British Airways task 2 feature engineering and predictive model .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1f85840b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bdb6549a",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_passengers</th>\n",
       "      <th>sales_channel</th>\n",
       "      <th>trip_type</th>\n",
       "      <th>purchase_lead</th>\n",
       "      <th>length_of_stay</th>\n",
       "      <th>flight_hour</th>\n",
       "      <th>flight_day</th>\n",
       "      <th>route</th>\n",
       "      <th>booking_origin</th>\n",
       "      <th>wants_extra_baggage</th>\n",
       "      <th>wants_preferred_seat</th>\n",
       "      <th>wants_in_flight_meals</th>\n",
       "      <th>flight_duration</th>\n",
       "      <th>booking_complete</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>262</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>Sat</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>112</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>Sat</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>243</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>Wed</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>96</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>Sat</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>68</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>Wed</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   num_passengers sales_channel  trip_type  purchase_lead  length_of_stay  \\\n",
       "0               2      Internet  RoundTrip            262              19   \n",
       "1               1      Internet  RoundTrip            112              20   \n",
       "2               2      Internet  RoundTrip            243              22   \n",
       "3               1      Internet  RoundTrip             96              31   \n",
       "4               2      Internet  RoundTrip             68              22   \n",
       "\n",
       "   flight_hour flight_day   route booking_origin  wants_extra_baggage  \\\n",
       "0            7        Sat  AKLDEL    New Zealand                    1   \n",
       "1            3        Sat  AKLDEL    New Zealand                    0   \n",
       "2           17        Wed  AKLDEL          India                    1   \n",
       "3            4        Sat  AKLDEL    New Zealand                    0   \n",
       "4           15        Wed  AKLDEL          India                    1   \n",
       "\n",
       "   wants_preferred_seat  wants_in_flight_meals  flight_duration  \\\n",
       "0                     0                      0             5.52   \n",
       "1                     0                      0             5.52   \n",
       "2                     1                      0             5.52   \n",
       "3                     0                      1             5.52   \n",
       "4                     0                      1             5.52   \n",
       "\n",
       "   booking_complete  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"/Users/ayodejioyesanya/Desktop/customer_booking.csv\", encoding=\"ISO-8859-1\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "23da6ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 14 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   num_passengers         50000 non-null  int64  \n",
      " 1   sales_channel          50000 non-null  object \n",
      " 2   trip_type              50000 non-null  object \n",
      " 3   purchase_lead          50000 non-null  int64  \n",
      " 4   length_of_stay         50000 non-null  int64  \n",
      " 5   flight_hour            50000 non-null  int64  \n",
      " 6   flight_day             50000 non-null  object \n",
      " 7   route                  50000 non-null  object \n",
      " 8   booking_origin         50000 non-null  object \n",
      " 9   wants_extra_baggage    50000 non-null  int64  \n",
      " 10  wants_preferred_seat   50000 non-null  int64  \n",
      " 11  wants_in_flight_meals  50000 non-null  int64  \n",
      " 12  flight_duration        50000 non-null  float64\n",
      " 13  booking_complete       50000 non-null  int64  \n",
      "dtypes: float64(1), int64(8), object(5)\n",
      "memory usage: 5.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ea7ac288",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sat', 'Wed', 'Thu', 'Mon', 'Sun', 'Tue', 'Fri'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"flight_day\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b33aa362",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_passengers</th>\n",
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       "      <th>length_of_stay</th>\n",
       "      <th>flight_hour</th>\n",
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       "  </thead>\n",
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       "      <th>count</th>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.00000</td>\n",
       "      <td>50000.00000</td>\n",
       "      <td>50000.000000</td>\n",
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       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.591240</td>\n",
       "      <td>84.940480</td>\n",
       "      <td>23.04456</td>\n",
       "      <td>9.06634</td>\n",
