d23",
   "metadata": {
    "papermill": {
     "duration": 0.029695,
     "end_time": "2023-07-12T15:12:27.764155",
     "exception": false,
     "start_time": "2023-07-12T15:12:27.734460",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    1. Gather a list of the categories that are numerical (int64) and those that are categorical (object)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c3d6fd4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:27.826174Z",
     "iopub.status.busy": "2023-07-12T15:12:27.825705Z",
     "iopub.status.idle": "2023-07-12T15:12:27.837100Z",
     "shell.execute_reply": "2023-07-12T15:12:27.835753Z"
    },
    "papermill": {
     "duration": 0.045089,
     "end_time": "2023-07-12T15:12:27.839640",
     "exception": false,
     "start_time": "2023-07-12T15:12:27.794551",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "numeric_columns = train.select_dtypes(include='number')\n",
    "categorical_columns = train.select_dtypes(exclude='number')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a40b0ae4",
   "metadata": {
    "papermill": {
     "duration": 0.02899,
     "end_time": "2023-07-12T15:12:27.897905",
     "exception": false,
     "start_time": "2023-07-12T15:12:27.868915",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    2. make a copy of our train dataset using train.copy() and assign it to a variable called train_display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "320160b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:27.960090Z",
     "iopub.status.busy": "2023-07-12T15:12:27.959631Z",
     "iopub.status.idle": "2023-07-12T15:12:27.969636Z",
     "shell.execute_reply": "2023-07-12T15:12:27.968399Z"
    },
    "papermill": {
     "duration": 0.044316,
     "end_time": "2023-07-12T15:12:27.972150",
     "exception": false,
     "start_time": "2023-07-12T15:12:27.927834",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_display = train.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a22c5b0",
   "metadata": {
    "papermill": {
     "duration": 0.029306,
     "end_time": "2023-07-12T15:12:28.030988",
     "exception": false,
     "start_time": "2023-07-12T15:12:28.001682",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    3. Label encode the categorical columns \n",
    "        a. To do this assign the object LabelEncoder() to the variable le"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c4615719",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:28.091340Z",
     "iopub.status.busy": "2023-07-12T15:12:28.090890Z",
     "iopub.status.idle": "2023-07-12T15:12:28.096948Z",
     "shell.execute_reply": "2023-07-12T15:12:28.095693Z"
    },
    "papermill": {
     "duration": 0.039228,
     "end_time": "2023-07-12T15:12:28.099497",
     "exception": false,
     "start_time": "2023-07-12T15:12:28.060269",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49ddbe97",
   "metadata": {
    "papermill": {
     "duration": 0.029163,
     "end_time": "2023-07-12T15:12:28.158856",
     "exception": false,
     "start_time": "2023-07-12T15:12:28.129693",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "        b. use a for loop to re-assign each categorical column train_display[col] to its label encoded version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7676c4b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:28.220457Z",
     "iopub.status.busy": "2023-07-12T15:12:28.219926Z",
     "iopub.status.idle": "2023-07-12T15:12:28.380800Z",
     "shell.execute_reply": "2023-07-12T15:12:28.379426Z"
    },
    "papermill": {
     "duration": 0.195433,
     "end_time": "2023-07-12T15:12:28.383683",
     "exception": false,
     "start_time": "2023-07-12T15:12:28.188250",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for col in categorical_columns.columns:\n",
    "     train_display[col] = le.fit_transform(train[col])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d21f6b2",
   "metadata": {
    "papermill": {
     "duration": 0.029016,
     "end_time": "2023-07-12T15:12:28.442212",
     "exception": false,
     "start_time": "2023-07-12T15:12:28.413196",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The below code should now output a list of each category and its correlation with satisfaction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "351c2693",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:28.502872Z",
     "iopub.status.busy": "2023-07-12T15:12:28.502352Z",
     "iopub.status.idle": "2023-07-12T15:12:28.581902Z",
     "shell.execute_reply": "2023-07-12T15:12:28.580534Z"
    },
    "papermill": {
     "duration": 0.113334,
     "end_time": "2023-07-12T15:12:28.584857",
     "exception": false,
     "start_time": "2023-07-12T15:12:28.471523",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction                         1.000000\n",
       "Class                                0.449321\n",
       "Type of Travel                       0.449000\n",
       "Seat comfort                         0.349459\n",
       "Cleanliness                          0.305198\n",
       "Flight Distance                      0.298780\n",
       "Inflight wifi service                0.284245\n",
       "Checkin service                      0.236174\n",
       "Age                                  0.137167\n",
       "Departure/Arrival time convenient    0.051601\n",
       "Departure Delay in Minutes           0.050494\n",
       "id                                   0.013734\n",
       "Gender                               0.012211\n",
       "Name: satisfaction, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_display.corr().loc['satisfaction'].abs().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ffaad6b",
   "metadata": {
    "papermill": {
     "duration": 0.030138,
     "end_time": "2023-07-12T15:12:28.646320",
     "exception": false,
     "start_time": "2023-07-12T15:12:28.616182",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### **🛩 Exploratory data analysis**\n",
    "****"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15295e58",
   "metadata": {
    "papermill": {
     "duration": 0.031912,
     "end_time": "2023-07-12T15:12:28.708742",
     "exception": false,
     "start_time": "2023-07-12T15:12:28.676830",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "6. Create some count plots of non-numerical categorical columns to view how many of each unique valeue that there is? (q: what insights can we get from this?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e7cee26f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:28.773879Z",
     "iopub.status.busy": "2023-07-12T15:12:28.773129Z",
     "iopub.status.idle": "2023-07-12T15:12:30.214124Z",
     "shell.execute_reply": "2023-07-12T15:12:30.212713Z"
    },
    "papermill": {
     "duration": 1.478354,
     "end_time": "2023-07-12T15:12:30.217534",
     "exception": false,
     "start_time": "2023-07-12T15:12:28.739180",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 4, figsize=(15, 5))\n",
    "for i, column in enumerate(categorical_columns.columns):\n",
    "    ax = axes[i]\n",
    "    sns.countplot(data=categorical_columns, x=column, ax=ax)\n",
    "    ax.set_title(column)\n",
    "    ax.tick_params(axis='x', rotation=45)\n",
    "\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce84e017",
   "metadata": {
    "papermill": {
     "duration": 0.032307,
     "end_time": "2023-07-12T15:12:30.282605",
     "exception": false,
     "start_time": "2023-07-12T15:12:30.250298",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    1. Fill in the blanks below"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fe6c005",
   "metadata": {
    "papermill": {
     "duration": 0.031804,
     "end_time": "2023-07-12T15:12:30.348638",
     "exception": false,
     "start_time": "2023-07-12T15:12:30.316834",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<div class=\"alert alert-block alert-info\" style=\"background-color: #C2F3D3; color: black;\">\n",
