Task3Cognizant.ipynb

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      "source": [
        "# Task 3: Model Building"
      ],
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      "cell_type": "code",
      "source": [
        "#Import all libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from datetime import datetime\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "2Jb2JiDXwtMz"
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      "execution_count": 1,
      "outputs": []
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      "cell_type": "markdown",
      "source": [
        "### Section 1 - Data loading"
      ],
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        "id": "nhlPNslBy7y3"
      }
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    {
      "cell_type": "code",
      "source": [
        "sales_df = pd.read_csv('/content/sample_sales_data (1).csv')\n",
        "sales_df.drop(columns=['Unnamed: 0'], inplace=True)\n",
        "sales_df.head"
      ],
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              "<bound method NDFrame.head of                             transaction_id         timestamp  \\\n",
              "0     a1c82654-c52c-45b3-8ce8-4c2a1efe63ed  02/03/2022 09:51   \n",
              "1     931ad550-09e8-4da6-beaa-8c9d17be9c60  06/03/2022 10:33   \n",
              "2     ae133534-6f61-4cd6-b6b8-d1c1d8d90aea  04/03/2022 17:20   \n",
              "3     157cebd9-aaf0-475d-8a11-7c8e0f5b76e4  02/03/2022 17:23   \n",
              "4     a81a6cd3-5e0c-44a2-826c-aea43e46c514  05/03/2022 14:32   \n",
              "...                                    ...               ...   \n",
              "7824  6c19b9fc-f86d-4526-9dfe-d8027a4d13ee  03/03/2022 18:22   \n",
              "7825  1c69824b-e399-4b79-a5e7-04a3a7db0681  04/03/2022 19:14   \n",
              "7826  79aee7d6-1405-4345-9a15-92541e9e1e74  03/03/2022 14:00   \n",
              "7827  e5cc4f88-e5b7-4ad5-bc1b-12a828a14f55  04/03/2022 15:11   \n",
              "7828  afd70b4f-ee21-402d-8d8f-0d9e13c2bea6  06/03/2022 13:50   \n",
              "\n",
              "                                product_id           category customer_type  \\\n",
              "0     3bc6c1ea-0198-46de-9ffd-514ae3338713              fruit          gold   \n",
              "1     ad81b46c-bf38-41cf-9b54-5fe7f5eba93e              fruit      standard   \n",
              "2     7c55cbd4-f306-4c04-a030-628cbe7867c1              fruit       premium   \n",
              "3     80da8348-1707-403f-8be7-9e6deeccc883              fruit          gold   \n",
              "4     7f5e86e6-f06f-45f6-bf44-27b095c9ad1d              fruit         basic   \n",
              "...                                    ...                ...           ...   \n",
              "7824  bc6187a9-d508-482b-9ca6-590d1cc7524f  cleaning products         basic   \n",
              "7825  707e4237-191c-4cc9-85af-383a6c1cb2ab  cleaning products      standard   \n",
              "7826  a9325c1a-2715-41df-b7f4-3078fa5ecd97  cleaning products         basic   \n",
              "7827  707e4237-191c-4cc9-85af-383a6c1cb2ab  cleaning products         basic   \n",
              "7828  d6ccd088-11be-4c25-aa1f-ea87c01a04db  cleaning products    non-member   \n",
              "\n",
              "      unit_price  quantity  total payment_type  \n",
              "0           3.99         2   7.98     e-wallet  \n",
              "1           3.99         1   3.99     e-wallet  \n",
              "2           0.19         2   0.38     e-wallet  \n",
              "3           0.19         4   0.76     e-wallet  \n",
              "4           4.49         2   8.98   debit card  \n",
              "...          ...       ...    ...          ...  \n",
              "7824       14.19         2  28.38     e-wallet  \n",
              "7825       16.99         1  16.99  credit card  \n",
              "7826       14.19         2  28.38  credit card  \n",
              "7827       16.99         4  67.96         cash  \n",
              "7828       14.99         4  59.96   debit card  \n",
              "\n",
              "[7829 rows x 9 columns]>"
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      "source": [
        "stock_df = pd.read_csv('/content/sensor_stock_levels.csv')\n",
        "stock_df.drop(columns=['Unnamed: 0'], inplace=True)\n",
        "stock_df.head()"
      ],
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              "                                     id            timestamp  \\\n",
              "0  4220e505-c247-478d-9831-6b9f87a4488a  2022-03-07 12:13:02   \n",
              "1  f2612b26-fc82-49ea-8940-0751fdd4d9ef  2022-03-07 16:39:46   \n",
              "2  989a287f-67e6-4478-aa49-c3a35dac0e2e  2022-03-01 18:17:43   \n",
              "3  af8e5683-d247-46ac-9909-1a77bdebefb2  2022-03-02 14:29:09   \n",
              "4  08a32247-3f44-4002-85fb-c198434dd4bb  2022-03-02 13:46:18   \n",
              "\n",
              "                             product_id  estimated_stock_pct  \n",
              "0  f658605e-75f3-4fed-a655-c0903f344427                 0.75  \n",
              "1  de06083a-f5c0-451d-b2f4-9ab88b52609d                 0.48  \n",
              "2  ce8f3a04-d1a4-43b1-a7c2-fa1b8e7674c8                 0.58  \n",
              "3  c21e3ba9-92a3-4745-92c2-6faef73223f7                 0.79  \n",
              "4  7f478817-aa5b-44e9-9059-8045228c9eb0                 0.22  "
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              "      <th>0</th>\n",
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              "      <td>2022-03-07 12:13:02</td>\n",
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              "      <td>0.75</td>\n",
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              "      <th>1</th>\n",
              "      <td>f2612b26-fc82-49ea-8940-0751fdd4d9ef</td>\n",
              "      <td>2022-03-07 16:39:46</td>\n",
              "      <td>de06083a-f5c0-451d-b2f4-9ab88b52609d</td>\n",
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              "      <th>2</th>\n",
              "      <td>989a287f-67e6-4478-aa49-c3a35dac0e2e</td>\n",
              "      <td>2022-03-01 18:17:43</td>\n",
              "      <td>ce8f3a04-d1a4-43b1-a7c2-fa1b8e7674c8</td>\n",
              "      <td>0.58</td>\n",
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              "      <th>3</th>\n",
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              "      <td>2022-03-02 14:29:09</td>\n",
              "      <td>c21e3ba9-92a3-4745-92c2-6faef73223f7</td>\n",
              "      <td>0.79</td>\n",