       "      <td>0.668780</td>\n",
       "      <td>0.296960</td>\n",
       "      <td>0.427140</td>\n",
       "      <td>7.277561</td>\n",
       "      <td>0.149560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.020165</td>\n",
       "      <td>90.451378</td>\n",
       "      <td>33.88767</td>\n",
       "      <td>5.41266</td>\n",
       "      <td>0.470657</td>\n",
       "      <td>0.456923</td>\n",
       "      <td>0.494668</td>\n",
       "      <td>1.496863</td>\n",
       "      <td>0.356643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.670000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.620000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>17.00000</td>\n",
       "      <td>9.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.570000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>115.000000</td>\n",
       "      <td>28.00000</td>\n",
       "      <td>13.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>8.830000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>867.000000</td>\n",
       "      <td>778.00000</td>\n",
       "      <td>23.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_passengers  purchase_lead  length_of_stay  flight_hour  \\\n",
       "count    50000.000000   50000.000000     50000.00000  50000.00000   \n",
       "mean         1.591240      84.940480        23.04456      9.06634   \n",
       "std          1.020165      90.451378        33.88767      5.41266   \n",
       "min          1.000000       0.000000         0.00000      0.00000   \n",
       "25%          1.000000      21.000000         5.00000      5.00000   \n",
       "50%          1.000000      51.000000        17.00000      9.00000   \n",
       "75%          2.000000     115.000000        28.00000     13.00000   \n",
       "max          9.000000     867.000000       778.00000     23.00000   \n",
       "\n",
       "       wants_extra_baggage  wants_preferred_seat  wants_in_flight_meals  \\\n",
       "count         50000.000000          50000.000000           50000.000000   \n",
       "mean              0.668780              0.296960               0.427140   \n",
       "std               0.470657              0.456923               0.494668   \n",
       "min               0.000000              0.000000               0.000000   \n",
       "25%               0.000000              0.000000               0.000000   \n",
       "50%               1.000000              0.000000               0.000000   \n",
       "75%               1.000000              1.000000               1.000000   \n",
       "max               1.000000              1.000000               1.000000   \n",
       "\n",
       "       flight_duration  booking_complete  \n",
       "count     50000.000000      50000.000000  \n",
       "mean          7.277561          0.149560  \n",
       "std           1.496863          0.356643  \n",
       "min           4.670000          0.000000  \n",
       "25%           5.620000          0.000000  \n",
       "50%           7.570000          0.000000  \n",
       "75%           8.830000          0.000000  \n",
       "max           9.500000          1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a33c16df",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0e7ebee5",
   "metadata": {},
   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>243</td>\n",
       "      <td>22</td>\n",
       "      <td>17</td>\n",
       "      <td>Wed</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.52</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>96</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>Sat</td>\n",
       "      <td>AKLDEL</td>\n",
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       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>Internet</td>\n",
       "      <td>RoundTrip</td>\n",
       "      <td>68</td>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "      <td>Wed</td>\n",
       "      <td>AKLDEL</td>\n",
       "      <td>India</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "   num_passengers sales_channel  trip_type  purchase_lead  length_of_stay  \\\n",
       "0               2      Internet  RoundTrip            262              19   \n",
       "1               1      Internet  RoundTrip            112              20   \n",
       "2               2      Internet  RoundTrip            243              22   \n",
       "3               1      Internet  RoundTrip             96              31   \n",
       "4               2      Internet  RoundTrip             68              22   \n",
       "\n",
       "   flight_hour flight_day   route booking_origin  wants_extra_baggage  \\\n",
       "0            7        Sat  AKLDEL    New Zealand                    1   \n",
       "1            3        Sat  AKLDEL    New Zealand                    0   \n",
       "2           17        Wed  AKLDEL          India                    1   \n",
       "3            4        Sat  AKLDEL    New Zealand                    0   \n",
       "4           15        Wed  AKLDEL          India                    1   \n",
       "\n",
       "   wants_preferred_seat  wants_in_flight_meals  flight_duration  \\\n",
       "0                     0                      0             5.52   \n",
       "1                     0                      0             5.52   \n",
