monthly_weekly_daily_sales.ipynb

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      "metadata": {
        "id": "YNZTn2y09UYu"
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      "source": [
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from xgboost import XGBRegressor\n",
        "import datetime as dt\n",
        "\n",
        "import xgboost as xgb\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.metrics import mean_absolute_error , mean_squared_error, r2_score\n",
        "\n",
        "import seaborn as sns\n",
        "color_pal = sns.color_palette()\n",
        "import plotly.express as px\n",
        "\n"
      ],
      "metadata": {
        "id": "tQQzjo2B9miU"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sales_pred = pd.read_csv(\"deb.csv\")\n",
        "sales_pred.head(3)"
      ],
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              "  TRANSACTION DATE  TRANSACTION MONTH TRANSACTION TYPE AGENT CODE  \\\n",
              "0       11/25/2022                 11          Invoice     ROGR03   \n",
              "1       11/25/2022                 11          Invoice     ROGR03   \n",
              "2       11/25/2022                 11          Invoice     ROGR03   \n",
              "\n",
              "           AGENT NAME      ORDER NO                    UCR NUMBER  \\\n",
              "0  RIVERSIDE ORCHARDS  LO23TUL04196  2ZA01779148CDELLO23TUL04196M   \n",
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              "  INVOICE NUMBER INCOTERM ATD (Sailing Date)  ...  FOREIGN CURRENCY  \\\n",
              "0      IN2315338      EXW         12/12/2022  ...               ZAR   \n",
              "1      IN2315339      EXW         11/21/2022  ...               ZAR   \n",
              "2      IN2315340      EXW         11/21/2022  ...               ZAR   \n",
              "\n",
              "   CURRENCY AMOUNT  ROE HOME CURRENCY AMOUNT ACCRUAL  Season  \\\n",
              "0         332640.0  1.0             332640.0     NaN    2023   \n",
              "1         120960.0  1.0             120960.0     NaN    2023   \n",
              "2         211680.0  1.0             211680.0     NaN    2023   \n",
              "\n",
              "   ORGANIZATION CODE     TRADER INTAKE COUNTRY  TARGET REGION CODE  \n",
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              "1                 LO  IKARABELE            NaN                 AFR  \n",
              "2                 LO  IKARABELE            NaN                 AFR  \n",
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      "source": [
        "#Check for null values\n"
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      "outputs": []
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        "sales_pred.info()"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 42041 entries, 0 to 42040\n",
            "Data columns (total 24 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   TRANSACTION DATE      42041 non-null  object \n",
            " 1   TRANSACTION MONTH     42041 non-null  int64  \n",
            " 2   TRANSACTION TYPE      42041 non-null  object \n",
            " 3   AGENT CODE            42041 non-null  object \n",
            " 4   AGENT NAME            42041 non-null  object \n",
            " 5   ORDER NO              42038 non-null  object \n",
            " 6   UCR NUMBER            42037 non-null  object \n",
            " 7   INVOICE NUMBER        42041 non-null  object \n",
            " 8   INCOTERM              37063 non-null  object \n",
            " 9   ATD (Sailing Date)    42041 non-null  object \n",
            " 10  EXPORT WEEK           42041 non-null  int64  \n",
            " 11  EXPORT MONTH          42041 non-null  int64  \n",
            " 12  RECEIPT #             18449 non-null  object \n",
            " 13  REFERENCE #           23886 non-null  object \n",
            " 14  FOREIGN CURRENCY      42041 non-null  object \n",
            " 15  CURRENCY AMOUNT       42041 non-null  float64\n",
            " 16  ROE                   42041 non-null  float64\n",
            " 17  HOME CURRENCY AMOUNT  42041 non-null  float64\n",
            " 18  ACCRUAL               18449 non-null  object \n",
            " 19  Season                42041 non-null  int64  \n",
            " 20  ORGANIZATION CODE     42041 non-null  object \n",
            " 21  TRADER                31919 non-null  object \n",
            " 22  INTAKE COUNTRY        17759 non-null  object \n",
            " 23  TARGET REGION CODE    36276 non-null  object \n",
            "dtypes: float64(3), int64(4), object(17)\n",
            "memory usage: 7.7+ MB\n"
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      "cell_type": "code",
      "source": [
        "#drop other columns not needed for ml \n",
        "\n",
        "#sales_pred = sales_pred.drop([\"name the columns you want to drop\"], axis = 1)\n",
        "\n",
        "#Overwrite the sales data Frame so we have only the information we need\n",
        "predict_sales = sales_pred[[\"TRANSACTION DATE\", \"HOME CURRENCY AMOUNT\"]].copy()\n",
        "sales_pred.head(1)"
      ],
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        "id": "IB8_hcsF-_ft",
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              "  TRANSACTION DATE  TRANSACTION MONTH TRANSACTION TYPE AGENT CODE  \\\n",
              "0       11/25/2022                 11          Invoice     ROGR03   \n",
              "\n",
              "           AGENT NAME      ORDER NO                    UCR NUMBER  \\\n",
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              "   CURRENCY AMOUNT  ROE HOME CURRENCY AMOUNT ACCRUAL  Season  \\\n",
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              "   ORGANIZATION CODE     TRADER INTAKE COUNTRY  TARGET REGION CODE  \n",
              "0                 LO  IKARABELE            NaN                 AFR  \n",
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        "predict_sales.head(1)"
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      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "  TRANSACTION DATE  HOME CURRENCY AMOUNT\n",
              "0       11/25/2022              332640.0"
            ],
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    {
      "cell_type": "code",
      "source": [
        "predict_sales.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AdwMYKOuAAsJ",
        "outputId": "59f3f41d-e3e6-4c94-ce88-80e9154ebe4c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 42041 entries, 0 to 42040\n",
            "Data columns (total 2 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   TRANSACTION DATE      42041 non-null  object \n",
            " 1   HOME CURRENCY AMOUNT  42041 non-null  float64\n",
            "dtypes: float64(1), object(1)\n",
            "memory usage: 657.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Covert TRANSACTION DATE to datetime data type\n",
        "predict_sales['TRANSACTION DATE'] = pd.to_datetime(predict_sales['TRANSACTION DATE'])\n"
      ],
      "metadata": {
        "id": "0YYkXLYeALmr"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#sanity check\n",
        "predict_sales.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T3jGNvO2AwIK",
        "outputId": "07f76442-603a-484c-93f5-be7f46b3f28b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 42041 entries, 0 to 42040\n",
            "Data columns (total 2 columns):\n",
            " #   Column                Non-Null Count  Dtype         \n",
            "---  ------                --------------  -----         \n",
            " 0   TRANSACTION DATE      42041 non-null  datetime64[ns]\n",
            " 1   HOME CURRENCY AMOUNT  42041 non-null  float64       \n",
            "dtypes: datetime64[ns](1), float64(1)\n",
            "memory usage: 657.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_xgb = predict_sales.copy()"
      ],
      "metadata": {
        "id": "VEx7EFY_h9Db"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Because we want to predict monthly sales, Convert date to month period"
      ],
      "metadata": {
        "id": "spy8SBusAz-a"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predict_sales[\"TRANSACTION DATE\"] = predict_sales[\"TRANSACTION DATE\"].dt.to_period(\"M\")\n",
        "monthly_income = predict_sales.groupby(\"TRANSACTION DATE\").sum().reset_index()"
      ],
      "metadata": {
        "id": "TPOs4d3FBVhe"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#convert date to time stamp datatype"
      ],
      "metadata": {
        "id": "MZbH8isGDb8P"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "monthly_income[\"TRANSACTION DATE\"] = monthly_income[\"TRANSACTION DATE\"].dt.to_timestamp()"
      ],
      "metadata": {
        "id": "8H5rMrddDiCX"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "monthly_income.tail(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "wy-aYfhZEBNR",
        "outputId": "3374b31e-4c39-4e6f-c728-c816a9fe3048"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   TRANSACTION DATE  HOME CURRENCY AMOUNT\n",
              "47       2023-01-01           12335101.54\n",
              "48       2023-02-01           13568010.07"
            ],
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualize the above\n",
        "\n",
        "plt.figure(figsize = (15,5))\n",