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              "      <th>4</th>\n",
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              "      <td>2022-03-02 13:46:18</td>\n",
              "      <td>7f478817-aa5b-44e9-9059-8045228c9eb0</td>\n",
              "      <td>0.22</td>\n",
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      "source": [
        "temp_df = pd.read_csv('/content/sensor_storage_temperature.csv')\n",
        "temp_df.drop(columns=['Unnamed: 0'], inplace=True)\n",
        "temp_df.head()"
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          "data": {
            "text/plain": [
              "                                     id            timestamp  temperature\n",
              "0  d1ca1ef8-0eac-42fc-af80-97106efc7b13  2022-03-07 15:55:20         2.96\n",
              "1  4b8a66c4-0f3a-4f16-826f-8cf9397e9d18  2022-03-01 09:18:22         1.88\n",
              "2  3d47a0c7-1e72-4512-812f-b6b5d8428cf3  2022-03-04 15:12:26         1.78\n",
              "3  9500357b-ce15-424a-837a-7677b386f471  2022-03-02 12:30:42         2.18\n",
              "4  c4b61fec-99c2-4c6d-8e5d-4edd8c9632fa  2022-03-05 09:09:33         1.38"
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              "      <th>0</th>\n",
              "      <td>d1ca1ef8-0eac-42fc-af80-97106efc7b13</td>\n",
              "      <td>2022-03-07 15:55:20</td>\n",
              "      <td>2.96</td>\n",
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              "      <th>1</th>\n",
              "      <td>4b8a66c4-0f3a-4f16-826f-8cf9397e9d18</td>\n",
              "      <td>2022-03-01 09:18:22</td>\n",
              "      <td>1.88</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3d47a0c7-1e72-4512-812f-b6b5d8428cf3</td>\n",
              "      <td>2022-03-04 15:12:26</td>\n",
              "      <td>1.78</td>\n",
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              "      <th>3</th>\n",
              "      <td>9500357b-ce15-424a-837a-7677b386f471</td>\n",
              "      <td>2022-03-02 12:30:42</td>\n",
              "      <td>2.18</td>\n",
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              "      <th>4</th>\n",
              "      <td>c4b61fec-99c2-4c6d-8e5d-4edd8c9632fa</td>\n",
              "      <td>2022-03-05 09:09:33</td>\n",
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              "<style>\n",
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              "    border: none;\n",
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              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "          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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              "      </script>\n",
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              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Section 2 - Data cleaning"
      ],
      "metadata": {
        "id": "OgbPAsK1zKAV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Let's check that we have the correct datatypes\n",
        "sales_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xylE4b5zzPYf",
        "outputId": "fbd048a8-e889-49e2-f8ba-1440f4083a8f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7829 entries, 0 to 7828\n",
            "Data columns (total 9 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   transaction_id  7829 non-null   object \n",
            " 1   timestamp       7829 non-null   object \n",
            " 2   product_id      7829 non-null   object \n",
            " 3   category        7829 non-null   object \n",
            " 4   customer_type   7829 non-null   object \n",
            " 5   unit_price      7829 non-null   float64\n",
            " 6   quantity        7829 non-null   int64  \n",
            " 7   total           7829 non-null   float64\n",
            " 8   payment_type    7829 non-null   object \n",
            "dtypes: float64(2), int64(1), object(6)\n",
            "memory usage: 550.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We notice that the values in the 'timestamp' column should be converted into a datetime format."
      ],
      "metadata": {
        "id": "pxkL0gq1z3hF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sales_df['timestamp'] = pd.to_datetime(sales_df['timestamp'])"
      ],
      "metadata": {
        "id": "3AqbnU4nzjsh"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stock_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3tk5_30EzlZB",
        "outputId": "03bfcf72-abf8-49fa-f3ce-c01554286211"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 15000 entries, 0 to 14999\n",
            "Data columns (total 4 columns):\n",
            " #   Column               Non-Null Count  Dtype  \n",
            "---  ------               --------------  -----  \n",
            " 0   id                   15000 non-null  object \n",
            " 1   timestamp            15000 non-null  object \n",
            " 2   product_id           15000 non-null  object \n",
            " 3   estimated_stock_pct  15000 non-null  float64\n",
            "dtypes: float64(1), object(3)\n",
            "memory usage: 468.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock_df['timestamp'] = pd.to_datetime(stock_df['timestamp'])"
      ],
      "metadata": {
        "id": "gYIDK8CEzokf"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "temp_df.info()"
      ],
      "metadata": {
        "id": "Vdz3qXFezv6L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "temp_df['timestamp'] = pd.to_datetime(temp_df['timestamp'])"
      ],
      "metadata": {
        "id": "cUdCY7VN0HpP"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Section 3 - Merge data"
      ],
      "metadata": {
        "id": "5oxfqqGU0R6g"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Currently we have 3 datasets. In order to include all of this data within a predictive model, we need to merge them together into 1 dataframe.\n",
        "\n",
        "If we revisit the problem statement:\n",
        "\n",
        "“Can we accurately predict the stock levels of products, based on sales data and sensor data,\n",
        "on an hourly basis in order to more intelligently procure products from our suppliers.”\n",
        "The client indicates that they want the model to predict on an hourly basis. Looking at the data model, we can see that only column that we can use to merge the 3 datasets together is timestamp.\n",
        "\n",
        "So, we must first transform the timestamp column in all 3 datasets to be based on the hour of the day, then we can merge the datasets together."