    "- There is an equal number of male and female participants in the survey. </br>\n",
    "- Most customers on this airline are business travel customers and most of them purchase tickets for Business class, followed by Eco class. </br>\n",
    "- A large proportion of customers felt neutral or dissatisfied with the airline.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f149736",
   "metadata": {
    "papermill": {
     "duration": 0.031975,
     "end_time": "2023-07-12T15:12:30.413153",
     "exception": false,
     "start_time": "2023-07-12T15:12:30.381178",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "7. Create some count plots of numerical categorical columns to view how many of each unique valeue that there is? (q: what insights can we get from this?)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81d72b93",
   "metadata": {
    "papermill": {
     "duration": 0.032162,
     "end_time": "2023-07-12T15:12:30.478144",
     "exception": false,
     "start_time": "2023-07-12T15:12:30.445982",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    1. Create a list of all of the columns that are numerical but have only a few unique values, you can do this by running 'train[col].unique()' for all columns where 'col' is the column name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4614d2eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:30.544439Z",
     "iopub.status.busy": "2023-07-12T15:12:30.543694Z",
     "iopub.status.idle": "2023-07-12T15:12:30.549390Z",
     "shell.execute_reply": "2023-07-12T15:12:30.548390Z"
    },
    "papermill": {
     "duration": 0.042184,
     "end_time": "2023-07-12T15:12:30.552110",
     "exception": false,
     "start_time": "2023-07-12T15:12:30.509926",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "categoric = [\n",
    "              \"Cleanliness\",  \"Checkin service\",  \n",
    "              \"Seat comfort\", \"Departure/Arrival time convenient\",\n",
    "             \"Inflight wifi service\"\n",
    "             ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f2f892d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:30.618476Z",
     "iopub.status.busy": "2023-07-12T15:12:30.617711Z",
     "iopub.status.idle": "2023-07-12T15:12:31.688671Z",
     "shell.execute_reply": "2023-07-12T15:12:31.687214Z"
    },
    "papermill": {
     "duration": 1.107371,
     "end_time": "2023-07-12T15:12:31.691462",
     "exception": false,
     "start_time": "2023-07-12T15:12:30.584091",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 5, figsize=(15, 5))\n",
    "for i, column in enumerate(categoric):\n",
    "    col = i % 5\n",
    "    \n",
    "    ax = axes[col]\n",
    "    sns.countplot(data=train, x=column, ax=ax)\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69f84649",
   "metadata": {
    "papermill": {
     "duration": 0.032319,
     "end_time": "2023-07-12T15:12:31.756178",
     "exception": false,
     "start_time": "2023-07-12T15:12:31.723859",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "8. Create some Kernel density plots for view the distribution of continuous variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "885ae547",
   "metadata": {
    "papermill": {
     "duration": 0.033707,
     "end_time": "2023-07-12T15:12:31.822406",
     "exception": false,
     "start_time": "2023-07-12T15:12:31.788699",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    1. Find the numerical columns that are continuous, you can do this by running 'train[col].unique()' for all columns where 'col' is the column name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d93902c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:31.890774Z",
     "iopub.status.busy": "2023-07-12T15:12:31.890272Z",
     "iopub.status.idle": "2023-07-12T15:12:31.896098Z",
     "shell.execute_reply": "2023-07-12T15:12:31.894785Z"
    },
    "papermill": {
     "duration": 0.04403,
     "end_time": "2023-07-12T15:12:31.898741",
     "exception": false,
     "start_time": "2023-07-12T15:12:31.854711",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "numeric = [\"Age\", \"Flight Distance\", \"Departure Delay in Minutes\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "aef59827",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:31.966663Z",
     "iopub.status.busy": "2023-07-12T15:12:31.966194Z",
     "iopub.status.idle": "2023-07-12T15:12:34.766619Z",
     "shell.execute_reply": "2023-07-12T15:12:34.765692Z"
    },
    "papermill": {
     "duration": 2.83701,
     "end_time": "2023-07-12T15:12:34.768852",
     "exception": false,
     "start_time": "2023-07-12T15:12:31.931842",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(15, 5))\n",
    "for i, column in enumerate(numeric):\n",
    "    row = i // 2\n",
    "    col = i % 2\n",
    "    \n",
    "    ax = axes[row, col]\n",
    "    sns.kdeplot(data=train, x=column, ax=ax)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7100d16c",
   "metadata": {
    "papermill": {
     "duration": 0.034451,
     "end_time": "2023-07-12T15:12:34.837526",
     "exception": false,
     "start_time": "2023-07-12T15:12:34.803075",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    2. Fill in the gaps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "300f678a",
   "metadata": {
    "papermill": {
     "duration": 0.03274,
     "end_time": "2023-07-12T15:12:34.904469",
     "exception": false,
     "start_time": "2023-07-12T15:12:34.871729",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<div class=\"alert alert-block alert-info\" style=\"background-color: #C2F3D3; color: black;\">\n",
    "The variables \"Flight Distance\" and \"Departure Delay in Minutes\" are all heavily right-skewed. \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c30fe03",
   "metadata": {
    "papermill": {
     "duration": 0.032395,
     "end_time": "2023-07-12T15:12:34.969657",
     "exception": false,
     "start_time": "2023-07-12T15:12:34.937262",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### **🛩 Machine learning preparation**\n",
    "****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5323d25d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:35.040632Z",
     "iopub.status.busy": "2023-07-12T15:12:35.040112Z",
     "iopub.status.idle": "2023-07-12T15:12:35.057229Z",
     "shell.execute_reply": "2023-07-12T15:12:35.055878Z"
    },
    "papermill": {
     "duration": 0.054918,
     "end_time": "2023-07-12T15:12:35.060016",
     "exception": false,
     "start_time": "2023-07-12T15:12:35.005098",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "clean_train = train.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a390712",
   "metadata": {
    "papermill": {
     "duration": 0.032896,
     "end_time": "2023-07-12T15:12:35.126587",
     "exception": false,
     "start_time": "2023-07-12T15:12:35.093691",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    1. Fill in the gaps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4882c66",
   "metadata": {
    "papermill": {
     "duration": 0.032477,
     "end_time": "2023-07-12T15:12:35.192102",
     "exception": false,
     "start_time": "2023-07-12T15:12:35.159625",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "9. Finding the logarithm can help in the following ways:\n",
    "    - Normalize the data: Transformation can help in reducing the impact of extreme values and make the data conform more closely to a normal distribution.\n",
    "\n",
    "    - Linearize relationships: In some cases, taking the logarithm of a variable can help linearize relationships between variables, making them more suitable for linear regression or other linear-based models.\n",
    "\n",
    "    - Reduce skewness: Skewed data can violate the assumptions of certain models. By applying the logarithm transformation, you can reduce the skewness and make the data more symmetric."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74e581ee",
