BA_prediction.ipynb

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        "<a href=\"https://colab.research.google.com/github/i-ninte/data-analytics-R-python/blob/main/BA_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "Here is the background information on your task\n",
        "Customers are more empowered than ever because they have access to a wealth of information at their fingertips. This is one of the reasons the buying cycle is very different to what it used to be. Today, if you’re hoping that a customer purchases your flights or holidays as they come into the airport, you’ve already lost! Being reactive in this situation is not ideal; airlines must be proactive in order to acquire customers before they embark on their holiday.\n",
        "\n",
        "This is possible with the use of data and predictive models. The most important factor with a predictive model is the quality of the data you use to train the machine learning algorithms. For this task, you must manipulate and prepare the provided customer booking data so that you can build a high-quality predictive model.\n",
        "\n",
        "With your predictive model, it is important to interpret the results in order to understand how “predictive” the data really was and whether we can feasibly use it to predict the target outcome (customers buying holidays). Therefore, you should evaluate the model's performance and output how each variable contributes to the predictive model's power."
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        "###Build a predictive model to understand factors that influence buying behaviour"
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        "##Here is your task\n",
        "##Explore and prepare the dataset\n",
        "First, spend some time exploring the dataset in the “Getting Started” Jupyter Notebook provided in the Resources section below to understand the different columns and some basic statistics of the dataset. Then, you should consider how to prepare the dataset for a predictive model. You should think about any new features you want to create in order to make your model even better. You can make use of the Resources provided to get you started with this task.\n",
        "\n",
        "##Train a machine learning model\n",
        "When your data is ready for modelling, you should train a machine learning model to be able to predict the target outcome, which is a customer making a booking. For this task, you should use an algorithm that easily allows you to output information about how each variable within the model contributes to its predictive power. For example, a RandomForest is very good for this purpose.\n",
        "\n",
        "##Evaluate model and present findings\n",
        "After training your model, you should evaluate how well it performed by conducting cross-validation and outputting appropriate evaluation metrics. Furthermore, you should create a visualisation to interpret how each variable contributed to the model. Finally, you should summarise your findings in a single slide to be sent to your manager. Use the “PowerPoint Template” provided in the Resources section below to create your summary and make use of the links provided to help with this task.\n",
        "\n",
        "It is recommended that the analysis portion of this task is done in Python.\n",
        "\n",
        "Once you’ve completed your PowerPoint, please submit your document below."
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        "# Task 2\n",
        "\n",
        "---\n",
        "\n",
        "## Predictive modeling of customer bookings\n",
        "\n",
        "This Jupyter notebook includes some code to get you started with this predictive modeling task. We will use various packages for data manipulation, feature engineering and machine learning.\n",
        "\n",
        "### Exploratory data analysis\n",
        "\n",
        "First, we must explore the data in order to better understand what we have and the statistical properties of the dataset."
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        "import pandas as pd"
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        "df = pd.read_csv(\"customer_booking.csv\", encoding=\"ISO-8859-1\")\n",
        "df.head()"
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              "   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",
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        "df.info()"
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            "<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"
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        "The `.info()` method gives us a data description, telling us the names of the columns, their data types and how many null values we have. Fortunately, we have no null values. It looks like some of these columns should be converted into different data types, e.g. flight_day.\n",
        "\n",
        "To provide more context, below is a more detailed data description, explaining exactly what each column means:\n",
        "\n",
        "- `num_passengers` = number of passengers travelling\n",
        "- `sales_channel` = sales channel booking was made on\n",
        "- `trip_type` = trip Type (Round Trip, One Way, Circle Trip)\n",
        "- `purchase_lead` = number of days between travel date and booking date\n",
        "- `length_of_stay` = number of days spent at destination\n",
        "- `flight_hour` = hour of flight departure\n",
        "- `flight_day` = day of week of flight departure\n",
        "- `route` = origin -> destination flight route\n",
        "- `booking_origin` = country from where booking was made\n",
        "- `wants_extra_baggage` = if the customer wanted extra baggage in the booking\n",
        "- `wants_preferred_seat` = if the customer wanted a preferred seat in the booking\n",
        "- `wants_in_flight_meals` = if the customer wanted in-flight meals in the booking\n",
        "- `flight_duration` = total duration of flight (in hours)\n",
        "- `booking_complete` = flag indicating if the customer completed the booking\n",
        "\n",
        "Before we compute any statistics on the data, lets do any necessary data conversion"
      ],
      "metadata": {
        "id": "nFTt0hmTJX8n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"flight_day\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SrbyaYymJOo0",
        "outputId": "acfc1a1b-0fc9-40f6-edb4-1971f99111e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Sat', 'Wed', 'Thu', 'Mon', 'Sun', 'Tue', 'Fri'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mapping = {\n",
        "    \"Mon\": 1,\n",
        "    \"Tue\": 2,\n",
        "    \"Wed\": 3,\n",
        "    \"Thu\": 4,\n",
        "    \"Fri\": 5,\n",
        "    \"Sat\": 6,\n",
        "    \"Sun\": 7,\n",
        "}\n",
        "\n",
        "df[\"flight_day\"] = df[\"flight_day\"].map(mapping)"
      ],
      "metadata": {
        "id": "fCLRlpgFJdy9"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"flight_day\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CN4G8BxhJgYL",
        "outputId": "32c438af-aca0-4968-f987-f130a2ecff83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6, 3, 4, 1, 7, 2, 5])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "aSzuw9RLJjh_",
        "outputId": "56c37ef6-6cfc-4403-b8b2-4d0928cdfd61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "         flight_day  wants_extra_baggage  wants_preferred_seat  \\\n",
              "count  50000.000000         50000.000000          50000.000000   \n",
              "mean       3.814420             0.668780              0.296960   \n",
              "std        1.992792             0.470657              0.456923   \n",
              "min        1.000000             0.000000              0.000000   \n",
              "25%        2.000000             0.000000              0.000000   \n",
              "50%        4.000000             1.000000              0.000000   \n",
              "75%        5.000000             1.000000              1.000000   \n",
              "max        7.000000             1.000000              1.000000   \n",
              "\n",
              "       wants_in_flight_meals  flight_duration  booking_complete  \n",
              "count           50000.000000     50000.000000      50000.000000  \n",
              "mean                0.427140         7.277561          0.149560  \n",
              "std                 0.494668         1.496863          0.356643  \n",
              "min                 0.000000         4.670000          0.000000  \n",
              "25%                 0.000000         5.620000          0.000000  \n",
              "50%                 0.000000         7.570000          0.000000  \n",
              "75%                 1.000000         8.830000          0.000000  \n",
              "max                 1.000000         9.500000          1.000000  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>num_passengers</th>\n",
              "      <th>purchase_lead</th>\n",
              "      <th>length_of_stay</th>\n",
              "      <th>flight_hour</th>\n",
              "      <th>flight_day</th>\n",
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              "      <th>flight_duration</th>\n",
              "      <th>booking_complete</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <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",
              "      <td>50000.000000</td>\n",
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              "      <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",
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              "      <td>9.06634</td>\n",
              "      <td>3.814420</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>1.992792</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>1.000000</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>2.000000</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>4.000000</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>5.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>8.830000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.500000</td>\n",
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              "    async function quickchart(key) {\n",
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The `.describe()` method gives us a summary of descriptive statistics over the entire dataset (only works for numeric columns). This gives us a quick overview of a few things such as the mean, min, max and overall distribution of each column.\n",
        "\n",
        "From this point, you should continue exploring the dataset with some visualisations and other metrics that you think may be useful. Then, you should prepare your dataset for predictive modelling. Finally, you should train your machine learning model, evaluate it with performance metrics and output visualisations for the contributing variables. All of this analysis should be summarised in your single slide."