      ],
      "metadata": {
        "id": "O7xbO3kR0gSF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def convert_timestamp_to_hourly(data: pd.DataFrame = None, column: str = None):\n",
        "  dummy = data.copy()\n",
        "  new_ts = dummy[column].tolist()\n",
        "  new_ts = [i.strftime('%Y-%m-%d %H:00:00') for i in new_ts]\n",
        "  new_ts = [datetime.strptime(i, '%Y-%m-%d %H:00:00') for i in new_ts]\n",
        "  dummy[column] = new_ts\n",
        "  return dummy"
      ],
      "metadata": {
        "id": "XSpgpxO60XRs"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sales_df = convert_timestamp_to_hourly(sales_df, 'timestamp')\n",
        "\n",
        "stock_df = convert_timestamp_to_hourly(stock_df, 'timestamp')\n",
        "\n",
        "temp_df = convert_timestamp_to_hourly(temp_df, 'timestamp')"
      ],
      "metadata": {
        "id": "Ur9mAUXG0sqp"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we need to aggregate the datasets in order to combine rows which have the same value for timestamp."
      ],
      "metadata": {
        "id": "Gpa8y7a91B3V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sales_agg = sales_df.groupby(['timestamp', 'product_id']).agg({'quantity': 'sum'}).reset_index()\n",
        "sales_agg.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "N-0X9Nya1JkK",
        "outputId": "7d491324-65e2-4d62-cb04-be9dda4a83d0"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            timestamp                            product_id  quantity\n",
              "0 2022-01-03 09:00:00  00e120bb-89d6-4df5-bc48-a051148e3d03         3\n",
              "1 2022-01-03 09:00:00  01f3cdd9-8e9e-4dff-9b5c-69698a0388d0         3\n",
              "2 2022-01-03 09:00:00  03a2557a-aa12-4add-a6d4-77dc36342067         3\n",
              "3 2022-01-03 09:00:00  049b2171-0eeb-4a3e-bf98-0c290c7821da         7\n",
              "4 2022-01-03 09:00:00  04da844d-8dba-4470-9119-e534d52a03a0        11"
            ],
            "text/html": [
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              "\n",
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              "          const element = document.querySelector('#df-13fd54a2-1dfe-492e-94a9-ce3531adea13');\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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stock_agg = stock_df.groupby(['timestamp', 'product_id']).agg({'estimated_stock_pct': 'mean'}).reset_index()\n",
        "stock_agg.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VJ04fl8Y1MXe",
        "outputId": "7610f0c9-5e82-4661-998b-735689030702"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            timestamp                            product_id  \\\n",
              "0 2022-03-01 09:00:00  00e120bb-89d6-4df5-bc48-a051148e3d03   \n",
              "1 2022-03-01 09:00:00  01f3cdd9-8e9e-4dff-9b5c-69698a0388d0   \n",
              "2 2022-03-01 09:00:00  01ff0803-ae73-4234-971d-5713c97b7f4b   \n",
              "3 2022-03-01 09:00:00  0363eb21-8c74-47e1-a216-c37e565e5ceb   \n",
              "4 2022-03-01 09:00:00  03f0b20e-3b5b-444f-bc39-cdfa2523d4bc   \n",
              "\n",
              "   estimated_stock_pct  \n",
              "0                 0.89  \n",
              "1                 0.14  \n",
              "2                 0.67  \n",
              "3                 0.82  \n",
              "4                 0.05  "
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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": [
        "temp_agg = temp_df.groupby(['timestamp']).agg({'temperature': 'mean'}).reset_index()\n",
        "temp_agg.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "LXQCDIW01Pll",
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      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            timestamp  temperature\n",
              "0 2022-03-01 09:00:00    -0.028850\n",
              "1 2022-03-01 10:00:00     1.284314\n",
              "2 2022-03-01 11:00:00    -0.560000\n",
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              "    [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-84d3b11b-7962-48e0-999c-6e4199bd1bb3 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-84d3b11b-7962-48e0-999c-6e4199bd1bb3');\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": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can now merge our datasets."