       "2                     1                      0             5.52   \n",
       "3                     0                      1             5.52   \n",
       "4                     0                      1             5.52   \n",
       "\n",
       "   booking_complete  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 0  \n",
       "3                 0  \n",
       "4                 0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"/Users/ayodejioyesanya/Desktop/customer_booking.csv\", encoding=\"ISO-8859-1\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fd12c3a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num_passengers             int64\n",
       "sales_channel             object\n",
       "trip_type                 object\n",
       "purchase_lead              int64\n",
       "length_of_stay             int64\n",
       "flight_hour                int64\n",
       "flight_day                object\n",
       "route                     object\n",
       "booking_origin            object\n",
       "wants_extra_baggage        int64\n",
       "wants_preferred_seat       int64\n",
       "wants_in_flight_meals      int64\n",
       "flight_duration          float64\n",
       "booking_complete           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data analysis \n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8d4252ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 14)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n",
    "#the shape i.e. number of rows and number of columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7d10ad10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num_passengers           0\n",
       "sales_channel            0\n",
       "trip_type                0\n",
       "purchase_lead            0\n",
       "length_of_stay           0\n",
       "flight_hour              0\n",
       "flight_day               0\n",
       "route                    0\n",
       "booking_origin           0\n",
       "wants_extra_baggage      0\n",
       "wants_preferred_seat     0\n",
       "wants_in_flight_meals    0\n",
       "flight_duration          0\n",
       "booking_complete         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to checknunassigned and where values -0 \n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e225f3c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num_passengers             int64\n",
       "sales_channel             object\n",
       "trip_type                 object\n",
       "purchase_lead              int64\n",
       "length_of_stay             int64\n",
       "flight_hour                int64\n",
       "flight_day                object\n",
       "route                     object\n",
       "booking_origin            object\n",
       "wants_extra_baggage        int64\n",
       "wants_preferred_seat       int64\n",
       "wants_in_flight_meals      int64\n",
       "flight_duration          float64\n",
       "booking_complete           int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e84ef87c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "booking_complete\n",
       "0    42522\n",
       "1     7478\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.booking_complete.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ae59a94a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X= df.drop('booking_complete', axis=1)\n",
    "y= df.booking_complete \n",
    "#changing object dtype oject to integer type \n",
    "for colname in X.select_dtypes(\"object\"):\n",
    "    X[colname], _ = X[colname].factorize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "77029333",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num_passengers             int64\n",
       "sales_channel              int64\n",
       "trip_type                  int64\n",
       "purchase_lead              int64\n",
       "length_of_stay             int64\n",
       "flight_hour                int64\n",
       "flight_day                 int64\n",
       "route                      int64\n",
       "booking_origin             int64\n",
       "wants_extra_baggage        int64\n",
       "wants_preferred_seat       int64\n",
       "wants_in_flight_meals      int64\n",
       "flight_duration          float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3859af7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "route                    0.052800\n",
       "booking_origin           0.047140\n",
       "flight_duration          0.013973\n",
       "wants_extra_baggage      0.010382\n",
       "length_of_stay           0.007865\n",
       "sales_channel            0.005153\n",
       "wants_in_flight_meals    0.004354\n",
       "flight_day               0.003341\n",
       "wants_preferred_seat     0.003017\n",
       "flight_hour              0.001439\n",
       "num_passengers           0.001423\n",
       "purchase_lead            0.000791\n",
       "trip_type                0.000668\n",
       "Name: MI Scores, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_selection import mutual_info_classif\n",
    "\n",
    "mi_scores = mutual_info_classif(X, y)\n",
    "mi_scores = pd.Series(mi_scores, name=\"MI Scores\", index=X.columns)\n",