        "plt.plot(monthly_income[\"TRANSACTION DATE\"], monthly_income[\"HOME CURRENCY AMOUNT\"])\n",
        "plt.xlabel(\"TRANSACTION DATE\")\n",
        "plt.ylabel(\"HOME CURRENCY AMOUNT\")\n",
        "plt.title(\"Monthly Sales per home currency\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "3FMEgoqvEuy_",
        "outputId": "3d2f202b-fff7-43a9-ea9a-2858b26a28f0"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#call the diference to make the home currency amount stationery"
      ],
      "metadata": {
        "id": "srCbiCcnEx4C"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "monthly_income[\"Sales_incr/decr(+-)\"] = monthly_income[\"HOME CURRENCY AMOUNT\"].diff()\n",
        "monthly_income = monthly_income.dropna()\n",
        "monthly_income.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ERACasrJH9S3",
        "outputId": "e3752e0f-dcdd-43e9-bb45-fcbc0e76815f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  TRANSACTION DATE  HOME CURRENCY AMOUNT  Sales_incr/decr(+-)\n",
              "1       2019-03-01          7.437938e+06           7229757.43\n",
              "2       2019-04-01          3.847417e+07          31036235.23\n",
              "3       2019-05-01          1.013867e+08          62912547.94\n",
              "4       2019-06-01          1.458842e+08          44497473.82\n",
              "5       2019-07-01          2.315043e+08          85620137.47"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-53c1d8b2-a2fc-415e-8558-3d1c5a7a5ec0\">\n",
              "    <div 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>TRANSACTION DATE</th>\n",
              "      <th>HOME CURRENCY AMOUNT</th>\n",
              "      <th>Sales_incr/decr(+-)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-03-01</td>\n",
              "      <td>7.437938e+06</td>\n",
              "      <td>7229757.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-04-01</td>\n",
              "      <td>3.847417e+07</td>\n",
              "      <td>31036235.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-05-01</td>\n",
              "      <td>1.013867e+08</td>\n",
              "      <td>62912547.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-06-01</td>\n",
              "      <td>1.458842e+08</td>\n",
              "      <td>44497473.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2019-07-01</td>\n",
              "      <td>2.315043e+08</td>\n",
              "      <td>85620137.47</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-53c1d8b2-a2fc-415e-8558-3d1c5a7a5ec0')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-53c1d8b2-a2fc-415e-8558-3d1c5a7a5ec0 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-53c1d8b2-a2fc-415e-8558-3d1c5a7a5ec0');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot the above\n",
        "plt.figure(figsize = (15,5))\n",
        "plt.plot(monthly_income[\"TRANSACTION DATE\"], monthly_income[\"Sales_incr/decr(+-)\"])\n",
        "plt.xlabel(\"TRANSACTION DATE\")\n",
        "plt.ylabel(\"Sales_incr/decr(+-)\")\n",
        "plt.title(\"Monthly income difference\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "duCx6rw6J8AQ",
        "outputId": "13f83639-1b24-485a-cdcc-896911e19b4f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Dropping off sales and data\n",
        "supervised_data = monthly_income.drop([\"TRANSACTION DATE\", \"HOME CURRENCY AMOUNT\"], axis = 1)"
      ],
      "metadata": {
        "id": "kG4B-9o3LKmQ"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#preparing supervised data"
      ],
      "metadata": {
        "id": "B7DIcU5yMUyU"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range (1,13):\n",
        "  col_name = \"month_\"+str(i)\n",
        "  supervised_data[col_name] = supervised_data[\"Sales_incr/decr(+-)\"].shift(i)\n",
        "supervised_data = supervised_data.dropna().reset_index(drop=True)\n",
        "supervised_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "0SVqpfI-L0k0",
        "outputId": "dea6baff-a3d2-4984-85ac-d8e8d925b103"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Sales_incr/decr(+-)      month_1      month_2      month_3      month_4  \\\n",
              "0         6.168518e+07  -5057761.42 -27182468.15 -67151161.24 -58321132.90   \n",
              "1         7.891946e+07  61685176.31  -5057761.42 -27182468.15 -67151161.24   \n",
              "2         7.445959e+07  78919456.62  61685176.31  -5057761.42 -27182468.15   \n",
              "3         6.071708e+07  74459585.33  78919456.62  61685176.31  -5057761.42   \n",
              "4         1.065451e+08  60717083.07  74459585.33  78919456.62  61685176.31   \n",
              "\n",
              "       month_5      month_6      month_7      month_8      month_9  \\\n",
              "0 -94672347.66  -6662704.82  35330750.58  85620137.47  44497473.82   \n",
              "1 -58321132.90 -94672347.66  -6662704.82  35330750.58  85620137.47   \n",
              "2 -67151161.24 -58321132.90 -94672347.66  -6662704.82  35330750.58   \n",
              "3 -27182468.15 -67151161.24 -58321132.90 -94672347.66  -6662704.82   \n",
              "4  -5057761.42 -27182468.15 -67151161.24 -58321132.90 -94672347.66   \n",
              "\n",
              "      month_10     month_11     month_12  \n",
              "0  62912547.94  31036235.23   7229757.43  \n",
              "1  44497473.82  62912547.94  31036235.23  \n",
              "2  85620137.47  44497473.82  62912547.94  \n",
              "3  35330750.58  85620137.47  44497473.82  \n",
              "4  -6662704.82  35330750.58  85620137.47  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1b7be6c1-9556-4ced-ada6-0070a9c299f2\">\n",
              "    <div 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>Sales_incr/decr(+-)</th>\n",
              "      <th>month_1</th>\n",
              "      <th>month_2</th>\n",
              "      <th>month_3</th>\n",
              "      <th>month_4</th>\n",
              "      <th>month_5</th>\n",
              "      <th>month_6</th>\n",
              "      <th>month_7</th>\n",
              "      <th>month_8</th>\n",
              "      <th>month_9</th>\n",
              "      <th>month_10</th>\n",
              "      <th>month_11</th>\n",
              "      <th>month_12</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.168518e+07</td>\n",
              "      <td>-5057761.42</td>\n",
              "      <td>-27182468.15</td>\n",
              "      <td>-67151161.24</td>\n",
              "      <td>-58321132.90</td>\n",
              "      <td>-94672347.66</td>\n",
              "      <td>-6662704.82</td>\n",
              "      <td>35330750.58</td>\n",
              "      <td>85620137.47</td>\n",
              "      <td>44497473.82</td>\n",
              "      <td>62912547.94</td>\n",
              "      <td>31036235.23</td>\n",
              "      <td>7229757.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.891946e+07</td>\n",
              "      <td>61685176.31</td>\n",
              "      <td>-5057761.42</td>\n",
              "      <td>-27182468.15</td>\n",
              "      <td>-67151161.24</td>\n",
              "      <td>-58321132.90</td>\n",
              "      <td>-94672347.66</td>\n",
              "      <td>-6662704.82</td>\n",
              "      <td>35330750.58</td>\n",
              "      <td>85620137.47</td>\n",
              "      <td>44497473.82</td>\n",
              "      <td>62912547.94</td>\n",
              "      <td>31036235.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.445959e+07</td>\n",
              "      <td>78919456.62</td>\n",
              "      <td>61685176.31</td>\n",
              "      <td>-5057761.42</td>\n",
              "      <td>-27182468.15</td>\n",
              "      <td>-67151161.24</td>\n",
              "      <td>-58321132.90</td>\n",
              "      <td>-94672347.66</td>\n",
              "      <td>-6662704.82</td>\n",
              "      <td>35330750.58</td>\n",
              "      <td>85620137.47</td>\n",
              "      <td>44497473.82</td>\n",
              "      <td>62912547.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.071708e+07</td>\n",
              "      <td>74459585.33</td>\n",
              "      <td>78919456.62</td>\n",
              "      <td>61685176.31</td>\n",
              "      <td>-5057761.42</td>\n",
              "      <td>-27182468.15</td>\n",
              "      <td>-67151161.24</td>\n",
              "      <td>-58321132.90</td>\n",
              "      <td>-94672347.66</td>\n",
              "      <td>-6662704.82</td>\n",
              "      <td>35330750.58</td>\n",
              "      <td>85620137.47</td>\n",
              "      <td>44497473.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.065451e+08</td>\n",
              "      <td>60717083.07</td>\n",
              "      <td>74459585.33</td>\n",
              "      <td>78919456.62</td>\n",
              "      <td>61685176.31</td>\n",
              "      <td>-5057761.42</td>\n",
              "      <td>-27182468.15</td>\n",
              "      <td>-67151161.24</td>\n",
              "      <td>-58321132.90</td>\n",
              "      <td>-94672347.66</td>\n",
              "      <td>-6662704.82</td>\n",
              "      <td>35330750.58</td>\n",
              "      <td>85620137.47</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_GPyOVkIOGHp"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split data into train and test"