      ],
      "metadata": {
        "id": "Np6GsPLg1Zgj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df = stock_agg.merge(sales_agg, on=['timestamp', 'product_id'], how='left')\n",
        "merged_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "n90twSmR1We1",
        "outputId": "be244429-49d7-4dfd-effc-f06ac1f97e0f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            timestamp                            product_id  \\\n",
              "0 2022-03-01 09:00:00  00e120bb-89d6-4df5-bc48-a051148e3d03   \n",
              "1 2022-03-01 09:00:00  01f3cdd9-8e9e-4dff-9b5c-69698a0388d0   \n",
              "2 2022-03-01 09:00:00  01ff0803-ae73-4234-971d-5713c97b7f4b   \n",
              "3 2022-03-01 09:00:00  0363eb21-8c74-47e1-a216-c37e565e5ceb   \n",
              "4 2022-03-01 09:00:00  03f0b20e-3b5b-444f-bc39-cdfa2523d4bc   \n",
              "\n",
              "   estimated_stock_pct  quantity  \n",
              "0                 0.89       NaN  \n",
              "1                 0.14       NaN  \n",
              "2                 0.67       NaN  \n",
              "3                 0.82       NaN  \n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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-8c7f75fc-2378-4dc4-90ef-1fd0b29e7d6d 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-8c7f75fc-2378-4dc4-90ef-1fd0b29e7d6d');\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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              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df = merged_df.merge(temp_agg, on='timestamp', how='left')\n",
        "merged_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "vXAendbc1lqh",
        "outputId": "bb6dd68f-67c6-4098-eb8b-4370305666ec"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            timestamp                            product_id  \\\n",
              "0 2022-03-01 09:00:00  00e120bb-89d6-4df5-bc48-a051148e3d03   \n",
              "1 2022-03-01 09:00:00  01f3cdd9-8e9e-4dff-9b5c-69698a0388d0   \n",
              "2 2022-03-01 09:00:00  01ff0803-ae73-4234-971d-5713c97b7f4b   \n",
              "3 2022-03-01 09:00:00  0363eb21-8c74-47e1-a216-c37e565e5ceb   \n",
              "4 2022-03-01 09:00:00  03f0b20e-3b5b-444f-bc39-cdfa2523d4bc   \n",
              "\n",
              "   estimated_stock_pct  quantity  temperature  \n",
              "0                 0.89       NaN     -0.02885  \n",
              "1                 0.14       NaN     -0.02885  \n",
              "2                 0.67       NaN     -0.02885  \n",
              "3                 0.82       NaN     -0.02885  \n",
              "4                 0.05       NaN     -0.02885  "
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
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              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-be29e9ea-f5ab-4a03-8775-34b0d31c72c1 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ff8eff10-ecec-4040-b3bf-ef5af7d30659');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "            '<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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              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tmhtwcGf1pYG",
        "outputId": "be392a1c-5770-4c7d-beb8-c1c613355dd8"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 10845 entries, 0 to 10844\n",
            "Data columns (total 5 columns):\n",
            " #   Column               Non-Null Count  Dtype         \n",
            "---  ------               --------------  -----         \n",
            " 0   timestamp            10845 non-null  datetime64[ns]\n",
            " 1   product_id           10845 non-null  object        \n",
            " 2   estimated_stock_pct  10845 non-null  float64       \n",
            " 3   quantity             418 non-null    float64       \n",
            " 4   temperature          10845 non-null  float64       \n",
            "dtypes: datetime64[ns](1), float64(3), object(1)\n",
            "memory usage: 508.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that we have some null values for the 'quantity' column. We can assume that if there is a null value for this column, it represents that there were 0 sales of this product within this hour. So, lets replace the null values with 0."
      ],
      "metadata": {
        "id": "a37rfygJ1ql9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df['quantity'] = merged_df['quantity'].fillna(0)\n",
        "merged_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IzThXMks2MLm",
        "outputId": "e23797fa-4ea5-4479-c5e0-205770e8a3c9"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 10845 entries, 0 to 10844\n",
            "Data columns (total 5 columns):\n",
            " #   Column               Non-Null Count  Dtype         \n",
            "---  ------               --------------  -----         \n",
            " 0   timestamp            10845 non-null  datetime64[ns]\n",
            " 1   product_id           10845 non-null  object        \n",
            " 2   estimated_stock_pct  10845 non-null  float64       \n",
            " 3   quantity             10845 non-null  float64       \n",
            " 4   temperature          10845 non-null  float64       \n",
            "dtypes: datetime64[ns](1), float64(3), object(1)\n",
            "memory usage: 508.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can combine some more features onto this table too, including 'category' and 'unit_price'."
      ],
      "metadata": {
        "id": "6x6OtOt-2Pvc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "product_categories = sales_df[['product_id', 'category']]\n",
        "product_categories = product_categories.drop_duplicates()\n",
        "\n",
        "product_price = sales_df[['product_id', 'unit_price']]\n",
        "product_price = product_price.drop_duplicates()"
      ],
      "metadata": {
        "id": "BxuXf5di2NKD"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df = merged_df.merge(product_categories, on=\"product_id\", how=\"left\")\n",
        "merged_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "jQz2KY_52XoI",
        "outputId": "98daaf42-d738-42d3-a580-4992f5b114fe"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            timestamp                            product_id  \\\n",
              "0 2022-03-01 09:00:00  00e120bb-89d6-4df5-bc48-a051148e3d03   \n",
              "1 2022-03-01 09:00:00  01f3cdd9-8e9e-4dff-9b5c-69698a0388d0   \n",
              "2 2022-03-01 09:00:00  01ff0803-ae73-4234-971d-5713c97b7f4b   \n",
              "3 2022-03-01 09:00:00  0363eb21-8c74-47e1-a216-c37e565e5ceb   \n",
              "4 2022-03-01 09:00:00  03f0b20e-3b5b-444f-bc39-cdfa2523d4bc   \n",
              "\n",
              "   estimated_stock_pct  quantity  temperature       category  \n",
              "0                 0.89       0.0     -0.02885        kitchen  \n",
              "1                 0.14       0.0     -0.02885     vegetables  \n",
              "2                 0.67       0.0     -0.02885  baby products  \n",
              "3                 0.82       0.0     -0.02885      beverages  \n",
              "4                 0.05       0.0     -0.02885           pets  "
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df = merged_df.merge(product_price, on=\"product_id\", how=\"left\")\n",
        "merged_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "PZNpEzat2ZM6",
        "outputId": "8be77ade-aaff-4318-80fa-2f4133948310"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            timestamp                            product_id  \\\n",
              "0 2022-03-01 09:00:00  00e120bb-89d6-4df5-bc48-a051148e3d03   \n",
              "1 2022-03-01 09:00:00  01f3cdd9-8e9e-4dff-9b5c-69698a0388d0   \n",
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              "4 2022-03-01 09:00:00  03f0b20e-3b5b-444f-bc39-cdfa2523d4bc   \n",
              "\n",
              "   estimated_stock_pct  quantity  temperature       category  unit_price  \n",