      ],
      "metadata": {
        "id": "frJvQjePJppX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uUv0yeDyOUKQ",
        "outputId": "22154d4e-abb3-43c0-bdef-b36557190dd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(50000, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Sales Channel"
      ],
      "metadata": {
        "id": "2HXvuwx7Od1w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "per_internet = (df.sales_channel.value_counts().values[0] / df.sales_channel.count()) * 100\n",
        "per_mobile = (df.sales_channel.value_counts().values[1] / df.sales_channel.count()) * 100\n"
      ],
      "metadata": {
        "id": "wi_O41yEOhk9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"percentage of bookings made through the internet: \", per_internet)\n",
        "print(\"percentage of bookings made through phone calls: \", per_mobile)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SJTpdd9CPOKx",
        "outputId": "65a1dbd2-5dd5-4dfa-902f-7aa22efe7e99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "percentage of bookings made through the internet:  88.764\n",
            "percentage of bookings made through phone calls:  11.236\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Most bookings were made over the internet"
      ],
      "metadata": {
        "id": "wvVnVd4aUTyq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Trip type"
      ],
      "metadata": {
        "id": "ODmmZWz7TjRr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "per_round= df.trip_type.value_counts().values[0]/df.trip_type.count() * 100\n",
        "per_oneway= df.trip_type.value_counts().values[1]/df.trip_type.count() * 100\n",
        "per_circle= df.trip_type.value_counts().values[2]/df.trip_type.count() * 100"
      ],
      "metadata": {
        "id": "DUaMCibJTlZd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"percentage of round trips: \", per_round)\n",
        "print(\"percentage of one way trips: \", per_oneway)\n",
        "print(\"percentage of circle trips: \", per_circle)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_HV3mjW_UcTU",
        "outputId": "a8795722-dc5c-4999-c1c4-3739b9a18ec5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "percentage of round trips:  98.994\n",
            "percentage of one way trips:  0.774\n",
            "percentage of circle trips:  0.232\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "TegGHA6TQfV9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Purchase Lead"
      ],
      "metadata": {
        "id": "AwN3i7Z0Qi9a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "plt.figure(figsize=(15,5))\n",
        "sns.histplot(data=df, x=\"purchase_lead\", binwidth=20, kde=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "id": "Q8r1Feu-Ql12",
        "outputId": "485c04a9-8dcf-4a74-8840-698cc1f12c58"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='purchase_lead', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
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I1fgC7NoKAAAAAABCAuEcmqSypv2PnDMMIzh67s0vDllcDQAAAAAAAOEcmig4rdXdfkfOSdLlg9MkSct3HFZpVZ3F1QAAAAAAgM6OcA5N0t43hGjQJyVKvZMiVesPaPFWprYCAAAAAABrEc6hSao6wIYQDRpGz7FrKwAAAAAAsBrhHJqko4yck6TLh9SvO/fJzkIVV9RaXA0AAAAAAOjMCOdwSgFTqq4LSOoY4VyvLpHqnxotX8DUoi15VpcDAAAAAAA6McI5nNLRjVplGJLH2f7DOenr0XNMbQUAAAAAAFYinMMpVQcMSVK40y7DMCyupmVcPqh+3bmVu47ocFmNxdUAAAAAAIDOinAOp9Qwci7c3f43g2iQkRCuId1iFDClRZsPWV0OAAAAAADopAjncErV/qMj5zrAenPHati19c2NhHMAAAAAAMAahHM4peDIuQ4Wzk0cXL/u3Jo9Rcorrba4GgAAAAAA0BkRzuGUgmvOuTrOtFZJSov1aGT3OJmm9PYmRs8BAAAAAIC2RziHU6ruoCPnJOnywezaCgAAAAAArEM4h1OqObrmXEQHGzknSRMGpcowpM9zS7S/uNLqcgAAAAAAQCdDOIdT6qgbQkhSUnSYxmTGS5LeZmMIAAAAAADQxjreUCi0uOpA/XN7DedycnJOen5IrE+fSfrXZ7s0Kqr0tPpOTExURkbGGVQHAAAAAAA6M8I5nJzNobqGDSHc7euPi7fosCRpypQpJ21n80Sr28x/aFexNObSy+UrafoIOk94uLbl5BDQAQAAAACAZmlfaQvanD0iRpJkM6QwR/uaBV1V7pUkTbzxbvUZPOKkbT8uMFRQLY2//Vn1jQk0qf/83F1a8NBtKiwsJJwDAAAAAADNQjiHk7JHxEmSwl0OGYZhcTXNk5DWXd16Dzhpm8ERpfogp0B5/nCN6929jSoDAAAAAACdXfsaCoU2Zw+PldR+15trql5dImUzpCPltSqqqLW6HAAAAAAA0EkQzuGk7JENI+c6djgX5rQrIz5ckrQjv8ziagAAAAAAQGfRrHCuZ8+eOnLkyLeOl5SUqGfPnmdcFEKHLThyruPPgD47OUpSfThnmqbF1QAAAAAAgM6gWeHcnj175Pf7v3W8pqZGBw4cOOOiEDq+XnOuY4+ck6SeXSJktxkqrqxTYTlTWwEAAAAAQOs7reFQb7zxRvD1e++9p5iYmOB7v9+vJUuWqEePHi1WHKxnj4iV1DnCObfDrh4J4dp1uEI78svUJcptdUkAAAAAAKCDO61w7sorr5QkGYahqVOnNjrndDrVo0cPPfrooy1WHKzXMHIuwt3xp7VK9VNbdx2u0M6Ccp3TK6Hd7lALAAAAAADah9NKXAKBgCQpMzNTa9asUWJiYqsUhdDRmUbOSVJmYoQcNkOlVXUqKKtRcnSY1SUBAAAAAIAOrFlrzu3evZtgrpOwBdec6xwj55x2mzITIySxaysAAAAAAGh9zU5clixZoiVLlqigoCA4oq7B3/72tzMuDNar9Zuyh0VK6jwj56T6qa07C8q1I79c552VyNRWAAAAAADQapoVzt1///164IEHNHLkSKWmphJedFCl1fWhq02m3I5mDbJsl3okhMtlt6m8xqeDpdXqGuuxuiQAAAAAANBBNSucmz9/vl544QVde+21LV0PQkhJtV+S5LarUwWwDrtNvZIilHOoTNvzygjnAAAAAABAq2nWcKja2lqdc845LV0LQkxJTf3IuTC7aXElba9PcpQk6cuCcvkDne