   "metadata": {
    "papermill": {
     "duration": 0.032358,
     "end_time": "2023-07-12T15:12:35.257241",
     "exception": false,
     "start_time": "2023-07-12T15:12:35.224883",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    2. From the graphs in section 8, make a list of the categories that were skewed and assign them to a variable called right_skewed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "03892479",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:35.325626Z",
     "iopub.status.busy": "2023-07-12T15:12:35.324758Z",
     "iopub.status.idle": "2023-07-12T15:12:35.329509Z",
     "shell.execute_reply": "2023-07-12T15:12:35.328638Z"
    },
    "papermill": {
     "duration": 0.041534,
     "end_time": "2023-07-12T15:12:35.331786",
     "exception": false,
     "start_time": "2023-07-12T15:12:35.290252",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "right_skewed = [\"Flight Distance\", \"Departure Delay in Minutes\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "048a70ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:35.399694Z",
     "iopub.status.busy": "2023-07-12T15:12:35.399202Z",
     "iopub.status.idle": "2023-07-12T15:12:35.440156Z",
     "shell.execute_reply": "2023-07-12T15:12:35.438596Z"
    },
    "papermill": {
     "duration": 0.07858,
     "end_time": "2023-07-12T15:12:35.443162",
     "exception": false,
     "start_time": "2023-07-12T15:12:35.364582",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\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>id</th>\n",
       "      <th>Gender</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>Seat comfort</th>\n",
       "      <th>Checkin service</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Departure Delay in Minutes</th>\n",
       "      <th>satisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70172</td>\n",
       "      <td>Male</td>\n",
       "      <td>13</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco Plus</td>\n",
       "      <td>6.131226</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3.218876</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5047</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>5.459586</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>110028</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>7.040536</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>-inf</td>\n",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24026</td>\n",
       "      <td>Female</td>\n",
       "      <td>25</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>6.331502</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>119299</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>5.365976</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-inf</td>\n",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103899</th>\n",
       "      <td>94171</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>5.257495</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103900</th>\n",
       "      <td>73097</td>\n",
       "      <td>Male</td>\n",
       "      <td>49</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>7.760893</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>-inf</td>\n",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103901</th>\n",
       "      <td>68825</td>\n",
       "      <td>Male</td>\n",
       "      <td>30</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>7.598399</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103902</th>\n",
       "      <td>54173</td>\n",
       "      <td>Female</td>\n",
       "      <td>22</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>6.907755</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>-inf</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103903</th>\n",
       "      <td>62567</td>\n",
       "      <td>Male</td>\n",
       "      <td>27</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>7.451822</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>-inf</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103904 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  Gender  Age   Type of Travel     Class  Flight Distance  \\\n",
       "0        70172    Male   13  Personal Travel  Eco Plus         6.131226   \n",
       "1         5047    Male   25  Business travel  Business         5.459586   \n",
       "2       110028  Female   26  Business travel  Business         7.040536   \n",
       "3        24026  Female   25  Business travel  Business         6.331502   \n",
       "4       119299    Male   61  Business travel  Business         5.365976   \n",
       "...        ...     ...  ...              ...       ...              ...   \n",
       "103899   94171  Female   23  Business travel       Eco         5.257495   \n",
       "103900   73097    Male   49  Business travel  Business         7.760893   \n",
       "103901   68825    Male   30  Business travel  Business         7.598399   \n",
       "103902   54173  Female   22  Business travel       Eco         6.907755   \n",
       "103903   62567    Male   27  Business travel  Business         7.451822   \n",
       "\n",
       "        Inflight wifi service  Departure/Arrival time convenient  \\\n",
       "0                           3                                  4   \n",
       "1                           3                                  2   \n",
       "2                           2                                  2   \n",
       "3                           2                                  5   \n",
       "4                           3                                  3   \n",
       "...                       ...                                ...   \n",
       "103899                      2                                  1   \n",
       "103900                      4                                  4   \n",
       "103901                      1                                  1   \n",
       "103902                      1                                  1   \n",
       "103903                      1                                  3   \n",
       "\n",
       "        Seat comfort  Checkin service  Cleanliness  \\\n",
       "0                  5                4            5   \n",
       "1                  1                1            1   \n",
       "2                  5                4            5   \n",
       "3                  2                1            2   \n",
       "4                  5                3            3   \n",
       "...              ...              ...          ...   \n",
       "103899             2                2            2   \n",
       "103900             5                5            4   \n",
       "103901             5                5            4   \n",
       "103902             1                5            1   \n",
       "103903             1                4            1   \n",
       "\n",
       "        Departure Delay in Minutes             satisfaction  \n",
       "0                         3.218876  neutral or dissatisfied  \n",
       "1                         0.000000  neutral or dissatisfied  \n",
       "2                             -inf                satisfied  \n",
       "3                         2.397895  neutral or dissatisfied  \n",
       "4                             -inf                satisfied  \n",
       "...                            ...                      ...  \n",
       "103899                    1.098612  neutral or dissatisfied  \n",
       "103900                        -inf                satisfied  \n",
       "103901                    1.945910  neutral or dissatisfied  \n",
       "103902                        -inf  neutral or dissatisfied  \n",
       "103903                        -inf  neutral or dissatisfied  \n",
