data_science_tasks .ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Mounted the google drive to load the data."
      ],
      "metadata": {
        "id": "Bz5JLmopgPM3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pWg383e7niqR",
        "outputId": "f1508814-80ab-4a7c-f323-acee5279d24f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CqhvGD37mg8Z"
      },
      "source": [
        "## Data description for the file 'data.csv'\n",
        "\n",
        "#### About the data\n",
        "This data have the daily sales records of 5 products for two bakery stores. The data gathered represent the sales in a whole year. In addition to that, the prediction of the AI for each each day is included.\n",
        "\n",
        "#### Data fields\n",
        "\n",
        "* **date**: The date of the sales record\n",
        "* **item_id**: The item id of the product as used in the database\n",
        "* **name**: The name of the product\n",
        "* **unit_price**: The price of a single piece of the product in Euros.\n",
        "* **waste**: The quantity in pieces of the product went to waste at the end of the day.\n",
        "* **sales_qntty**: The quantity in pieces of what the store have sold during the day.\n",
        "* **category**: To which category does this product belonges to\n",
        "* **store_name**: The name of the bakery store\n",
        "* **prediction**: The prediction by the AI of the expected quantity to be sold on a given day."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Loading"
      ],
      "metadata": {
        "id": "-xPpm4vGgtz2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "41DM_xPmmg8b"
      },
      "outputs": [],
      "source": [
        "# load the libraries\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LinearRegression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-MU3U2ramg8c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "afe49d25-0c3b-4d11-9aef-dd7c66c807c8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            date  item_id                  name  unit_price  waste  \\\n",
              "0     2020-06-02      102       Schnittbrötchen        0.36   19.0   \n",
              "1     2020-06-03      102       Schnittbrötchen        0.36    0.0   \n",
              "2     2020-06-04      102       Schnittbrötchen        0.36   53.0   \n",
              "3     2020-06-02      121      Mehrkornbrötchen        0.70   41.0   \n",
              "4     2020-06-03      121      Mehrkornbrötchen        0.70    0.0   \n",
              "...          ...      ...                   ...         ...    ...   \n",
              "3244  2021-05-31      102       Schnittbrötchen        0.36   27.0   \n",
              "3245  2021-05-31      121      Mehrkornbrötchen        0.70   27.0   \n",
              "3246  2021-05-31      101                  Ebbi        0.40   68.0   \n",
              "3247  2021-05-31      268  Der Ebbing - MK Brot        3.40    8.5   \n",
              "3248  2021-05-31      128         Laugenstangen        1.00   13.0   \n",
              "\n",
              "      sales_qntty                   category store_name  prediction  \n",
              "0           383.0  Ebbis und Schnittbrötchen        one  370.687347  \n",
              "1           360.0  Ebbis und Schnittbrötchen        one  331.705994  \n",
              "2           353.0  Ebbis und Schnittbrötchen        one  350.590912  \n",
              "3           120.0         Gemischte Brötchen        one   84.045364  \n",
              "4            92.0         Gemischte Brötchen        one   80.833221  \n",
              "...           ...                        ...        ...         ...  \n",
              "3244        340.0  Ebbis und Schnittbrötchen        two  365.815094  \n",
              "3245         42.0         Gemischte Brötchen        two   67.243301  \n",
              "3246        155.0  Ebbis und Schnittbrötchen        two  180.313950  \n",
              "3247         16.5                      Brote        two   19.608158  \n",
              "3248         48.0         Gemischte Brötchen        two   42.722023  \n",
              "\n",
              "[3249 rows x 9 columns]"
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 3249,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 359,\n        \"samples\": [\n          \"2021-02-24\",\n          \"2020-07-22\",\n          \"2021-05-10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"item_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61,\n        \"min\": 101,\n        \"max\": 268,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          121,\n          128,\n          101\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Mehrkornbr\\u00f6tchen\",\n          \"Laugenstangen\",\n          \"Ebbi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unit_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1090489367180907,\n        \"min\": 0.36,\n        \"max\": 3.4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.7,\n          1.0,\n          0.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"waste\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 46.5223752536421,\n        \"min\": 0.0,\n        \"max\": 559.0,\n        \"num_unique_values\": 235,\n        \"samples\": [\n          123.0,\n          155.0,\n          217.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales_qntty\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 181.13516241339633,\n        \"min\": 4.0,\n        \"max\": 914.0,\n        \"num_unique_values\": 639,\n        \"samples\": [\n          277.0,\n          140.0,\n          709.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Ebbis und Schnittbr\\u00f6tchen\",\n          \"Gemischte Br\\u00f6tchen\",\n          \"Brote\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"two\",\n          \"one\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 185.24307500372203,\n        \"min\": 11.1263151169,\n        \"max\": 941.9942626953,\n        \"num_unique_values\": 3249,\n        \"samples\": [\n          39.8618431091,\n          37.4039535522\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "# load the data and convert the date column into date data type\n",
        "data = pd.read_csv('/content/drive/MyDrive/data.csv')\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XYo4EM0ksWDN",
        "outputId": "30974276-af47-4311-e059-858a99f40b97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3249 entries, 0 to 3248\n",
            "Data columns (total 9 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   date         3249 non-null   object \n",
            " 1   item_id      3249 non-null   int64  \n",
            " 2   name         3249 non-null   object \n",
            " 3   unit_price   3249 non-null   float64\n",
            " 4   waste        3249 non-null   float64\n",
            " 5   sales_qntty  3249 non-null   float64\n",
            " 6   category     3249 non-null   object \n",
            " 7   store_name   3249 non-null   object \n",
            " 8   prediction   3249 non-null   float64\n",
            "dtypes: float64(4), int64(1), object(4)\n",
            "memory usage: 228.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Converted all columns representing quantities to integer values using the ceiling function, as quantities are typically whole numbers."
      ],
      "metadata": {
        "id": "I4DdpCl-uBR0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# used  ceiling  function.\n",
        "data[['prediction', 'waste', 'sales_qntty']] = np.ceil(data[['prediction', 'waste', 'sales_qntty']]).astype(int)\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l-JX8b7GpoUJ",
        "outputId": "fab09421-9791-4450-c061-cb94f79a8006"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            date  item_id                  name  unit_price  waste  \\\n",
              "0     2020-06-02      102       Schnittbrötchen        0.36     19   \n",
              "1     2020-06-03      102       Schnittbrötchen        0.36      0   \n",
              "2     2020-06-04      102       Schnittbrötchen        0.36     53   \n",
              "3     2020-06-02      121      Mehrkornbrötchen        0.70     41   \n",
              "4     2020-06-03      121      Mehrkornbrötchen        0.70      0   \n",
              "...          ...      ...                   ...         ...    ...   \n",
              "3244  2021-05-31      102       Schnittbrötchen        0.36     27   \n",
              "3245  2021-05-31      121      Mehrkornbrötchen        0.70     27   \n",
              "3246  2021-05-31      101                  Ebbi        0.40     68   \n",
              "3247  2021-05-31      268  Der Ebbing - MK Brot        3.40      9   \n",
              "3248  2021-05-31      128         Laugenstangen        1.00     13   \n",
              "\n",
              "      sales_qntty                   category store_name  prediction  \n",
              "0             383  Ebbis und Schnittbrötchen        one         371  \n",
              "1             360  Ebbis und Schnittbrötchen        one         332  \n",
              "2             353  Ebbis und Schnittbrötchen        one         351  \n",
              "3             120         Gemischte Brötchen        one          85  \n",
              "4              92         Gemischte Brötchen        one          81  \n",
              "...           ...                        ...        ...         ...  \n",
              "3244          340  Ebbis und Schnittbrötchen        two         366  \n",
              "3245           42         Gemischte Brötchen        two          68  \n",
              "3246          155  Ebbis und Schnittbrötchen        two         181  \n",
              "3247           17                      Brote        two          20  \n",
              "3248           48         Gemischte Brötchen        two          43  \n",
              "\n",
              "[3249 rows x 9 columns]"
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5d00ade8-d00f-471a-ac85-f41834a4d861 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 3249,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 359,\n        \"samples\": [\n          \"2021-02-24\",\n          \"2020-07-22\",\n          \"2021-05-10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"item_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61,\n        \"min\": 101,\n        \"max\": 268,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          121,\n          128,\n          101\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Mehrkornbr\\u00f6tchen\",\n          \"Laugenstangen\",\n          \"Ebbi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unit_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1090489367180907,\n        \"min\": 0.36,\n        \"max\": 3.4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.7,\n          1.0,\n          0.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"waste\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 46,\n        \"min\": 0,\n        \"max\": 559,\n        \"num_unique_values\": 216,\n        \"samples\": [\n          249,\n          258,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales_qntty\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 181,\n        \"min\": 4,\n        \"max\": 914,\n        \"num_unique_values\": 600,\n        \"samples\": [\n          71,\n          154,\n          379\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Ebbis und Schnittbr\\u00f6tchen\",\n          \"Gemischte Br\\u00f6tchen\",\n          \"Brote\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"two\",\n          \"one\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 185,\n        \"min\": 12,\n        \"max\": 942,\n        \"num_unique_values\": 595,\n        \"samples\": [\n          78,\n          69\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lUbVyQFIg_vq",
        "outputId": "fb5b6b66-a11e-4c14-cdd9-abbdbe397e03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date           0\n",
              "item_id        0\n",
              "name           0\n",
              "unit_price     0\n",
              "waste          0\n",
              "sales_qntty    0\n",
              "category       0\n",
              "store_name     0\n",
              "prediction     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vx_BARhOg5LM",
        "outputId": "11eec69d-cdf8-48b9-a656-a28c85b4fefa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3249 entries, 0 to 3248\n",
            "Data columns (total 9 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   date         3249 non-null   object \n",
            " 1   item_id      3249 non-null   int64  \n",
            " 2   name         3249 non-null   object \n",
            " 3   unit_price   3249 non-null   float64\n",
            " 4   waste        3249 non-null   int64  \n",
            " 5   sales_qntty  3249 non-null   int64  \n",
            " 6   category     3249 non-null   object \n",
            " 7   store_name   3249 non-null   object \n",
            " 8   prediction   3249 non-null   int64  \n",
            "dtypes: float64(1), int64(4), object(4)\n",
            "memory usage: 228.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Converted the data type of the date column from object to date."