              "0                 0.89       0.0     -0.02885        kitchen       11.19  \n",
              "1                 0.14       0.0     -0.02885     vegetables        1.49  \n",
              "2                 0.67       0.0     -0.02885  baby products       14.19  \n",
              "3                 0.82       0.0     -0.02885      beverages       20.19  \n",
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              "      <th>0</th>\n",
              "      <td>2022-03-01 09:00:00</td>\n",
              "      <td>00e120bb-89d6-4df5-bc48-a051148e3d03</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.02885</td>\n",
              "      <td>kitchen</td>\n",
              "      <td>11.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2022-03-01 09:00:00</td>\n",
              "      <td>01f3cdd9-8e9e-4dff-9b5c-69698a0388d0</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.02885</td>\n",
              "      <td>vegetables</td>\n",
              "      <td>1.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2022-03-01 09:00:00</td>\n",
              "      <td>01ff0803-ae73-4234-971d-5713c97b7f4b</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.02885</td>\n",
              "      <td>baby products</td>\n",
              "      <td>14.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2022-03-01 09:00:00</td>\n",
              "      <td>0363eb21-8c74-47e1-a216-c37e565e5ceb</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.02885</td>\n",
              "      <td>beverages</td>\n",
              "      <td>20.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2022-03-01 09:00:00</td>\n",
              "      <td>03f0b20e-3b5b-444f-bc39-cdfa2523d4bc</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.02885</td>\n",
              "      <td>pets</td>\n",
              "      <td>8.19</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-442980a5-f6bc-4c8b-9766-fd1d5912fb35')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "              title=\"Suggest charts.\"\n",
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              "      </button>\n",
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              "<style>\n",
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              "    display: none;\n",
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              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "\n",
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              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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              "\n",
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              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        displayQuickchartButton(document);\n",
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              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\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-442980a5-f6bc-4c8b-9766-fd1d5912fb35 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-442980a5-f6bc-4c8b-9766-fd1d5912fb35');\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": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T62DkN7V2aa9",
        "outputId": "402a509f-3f38-47c8-9caa-ac8daed42280"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 10845 entries, 0 to 10844\n",
            "Data columns (total 7 columns):\n",
            " #   Column               Non-Null Count  Dtype         \n",
            "---  ------               --------------  -----         \n",
            " 0   timestamp            10845 non-null  datetime64[ns]\n",
            " 1   product_id           10845 non-null  object        \n",
            " 2   estimated_stock_pct  10845 non-null  float64       \n",
            " 3   quantity             10845 non-null  float64       \n",
            " 4   temperature          10845 non-null  float64       \n",
            " 5   category             10845 non-null  object        \n",
            " 6   unit_price           10845 non-null  float64       \n",
            "dtypes: datetime64[ns](1), float64(4), object(2)\n",
            "memory usage: 677.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Section 4 - Feature engineering\n",
        "We have our cleaned and merged data. Now we must transform this data so that the columns are in a suitable format for a machine learning model. In other terms, every column must be numeric."
      ],
      "metadata": {
        "id": "MeEx7I2a2cuU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df['timestamp_day_of_month'] = merged_df['timestamp'].dt.day\n",
        "merged_df['timestamp_day_of_week'] = merged_df['timestamp'].dt.dayofweek\n",
        "merged_df['timestamp_hour'] = merged_df['timestamp'].dt.hour\n",
        "merged_df.drop(columns=['timestamp'], inplace=True)\n",
        "merged_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "viVd6xXc2pHO",
        "outputId": "a00af4d2-a12d-4019-85f8-22a4648cc4b7"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             product_id  estimated_stock_pct  quantity  \\\n",
              "0  00e120bb-89d6-4df5-bc48-a051148e3d03                 0.89       0.0   \n",
              "1  01f3cdd9-8e9e-4dff-9b5c-69698a0388d0                 0.14       0.0   \n",
              "2  01ff0803-ae73-4234-971d-5713c97b7f4b                 0.67       0.0   \n",
              "3  0363eb21-8c74-47e1-a216-c37e565e5ceb                 0.82       0.0   \n",
              "4  03f0b20e-3b5b-444f-bc39-cdfa2523d4bc                 0.05       0.0   \n",
              "\n",
              "   temperature       category  unit_price  timestamp_day_of_month  \\\n",
              "0     -0.02885        kitchen       11.19                       1   \n",
              "1     -0.02885     vegetables        1.49                       1   \n",
              "2     -0.02885  baby products       14.19                       1   \n",
              "3     -0.02885      beverages       20.19                       1   \n",
              "4     -0.02885           pets        8.19                       1   \n",
              "\n",
              "   timestamp_day_of_week  timestamp_hour  \n",
              "0                      1               9  \n",
              "1                      1               9  \n",
              "2                      1               9  \n",
              "3                      1               9  \n",
              "4                      1               9  "
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              "      <td>20.19</td>\n",
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              "      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",
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              "\n",
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              "      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",
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              "    }\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-84339757-9d66-4e80-a858-c857d53e9216');\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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df = pd.get_dummies(merged_df, columns=['category'])\n",
        "merged_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "6BZOT1IN2tFC",
        "outputId": "016f07fb-f894-43f7-bec2-999585b55d71"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             product_id  estimated_stock_pct  quantity  \\\n",
              "0  00e120bb-89d6-4df5-bc48-a051148e3d03                 0.89       0.0   \n",
              "1  01f3cdd9-8e9e-4dff-9b5c-69698a0388d0                 0.14       0.0   \n",
              "2  01ff0803-ae73-4234-971d-5713c97b7f4b                 0.67       0.0   \n",
              "3  0363eb21-8c74-47e1-a216-c37e565e5ceb                 0.82       0.0   \n",
              "4  03f0b20e-3b5b-444f-bc39-cdfa2523d4bc                 0.05       0.0   \n",
              "\n",
              "   temperature  unit_price  timestamp_day_of_month  timestamp_day_of_week  \\\n",