       "\n",
       "[103904 rows x 13 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalize the numeric variables \n",
    "for x in right_skewed:\n",
    "    clean_train[x] = np.log(clean_train[x])\n",
    "\n",
    "clean_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8436ea70",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:35.512496Z",
     "iopub.status.busy": "2023-07-12T15:12:35.512065Z",
     "iopub.status.idle": "2023-07-12T15:12:37.704812Z",
     "shell.execute_reply": "2023-07-12T15:12:37.703538Z"
    },
    "papermill": {
     "duration": 2.230854,
     "end_time": "2023-07-12T15:12:37.707509",
     "exception": false,
     "start_time": "2023-07-12T15:12:35.476655",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric = [\"Age\", \"Flight Distance\", \"Departure Delay in Minutes\"]\n",
    "\n",
    "fig, axes = plt.subplots(2, 2, figsize=(15, 5))\n",
    "for i, column in enumerate(numeric):\n",
    "    row = i // 2\n",
    "    col = i % 2\n",
    "    \n",
    "    ax = axes[row, col]\n",
    "    sns.kdeplot(data= clean_train, x=column, ax=ax)\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33fa8e9d",
   "metadata": {
    "papermill": {
     "duration": 0.036125,
     "end_time": "2023-07-12T15:12:37.780140",
     "exception": false,
     "start_time": "2023-07-12T15:12:37.744015",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    1. Fill in the gap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38618c49",
   "metadata": {
    "papermill": {
     "duration": 0.035825,
     "end_time": "2023-07-12T15:12:37.852340",
     "exception": false,
     "start_time": "2023-07-12T15:12:37.816515",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<div class=\"alert alert-block alert-info\" style=\"background-color: #C2F3D3; color: black;\">\n",
    "\"Departure Delay in Minutes\" and \"Fight Distance\" are now more normally distributed. \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0f9bc25e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:37.924913Z",
     "iopub.status.busy": "2023-07-12T15:12:37.924404Z",
     "iopub.status.idle": "2023-07-12T15:12:37.944659Z",
     "shell.execute_reply": "2023-07-12T15:12:37.943379Z"
    },
    "papermill": {
     "duration": 0.059626,
     "end_time": "2023-07-12T15:12:37.947251",
     "exception": false,
     "start_time": "2023-07-12T15:12:37.887625",
     "status": "completed"
    },
    "tags": []
   },
   "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",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Gender</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>Seat comfort</th>\n",
       "      <th>Checkin service</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Departure Delay in Minutes</th>\n",
       "      <th>satisfaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70172</td>\n",
       "      <td>Male</td>\n",
       "      <td>13</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco Plus</td>\n",
       "      <td>6.131226</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3.218876</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5047</td>\n",
       "      <td>Male</td>\n",
       "      <td>25</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>5.459586</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>110028</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>7.040536</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>-inf</td>\n",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24026</td>\n",
       "      <td>Female</td>\n",
       "      <td>25</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>6.331502</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>neutral or dissatisfied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>119299</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>5.365976</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>-inf</td>\n",
       "      <td>satisfied</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  Gender  Age   Type of Travel     Class  Flight Distance  \\\n",
       "0   70172    Male   13  Personal Travel  Eco Plus         6.131226   \n",
       "1    5047    Male   25  Business travel  Business         5.459586   \n",
       "2  110028  Female   26  Business travel  Business         7.040536   \n",
       "3   24026  Female   25  Business travel  Business         6.331502   \n",
       "4  119299    Male   61  Business travel  Business         5.365976   \n",
       "\n",
       "   Inflight wifi service  Departure/Arrival time convenient  Seat comfort  \\\n",
       "0                      3                                  4             5   \n",
       "1                      3                                  2             1   \n",
       "2                      2                                  2             5   \n",
       "3                      2                                  5             2   \n",
       "4                      3                                  3             5   \n",
       "\n",
       "   Checkin service  Cleanliness  Departure Delay in Minutes  \\\n",
       "0                4            5                    3.218876   \n",
       "1                1            1                    0.000000   \n",
       "2                4            5                        -inf   \n",
       "3                1            2                    2.397895   \n",
       "4                3            3                        -inf   \n",
       "\n",
       "              satisfaction  \n",
       "0  neutral or dissatisfied  \n",
       "1  neutral or dissatisfied  \n",
       "2                satisfied  \n",
       "3  neutral or dissatisfied  \n",
       "4                satisfied  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_train.head() "
   ]
  },
  {
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   "source": [
    "    1.Define the X and Y variables (hint: x is all categories apart from the id and the one that we are trying to predict and the y variables is only the column that we are trying to predict)"
   ]
  },
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       "      <th>103899</th>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Eco</td>\n",
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       "    <tr>\n",
       "      <th>103900</th>\n",
       "      <td>Male</td>\n",
       "      <td>49</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
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       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
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       "      <td>5</td>\n",
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       "      <th>103902</th>\n",
       "      <td>Female</td>\n",
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       "      <td>Business travel</td>\n",
       "      <td>Eco</td>\n",
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       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
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       "<p>103904 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Gender  Age   Type of Travel     Class  Flight Distance  \\\n",
       "0         Male   13  Personal Travel  Eco Plus         6.131226   \n",
       "1         Male   25  Business travel  Business         5.459586   \n",
       "2       Female   26  Business travel  Business         7.040536   \n",
       "3       Female   25  Business travel  Business         6.331502   \n",
       "4         Male   61  Business travel  Business         5.365976   \n",
       "...        ...  ...              ...       ...              ...   \n",
       "103899  Female   23  Business travel       Eco         5.257495   \n",
       "103900    Male   49  Business travel  Business         7.760893   \n",
       "103901    Male   30  Business travel  Business         7.598399   \n",