      ],
      "metadata": {
        "id": "ijvk0smOh7Sx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['date'] = pd.to_datetime(data['date'])\n",
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "40Jmv0GYg1yX",
        "outputId": "328a7b21-3ff7-48e8-9a06-4df039ba13f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3249 entries, 0 to 3248\n",
            "Data columns (total 9 columns):\n",
            " #   Column       Non-Null Count  Dtype         \n",
            "---  ------       --------------  -----         \n",
            " 0   date         3249 non-null   datetime64[ns]\n",
            " 1   item_id      3249 non-null   int64         \n",
            " 2   name         3249 non-null   object        \n",
            " 3   unit_price   3249 non-null   float64       \n",
            " 4   waste        3249 non-null   int64         \n",
            " 5   sales_qntty  3249 non-null   int64         \n",
            " 6   category     3249 non-null   object        \n",
            " 7   store_name   3249 non-null   object        \n",
            " 8   prediction   3249 non-null   int64         \n",
            "dtypes: datetime64[ns](1), float64(1), int64(4), object(3)\n",
            "memory usage: 228.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **1. What are the start and end dates of the data?**"
      ],
      "metadata": {
        "id": "5u0Zw8izfIKF"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HVxW94nsmg8c",
        "outputId": "18752f37-b01d-4a07-ac90-e391c3013e4b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "starting date: 2020-06-02 00:00:00\n",
            "ending date: 2021-05-31 00:00:00\n"
          ]
        }
      ],
      "source": [
        "# 1. what are the start and end dates of the data?\n",
        "initial_date = data['date'].min()\n",
        "last_date = data['date'].max()\n",
        "print(\"starting date:\", initial_date)\n",
        "print(\"ending date:\", last_date)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **2. Show a summary table of the sales and wastes of each store. The summary of each store should show the total waste and sales in euros for the whole year.**"
      ],
      "metadata": {
        "id": "b4JEPb_5fN7a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# explored the  data\n",
        "data.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "eGmSNK9iosEV",
        "outputId": "4b03d84a-a24b-4023-afc6-f2ebc2b88bca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           date  item_id                  name  unit_price  waste  \\\n",
              "82   2020-06-24      102       Schnittbrötchen        0.36     21   \n",
              "341  2020-08-20      101                  Ebbi        0.40     13   \n",
              "996  2021-01-26      268  Der Ebbing - MK Brot        3.40      6   \n",
              "1722 2020-07-15      121      Mehrkornbrötchen        0.70     28   \n",
              "2791 2021-02-24      102       Schnittbrötchen        0.36     56   \n",
              "\n",
              "      sales_qntty                   category store_name  prediction  \n",
              "82            367  Ebbis und Schnittbrötchen        one         344  \n",
              "341           266  Ebbis und Schnittbrötchen        one         307  \n",
              "996            26                      Brote        one          29  \n",
              "1722           57         Gemischte Brötchen        two          52  \n",
              "2791          269  Ebbis und Schnittbrötchen        two         331  "
            ],
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
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              "        document.querySelector('#df-55cf513b-d489-43d5-8f57-69711f23cdfb button');\n",
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              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2020-06-24 00:00:00\",\n        \"max\": \"2021-02-24 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2020-08-20 00:00:00\",\n          \"2021-02-24 00:00:00\",\n          \"2021-01-26 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"item_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 72,\n        \"min\": 101,\n        \"max\": 268,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          101,\n          121,\n          102\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Ebbi\",\n          \"Mehrkornbr\\u00f6tchen\",\n          \"Schnittbr\\u00f6tchen\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unit_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3247188380935782,\n        \"min\": 0.36,\n        \"max\": 3.4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.4,\n          0.7,\n          0.36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"waste\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 6,\n        \"max\": 56,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          13,\n          56,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales_qntty\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 148,\n        \"min\": 26,\n        \"max\": 367,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          266,\n          269,\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Ebbis und Schnittbr\\u00f6tchen\",\n          \"Brote\",\n          \"Gemischte Br\\u00f6tchen\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"two\",\n          \"one\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 157,\n        \"min\": 29,\n        \"max\": 344,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          307,\n          331\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Summary  report  of **TOTAL items**  sold  by  each  store in the entire TIME duration."
      ],
      "metadata": {
        "id": "ut4bH55W5LIT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['cost_price'] = data['unit_price']*0.5 # created new column cost_price that is production cost"
      ],
      "metadata": {
        "id": "pu2ivqZqFvd7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# groupped by store_name and aggregate required fields\n",
        "summary_table = data.groupby('store_name').agg({'waste': 'sum', 'unit_price': 'sum', 'sales_qntty': 'sum', 'cost_price': 'sum', 'prediction': 'sum'})\n",
        "\n",
        "summary_table['produced'] = summary_table['waste'] + summary_table['sales_qntty'] # calculated total production\n",
        "summary_table['Ai_difference'] = (summary_table['produced'] - summary_table['prediction']).abs() # calculated the difference with AI prediction\n",
        "summary_table['total_sales'] = data.groupby('store_name').apply(lambda x: (x['unit_price'] * x['sales_qntty']).sum()).values # calculated total sales in Euros\n",
        "summary_table['waste_loss_money'] = data.groupby('store_name').apply(lambda x: (x['waste'] * x['cost_price']).sum()).values # calculated loss money because of waste\n",
        "summary_table = summary_table.rename(columns={'waste': 'Waste_quantity', 'total_sales': 'Total Sales (Euros)', 'waste_loss_money': 'Waste Loss (Euros)'}) # renamed columns\n",
        "summary_table = summary_table.drop(columns=['unit_price']) # dropped unnecessary column\n",
        "\n",
        "print(\"\\nSummary table of sales and waste of each store for all items:\")\n",
        "summary_table"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "SX_6Y8Qf1uKB",
        "outputId": "68821f4b-5a92-43cc-be93-8d33716401b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Summary table of sales and waste of each store for all items:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Waste_quantity  sales_qntty  cost_price  prediction  produced  \\\n",
              "store_name                                                                  \n",
              "one                  36833       285172      884.86      297730    322005   \n",
              "two                  48919       286866      956.67      296960    335785   \n",
              "\n",
              "            Ai_difference  Total Sales (Euros)  Waste Loss (Euros)  \n",
              "store_name                                                          \n",
              "one                 24275            150490.34             8628.15  \n",
              "two                 38825            142098.54            11811.44  "
            ],
            "text/html": [
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              "\n",
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              "      background-color: #3B4455;\n",
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              "\n",
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              "        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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
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              "      }\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "summary_table",
              "summary": "{\n  \"name\": \"summary_table\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"store_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"two\",\n          \"one\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Waste_quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8546,\n        \"min\": 36833,\n        \"max\": 48919,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          48919,\n          36833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales_qntty\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1197,\n        \"min\": 285172,\n        \"max\": 286866,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          286866,\n          285172\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cost_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50.77733795700594,\n        \"min\": 884.86,\n        \"max\": 956.67,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          956.67,\n          884.86\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 544,\n        \"min\": 296960,\n        \"max\": 297730,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          296960,\n          297730\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"produced\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9743,\n        \"min\": 322005,\n        \"max\": 335785,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          335785,\n          322005\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ai_difference\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10288,\n        \"min\": 24275,\n        \"max\": 38825,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          38825,\n          24275\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total Sales (Euros)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5933.898686361282,\n        \"min\": 142098.53999999998,\n        \"max\": 150490.34,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          142098.53999999998,\n          150490.34\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Waste Loss (Euros)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2250.9259454833254,\n        \"min\": 8628.15,\n        \"max\": 11811.44,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          11811.44,\n          8628.15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Summary  report  of **each item**  sold  by  each   store in the entire TIME duration."