    "mi_scores = mi_scores.sort_values(ascending=False)\n",
    "\n",
    "mi_scores # show a few features with their MI scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0d599781",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ee453ff1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_mi_scores(scores):\n",
    "    scores = scores.sort_values(ascending=True)\n",
    "    width = np.arange(len(scores))\n",
    "    ticks = list(scores.index)\n",
    "    plt.barh(width, scores)\n",
    "    plt.yticks(width, ticks)\n",
    "    plt.title(\"Mutual Information Scores\")\n",
    "\n",
    "\n",
    "plt.figure(dpi=100, figsize=(8, 5))\n",
    "plot_mi_scores(mi_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "606c9bcb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: seaborn in /Users/ayodejioyesanya/anaconda3/lib/python3.10/site-packages (0.13.0)\n",
      "Requirement already satisfied: pandas>=1.2 in /Users/ayodejioyesanya/anaconda3/lib/python3.10/site-packages (from seaborn) (2.1.3)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.3 in /Users/ayodejioyesanya/anaconda3/lib/python3.10/site-packages (from seaborn) (3.8.1)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /Users/ayodejioyesanya/anaconda3/lib/python3.10/site-packages (from seaborn) (1.26.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/ayodejioyesanya/anaconda3/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (3.1.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/ayodejioyesanya/anaconda3/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (0.12.1)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /Users/ayodejioyesanya/anaconda3/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (1.4.5)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/ayodejioyesanya/anaconda3/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (23.1)\n",
      "Requirement already satisfied: pillow>=8 in /Users/ayodejioyesanya/anaconda3/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (10.0.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/ayodejioyesanya/anaconda3/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (2.8.2)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/ayodejioyesanya/anaconda3/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (4.44.0)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Users/ayodejioyesanya/anaconda3/lib/python3.10/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (1.2.0)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /Users/ayodejioyesanya/anaconda3/lib/python3.10/site-packages (from pandas>=1.2->seaborn) (2023.3)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/ayodejioyesanya/anaconda3/lib/python3.10/site-packages (from pandas>=1.2->seaborn) (2023.3.post1)\n",
      "Requirement already satisfied: six>=1.5 in /Users/ayodejioyesanya/anaconda3/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.3->seaborn) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4bfff593",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "be935756",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x7f9d81358430>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(data=df,x='route',y='booking_origin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8a2f44ab",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (2875308748.py, line 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[21], line 4\u001b[0;36m\u001b[0m\n\u001b[0;31m    flight duration\u001b[0m\n\u001b[0m           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "#top 5 features which are dependant with booking_complete feature \n",
    "route\n",
    "booking_orgin\n",
    "flight duration \n",
    "wants_extra_bags\n",
    "length_of_stay\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d37cbd40",
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install -U scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0dd986b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#test train split\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# dataset split\n",
    "# creating a function for dataset split\n",
    "def dataset(X,y):\n",
    "    train_full_X, val_X, train_full_y, val_y = train_test_split(X, y,test_size=0.2,random_state = 0)\n",
    "\n",
    "# Use the same function above for the validation set\n",
    "    train_X, test_X, train_y, test_y = train_test_split(train_full_X, train_full_y, test_size=0.25,random_state = 0)\n",
    "    return (train_X, val_X, train_y, val_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c79aac08",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "def scale(X):\n",
    "    scaler = MinMaxScaler()\n",
    "    scaler.fit(X)\n",
    "    return X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "68c8cc19",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (2796649617.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[24], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    Model: Random forest classifier with top 6  features\u001b[0m\n\u001b[0m                  ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "Model: Random forest classifier with top 6  features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "911569d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACCURACY:  83.36\n",
      "AUC score:  0.5657818407546988\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "\n",