       "103902  Female   22  Business travel       Eco         6.907755   \n",
       "103903    Male   27  Business travel  Business         7.451822   \n",
       "\n",
       "        Inflight wifi service  Departure/Arrival time convenient  \\\n",
       "0                           3                                  4   \n",
       "1                           3                                  2   \n",
       "2                           2                                  2   \n",
       "3                           2                                  5   \n",
       "4                           3                                  3   \n",
       "...                       ...                                ...   \n",
       "103899                      2                                  1   \n",
       "103900                      4                                  4   \n",
       "103901                      1                                  1   \n",
       "103902                      1                                  1   \n",
       "103903                      1                                  3   \n",
       "\n",
       "        Seat comfort  Checkin service  Cleanliness  Departure Delay in Minutes  \n",
       "0                  5                4            5                    3.218876  \n",
       "1                  1                1            1                    0.000000  \n",
       "2                  5                4            5                        -inf  \n",
       "3                  2                1            2                    2.397895  \n",
       "4                  5                3            3                        -inf  \n",
       "...              ...              ...          ...                         ...  \n",
       "103899             2                2            2                    1.098612  \n",
       "103900             5                5            4                        -inf  \n",
       "103901             5                5            4                    1.945910  \n",
       "103902             1                5            1                        -inf  \n",
       "103903             1                4            1                        -inf  \n",
       "\n",
       "[103904 rows x 11 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = clean_train.drop([\"satisfaction\", 'id'], axis = 1)\n",
    "y = clean_train[\"satisfaction\"]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "49822069",
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    {
     "data": {
      "text/plain": [
       "0         neutral or dissatisfied\n",
       "1         neutral or dissatisfied\n",
       "2                       satisfied\n",
       "3         neutral or dissatisfied\n",
       "4                       satisfied\n",
       "                   ...           \n",
       "103899    neutral or dissatisfied\n",
       "103900                  satisfied\n",
       "103901    neutral or dissatisfied\n",
       "103902    neutral or dissatisfied\n",
       "103903    neutral or dissatisfied\n",
       "Name: satisfaction, Length: 103904, dtype: object"
      ]
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   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
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     "status": "completed"
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   "source": [
    "9. We next need to normalize the numeric variables, this is the process of transforming the variables to a common scale or range to ensure a fair comparison between variables and prevent one variable from dominating others due to its larger magnitude, we will normalize using the standard scalar object"
   ]
  },
  {
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   "source": [
    "    1. Assign a variable named scaler to the object 'StandardScaler()'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b225762a",
   "metadata": {
    "execution": {
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   "source": [
    "# Scaling\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "eb511b31",
   "metadata": {
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   "source": [
    "\n",
    "numeric_col = [\"Age\", \"Flight Distance\", \"Departure Delay in Minutes\"]\n",
    "\n",
    "# Handle the problematic values in X\n",
    "for numeric_val in numeric_col:\n",
    "    X[numeric_val] = X[numeric_val].replace([np.inf, -np.inf], np.nan)\n",
    "    X[numeric_val] = X[numeric_val].clip(lower=-1e6, upper=1e6)  # Replace extremely large values with a suitable range\n",
    "    X[numeric_val].fillna(X[numeric_val].mean(), inplace=True)  # Replace NaN values with column mean\n"
   ]
  },
  {
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   "id": "39f347fa",
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   "source": [
    "    2. Scale all of the values in X[numeric_col] by using the method scaler.fit_transform(__) and assign it to the variable X[numerical_col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
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     "duration": 0.065328,
     "end_time": "2023-07-12T15:12:38.731790",
     "exception": false,
     "start_time": "2023-07-12T15:12:38.666462",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X[numeric_col] = scaler.fit_transform(X[numeric_col])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cd88f59",
   "metadata": {
    "papermill": {
     "duration": 0.036897,
     "end_time": "2023-07-12T15:12:38.806850",
     "exception": false,
     "start_time": "2023-07-12T15:12:38.769953",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "10. We now want to turn the categorical columns into a machine readable format so it can be interpretted by our model, we do this using the Label encoder object"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8304875a",
   "metadata": {
    "papermill": {
     "duration": 0.037339,
     "end_time": "2023-07-12T15:12:38.882206",
     "exception": false,
     "start_time": "2023-07-12T15:12:38.844867",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    1. Assign a variable called le to the object LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1b402f27",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:38.959500Z",
     "iopub.status.busy": "2023-07-12T15:12:38.958583Z",
     "iopub.status.idle": "2023-07-12T15:12:38.964210Z",
     "shell.execute_reply": "2023-07-12T15:12:38.963109Z"
    },
    "papermill": {
     "duration": 0.047779,
     "end_time": "2023-07-12T15:12:38.966895",
     "exception": false,
     "start_time": "2023-07-12T15:12:38.919116",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fdeb309",
   "metadata": {
    "papermill": {
     "duration": 0.039219,
     "end_time": "2023-07-12T15:12:39.044989",
     "exception": false,
     "start_time": "2023-07-12T15:12:39.005770",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    2. Assign a list of the non-numerical categorical columns to a variable called 'cat_col'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2c22ba63",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:39.123782Z",
     "iopub.status.busy": "2023-07-12T15:12:39.122881Z",
     "iopub.status.idle": "2023-07-12T15:12:39.128427Z",
     "shell.execute_reply": "2023-07-12T15:12:39.127481Z"
    },
    "papermill": {
     "duration": 0.047886,
     "end_time": "2023-07-12T15:12:39.130960",
     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cat_col = [\"Gender\", \"Type of Travel\", \"Class\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7024bd54",
   "metadata": {
    "papermill": {
     "duration": 0.03655,
     "end_time": "2023-07-12T15:12:39.205133",
     "exception": false,