      ],
      "metadata": {
        "id": "aC1UGXRe5et8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_summary_table(data):\n",
        "    data['cost_price'] = data['unit_price']*0.5     # calculated the cost price\n",
        "    summary_table = data.groupby('name').agg({'waste': 'sum', 'sales_qntty': 'sum', 'prediction': 'sum'})  # groupped by 'name' and aggregate necessary fields\n",
        "    summary_table['produced'] = summary_table['waste'] + summary_table['sales_qntty'] # calculated total production\n",
        "    summary_table['Ai_difference'] = (summary_table['produced'] - summary_table['prediction']).abs() # calculated the difference with AI prediction\n",
        "    summary_table['Total Sales (Euros)'] = data.groupby('name').apply(lambda x: (x['unit_price'] * x['sales_qntty']).sum()).values # calculated total sales in Euros\n",
        "    summary_table['Waste Loss (Euros)'] = data.groupby('name').apply(lambda x: (x['waste'] * x['cost_price']).sum()).values # calculated loss money because of waste\n",
        "    summary_table = summary_table.rename(columns={'waste': 'Waste_quantity'}) # renamed columns\n",
        "    return summary_table"
      ],
      "metadata": {
        "id": "qHRoI6m7MucT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# filtered data for each store\n",
        "store_1 = data[data['store_name'] == 'one']\n",
        "store_2 = data[data['store_name'] == 'two']\n",
        "\n",
        "# created summary tables for each store\n",
        "summary_table_store_1 = create_summary_table(store_1)\n",
        "summary_table_store_2 = create_summary_table(store_2)"
      ],
      "metadata": {
        "id": "w_YuSniWUQg9",
        "outputId": "db16f48d-1fdc-4e37-c357-d643c8b955e3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-6e42f2972f3b>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['cost_price'] = data['unit_price']*0.5     # calculated the cost price\n",
            "<ipython-input-13-6e42f2972f3b>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data['cost_price'] = data['unit_price']*0.5     # calculated the cost price\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# added MultiIndex of store names to separate\n",
        "summary_table_store_1.index = pd.MultiIndex.from_product([['one'], summary_table_store_1.index])\n",
        "summary_table_store_2.index = pd.MultiIndex.from_product([['two'], summary_table_store_2.index])\n",
        "\n",
        "# joined summary tables\n",
        "summary_table_combined = pd.concat([summary_table_store_1, summary_table_store_2])\n",
        "\n",
        "# Print combined summary table of sales and waste for both stores\n",
        "print(\"\\nCombined summary table of sales and waste for both stores:\")\n",
        "summary_table_combined"
      ],
      "metadata": {
        "id": "0u_Cs_bYUVpp",
        "outputId": "3b8ae445-de6e-490f-9402-dc505af742dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 429
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Combined summary table of sales and waste for both stores:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          Waste_quantity  sales_qntty  prediction  produced  \\\n",
              "    name                                                                      \n",
              "one Der Ebbing - MK Brot             395         7801        8231      8196   \n",
              "    Ebbi                           14280       107009      112579    121289   \n",
              "    Laugenstangen                   1756        15509       16105     17265   \n",
              "    Mehrkornbrötchen                3237        29139       29731     32376   \n",
              "    Schnittbrötchen                17165       125714      131084    142879   \n",
              "two Der Ebbing - MK Brot             897         5483        5704      6380   \n",
              "    Ebbi                           17607        79206       81831     96813   \n",
              "    Laugenstangen                   2254        16145       16582     18399   \n",
              "    Mehrkornbrötchen                3348        25463       26146     28811   \n",
              "    Schnittbrötchen                24813       160569      166697    185382   \n",
              "\n",
              "                          Ai_difference  Total Sales (Euros)  \\\n",
              "    name                                                       \n",
              "one Der Ebbing - MK Brot             35             26523.40   \n",
              "    Ebbi                           8710             42803.60   \n",
              "    Laugenstangen                  1160             15509.00   \n",
              "    Mehrkornbrötchen               2645             20397.30   \n",
              "    Schnittbrötchen               11795             45257.04   \n",
              "two Der Ebbing - MK Brot            676             18642.20   \n",
              "    Ebbi                          14982             31682.40   \n",
              "    Laugenstangen                  1817             16145.00   \n",
              "    Mehrkornbrötchen               2665             17824.10   \n",
              "    Schnittbrötchen               18685             57804.84   \n",
              "\n",
              "                          Waste Loss (Euros)  \n",
              "    name                                      \n",
              "one Der Ebbing - MK Brot              671.50  \n",
              "    Ebbi                             2856.00  \n",
              "    Laugenstangen                     878.00  \n",
              "    Mehrkornbrötchen                 1132.95  \n",
              "    Schnittbrötchen                  3089.70  \n",
              "two Der Ebbing - MK Brot             1524.90  \n",
              "    Ebbi                             3521.40  \n",
              "    Laugenstangen                    1127.00  \n",
              "    Mehrkornbrötchen                 1171.80  \n",
              "    Schnittbrötchen                  4466.34  "
            ],
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              "      <th>Der Ebbing - MK Brot</th>\n",
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            }
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **3. Show the day with most sales of Schnittbrötchen (item_id = 102).**"
      ],
      "metadata": {
        "id": "r5aVRyCdffqi"
      }
    },
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        {
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          "text": [
            "Day with most sales of Schnittbrötchen : 2020-08-08 00:00:00 \n",
            "\n",
            "Top 5 days with most sales of Schnittbrötchen\n"
          ]
        },
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              "           date  sales_qntty\n",
              "285  2020-08-08          914\n",
              "2968 2021-04-03          908\n",
              "1933 2020-08-29          897\n",
              "3227 2021-05-29          895\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "top_days",
              "summary": "{\n  \"name\": \"top_days\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2020-08-08 00:00:00\",\n        \"max\": \"2021-05-29 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2021-04-03 00:00:00\",\n          \"2021-04-24 00:00:00\",\n          \"2020-08-29 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales_qntty\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 848,\n        \"max\": 914,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          908,\n          848,\n          897\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# 3. show the day with most sales of Schnittbrötchen (item_id = 102)\n",
        "\n",
        "# filterd  the  datframe for  the item_id = 102\n",
        "schnittbroetchen_data = data[data['item_id'] == 102]\n",
        "\n",
        "# sorted the data by  Descending order of SALES QUANTITY\n",
        "sorted_data = schnittbroetchen_data.sort_values(by='sales_qntty', ascending=False)\n",
        "\n",
        "# created the dateframe to fetch most  selling  date.\n",
        "i = 5\n",
        "top_days = sorted_data.head(i)[['date','sales_qntty']]\n",
        "for day in top_days['date']:\n",
        "    y = day\n",
        "    break\n",
        "print(\"Day with most sales of Schnittbrötchen : {} \".format(y))\n",
        "print(\"\\nTop {} days with most sales of Schnittbrötchen\".format(i))\n",
        "top_days"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **4. Show the record with the worst prediction in Euros**"
      ],
      "metadata": {
        "id": "8G7ZunXPfjnr"
      }
    },
    {
      "cell_type": "code",
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        "id": "leM6NBGTmg8d",
        "outputId": "b9d2d444-7f3b-4a4c-e938-49fb929f1dfb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           date  item_id                  name  unit_price  waste  \\\n",
              "0    2020-06-02      102       Schnittbrötchen        0.36     19   \n",
              "1    2020-06-03      102       Schnittbrötchen        0.36      0   \n",
              "2    2020-06-04      102       Schnittbrötchen        0.36     53   \n",
              "3    2020-06-02      121      Mehrkornbrötchen        0.70     41   \n",
              "4    2020-06-03      121      Mehrkornbrötchen        0.70      0   \n",
              "...         ...      ...                   ...         ...    ...   \n",
              "3244 2021-05-31      102       Schnittbrötchen        0.36     27   \n",
              "3245 2021-05-31      121      Mehrkornbrötchen        0.70     27   \n",
              "3246 2021-05-31      101                  Ebbi        0.40     68   \n",
              "3247 2021-05-31      268  Der Ebbing - MK Brot        3.40      9   \n",
              "3248 2021-05-31      128         Laugenstangen        1.00     13   \n",
              "\n",
              "      sales_qntty                   category store_name  prediction  \\\n",
              "0             383  Ebbis und Schnittbrötchen        one         371   \n",
              "1             360  Ebbis und Schnittbrötchen        one         332   \n",
              "2             353  Ebbis und Schnittbrötchen        one         351   \n",
              "3             120         Gemischte Brötchen        one          85   \n",
              "4              92         Gemischte Brötchen        one          81   \n",
              "...           ...                        ...        ...         ...   \n",
              "3244          340  Ebbis und Schnittbrötchen        two         366   \n",
              "3245           42         Gemischte Brötchen        two          68   \n",
              "3246          155  Ebbis und Schnittbrötchen        two         181   \n",