              "0     -0.02885       11.19                       1                      1   \n",
              "1     -0.02885        1.49                       1                      1   \n",
              "2     -0.02885       14.19                       1                      1   \n",
              "3     -0.02885       20.19                       1                      1   \n",
              "4     -0.02885        8.19                       1                      1   \n",
              "\n",
              "   timestamp_hour  category_baby products  category_baked goods  ...  \\\n",
              "0               9                       0                     0  ...   \n",
              "1               9                       0                     0  ...   \n",
              "2               9                       1                     0  ...   \n",
              "3               9                       0                     0  ...   \n",
              "4               9                       0                     0  ...   \n",
              "\n",
              "   category_meat  category_medicine  category_packaged foods  \\\n",
              "0              0                  0                        0   \n",
              "1              0                  0                        0   \n",
              "2              0                  0                        0   \n",
              "3              0                  0                        0   \n",
              "4              0                  0                        0   \n",
              "\n",
              "   category_personal care  category_pets  category_refrigerated items  \\\n",
              "0                       0              0                            0   \n",
              "1                       0              0                            0   \n",
              "2                       0              0                            0   \n",
              "3                       0              0                            0   \n",
              "4                       0              1                            0   \n",
              "\n",
              "   category_seafood  category_snacks  category_spices and herbs  \\\n",
              "0                 0                0                          0   \n",
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              "2                 0                0                          0   \n",
              "3                 0                0                          0   \n",
              "4                 0                0                          0   \n",
              "\n",
              "   category_vegetables  \n",
              "0                    0  \n",
              "1                    1  \n",
              "2                    0  \n",
              "3                    0  \n",
              "4                    0  \n",
              "\n",
              "[5 rows x 30 columns]"
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              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "\n",
              "    <script>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      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-a89ff1a3-2f18-4e73-a1a7-eea3b19b89f3 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-a89ff1a3-2f18-4e73-a1a7-eea3b19b89f3');\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": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df.drop(columns=['product_id'], inplace=True)\n",
        "merged_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "79v9l7XL2xJ1",
        "outputId": "5ac5ef6b-9597-472b-b125-7c0688c6c142"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   estimated_stock_pct  quantity  temperature  unit_price  \\\n",
              "0                 0.89       0.0     -0.02885       11.19   \n",
              "1                 0.14       0.0     -0.02885        1.49   \n",
              "2                 0.67       0.0     -0.02885       14.19   \n",
              "3                 0.82       0.0     -0.02885       20.19   \n",
              "4                 0.05       0.0     -0.02885        8.19   \n",
              "\n",
              "   timestamp_day_of_month  timestamp_day_of_week  timestamp_hour  \\\n",
              "0                       1                      1               9   \n",
              "1                       1                      1               9   \n",
              "2                       1                      1               9   \n",
              "3                       1                      1               9   \n",
              "4                       1                      1               9   \n",
              "\n",
              "   category_baby products  category_baked goods  category_baking  ...  \\\n",
              "0                       0                     0                0  ...   \n",
              "1                       0                     0                0  ...   \n",
              "2                       1                     0                0  ...   \n",
              "3                       0                     0                0  ...   \n",
              "4                       0                     0                0  ...   \n",
              "\n",
              "   category_meat  category_medicine  category_packaged foods  \\\n",
              "0              0                  0                        0   \n",
              "1              0                  0                        0   \n",
              "2              0                  0                        0   \n",
              "3              0                  0                        0   \n",
              "4              0                  0                        0   \n",
              "\n",
              "   category_personal care  category_pets  category_refrigerated items  \\\n",
              "0                       0              0                            0   \n",
              "1                       0              0                            0   \n",
              "2                       0              0                            0   \n",
              "3                       0              0                            0   \n",
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          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Section 5 - Modelling\n",
        "We can now train a machine learning model, and we will use 'estimated_stock_pct' as the target variable, since the problem statement was focused on being able to predict the stock levels of products on an hourly basis.\n",
        "\n",
        "Whilst training the machine learning model, we will use cross-validation, which is a technique where we hold back a portion of the dataset for testing in order to compute how well the trained machine learning model is able to predict the target variable.\n",
        "\n",
        "Finally, to ensure that the trained machine learning model is able to perform robustly, we will want to test it several times on random samples of data, not just once. Hence, we will use a K-fold strategy to train the machine learning model on K (K is an integer to be decided) random samples of the data.\n",
        "\n",
        "First, let's create our target variable y and independent variables X."
      ],
      "metadata": {
        "id": "9sy0nCEd20nE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = merged_df.drop(columns=['estimated_stock_pct'])\n",
        "y = merged_df['estimated_stock_pct']\n",
        "print(X.shape)\n",
        "print(y.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZZ7GjCSX23fZ",
        "outputId": "cb461ba4-e457-4b57-ec95-42d097bf94af"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(10845, 28)\n",
            "(10845,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "K = 10\n",
        "split = 0.75"
      ],
      "metadata": {
        "id": "8ForFMQb3PG2"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are going to use a RandomForestRegressor model."