/6AQAAAABA22hWOPfTn/5UCxcubOlaEGJKqurDOXfnmdEalB4XLo/Trqo6v/YVV1pdDgAAAAAA6KCaNa21urpazz33nD744AMNHjxYTqez0fnHHnusRYqDtUpq6qe1dsaRczabod7Jkdq4v1Tb88rUIyHC6pIAAAAAAEAH1KxwbuPGjRo6dKgkafPmzY3Odaa1yTq6kuqGaa0WF2KRPslR2ri/VLsOl8vnD8hh74RDCAEAAAAAQKtqVjj34YcftnQdCEEN4Zy7E46ck6TUmDBFhTlUVu3T7sIK9T66Dh0AAAAAAEBLYSgQTqhht9bOOK1Vqh8F2rAxxPb8MourAQAAAAAAHVGzRs5dfPHFJ52+unTp0mYXhNARHDnXiSPcs5OjtHZvsfYcqVRNnV9uZyed4wsAAAAAAFpFs2KXoUOHasiQIcFH//79VVtbq/Xr12vQoEEtWuCBAwc0ZcoUJSQkyOPxaNCgQVq7dm3wvGmamjNnjlJTU+XxeDRu3Djt3LmzUR9FRUWaPHmyoqOjFRsbq2nTpqm8vLxRm40bN+r8889XWFiY0tPT9fDDD7fodbRHpTUNa851zpFzkpQY6VJ8hEv+gKkvD5ef+gMAAAAAAACnoVkj5x5//PHjHr/vvvu+FXqdieLiYp177rm6+OKL9e6776pLly7auXOn4uLigm0efvhhPfXUU3rxxReVmZmpe++9V9nZ2dq6davCwsIkSZMnT9ahQ4e0ePFi1dXV6Sc/+YmmT5+uhQsXSpK8Xq/Gjx+vcePGaf78+dq0aZOuv/56xcbGavr06S12Pe1JdZ1flXX1oVxn3RBC+npq68qvjmhHfrkGpMVYXRIAAAAAAOhAmhXOnciUKVM0evRo/eEPf2iR/h566CGlp6fr+eefDx7LzMwMvjZNU0888YTuueceXXHFFZKkv//970pOTtbrr7+uq6++Wjk5OVq0aJHWrFmjkSNHSpKefvppTZgwQX/4wx+UlpamBQsWqLa2Vn/729/kcrk0YMAAbdiwQY899linDeeqav0anurWyrVfyJHe0+pyLHV2cqRWfnVE+4oqVVHjU4S7RW8bAAAAAADQibXoamIrV64MjlZrCW+88YZGjhypH/zgB0pKStKwYcP05z//OXh+9+7dysvL07hx44LHYmJiNGbMGK1cuTJYU2xsbDCYk6Rx48bJZrNp1apVwTYXXHCBXC5XsE12dra2b9+u4uLi49ZWU1Mjr9fb6NGRxEW4dM/58cr75y91kuUFO4XYcJeSo90yJe0sYGorAAAAAABoOc0aAjRp0qRG703T1KFDh7R27Vrde++9LVKYJH311Vd69tlnNXv2bP3qV7/SmjVrdPPNN8vlcmnq1KnKy8uTJCUnJzf6XHJycvBcXl6ekpKSGp13OByKj49v1ObYEXnH9pmXl9doGm2DuXPn6v7772+ZC0XI65McpXxvjXbkl2loeqzV5QAAAAAAgA6iWeFcTEzjdbdsNpv69OmjBx54QOPHj2+RwiQpEAho5MiR+t3vfidJGjZsmDZv3qz58+dr6tSpLfY9zXHXXXdp9uzZwfder1fp6ekWVoTWdHZylD7eWahDpdUqrapTjMdpdUkAAAAAAKADaFY4d+wacK0pNTVV/fv3b3SsX79++r//+z9JUkpKiiQpPz9fqampwTb5+fkaOnRosE1BQUGjPnw+n4qKioKfT0lJUX5+fqM2De8b2nyT2+2W2+1u5pWhvYlwO5QeH67cokptO+TVmJ4JVpcEAAAAAAA6gDNac27dunX65z//qX/+85/6/PPPW6qmoHPPPVfbt29vdGzHjh3q3r27pPrNIVJSUrRkyZLgea/Xq1WrVikrK0uSlJWVpZKSEq1bty7YZunSpQoEAhozZkywzfLly1VXVxdss3jxYvXp0+e4U1rROfVLiZIk5eSVyTRNi6sBAAAAAAAdQbPCuYKCAl1yySUaNWqUbr75Zt18880aMWKELr30Uh0+fLjFirv11lv12Wef6Xe/+52+/PJLLVy4UM8995xmzJghSTIMQ7fccot++9vf6o033tCmTZt03XXXKS0tTVdeeaWk+pF2l112mW644QatXr1an376qWbOnKmrr75aaWlpkqQf//jHcrlcmjZtmrZs2aJXXnlFTz75ZKNpq0CvpEg57YZKq+p0qLTa6nIAAAAAAEAH0Kxw7qabblJZWZm2bNmioqIiFRUVafPmzfJ6vbr55ptbrLhRo0bptdde00svvaSBAwfqN7/5jZ544glNnjw52Ob222/XTTfdpOnTp2vUqFEqLy/XokWLGu0au2DBAvXt21eXXnqpJkyYoPPOO0/PPfdc8HxMTIzef/997d69WyNGjNAvfvELzZkzR9OnT2+xa0H757TbdFaXSEnStrwyi6sBAAAAAAAdQbPWnFu0aJE++OAD9evXL3isf//+mjdvXotuCCFJl19+uS6//PITnjcMQw888IAeeOCBE7aJj4/XwoULT/o9gwcP1scff9zsOtE59E2NVk5emXbkl6lX6qnbAwAAAAAAnEyzRs4FAgE5nd/erdLpdCoQCJxxUUCo6hbnUaTboRpfQHlVhtXlAAAAAACAdq5Z4dwll1yiWbNm6eDBg8FjBw4c0K233qpLL720xYoDQo3NMNTn6MYQeyvsFlcDAAAAAADau2aFc88884y8Xq969OihXr16qVevXsrMzJTX69XTTz/d0jUCIaVh19a8KkM2T7TF1QAAAAAAgPasWWvOpaena/369frggw+0bds2SfW7oo4bN65FiwNCUUKkW0lRbhWU1Sii3wVWlwMAAAAAANqx0xo5t3TpUvXv319er1eGYeg73/mObrrpJt10000aNWqUBgwYwKYK6BT6Hh09FzHgYosrAQAAAAAA7dlphXNPPPGEbrjhBkVHf3sqX0xMjG688UY99thjLVYcEKr6pETJkCl3Wh/t9/qsLgcAAAAAALRTpxXOffHFF7rssstOeH78+PFat27dGRcFhLpwl0PJYaYkadneSourAQAAAAAA7dVphXP5+flyOp0nPO9wOHT48OEzLgpoD7pHBCRJy/ZWKxAwLa4GAAAAAAC0R6cVznXt2lWbN28+4fmNGzcqNTX1jIsC2oNUT0CBmgoVVvr12VdHrC4HAAAAAAC0Q6cVzk2YMEH33nuvqqurv3WuqqpKv/71r3X55Ze3WHFAKLPbpIqtyyVJL63ZZ3E1AAAAAACgPXKcTuN77rlH//73v3X22Wdr5syZ6tOnjyRp27Ztmjdvnvx+v+6+++5WKRQIReVfLFLUsO9q0eZDOlLeXwmRbqtLAgAAAAAA7chphXPJyclasWKF/vd//1d33XWXTLN+nS3DMJSdna158+YpOTm5VQoFQlFt/i71inNqV3Gd/m/9fk2/oJfVJQEAAAAAgHbktMI5SerevbveeecdFRcX68svv5Rpmurdu7fi4uJaoz4g5I3vGa5n15XqpdX7dMP5PWUYhtUlAQAAAACAduK01pw7VlxcnEaNGqXRo0cTzKFTOy8jTBEuu3YXVuizr4qsLgcAAAAAALQjzQ7nANTzOG26YlhXSdLC1bkWVwMAAAAAANoTwjmgBfx4dIYk6b3NeTpSXmNxNQAAAAAAoL0gnANawMCuMRrcLUa1/oD+vf6A1eUAAAAAAIB2gnAOaCHXHB0999Lq3OBOxgAAAAAAACdDOAe0kP8akqYIl11fsTEEAAAAAABoIsI5oIVEuB3BjSFeYmMIAAAAAADQBIRzQAtq2Bhi0eY8FVXUWlwNAAAAAAAIdYRzQAs6dmOIf63dZ3U5AAAAAAAgxBHOAS1sytjukqQXV+xRnT9gcTUAAAAAACCUEc4BLeyKoWlKjHTrUGm13tl0yOpyAAAAAABACCOcA1qY22HX/5xTP3ruueVfyTRNiysCAAAAAAChinAOaAWTx3RXmNOmLQe9WvnVEavLAQAAAAAAIYpwDmgFcREu/WBEuiTpLx/vtrgaAAAAAAAQqgjngFYy7bxMGYa0dFuBviwos7ocAAAAAAAQggjngFbSIzFC4/snS2L0HAAAAAAAOD7COaAV3XB+T0nSv9cf0OGyGourAQAAAAAAoYZwDmhFI7rHaVhGrGr9Af1j5R6rywEAAAAAACGGcA5oRYZhBEfP/eOzvaqq9VtcEQAAAAAACCWEc0Aryx6QovR4j4or6/T/1u+3uhwAAAAAABBCCOeAVma3GZp2bqYk6bnlu1TnD1hcEQAAAAAACBWEc0Ab+NGoDCVGurWvqEr/t47RcwAAAAAAoJ7D6gKA9i4nJ6dJ7b7Xy6Xnv6jRo4u2KtM4LKfdOOVnEhMTlZGRcaYlAgAAAACAEEU4BzSTt+iwJGnKlClNam84XEq78S86rHiNu+EelX+x6JSf8YSHa1tODgEdAAAAAAAdFOEc0ExV5V5J0sQb71afwSOa9Jkvy2z6oljqNnGGsm+YrpMNnsvP3aUFD92mwsJCwjkAAAAAADoowjngDCWkdVe33gOa1DbFH9CXK/eoosavkvCuGtIttnWLAwAAAAAAIY0NIYA25LDbNKp7vCRp7Z5i+di5FQAAAACATo1wDmhjA7pGK9LtUHmNT5sPeq0uBwAAAAAAWIhwDmhjDptNo3rESZLW7ili9BwAAAAAAJ0Y4RxggQFpMYoKc6ii1q9NB0qtLgcAAAAAAFiEcA6wgN1maHSPo2vP7S1WrY/RcwAAAAAAdEaEc4BF+qVGK8bjVGWtX+tzi60uBwAAAAAAWIBwDrCI3Wbo3F4JkqT1ucWqqPFZXBEAAAAAAGhrhHOAhc5KilRKdJjq/KY+233E6nIAAAAAAEAbI5wDLGQYhs47K1GStOWgV0UVtRZXBAAAAAAA2hLhHGCxrnEe9UyMkGlKn35ZaHU5AAAAAACgDRHOASHg3LMSZRjSV4UVOlBcZXU5AAAAAACgjRDOASEgPsKlgWkxkqSPvzws0zQtrggAAAAAALQFwjkgRIzJjJfTbijfW6OdBeVWlwMAAAAAANpAuwrnfv/738swDN1yyy3BY9XV1ZoxY4YSEhIUGRmpq666Svn5+Y0+l5ubq4kTJyo8PFxJSUm67bbb5PP5GrX56KOPNHz4cLndbp111ll64YUX2uCKgK9FuB0akREnSVqx64j8DJ4DAAAAAKDDazfh3Jo1a/SnP/1JgwcPbnT81ltv1ZtvvqlXX31Vy5Yt08GDBzVp0qTgeb/fr4kTJ6q2tlYrVqzQiy++qBdeeEFz5swJttm9e7cmTpyoiy++WBs2bNAtt9yin/70p3rvvffa7PoASRrePU4RLrtKq+q0q6zd3J4AAAAAAKCZ2sX//ZeXl2vy5Mn685//rLi4uODx0tJS/fWvf9Vjjz2mSy65RCNGjNDzzz+vFStW6LPPPpMkvf/++9q6dav++c9/aujQofrud7+r3/zmN5o3b55qa2slSfPnz1dmZqYeffRR9evXTzNnztR///d/6/HHH7fketF5Oe02ZfVKkCTllNplC4+xuCIAAAAAANCa2kU4N2PGDE2cOFHjxo1rdHzdunWqq6trdLxv377KyMjQypUrJUkrV67UoEGDlJycHGyTnZ0tr9erLVu2BNt8s+/s7OxgH8dTU1Mjr9fb6AG0hP6p0UqKcstnGoo9/1qrywEAAAAAAK0o5MO5l19+WevXr9fcuXO/dS4vL08ul0uxsbGNjicnJysvLy/Y5thgruF8w7mTtfF6vaqqqjpuXXPnzlVMTEzwkZ6e3qzrA77JMAxdcHYXSVLkkPHaXVxncUUAAAAAAKC1hHQ4t2/fPs2aNUsLFixQWFiY1eU0ctddd6m0tDT42Ldvn9UloQPpGutRt3C/DMOmv23wyjTZHQIAAAAAgI4opMO5devWqaCgQMOHD5fD4ZDD4dCyZcv01FNPyeFwKDk5WbW1tSopKWn0ufz8fKWkpEiSUlJSvrV7a8P7U7WJjo6Wx+M5bm1ut1vR0dGNHkBLGhTrV6CuRlsO1+rdzXlWlwMAAAAAAFpBSIdzl156qTZt2qQNGzYEHyNHjtTkyZODr51Op5YsWRL8zPbt25Wbm6usrCxJUlZWljZt2qSCgoJgm8WLFys6Olr9+/cPtjm2j4Y2DX0AVgh3SN7V/ydJ+t07Oaqu81tcEQAAAAAAaGkOqws4maioKA0cOLDRsYiICCUkJASPT5s2TbNnz1Z8fLyio6N10003KSsrS2PHjpUkjR8/Xv3799e1116rhx9+WHl5ebrnnns0Y8YMud1uSdLPfvYzPfPMM7r99tt1/fXXa+nSpfrXv/6lt99+u20vGPgG76r/U89xU7S/uEp//WS3Zlx8ltUlAQAAAACAFhTSI+ea4vHHH9fll1+uq666ShdccIFSUlL073//O3jebrfrrbfekt1uV1ZWlqZMmaLrrrtODzzwQLBNZmam3n77bS1evFhDhgzRo48+qr/85S/Kzs624pKAILOuRtcOrp8yPe/DL5Xvrba4IgAAAAAA0JJCeuTc8Xz00UeN3oeFhWnevHmaN2/eCT/TvXt3vfPOOyft96KLLtLnn3/eEiUCLeqCjDAtPxSrz3NL9Pt3t+nxHw21uiQAAAAAANBC2v3IOaCjMwxD9//XABmG9NrnB7TqqyNWlwQAAAAAAFoI4RzQDgzuFqtrRmdIkub8Z4vq/AGLKwIAAAAAAC2BcA5oJ24b30ex4U5tzy/T31futbocAAAAAADQAgjngHYiLsKlOy7rK0l6YvEOFZSxOQQAAAAAAO0d4RzQjvxoZLqGdItRWY1Pv39nm9XlAAAAAACAM0Q4B7QjNpuhB64YKMOQ/v35Aa3eXWR1SQAAAAAA4AwQzgHtzJD0WF09qmFziM3ysTkEAAAAAADtFuEc0A7dnl2/OcS2PDaHAAAAAACgPSOcA9qhuAiXbs+u3xziD+9v1/7iSosrAgAAAAAAzUE4B7RTV49K1+ge8aqs9evu1zbLNE2rSwIAAAAAAKeJcA5op2w2Q3OvGiSXw6ZlOw7r9Q0HrC4JAAAAAACcJsI5oB3r1SVSsy7tLUl64M2tOlJeY3FFAAAAAADgdBDOAe3c9At6ql9qtIor63T/m1utLgcAAAAAAJwGh9UFADi5nJycU7b5yQCn7syT3vjioAZGVWlkWliT+k5MTFRGRsaZlggAAAAAAJqJcA4IUd6iw5KkKVOmNKl97MXXK2b0JD3wzg4d/OvPZdZWnfIznvBwbcvJIaADAAAAAMAihHNAiKoq90qSJt54t/oMHnHK9r6A9EGeqYroLrro3pc1LN5/0vb5ubu04KHbVFhYSDgHAAAAAIBFCOeAEJeQ1l3deg9oUtvsLpX69+cH9FW5XQN6dVPPxMhWrg4AAAAAAJwJNoQAOpD0+HANTY+VJC3emq/yap+1BQEAAAAAgJMinAM6mHPPSlCXKLeq6wJatCVPAdO0uiQAAAAAAHAChHNAB+Ow2fTdgSly2g0dKKnSmj1FVpcEAAAAAABOgHAO6IDiwl26pE+SJGnVV0U6UHzqnVsBAAAAAEDbI5wDOqi+qdHqlxIlU9KiLXmqrjv57q0AAAAAAKDtEc4BHdhFfZIU63GqvManxVvzZbL+HAAAAAAAIYVwDujAXA6bvjsoRXbD0FeFFVr51RGrSwIAAAAAAMcgnAM6uKSoMF3St379uTV7irXpQKnFFQEAAAAAgAYOqwsA0Pr6p0XLW12nVbuL9OG2AkW6HXJaXRQAAAAAAGDkHNBZjMmMV//UaJmS3tl0SMU1htUlAQAAAADQ6RHOAZ2EYRi6pG+SMuLD5QuY+vSwQ46YZKvLAgAAAACgUyOcAzoRu83QhEEpSox0qSZgKOkH96m02m91WQAAAAAAdFqEc0An43bYdcWQrvLYTTkT0nXvR0U6VFpldVkAAAAAAHRKhHNAJxQZ5tD5SXXyeQ9rv9en/352pfYUVlhdFgAAAAAAnQ7hHNBJRTmlvAW3KzXSrgMlVfrv+SuVc8hrdVkAAAAAAHQqhHNAJ+b3HtaDlySoX2q0Cstr9KM/rdS6vcVWlwUAAAAAQKdBOAd0crFhdr08faxGdo+Tt9qnKX9ZpQ+3FVhdFgAAAAAAnQLhHADFeJz6+7TRuuDsLqqq8+v6F9fojx99KdM0rS4NAAAAAIAOjXAOgCQp3OXQX64bqWtGZ8g0pYcXbdfMlz5XZa3P6tIAAAAAAOiwCOcABLkcNs2dNEgPfn+gHDZDb288pKueXal9RZVWlwYAAAAAQIdEOAfgWyaP6a6Xpo9VYqRLOYe8+q9nPtGnXxZaXRYAAAAAAB2Ow+oCAFgrJyfnuMftkn53Uawe+rRYu4rrdO1fV2nKoChd0SdChmE0qe/ExERlZGS0YLUAAAAAAHQshHNAJ+UtOixJmjJlyknbGQ6X4sf/XJGDxunvG8v07L/e1ZF3n5RZW3XK7/CEh2tbTg4BHQAAAAAAJ0A4B3RSVeVeSdLEG+9Wn8EjTtrWNKWvyn36otiuiL7nKWXguRrbpU7RzhN/Jj93lxY8dJsKCwsJ5wAAAAAAOAHCOaCTS0jrrm69B5yyXbqks0ur9PamQyqr8WtZQZi+0z9ZZyVFtn6RAAAAAAB0UGwIAaDJUmM8umZUhrrGelTrD+jtTYf06ZeFCpim1aUBAAAAANAuEc4BOC0Rboe+P6yrhmXESpLW7i3W6xsOqKrWb21hAAAAAAC0Q4RzAE6b3Wbogt5d9N2BKXLYDO0rqtJLa3KV7622ujQAAAAAANoVwjkAzXZ2cpR+NCpdsR6nyqp9enXdfm0+WGp1WQAAAAAAtBuEcwDOSGKkW1ePTlfPxAj5A6aW5BToo+0FCrAMHQAAAAAAp0Q4B+CMuR12XT44VVk9EyRJX+wv1aeHHTLcERZXBgAAAABAaCOcA9AiDMPQ6Mx4TRyUKofNUEG1TanX/kGHynxWlwYAAAAAQMginAPQos5KitQPRnaTx27KmZCuO5YUasWuQqvLAgAAAAAgJBHOAWhxSVFhujilTjUHt6m81tR1f12thatyrS4LAAAAAICQQzgHoFV47FL+S7/S+Rlh8gVM/eq1Tbr/zS3y+QNWlwYAAAAAQMggnAPQakxfrW4ZE6tfjj9bkvT8p3t0/Ytr5a2us7gyAAAAAABCQ0iHc3PnztWoUaMUFRWlpKQkXXnlldq+fXujNtXV1ZoxY4YSEhIUGRmpq666Svn5+Y3a5ObmauLEiQoPD1dSUpJuu+02+XyNF6n/6KOPNHz4cLndbp111ll64YUXWvvygE7BMAzNvKS3np08XB6nXct3HNakP67Q3iMVVpcGAAAAAIDlQjqcW7ZsmWbMmKHPPvtMixcvVl1dncaPH6+Kiq//p/7WW2/Vm2++qVdffVXLli3TwYMHNWnSpOB5v9+viRMnqra2VitWrNCLL76oF154QXPmzAm22b17tyZOnKiLL75YGzZs0C233KKf/vSneu+999r0eoGO7LuDUvXqz7KUEh2mLwvKdcW8T7Vy1xGrywIAAAAAwFIOqws4mUWLFjV6/8ILLygpKUnr1q3TBRdcoNLSUv31r3/VwoULdckll0iSnn/+efXr10+fffaZxo4dq/fff19bt27VBx98oOTkZA0dOlS/+c1vdMcdd+i+++6Ty+XS/PnzlZmZqUcffVSS1K9fP33yySd6/PHHlZ2dfdzaampqVFNTE3zv9Xpb6VcAOo6BXWP0xsxzdcPf1+qL/aW69q+r9NsrB+rq0RlWlwYAAAAAgCVCeuTcN5WWlkqS4uPjJUnr1q1TXV2dxo0bF2zTt29fZWRkaOXKlZKklStXatCgQUpOTg62yc7Oltfr1ZYtW4Jtju2joU1DH8czd+5cxcTEBB/p6ektc5FAB5cUHaZXbszS94akyRcwdee/N+mBN7fKHzCtLg0AAAAAgDbXbsK5QCCgW265Reeee64GDhwoScrLy5PL5VJsbGyjtsnJycrLywu2OTaYazjfcO5kbbxer6qqqo5bz1133aXS0tLgY9++fWd8jUBnEea066mrh2r2d+o3ivjbp7s17cU1bBQBAAAAAOh02k04N2PGDG3evFkvv/yy1aVIktxut6Kjoxs9ADSdYRi6+dLe+uPk4Qpz2vTRdjaKAAAAAAB0PiG95lyDmTNn6q233tLy5cvVrVu34PGUlBTV1taqpKSk0ei5/Px8paSkBNusXr26UX8Nu7ke2+abO7zm5+crOjpaHo+nNS4J6DRycnJOej5F0m8ujNfcT4v0ZUG5Jj65TDePjtXItLBT9p2YmKiMDNarAwAAAAC0XyEdzpmmqZtuukmvvfaaPvroI2VmZjY6P2LECDmdTi1ZskRXXXWVJGn79u3Kzc1VVlaWJCkrK0sPPvigCgoKlJSUJElavHixoqOj1b9//2Cbd955p1HfixcvDvYB4PR5iw5LkqZMmdKk9vbIeHX5/t1SWh/97pNila76P5Us/7sU8J/wM57wcG3LySGgAwAAAAC0WyEdzs2YMUMLFy7Uf/7zH0VFRQXXiIuJiZHH41FMTIymTZum2bNnKz4+XtHR0brpppuUlZWlsWPHSpLGjx+v/v3769prr9XDDz+svLw83XPPPZoxY4bcbrck6Wc/+5meeeYZ3X777br++uu1dOlS/etf/9Lbb79t2bUD7V1Vef0OxhNvvFt9Bo9o0mcCprSpxK8vy+yKGXOVep7/fY1O9Cn8OP+mys/dpQUP3abCwkLCOQAAAABAuxXS4dyzzz4rSbrooosaHX/++ef1P//zP5Kkxx9/XDabTVdddZVqamqUnZ2tP/7xj8G2drtdb731lv73f/9XWVlZioiI0NSpU/XAAw8E22RmZurtt9/WrbfeqieffFLdunXTX/7yF2VnZ7f6NQIdXUJad3XrPaDJ7TMkfVlQrsU5+TpSK314OEzj+6coMzGi9YoEAAAAAMAiIR3OmaZ5yjZhYWGaN2+e5s2bd8I23bt3/9a01W+66KKL9Pnnn592jQBa3llJkeoS5dY7mw6poKxGb3xxUAPTonVe70S5HXarywMAAAAAoMW0m91aAXQuMR6nfjCym4Z2i5UkbT7o1T8/y9VXheXWFgYAAAAAQAsK6ZFzADo3h82mC/t0Ua+kCH2QU6DSqjq9+cUh9UmJUm8G0AEAAAAAOgBGzgEIed3iwjV5TIaGZ8TKkLQ9r0yLDzkVOeQy+QKnnv4OAAAAAECoIpwD0C447Tad37uLfjgqXQkRLtUEDCVcNlOzFh3WfzYcUICQDgAAAADQDhHOAWhXUqLDdM3oDA2J88lfUaJD5X7NenmDJj79iT7cVtCkjWQAAAAAAAgVhHMA2h27zdBZUQEd+NNPdc3ASEW5Hco55NVPXlijCU99on+s3CNvdZ3VZQIAAAAAcEqEcwDaLbOuWj/oH6Xlt1+s6Rf0lNthU84hr+79zxaNfvAD3fbqF1qfW8xoOgAAAABAyGK3VgDtXlyES7+a0E8/v6iXXvv8gF5anasd+eV6dd1+vbpuv3okhOuiPkm68OwuGtszQR4XW70CAAAAAEID4RyADiM23KWfnJup/zmnh9bnFmvhqn16a+NB7TlSqRdW7NELK/bI5bBpTGa8zu+dqEFdYzWwa7SiwpxWlw4AAAAA6KQI5wC0azk5Occ9bkiafJY0qXsXbSyo1fpDNfo8r1qFlQF9vLNQH+8sDLZNi7KrV5xTmbFOpcc41DXKoS7hdiUndVFGRkYbXQkAAAAAoDMinAPQLnmLDkuSpkyZclqfcyakK6znSIV17SdXyllyxCTpYJlfB8v8+ji3OtguUFcjf+lnGj9miAb3SFKvpEj16hKhnomRTIsFAAAAALQYwjkA7VJVuVeSNPHGu9Vn8Ihm91Pjr1VxraHiWkOltTaV+aTyOkNyumVL7KEPd5Xqw12lwfaGIXWN9ahPcpQGpEWrf1qMBqRFq1ucR4ZhnPF1AQAAAAA6F8I5AO1aQlp3des9oEX7DARM7di2Vf94eq6um3m76jzx2u/16UCZT+W1pvYXV2l/cZWWbCsIfibSZahXnFP9u7g0oItbveOdctpPHtYlJiYybRYAAAAAOjnCOQD4BpvNUKDssKp2rdGfbv1B43OeaDkT0uVKypQruZdcyb3kTMxQea1DX+TX6ov8WknlMn21qjm4XdW5m1S1a41q876UZDbqyxMerm05OQR0AAAAANCJEc4BwHGczrRZvxmQt65OR2oMFdYYKqy2qcbhUljGIIVlDFLseT9WmM1UiiegVE9ASWGmjuzfpQUP3abCwkLCOQAAAADoxAjnAOAkmjNt1jRNFVfW6UBJlXKPVGpvUYWq/dKeCrv2VNhltxnqEtZHkUMu05FKfytVDgAAAABoDwjnAKCFGYah+AiX4iNcGtQ1Rr5AQAeKq7S7sEJfFVaorNqnvGqbEi6bqRveKtDAzz/WpX2T9Z3+yRqQFs3GEgAAAADQiRDOAUArc9hs6p4Qoe4JEbrwbFNHKmq1YdtXWr/1S4V17avNB7zafMCrJ5fsVEp0mC7tl6Rx/ZOV1TNBYU671eUDAAAAAFoR4RwAtCHDMJQY6VbfmIDe+ecvteTT1Sp0peiDrfn6eGeh8rzVWrAqVwtW5SrcZde5ZyXqgt6JOr93F3VPCGdUHQAAAAB0MIRzAGCh2DC7Lhmerh+OTFd1nV8rvzqiD7bm64OcfOV7a7R4a74Wb82XJHWL8+j83l107lkJGtE9TqkxHourBwAAAACcKcI5ALBQTk5Oo/cxkq7qLk3KiNNXxT59nlejjfk12nakVvuLq/TS6ly9tDpXkpQYbtPZCS71SXDp7ASnusc4FOawBftKTExkJ1gAAAAACHGEcwBgAW/RYUnSlClTmtTecIbJnT5QnsxhCksfKGeXHiqslAorq7ViX7UkyTQD8pXkq65wr+oO75W8eXrlL09pVL+eig13MiUWAAAAAEIQ4RwAWKCq3CtJmnjj3eozeMRpf94X8KuoNqCiGkNHamwqrjVUE7DJGZcqZ1yq1HusJGn6/9slaZci3Q51i/MoIz5cabEeJUeHKTnaHXzuEhmmqDCHbDYCPAAAAABoS4RzAGChhLTu6tZ7QLM+2+Mb7ytrfSqqqNWR8lrtPZinnJxt6pLZT2U+m8prfNqWV6ZteWUn7M9mSFEum6LdNkW5bYp0GYpw2uRxGAp32uRxGvI4DDlthhx2KTY6WqnJSXI5DDlsNjnttuO/dtjktBty2W1yOWzyOO2M4gMAAACAowjnAKCDCHc5FO5yqFtcuJwHN+ijhXcoX5LhcMkenSRHbIocMclyRCXKHhkve1R8/XNEvOyeKAVMqbQmoNKaQBO/sVTSvtOu02ZI4c76wC/cWR8AJnhs6hJhV2K4XV2OPgb0SNZZmT1Ou38AAAAAaE8I5wCgAzrdabN+s1a1fqk2YKgmINX66599puQLGKo7+rouYChgShXlXh0+kCvZHTJsDhl2x7dff/O9Ub9ZRcCUymtNldf6T1qT6c/TWUm7NKR7gvqnRqt/arQGdI1RjMd55j8QAAAAAIQIwjkA6MDOZNrsyaxb8oYWvPyr01gzzyfTlPymVBeQ6o4GfXWB+udKv1TpM1TpM1Tll8rrTPntDu06Uq1dRw7o3zogSTIMqV9KtEZnxmtsz3iN6hGvhEh3i18fAAAAALQVwjkAQLO1Vvi3b8cWPXXXNN350DPyRaZoT4lPu0vqlF/h19ZDXm095NULK/ZIktKjHRqa4tawFLf6d3HJZW/aenaJiYnKyMho8doBAAAA4HQQzgEAQk5Z8WH5ywr14M+vbnTcHhEnd/oAhaUPlDt9oFxdemif16d9Xp/e3FGhQF21qnM3qfqrdaravV6+4oMn/A5PeLi25eQQ0AEAAACwFOEcACDkNHXNvBp/rQ7XGMqvsimv2qZqhSm81yiF9xolSQq3m0rxBJQcFlCXMFPO+mXvlJ+7Swseuk2FhYWEcwAAAAAsRTgHAAhZTZk22+vos2maOlJRq71HKrX3SIUOlFSp0m/oq3K7viq3y2ZIabEedU8IlyelaVNfAQAAAKC1Ec4BADoEwzCUGOlWYqRbI7rHqdYX0P6SyqNhXaVKq+q0v7hK+4urJDnV9ecv6unVJZpkP6jzeycqNtxl9SUAAAAA6IQI5wAAHZLLYVPPxEj1TIyUJJVU1o+q23OkQvuKKqSoBH24p0of7vlcNkMakh6rC8/uogvP7qLB3WJltzG6DgAAAEDrI5wDAHQKseEuxYa7NCQ9Vnt3bNGfHrlf03/9hLaV2LQ9v0yf55bo89wSPfHBTkWHOTS8e5xGZMRpRPc4DUmPVYSb/2QCAAAAaHn8nwYAoNOxG1L13i80dUi0hg8frkOlVVq+47CW7Tisj3cWylvt00fbD+uj7Yfr29sM9U2JUr/UaPVNiVKflCj1TYlWlyi3xVcCAAAAoL0jnAMAdFo5OTnB173tUu9+hn7SJ1F7SnzadqRW2wtrte1IrQorA9py0KstB72NPh/ttik10q6kCLu6hNuVFOFQlwi7Ejw2ndW1iwb0zpRhMD0WAAAAwIkRzgEAOh1vUf2IuClTpjSpvT0qUa7U3nJ16SFnlx5ydekhR1yqvDWStyag7UfqjvOpQtmNHMV5bIoLsys27Nhnm+I8jY857acX4iUmJiojI+O0PgMAAAAg9BDOAQA6nary+hFwE2+8W30Gj2hWH76AT2U+QxU+qdJnqMJnqNJnqNIvVdT65Tec8ptSYWVAhZWBU/bnryqTv6JI/vJi+Ury5Cs+qLrig/IVHVBdSZ7kbxwAesLDtS0nh4AOAAAAaOcI5wAAnVZCWnd16z2gxftdt+QNLfjDr/SdG+5R17MHq8ov1fgNVfsNVQdU/+z/+tmUIbsnSnZPlJTY/Tg9mopwSFEOU9FOU2ZZvpb//Q/ad6iAcA4AAABo5wjnAABoDf46paamavDA/idtZpqmanwBVdT4VFHrV0WNTyWVdSqpqq1/rqxTrT+gCp9U4TOUVy1JaUq97jFNfi1faUuXqFdSpHonRal3cqR6dYlU1ziPkqPccthtbXKpAAAAAJqPcA4AAAsZhqEwp11hTrsSjnPeNE1V1vpVXFmroor6x8HDxTpUWCxHVIIOllbrYGm1Pt5Z2OhzdpuhlOgwdY3zqGusR3HhLsWGO48+XIrxOOVx2uW0G3LabXI5bHLa69e+c9mPvnZ8/Z6NLQAAAIDWQTgHAEAIMwxDEW6HItwOdYsLlyTttx3WYw9M1bKVaxSR2lM788u1s6BcXxaUa3dhhQ6VVqnOb+pASZUOlFS1SB1Om44GdYacNkMu+9fvXTbjaMgnRThtinDaFOkyFOE6+uy0KfLo6/Cjr9OSuzAlFwAAABDhHAAA7da+XdvVz2XT2Q7p7DRJaQ5JMfIHolVSHdDhSr8OV/pVWOlXeW1A5bUBldWa9c81AdUFTPkCkq/Rs6k6v2R+47vqAlJdwFRl3TfPNI9Zt0dpCduUHBOhxEi3EiNdSoh0KTHSrYSj7+uPuxUX7mTkHgAAADoswjkAANoZb9FhSdKUKVNa70sMm6646X5l9B4gvykFTB19NuQPvv72sTpTqg0YqgtItQGpLmB841mSDBnOMB3y1umQt+SUpbjsUmK4XYnhdnVpeETUPyeG25XgqZ+ee6zExERG5gEAAKBdIJwDAKCdqSr3SpIm3ni3+gwe0eL956xepndffFIxCcnK7NOyu9mapqmNq5ZrwWO/lj0iRrbwWNnDY2WPiK1/HRFzzPsY2T3RqvVLB8v8OljmP0GfAfnLi+XzFshfmi9faYGMqmI999hcDe/TXWmxHoU57S16HQAAAEBLIZwDAKCdSkjrrm69WzY8k6T83F0t3mcDwzDkqyiVr+SQsn90/SnDRb9Zqyq/VOkzVOUzVHn0daXfOPosBWSTIypBjqgEqWu/4Gd/+dYe6a09kqQuUW51i/OoW1y4usZ6lBjpUnxE/SMhwq24CKei3E6FuWxsgAEAAIA2RTgHAAAs0RLhommaqqrzy1vtU1l1ncqqfDqYl69NG9ap74hzVFhVv9vt4bIaHS6r0ee5Jafs02ZIHqddHpc9GNTZbfUPmyHZDEN+n09mwCebYcgwdPR4/TmbIdnU+L1xzHmX3VCY43gPmzxHX6ckxqlnRlfFeJyK8TgZ+QcAANCBEc4BAIB2yzAMhbscCnc5lBIdJklKqj2opf/3gB7//j/Vt29fldeaKqjwq6DSp4IKvw5X+OWtCchbG6h/PvrwBer7DJhSRa1fFbXHn0bbNookfT2C0WnT0d1v63e9jXTaGu2GGxNmU1yYXXEem+KOvv7mOnzHYk0+AACA0EE4BwAAOpRmb5hhs8twuGVzhclwuGU4w2TY7ZJhk2GzScbR14Yh2Ww658r/UXK37jLNr3e3NU1DpurfNxz/5vuAKfkChnym5DMlf0DymUffB6TKqiqVlZXJ5vLIFhYpw2ZXXUAqqQ6opDrQ5MvxV5XJX1Ekf3mx/OVF8lcUy19+RL6SPNlrSrXmw3fVt1fm6f1GAAAAaHGEcwAAoENpqw0zusWFa+jg/i3e/7olb2jBn27TxBvv1tl9Rshn+r+1423tN3bBrfEbqvZL1UefAzJk90TJ7omSErsf93su+/NWJUZ+qfT4cPXqEqneSZHqnRyp3klR6hrrkc3GunsAAABtgXAOAAB0SO1xw4xjJaR1V/rZp1+/aZqq8QVUUeOrn55b41Pl0eeyGp8KS7w6UlohuydKheW1Kiyv/dZafGFOm85Kqg/q6p8jdVZSpDLiw+Ww21roCgEAACARzgEAAHQohmEozGlXmNOuhOOc37+zSI/95hr9+cUFiu3aS3nlPu33+rTPW/98oMyn6rqANh/wavMBb6PPOmxSaqRDXaMc6hptr3+OcigtyqEI19ehHWvaAQAANB3h3DfMmzdPjzzyiPLy8jRkyBA9/fTTGj16tNVlAQAAtIiGNflumDr5+A0MmxyxKXImZsiZkP71c0I3+Zxh2nc0yNOBxh/zlR2Rr+iA6or2S2UFeup3czSkd4ZSY8IUH+GqX6sPAAAA30I4d4xXXnlFs2fP1vz58zVmzBg98cQTys7O1vbt25WUlGR1eQAAAGesuWvymaZU6a9VWZ1R//AZKj/6ujpgyBGVIEdUgsK6D5Yk3b0oV1qUK0lyO2zqGutRYpRbXSLdSoh0KfHoc6zHpQi3XVFhDkW4HYpwOYKvnUyhBQAAnQDh3DEee+wx3XDDDfrJT34iSZo/f77efvtt/e1vf9Odd95pcXUAAAAtpyXX5Kvx+VVcUafiylrt3X9Q61evVJ9hY1VuulRSHVCNL6CvCiv0VWHFafXrtEkep00eh6EwhyGHzZDdJtlkymm3yW4z5DAku03HvDbUsJeFIal+wJ5xzOtvntO3zoV7PIqOjm7cxqjvQ0Z9b4ZR/7nOqLTUq6qqyia3N0/dpHF7U/L7/bLb7af5yabzHP1n3Fq8Xq+qqqparX9P+KnrN1r4T2hLD35tye5avrbO89vVd9hyPbZ0bfyzPYO+2uA/UuYJ/gX//eFd1S0uvPUL6EAI546qra3VunXrdNdddwWP2Ww2jRs3TitXrvxW+5qaGtXU1ATfl5aWSqr/D3FHUV5eLknav3OLak7jL2BN1bCgdt6eHdoV0fI3Lv3TP/3TP/3TP/23bf8uSc6vPlfhfx5V4X+OHrTZZY9KkD0qUfbwWNnDo2X3RMsWESebJ0a2sHDZnB4ZrjDZXB4ZTo9sTrckqUZSTZVU0qJVAgCA1uQu3au+ia5mfTYlJUUpKSktXJF1GjIi80RJ5lGGeaoWncTBgwfVtWtXrVixQllZWcHjt99+u5YtW6ZVq1Y1an/ffffp/vvvb+syAQAAAAAA0I7s27dP3bp1O+F5Rs4101133aXZs2cH3wcCARUVFSkhIaHDLHjs9XqVnp6uffv2teqwfwAtj/sXaL+4f4H2jXsYaL+4f9HSTNNUWVmZ0tLSTtqOcO6oxMRE2e125efnNzqen59/3CGVbrdbbre70bHY2NjWLNEy0dHR/IsJaKe4f4H2i/sXaN+4h4H2i/sXLSkmJuaUbdgC6yiXy6URI0ZoyZIlwWOBQEBLlixpNM0VAAAAAAAAaCmMnDvG7NmzNXXqVI0cOVKjR4/WE088oYqKiuDurQAAAAAAAEBLIpw7xo9+9CMdPnxYc+bMUV5enoYOHapFixYpOTnZ6tIs4Xa79etf//pb03cBhD7uX6D94v4F2jfuYaD94v6FVditFQAAAAA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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "here are a few bookings made more than 2 years before the travel date, which seems unlikely as they were not likely to be made in advance. These outliers could be due to customer cancellations and subsequent rebookings within a 6-month period"
      ],
      "metadata": {
        "id": "DdUJ-PIYVqec"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(df.purchase_lead > 600).value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VbhWbImMWz8T",
        "outputId": "a4381e95-e4d8-4ab2-a99c-c42b0aaa71f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    49992\n",
              "True         8\n",
              "Name: purchase_lead, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's assume that no customer makes a booking more than a year and a half in advance. Therefore, we will remove all purchase_lead times greater than 600 days."