      ],
      "metadata": {
        "id": "i0Ww3TsjOG3X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = supervised_data[:-12]\n",
        "test_data = supervised_data[-12:]\n",
        "print(\"train data shape\", train_data.shape)\n",
        "print(\"test data shape\", test_data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oLjRkIlBON8U",
        "outputId": "68ce80e4-1ae0-42dc-b50a-cb4665e70ea5"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train data shape (24, 13)\n",
            "test data shape (12, 13)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "min max scaler"
      ],
      "metadata": {
        "id": "W7b1RF43PKOK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler(feature_range=(-1,1))\n",
        "scaler.fit(train_data)\n",
        "train_data=scaler.transform(train_data)\n",
        "test_data=scaler.transform(test_data)"
      ],
      "metadata": {
        "id": "fBs_KwyWPI01"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, y_train = train_data[:,1:], train_data[:,0:1]\n",
        "x_test, y_test = test_data[:,1:], test_data[:,0:1]\n",
        "y_train = y_train.ravel()\n",
        "y_test = y_test.ravel()\n",
        "print(\"X_train shape\", x_train.shape)\n",
        "print(\"y_train shape\", y_train.shape)\n",
        "print(\"x_test shape\", x_test.shape)\n",
        "print(\"y_test shape\", y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6FCQ6gHsP8Lz",
        "outputId": "c11bf814-fd46-44d2-89a4-1bf02b03c1ad"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train shape (24, 12)\n",
            "y_train shape (24,)\n",
            "x_test shape (12, 12)\n",
            "y_test shape (12,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "r4hk_nRiVOBo"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1JPHO74fVKVe"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make a prediction data frame to merge the predicted income of all trained algorithms"
      ],
      "metadata": {
        "id": "gKGpMi5BU_aY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Income_dates = monthly_income[\"TRANSACTION DATE\"][-12:].reset_index(drop=True)\n",
        "#Income_dates = monthly_income[\"TRANSACTION DATE\"][:24].reset_index(drop=True)\n",
        "predict_df = pd.DataFrame(Income_dates)"
      ],
      "metadata": {
        "id": "LUZbnsFlVPLe"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#actual income in the last 12 months\n",
        "actual_Income = monthly_income['HOME CURRENCY AMOUNT'][13:].to_list()\n",
        "print(actual_Income)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9CKhLMD7V9Iy",
        "outputId": "25208f45-90b7-4143-efa4-2cb4f7ed7715"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[148392140.27, 222851725.6, 283568808.67, 390113891.72, 440317781.58, 327104413.93, 222029049.61, 112010258.53, 35336252.86, 29362459.51, 44812394.22, 66931288.0, 145098466.27, 191787355.88, 240219568.01, 252103423.04, 322256898.88, 281775787.47, 206227654.89, 122693965.7, 114198667.91, 39798842.61, 37186032.99, 59160491.11, 62249470.64, 103392866.59, 165596974.19, 255742635.52, 295275588.83, 321708631.84, 193134160.54, 121288976.32, 72591061.68, 12335101.540000001, 13568010.07]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Forecast Sales using linear regression**"
      ],
      "metadata": {
        "id": "0146aQbaXDhC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr_model = LinearRegression()\n",
        "lr_model.fit(x_train, y_train)\n",
        "lr_predict = lr_model.predict(x_test)"
      ],
      "metadata": {
        "id": "hsKbnFHYXICa"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"--------------------------------------------------------------------------------------------\")"
      ],
      "metadata": {
        "id": "LmhGiD0AW-bO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "15b0899b-aefc-4e23-da56-9ca230a918f7"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--------------------------------------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create a DataFrame with the next 12 months as the index\n",
        "#next_12_months_df = pd.DataFrame(index=next_12_months)\n"
      ],
      "metadata": {
        "id": "wJNryKVHa4Q4"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create additional features for the next 12 months using lagged values of the predicted sales\n",
        "#for i in range(1, 12):\n",
        "    #next_12_months_df[f'Predicted Sales Lag {i}'] = next_12_months_df['Predicted Sales'].shift(i)"
      ],
      "metadata": {
        "id": "o6H1kjaka8VF"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# save the predicted sales to a CSV file\n",
        "#next_12_months_df.to_csv('predicted_sales.csv')\n",
        "\n"
      ],
      "metadata": {
        "id": "abaG291ObC49"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# display the predicted sales for the next 12 months\n",
        "#print(next_12_months_df)"
      ],
      "metadata": {
        "id": "qSGbgVKfbF2K"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#print(\"--------------------------------------------------------------------------------------------\")"
      ],
      "metadata": {
        "id": "CpA6awEhXEdS"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#reverse scalling\n",
        "lr_predict = lr_predict.reshape(-1,1) \n",
        "#this is a set metrix - con tains the input features of the test data nad the predicted output\n",
        "lr_predict_test_set = np.concatenate([lr_predict , x_test], axis = 1)\n",
        "lr_predict_test_set = scaler.inverse_transform(lr_predict_test_set)"
      ],
      "metadata": {
        "id": "JaKt-I6SX_LV"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#calculate the predicted sales values\n",
        "\n",
        "result_list = []\n",
        "for index in range (0, len(lr_predict_test_set)):\n",
        "  result_list.append(lr_predict_test_set[index][0] + actual_Income[index])\n",
        "lr_predict_series = pd.Series(result_list, name = \"linear Prediction\")\n",
        "predict_df = predict_df.merge(lr_predict_series, left_index = True , right_index = True)\n"
      ],
      "metadata": {
        "id": "v7jXXqPnZ8DP"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1V2sD0JZdSil"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(predict_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t5jWzv1EdvRX",
        "outputId": "65ff1fb8-8e9c-464a-bc8b-8460eba94d06"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   TRANSACTION DATE  linear Prediction\n",
            "0        2022-03-01       1.764869e+08\n",
            "1        2022-04-01       3.113808e+08\n",
            "2        2022-05-01       3.017862e+08\n",
            "3        2022-06-01       4.742630e+08\n",
            "4        2022-07-01       4.803565e+08\n",
            "5        2022-08-01       3.795257e+08\n",
            "6        2022-09-01       2.049663e+08\n",
            "7        2022-10-01       7.208569e+07\n",
            "8        2022-11-01      -4.062744e+07\n",
            "9        2022-12-01      -3.052279e+07\n",
            "10       2023-01-01       1.862724e+07\n",
            "11       2023-02-01       2.340074e+07\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr_mse = np.sqrt(mean_squared_error(predict_df[\"linear Prediction\"], monthly_income['HOME CURRENCY AMOUNT'][-12:]))\n",
        "lr_mae = mean_absolute_error(predict_df[\"linear Prediction\"], monthly_income['HOME CURRENCY AMOUNT'][-12:])\n",
        "lr_r2 = r2_score(predict_df[\"linear Prediction\"], monthly_income['HOME CURRENCY AMOUNT'][-12:])\n",
        "\n",
        "print(\"Linear Regression MSE:\", lr_mse)\n",
        "print(\"Linear Regression MAE:\", lr_mae)\n",
        "print(\"Linear Regression R2:\", lr_r2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RCD_k0v4gdTI",
        "outputId": "2f8773a2-ffcc-4e69-c5d9-0cf2659d3a1a"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linear Regression MSE: 166654125.9652763\n",
            "Linear Regression MAE: 141777253.77597436\n",
            "Linear Regression R2: 0.16848470070980048\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualize predictions vs actual sales\n",
        "plt.figure(figsize=(15,5))\n",
        "#actual income\n",
        "plt.plot(monthly_income[\"TRANSACTION DATE\"],monthly_income['HOME CURRENCY AMOUNT'])\n",
        "\n",
        "#predicted income\n",
        "\n",
        "plt.plot(predict_df[\"TRANSACTION DATE\"],predict_df[\"linear Prediction\"])\n",
        "plt.title(\"Forecast using linear regression model\")\n",
        "plt.xlabel(\"TRANSACTION DATE\")\n",
        "plt.ylabel(\"HOME CURRENCY AMOUNT\")\n",
        "plt.legend([\"Actual Income\", \"Predicted Income\"])\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "QJOrhG42gdDI",
        "outputId": "684114fc-c8b2-4bc4-9c29-047dfe496a6e"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Forecast Sales using XGB boost**"
      ],
      "metadata": {
        "id": "m3B21_8xfUq2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfx = monthly_income[[\"TRANSACTION DATE\",\"HOME CURRENCY AMOUNT\"]].copy()"
      ],
      "metadata": {
        "id": "0ILSTovEgPyE"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dfxplt = dfx.set_index('TRANSACTION DATE')\n",
        "#dfx.index = pd.to_datetime(df_pred.index)\n",
        "#sanity check\n",