      ],
      "metadata": {
        "id": "-F4PVg463RGb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = []\n",
        "\n",
        "for fold in range(0, K):\n",
        "\n",
        "  # Instantiate algorithm\n",
        "  model = RandomForestRegressor()\n",
        "  scaler = StandardScaler()\n",
        "\n",
        "  # Create training and test samples\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=split, random_state=42)\n",
        "\n",
        "  # Scale X data, we scale the data because it helps the algorithm to converge\n",
        "  # and helps the algorithm to not be greedy with large values\n",
        "  scaler.fit(X_train)\n",
        "  X_train = scaler.transform(X_train)\n",
        "  X_test = scaler.transform(X_test)\n",
        "\n",
        "  # Train model\n",
        "  trained_model = model.fit(X_train, y_train)\n",
        "\n",
        "  # Generate predictions on test sample\n",
        "  y_pred = trained_model.predict(X_test)\n",
        "\n",
        "  # Compute accuracy, using mean absolute error\n",
        "  mae = mean_absolute_error(y_true=y_test, y_pred=y_pred)\n",
        "  accuracy.append(mae)\n",
        "  print(f\"Fold {fold + 1}: MAE = {mae:.3f}\")\n",
        "\n",
        "print(f\"Average MAE: {(sum(accuracy) / len(accuracy)):.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rBvBKfIA3QLc",
        "outputId": "94ddc0a2-9213-4ef9-9081-fcc82d662b2d"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fold 1: MAE = 0.241\n",
            "Fold 2: MAE = 0.240\n",
            "Fold 3: MAE = 0.240\n",
            "Fold 4: MAE = 0.240\n",
            "Fold 5: MAE = 0.241\n",
            "Fold 6: MAE = 0.241\n",
            "Fold 7: MAE = 0.241\n",
            "Fold 8: MAE = 0.241\n",
            "Fold 9: MAE = 0.241\n",
            "Fold 10: MAE = 0.241\n",
            "Average MAE: 0.24\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that the mean absolute error (MAE) is almost exactly the same each time. It shows that the performance of the model is consistent across different random samples of the data.\n",
        "\n",
        "Even though the model is predicting robustly, this value for MAE is not so good. In an ideal world, we would want the MAE to be as low as possible. At this stage, since we only have small samples of the data, we can report back to the business with these findings and recommend that the the dataset needs to be further engineered, or more datasets need to be added.\n",
        "\n",
        "As a final note, we can use the trained model to intepret which features were signficant when the model was predicting the target variable."
      ],
      "metadata": {
        "id": "QBuJ9kUW4Ujq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features = [i.split(\"__\")[0] for i in X.columns]\n",
        "importances = model.feature_importances_\n",
        "indices = np.argsort(importances)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 20))\n",
        "plt.title('Feature Importances')\n",
        "plt.barh(range(len(indices)), importances[indices], color='y', align='center')\n",
        "plt.yticks(range(len(indices)), [features[i] for i in indices])\n",
        "plt.xlabel('Relative Importance')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9duA0I0W3iQe",
        "outputId": "9a1989cd-6018-4f25-c513-aa822f0a6a24"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x2000 with 1 Axes>"
            ],