              "3247           17                      Brote        two          20   \n",
              "3248           48         Gemischte Brötchen        two          43   \n",
              "\n",
              "      cost_price  Ai_sale_difference  \n",
              "0           0.18                  12  \n",
              "1           0.18                  28  \n",
              "2           0.18                   2  \n",
              "3           0.35                  35  \n",
              "4           0.35                  11  \n",
              "...          ...                 ...  \n",
              "3244        0.18                  26  \n",
              "3245        0.35                  26  \n",
              "3246        0.20                  26  \n",
              "3247        1.70                   3  \n",
              "3248        0.50                   5  \n",
              "\n",
              "[3249 rows x 11 columns]"
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              "      <td>2020-06-03</td>\n",
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              "      <td>one</td>\n",
              "      <td>332</td>\n",
              "      <td>0.18</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-06-04</td>\n",
              "      <td>102</td>\n",
              "      <td>Schnittbrötchen</td>\n",
              "      <td>0.36</td>\n",
              "      <td>53</td>\n",
              "      <td>353</td>\n",
              "      <td>Ebbis und Schnittbrötchen</td>\n",
              "      <td>one</td>\n",
              "      <td>351</td>\n",
              "      <td>0.18</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-06-02</td>\n",
              "      <td>121</td>\n",
              "      <td>Mehrkornbrötchen</td>\n",
              "      <td>0.70</td>\n",
              "      <td>41</td>\n",
              "      <td>120</td>\n",
              "      <td>Gemischte Brötchen</td>\n",
              "      <td>one</td>\n",
              "      <td>85</td>\n",
              "      <td>0.35</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-06-03</td>\n",
              "      <td>121</td>\n",
              "      <td>Mehrkornbrötchen</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0</td>\n",
              "      <td>92</td>\n",
              "      <td>Gemischte Brötchen</td>\n",
              "      <td>one</td>\n",
              "      <td>81</td>\n",
              "      <td>0.35</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3244</th>\n",
              "      <td>2021-05-31</td>\n",
              "      <td>102</td>\n",
              "      <td>Schnittbrötchen</td>\n",
              "      <td>0.36</td>\n",
              "      <td>27</td>\n",
              "      <td>340</td>\n",
              "      <td>Ebbis und Schnittbrötchen</td>\n",
              "      <td>two</td>\n",
              "      <td>366</td>\n",
              "      <td>0.18</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3245</th>\n",
              "      <td>2021-05-31</td>\n",
              "      <td>121</td>\n",
              "      <td>Mehrkornbrötchen</td>\n",
              "      <td>0.70</td>\n",
              "      <td>27</td>\n",
              "      <td>42</td>\n",
              "      <td>Gemischte Brötchen</td>\n",
              "      <td>two</td>\n",
              "      <td>68</td>\n",
              "      <td>0.35</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3246</th>\n",
              "      <td>2021-05-31</td>\n",
              "      <td>101</td>\n",
              "      <td>Ebbi</td>\n",
              "      <td>0.40</td>\n",
              "      <td>68</td>\n",
              "      <td>155</td>\n",
              "      <td>Ebbis und Schnittbrötchen</td>\n",
              "      <td>two</td>\n",
              "      <td>181</td>\n",
              "      <td>0.20</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3247</th>\n",
              "      <td>2021-05-31</td>\n",
              "      <td>268</td>\n",
              "      <td>Der Ebbing - MK Brot</td>\n",
              "      <td>3.40</td>\n",
              "      <td>9</td>\n",
              "      <td>17</td>\n",
              "      <td>Brote</td>\n",
              "      <td>two</td>\n",
              "      <td>20</td>\n",
              "      <td>1.70</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3248</th>\n",
              "      <td>2021-05-31</td>\n",
              "      <td>128</td>\n",
              "      <td>Laugenstangen</td>\n",
              "      <td>1.00</td>\n",
              "      <td>13</td>\n",
              "      <td>48</td>\n",
              "      <td>Gemischte Brötchen</td>\n",
              "      <td>two</td>\n",
              "      <td>43</td>\n",
              "      <td>0.50</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 3249,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2020-06-02 00:00:00\",\n        \"max\": \"2021-05-31 00:00:00\",\n        \"num_unique_values\": 359,\n        \"samples\": [\n          \"2021-02-24 00:00:00\",\n          \"2020-07-22 00:00:00\",\n          \"2021-05-10 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"item_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 61,\n        \"min\": 101,\n        \"max\": 268,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          121,\n          128,\n          101\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Mehrkornbr\\u00f6tchen\",\n          \"Laugenstangen\",\n          \"Ebbi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unit_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1090489367180907,\n        \"min\": 0.36,\n        \"max\": 3.4,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.7,\n          1.0,\n          0.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"waste\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 46,\n        \"min\": 0,\n        \"max\": 559,\n        \"num_unique_values\": 216,\n        \"samples\": [\n          249,\n          258,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales_qntty\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 181,\n        \"min\": 4,\n        \"max\": 914,\n        \"num_unique_values\": 600,\n        \"samples\": [\n          71,\n          154,\n          379\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Ebbis und Schnittbr\\u00f6tchen\",\n          \"Gemischte Br\\u00f6tchen\",\n          \"Brote\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"two\",\n          \"one\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 185,\n        \"min\": 12,\n        \"max\": 942,\n        \"num_unique_values\": 595,\n        \"samples\": [\n          78,\n          69\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cost_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5545244683590453,\n        \"min\": 0.18,\n        \"max\": 1.7,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.35,\n          0.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ai_sale_difference\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24,\n        \"min\": 0,\n        \"max\": 268,\n        \"num_unique_values\": 135,\n        \"samples\": [\n          36,\n          50\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# 4. show the record with the worst prediction in Euros\n",
        "data['Ai_sale_difference'] = (data['sales_qntty'] - data['prediction']).abs() # created  new column to check the  difference\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Here worse is considered as the  highest  difference between actual sale and prediction\n",
        "worst_prediction_record = data.loc[data['Ai_sale_difference'].idxmax()]\n",
        "print(\"\\nRecord with the worst prediction in Euros:\")\n",
        "print(worst_prediction_record)\n",
        "\n",
        "# Sort the data by 'Ai_diffrence' column in descending order\n",
        "data.sort_values(by='Ai_sale_difference', ascending=False).head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "BnVRLSl2XGPp",
        "outputId": "a2c14875-939c-4e6a-bda6-bf804274bc7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Record with the worst prediction in Euros:\n",
            "date                        2021-02-07 00:00:00\n",
            "item_id                                     102\n",
            "name                            Schnittbrötchen\n",
            "unit_price                                 0.36\n",
            "waste                                       559\n",
            "sales_qntty                                 273\n",
            "category              Ebbis und Schnittbrötchen\n",
            "store_name                                  two\n",
            "prediction                                  541\n",
            "cost_price                                 0.18\n",
            "Ai_sale_difference                          268\n",
            "Name: 2697, dtype: object\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           date  item_id             name  unit_price  waste  sales_qntty  \\\n",
              "2697 2021-02-07      102  Schnittbrötchen        0.36    559          273   \n",
              "285  2020-08-08      102  Schnittbrötchen        0.36     12          914   \n",
              "3228 2021-05-30      102  Schnittbrötchen        0.36      0          560   \n",
              "3172 2021-05-15      102  Schnittbrötchen        0.36    125          566   \n",
              "1895 2020-08-22      102  Schnittbrötchen        0.36     56          660   \n",
              "\n",
              "                       category store_name  prediction  cost_price  \\\n",
              "2697  Ebbis und Schnittbrötchen        two         541        0.18   \n",
              "285   Ebbis und Schnittbrötchen        one         667        0.18   \n",
              "3228  Ebbis und Schnittbrötchen        two         761        0.18   \n",
              "3172  Ebbis und Schnittbrötchen        two         750        0.18   \n",
              "1895  Ebbis und Schnittbrötchen        two         836        0.18   \n",
              "\n",
              "      Ai_sale_difference  \n",
              "2697                 268  \n",
              "285                  247  \n",
              "3228                 201  \n",
              "3172                 184  \n",
              "1895                 176  "
            ],
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              "      <th>name</th>\n",
              "      <th>unit_price</th>\n",
              "      <th>waste</th>\n",
              "      <th>sales_qntty</th>\n",
              "      <th>category</th>\n",
              "      <th>store_name</th>\n",
              "      <th>prediction</th>\n",
              "      <th>cost_price</th>\n",
              "      <th>Ai_sale_difference</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2697</th>\n",
              "      <td>2021-02-07</td>\n",