        "dfxplt.head(20)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 708
        },
        "id": "ZygrkkJbg0Y5",
        "outputId": "200e1bcc-2d84-4175-a0b4-d1dcad88b595"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  HOME CURRENCY AMOUNT\n",
              "TRANSACTION DATE                      \n",
              "2019-03-01                7.437938e+06\n",
              "2019-04-01                3.847417e+07\n",
              "2019-05-01                1.013867e+08\n",
              "2019-06-01                1.458842e+08\n",
              "2019-07-01                2.315043e+08\n",
              "2019-08-01                2.668351e+08\n",
              "2019-09-01                2.601724e+08\n",
              "2019-10-01                1.655000e+08\n",
              "2019-11-01                1.071789e+08\n",
              "2019-12-01                4.002774e+07\n",
              "2020-01-01                1.284527e+07\n",
              "2020-02-01                7.787507e+06\n",
              "2020-03-01                6.947268e+07\n",
              "2020-04-01                1.483921e+08\n",
              "2020-05-01                2.228517e+08\n",
              "2020-06-01                2.835688e+08\n",
              "2020-07-01                3.901139e+08\n",
              "2020-08-01                4.403178e+08\n",
              "2020-09-01                3.271044e+08\n",
              "2020-10-01                2.220290e+08"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9bd0c11f-91d6-4cb7-bf0f-cf6e67804ccd\">\n",
              "    <div 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>HOME CURRENCY AMOUNT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TRANSACTION DATE</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-03-01</th>\n",
              "      <td>7.437938e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-04-01</th>\n",
              "      <td>3.847417e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-01</th>\n",
              "      <td>1.013867e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-01</th>\n",
              "      <td>1.458842e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-07-01</th>\n",
              "      <td>2.315043e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-08-01</th>\n",
              "      <td>2.668351e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-09-01</th>\n",
              "      <td>2.601724e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-10-01</th>\n",
              "      <td>1.655000e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-11-01</th>\n",
              "      <td>1.071789e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-12-01</th>\n",
              "      <td>4.002774e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-01-01</th>\n",
              "      <td>1.284527e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-02-01</th>\n",
              "      <td>7.787507e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-03-01</th>\n",
              "      <td>6.947268e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-04-01</th>\n",
              "      <td>1.483921e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-05-01</th>\n",
              "      <td>2.228517e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-01</th>\n",
              "      <td>2.835688e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-07-01</th>\n",
              "      <td>3.901139e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-08-01</th>\n",
              "      <td>4.403178e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-09-01</th>\n",
              "      <td>3.271044e+08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-10-01</th>\n",
              "      <td>2.220290e+08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9bd0c11f-91d6-4cb7-bf0f-cf6e67804ccd')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-9bd0c11f-91d6-4cb7-bf0f-cf6e67804ccd 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-9bd0c11f-91d6-4cb7-bf0f-cf6e67804ccd');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfxplt.plot(style='.',\n",
        "        figsize=(15, 5),\n",
        "        color=color_pal[0],\n",
        "        title='Home Currency Amount')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Ng60LBcOfU_l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "outputId": "3532300b-cd58-4d8a-fd81-dfe76a328842"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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+6667auedd1Z7e/uA7dN/F8Mdp6H2Kx9IwACUvca6Gp115NtJvgCgyEV9U+3888/XV7/6VXV2prqvt7W1admyZYNaeQ4//HBdeOGFmj9//oi/Y+LEiTr99NN1zjnn9CcQW7Zs0Q033CBJet/73qfrr79eUqq75MqVK3XkkUcOKue4445TIpHQtddeO2jd5z//efX29uq3v/1tznF9+ctf1tVXX62enh5J0vvf/35t27ZNS5Ys6d/mwQcfHNSlcijLly/XxRdfrE2bNmnTpk166qmn9NRTT6mjo2PAdpdccokWLVqkysrKAcvPO+88nX322XrttdckSXfddZfWrFmjk08+WVLuxylzv/KBBAwAAAAlI8qbascdd5xOO+00HXroodpvv/30b//2b7r++uu15557DtjOzHTuuecOanGRBo8Bu/LKKwdtc+mll2rq1Knaf//9NXv2bH3kIx/pHxP2/e9/XzfddJMaGhrU1NSkT37yk0OOsfr617+u7373u+rt7R0U30UXXaRvfetb/cvSx4B94AMfGFTWlClTNHfuXG3btq2/jFWrVumuu+7SvvvuqwMOOEAXXnih9thjj2GOYsqKFSs0d+7cAcvmzp3bP4FJn0MPPXTQeDVJ+sIXvqCDDjpI73rXuzRr1ix94xvf0M0336wJEyZIyv04Ze5XPlj6cwnCkkgkfCR9YwEAAIBMjzzyiN75zncWOgxgWNn+r5pZ0t0HDaSjBQwAAAAAIkICBgAAAAARIQEDAABAbOVjuAwQppH+HyUBAwAAQCyNHz9enZ2dJGGILXdXZ2enxo8fn/NneBAzAAAAYmnatGnavHmztmzZUuhQgCGNHz9e06ZNy3l7EjAAAADEUnV1taZPn17oMIBQ0QURAAAAACJCAgYAAAAAESEBAwAAAICIkIABAAAAQERIwAAAAAAgIiRgAAAAABAREjAAAAAAiAgJGAAAAABEJOcEzMwqzewvZnZrPgMCAAAAgFI1khawcyQ9kq9AAAAAAKDU5ZSAmdk0Sf8iaWl+wwEAAACA0pVrC9gVks6X1DvUBmZ2hpm1mlnrli1bwogNAAAAAErKsAmYmX1E0nPuntzRdu6+xN0T7p6YOnVqaAECAAAAQKnIpQXsnyUdZ2abJK2Q9H4zuz6vUQEAAABACRo2AXP3C919mrvXS5on6ffu/qm8RwYAOUh2dGnx6g1KdnQVOhQAAIBhVRU6AAAYrWRHlxYsbdH2nl6Nq6pQ88ImNdbVFDosAACAIY3oQczufre7fyRfwQDASLS0d2p7T696Xeru6VVLe2ehQwIAANihESVgABAnTTNqNa6qQpUmVVdVqGlGbaFDAgAA2CG6IAIoWo11NWpe2KSW9k41zail+yEAAIg9EjAARa2xrobECwAAFA26IAIAAABAREjAAAAAACAiJGAAAAAAEBESMAAAAACICAkYAAAAAESEBAwAAAAAIkICBgAAAAARIQEDAAAAgIiQgAEAAABAREjAAAAAACAiJGAAAAAAEBESMAAAAACICAkYAAAAAESEBAwAAAAAIkICBgAAAAARIQEDAAAAgIiQgAEAAABAREjAAAAAACAiJGAAAAAAEBESMAAAAACICAkYAAAAAESEBAwAAAAAIkICBgAAAAARIQEDAAAAgIiQgAEAAABAREjAAAAAACAiJGAAAAAAEBESMAAAAACICAkYAAAAAESEBAwAAAAAIkICBgAhSXZ0afHqDUp2dBU6FAAAEFNVhQ4AAEpBsqNLC5a2aHtPr8ZVVah5YZMa62oKHRYAAIgZWsAAIAQt7Z3a3tOrXpe6e3rV0t5Z6JAAAEAMkYABQAiaZtRqXFWFKk2qrqpQ04zaQocEFBRdcoH4on4WFl0QASAEjXU1al7YpJb2TjXNqKX7IcoaXXKB+KJ+Fh4JGACEpLGuhpMYoOxdcqkbQDxQPwuPLogAACBUdMkF4ov6WXjm7qEXmkgkvLW1NfRyAQBAcUh2dNElF4gp6mc0zCzp7onM5XRBBAAAoaNLLhBf1M/CogsiAAAAAESEBAwAAAAAIkICBgAAAAARIQEDAAAAgIiQgAEAAABAREjAAAAAACAiwyZgZjbezO43swfMbJ2Z/WcUgQEAAABAqcnlOWDbJL3f3beaWbWkNWZ2u7u35Dk2AAAAACgpwyZg7u6StgZvq4Mfz2dQAAAAAFCKchoDZmaVZtYm6TlJd7r7n7Jsc4aZtZpZ65YtW0IOEwAAAACKX04JmLu/4e4NkqZJOtjMZmfZZom7J9w9MXXq1JDDBAAAAMpbsqNLi1dvULKjq9ChYAxyGQPWz91fNLPVko6R9HB+QgIAAACQLtnRpQVLW7S9p1fjqirUvLBJjXU1hQ4Lo5DLLIhTzWxy8HqCpKMlPZrnuAAAAAAEWto7tb2nV70udff0qqW9s9AhYZRyaQHbU9K1ZlapVMK20t1vzW9YAAAAAPo0zajVuKoKdff0qrqqQk0zagsdEkYpl1kQH5T0nghiAQAAAJBFY12Nmhc2qaW9U00zaul+WMRGNAYMAAAAQGE01tWQeJWAnGZBBICwMZMTAAAoR7SAAYgcMzkByFWyo4suVwBKCgkYgMhlm8mJCysAmbhZA6AU0QURQOT6ZnKqNDGTE4AhMe02gFJECxiAyDGTE4BcMO02gFJk7h56oYlEwltbW0MvFwAAlBfGgAEoVmaWdPdE5nJawAAAQGwx7TaAUsMYMAAAAACICAkYAAAAAESEBAwAAAAAIkICVoSSHV1avHqDkh1dhQ4FAAAAwAgwCUeR4aGUAAAAQPGiBazI8FBKAAAAoHiRgBWZvodSVpp4KCUAAABQZOiCWGQa62rUvLBpzA+l5MGWAAAAQPRIwIrQWB9KyTgyAAAAoDDogliGGEcGAAAQHWawRjpawMpQ3ziy7p5expEBAADkET2PkIkErAyFNY4MAFB6GCMMhCtbzyPqVnkjAStTYx1HBgAoPdypB8JHzyNkIgEDAACSuFMP5AM9j5CJBAwAAEjiTj2QL/Q8QjoSMAAAIIk79QAQBRIwAADQjzv1AJBfPAcMAAAAACJCAgYAQAngQa8AUBzogggAQJFj+ngAKB60gAEAUOSyTR8PAIgnEjAAAIpc3/TxlSamjweAmKMLIgAARY7p4wGgeJCARSjZ0cXJEQCQF0wfDwDFgQQsIgyQBgAAAMAYsIgwQBoAAAAACVhEGCANAAAAgC6IEWGANAAAAAASsAgxQBqlgMlk8o9jDABA6SIBA5AzJpPJP44xAACljTFgAHLGZDL5xzEGAKC0kYAByBmTyeQfxxgAgNJm7h56oYlEwltbW0MvF0DhMT4p/zjGAAAUPzNLunsiczljwACMCJPJ5B/HGACA0kUXRAAAAACICAkYAAAAAESEBAwAAAAAIkICBgAASl6yo0uLV29QsqOr0KEAKHNMwgEAAEoaDzgHECe0gAEAgJLGA84BxMmwCZiZ7W1mq83sr2a2zszOiSIwAACAMPCAcwBxkksXxB5JX3H3tWa2i6Skmd3p7n/Nc2wAAABj1lhXo+aFTTzgHEAsDJuAufvTkp4OXr9sZo9I2ksSCRgAACgKPOAcQFyMaAyYmdVLeo+kP2VZd4aZtZpZ65YtW0IKDwAAAABKR84JmJlNknSjpC+6+0uZ6919ibsn3D0xderUMGMEAAAAgJKQUwJmZtVKJV/N7n5TfkMCAAAAEGc8W2/0hh0DZmYm6X8kPeLu381/SAAAlJdkRxcTRAAoGjxbb2xymQXxnyV9WtJDZtYWLPuau9+Wt6gAACgTXMgAKDbZnq3H363c5TIL4hpJFkEsAACUHS5kABSbvmfrdff08my9UcilBQwAAOQJFzIAig3P1hsbc/fQC00kEt7a2hp6uQAAlCLGgAFA6TGzpLsnMpfTAgYAQIHxkGAAKB8jehAzAAAAAGD0SMAAAAAAICIkYAAAAAAQERIwAAAAAIgICRgAAAAARIQEDAAAAAAiQgIGAAAAABEhAQMAAACAiJCAAQAAAEBESMAwJsmOLi1evUHJjq5ChwIAABAqrnOQD1WFDgDFK9nRpQVLW7S9p1fjqirUvLBJjXU1hQ4LAABgzLjOQb7QAoZRa2nv1PaeXvW61N3Tq5b2zkKHBAAAEAquc5AvJGAYtaYZtRpXVaFKk6qrKtQ0o7bQIQEAAISC6xzki7l76IUmEglvbW0NvVzET7KjSy3tnWqaUUuzPAAAKClc52AszCzp7onM5YwBw5g01tXwBwkAAJQkrnOQD3RBBAAAAICIkIABAAAAQERIwAAAAAAgIiRgQJngYZIAAACFxyQcQBngYZIAAADxQAtYDmg5QLHjYZJAfnB+AACMFC1gw6DlAKWg72GS3T29PEwSCAnnBwDAaJCADSNbywEnWBSbxroaNS9s4mGSQIg4PwAARoMEbBi0HKBU8DBJIFycHwAAo2HuHnqhiUTCW1tbQy+3UJIdXbQcAAAG4fwAABiKmSXdPZG5nBawHNByAADIhvMDAGCkmAURAAAAACJCAgYAAAAAESEBAwAAAICIkIABAAAAQERIwAAAAAAgIiRgAAAAABAREjAAAAAAiAgJGAAAQA6SHV1avHqDkh1dhQ4FQBHjQcwAAADDSHZ0acHSFm3v6dW4qgo1L2ziIdxACJIdXWpp71TTjNqyqVMkYAAAAMNoae/U9p5e9brU3dOrlvbOsrlYBPKlXG9s0AURAABgGE0zajWuqkKVJlVXVahpRm2hQwKKXrYbG+WAFjAAAIBhNNbVqHlhU9l1lQLyqe/GRndPb1nd2DB3D73QRCLhra2toZcLAABQ7MpxzAswlFKuD2aWdPdE5nJawACgRJXySQ0oVuU65gUYSmNdTdnVARIwAChBXOQB8cRkHgCYhAMASlC5DmwG4o7JPADQAoZYoKsUEK5yHdgMxB2TeQBgEg4UHF2lgPzgxgYAAIXDJByILfrDA/lRjgObAUDiBhTibdgEzMyukfQRSc+5++z8hxQeKl9xoKsUAAAICz1rEHe5tIAtk3SVpOvyG0q4qHzFg/7wAAAgLPSsQdwNm4C5+z1mVh9BLKGi8hUXukoBAIAw0LMGcRfaGDAzO0PSGZK0zz77hFXsqFH5AAAAyg89axB3Oc2CGLSA3ZrrGLC4zILIGDAAAAAAhVCWsyDSrQ0AAABAnFQUOgAAAAAAKBfDJmBmtlzSHyXNMrPNZnZ6/sMCAAAAgNKTyyyI86MIBAAAAABKHV0QAQAAACAiJGAAAAAAEBESMAAAAACICAkYAAAAAESEBAwAAAAAIkICBgAAAAARIQEDAAAAgIiQgAEAAABAREjAAAAAABS1ZEeXFq/eoGRHV6FDGVZVoQMAsGPJji61tHeqaUatGutqCh0OAABArCQ7urRgaYu29/RqXFWFmhc2xfqaiQQMiLFi+4MCAAAQtZb2Tm3v6VWvS909vWpp74z19RJdEIEYy/YHBcDYFVNXFQDAjjXNqNW4qgpVmlRdVaGmGbWFDmmHaAEDYqzvD0p3T29R/EEBigEtywBQWhrratS8sKlohmyQgAExVmx/UIBiUGxdVQAAw2usqymav+UkYEDMFdMfFKAY0LIMxBcTT6EckIABAMoKLctAfow1eaJ7MMoFCRgAoOzQsgyEK4zkie7BKBfMgggAAIAxCWPW3mKbyQ4YLVrAAAAAMCZhjK2kezDKhbl76IUmEglvbW0NvVwAAADEExNoAAOZWdLdE5nLaQEDAADAmDG2EsgNY8AAAAAAICIkYAAAAAAQERIwAAAAAIgICRgAoGgkO7q0ePUGJTu6Ch0KAACjwiQcAICiEMaDXgEAKDRawFBSuDsOlK4wHvQKlBLOeUBximULGM+RwGhwdxwobWE86BUoFZzzgOIVuwSMPygYrWx3x/m/A5SOxroaNS9s4gYdoHDPedz4BqIVuwSMi2iMFnfHgfyI08UZD3oFUsI653HjG4he7BIwLqIxWtwdB8LHxRkQT2Gd87jxDUQvdgkYF9EYC+6OA+Hi4gyIrzDOedz4BqIXuwRM4iIaAOIizG5O3FgD4ocb30D0zN1DLzSRSHhra2vo5QIAojfW5IlujACAcmRmSXdPZC6PZQsYACA+xtorgW6MAAC8iQcxAwDyqq8bY6WJMSYAgLJHCxgAIK8YYwIAwJtIwAAAecfkSgAApNAFEQAAAAAiQgIGAAAAABEhAQMAAACAiJCAAQAAAECIkh1dqpxUu0e2dSRgQBbJji4tXr1ByY6uQocCAACAiIRxDZjs6NKCpS2q3Llmr2zrmQURyNBXabb39GpcVYWaFzYxexsAAECJC+sasKW9U9t7eiXLvp4WMCBDX6Xpdam7p1ct7Z2FDgkAAAB5FtY1YNOMWo2rqpDcPdv6vCRgz728ja5bKFp9labSpOqqCjXNqB1VOXRjBAAAKB5hXQM21tWoeWGT3njlxaeyrbchErMx2WnPmT594ZV03ULRSnZ0qaW9U00zakf1f5hujAAAAMVnrNeA6cws6e6JzOV5GwPW12zHRSeKUWNdzZj+72ZrwqYuAAAAxNtYrwFzkVMXRDM7xsz+ZmYbzOyCXD4zlmY7oNiF1YQNAACA0jJsF0Qzq5S0XtLRkjZL+rOk+e7+16E+s8+sd/mqO+7hjj/KWphN2AAAACguY+mCeLCkDe7eHhS0QtLHJA2ZgL11l5244ETZi6IJGwAAAMUlly6Ie0l6Iu395mDZAGZ2hpm1mlnrli1bwooPAAAAAEpGaNPQu/sSd0+4e2Lq1KlhFQsAAAAAJSOXBOxJSXunvZ8WLAMAAAAAjEAuCdifJc00s+lmNk7SPEm/zm9YAAAAAFB6hp2Ew917zOzzkn4rqVLSNe6+Lu+RAQAAAECJyelBzO5+m6Tb8hwLAAAAAJS00CbhAAAAAADsGAkYAAAAAETE3D38Qs3+IenvYyxmN0n/CCEcyimOWOJYzhRJz8ckFsrJfzlxiqVUywmjToUVS6mWE6dYSrWcOMUilWa9ilMslFM8sYRZTlj1aqa77zZoqbuH/iNpSRzKoJziiSWm5bTGKBbKyXM5cYqlVMsJo07FbZ/iVk6cYinVcuIUS1BOydWrOMVCOcUTS8jl5LVe5asL4i0xKYNy8l9GKZcThrjtE+XktwzKiUbc9ilO5cQpllItJ06xhClO+xWnWCgn/2XEsZywZI0nL10QgVJgZq3unih0HECpoE4B4aNeAeHLd71iEg5gaEsKHQBQYqhTQPioV0D48lqvaAEDAAAAgIjQApZHZrZ1mPV3mxndBoARoF4B4aNeAeGiTmFHSMAAAAAAICIkYHlmZkeY2a1p768ys1MLGBLSDHeHCvFEvYo36lVxol7FG/Wq+FCn4q9Q9YoEDAAAAAAiQgKGsmdmk8zsd2a21sweMrOPBcvrzewRM/uJma0zszvMbEKh4wWKAfUKCB/1CghfIeoVCVj+9WjgcR5fqEAwpNclzXX3AyUdKek7ZmbBupmSFrv7AZJelHRCYUJEBupV/FGvig/1Kv6oV8WFOlUcIq9XJGD51yFpfzPbycwmSzqqwPFgMJP0X2b2oKS7JO0lafdg3WPu3ha8Tkqqjzw6ZEO9ij/qVfGhXsUf9aq4UKeKQ+T1qiqMQjCYmVVJ2ubuT5jZSkkPS3pM0l8KGxmyWCBpqqRGd+82s0168y7VtrTt3pBEl44Col4VFepVkaBeFRXqVRGgThWdyOsVCVj+HCBpoyS5+/mSzs/cwN2PiDgmZLebpOeCSnekpLpCB4QhUa+KB/WqeFCvigf1qjhQp4pL5PWKBCwPzOxMSWdL+mKBQ8EO9N2hktQs6RYze0hSq6RHCxoYsqJeFQfqVXGhXhUH6lXxoE4Vj0LWK3P3fH8HEEtmNkfST9z94ELHApQK6hUQPuoVEL5C1ism4UBZCu5QLZd0UaFjAUoF9QoIH/UKCF+h6xUtYAAAAAAQEVrAAAAAACAiJGAoG2a2t5mtNrO/Bk80PydY/hYzu9PM/h78WxMsX2BmDwZPRb8v6CvcV9YxZvY3M9tgZhcUap+AQgq5Tl1jZs+Z2cOF2h8gDsKqV0OVA5SjEOvVeDO738weCMr5z1HFQxdElAsz21PSnu6+1sx2UeqBesdLOlXSC+7+30EyVePuXzWzQyU94u5dZnaspIvd/Z/MrFLSeklHS9os6c+S5rv7XwuwW0DBhFWngrLeK2mrpOvcfXYh9geIgxDPVVnL4VyFchRivTJJO7v7VjOrlrRG0jnu3jKSeGgBQ9lw96fdfW3w+mVJjyj1tPOPSbo22OxapSqk3P0+d+8KlrdImha8PljSBndvd/ftklYEZQBlJcQ6JXe/R9IL0UQOxFdY9WoH5QBlJ8R65e6+NVheHfyMuDWLBAxlyczqJb1H0p8k7e7uTwernpG0e5aPnC7p9uD1XpKeSFu3WZzUUObGWKcAZBFWvcooByhrY61XZlZpZm2SnpN0p7uPuF7xIGaUHTObJOlGSV9095dSrckp7u5m5hnbH6lU5Tss0kCBIkGdAsIXVr3KLCfvgQMxFka9cvc3JDWY2WRJq8xstruPaPwyLWAoK0F/3RslNbv7TcHiZ4O+wX19hJ9L2/7dkpZK+pi7dwaLn5S0d1qx04JlQNkJqU4BSBNWvRqiHKAshX2+cvcXJa2WdMxIYyEBQ9kIBk7+j1KDKr+bturXkk4JXp8i6eZg+30k3STp0+6+Pm37P0uaaWbTzWycpHlBGUBZCbFOAQiEVa92UA5QdkKsV1ODli+Z2QSlJmR7dMTxMAsiyoWZHSbpXkkPSeoNFn9NqT7AKyXtI6lD0onu/oKZLZV0QrBMknrcPRGU9WFJV0iqlHSNu38zqv0A4iLkOrVc0hGSpkh6VtJ/uPv/RLQrQGyEVa+GKsfdb4tmT4D4CLFevVupyToqlWrIWunul4w4HhIwAAAAAIgGXRABAAAAICIkYAAAAAAQERIwAAAAAIgICRgAAAAARIQEDAAAAAAiQgIGAOhnZrVm1hb8PGNmT6a99+Dfh83slr5noaR9ts3MVmQsWxaUsVPwfoqZbQpeV5jZlUF5D5nZn81setpnp5hZt5mdmVHmHma2wsw2mlnSzG4zszlpcb5gZo8Fr+8ys3ozezjt84eZ2f1m9mjwc0bauovN7FUze2vasq1DHKtNQdwPmdlfzexSMxufsc0Vwf5XmNm7honxtbT1bWb2mdx/cwCAYlFV6AAAAPHh7p2SGqRUMiJpq7t/O3i/1d371l0r6SxJ3wzev1Op56IcbmY7u/sracW+Iek0ST/K+LqTJL1N0rvdvdfMpklK/9wnJbVImi/px8H3mKRVkq5193nBsjmSdk2LbZmkW939l8H7+r4CzWwPST+XdLy7rzWzKZJ+a2ZPuvv/Bps9L+krkr6awyE70t2fN7NJkpZIulrBQz3NrELSXElPSHqfu69OO7bZYtzYtw8AgNJFCxgAYDT+KGmvtPfzJf1M0h2SPpax7RWSvmRmmTf99pT0tLv3SpK7b3b3rowyvyJpryA5k6QjJXW7+4/7NnL3B9z93hzjPkvSMndfG3z2eUnnS7ogbZtrJJ1kZm/JsUy5+1ZJZ0o6Pu1zR0hap1TiOT/XsgAApY0EDAAwImZWKekoSb9OW3ySpBWSlmtwsvG4pDWSPp2xfKWkjwbd7b5jZu9J+469Je3p7vcH250UrJotKTmG8A/I8vnWYHmfrUolYeeMpGB3f0nSY5JmBovmK3U8Vkn6FzOrHqaIfTO6IB4+ku8HABQHEjAAQK4mmFmbpGck7S7pTkkys4Sk5939cUm/k/SeLK1Hl0k6T2nnHXffLGmWpAsl9Ur6nZkdFaw+SanES0oldlG3IF0p6RQz22WEnzNJMrNxkj4s6VdBYvYnSR8a5rMb3b0h7SfXVj0AQBEhAQMA5Oq1YIxSnVKJxlnB8vmS9gsm19goaVdJJ6R/0N3/LqlN0okZy7e5++3ufp6k/5J0fFqZpwZl/lrSu81splJd+hrHsA9/zfL5xqDc9LheVGqs2FnKUZCs1Utar1SyNVnSQ8E+HCa6IQIARAIGABghd39V0tmSvhK09Jwo6V3uXu/u9UqNAcuWbHxT0rl9b8zsQDN7W/C6QtK7JXWY2TskTXL3vdLKvCwo8/eSdsqYufDdI+iut1ipxK4h+GytpEWSvpVl2+9K+qxymLAqmITjh0q1eHUFsS5Mi3+6pKPNbGKOcQIAShQJGABgxNz9L5IeVKr74JPu/lTa6nsk7W9me2Z8Zp2ktWmL3irplmCK+Acl9Ui6SqnkZVXGV94oab67u1IzC34gmIZ+nVLJ2TM5xv20pE9J+omZPSrpPknXuPstWbZ9Pohjpx0UuTqI/36lxrp9NkiyjpHUN6uiglkh10j66A7KyhwDdnYu+wQAKC6WOpcBAAAAAPKNFjAAAAAAiAgJGAAAAABEhAQMAAAAACJCAgYAAAAAESEBAwAAAICIkIABAAAAQERIwAAAAAAgIv8H4awclMeZq5EAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Train / Test Split**"
      ],
      "metadata": {
        "id": "ElVljNSVipy1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfx.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dbpn9NNQki1V",
        "outputId": "6a35622d-e243-454a-a4dc-36a7c73c648d"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 48 entries, 1 to 48\n",
            "Data columns (total 2 columns):\n",
            " #   Column                Non-Null Count  Dtype         \n",
            "---  ------                --------------  -----         \n",
            " 0   TRANSACTION DATE      48 non-null     datetime64[ns]\n",
            " 1   HOME CURRENCY AMOUNT  48 non-null     float64       \n",
            "dtypes: datetime64[ns](1), float64(1)\n",
            "memory usage: 1.1 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Train**"
      ],
      "metadata": {
        "id": "212KvQP3mhiF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train = dfx.loc[dfx[\"TRANSACTION DATE\"] < '01-01-2022' ]"
      ],
      "metadata": {
        "id": "h4F8VoN5mp8q"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "NdWVddH3myIK",
        "outputId": "294091c9-20f3-4575-dc82-4bf7434512b9"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  TRANSACTION DATE  HOME CURRENCY AMOUNT\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "a= train.copy()\n",
        "a[\"year\"] = a[\"TRANSACTION DATE\"].dt.year\n",
        "a[\"month\"] = a[\"TRANSACTION DATE\"].dt.month\n",
        "px.box(a, x=\"year\", y=\"HOME CURRENCY AMOUNT\" , color = \"month\", title = \"Rate of Exchange\")"
      ],
      "metadata": {
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        "id": "K3OY1q4dm4a2",
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      "execution_count": 47,
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        "test = dfx.loc[dfx[\"TRANSACTION DATE\"] > '01-01-2022' ]"
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      "metadata": {
        "id": "TeAGu-Kois_o"
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      "execution_count": 48,
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              "   TRANSACTION DATE  HOME CURRENCY AMOUNT\n",
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              "36       2022-02-01          3.718603e+07\n",
              "37       2022-03-01          5.916049e+07\n",
              "38       2022-04-01          6.224947e+07\n",
              "39       2022-05-01          1.033929e+08\n",
              "40       2022-06-01          1.655970e+08\n",
              "41       2022-07-01          2.557426e+08\n",
              "42       2022-08-01          2.952756e+08\n",
              "43       2022-09-01          3.217086e+08\n",
              "44       2022-10-01          1.931342e+08\n",
              "45       2022-11-01          1.212890e+08\n",
              "46       2022-12-01          7.259106e+07\n",
              "47       2023-01-01          1.233510e+07\n",
              "48       2023-02-01          1.356801e+07"
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              "      <th>36</th>\n",
              "      <td>2022-02-01</td>\n",
              "      <td>3.718603e+07</td>\n",
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              "      <th>37</th>\n",
              "      <td>2022-03-01</td>\n",
              "      <td>5.916049e+07</td>\n",
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              "      <th>38</th>\n",
              "      <td>2022-04-01</td>\n",
              "      <td>6.224947e+07</td>\n",
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              "      <th>39</th>\n",
              "      <td>2022-05-01</td>\n",
              "      <td>1.033929e+08</td>\n",
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              "      <th>40</th>\n",
              "      <td>2022-06-01</td>\n",