    "\n",
    "#assigning the features under a list\n",
    "\n",
    "features=['route','booking_origin','flight_duration','wants_extra_baggage', 'length_of_stay','num_passengers']\n",
    "X= df[features]\n",
    "#one hot encoding\n",
    "X = pd.get_dummies(X, columns=features)\n",
    "X= scale(X)\n",
    "y= df.booking_complete       \n",
    "\n",
    "X_train,X_val,y_train,y_val= dataset(X,y)\n",
    "\n",
    "forest_model= RandomForestClassifier(random_state=1)\n",
    "forest_model.fit(X_train, y_train)\n",
    "preds= forest_model.predict(X_val)\n",
    "\n",
    "print('ACCURACY: ',accuracy_score(y_val,preds)*100)\n",
    "print('AUC score: ',roc_auc_score(y_val,preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90e980a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "Model 2: Random forest classifier with all features \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "baedd0d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACCURACY:  84.76\n",
      "AUC score:  0.5479604084813514\n"
     ]
    }
   ],
   "source": [
    "X= df.drop('booking_complete',axis=1)\n",
    "#one hot encoding\n",
    "X = pd.get_dummies(X)\n",
    "X= scale(X)\n",
    "y= df.booking_complete       \n",
    "\n",
    "X_train,X_val,y_train,y_val= dataset(X,y)\n",
    "\n",
    "forest_model= RandomForestClassifier(random_state=1)\n",
    "forest_model.fit(X_train, y_train)\n",
    "preds= forest_model.predict(X_val)\n",
    "\n",
    "print('ACCURACY: ',accuracy_score(y_val,preds)*100)\n",
    "print('AUC score: ',roc_auc_score(y_val,preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b42d99d",
   "metadata": {},
   "outputs": [],
   "source": [
    "Model 3: XGB classifier with top 6 features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "16b4f7b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: xgboost in /Users/ayodejioyesanya/anaconda3/lib/python3.10/site-packages (2.0.2)\n",
      "Requirement already satisfied: numpy in /Users/ayodejioyesanya/anaconda3/lib/python3.10/site-packages (from xgboost) (1.26.0)\n",
      "Requirement already satisfied: scipy in /Users/ayodejioyesanya/anaconda3/lib/python3.10/site-packages (from xgboost) (1.11.4)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ed8f2447",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACCURACY:  84.72\n",
      "AUC score:  0.5189398567740671\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "\n",
    "X= df[features]\n",
    "\n",
    "#one hot encoding\n",
    "X = pd.get_dummies(X, columns=features)\n",
    "X= scale(X)\n",
    "\n",
    "y= df.booking_complete    \n",
    "\n",
    "X_train,X_val,y_train,y_val= dataset(X,y)\n",
    "xgb_model = XGBClassifier()\n",
    "\n",
    "xgb_model.fit(X_train, y_train)\n",
    "prediction_xgb = xgb_model.predict(X_val)\n",
    "print('ACCURACY: ',accuracy_score(y_val, prediction_xgb)*100)\n",
    "print('AUC score: ',roc_auc_score(y_val,prediction_xgb))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15e2ad92",
   "metadata": {},
   "outputs": [],
   "source": [
    "Model 4:XGB classifier with all features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d80958bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACCURACY:  84.98\n",
      "AUC score:  0.5424676175552812\n"
     ]
    }
   ],
   "source": [
    "X= df.drop('booking_complete',axis=1)\n",
    "#one hot encoding\n",
    "X = pd.get_dummies(X)\n",
    "X= scale(X)\n",
    "y= df.booking_complete \n",
    "\n",
    "X_train,X_val,y_train,y_val= dataset(X,y)\n",
    "\n",
    "\n",
    "xgb_model = XGBClassifier()\n",
    "xgb_model.fit(X_train, y_train)\n",
    "prediction_xgb = xgb_model.predict(X_val)\n",
    "print('ACCURACY: ',accuracy_score(y_val, prediction_xgb)*100)\n",
    "print('AUC score: ',roc_auc_score(y_val,prediction_xgb))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e395c74",
   "metadata": {},
   "outputs": [],
   "source": [
    "Finalising random forest model with all features as final model, \n",
    "as it has good accuracy and higher AUC score compared to the other models "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b003645b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Validating with test data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2524cbd6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACCURACY:  85.09\n",
      "AUC score:  0.5577796717361984\n"
     ]
    }
   ],
   "source": [
    "X= df.drop('booking_complete',axis=1)\n",
    "#one hot encoding\n",
    "X = pd.get_dummies(X)\n",
    "X= scale(X)\n",
    "y= df.booking_complete       \n",
    "\n",
    "train_full_X,test_X, train_full_y, test_y = train_test_split(X, y,test_size=0.2,random_state = 0)\n",
    "\n",
    "forest_model= RandomForestClassifier(random_state=1)\n",
    "forest_model.fit(train_full_X, train_full_y)\n",
    "preds= forest_model.predict(test_X)\n",
    "\n",
    "print('ACCURACY: ',accuracy_score(test_y,preds)*100)\n",
    "print('AUC score: ',roc_auc_score(test_y,preds))\n",
    "     \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "601a2fba",
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install matplotlib\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dada532",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract features (X) and target variable (y)\n",