              "      <td>102</td>\n",
              "      <td>Schnittbrötchen</td>\n",
              "      <td>0.36</td>\n",
              "      <td>559</td>\n",
              "      <td>273</td>\n",
              "      <td>Ebbis und Schnittbrötchen</td>\n",
              "      <td>two</td>\n",
              "      <td>541</td>\n",
              "      <td>0.18</td>\n",
              "      <td>268</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>285</th>\n",
              "      <td>2020-08-08</td>\n",
              "      <td>102</td>\n",
              "      <td>Schnittbrötchen</td>\n",
              "      <td>0.36</td>\n",
              "      <td>12</td>\n",
              "      <td>914</td>\n",
              "      <td>Ebbis und Schnittbrötchen</td>\n",
              "      <td>one</td>\n",
              "      <td>667</td>\n",
              "      <td>0.18</td>\n",
              "      <td>247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3228</th>\n",
              "      <td>2021-05-30</td>\n",
              "      <td>102</td>\n",
              "      <td>Schnittbrötchen</td>\n",
              "      <td>0.36</td>\n",
              "      <td>0</td>\n",
              "      <td>560</td>\n",
              "      <td>Ebbis und Schnittbrötchen</td>\n",
              "      <td>two</td>\n",
              "      <td>761</td>\n",
              "      <td>0.18</td>\n",
              "      <td>201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3172</th>\n",
              "      <td>2021-05-15</td>\n",
              "      <td>102</td>\n",
              "      <td>Schnittbrötchen</td>\n",
              "      <td>0.36</td>\n",
              "      <td>125</td>\n",
              "      <td>566</td>\n",
              "      <td>Ebbis und Schnittbrötchen</td>\n",
              "      <td>two</td>\n",
              "      <td>750</td>\n",
              "      <td>0.18</td>\n",
              "      <td>184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1895</th>\n",
              "      <td>2020-08-22</td>\n",
              "      <td>102</td>\n",
              "      <td>Schnittbrötchen</td>\n",
              "      <td>0.36</td>\n",
              "      <td>56</td>\n",
              "      <td>660</td>\n",
              "      <td>Ebbis und Schnittbrötchen</td>\n",
              "      <td>two</td>\n",
              "      <td>836</td>\n",
              "      <td>0.18</td>\n",
              "      <td>176</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2020-08-08 00:00:00\",\n        \"max\": \"2021-05-30 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2020-08-08 00:00:00\",\n          \"2020-08-22 00:00:00\",\n          \"2021-05-30 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"item_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 102,\n        \"max\": 102,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          102\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Schnittbr\\u00f6tchen\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unit_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.36,\n        \"max\": 0.36,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"waste\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 233,\n        \"min\": 0,\n        \"max\": 559,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          12\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales_qntty\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 230,\n        \"min\": 273,\n        \"max\": 914,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          914\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Ebbis und Schnittbr\\u00f6tchen\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"one\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 112,\n        \"min\": 541,\n        \"max\": 836,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cost_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.18,\n        \"max\": 0.18,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ai_sale_difference\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 40,\n        \"min\": 176,\n        \"max\": 268,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          247\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **5. Do the stores sell more products in winter or summer?**"
      ],
      "metadata": {
        "id": "opFanRo9fnSu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I've categorized *December, January, and February* as **winter** months, and *June, July, and August* as **summer** months."
      ],
      "metadata": {
        "id": "Sy_2GZ2JQgui"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D6SohJkamg8d",
        "outputId": "50ed2299-334a-4f4c-ed91-ce0ca995a09e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sales in Winter months is :  141057\n",
            "Sales in Summer months is :  139229\n",
            "Winter Sales are 1.313% more than Summer sales.\n"
          ]
        }
      ],
      "source": [
        "# 5. do the stores sell more products in winter or summer?\n",
        "\n",
        "# filtered the data according to the seasons and  applied sum aggregate on sales quantity column.\n",
        "winter_sales = data[(data['date'].dt.month >= 12) | (data['date'].dt.month <= 2)]['sales_qntty'].sum() #Winter month filter\n",
        "summer_sales = data[(data['date'].dt.month >= 6) & (data['date'].dt.month <= 8)]['sales_qntty'].sum() #summer month filter\n",
        "\n",
        "print(\"Sales in Winter months is : \",winter_sales)\n",
        "print(\"Sales in Summer months is : \",summer_sales)\n",
        "per_difference = (winter_sales - summer_sales) / summer_sales * 100\n",
        "print(\"Winter Sales are {}% more than Summer sales.\".format(per_difference.round(3)))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **6. Show a plot (line plot) of the sales and predictions in quantity for the period between 11.1.2021 and 17.1.2021 for the product Mehrkornbrötchen(item_id = 121) within the second store.**"
      ],
      "metadata": {
        "id": "2s2AEYeZfr1L"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "ghEy-W2wmg8e",
        "outputId": "9b1a962f-4c5a-4ba8-d533-e44597f528af"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           date  item_id              name  unit_price  waste  sales_qntty  \\\n",
              "2581 2021-01-11      121  Mehrkornbrötchen         0.7     17           63   \n",
              "2582 2021-01-12      121  Mehrkornbrötchen         0.7      0           63   \n",
              "2583 2021-01-13      121  Mehrkornbrötchen         0.7     32           48   \n",
              "2584 2021-01-14      121  Mehrkornbrötchen         0.7      3           63   \n",
              "2601 2021-01-15      121  Mehrkornbrötchen         0.7      0           79   \n",
              "2602 2021-01-16      121  Mehrkornbrötchen         0.7      0          112   \n",
              "2603 2021-01-17      121  Mehrkornbrötchen         0.7     24           75   \n",
              "\n",
              "                category store_name  prediction  cost_price  \\\n",
              "2581  Gemischte Brötchen        two          61        0.35   \n",
              "2582  Gemischte Brötchen        two          53        0.35   \n",
              "2583  Gemischte Brötchen        two          51        0.35   \n",
              "2584  Gemischte Brötchen        two          65        0.35   \n",
              "2601  Gemischte Brötchen        two          78        0.35   \n",
              "2602  Gemischte Brötchen        two         110        0.35   \n",
              "2603  Gemischte Brötchen        two          85        0.35   \n",
              "\n",
              "      Ai_sale_difference  \n",
              "2581                   2  \n",
              "2582                  10  \n",
              "2583                   3  \n",
              "2584                   2  \n",
              "2601                   1  \n",
              "2602                   2  \n",
              "2603                  10  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
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              "      <td>121</td>\n",
              "      <td>Mehrkornbrötchen</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0</td>\n",
              "      <td>63</td>\n",
              "      <td>Gemischte Brötchen</td>\n",
              "      <td>two</td>\n",
              "      <td>53</td>\n",
              "      <td>0.35</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2583</th>\n",
              "      <td>2021-01-13</td>\n",
              "      <td>121</td>\n",
              "      <td>Mehrkornbrötchen</td>\n",
              "      <td>0.7</td>\n",
              "      <td>32</td>\n",
              "      <td>48</td>\n",
              "      <td>Gemischte Brötchen</td>\n",
              "      <td>two</td>\n",
              "      <td>51</td>\n",
              "      <td>0.35</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2584</th>\n",
              "      <td>2021-01-14</td>\n",
              "      <td>121</td>\n",
              "      <td>Mehrkornbrötchen</td>\n",
              "      <td>0.7</td>\n",
              "      <td>3</td>\n",
              "      <td>63</td>\n",
              "      <td>Gemischte Brötchen</td>\n",
              "      <td>two</td>\n",
              "      <td>65</td>\n",
              "      <td>0.35</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2601</th>\n",
              "      <td>2021-01-15</td>\n",
              "      <td>121</td>\n",
              "      <td>Mehrkornbrötchen</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0</td>\n",
              "      <td>79</td>\n",
              "      <td>Gemischte Brötchen</td>\n",
              "      <td>two</td>\n",
              "      <td>78</td>\n",
              "      <td>0.35</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2602</th>\n",
              "      <td>2021-01-16</td>\n",
              "      <td>121</td>\n",
              "      <td>Mehrkornbrötchen</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0</td>\n",
              "      <td>112</td>\n",
              "      <td>Gemischte Brötchen</td>\n",
              "      <td>two</td>\n",
              "      <td>110</td>\n",
              "      <td>0.35</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2603</th>\n",
              "      <td>2021-01-17</td>\n",
              "      <td>121</td>\n",
              "      <td>Mehrkornbrötchen</td>\n",
              "      <td>0.7</td>\n",
              "      <td>24</td>\n",
              "      <td>75</td>\n",
              "      <td>Gemischte Brötchen</td>\n",
              "      <td>two</td>\n",
              "      <td>85</td>\n",
              "      <td>0.35</td>\n",
              "      <td>10</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "Jan_week",