            "image/png": 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Ki4tt28mTJ+3q8HO29tX0veVn7c3hSu/rrFmzjE8//dQ4ePCgsW/fPmPOnDmGg4ODsWzZMlsd/s7+jNAAuIW9/vrrRrNmzQxHR0ejU6dOxpdffmkr6969uzF06FC7+u+//74RFBRkODo6Gu3atTPWr19vV15ZWWkkJiYaTZo0MZycnIyePXsa+/fvvxFDwQVq8r6eOnXK6N27t+Hl5WXUrVvX8Pf3N0aO/P/au/+YqOs/DuDPjxx33OGBliSOqeAgdmwmSsvwj6DSQeY6ZiXgkbAI/SMWoiYru7T1A3/M5MBWmxqi80Rno5jEzH5AximuH9JEhnGDqMZyOIuADhBe3z+cn/nJjybfFFSej42Ne/94vd/3/owd97r35305fFM5SoZzbVtbWwXAVT8JCQk3HJNGzs2+tqmpqTJlyhQxGo0SFhYmqamp0tLSMoLPiESGd12nT5+ue13Xr1+vtuHr7O3jZl5bvtbePoZzXdetWyeRkZESEBAgEydOlPj4eCkvL9fE49/sJYqIyMjubSAiIiIiIiKiOwHPNCAiIiIiIiIiXUwaEBEREREREZEuJg2IiIiIiIiISBeTBkRERERERESki0kDIiIiIiIiItLFpAERERERERER6WLSgIiIiIiIiIh0MWlARERERERERLqYNCAiIiL6FzU1NVAUBX/88cdtEYeIiGikMGlAREREd7WsrCwoigJFUeDv74+IiAisXbsWPp/vlo6bmJiIlStXasrmzZuHjo4OBAcH37Jx29raoCgKTp06dcvG+K+ysrKQkpIy2tMgIqIbYBjtCRARERHdasnJySgtLcXAwAC+++47ZGZmQlEUbNq0aUTnYTQaERoaOqJj3k4GBwehKMpoT4OIiIaBOw2IiIjormcymRAaGoqpU6ciJSUF8+fPx9GjR9X6oaEhFBYWIiIiAmazGbNmzcKhQ4euGe/8+fNIT09HWFgYLBYLZs6cif3796v1WVlZqK2thcvlUnc5tLW1aW5P6OrqgtlsRnV1tSZ2RUUFrFYrent7AQC//PILlixZggkTJuCee+6B3W5HW1vbDT/3y2MeOXIEs2fPhtlsxmOPPYZz586huroaNpsNQUFBWLp0qTomcGmnRG5uLnJzcxEcHIxJkybB6XRCRNQ2Fy5cwLJlyzBx4kRYLBY88cQT+Omnn9T63bt3Y8KECaisrERMTAxMJhOef/55lJWV4ZNPPlHXpqamBgBQUFCA+++/HxaLBTNmzIDT6cTAwIAab8OGDYiNjcXevXsRHh6O4OBgpKWl4a+//tJcy82bNyMyMhImkwnTpk3D22+/rdb/1/UkIhprmDQgIiKiMeX06dPweDwwGo1qWWFhIfbs2YMPPvgAjY2NyM/PR0ZGBmpra3Vj+Hw+xMXFoaqqCqdPn8by5cvx3HPP4eTJkwAAl8uF+Ph45OTkoKOjAx0dHZg6daomRlBQEBYtWgS3260p37dvH1JSUmCxWDAwMICkpCRYrVYcO3YMdXV1GD9+PJKTk9Hf3z+s571hwwZs374dHo9HfeNcVFQEt9uNqqoqfPbZZygpKdH0KSsrg8FgwMmTJ+FyufDuu+9i586dan1WVha+/fZbVFZW4vjx4xARLFy4UPNGv7e3F5s2bcLOnTvR2NiI4uJiLFmyBMnJyerazJs3DwBgtVqxe/dunDlzBi6XCzt27MC2bds0c/J6vfj4449x+PBhHD58GLW1tdi4caNa/8orr2Djxo1wOp04c+YM3G43Jk+eDAA3dT2JiMYMISIiIrqLZWZmip+fnwQGBorJZBIAMm7cODl06JCIiPh8PrFYLOLxeDT9srOzJT09XUREvvrqKwEgFy5cuOY4Tz75pKxevVp9nJCQIHl5eZo2/4xTUVEh48ePl56eHhER+fPPPyUgIECqq6tFRGTv3r0SHR0tQ0NDaoy+vj4xm81y5MgR3Xm0trYKAPnhhx80Y37++edqm8LCQgEgXq9XLVuxYoUkJSVp5m+z2TRjFxQUiM1mExGRs2fPCgCpq6tT6zs7O8VsNsvBgwdFRKS0tFQAyKlTpzRzzMzMFLvdrjv/K23ZskXi4uLUx+vXrxeLxSJdXV1q2csvvyxz584VEZGuri4xmUyyY8cO3Xj/z3oSEY11PNOAiIiI7nqPPvoo3n//ffT09GDbtm0wGAx4+umnAQAtLS3o7e3FggULNH36+/sxe/Zs3XiDg4N45513cPDgQfz222/o7+9HX18fLBbLsOa1cOFC+Pv7o7KyEmlpafjoo48QFBSE+fPnAwAaGhrQ0tICq9Wq6efz+eD1eoc11gMPPKD+PnnyZPUWgCvLLu+UuOzhhx/WnEEQHx+PrVu3YnBwEE1NTTAYDJg7d65af++99yI6OhpNTU1qmdFo1Ix9PQcOHEBxcTG8Xi+6u7tx8eJFBAUFadqEh4dr1mPKlCk4d+4cAKCpqQl9fX14/PHHdePfzPUkIhormDQgIiKiu15gYCAiIyMBAB9++CFmzZqFXbt2ITs7G93d3QCAqqoqhIWFafqZTCbdeFu2bIHL5UJRURFmzpyJwMBArFy5cthb3I1GI5555hm43W6kpaXB7XYjNTUVBsOlf9G6u7sRFxeHffv2XdU3JCRkWGP5+/urv1/+JokrKYqCoaGhYcW8EWaz+YYOPzx+/DgcDgfeeOMNJCUlITg4GOXl5di6daum3fXmbTabrzvGzVxPIqKxgkkDIiIiGlPGjRuHV199FatWrcLSpUvVA/ra29uRkJBwQzHq6upgt9uRkZEB4NLhe2fPnkVMTIzaxmg0YnBw8F9jORwOLFiwAI2Njfjyyy/x1ltvqXVz5szBgQMHcN999131iftIqK+v1zw+ceIEoqKi4OfnB5vNhosXL6K+vl49k+D8+fNobm7WrIMevbXxeDyYPn061q1bp5b9/PPPw5pvVFQUzGYzvvjiC7zwwgtX1Y/2ehIR3Yl4ECIRERGNOc8++yz8/Pzw3nvvwWq1Ys2aNcjPz0dZWRm8Xi++//57lJSUoKysTLd/VFQUjh49Co/Hg6amJqxYsQK///67pk14eDjq6+vR1taGzs7Oa36K/8gjjyA0NBQOhwMRERGa7f4OhwOTJk2C3W7HsWPH0NraipqaGrz00kv49ddfb96CXEN7eztWrVqF5uZm7N+/HyUlJcjLywNwaQ3sdjtycnLwzTffoKGhARkZGQgLC4Pdbr9u3PDwcPz4449obm5GZ2cnBgYGEBUVhfb2dpSXl8Pr9aK4uBgVFRXDmm9AQAAKCgqwdu1a7NmzB16vFydOnMCuXbsAjP56EhHdiZg0ICIiojHHYDAgNzcXmzdvRk9PD9588004nU4UFhbCZrMhOTkZVVVViIiI0O3/2muvYc6cOUhKSkJiYiJCQ0ORkpKiabNmzRr4+fkhJiYGISEhaG9v142lKArS09PR0NAAh8OhqbNYLPj6668xbdo0LF68GDabDdnZ2fD5fCPySfmyZcvw999/46GHHsKLL76IvLw8LF++XK0vLS1FXFwcFi1ahPj4eIgIPv3006tuIfinnJwcREdH48EHH0RISAjq6urw1FNPIT8/H7m5uYiNjYXH44HT6Rz2nJ1OJ1avXo3XX38dNpsNqamp6pkHo72eRER3IkXkii/bJSIiIiICkJiYiNjYWBQVFY32VIiIaBRxpwERERERERER6WLSgIiIiIiIiIh08fYEIiIiIiIiItLFnQZEREREREREpItJAyIiIiIiIiLSxaQBEREREREREeli0oCIiIiIiIiIdDFpQERERERERES6mDQgIiIiIiIiIl1MGhARERERERGRLiYNiIiIiIiIiEjX/wDJpZi1P9YzjAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This feature importance visualisation tells us:\n",
        "\n",
        "*   The product categories were not that important\n",
        "*   The unit price and temperature were important in predicting stock\n",
        "*   The hour of day was also important for predicting stock"
      ],
      "metadata": {
        "id": "TgkZK9cm4ygt"
      }
    }
  ]
}