      ],
      "metadata": {
        "id": "o2zcEYDPXOAv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[df.purchase_lead > 600]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "7Qg3Nl-EXJxP",
        "outputId": "b8ba0a67-df18-4d13-8093-d73513011f39"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       num_passengers sales_channel  trip_type  purchase_lead  length_of_stay  \\\n",
              "835                 3      Internet  RoundTrip            641              46   \n",
              "6148                1      Internet  RoundTrip            614              19   \n",
              "24119               1      Internet  RoundTrip            704              23   \n",
              "38356               2      Internet  RoundTrip            633               5   \n",
              "39417               1        Mobile  RoundTrip            625               5   \n",
              "42916               1        Mobile  RoundTrip            605               6   \n",
              "46716               2      Internet  RoundTrip            606               6   \n",
              "48259               3      Internet  RoundTrip            867               6   \n",
              "\n",
              "       flight_hour  flight_day   route   booking_origin  wants_extra_baggage  \\\n",
              "835              6           7  AKLKUL         Malaysia                    1   \n",
              "6148            11           3  COKMEL        Australia                    0   \n",
              "24119            8           2  PNHSYD        Australia                    0   \n",
              "38356           10           6  HKTOOL        Australia                    0   \n",
              "39417           15           5  ICNRGN  Myanmar (Burma)                    0   \n",
              "42916           18           4  BLRMEL            India                    0   \n",
              "46716            6           5  HKTTPE    United States                    0   \n",
              "48259            7           1  KIXMLE            Japan                    0   \n",
              "\n",
              "       wants_preferred_seat  wants_in_flight_meals  flight_duration  \\\n",
              "835                       0                      1             8.83   \n",
              "6148                      0                      0             8.83   \n",
              "24119                     0                      0             8.58   \n",
              "38356                     0                      1             8.83   \n",
              "39417                     0                      0             6.62   \n",
              "42916                     0                      0             8.83   \n",
              "46716                     0                      1             4.67   \n",
              "48259                     0                      1             7.00   \n",
              "\n",
              "       booking_complete  \n",
              "835                   1  \n",
              "6148                  0  \n",
              "24119                 0  \n",
              "38356                 0  \n",
              "39417                 0  \n",
              "42916                 0  \n",
              "46716                 0  \n",
              "48259                 1  "
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              "      <th>num_passengers</th>\n",
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              "      <th>835</th>\n",
              "      <td>3</td>\n",
              "      <td>Internet</td>\n",
              "      <td>RoundTrip</td>\n",
              "      <td>641</td>\n",
              "      <td>46</td>\n",
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              "      <td>8.83</td>\n",
              "      <td>0</td>\n",
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              "      <th>24119</th>\n",
              "      <td>1</td>\n",
              "      <td>Internet</td>\n",
              "      <td>RoundTrip</td>\n",
              "      <td>704</td>\n",
              "      <td>23</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>PNHSYD</td>\n",
              "      <td>Australia</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.58</td>\n",
              "      <td>0</td>\n",
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              "      <th>38356</th>\n",
              "      <td>2</td>\n",
              "      <td>Internet</td>\n",
              "      <td>RoundTrip</td>\n",
              "      <td>633</td>\n",
              "      <td>5</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>HKTOOL</td>\n",
              "      <td>Australia</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8.83</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39417</th>\n",
              "      <td>1</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>RoundTrip</td>\n",
              "      <td>625</td>\n",
              "      <td>5</td>\n",
              "      <td>15</td>\n",
              "      <td>5</td>\n",
              "      <td>ICNRGN</td>\n",
              "      <td>Myanmar (Burma)</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>6.62</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42916</th>\n",
              "      <td>1</td>\n",
              "      <td>Mobile</td>\n",
              "      <td>RoundTrip</td>\n",
              "      <td>605</td>\n",
              "      <td>6</td>\n",
              "      <td>18</td>\n",
              "      <td>4</td>\n",
              "      <td>BLRMEL</td>\n",
              "      <td>India</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.83</td>\n",
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              "    <tr>\n",
              "      <th>46716</th>\n",
              "      <td>2</td>\n",
              "      <td>Internet</td>\n",
              "      <td>RoundTrip</td>\n",
              "      <td>606</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>HKTTPE</td>\n",
              "      <td>United States</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4.67</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48259</th>\n",
              "      <td>3</td>\n",
              "      <td>Internet</td>\n",
              "      <td>RoundTrip</td>\n",
              "      <td>867</td>\n",
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              "      <td>KIXMLE</td>\n",
              "      <td>Japan</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7.00</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df= df[df.purchase_lead < 600]"
      ],
      "metadata": {
        "id": "cVJz80UTX00J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I6xqmqFYYGr0",
        "outputId": "ff545675-9a62-448d-dac5-b02f21397399"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['num_passengers', 'sales_channel', 'trip_type', 'purchase_lead',\n",
              "       'length_of_stay', 'flight_hour', 'flight_day', 'route',\n",
              "       'booking_origin', 'wants_extra_baggage', 'wants_preferred_seat',\n",
              "       'wants_in_flight_meals', 'flight_duration', 'booking_complete'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "correlation_matrix = df.corr()\n",
        "\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)\n",
        "plt.title(\"Correlation Heatmap\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        },
        "id": "o3KekWx_Yh7l",
        "outputId": "f3921372-e04d-4c2c-ddc6-592e45122f73"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-26-60234f466601>:5: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  correlation_matrix = df.corr()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Predictive Model"
      ],
      "metadata": {
        "id": "cKHW95JDYoLg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.reset_index(drop=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "yr_tgBrIbI5q",
        "outputId": "65ffd5d3-996c-4745-fbe4-fe8a1ce58a1c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "49995               2      Internet  RoundTrip             27               6   \n",
              "49996               1      Internet  RoundTrip            111               6   \n",
              "49997               1      Internet  RoundTrip             24               6   \n",
              "49998               1      Internet  RoundTrip             15               6   \n",
              "49999               1      Internet  RoundTrip             19               6   \n",
              "\n",
              "       flight_hour  flight_day   route booking_origin  wants_extra_baggage  \\\n",
              "0                7           6  AKLDEL    New Zealand                    1   \n",
              "1                3           6  AKLDEL    New Zealand                    0   \n",
              "2               17           3  AKLDEL          India                    1   \n",
              "3                4           6  AKLDEL    New Zealand                    0   \n",
              "4               15           3  AKLDEL          India                    1   \n",
              "...            ...         ...     ...            ...                  ...   \n",
              "49995            9           6  PERPNH      Australia                    1   \n",
              "49996            4           7  PERPNH      Australia                    0   \n",
              "49997           22           6  PERPNH      Australia                    0   \n",
              "49998           11           1  PERPNH      Australia                    1   \n",
              "49999           10           4  PERPNH      Australia                    0   \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",
              "49995                     0                      1             5.62   \n",
              "49996                     0                      0             5.62   \n",
              "49997                     0                      1             5.62   \n",
              "49998                     0                      1             5.62   \n",
              "49999                     1                      0             5.62   \n",
              "\n",
              "       booking_complete  \n",
              "0                     0  \n",
              "1                     0  \n",
              "2                     0  \n",
              "3                     0  \n",
              "4                     0  \n",
              "...                 ...  \n",
              "49995                 0  \n",
              "49996                 0  \n",
              "49997                 0  \n",
              "49998                 0  \n",
              "49999                 0  \n",
              "\n",
              "[50000 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3c4cc030-6a7b-4efe-81aa-946b3f100555\" class=\"colab-df-container\">\n",