              "summary": "{\n  \"name\": \"Jan_week\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2021-01-11 00:00:00\",\n        \"max\": \"2021-01-17 00:00:00\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"2021-01-11 00:00:00\",\n          \"2021-01-12 00:00:00\",\n          \"2021-01-16 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"item_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 121,\n        \"max\": 121,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          121\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Mehrkornbr\\u00f6tchen\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unit_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.199177923332954e-16,\n        \"min\": 0.7,\n        \"max\": 0.7,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"waste\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 0,\n        \"max\": 32,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales_qntty\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 48,\n        \"max\": 112,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Gemischte Br\\u00f6tchen\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"two\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20,\n        \"min\": 51,\n        \"max\": 110,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cost_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.99588961666477e-17,\n        \"min\": 0.35,\n        \"max\": 0.35,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ai_sale_difference\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 10,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "# 6. show a plot (line plot) of the sales and predictions in quantity for the\n",
        "# period between 11.1.2021 and 17.1.2021 for the product Mehrkornbrötchen\n",
        "# (item_id = 121) within the second store.\n",
        "\n",
        "# filted the  data as the  required duration and sotre  for the  perticular  item.\n",
        "Jan_week = data[(data['date'] >= '2021-01-11') & (data['date'] <= '2021-01-17') & (data['item_id'] == 121) & (data['store_name'] == 'two')]\n",
        "Jan_week"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plotted LINE plot of the given duration.\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(Jan_week['date'], Jan_week['sales_qntty'], marker='o', label='Actual Sales')\n",
        "plt.plot(Jan_week['date'], Jan_week['prediction'], marker='x', label='Predictions')\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Quantity')\n",
        "plt.title('Sales vs Predictions for Mehrkornbrötchen (Store \\\"two\\\")')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "yDYS6JJZV7Hw",
        "outputId": "5da0a81f-4aa1-444f-ec96-275aaeb9aa1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **7. on which days of the week is each store open?**"
      ],
      "metadata": {
        "id": "9-TGYSbrf2Gy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NbVD0quCmg8e",
        "outputId": "42395186-abfe-4b60-8e60-ef22946768f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Days when store \"one\"is open:\n",
            " ['Tuesday' 'Wednesday' 'Thursday' 'Friday' 'Saturday' 'Monday']\n",
            "\n",
            "Days when store \"two\"is open:\n",
            " ['Tuesday' 'Wednesday' 'Thursday' 'Friday' 'Saturday' 'Sunday' 'Monday']\n"
          ]
        }
      ],
      "source": [
        "# 7. on which days of the week is each store open?\n",
        "\n",
        "# created day name column\n",
        "data['day_name'] = data['date'].dt.day_name()\n",
        "\n",
        "# filtered all the  unique day names from the  day_name column and created datafram.\n",
        "store_open_days = data.groupby('store_name')['day_name'].unique().reset_index()\n",
        "store_open_days.columns = ['store_name', 'open_days']\n",
        "\n",
        "print(\"Days when store \\\"one\\\"is open:\\n\",store_open_days['open_days'][0])\n",
        "print(\"\\nDays when store \\\"two\\\"is open:\\n\",store_open_days['open_days'][1])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **8. if the second store used the AI predictions, how much would they have saved/lost in Euros? Suppose the product cost is half its price.**"
      ],
      "metadata": {
        "id": "ex4EQLb7f76y"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-ugXFaN7mg8e",
        "outputId": "f5d44112-dd08-4736-fbe1-111febf2a37a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Production cost based on AI Prediction: 152072.53\n",
            "Production cost based on WITHOUT AI Prediction: 166734.03\n",
            "\n",
            "Since the production cost with AI prediction is lower than without it,\n",
            "If store \"two\" had utilized AI prediction, it could have saved 14661.5 euros.\n"
          ]
        }
      ],
      "source": [
        "# 8. if the second store used the AI predictions, how much would they have\n",
        "# saved/lost in Euros? Suppose the product cost is half its price.\n",
        "\n",
        "data['produced'] = data['waste'] + data['sales_qntty'] # created  new column for  total production\n",
        "data['Ai_prod_difference'] = (data['produced'] - data['prediction']).abs()\n",
        "\n",
        "cost_by_AI = ((data['unit_price']*0.5)*data['prediction']).sum()\n",
        "actual_production_cost = ((data['unit_price']*0.5)*data['produced']).sum()\n",
        "\n",
        "print(\"Production cost based on AI Prediction:\", cost_by_AI)\n",
        "print(\"Production cost based on WITHOUT AI Prediction:\", actual_production_cost)\n",
        "print(\"\\nSince the production cost with AI prediction is lower than without it,\")\n",
        "\n",
        "# Calculating and displaying the cost differece if AI prediction was used.\n",
        "savings = actual_production_cost - cost_by_AI\n",
        "print(\"If store \\\"two\\\" had utilized AI prediction, it could have saved {} euros.\".format(savings))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **9. Create a linear model for the sales of Schnittbrötchen (item_id = 102) at store two. use the data from 6.2020 ~ 3.2021 for training. Don't use external data like public holidays, etc.**"
      ],
      "metadata": {
        "id": "uKvS1s6FgBvr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# imported necessary  libraries\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score"
      ],
      "metadata": {
        "id": "wP1LL8aJCH-y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# filtered the data as required\n",
        "filtered_data = data[(data['item_id'] == 102) & (data['date'] >= '2020-06-01') & (data['date'] <= '2021-03-31')]\n",
        "\n",
        "# Extracted day of the week from the date\n",
        "filtered_data['day_of_week'] = filtered_data['date'].dt.dayofweek\n",
        "\n",
        "# Dropped uslesscolumn which  will not be helpful in modal training.\n",
        "filtered_data = filtered_data.drop(columns=['name', 'category', 'item_id', 'unit_price', 'day_name']).reset_index(drop=True)\n",
        "filtered_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "n0agq238Ep_F",
        "outputId": "d395a369-0b77-44d0-836c-f4f73ba28e8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-25-c05383763d4a>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  filtered_data['day_of_week'] = filtered_data['date'].dt.dayofweek\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          date  waste  sales_qntty store_name  prediction  cost_price  \\\n",
              "0   2020-06-02     19          383        one         371        0.18   \n",
              "1   2020-06-03      0          360        one         332        0.18   \n",
              "2   2020-06-04     53          353        one         351        0.18   \n",
              "3   2020-06-05     42          445        one         469        0.18   \n",
              "4   2020-06-06     10          674        one         660        0.18   \n",
              "..         ...    ...          ...        ...         ...         ...   \n",
              "550 2021-03-27      0          675        two         825        0.18   \n",
              "551 2021-03-28    151          616        two         590        0.18   \n",
              "552 2021-03-29     55          453        two         445        0.18   \n",
              "553 2021-03-30     10          394        two         419        0.18   \n",
              "554 2021-03-31      3          445        two         490        0.18   \n",
              "\n",
              "     Ai_sale_difference  produced  Ai_prod_difference  day_of_week  \n",
              "0                    12       402                  31            1  \n",
              "1                    28       360                  28            2  \n",
              "2                     2       406                  55            3  \n",
              "3                    24       487                  18            4  \n",
              "4                    14       684                  24            5  \n",
              "..                  ...       ...                 ...          ...  \n",
              "550                 150       675                 150            5  \n",
              "551                  26       767                 177            6  \n",
              "552                   8       508                  63            0  \n",
              "553                  25       404                  15            1  \n",
              "554                  45       448                  42            2  \n",
              "\n",
              "[555 rows x 10 columns]"
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1a331739-ec69-4e38-a505-32f686f47046 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "filtered_data",
              "summary": "{\n  \"name\": \"filtered_data\",\n  \"rows\": 555,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2020-06-02 00:00:00\",\n        \"max\": \"2021-03-31 00:00:00\",\n        \"num_unique_values\": 300,\n        \"samples\": [\n          \"2021-01-30 00:00:00\",\n          \"2020-08-16 00:00:00\",\n          \"2020-11-28 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"waste\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 71,\n        \"min\": 0,\n        \"max\": 559,\n        \"num_unique_values\": 181,\n        \"samples\": [\n          141,\n          77,\n          49\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales_qntty\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 154,\n        \"min\": 200,\n        \"max\": 914,\n        \"num_unique_values\": 296,\n        \"samples\": [\n          386,\n          346,\n          524\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"two\",\n          \"one\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 143,\n        \"min\": 252,\n        \"max\": 836,\n        \"num_unique_values\": 272,\n        \"samples\": [\n          336,\n          420\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cost_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7780614480094495e-17,\n        \"min\": 0.18,\n        \"max\": 0.18,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ai_sale_difference\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35,\n        \"min\": 0,\n        \"max\": 268,\n        \"num_unique_values\": 123,\n        \"samples\": [\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"produced\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 170,\n        \"min\": 250,\n        \"max\": 1100,\n        \"num_unique_values\": 312,\n        \"samples\": [\n          486\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ai_prod_difference\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62,\n        \"min\": 0,\n        \"max\": 427,\n        \"num_unique_values\": 167,\n        \"samples\": [\n          130\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day_of_week\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# handled categotical columns  by  one hot encoding\n",