              "      <td>1.655970e+08</td>\n",
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              "      <th>41</th>\n",
              "      <td>2022-07-01</td>\n",
              "      <td>2.557426e+08</td>\n",
              "    </tr>\n",
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              "      <th>42</th>\n",
              "      <td>2022-08-01</td>\n",
              "      <td>2.952756e+08</td>\n",
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              "      <th>43</th>\n",
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              "      <th>44</th>\n",
              "      <td>2022-10-01</td>\n",
              "      <td>1.931342e+08</td>\n",
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              "      <td>2022-11-01</td>\n",
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              "      <td>2023-02-01</td>\n",
              "      <td>1.356801e+07</td>\n",
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        "b= test.copy()\n",
        "b[\"year\"] = b[\"TRANSACTION DATE\"].dt.year\n",
        "b[\"month\"] = b[\"TRANSACTION DATE\"].dt.month\n",
        "px.box(b, x=\"year\", y=\"HOME CURRENCY AMOUNT\" , color = \"month\", title = \"Rate of Exchange\")"
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    {
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      "source": [
        "trs = train.set_index('TRANSACTION DATE')\n",
        "trs.index = pd.to_datetime(trs.index)"
      ],
      "metadata": {
        "id": "AuY_t6cx37Qy"
      },
      "execution_count": 52,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "\n",
        "tts = test.set_index('TRANSACTION DATE')\n",
        "tts.index = pd.to_datetime(tts.index)\n"
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      "metadata": {
        "id": "Ji52BNH95jMb"
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      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "fig, ax = plt.subplots(figsize=(15, 5))\n",
        "trs.plot(ax=ax, label='Training Set', title='Data Train/Test Split')\n",
        "tts.plot(ax=ax, label='Test Set')\n",
        "ax.axvline('01-01-2019', color='black', ls='--')\n",
        "ax.legend(['Training Set', 'Test Set'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "E-ktf_Uji1C0",
        "outputId": "9552a484-9c65-4eed-efe4-3a2f16e8eeed"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Feature Creation**"
      ],
      "metadata": {
        "id": "PpyRInsL7sox"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_features(dfxplt):\n",
        "    \"\"\"\n",
        "    Create time series features based on time series index.\n",
        "    \"\"\"\n",
        "    dfxplt = dfxplt.copy()\n",
        "    dfxplt['hour'] = dfxplt.index.hour\n",
        "    dfxplt['dayofweek'] = dfxplt.index.dayofweek\n",
        "    dfxplt['quarter'] = dfxplt.index.quarter\n",
        "    dfxplt['month'] = dfxplt.index.month\n",
        "    dfxplt['year'] = dfxplt.index.year\n",
        "    dfxplt['dayofyear'] = dfxplt.index.dayofyear\n",
        "    dfxplt['dayofmonth'] = dfxplt.index.day\n",
        "    dfxplt['weekofyear'] = dfxplt.index.isocalendar().week\n",
        "    return dfxplt\n",
        "\n",
        "dfplt = create_features(dfxplt)"
      ],
      "metadata": {
        "id": "7PNGu8eP7xbG"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualize our Feature / Target Relationship**\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "FQFFGlSZ8smx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(10, 8))\n",
        "sns.boxplot(data=dfplt, x='dayofweek', y='HOME CURRENCY AMOUNT')\n",
        "ax.set_title('HOME CURRENCY AMOUNT by dayofweek')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "AV5rLC_f8vn2",
        "outputId": "a5a3ade1-6c85-4092-c46a-f6d10aa946f8"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Modeling**"
      ],
      "metadata": {
        "id": "YKzbhpGI9qL9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train.head(1)\n",
        "trs = train.set_index('TRANSACTION DATE')\n",
        "trs.index = pd.to_datetime(trs.index)"
      ],
      "metadata": {
        "id": "2Afj0vTT-NwU"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tts.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "XltugWlPlirE",
        "outputId": "881cc1f9-911f-4484-c8ed-b2f68a5ef742"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  HOME CURRENCY AMOUNT\n",
              "TRANSACTION DATE                      \n",
              "2022-02-01                 37186032.99"
            ],
            "text/html": [
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HOME CURRENCY AMOUNT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TRANSACTION DATE</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-02-01</th>\n",
              "      <td>37186032.99</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5a4103bd-4ae2-41fd-a9d7-49dd82d32739')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5a4103bd-4ae2-41fd-a9d7-49dd82d32739 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-5a4103bd-4ae2-41fd-a9d7-49dd82d32739');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trs.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "q24UnFxvllVO",
        "outputId": "e2b9e54c-5668-4a9c-a32e-67833c80b9fd"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  HOME CURRENCY AMOUNT\n",
              "TRANSACTION DATE                      \n",
              "2019-03-01                  7437938.49"
            ],
            "text/html": [
              "\n",
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              "    <div 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",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>HOME CURRENCY AMOUNT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TRANSACTION DATE</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-03-01</th>\n",
              "      <td>7437938.49</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-250785f9-94e9-42e6-93f0-2aaceaeb3338')\"\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 0 24 24\"\n",
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              "    .colab-df-convert {\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-250785f9-94e9-42e6-93f0-2aaceaeb3338');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = create_features(trs)\n",
        "test = create_features(tts)\n",
        "\n",
        "FEATURES = ['dayofyear', 'dayofweek', 'quarter', 'month', 'year']\n",
        "TARGET = 'HOME CURRENCY AMOUNT'\n",
        "\n",
        "X_train = train[FEATURES]\n",
        "y_train = train[TARGET]\n",
        "\n",
        "X_test = test[FEATURES]\n",
        "y_test = test[TARGET]"
      ],
      "metadata": {
        "id": "DciDlvbk9ng0"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "5ZgRzvGxckMj",
        "outputId": "ac91c484-016b-47e8-89c5-70e406e93193"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  HOME CURRENCY AMOUNT  hour  dayofweek  quarter  month  year  \\\n",
              "TRANSACTION DATE                                                                \n",
              "2019-03-01                  7437938.49     0          4        1      3  2019   \n",
              "\n",
              "                  dayofyear  dayofmonth  weekofyear  \n",
              "TRANSACTION DATE                                     \n",
              "2019-03-01               60           1           9  "
            ],
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th>HOME CURRENCY AMOUNT</th>\n",
              "      <th>hour</th>\n",
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              "      <th>quarter</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2019-03-01</th>\n",
              "      <td>7437938.49</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2019</td>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-87188ad5-98f1-49d6-9d0a-022d93bc71e8 button.colab-df-convert');\n",
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              "\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "reg = xgb.XGBRegressor(base_score=0.5, booster='gbtree',    \n",
        "                       n_estimators=1000,\n",
        "                       early_stopping_rounds=50,\n",
        "                       objective='reg:linear',\n",
        "                       max_depth=3,\n",
        "                       learning_rate=0.01)\n",
        "reg.fit(X_train, y_train,\n",
        "        eval_set=[(X_train, y_train), (X_test, y_test)],\n",
        "        verbose=100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "Hf7mbjvY-sDE",
        "outputId": "348b12e7-becb-4af8-d1e4-ebfac7eee7b2"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[12:45:38] WARNING: ../src/objective/regression_obj.cu:213: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[0]\tvalidation_0-rmse:199722367.83136\tvalidation_1-rmse:164951593.73598\n",
            "[100]\tvalidation_0-rmse:89494365.57015\tvalidation_1-rmse:61672036.45810\n",
            "[200]\tvalidation_0-rmse:45251793.62267\tvalidation_1-rmse:29910906.28558\n",
            "[264]\tvalidation_0-rmse:31851227.72824\tvalidation_1-rmse:31797059.16840\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=50,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=3, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n",
              "             objective='reg:linear', predictor=None, ...)"