              "    <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>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>6</td>\n",
              "      <td>AKLDEL</td>\n",
              "      <td>New Zealand</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5.52</td>\n",
              "      <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>6</td>\n",
              "      <td>AKLDEL</td>\n",
              "      <td>New Zealand</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5.52</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <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>3</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>6</td>\n",
              "      <td>AKLDEL</td>\n",
              "      <td>New Zealand</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.52</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <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>3</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",
              "    </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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49995</th>\n",
              "      <td>2</td>\n",
              "      <td>Internet</td>\n",
              "      <td>RoundTrip</td>\n",
              "      <td>27</td>\n",
              "      <td>6</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>PERPNH</td>\n",
              "      <td>Australia</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.62</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49996</th>\n",
              "      <td>1</td>\n",
              "      <td>Internet</td>\n",
              "      <td>RoundTrip</td>\n",
              "      <td>111</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "      <td>PERPNH</td>\n",
              "      <td>Australia</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5.62</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49997</th>\n",
              "      <td>1</td>\n",
              "      <td>Internet</td>\n",
              "      <td>RoundTrip</td>\n",
              "      <td>24</td>\n",
              "      <td>6</td>\n",
              "      <td>22</td>\n",
              "      <td>6</td>\n",
              "      <td>PERPNH</td>\n",
              "      <td>Australia</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.62</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49998</th>\n",
              "      <td>1</td>\n",
              "      <td>Internet</td>\n",
              "      <td>RoundTrip</td>\n",
              "      <td>15</td>\n",
              "      <td>6</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>PERPNH</td>\n",
              "      <td>Australia</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5.62</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49999</th>\n",
              "      <td>1</td>\n",
              "      <td>Internet</td>\n",
              "      <td>RoundTrip</td>\n",
              "      <td>19</td>\n",
              "      <td>6</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>PERPNH</td>\n",
              "      <td>Australia</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>5.62</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>50000 rows × 14 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3c4cc030-6a7b-4efe-81aa-946b3f100555')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3c4cc030-6a7b-4efe-81aa-946b3f100555 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3c4cc030-6a7b-4efe-81aa-946b3f100555');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d5b96885-03df-4b59-86d6-6aa6ef601684\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d5b96885-03df-4b59-86d6-6aa6ef601684')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d5b96885-03df-4b59-86d6-6aa6ef601684 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#SCALING"
      ],
      "metadata": {
        "id": "5p2v2tD2cU3k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['sales_channel']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N_fiLza4fkin",
        "outputId": "5b740dae-ba10-480b-f966-277fa6fb0dd2"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        Internet\n",
              "1        Internet\n",
              "2        Internet\n",
              "3        Internet\n",
              "4        Internet\n",
              "           ...   \n",
              "49995    Internet\n",
              "49996    Internet\n",
              "49997    Internet\n",
              "49998    Internet\n",
              "49999    Internet\n",
              "Name: sales_channel, Length: 50000, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Replace values in the \"sales_channel\" column\n",
        "df['sales_channel'].replace({'Mobile': 0, 'Internet': 1}, inplace=True)\n"
      ],
      "metadata": {
        "id": "wCqorWmNgi9N"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['trip_type'].replace({'RoundTrip': 0, 'OneWay': 1, 'CircleTrip':2}, inplace=True)"
      ],
      "metadata": {
        "id": "D_c5n-EVmch9"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_final = df.drop([\"route\", \"booking_origin\"], axis=1)\n"
      ],
      "metadata": {
        "id": "5QicnDb4qff0"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler= StandardScaler()\n",
        "\n",
        "scaled_df= scaler.fit_transform(df_final)"
      ],
      "metadata": {
        "id": "Iqz4xGAycW9l"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#create a dataframe of scaled data\n",
        "scaled_df= pd.DataFrame(scaled_df, columns=df_final.columns)"
      ],
      "metadata": {
        "id": "3M3ScJVZssJx"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "6CMWA8cdtsvb",
        "outputId": "a511a6c8-accf-4341-9374-11835c7bf3bb"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   num_passengers  sales_channel  trip_type  purchase_lead  length_of_stay  \\\n",
              "0        0.400684       0.355785  -0.095325       1.957530       -0.119353   \n",
              "1       -0.579559       0.355785  -0.095325       0.299164       -0.089844   \n",
              "2        0.400684       0.355785  -0.095325       1.747470       -0.030824   \n",
              "3       -0.579559       0.355785  -0.095325       0.122272        0.234761   \n",
              "4        0.400684       0.355785  -0.095325      -0.187290       -0.030824   \n",
              "\n",
              "   flight_hour  flight_day  wants_extra_baggage  wants_preferred_seat  \\\n",
              "0    -0.381764    1.096754             0.703747             -0.649919   \n",
              "1    -1.120780    1.096754            -1.420965             -0.649919   \n",
              "2     1.465775   -0.408687             0.703747              1.538654   \n",
              "3    -0.936026    1.096754            -1.420965             -0.649919   \n",
              "4     1.096267   -0.408687             0.703747             -0.649919   \n",
              "\n",
              "   wants_in_flight_meals  flight_duration  booking_complete  \n",
              "0              -0.863497        -1.174175         -0.419359  \n",
              "1              -0.863497        -1.174175         -0.419359  \n",
              "2              -0.863497        -1.174175         -0.419359  \n",
              "3               1.158082        -1.174175         -0.419359  \n",
              "4               1.158082        -1.174175         -0.419359  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fb9082a6-d345-4a37-8916-f19935d56eae\" class=\"colab-df-container\">\n",
              "    <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>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>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>0.400684</td>\n",
              "      <td>0.355785</td>\n",
              "      <td>-0.095325</td>\n",
              "      <td>1.957530</td>\n",
              "      <td>-0.119353</td>\n",
              "      <td>-0.381764</td>\n",
              "      <td>1.096754</td>\n",
              "      <td>0.703747</td>\n",
              "      <td>-0.649919</td>\n",
              "      <td>-0.863497</td>\n",
              "      <td>-1.174175</td>\n",
              "      <td>-0.419359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.579559</td>\n",
              "      <td>0.355785</td>\n",
              "      <td>-0.095325</td>\n",
              "      <td>0.299164</td>\n",
              "      <td>-0.089844</td>\n",
              "      <td>-1.120780</td>\n",
              "      <td>1.096754</td>\n",
              "      <td>-1.420965</td>\n",
              "      <td>-0.649919</td>\n",
              "      <td>-0.863497</td>\n",
              "      <td>-1.174175</td>\n",
              "      <td>-0.419359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.400684</td>\n",
              "      <td>0.355785</td>\n",
              "      <td>-0.095325</td>\n",
              "      <td>1.747470</td>\n",
              "      <td>-0.030824</td>\n",
              "      <td>1.465775</td>\n",
              "      <td>-0.408687</td>\n",
              "      <td>0.703747</td>\n",
              "      <td>1.538654</td>\n",
              "      <td>-0.863497</td>\n",
              "      <td>-1.174175</td>\n",
              "      <td>-0.419359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.579559</td>\n",
              "      <td>0.355785</td>\n",
              "      <td>-0.095325</td>\n",
              "      <td>0.122272</td>\n",
              "      <td>0.234761</td>\n",
              "      <td>-0.936026</td>\n",
              "      <td>1.096754</td>\n",
              "      <td>-1.420965</td>\n",
              "      <td>-0.649919</td>\n",
              "      <td>1.158082</td>\n",
              "      <td>-1.174175</td>\n",
              "      <td>-0.419359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.400684</td>\n",
              "      <td>0.355785</td>\n",
              "      <td>-0.095325</td>\n",
              "      <td>-0.187290</td>\n",
              "      <td>-0.030824</td>\n",
              "      <td>1.096267</td>\n",
              "      <td>-0.408687</td>\n",
              "      <td>0.703747</td>\n",
              "      <td>-0.649919</td>\n",
              "      <td>1.158082</td>\n",
              "      <td>-1.174175</td>\n",
              "      <td>-0.419359</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fb9082a6-d345-4a37-8916-f19935d56eae')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fb9082a6-d345-4a37-8916-f19935d56eae button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fb9082a6-d345-4a37-8916-f19935d56eae');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c8fea225-8f1d-4153-ac53-dff31cca2800\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c8fea225-8f1d-4153-ac53-dff31cca2800')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c8fea225-8f1d-4153-ac53-dff31cca2800 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X="
      ],
      "metadata": {
        "id": "ON6hpFpVYulW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}