        "columns_to_encode = ['store_name', 'day_of_week']\n",
        "\n",
        "# used  onehotencoder  class  and  created and   object  of  it.\n",
        "onehot_encoder = OneHotEncoder()\n",
        "encoded_data = onehot_encoder.fit_transform(filtered_data[columns_to_encode])\n",
        "\n",
        "# Convert encoded data to DataFrame\n",
        "encoded_data = pd.DataFrame(encoded_data.toarray(), columns=onehot_encoder.get_feature_names_out(columns_to_encode))\n",
        "\n",
        "# Concatenate original data with encoded data\n",
        "filtered_data_encoded = pd.concat([filtered_data.drop(columns=columns_to_encode), encoded_data], axis=1)\n",
        "filtered_data_encoded"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "PKga9yepFT_N",
        "outputId": "b99c773c-0f6f-4635-dfcd-bde9edd4dd22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          date  waste  sales_qntty  prediction  cost_price  \\\n",
              "0   2020-06-02     19          383         371        0.18   \n",
              "1   2020-06-03      0          360         332        0.18   \n",
              "2   2020-06-04     53          353         351        0.18   \n",
              "3   2020-06-05     42          445         469        0.18   \n",
              "4   2020-06-06     10          674         660        0.18   \n",
              "..         ...    ...          ...         ...         ...   \n",
              "550 2021-03-27      0          675         825        0.18   \n",
              "551 2021-03-28    151          616         590        0.18   \n",
              "552 2021-03-29     55          453         445        0.18   \n",
              "553 2021-03-30     10          394         419        0.18   \n",
              "554 2021-03-31      3          445         490        0.18   \n",
              "\n",
              "     Ai_sale_difference  produced  Ai_prod_difference  store_name_one  \\\n",
              "0                    12       402                  31             1.0   \n",
              "1                    28       360                  28             1.0   \n",
              "2                     2       406                  55             1.0   \n",
              "3                    24       487                  18             1.0   \n",
              "4                    14       684                  24             1.0   \n",
              "..                  ...       ...                 ...             ...   \n",
              "550                 150       675                 150             0.0   \n",
              "551                  26       767                 177             0.0   \n",
              "552                   8       508                  63             0.0   \n",
              "553                  25       404                  15             0.0   \n",
              "554                  45       448                  42             0.0   \n",
              "\n",
              "     store_name_two  day_of_week_0  day_of_week_1  day_of_week_2  \\\n",
              "0               0.0            0.0            1.0            0.0   \n",
              "1               0.0            0.0            0.0            1.0   \n",
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              "3               0.0            0.0            0.0            0.0   \n",
              "4               0.0            0.0            0.0            0.0   \n",
              "..              ...            ...            ...            ...   \n",
              "550             1.0            0.0            0.0            0.0   \n",
              "551             1.0            0.0            0.0            0.0   \n",
              "552             1.0            1.0            0.0            0.0   \n",
              "553             1.0            0.0            1.0            0.0   \n",
              "554             1.0            0.0            0.0            1.0   \n",
              "\n",
              "     day_of_week_3  day_of_week_4  day_of_week_5  day_of_week_6  \n",
              "0              0.0            0.0            0.0            0.0  \n",
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              "4              0.0            0.0            1.0            0.0  \n",
              "..             ...            ...            ...            ...  \n",
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              "553            0.0            0.0            0.0            0.0  \n",
              "554            0.0            0.0            0.0            0.0  \n",
              "\n",
              "[555 rows x 17 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>waste</th>\n",
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              "      <td>2020-06-05</td>\n",
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              "<p>555 rows × 17 columns</p>\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",
              "        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",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0ef10e39-29e5-45e5-810e-4266b295e903')\"\n",
              "            title=\"Suggest charts\"\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0ef10e39-29e5-45e5-810e-4266b295e903 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "filtered_data_encoded",
              "summary": "{\n  \"name\": \"filtered_data_encoded\",\n  \"rows\": 555,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2020-06-02 00:00:00\",\n        \"max\": \"2021-03-31 00:00:00\",\n        \"num_unique_values\": 300,\n        \"samples\": [\n          \"2021-01-30 00:00:00\",\n          \"2020-08-16 00:00:00\",\n          \"2020-11-28 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"waste\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 71,\n        \"min\": 0,\n        \"max\": 559,\n        \"num_unique_values\": 181,\n        \"samples\": [\n          141,\n          77,\n          49\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales_qntty\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 154,\n        \"min\": 200,\n        \"max\": 914,\n        \"num_unique_values\": 296,\n        \"samples\": [\n          386,\n          346,\n          524\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 143,\n        \"min\": 252,\n        \"max\": 836,\n        \"num_unique_values\": 272,\n        \"samples\": [\n          336,\n          420,\n          366\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cost_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7780614480094495e-17,\n        \"min\": 0.18,\n        \"max\": 0.18,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ai_sale_difference\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35,\n        \"min\": 0,\n        \"max\": 268,\n        \"num_unique_values\": 123,\n        \"samples\": [\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"produced\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 170,\n        \"min\": 250,\n        \"max\": 1100,\n        \"num_unique_values\": 312,\n        \"samples\": [\n          486\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ai_prod_difference\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62,\n        \"min\": 0,\n        \"max\": 427,\n        \"num_unique_values\": 167,\n        \"samples\": [\n          130\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_name_one\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.49880332941966266,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"store_name_two\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.49880332941966266,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day_of_week_0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.36218818002617764,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day_of_week_1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3655934467623948,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day_of_week_2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3655934467623948,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day_of_week_3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3604599498123752,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day_of_week_4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.35516987516547993,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day_of_week_5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3569510563871942,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day_of_week_6\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2675887398557655,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Did train test split and  also  dropped  \"date\" column.\n",
        "X = filtered_data_encoded.drop(columns=[\"sales_qntty\", 'date'])\n",
        "y = filtered_data_encoded[\"sales_qntty\"]\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Initialize and train the Linear Regression model\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "BEGjZnpTF1K5",
        "outputId": "d6b36f91-c44b-49cd-fc20-289d356fa878"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Made predictions\n",
        "y_pred_train = model.predict(X_train)\n",
        "y_pred_test = model.predict(X_test)"
      ],
      "metadata": {
        "id": "k-ujpdCGGKXB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluation of the model\n",
        "train_rmse = mean_squared_error(y_train, y_pred_train, squared=False)\n",
        "test_rmse = mean_squared_error(y_test, y_pred_test, squared=False)\n",
        "\n",
        "train_r2 = r2_score(y_train, y_pred_train)\n",
        "test_r2 = r2_score(y_test, y_pred_test)\n",
        "\n",
        "print(f\"Train RMSE: {train_rmse}\")\n",
        "print(f\"Test RMSE: {test_rmse}\")\n",
        "print(f\"Train R^2 Score: {train_r2}\")\n",