     "start_time": "2023-07-12T15:12:39.168583",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    3. use a for loop to re-assign each categorical column X[cols] to its label encoded version by using le.fit_transform(X[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "70123591",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:39.282856Z",
     "iopub.status.busy": "2023-07-12T15:12:39.281926Z",
     "iopub.status.idle": "2023-07-12T15:12:39.400337Z",
     "shell.execute_reply": "2023-07-12T15:12:39.398796Z"
    },
    "papermill": {
     "duration": 0.1604,
     "end_time": "2023-07-12T15:12:39.403279",
     "exception": false,
     "start_time": "2023-07-12T15:12:39.242879",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for cols in cat_col: \n",
    "    X[cols] = le.fit_transform(X[cols])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e90668af",
   "metadata": {
    "papermill": {
     "duration": 0.035817,
     "end_time": "2023-07-12T15:12:39.475566",
     "exception": false,
     "start_time": "2023-07-12T15:12:39.439749",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "11. We also want to do the same thing to our target variable"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e5962f0",
   "metadata": {
    "papermill": {
     "duration": 0.035651,
     "end_time": "2023-07-12T15:12:39.547038",
     "exception": false,
     "start_time": "2023-07-12T15:12:39.511387",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    1. Do the same thing to our variable y:\n",
    "       a. Assign the object LabelEncoder() to le"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f49ab0b1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:39.621269Z",
     "iopub.status.busy": "2023-07-12T15:12:39.620489Z",
     "iopub.status.idle": "2023-07-12T15:12:39.625012Z",
     "shell.execute_reply": "2023-07-12T15:12:39.624160Z"
    },
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     "duration": 0.044434,
     "end_time": "2023-07-12T15:12:39.627143",
     "exception": false,
     "start_time": "2023-07-12T15:12:39.582709",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8e90c88",
   "metadata": {
    "papermill": {
     "duration": 0.035747,
     "end_time": "2023-07-12T15:12:39.698833",
     "exception": false,
     "start_time": "2023-07-12T15:12:39.663086",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "        b. Use fit_transform to change the variable y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "68c6d580",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:39.774230Z",
     "iopub.status.busy": "2023-07-12T15:12:39.773007Z",
     "iopub.status.idle": "2023-07-12T15:12:39.816967Z",
     "shell.execute_reply": "2023-07-12T15:12:39.815700Z"
    },
    "papermill": {
     "duration": 0.085343,
     "end_time": "2023-07-12T15:12:39.819874",
     "exception": false,
     "start_time": "2023-07-12T15:12:39.734531",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y = le.fit_transform(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fdcafb5",
   "metadata": {
    "papermill": {
     "duration": 0.043289,
     "end_time": "2023-07-12T15:12:39.901633",
     "exception": false,
     "start_time": "2023-07-12T15:12:39.858344",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "12. We are then splitting our training dataset into train and a test datasets, this will allow us to evaluate which model is best for predicting, we also cannot use the actual test dataset because this results in data leakage, data leakage occurs when information from the testing set is inadvertently used during model training. This can lead to overly optimistic performance estimates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "020704f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:39.976156Z",
     "iopub.status.busy": "2023-07-12T15:12:39.975739Z",
     "iopub.status.idle": "2023-07-12T15:12:40.060807Z",
     "shell.execute_reply": "2023-07-12T15:12:40.059275Z"
    },
    "papermill": {
     "duration": 0.126282,
     "end_time": "2023-07-12T15:12:40.063876",
     "exception": false,
     "start_time": "2023-07-12T15:12:39.937594",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape:  (83123, 11)\n",
      "X_val shape:  (20781, 11)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size = 0.2, random_state = 42, stratify = y)\n",
    "\n",
    "print(f\"X_train shape: \", X_train.shape)\n",
    "print(f\"X_val shape: \", X_val.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c217b3e3",
   "metadata": {
    "papermill": {
     "duration": 0.035813,
     "end_time": "2023-07-12T15:12:40.135895",
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     "start_time": "2023-07-12T15:12:40.100082",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### **🛩 Modelling**\n",
    "****"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "856bb7d9",
   "metadata": {
    "papermill": {
     "duration": 0.036377,
     "end_time": "2023-07-12T15:12:40.208351",
     "exception": false,
     "start_time": "2023-07-12T15:12:40.171974",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    1. fill in the gaps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d9ec321",
   "metadata": {
    "papermill": {
     "duration": 0.036018,
     "end_time": "2023-07-12T15:12:40.280502",
     "exception": false,
     "start_time": "2023-07-12T15:12:40.244484",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "- Accuracy: The overall correctness of a classification model in predicting the correct labels.\n",
    "\n",
    "- F1 Score: A measure of a model's accuracy that considers both precision and recall, providing a balanced assessment.\n",
    "\n",
    "- Precision: The proportion of true positive predictions (correctly predicted positives) out of all positive predictions made.\n",
    "\n",
    "- Recall: The proportion of true positive predictions (correctly predicted positives) out of all actual positive instances.\n",
    "\n",
    "- AUC (Area Under the Curve): The measure of a model's performance by calculating the area under the receiver operating characteristic (ROC) curve."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "586b5192",
   "metadata": {
    "papermill": {
     "duration": 0.035434,
     "end_time": "2023-07-12T15:12:40.352228",
     "exception": false,
     "start_time": "2023-07-12T15:12:40.316794",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    2. Assign the object 'LogisticRegression()' to the variable lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "21dc13d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:40.426617Z",
     "iopub.status.busy": "2023-07-12T15:12:40.426161Z",
     "iopub.status.idle": "2023-07-12T15:12:40.432653Z",
     "shell.execute_reply": "2023-07-12T15:12:40.431049Z"
    },
    "papermill": {
     "duration": 0.047138,
     "end_time": "2023-07-12T15:12:40.435295",
     "exception": false,
     "start_time": "2023-07-12T15:12:40.388157",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74ec8a73",
   "metadata": {
    "papermill": {
     "duration": 0.035609,
     "end_time": "2023-07-12T15:12:40.507266",
     "exception": false,
     "start_time": "2023-07-12T15:12:40.471657",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    3. Use the lr.fit() method to train the algorithm on the X_train and y_train lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "02836588",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:40.583011Z",
     "iopub.status.busy": "2023-07-12T15:12:40.582011Z",
     "iopub.status.idle": "2023-07-12T15:12:40.974554Z",
     "shell.execute_reply": "2023-07-12T15:12:40.972989Z"
    },
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "220ac792",
   "metadata": {
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     "duration": 0.044922,
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     "exception": false,
     "start_time": "2023-07-12T15:12:41.058562",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    4. Use the lr.predict() method to predict the values of X_val and assign this to a variable called y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a88dc690",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:41.178177Z",