    "X = scaled_df.drop('target_column', axis=1)\n",
    "y = scaled_df['target_column']\n",
    "\n",
    "# Instantiate and train a Random Forest classifier\n",
    "clf_rf = RandomForestClassifier(random_state=1)\n",
    "clf_rf.fit(X, y)\n",
    "\n",
    "# Plot feature importances\n",
    "plt.figure(figsize=(10, 5))\n",
    "sorted_idx = clf_rf.feature_importances_.argsort()\n",
    "plt.barh(X.columns[sorted_idx], clf_rf.feature_importances_[sorted_idx])\n",
    "plt.xlabel(\"Random Forest Feature Importance\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1e775c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = ConfusionMatrix(clf_rf, classes=[0,1])\n",
    "cm.fit(x_train, y_train)           \n",
    "cm.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "50dcea6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
    "\n",
    "# Assuming you have the dataset loading and model training code before this point\n",
    "# ...\n",
    "\n",
    "# Assuming you have 'X_train', 'y_train', 'X_val', 'y_val' defined from your dataset function\n",
    "\n",
    "# Training Random Forest model\n",
    "forest_model = RandomForestClassifier(random_state=1)\n",
    "forest_model.fit(X_train, y_train)\n",
    "rf_preds = forest_model.predict(X_val)\n",
    "\n",
    "# Training XGBoost model\n",
    "xgb_model = XGBClassifier()\n",
    "xgb_model.fit(X_train, y_train)\n",
    "xgb_preds = xgb_model.predict(X_val)\n",
    "\n",
    "# Calculating AUC scores\n",
    "rf_auc_score = roc_auc_score(y_val, rf_preds)\n",
    "xgb_auc_score = roc_auc_score(y_val, xgb_preds)\n",
    "\n",
    "# Creating a bar chart\n",
    "def plot_scores(rf_score, xgb_score, score_type1, score_type2):\n",
    "    scores = pd.Series([rf_score, xgb_score], index=[score_type1, score_type2])\n",
    "    scores = scores.sort_values(ascending=True)\n",
    "\n",
    "    # Creating indices and ticks\n",
    "    indices = np.arange(len(scores))\n",
    "    ticks = list(scores.index)\n",
    "\n",
    "    # Creating subplots\n",
    "    fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "    # Plotting scores\n",
    "    ax.barh(indices, scores, color=['blue', 'orange'])\n",
    "\n",
    "    # Adding labels and title\n",
    "    ax.set_yticks(indices)\n",
    "    ax.set_yticklabels(ticks)\n",
    "    ax.set_xlabel('AUC Scores')\n",
    "    ax.set_title(f\"{score_type1} vs {score_type2}\")\n",
    "\n",
    "    # Displaying the plot\n",
    "    plt.show()\n",
    "\n",
    "# Calling the function to plot the bar chart\n",
    "plot_scores(rf_auc_score, xgb_auc_score, 'Random Forest', 'XGBoost')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "89c5114c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Assuming you have the dataset loading and model training code before this point\n",
    "# ...\n",
    "\n",
    "# Assigning the features under a list\n",
    "features = ['route', 'booking_origin', 'flight_duration', 'wants_extra_baggage', 'length_of_stay', 'num_passengers']\n",
    "X = df[features]\n",
    "\n",
    "# One hot encoding\n",
    "X = pd.get_dummies(X, columns=features)\n",
    "\n",
    "# Scaling features using StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X)\n",
    "\n",
    "y = df.booking_complete\n",
    "\n",
    "X_train, X_val, y_train, y_val = dataset(X, y)\n",
    "\n",
    "# Training Random Forest model\n",
    "forest_model = RandomForestClassifier(random_state=1)\n",
    "forest_model.fit(X_train, y_train)\n",
    "rf_preds = forest_model.predict(X_val)\n",
    "\n",
    "# Training XGBoost model\n",
    "xgb_model = XGBClassifier()\n",
    "xgb_model.fit(X_train, y_train)\n",
    "xgb_preds = xgb_model.predict(X_val)\n",
    "\n",
    "# Calculating AUC scores\n",
    "rf_auc_score = roc_auc_score(y_val, rf_preds)\n",
    "xgb_auc_score = roc_auc_score(y_val, xgb_preds)\n",
    "\n",
    "# Creating a bar chart with data labels\n",
    "def plot_scores(rf_score, xgb_score, score_type1, score_type2):\n",
    "    scores = pd.Series([rf_score, xgb_score], index=[score_type1, score_type2])\n",
    "    scores = scores.sort_values(ascending=True)\n",
    "\n",
    "    # Creating indices and ticks\n",
    "    indices = np.arange(len(scores))\n",
    "    ticks = list(scores.index)\n",
    "\n",
    "    # Creating subplots\n",
    "    fig, ax = plt.subplots(figsize=(8, 5))\n",
    "\n",
    "    # Plotting scores\n",
    "    bars = ax.barh(indices, scores, color=['blue', 'red'])\n",
    "\n",
    "    # Adding labels and title\n",
    "    ax.set_yticks(indices)\n",
    "    ax.set_yticklabels(ticks)\n",
    "    ax.set_xlabel('AUC Scores')\n",
    "    ax.set_title(f\"{score_type1} vs {score_type2}\")\n",
    "\n",
    "    # Adding data labels on bars\n",
    "    for bar in bars:\n",
    "        yval = bar.get_width()\n",
    "        plt.text(yval, bar.get_y() + bar.get_height()/2, round(yval, 4), ha='left', va='center')\n",
    "\n",
    "    # Displaying the plot\n",
    "    plt.show()\n",
    "\n",
    "# Calling the function to plot the bar chart with data labels\n",
    "plot_scores(rf_auc_score, xgb_auc_score, 'Random Forest', 'XGBoost')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "addfd7c7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}