        "print(f\"Test R^2 Score: {test_r2}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EkD6KX3YGQY6",
        "outputId": "0e08bc22-5032-4f59-a908-bdeaa2006b4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train RMSE: 6.53732714059602e-14\n",
            "Test RMSE: 6.228053151063119e-14\n",
            "Train R^2 Score: 1.0\n",
            "Test R^2 Score: 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **10. Compute the L2 error of the model on data from 4.2021 ~ 5.2021.**"
      ],
      "metadata": {
        "id": "2-sIAMi5gIYs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# filtered the data as required\n",
        "test_data = data[(data['item_id'] == 102) & (data['date'] >= '2021-04-01') & (data['date'] <= '2021-05-31')]"
      ],
      "metadata": {
        "id": "YFENcJHNHlMX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# extracted day of the week from the date\n",
        "test_data['day_of_week'] = test_data['date'].dt.dayofweek\n",
        "test_data = test_data.drop(columns=['name', 'category', 'item_id', 'unit_price', 'day_name']).reset_index(drop=True)\n",
        "\n",
        "# used One-hot encoding to handle the categorical columns\n",
        "encoded_test_data = onehot_encoder.transform(test_data[columns_to_encode])\n",
        "encoded_test_data = pd.DataFrame(encoded_test_data.toarray(), columns=onehot_encoder.get_feature_names_out(columns_to_encode))\n",
        "\n",
        "# merged the encoded dataframe\n",
        "test_data_encoded = pd.concat([test_data.drop(columns=columns_to_encode), encoded_test_data], axis=1)\n",
        "test_data_encoded"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 721
        },
        "id": "u-0fdMpEH7HA",
        "outputId": "01e8f364-be86-4970-feb6-3efcc76e952d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-31-9ccb84e0264e>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  test_data['day_of_week'] = test_data['date'].dt.dayofweek\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          date  waste  sales_qntty  prediction  cost_price  \\\n",
              "0   2021-04-01      5          645         594        0.18   \n",
              "1   2021-04-03      0          814         859        0.18   \n",
              "2   2021-04-06     46          436         469        0.18   \n",
              "3   2021-04-07      2          321         348        0.18   \n",
              "4   2021-04-08    208          326         387        0.18   \n",
              "..         ...    ...          ...         ...         ...   \n",
              "101 2021-05-27     93          333         376        0.18   \n",
              "102 2021-05-28      0          449         468        0.18   \n",
              "103 2021-05-29      0          895         761        0.18   \n",
              "104 2021-05-30      0          560         761        0.18   \n",
              "105 2021-05-31     27          340         366        0.18   \n",
              "\n",
              "     Ai_sale_difference  produced  Ai_prod_difference  store_name_one  \\\n",
              "0                    51       650                  56             1.0   \n",
              "1                    45       814                  45             1.0   \n",
              "2                    33       482                  13             1.0   \n",
              "3                    27       323                  25             1.0   \n",
              "4                    61       534                 147             1.0   \n",
              "..                  ...       ...                 ...             ...   \n",
              "101                  43       426                  50             0.0   \n",
              "102                  19       449                  19             0.0   \n",
              "103                 134       895                 134             0.0   \n",
              "104                 201       560                 201             0.0   \n",
              "105                  26       367                   1             0.0   \n",
              "\n",
              "     store_name_two  day_of_week_0  day_of_week_1  day_of_week_2  \\\n",
              "0               0.0            0.0            0.0            0.0   \n",
              "1               0.0            0.0            0.0            0.0   \n",
              "2               0.0            0.0            1.0            0.0   \n",
              "3               0.0            0.0            0.0            1.0   \n",
              "4               0.0            0.0            0.0            0.0   \n",
              "..              ...            ...            ...            ...   \n",
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              "105             1.0            1.0            0.0            0.0   \n",
              "\n",
              "     day_of_week_3  day_of_week_4  day_of_week_5  day_of_week_6  \n",
              "0              1.0            0.0            0.0            0.0  \n",
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              "4              1.0            0.0            0.0            0.0  \n",
              "..             ...            ...            ...            ...  \n",
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              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>waste</th>\n",
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              "      <th>0</th>\n",
              "      <td>2021-04-01</td>\n",
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              "      <td>2021-04-03</td>\n",
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              "      <td>2021-04-06</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-04-07</td>\n",
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              "      <td>348</td>\n",
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              "      <td>27</td>\n",
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              "      <th>4</th>\n",
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              "      <td>326</td>\n",
              "      <td>387</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>2021-05-27</td>\n",
              "      <td>93</td>\n",
              "      <td>333</td>\n",
              "      <td>376</td>\n",
              "      <td>0.18</td>\n",
              "      <td>43</td>\n",
              "      <td>426</td>\n",
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              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>2021-05-28</td>\n",
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              "      <td>449</td>\n",
              "      <td>468</td>\n",
              "      <td>0.18</td>\n",
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              "      <td>449</td>\n",
              "      <td>19</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>2021-05-29</td>\n",
              "      <td>0</td>\n",
              "      <td>895</td>\n",
              "      <td>761</td>\n",
              "      <td>0.18</td>\n",
              "      <td>134</td>\n",
              "      <td>895</td>\n",
              "      <td>134</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>2021-05-30</td>\n",
              "      <td>0</td>\n",
              "      <td>560</td>\n",
              "      <td>761</td>\n",
              "      <td>0.18</td>\n",
              "      <td>201</td>\n",
              "      <td>560</td>\n",
              "      <td>201</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>2021-05-31</td>\n",
              "      <td>27</td>\n",
              "      <td>340</td>\n",
              "      <td>366</td>\n",
              "      <td>0.18</td>\n",
              "      <td>26</td>\n",
              "      <td>367</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>106 rows × 17 columns</p>\n",
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              "        document.querySelector('#df-905d3ef8-d1bd-4ca4-875f-e7e29f36ae1e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "variable_name": "test_data_encoded",
              "summary": "{\n  \"name\": \"test_data_encoded\",\n  \"rows\": 106,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2021-04-01 00:00:00\",\n        \"max\": \"2021-05-31 00:00:00\",\n        \"num_unique_values\": 59,\n        \"samples\": [\n          \"2021-04-01 00:00:00\",\n          \"2021-04-09 00:00:00\",\n          \"2021-05-15 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"waste\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 64,\n        \"min\": 0,\n        \"max\": 300,\n        \"num_unique_values\": 68,\n        \"samples\": [\n          55,\n          56,\n          208\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sales_qntty\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 170,\n        \"min\": 245,\n        \"max\": 908,\n        \"num_unique_values\": 93,\n        \"samples\": [\n          382,\n          305,\n          338\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prediction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 165,\n        \"min\": 302,\n        \"max\": 942,\n        \"num_unique_values\": 87,\n        \"samples\": [\n          649,\n          594,\n          305\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cost_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.18,\n        \"max\": 0.18,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 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\"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day_of_week_4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3686989339060152,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day_of_week_5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.36869893390601527,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day_of_week_6\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2800654524962704,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Did train test split and  dropped  date column\n",
        "X_test = test_data_encoded.drop(columns=[\"sales_qntty\", 'date'])\n",
        "y_test = test_data_encoded[\"sales_qntty\"]"
      ],
      "metadata": {
        "id": "4nwmjSqcIYfu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Made predictions on test data using the previously trained model\n",
        "y_pred_test = model.predict(X_test)\n",
        "\n",
        "# Compute L2 error\n",
        "l2_error = mean_squared_error(y_test, y_pred_test, squared=False)\n",
        "print(\"L2 Error:\", l2_error)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6a4ywAKsIscq",
        "outputId": "cc07c916-fd1d-4fe2-a170-45af4be4984a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "L2 Error: 5.157152524116973e-14\n"
          ]
        }
      ]
    }
  ],
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      "display_name": "conda_python3",
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