     "iopub.status.busy": "2023-07-12T15:12:41.177730Z",
     "iopub.status.idle": "2023-07-12T15:12:41.188020Z",
     "shell.execute_reply": "2023-07-12T15:12:41.186378Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_pred = lr.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c1394545",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:41.325722Z",
     "iopub.status.busy": "2023-07-12T15:12:41.325274Z",
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    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8213271738607382\n",
      "AUC Score: 0.8185043635178524\n",
      "F1 Score: 0.79455541415371\n",
      "Recall Score: 0.7973348139922265\n",
      "Precision Score: 0.7917953242170269\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_val, y_pred)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "auc_score = roc_auc_score(y_val, y_pred)\n",
    "print(\"AUC Score:\", auc_score)\n",
    "\n",
    "\n",
    "F1_score = f1_score(y_val, y_pred)\n",
    "print(\"F1 Score:\", F1_score)\n",
    "\n",
    "Recall_score = recall_score(y_val, y_pred)\n",
    "print(\"Recall Score:\", Recall_score)\n",
    "\n",
    "Precision_score = precision_score(y_val, y_pred)\n",
    "print(\"Precision Score:\", Precision_score)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c925b31d",
   "metadata": {
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "#### **🛩 Test prediction**\n",
    "****"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0b2585a",
   "metadata": {
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     "status": "completed"
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   "source": [
    "15. we now implement all of our necessary changes to the test dataset that we had previously done to the train dataset as follows\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cfaa64d",
   "metadata": {
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     "status": "completed"
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   },
   "source": [
    "    1. Read the test csv file and assign it to a variable called test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f033f13c",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.describe of           id  Gender  Age   Type of Travel     Class  Flight Distance  \\\n",
       "0      19556  Female   52  Business travel       Eco              160   \n",
       "1      90035  Female   36  Business travel  Business             2863   \n",
       "2      12360    Male   20  Business travel       Eco              192   \n",
       "3      77959    Male   44  Business travel  Business             3377   \n",
       "4      36875  Female   49  Business travel       Eco             1182   \n",
       "...      ...     ...  ...              ...       ...              ...   \n",
       "25971  78463    Male   34  Business travel  Business              526   \n",
       "25972  71167    Male   23  Business travel  Business              646   \n",
       "25973  37675  Female   17  Personal Travel       Eco              828   \n",
       "25974  90086    Male   14  Business travel  Business             1127   \n",
       "25975  34799  Female   42  Personal Travel       Eco              264   \n",
       "\n",
       "       Inflight wifi service  Departure/Arrival time convenient  Seat comfort  \\\n",
       "0                          5                                  4             3   \n",
       "1                          1                                  1             5   \n",
       "2                          2                                  0             2   \n",
       "3                          0                                  0             4   \n",
       "4                          2                                  3             2   \n",
       "...                      ...                                ...           ...   \n",
       "25971                      3                                  3             4   \n",
       "25972                      4                                  4             4   \n",
       "25973                      2                                  5             2   \n",
       "25974                      3                                  3             4   \n",
       "25975                      2                                  5             2   \n",
       "\n",
       "       Checkin service  Cleanliness  Departure Delay in Minutes  \n",
       "0                    2            5                          50  \n",
       "1                    3            5                           0  \n",
       "2                    2            2                           0  \n",
       "3                    3            4                           0  \n",
       "4                    4            4                           0  \n",
       "...                ...          ...                         ...  \n",
       "25971                4            4                           0  \n",
       "25972                5            4                           0  \n",
       "25973                5            2                           0  \n",
       "25974                4            4                           0  \n",
       "25975                1            1                           0  \n",
       "\n",
       "[25976 rows x 12 columns]>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0c6e46c4",
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    "execution": {
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>Gender</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>Seat comfort</th>\n",
       "      <th>Checkin service</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Departure Delay in Minutes</th>\n",
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       "      <td>19556</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12360</td>\n",
       "      <td>Male</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>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>77959</td>\n",
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       "      <td>44</td>\n",
       "      <td>Business travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>3377</td>\n",
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      "text/plain": [
       "      id  Gender  Age   Type of Travel     Class  Flight Distance  \\\n",
       "0  19556  Female   52  Business travel       Eco              160   \n",
       "1  90035  Female   36  Business travel  Business             2863   \n",
       "2  12360    Male   20  Business travel       Eco              192   \n",
       "3  77959    Male   44  Business travel  Business             3377   \n",
       "4  36875  Female   49  Business travel       Eco             1182   \n",
       "\n",
       "   Inflight wifi service  Departure/Arrival time convenient  Seat comfort  \\\n",
       "0                      5                                  4             3   \n",
       "1                      1                                  1             5   \n",
       "2                      2                                  0             2   \n",
       "3                      0                                  0             4   \n",
       "4                      2                                  3             2   \n",
       "\n",
       "   Checkin service  Cleanliness  Departure Delay in Minutes  \n",
       "0                2            5                          50  \n",
       "1                3            5                           0  \n",
       "2                2            2                           0  \n",
       "3                3            4                           0  \n",