            ],
            "text/html": [
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              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=50,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=3, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n",
              "             objective=&#x27;reg:linear&#x27;, predictor=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=50,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=0.01, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=3, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=1000, n_jobs=None, num_parallel_tree=None,\n",
              "             objective=&#x27;reg:linear&#x27;, predictor=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Feature Importance**"
      ],
      "metadata": {
        "id": "TWUxkyoA_I_s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fi = pd.DataFrame(data=reg.feature_importances_,\n",
        "             index=reg.feature_names_in_,\n",
        "             columns=['importance'])\n",
        "fi.sort_values('importance').plot(kind='barh', title='Feature Importance')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "s09U0cWT_EBR",
        "outputId": "7f34ba5b-b751-4401-c5e1-da6108b12c27"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test['prediction'] = reg.predict(X_test)\n",
        "dfplt = dfplt.merge(test[['prediction']], how='left', left_index=True, right_index=True)\n",
        "\n",
        "ax = dfplt[['HOME CURRENCY AMOUNT']].plot(figsize=(15, 5))\n",
        "dfplt['prediction'].plot(ax=ax, style='--')\n",
        "plt.legend(['Truth Data', 'Predictions'])\n",
        "ax.set_title('Raw Dat and Prediction')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "3a0q6VjV_RpX",
        "outputId": "a304d83c-7ab4-4349-896e-9f68c6ca130f"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Score (RMSE)**"
      ],
      "metadata": {
        "id": "Yb62E77MDYYB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rmse_score = np.sqrt(mean_squared_error(test['HOME CURRENCY AMOUNT'], test['prediction']))\n",
        "print(f'RMSE Score on Test set: {rmse_score:0.2f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6b9P1mqWDcAP",
        "outputId": "e0fd459e-7529-4271-8b22-98c83fb7935d"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE Score on Test set: 29465133.11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Error**"
      ],
      "metadata": {
        "id": "jfnCZ755Dw5V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test['error'] = np.abs(test[TARGET] - test['prediction'])\n",
        "test['date'] = test.index.date\n",
        "test.groupby(['date'])['error'].mean().sort_values(ascending=False).head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SLQerY-KD47e",
        "outputId": "d42a9858-3796-4260-d0f9-f03cbdea5803"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date\n",
              "2022-09-01    55292631.84\n",
              "2022-05-01    51367533.41\n",
              "2022-04-01    47656793.36\n",
              "2022-06-01    33022337.81\n",
              "2022-07-01    20032395.52\n",
              "2022-11-01    18924272.32\n",
              "2022-10-01    18268816.54\n",
              "2022-03-01    18202731.11\n",
              "2022-08-01    18063460.83\n",
              "2023-01-01    13799512.46\n",
              "Name: error, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Monthly"
      ],
      "metadata": {
        "id": "H51q85n6xRvW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Predict home currency sales for the next 18 Months**"
      ],
      "metadata": {
        "id": "2urjGEXcnHeI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "s_pred = pd.read_csv(\"test_set.csv\")\n",
        "s_pred.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "vC_UHDhmnFw0",
        "outputId": "68f5a73d-1d10-4c31-dc5a-483c5ccee624"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date  dayofyear  dayofweek  quarter  month  year\n",
              "0  3/31/2023         90          5        1      3  2023\n",
              "1  4/30/2023        120          0        2      4  2023\n",
              "2  5/31/2023        151          3        2      5  2023\n",
              "3  6/30/2023        181          5        2      6  2023\n",
              "4  7/31/2023        212          1        3      7  2023"
            ],
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              "          document.querySelector('#df-5affc4e4-eb9d-49e1-972e-b2c3fbc73352 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-5affc4e4-eb9d-49e1-972e-b2c3fbc73352');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s_pred.rename(columns={'Date': 'TRANSACTION DATE'}, inplace=True)\n",
        "s_pred.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "Ml9IsbujpBN7",
        "outputId": "fabe7038-121d-4756-ca7a-3fa1081e0058"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  TRANSACTION DATE  dayofyear  dayofweek  quarter  month  year\n",
              "0        3/31/2023         90          5        1      3  2023\n",
              "1        4/30/2023        120          0        2      4  2023"
            ],
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              "\n",
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            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#s_pred = s_pred.set_index('TRANSACTION DATE')\n",
        "#s_pred.index = pd.to_datetime(s_pred.index)\n",
        "#s_pred.head(1)"
      ],
      "metadata": {
        "id": "DazFm0EDoUW_"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_set = s_pred[['dayofyear','dayofweek', 'quarter','month','year']].copy()"
      ],
      "metadata": {
        "id": "GZuZQT6YnFzr"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_set.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "23k09Fmnqcp0",
        "outputId": "10cb0bbf-6742-4f64-ef31-b167dcd9198e"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   dayofyear  dayofweek  quarter  month  year\n",
              "0         90          5        1      3  2023\n",
              "1        120          0        2      4  2023\n",
              "2        151          3        2      5  2023\n",
              "3        181          5        2      6  2023\n",
              "4        212          1        3      7  2023"
            ],
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              "\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-095f1792-3fb7-4686-83ac-7191005ed03e');\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",
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              "  "
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          "metadata": {},
          "execution_count": 71
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = reg.predict(train_set)"
      ],
      "metadata": {
        "id": "5MClbl5w7Aue"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create a dataframe with the predicted sales for the next 18 months\n",
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SvVhvBffs1yQ",
        "outputId": "62ea9868-4f2b-45bd-8bb0-aa24054c5a74"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.09906264e+08, 1.54760400e+08, 1.98619312e+08, 2.35710240e+08,\n",
              "       2.77212128e+08, 2.66416000e+08, 1.74865344e+08, 1.02364704e+08,\n",
              "       7.52162640e+07, 4.25769560e+07, 2.63652380e+07, 4.09577600e+07,\n",
              "       8.44973840e+07, 1.54760400e+08, 1.98619312e+08, 2.35710240e+08,\n",
              "       2.77212128e+08, 2.66416000e+08], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s_pred.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cy3RHurzzids",
        "outputId": "cd8aa8f8-8138-498c-fb3c-94392aa6ff8e"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 18 entries, 0 to 17\n",
            "Data columns (total 6 columns):\n",
            " #   Column            Non-Null Count  Dtype \n",
            "---  ------            --------------  ----- \n",
            " 0   TRANSACTION DATE  18 non-null     object\n",
            " 1   dayofyear         18 non-null     int64 \n",
            " 2   dayofweek         18 non-null     int64 \n",
            " 3   quarter           18 non-null     int64 \n",
            " 4   month             18 non-null     int64 \n",
            " 5   year              18 non-null     int64 \n",
            "dtypes: int64(5), object(1)\n",
            "memory usage: 992.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s_pred['Predicted_Income'] = y_pred.tolist()"
      ],
      "metadata": {
        "id": "aUoMif_PvAZs"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "s_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "JShB90AcsYVQ",
        "outputId": "1aa74185-d0e5-45b4-e4ed-410596d1a181"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   TRANSACTION DATE  dayofyear  dayofweek  quarter  month  year  \\\n",
              "0         3/31/2023         90          5        1      3  2023   \n",
              "1         4/30/2023        120          0        2      4  2023   \n",
              "2         5/31/2023        151          3        2      5  2023   \n",
              "3         6/30/2023        181          5        2      6  2023   \n",
              "4         7/31/2023        212          1        3      7  2023   \n",
              "5         8/31/2023        243          4        3      8  2023   \n",
              "6         9/30/2023        273          6        3      9  2023   \n",
              "7        10/31/2023        304          2        4     10  2023   \n",
              "8        11/30/2023        334          4        4     11  2023   \n",
              "9        12/31/2023        365          0        4     12  2023   \n",
              "10        1/31/2024         31          3        1      1  2024   \n",
              "11        2/29/2024         60          4        1      2  2024   \n",
              "12        3/31/2024         91          0        1      3  2024   \n",
              "13        4/30/2024        121          2        2      4  2024   \n",
              "14        5/31/2024        152          5        2      5  2024   \n",
              "15        6/30/2024        182          0        2      6  2024   \n",
              "16        7/31/2024        213          3        3      7  2024   \n",
              "17        8/31/2024        244          6        3      8  2024   \n",
              "\n",
              "    Predicted_Income  \n",
              "0        109906264.0  \n",
              "1        154760400.0  \n",
              "2        198619312.0  \n",
              "3        235710240.0  \n",
              "4        277212128.0  \n",
              "5        266416000.0  \n",
              "6        174865344.0  \n",
              "7        102364704.0  \n",
              "8         75216264.0  \n",
              "9         42576956.0  \n",
              "10        26365238.0  \n",
              "11        40957760.0  \n",
              "12        84497384.0  \n",
              "13       154760400.0  \n",
              "14       198619312.0  \n",
              "15       235710240.0  \n",
              "16       277212128.0  \n",
              "17       266416000.0  "
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              "\n",
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              "      <th></th>\n",
              "      <th>TRANSACTION DATE</th>\n",
              "      <th>dayofyear</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>quarter</th>\n",
              "      <th>month</th>\n",
              "      <th>year</th>\n",
              "      <th>Predicted_Income</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3/31/2023</td>\n",
              "      <td>90</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2023</td>\n",
              "      <td>109906264.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4/30/2023</td>\n",
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              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5/31/2023</td>\n",
              "      <td>151</td>\n",
              "      <td>3</td>\n",
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              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>2023</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>9/30/2023</td>\n",
              "      <td>273</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>2023</td>\n",
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              "      <th>7</th>\n",
              "      <td>10/31/2023</td>\n",
              "      <td>304</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>10</td>\n",
              "      <td>2023</td>\n",
              "      <td>102364704.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>11/30/2023</td>\n",
              "      <td>334</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>11</td>\n",
              "      <td>2023</td>\n",
              "      <td>75216264.0</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>12/31/2023</td>\n",
              "      <td>365</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>2023</td>\n",
              "      <td>42576956.0</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1/31/2024</td>\n",
              "      <td>31</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2024</td>\n",
              "      <td>26365238.0</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2/29/2024</td>\n",
              "      <td>60</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2024</td>\n",
              "      <td>40957760.0</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>3/31/2024</td>\n",
              "      <td>91</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2024</td>\n",
              "      <td>84497384.0</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>4/30/2024</td>\n",
              "      <td>121</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2024</td>\n",
              "      <td>154760400.0</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>5/31/2024</td>\n",
              "      <td>152</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2024</td>\n",
              "      <td>198619312.0</td>\n",
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              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>6/30/2024</td>\n",
              "      <td>182</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>2024</td>\n",
              "      <td>235710240.0</td>\n",
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              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>7/31/2024</td>\n",
              "      <td>213</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>7</td>\n",
              "      <td>2024</td>\n",
              "      <td>277212128.0</td>\n",
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              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>8/31/2024</td>\n",
              "      <td>244</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>8</td>\n",
              "      <td>2024</td>\n",
              "      <td>266416000.0</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s_pred.to_csv('Monthly_Prediction')"
      ],
      "metadata": {
        "id": "gnWXePJpnF2k"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Weekly"
      ],
      "metadata": {
        "id": "T1Jjl--1xV94"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "w_pred = pd.read_csv(\"data_2_weekly.csv\")\n",
        "w_pred.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "jUrH2K5Ixcwd",
        "outputId": "b7c5fd08-2185-4738-ce00-9d5dd6cd8fa1"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date  dayofyear  dayofweek  quarter  month  year\n",
              "0  2023/03/07         66          2        1      3  2023\n",
              "1  2023/03/14         73          2        1      3  2023\n",
              "2  2023/03/21         80          2        1      3  2023\n",
              "3  2023/03/28         87          2        1      3  2023\n",
              "4  2023/04/04         94          2        2      4  2023"
            ],
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              "      <td>2023/03/07</td>\n",
              "      <td>66</td>\n",
              "      <td>2</td>\n",
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              "      <td>2023</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2023/03/14</td>\n",
              "      <td>73</td>\n",
              "      <td>2</td>\n",
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              "      <td>2023</td>\n",
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              "      <td>2023/03/21</td>\n",
              "      <td>80</td>\n",
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              "      <td>3</td>\n",
              "      <td>2023</td>\n",
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              "      <td>87</td>\n",
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              "      <td>2023/04/04</td>\n",
              "      <td>94</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2023</td>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-694b0c05-79e0-478e-a470-59cdc6e007ed');\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",
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    {
      "cell_type": "code",
      "source": [
        "w_pred.rename(columns={'Date': 'TRANSACTION DATE'}, inplace=True)\n",
        "w_pred.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "rZKmF3zIDdGw",
        "outputId": "d9a23f25-f4a2-408b-dc9f-774fa51bc5b6"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  TRANSACTION DATE  dayofyear  dayofweek  quarter  month  year\n",
              "0       2023/03/07         66          2        1      3  2023\n",
              "1       2023/03/14         73          2        1      3  2023"
            ],
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_w = w_pred[['dayofyear','dayofweek', 'quarter','month','year']].copy()"
      ],
      "metadata": {
        "id": "ViW2ZPchDmFM"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_w.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
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        "id": "PiAYVNdxDo0-",
        "outputId": "f85d320c-bfcb-4b30-ea24-f5199b0cc741"
      },
      "execution_count": 81,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   dayofyear  dayofweek  quarter  month  year\n",
              "0         66          2        1      3  2023\n",
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              "                                                     [key], {});\n",
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              "\n",
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    {
      "cell_type": "code",
      "source": [
        "wy_pred = reg.predict(train_w)"
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      "metadata": {
        "id": "GNWkbbo9DsB9"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create a dataframe with the predicted sales for the next 18 months\n",
        "wy_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MkUXWTD_DvJi",
        "outputId": "8c82b06b-6268-4a0f-969e-0152b116158b"
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      "execution_count": 83,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
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              "       1.02364704e+08, 7.52162640e+07, 7.52162640e+07, 4.25769560e+07,\n",
              "       4.25769560e+07, 4.25769560e+07, 4.25769560e+07, 2.63652380e+07,\n",
              "       2.63652380e+07, 2.63652380e+07, 2.63652380e+07, 2.63652380e+07,\n",
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              "       2.35710240e+08], dtype=float32)"
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          },
          "metadata": {},
          "execution_count": 83
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    {
      "cell_type": "code",
      "source": [
        "w_pred.info()"
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        "colab": {
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        },
        "id": "tSr1NZVcDyIL",
        "outputId": "b104e9be-eb0d-4193-a2c9-3ad285ec5387"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 69 entries, 0 to 68\n",
            "Data columns (total 6 columns):\n",
            " #   Column            Non-Null Count  Dtype \n",
            "---  ------            --------------  ----- \n",
            " 0   TRANSACTION DATE  69 non-null     object\n",
            " 1   dayofyear         69 non-null     int64 \n",
            " 2   dayofweek         69 non-null     int64 \n",
            " 3   quarter           69 non-null     int64 \n",
            " 4   month             69 non-null     int64 \n",
            " 5   year              69 non-null     int64 \n",
            "dtypes: int64(5), object(1)\n",
            "memory usage: 3.4+ KB\n"
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        }
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    {
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      "source": [
        "w_pred['Predicted_Income'] = wy_pred.tolist()"
      ],
      "metadata": {
        "id": "CrUuBvP2D1SK"
      },
      "execution_count": 85,
      "outputs": []
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        "outputId": "b42fe166-c5f7-41f5-a67d-21401ead9ce1"
      },
      "execution_count": 86,
      "outputs": [
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            "text/plain": [
              "   TRANSACTION DATE  dayofyear  dayofweek  quarter  month  year  \\\n",
              "0        2023/03/07         66          2        1      3  2023   \n",
              "1        2023/03/14         73          2        1      3  2023   \n",
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              "3        2023/03/28         87          2        1      3  2023   \n",
              "4        2023/04/04         94          2        2      4  2023   \n",
              "..              ...        ...        ...      ...    ...   ...   \n",
              "64       2024/05/28        149          2        2      5  2024   \n",
              "65       2024/06/04        156          2        2      6  2024   \n",
              "66       2024/06/11        163          2        2      6  2024   \n",
              "67       2024/06/18        170          2        2      6  2024   \n",
              "68       2024/06/25        177          2        2      6  2024   \n",
              "\n",
              "    Predicted_Income  \n",
              "0         71871512.0  \n",
              "1         71871512.0  \n",
              "2        109906264.0  \n",
              "3        109906264.0  \n",
              "4        145803472.0  \n",
              "..               ...  \n",
              "64       198619312.0  \n",
              "65       235710240.0  \n",
              "66       235710240.0  \n",
              "67       235710240.0  \n",
              "68       235710240.0  \n",
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              "[69 rows x 7 columns]"
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              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2023</td>\n",
              "      <td>71871512.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023/03/21</td>\n",
              "      <td>80</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2023</td>\n",
              "      <td>109906264.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023/03/28</td>\n",
              "      <td>87</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2023</td>\n",
              "      <td>109906264.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2023/04/04</td>\n",
              "      <td>94</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2023</td>\n",
              "      <td>145803472.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>2024/05/28</td>\n",
              "      <td>149</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2024</td>\n",
              "      <td>198619312.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>2024/06/04</td>\n",
              "      <td>156</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>2024</td>\n",
              "      <td>235710240.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>2024/06/11</td>\n",
              "      <td>163</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>2024</td>\n",
              "      <td>235710240.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>2024/06/18</td>\n",
              "      <td>170</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>2024</td>\n",
              "      <td>235710240.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>2024/06/25</td>\n",
              "      <td>177</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>2024</td>\n",
              "      <td>235710240.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>69 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a0e26c6f-d8b0-4eab-86df-e09d60fa624b')\"\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 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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",
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              "\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",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-a0e26c6f-d8b0-4eab-86df-e09d60fa624b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a0e26c6f-d8b0-4eab-86df-e09d60fa624b');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "w_pred.to_csv('Weekly_Predictions')"
      ],
      "metadata": {
        "id": "AXHl6u7AD7c2"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Daily "
      ],
      "metadata": {
        "id": "DITg-40EEm2i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "d_pred = pd.read_csv(\"data_2_weekly.csv\")\n",
        "d_pred.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "BAvKxDBwFWCn",
        "outputId": "7a60d11f-d052-408b-8e14-fd56e4b97569"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date  dayofyear  dayofweek  quarter  month  year\n",
              "0  2023/03/07         66          2        1      3  2023\n",
              "1  2023/03/14         73          2        1      3  2023\n",
              "2  2023/03/21         80          2        1      3  2023\n",
              "3  2023/03/28         87          2        1      3  2023\n",
              "4  2023/04/04         94          2        2      4  2023"
            ],
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              "      <td>2023</td>\n",
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              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-27a04cfa-98a3-4f84-b5b2-0e598cf5d944 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-27a04cfa-98a3-4f84-b5b2-0e598cf5d944');\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",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "d_pred.rename(columns={'Date': 'TRANSACTION DATE'}, inplace=True)\n",
        "d_pred.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "C3VpCWCJFea9",
        "outputId": "bc9d5a3f-258c-483c-f9ea-b1e811ddfddf"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  TRANSACTION DATE  dayofyear  dayofweek  quarter  month  year\n",
              "0       2023/03/07         66          2        1      3  2023\n",
              "1       2023/03/14         73          2        1      3  2023"
            ],
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              "      <th>dayofweek</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>2023/03/07</td>\n",
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              "      <td>2023</td>\n",
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              "      <th>1</th>\n",
              "      <td>2023/03/14</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3332947d-59db-44ef-a25a-80327154598d')\"\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 0 24 24\"\n",
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              "      \n",
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              "\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          const element = document.querySelector('#df-3332947d-59db-44ef-a25a-80327154598d');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_d = d_pred[['dayofyear','dayofweek', 'quarter','month','year']].copy()"
      ],
      "metadata": {
        "id": "Ktca9FZ0FfQO"
      },
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_d.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QfP2jbw0Fmee",
        "outputId": "3e60464f-790e-4edf-ef73-b6bdf2ae85b2"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   dayofyear  dayofweek  quarter  month  year\n",
              "0         66          2        1      3  2023\n",
              "1         73          2        1      3  2023\n",
              "2         80          2        1      3  2023\n",
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              "                                                     [key], {});\n",
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              "\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",
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    {
      "cell_type": "code",
      "source": [
        "dy_pred = reg.predict(train_d)"
      ],
      "metadata": {
        "id": "1UN6sfJaFqrO"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create a dataframe with the predicted sales for the next 18 months\n",
        "dy_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lXzJ5XvMFraZ",
        "outputId": "7d6b9fbc-16f3-458d-d6ce-a4413760e90c"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([7.18715120e+07, 7.18715120e+07, 1.09906264e+08, 1.09906264e+08,\n",
              "       1.45803472e+08, 1.45803472e+08, 1.54760400e+08, 1.54760400e+08,\n",
              "       1.98619312e+08, 1.98619312e+08, 1.98619312e+08, 1.98619312e+08,\n",
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              "       1.02364704e+08, 7.52162640e+07, 7.52162640e+07, 4.25769560e+07,\n",
              "       4.25769560e+07, 4.25769560e+07, 4.25769560e+07, 2.63652380e+07,\n",
              "       2.63652380e+07, 2.63652380e+07, 2.63652380e+07, 2.63652380e+07,\n",
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              "       1.98619312e+08, 2.35710240e+08, 2.35710240e+08, 2.35710240e+08,\n",
              "       2.35710240e+08], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d_pred['Predicted_Income'] = dy_pred.tolist()"
      ],
      "metadata": {
        "id": "42RULHS9GD_Q"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "d_pred"
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      "metadata": {
        "colab": {
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        "id": "b_WFtMkGGH9O",
        "outputId": "70e3c651-2242-4588-b821-971e9979aed5"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   TRANSACTION DATE  dayofyear  dayofweek  quarter  month  year  \\\n",
              "0        2023/03/07         66          2        1      3  2023   \n",
              "1        2023/03/14         73          2        1      3  2023   \n",
              "2        2023/03/21         80          2        1      3  2023   \n",
              "3        2023/03/28         87          2        1      3  2023   \n",
              "4        2023/04/04         94          2        2      4  2023   \n",
              "..              ...        ...        ...      ...    ...   ...   \n",
              "64       2024/05/28        149          2        2      5  2024   \n",
              "65       2024/06/04        156          2        2      6  2024   \n",
              "66       2024/06/11        163          2        2      6  2024   \n",
              "67       2024/06/18        170          2        2      6  2024   \n",
              "68       2024/06/25        177          2        2      6  2024   \n",
              "\n",
              "    Predicted_Income  \n",
              "0         71871512.0  \n",
              "1         71871512.0  \n",
              "2        109906264.0  \n",
              "3        109906264.0  \n",
              "4        145803472.0  \n",
              "..               ...  \n",
              "64       198619312.0  \n",
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              "66       235710240.0  \n",
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              "      <th>65</th>\n",
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              "      <td>2024/06/11</td>\n",
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              "      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",
              "    [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-61400164-c6b0-4db4-9e75-b3dd035c55f2 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-61400164-c6b0-4db4-9e75-b3dd035c55f2');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d_pred.to_csv('daily_Prediction')"
      ],
      "metadata": {
        "id": "gEAq3AvOGKPX"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-f4p5MfWGoIZ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}