       "4                4            4                           0  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "548897a9",
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     "start_time": "2023-07-12T15:12:41.831089",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**a.** Taking the natural logarithm of the numerical variables that are skewed, the is done to make it more suitable for model predicting and reduce skewedness"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c4e684b",
   "metadata": {
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     "duration": 0.0391,
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     "start_time": "2023-07-12T15:12:41.909601",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    1. Take the natural logarithm of the 2 continuous numerical variables (hint: You previously did this to the train dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "26611aad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:42.029965Z",
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "test[\"Flight Distance\"] = np.log(test[\"Flight Distance\"])\n",
    "test[\"Departure Delay in Minutes\"] = np.log(test[\"Departure Delay in Minutes\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ef1a529",
   "metadata": {
    "papermill": {
     "duration": 0.039023,
     "end_time": "2023-07-12T15:12:42.124061",
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     "start_time": "2023-07-12T15:12:42.085038",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**d.** Define the columns for our x and y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "203c6f8a",
   "metadata": {
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     "duration": 0.038022,
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     "start_time": "2023-07-12T15:12:42.162815",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    2.Define the X and Y variables (hint: x is all categories apart from the id and the one that we are trying to predict and the y variables is only the column that we are trying to predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fe763339",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:42.281551Z",
     "iopub.status.busy": "2023-07-12T15:12:42.281146Z",
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     "end_time": "2023-07-12T15:12:42.291687",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = test.drop(['id'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ecd828a",
   "metadata": {
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     "duration": 0.038437,
     "end_time": "2023-07-12T15:12:42.369341",
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   },
   "source": [
    "**e.** We now normalise the numerical variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69f93717",
   "metadata": {
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    },
    "tags": []
   },
   "source": [
    "    1. Assign a variable named scaler to the object 'StandardScaler()'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "3250604e",
   "metadata": {
    "execution": {
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   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0fe10f82",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "\n",
    "numeric_col = [\"Age\", \"Flight Distance\", \"Departure Delay in Minutes\"]\n",
    "\n",
    "# Handle the problematic values in X\n",
    "for numeric_val in numeric_col:\n",
    "    X[numeric_val] = X[numeric_val].replace([np.inf, -np.inf], np.nan)\n",
    "    X[numeric_val] = X[numeric_val].clip(lower=-1e6, upper=1e6)  # Replace extremely large values with a suitable range\n",
    "    X[numeric_val].fillna(X[numeric_val].mean(), inplace=True)  # Replace NaN values with column mean\n",
    "\n",
    "X[numeric_col] = scaler.fit_transform(X[numeric_col])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6015f6a6",
   "metadata": {
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     "duration": 0.038175,
     "end_time": "2023-07-12T15:12:42.720148",
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   "source": [
    "**f.** We now encode our categorical variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4968e44",
   "metadata": {
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     "duration": 0.038035,
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    "tags": []
   },
   "source": [
    "    1. Assign a variable called le to the object LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "17428ebb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:42.881273Z",
     "iopub.status.busy": "2023-07-12T15:12:42.880890Z",
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   "outputs": [],
   "source": [
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d0dad33",
   "metadata": {
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    },
    "tags": []
   },
   "source": [
    "    2. Assign a list of the non-numerical categorical columns to a variable called 'cat_col'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4db673b0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:43.073340Z",
     "iopub.status.busy": "2023-07-12T15:12:43.072675Z",
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     "shell.execute_reply": "2023-07-12T15:12:43.079131Z"
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   "outputs": [],
   "source": [
    "cat_col = [\"Gender\", \"Type of Travel\", \"Class\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c81f8bdd",
   "metadata": {
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     "start_time": "2023-07-12T15:12:43.124095",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "    3. use a for loop to re-assign each categorical column X[cols] to its label encoded version by using le.fit_transform(X[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "17419ad1",
   "metadata": {
    "execution": {
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   "source": [
    "for cols in cat_col: \n",
    "    X[cols] = le.fit_transform(X[cols])"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "    1. Do the same thing to our variable y:\n",
    "       a. Assign the object LabelEncoder() to le"
   ]
  },
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   "cell_type": "code",
   "execution_count": 48,
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   "source": [
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "        b. Use fit_transform to change the variable y"
   ]
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  {
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   "execution_count": 49,
   "id": "72bcfed4",
   "metadata": {
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   "source": [
    "y = le.fit_transform(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b222b4b3",
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   "source": [
    "16. Lastly we used our pretrained model to predict whetehr airline passengers were satisfied or not from the test dataset and retreive the key metrics for our model"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "    1. Now we use the lr.predict() method to predict y using X. Assign the output of this to a variable called y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1e0cebe3",
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   "outputs": [],
   "source": [
    "y_pred = lr.predict(X)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 51,
   "id": "1c12e952",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "final_sub = pd.DataFrame({'id': test['id'], 'satisfaction': y_pred})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cacdbac0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-12T15:12:44.084104Z",
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   "outputs": [],
   "source": [
    "final_sub.to_csv('submission.csv', index=False)"
   ]
  }
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